Business Strategy and Organizational Performance - Pakistan ...

Business Strategy and Organizational

Performance: Measures and Relationships

By

Jamil Anwar

CIIT/SP12-PMS-006/ISB

PhD Thesis

In

Management Sciences

COMSATS University Islamabad

Islamabad Campus- Pakistan

Spring, 2017

ii

COMSATS University Islamabad

Business Strategy and Organizational Performance:

Measures and Relationships

A Thesis Presented to

COMSATS University, Islamabad

In partial fulfillment

of the requirement for the degree of

PhD (Management Sciences)

By

Jamil Anwar


Spring, 2017

iii

Business Strategy and Organizational Performance:

Measures and Relationships

A Post Graduate Thesis submitted to the Department of Management Sciences as

partial fulfillment of the requirement for the award of Degree of Ph.D in

Management Sciences.

Name Registration Number

Jamil Anwar CIIT/SP12-PMS-006/ISB

Supervisor

Dr. Syed Amjad Farid Hasnu

Professor, Department of Management Sciences

COMSATS University Islamabad, Abbottabad Campus

iv

Certificate of Approval

This is to certify that the research work presented in this thesis, entitled “Business

Strategy and Organizational Performance: Measures and Relationships” was conducted

by Mr. Jamil Anwar, CIIT/SP12-PMS-006/ISB, under the supervision of Dr. Syed Amjad

Farid Hasnu. No part of this thesis has been submitted anywhere else for any other

degree. This thesis is submitted to the Department of Management Sciences, COMSATS

University Islamabad, in the partial fulfillment of the requirement for the degree of

Doctor of Philosophy in the field of Management Sciences.

Student Name: Jamil Anwar Signature: __________________

Examinations Committee:

Signature

External Examiner 1:

(Designation& Office Address)

……………………………………

……………………………………

Signature

External Examiner 2:

(Designation& Office Address)

……………………………………

……………………………………

.


Supervisor

Department of Management Sciences,

CUI, Abbottabad Campus

.

Dr. Aneel Salman,

HoD

Department of Management

Sciences,


.

Dr. Samina Nawab

Chairperson,

Department of Management Sciences,

CUI

.

Dr. Khalid Riaz

Dean,

Faculty of Business

Administration, CUI

v

Author’s Declaration

I, Jamil Anwar, CIIT/SP12-PMS-006/ISB, hereby state that my PhD thesis titled

“Business Strategy and Organizational Performance: Measures and Relationships” is my

own work and has not been submitted previously by me for taking any degree from this

University i.e. COMSATS University, Islamabad or anywhere else in the country/world.

At any time if my statement is found to be incorrect even after I graduate, the University

has the right to withdraw my PhD degree.

Date: _________________ Signature: ________________________

Jamil Anwar


vi

Plagiarism Undertaking

I solemnly declare that research work presented in the thesis titled “Business Strategy and

Organizational Performance: Measures and Relationships” is solely my research work

with no significant contribution from any other person. Small contribution/help wherever

taken has been duly acknowledged and that complete thesis has been written by me.

I understand the zero tolerance policy of HEC and COMSATS University, Islamabad

towards plagiarism. Therefore, I as an author of the above titled thesis declare that no

portion of my thesis has been plagiarized and any material used as reference is properly

referred/cited.

I undertake if I am found guilty of any formal plagiarism in the above titled thesis even

after award of PhD Degree, the University reserves the right to withdraw/revoke my PhD

degree and that HEC and the university has the right to publish my name on the

HEC/university website on which names of students are placed who submitted

plagiarized thesis.

Date: __________________ Signature: _______________________

Jamil Anwar


vii

Certificate

It is certified that Jamil Anwar, CIIT/SP12-PMS-006/ISB has carried out all the work

related to this thesis under my supervision at the Department of Management Sciences,

COMSATS University, Islamabad and the work fulfills the requirements for award of

PhD degree.

Date:___________________

Supervisor:

________________________________


Professor

Department of Management Sciences

CUI, Abbottabad Campus

Head of Department:

______________________________

Dr. Aneel Salman

Head, Department of Management Sciences


viii

DEDICATION

Dedicated to my parents, wife, and kids for their love, care

and prayers

ix

ACKNOWLEDGEMENTS

Alhamdolillah. I am very thankful to Almighty Allah (SWT) for blessing me with the

opportunity, determination, and confidence to undertake this doctoral program.

While conducting this research, I received support from many people. It is my pleasure to

take this opportunity to thank all of them. First of all, I am deeply thankful to my

supervisor Prof. Dr. Syed Amjad Farid Hasnu for his continuous guidance, support and

kind heartedness. I am also thankful to the HODs and program coordinators at the

department of Management Sciences, COMSATS University, Islamabad for their

continuous support.

I am thankful to Muhammad Shafi, Joint Director, State Bank of Pakistan for helping me

in data collection in the proper format. Thanks to my colleagues and friends for their

continuous support and encouragement during the whole process.

Very special thanks to my loving parents, my wife, children and other family members

for their continuous support and prayers during this period. It would not be possible to

complete the thesis with in time without their prayers, support, encouragement, and

patience they showed throughout this time. May Allah (SWT) bless all of them. Ameen

Jamil Anwar


x

Publication

The following research has been published during the preparation of this thesis.

Impact Factor Publication

Jamil Anwar, SAF Hasnu (2017), Strategic Patterns and Firm Performance: Comparing

Consistent, Flexible and Reactor Strategies, Journal of Organizational Change

Management, 30(7), pp: 1015-1029

HEC Approved Journal Publication

Jamil Anwar, SAF Hasnu (2019), The Impact of Consistent, Flexible and Reactor

Strategy on Organizational Performance: A Comparative Analysis, Pakistan Business

Review, 20(1), pp:

Jamil Anwar, Said Shah, SAF Hasnu (2016). Business Strategy and Organizational

Performance: Measures and Relationships. Pakistan Economic and Social Review, 54(1),

97-122

ESCI/SCOPUS Publications

Jamil Anwar, SAF Hasnu (2016). Strategy-Performance Linkage: Methodological

Refinements and Empirical Analysis. Journal of Asia Business Studies, 10(3), pp: 303 -

317

Jamil Anwar, SAF Hasnu (2017), Strategy-Performance Relationship: A Comparative

Analysis of Pure, Hybrid, and Reactor Strategies, Journal of Advances in Management

Research, 13(4), pp: 446-465

Jamil Anwar, SAF Hasnu (2016). Business Strategy and Firm Performance: A Multi-

Industry Analysis. Journal of Strategy and Management, 9(3), 361 – 382

xi

Conference Proceedings

Jamil Anwar, SAF Hasnu (2015), Business Strategy and organizational Performance: An

Analysis of Textile Sector, 7th SAICON: International Conference on Meeting the

Challenge: Navigating the Future (http://ww2.comsats.edu.pk/saicon2015/)

Jamil Anwar, SAF Hasnu (2013), Organizational Structure and Performance: A proposed

Framework for Doctoral Research, 5th SAICON: International Conference on

Management, Innovation, Leadership, Economics and Strategies (MILES)

(http://www.ciit-atd.edu.pk/secure/newsandevents/NewsDetails.aspx?Id=253

http://ww2.comsats.edu.pk/saicon2015/

http://www.ciit-atd.edu.pk/secure/newsandevents/NewsDetails.aspx?Id=253

xii

ABSTRACT

Business Strategy and Organizational Performance: Measures and

Relationships

The contemporary literature is challenging the original idea that strategic purity leads to

superior performance. Similarly, there is an inconclusive debate in extant literature about

the superiority of strategic consistency over strategic flexibility. To address these issues,

the objectives of the research include: the refinement of scoring methodology for

classification of strategic types and strategic behavior of the firms; the comparison of

strategic groups’ performance across firm size and industry; strategy-performance, size-

performance, industry-performance, contingent effect of strategy, size, and industry on

performance; and the comparative analysis of single industry results with multi-industry

results for generalization. Drawing on the contingency theory perspective, Miles and

Snow typology is used for operationalization of strategic types using seven years

archived financial data of 307 joint stock companies listed at Pakistan Stock Exchange

(PSE). The findings reveal that the defending and analysing strategies perform better than

prospecting and reacting strategies. Hybrid strategies outperformed pure strategies while

both consistent and flexible strategies performed equally well and outperformed the

reactors. The performance of strategic types varies with the change in firm size and

industry. The results imply that for better performance, firms in Pakistan should either

compete on the basis of service, price, quality, and operational excellence or should focus

on a balance of innovation and core product development. Innovation, rapid growth and

new product developments is non-profitable. Similarly, inconsistent strategic behaviour

results in poor performance. The conceptualization of pure, hybrid, consistent, and

flexible strategic types, identification of strategic transition of the firms to find out

behaviour along with their empirical testing, refinements in methodology for objective

measures, a comparison of single industry vs multi-industry results are the major

contributions of the study. Typology-driven theorizing for hybrid, consistent, and flexible

strategic types is one of the promising area for future research

Keywords: Business Strategy; Strategic Typology; Strategic Behavior; Organizational

Performance; Contingency Theory; Scoring Methodology

xiii

TABLE OF CONTENTS

Certificate of Approval ......................................................................................... iv

Author’s Declaration .............................................................................................. v

Plagiarism Undertaking ........................................................................................ vi

Certificate .............................................................................................................. vii

DEDICATION ..................................................................................................... viii

ACKNOWLEDGEMENTS .................................................................................. ix

Publication ............................................................................................................... x

Introduction ............................................................................................................ 1

1.1 Overview ................................................................................................................................ 2

1.2. The Pakistan’s Business Context ........................................................................................ 10

1.3 Strategic Management in Pakistan ....................................................................................... 14

1.4 Motivation of the Study ....................................................................................................... 15

1.5 Problem Statement ............................................................................................................... 17

1.6 Objectives of the Study ........................................................................................................ 17

1.7 The Research Questions ....................................................................................................... 18

1.8 Research Methodology ........................................................................................................ 18

1.9 Contribution of the Study ..................................................................................................... 19

1.10 Structure of the Thesis ....................................................................................................... 20

Theoretical Literature Review ............................................................................ 21

2.1 Introduction .......................................................................................................................... 22

2.2 Strategic Management –A Historical Perspective ............................................................... 22

2.3 What is Strategy? ................................................................................................................. 28

2.4 Intended versus Realized Strategy ....................................................................................... 29

2.5 Strategic Purity versus Strategic Hybridization ................................................................... 31

2.6 Strategic Behaviour .............................................................................................................. 32

2.6.1 Strategic Consistency versus Strategic Flexibility ......................................................... 33

2.7 Strategy Levels..................................................................................................................... 36

xiv

2.7.1 Corporate-Level Strategy .............................................................................................. 36

2.7.2 Business-Level Strategy ................................................................................................. 37

2.7.3 Functional-Level Strategy .............................................................................................. 39

2.8 Organizational and Environmental Contingencies .............................................................. 40

2.9 Strategic Groups and Typologies ......................................................................................... 41

2.10 Miles and Snow Typology ................................................................................................. 45

2.10.1 Miles and Snow’s Strategic Types ............................................................................... 47

2.11 Contingency Theory ........................................................................................................... 51

2.12 Business Strategy and Organizational Performance .......................................................... 55

2.12.1 Miles and Snow’s Strategic Types and Organizational Performance.......................... 58

2.12.2 Strategy and Performance: A Contingency Theory Perspective ................................. 59

2.13 Summary ............................................................................................................................ 62

Empirical Literature Review ............................................................................... 64

3.1 Introduction .......................................................................................................................... 65

3.2. Evidence on Industries and Countries Studied, Distribution Patterns of Strategic Types,

and Methodologies Applied ....................................................................................................... 65

3.3 Strategy-Performance Relationships .................................................................................... 69

3.4 Methodological Development for Application of Miles and Snow Typology..................... 76

3.5 Strategic Management in Pakistan ....................................................................................... 81

3.6 Hypotheses Development .................................................................................................... 84

3.7 Summary .............................................................................................................................. 91

Research Methodology ......................................................................................... 93

4.1 Introduction .......................................................................................................................... 94

4.2 Research Paradigm............................................................................................................... 94

4.2.1 Positivist Approach ....................................................................................................... 95

4.3 Research Design................................................................................................................... 97

4.4 Strategy and Performance Variables, Sample size, Tools and Techniques ......................... 98

4.5 Sample and Data ................................................................................................................ 103

4.6 Measures of Strategy, Performance, and Contingent Variables ........................................ 105

4.6.1 Measures of Strategy: Independent Variables ........................................................... 105

4.6.2 Measuring Performance: Dependent Variables ......................................................... 107

xv

4.6.3 Measuring Contingency Variables .............................................................................. 108

4.7 Identification of Strategic Types ........................................................................................ 110

4.7.1 Cluster Analysis ........................................................................................................... 110

4.7.2 Conceptual Development for Scoring Method ........................................................... 111

4.8 Step-by-Step Process to Calculate the Strategy Types using SAS Codes.......................... 114

4.8.1 SAS Data Set ................................................................................................................ 115

4.8.2 Average Calculation .................................................................................................... 116

4.8.3 Rank Calculation .......................................................................................................... 116

4.8.4 Categorization of Firms ............................................................................................... 117

4.8.5 Comparison of Strategies Overtime and Identification of Consistent, Flexible and

Reactor Strategy................................................................................................................... 118

4.9 Data Analysis Techniques .................................................................................................. 120

4.9.1 Descriptive Statistics ................................................................................................... 120

4.9.2 ANOVA and Regression Analysis ................................................................................. 120

4.10 Conceptual Model ............................................................................................................ 122

4.11 Ethical Issues and their Resolution .................................................................................. 125

4.11.1 Ethical Issues ............................................................................................................. 125

4.11.2 Resolution of Ethical Issues ....................................................................................... 126

4.12 Summary .......................................................................................................................... 127

Results and Discussion ....................................................................................... 128

5.1 Introduction ........................................................................................................................ 129

5.2 Identification of Strategic Types ........................................................................................ 129

5.3 Strategic Types, Firm Size and Industry ............................................................................ 133

5.4 Strategy and Performance .................................................................................................. 137

5.5 Strategic Behavior and Performance .................................................................................. 138

5.6 Strategy, Firm Size, and Organizational Performance ....................................................... 140

5.7 Firm Size, Strategic Behavior, and Organizational Performance ...................................... 142

5.8 Strategy, Industry, and Performance .................................................................................. 143

5.9 Industry, Strategic Behavior, and Performance ................................................................. 145

5.10 Hypotheses Testing .......................................................................................................... 148

5.10.1 Proportionate Distribution of Strategic Types .............................................................. 148

xvi

5.10.2 Analysis of Variance (ANOVA) ................................................................................... 149

5.10.2.1 Performance Comparison among Viable Strategies .............................................. 149

5.10.2.2 Pair-wise Differences in Performance ................................................................... 153

5.10.2.3 Performance Comparison between Viable Strategies and Reactors ..................... 155

5.10.2.4 Performance Comparison of Pure, Hybrid, and Reactors ...................................... 157

5.10.2.5 Performance Comparison of Consistent, Flexible, and Reactors........................... 157

5.10.3 The Impact of Strategy, Firm Size, and Industry on Performance: Univariate Analysis

................................................................................................................................................. 160

5.10.3.1 Strategy-Performance Relationship ....................................................................... 160

5.10.3.2 Size-Performance Relationship .............................................................................. 163

5.10.3.3 Industry-Performance Relationship ....................................................................... 164

5.10.4 The impact of Strategy, Size, and Industry on Performance: Multivariate Analysis. 166

5.11 Textile Industry Analysis and its Comparison with Overall Results ............................... 169

5.11.1 Strategic Types Distribution ...................................................................................... 170

5.11.2 Strategic Types and Performance ............................................................................. 171

5.11.3 Strategic Behavior and Performance ........................................................................ 171

5.11.4 Strategy, Size and Performance ................................................................................ 172

5.11.5 Strategic Behavior, Firm Size, and Performance ....................................................... 172

5.11.6 Strategy, Industry, and Performance ........................................................................ 173

5.11.7 Industry, Strategic Behavior, and Performance ........................................................ 173

5.11.8 Strategy-Performance Relationship .......................................................................... 173

5.12 Comparative Summary of the Results: Multi-Industry (MI) versus Single Industry (SI) 175

5.13 Discussion ........................................................................................................................ 180

5.13.1 Refinement in Scoring Methodology ........................................................................ 180

5.13.2 Presence and Distribution of Strategic Types ........................................................... 181

5.13.3 Strategy-Performance Relationship: Pure Versus Hybrid Strategies ........................ 182

5.13.4 Strategy-Performance Relationship: Consistency versus Flexibility ......................... 183

5.13.5 Strategy-Performance Relationship: Miles and Snow Typology Perspective ........... 187

5.13.6 Strategy-Performance Relationship: Reactor Strategy ............................................ 189

5.13.7 Strategy-Performance Relationship: The Contingency Effect .................................. 190

5.14 Summary .......................................................................................................................... 192

Summary and Conclusion .................................................................................. 194

xvii

6.1 Introduction ........................................................................................................................ 195

6.2 Summary of the Research .................................................................................................. 195

6.2.1 Conclusion ................................................................................................................... 197

6.2.2 Contributions and Implications of the Study .............................................................. 198

6.2.4 Limitations of the Study .............................................................................................. 202

6.2.5 Opportunities for Future Research ............................................................................. 203

References ........................................................................................................... 204

Appendices .......................................................................................................... 222

A1: Step-by-step SAS coding for classification of strategic types and groups ........................ 223

A2: Firms Strategic Orientation: Industry Wise ...................................................................... 231

A3: Results -Textile Sector ...................................................................................................... 262

xviii

LIST OF FIGURES

Figure 2.1 Intended, Emergent, and Realized Strategy………………………....

Figure 2.2 Strategy Levels ……………………………………………..……….

Figure 3.1 Criteria for Classification of Strategic Types ………………..……...

Figure 4.1 Strategy Continuum and Reactors’ Domain ……………..…………

Figure 4.2 Conceptual Research Model ………………………...……………...

Figure 5.1 Strategy Continuum and Reactors’ Domain: Actual Position ………

Figure 5.2 Strategy Wise Performance (ROA, ROE, ROS, ROCE) …...………

Figure 5.3 Firm Size and Performance (ROA, ROE, ROS, ROCE) ……...…..

Figure 5.4 Industry and Performance (ROA, ROE, ROS, ROCE) ….......

Figure 5.5 Adjustment of Multiple Comparison: Tukey Kramer …………...….

30

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xix

LIST OF TABLES

Table 2.1 Characteristics of Strategic Consistency and Strategic Flexibility……

Table 2.2 Prevalent Strategic Typologies ………………………………………...

Table 3.1 Summary of Empirical Studies on Miles and Snow Typology ………..

Table 3.2 Research Evidence on Strategy-Performance Relationships …………..

Table 4.1 Summary of Strategy and Performance Variables where Archived

Data is used ………………………………………………………………………...

Table 4.2 Distribution of Firms According to Industry (Economic Groups) …….

Table 4.3 Strategy Measures, Their Implications, and Indicators ………………..

Table 4.4 Ranking, Scores, and Classification of Strategic Types and Firm Size .

Table 4.5 Strategic Orientation of the Firms Over Time …….…………………...

Table 5.1 Classification of Strategic Types and Their Transition Over the Time ...

Table 5.2 Categorization of Strategic Types: Overall (Long-term Orientation) …..

Table 5.3 Categorization of Strategic Types and Strategic Behaviors …………….

Table 5.4 Strategic Types and Firm Size ………………………………………....

Table 5.5 Strategic Behavior and Firm Size ………………………………...…....

Table 5.6 Strategic Types: Overall and Industry wise Distribution …...………....

Table 5.7 Strategic Behavior: Industry wise Distribution …...……………….......

Table 5.8 Strategic Types and Performance: Overall ….........................………....

Table 5.9 Strategic Behavior and Performance: Overall ........................………....

Table 5.10 Strategic Behavior and Performance: Strategy wise ...........………....

Table 5.11 Strategic Types and Performance: Firm Size wise ...............………....

Table 5.12 Firm Size, Strategic Behavior and Performance ……............………....

Table 5.13 Strategic Types and Performance: Industry wise ...............………......

Table 5.14 Industry, Strategic Behavior and Performance ……............…..……....

Table 5.15 Test for Equal Proportion of Strategic Types across Industry ….…....

Table 5.16a Test for Difference of Performance Means (ROA): Viable Strategies

Table 5.16b Test for Difference of Performance Means (ROE): Viable Strategies

Table 5.16c Test for Difference of Performance Means (ROS): Viable Strategies

Table 5.16d Test for Difference of Performance Means (ROCE):Viable Strategies

Table 5.17 Tukey’s Studentized Range Test (HSD) for ROS …………………….

Table 5.18a Test for Difference of Performance Means (ROA): Among Strategic

Types including Reactors …………………………………………………………...

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xx

Table 5.18b Test for Difference of Performance Means (ROE): Among Strategic


Table 5.18c Test for Difference of Performance Means (ROS): Among Strategic


Table 5.18d Test for Difference of Performance Means (ROCE): Among Strategic


Table 5.19a Test for Difference of Performance Means (ROA): Among Strategic

Behaviors …………………………………………………………………………...

Table 5.19b Test for Difference of Performance Means (ROE): Among Strategic

Behaviors …………………………………………………………………………...

Table 5.19c Test for Difference of Performance Means (ROS): Among Strategic

Behaviors …………………………………………………………………………...

Table 5.19d Test for Difference of Performance Means (ROCE): Among Strategic

Behaviors …………………………………………………………………………...

Table 5.20 The Results of Goodness of Fit Test (Strategy=Performance) ………...

Table 5.21 Parameter Estimates and Their Significance (Strategy=Performance) …

Table 5.22 The Results of Goodness of Fit Test (Strategic Behavior=Performance)

Table 5.23 Parameter Estimates and Their Significance (Strategic

Behavior=Performance) …………………………………………………………….

Table 5.24 The Results of Goodness of Fit Test (Size=Performance ) …………….

Table 5.25 Parameter Estimates and Their Significance (Size=Performance) …….

Table 5.26 The Results of Goodness of Fit Test (Industry=Performance ) ………..

Table 5.27 Parameter Estimates and Their Significance (Industry=Performance) ...

Table 5.28 Multivariate Analysis for Goodness of Fit ……………………………..

Table 5.29 Comparison of Multi-Industry and Single-Industry Analysis …………

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xxi

LIST OF ABBREVIATION

BCG Boston Consulting Group

BLS Business Level Strategy

CLS Corporate Level Strategy

CPEC China Pakistan Economic Corridor

DA-Like Defender-Analyzer-Like

FLS Functional Level Strategy

HBR Harvard Business Review

IO Industrial Organization

KSE Karachi Stock Exchange

M&S Miles and Snow

PA-Like Prospector-Analyzer-Like

PSE Pakistan Stock Exchange

RBV Resource Based View

ROA Return on Assets

ROE Return on Equity

ROS Return on Sales

ROCE Return on Capital Employed

SM Strategic Management

SSP Strategy-Structure-Performance

SBP State Bank of Pakistan

TCE Transaction Cost Economics

1

Chapter 1

Introduction

2

1.1 Overview

An organization is an established mechanism for achieving its articulated purpose. Most

of the organizations engage themselves in an ongoing process through questioning,

verifying, and redefining the ways of interaction with their environments for evaluating

their purposes. They constantly modify and refine the structure of roles, relationships,

and managerial process to complement strategy. Effective organizations create and

maintain a viable market for their goods or services whereas ineffective organizations fail

to do so (Miles and Snow, 1978; 2003). Management and organizational theorists view

strategy as the mechanism that provides integration for internal operations and guides for

environmental alignment.

In strategy-performance relationship, one of the central questions is to investigate why

firms succeed or fail in a given situation. This question has preoccupied the strategy field

since its inception (Boyd et al, 2012). The causes of success or failure of firms

encompass the questions such as: how firms chose strategies and create a fit; how they

behave over the time; why firms differ in their performance and structure; and how they

are managed etc (Porter, 1991). The strategy-performance relationship has been

examined widely through theoretical and empirical studies. In empirical studies, this

relationship is generally operationalized by using number of different measures and

models of causality powered by strategic typologies. Such type of research used to

distinguish different strategic types to investigate the relationship of strategy with varying

nature of performance measures (Luoma, 2015). In the prevailing complexity of

competitive market conditions and the fact that the business strategy is contingency

based, the existence of universal set of strategic choices is rare. This means that the

organizational effectiveness is dependent upon the amount of “congruence” or “fit”

between environmental and structural factors and the organization’s strategic response to

those factors (Pleshko et al., 2014).

The original idea that purity in strategic stance leads to the superior performance has been

widely challenged in the extant literature. There is rapidly increase in the evidences in

favour of the argument that the focus of the firms is shifting to hybrid form of strategic

3

choice instead of adapting pure strategy (Salavou, 2015). The complex and ever changing

challenges of the international market suggest that firms with hybrid strategies can

produce better performance. The adaptability and comfort ability of dealing with

uncertain strategic issues is now the fundamental requirement. Therefore, the

organizations have to blend their pure strategies to make hybrid strategies (Review,

2018a). This shift of focus has raised the fundamental question whether hybrid strategy

has become superior to the pure strategy and vice versa? Similarly, there is an

inconclusive debate in the literature about adaptation of strategic orientation or behaviour

by the management in a given environment over the time. The supporters for having

consistency in strategic choice argue that organizations perform better if they chose to

stick consistently for a longer period of time to a core strategy. The counter argument

states that for a superior performance, strategic flexibility is the better choice as doing so

firms are able to exploit the given situation and by adjusting their strategic stance for

competitive advantage (Fehre, Kronenwett, & Lindsta, 2016; Moss, Payne, & Moore,

2014; Review, 2018b). Also, investigating the influence of other contingency factors such

as industry in which a firm operates, the size of the firm, and the strategic choice, is an

important area for the enhancement of theory in the field of strategic management.

Furthermore, researchers, for example, Amitabh & Gupta (2010) and Ven et al., (2013)

have emphasized on the importance of expanding the methodological toolbox for

empirical research and stressed the need for longitudinal studies for exploring the

strategy-performance relationship.

One difficulty in business strategy level research is the fact that two matching strategic

settings seldom occur. Because of this difficulty, three primary approaches are used to

study strategy. These approaches are: the “situation-specific view”, “universal view”, and

“contingency view”. The situation-specific view sees strategy as an artful alignment of

internal strengths and weaknesses; environmental opportunities and threats; and

managerial values (Andrews, 1971; Martín-Alcázar et al., 2005). In contrast, the

universal laws of strategy exist to some extent in all settings (Delery & Doty, 1996;

Martín-Alcázar et al., 2005; Rozell & Terpstra, 1993). An example of this view is the

Boston Consulting Group’s “universally observable view” which implies that there is

4

only one grand type of setting and one universally sound competitive strategy (Hambrick

& Lei, 1985). Balancing these extreme views, contingency theory states that the

appropriateness of different strategies depends on the competitive settings of businesses.

It differs from the universal view by stressing that "it all depends." The situationalists

view differ from contingency in many ways. For example, it is difficult to generalize the

findings mostly because of case study approach whereas in contingency research the

findings can be generalized. Similarly, the prediction of the future behavior of the

organizations choices are difficult in situationalists approach while the contingency

research makes it possible to predict the future (Hambrick, 2003). The difference of

situation-specific view from contingency based view is also based on the fact that there

are classes of settings for which strategic generalizations can be made. Contingency

theory requires having a basis on which to divide competitive settings into discrete

classes. For this purpose, contingency variables are identified to categorize the firms into

discrete classes. Many researchers such as Hofer (1975), Mintzberg (1979), Porter

(1980), Donaldson (2001), Daft (2015) etc. identified some key contingent factors like

strategy, firm size, industry, technology, country etc. According to Hambrick & Lei

(1985), organizational and strategy scholars can make their greatest contributions through

the contingency view by dividing competitive settings into discrete classes.

The popularity of contingency based research can be attributed to a fundamental

assumption that there is no one best way to organize a fit that is equally effective under

all conditions. The reason of extending this assumption to the strategy context or

paradigm is based on the fact that it is rooted in the concept of matching organizational

resources with the corresponding environmental circumstances. In this approach, the

relationship between two variables is predicted by the third variable (Ginsberg &

Venkatraman 1985; Pleshko et al. 2014; Donaldson 2001). In line with these

perspectives, studies that focus on the contingent relationship between an independent or

contextual variable (for example strategic orientation, firm size, and industry etc.) and a

dependent variable (for example performance) across different contexts are considered as

legitimate contingency based research.

5

Contingency variables such as strategy, structure, firm size, industry etc significantly

influence the strategic stance and performance. For example, changes in firm size

influence the firm performance and exhibits different characteristics of organizational

design. As organizational size changes, complexities in roles, organizational structure and

behavior changes. Similarly, the fundamental differences between the two industries

seem to concern buyer dispersion, needs and knowledgeability, and demand uncertainty.

Such variations give rise to a significant differences in strategic thinking of the

management. The peculiarities of industry limit the managerial influence significantly as

it restricts the management to design and implement strategy proactively for higher

performance (Wilden et al. 2013; Hambrick & David Lei 1985; Madanoglu et al. 2014;

Ven et al. 2013; Jennings et al. 2003).

The contribution of strategic group analysis is noteworthy in highlighting the nature of

strategy-performance relationship. Strategic groups’ analyses help in identifying the

clusters of businesses, within most industries, that seek to execute similar business

strategies. Each strategic group contains the number of firms that peruse a similar

strategy so that they can benefit from the competitive advantage (DeSarbo, Grewal, &

Wang, 2009; Lin, Tsai, & Wu, 2014). Strategic groups provide empirical evidence of the

presence of a number of patterns of strategic behavior of the firms. Such analysis

provides a useful intermediate frame of reference between seeing each firm separately

and seeing the industry as a whole. Comparing the outcomes of groups’ differences and

similarities in a given industry helps to clarify the strategic features associated with the

performance of an organization (Zamani et al. 2013). For identification of strategic

groups and for investigating their relationship with performance, strategic typologies are

generally operationalized.

Strategic typologies helps in identify multiple competitive strategic choices available to

the organizations or business units and provide the theoretical foundation for

identification of groups across industries. Strategic management studies provide several

strategic typologies in this regard. These typologies are conceptually driven interrelated

sets of ideal categories meeting three criteria: (1) they contain quantifiable and clearly

defined construct, (2) they articulate the construct relationships, and (3) the assumptions

6

of the typology are testable (Doty & Glick, 1994). This means that a typology provides

multiple causal relationships in a given environment. Based on its conceptual and

methodological soundness, a typology reduces the complexity of classification and causal

relationships to a manageable level (Fiss & Peer, 2011).

Numerous typologies are developed to categorize the business-level strategy. The major

contribution comes from: strategic types of Miles and Snow (1978) known as

“Prospectors, Analyzers, Defenders, and Reactors”; generic strategies of Porter (1980) as

“Cost Leadership, Differentiation, and Focus”; high-performance gestalts of Miller

(1990) termed as “Craftsman, Builder, Pioneer, and Salesman”; and strategic types of

Treacy & Wiersema (1995) named as “Operational Excellence, Product Leadership, and

Customer Intimacy”. Theoretical foundations and practical approach of these typologies

have stimulated a widespread research in strategic management.

The contribution Miles and Snow’s typology is specifically appropriate for a context in

which strategy-performance relationship of firms from multi-industry settings is

investigated. The typology has been validated through a numerous tests in a wide range

of research settings (Hambrick 2003) making it suitable to study with both perceive

information and for archival financial data for analysis. Some examples of research using

this typology are how the strategy types differ: in their administrative practice

(environment scanning, power and influence process, organizational structure, and

reward settings); in their functional profiles and policies (vertical integration, R&D

intensity, fixed assets configuration, sales force management practices, and advertising);

and in their performance under various environmental conditions etc (Blackmore &

Nesbitt, 2013; Hambrick, 2003).

Miles and Snow (1978) offered two important elements for competitive advantage of

organizations. First element is a general model of the process of adaptation specifying the

major decisions needed by an organization regarding the entrepreneurial, engineering,

and administrative problems to maintain an effective alignment with its environment. The

second and most popular component of the framework is the strategic typology

portraying diverse pattern of adaptive conduct or behavior used by organizations within a

7

given industry or other group of industries. Accordingly, the choices and decisions of the

top management are the critical determinants of organizational structure and process.

These choices, which are complex in nature, are classified into three broad problems of

organizational adaptation. The entrepreneurial problem highlights the selection and

adjustment of the product-market domain. The issues relating to the engineering problem

include the problems regarding production and delivery of the products and services. The

administrative problems refer to the establishment of managerial roles, their inter-

relationships and the organizational processes to achieve the effectiveness. The typology

presented four distinct strategic types that an organization opts during its course of

business. Businesses that adapt defender strategy follow functional structure and they

prosper through the focus on stability and efficiency. On the other hand, prospectors

flourish by stimulating new product and market opportunities following divisional

structure. Analyzers flourish by adapting a matrix structure and by creating a balance

strategy. Analyzers are more innovative in their product-market initiative than defenders.

Their innovative approach is more cautious and selective than Prospectors. Reactors

depend on the environmental forces in their approaches and hence do not prosper at all

(Miles & Snow. 1978).

Hambrick (1983), termed Miles and Snow framework as the "configurational view" of

strategic choice which focuses on how management creates a fit between organizational

characteristics with its environment and how they match each other requirements.

Subsequent studies used this work to build the Strategy-Structure-Performance paradigm

that made this framework as "arguably the most important sub-stream of research on

structural contingency theory”. The paradigm focuses on the importance of the linkage

between strategic types and organizational performance rather than seeing strategy and

structure in isolation for the importance of their impact on performance. the theoretical

basis for the paradigm is that the performance of an organization is likely to be higher

when its strategy and structure are compatible with each other. The performance will be

lower if there is no such matching or compatibility (Wasserman, 2008). Hence, the

framework of Miles and Snow has been integrated into contingency research in

8

organizational theory and it has been extended by the configurrists to other organizational

processes as well (Hambrick 2003).

There is a great support for Miles and Snow typology’s basic assumption that “viable

strategies perform equally well in the long-run”. However, there are reasonable instances

where this assumption was violated (Blackmore & Nesbitt, 2013; Hambrick, 1983;

Parnell et al., 2015; Parnell & Wright, 1993; Smith et al., 1989; Zamani et al., 2013). One

of the many reasons for the significant differences in the performance among viable

strategic types is the varying nature and scope of performance measures. The difference

in environmental context is another reason in this regard. For instance, Hambrick (1983)

found that defenders performed better than prospectors when their performance was

compared in terms of profitability while in terms of market share, prospectors performed

better than defenders. In other studies, for example, prospectors showed higher

performance in terms of sales growth while analyzers provided higher return on assets

(ROA). Similarly, prospectors outperformed all other strategic types (Parnell & Wright,

1993; Zamani et al., 2013). Regarding environmental context, the inconsistencies and

differences in performance was also found when the performance comparisons were

made across the countries (Parnell et al., 2015). Comparing the performance of viable

strategies with that of reactor, it was also found that viable strategies performed

negatively although, they outperformed reactors. For example, the performance of

prospectors was negative in China while the performance of analyzers was recorded

negative in USA and Turkey (Parnell et al., 2012; Parnell et al., 2015). Reactors generally

performed poorly which support the main assumption of Miles and Snow typology but

reactors also performed better than viable strategies in some studies. For example, in

highly regulated industry reactors outperformed viable strategies (Snow & Hrebiniak,

1980). Blackmore & Nesbitt (2013) also found that reactors performed better in one of

the performance measure (ROA) over viable strategies. These findings supports the

assessment of Zahra & Pearce (1990) that “the preassumed inferiority of reactor strategy

to others is questionable”. Similarly, Conant et al. (1990) also argued that reactors have

the potential to perform better if they improve their strategic practices step by step. By

doing so, they can sufficiently sustain and exploit environmental conditions. The findings

9

of the studies also show that there is no consistency in the impact of contingencies as the

influence of contingent factors on strategy-performance relationship is inconclusive.

A number of researchers have commented on Miles and Snow’s work that there is a need

for further refinements in methodology, particularly the process for identification of

reactor strategy (Amitabh & Gupta, 2010; Conant et al., 1990; Desarbo, Benedetto et al.,

2005; Sarac, Ertan, & Yucel, 2014; Shortell & Zajac, 1990; Smith et al., 1989; Thomas &

Ramaswamy, 1996; Ven et al., 2013; Zahra & Pearce II, 1990). These researchers have

suggested for validation and empirical testing of the underlying assumptions of Miles and

Snow typology across industries and in different contexts specifically in countries other

than developed countries.

Following gaps are identified based on the detailed investigation of the previous research

on Miles and Snow typology:

Most of the empirical research using Miles and Snow typology is cross-sectional

using information collected through questionnaire or interviews which measure

the intended or perceived strategy. Few researchers have used archived data to

measure realized strategy and within this even lesser number used the financial

data for identification of strategic types.

The contemporary debate that does strategic purity matters or firms should

combine/hybridize the pure strategies to have competitive advantage is under

researched.

Very few attempts were made to investigate the transition of strategic orientation

over the time to find out the strategic behavior of the firms. To fill this gap, it is

important to know that in rapidly changing environment whether the firms with

consistency in strategic choice brings better results or firms should follow a

flexible strategy.

Generally, the operationalization of reactor strategy is neglected specifically in

longitudinal studies. Therefore, there is a need to develop a mechanism for

identification of this residual strategy.

10

There is a dearth of studies where a comparative analysis of single-industry and

multi-industry is done in one setting.

1.2. The Pakistan’s Business Context

Local, social, and cultural contexts shapes the behavior of the management. The

contextual environments in developing countries have different dynamics. There are

environmental complexities because these environments lack predictability and have

limited resources for development. From a strategic point of view, long-term planning is

generally compromised because of the turbulent and unpredictable business environment.

Therefore, the concepts and theories developed, practiced, and promoted in advanced

countries face intense difficulties and restrictions when they are perused in developing

nations. In this context, the attentiveness of business and management research in

developing and emerging countries represents significant gap in literature. This gap is

visible in both theoretical and empirical studies. This phenomenon, therefore, is even

more pertinent to the developing as well as the emerging economies of the Asian

countries (such as China, India, and Pakistan) those are at the verge of competitive

business pressures from MNCs and global integration of resources. The management in

these countries are faced with unique set of challenges because of differences in the

business contexts, developments and understanding of theories, and the management

practices. These challenges are significantly different from those faced by the developed

world where they have stable economic and institutional structure. (Janjua & Sobia,

2010; Parnell et al., 2015; Zamani et al., 2013)

Pakistan is occupying a strategic geographical location in South Asia. Its neighboring

countries include India, China, Iran, and Afghanistan. Pakistan falls in low income

country list having per capita income equal to US $ 1,629 in 2017. In GDP of Pakistan,

agriculture sector has been the largest contributor since its inception. This is now

replaced by the rising contribution of manufacturing and service sector showing a shifting

trend toward more modern and developed economic infrastructure. Being ranked at

number 6 in the world in terms of population, Because of its strategic location, large

potential consumer market, and cheap labor, Pakistan can be an attractive and lucrative

11

place for international investors. According to SBP, Foreign Direct Investment (FDI) in

Pakistan rose to US $ 2767.6 million at the end of June, 2018 (SBP, 2018). Pakistan

offers a promising investment and growth opportunity for international community

because its young educated generation is entering into job market, particularly in the

information and telecommunication sector. However, the drivers for change in the

external environment such as the law & order situation, global & national economic

situation, R&D and innovation etc. are expected to affect the business organizations in

the region. Similarly, the internal change drivers such as quality in production design and

service delivery are seen as the promising opportunity for business growth in future

(Government of Pakistan, 2017; Janjua & Sobia, 2010).

Historically, economic growth in Pakistan has remained volatile. Although, it lacks a

steady growth pattern that adds uncertainty to the economic development conditions, it is

widely acknowledged that Pakistan has immense economic potential. However, in recent

times, a smooth upward trend in economic growth rate has been witnessed since 2013-14.

Since then, the growth in Real GDP, which was above 4 percent in 2013-14, has steadily

increased and reached to the level of 5.28 percent in 2016-17. This growth rate is the

highest in 10 years. Pakistan is expected to become the world’s 20th largest economy by

the end of 2030 and 16th largest by the year 2050 according to a report published by

PricewaterhouseCoopers in 2017. Other reputed international publications, for example,

Bloomberg and Economist, have also acknowledged the impressive economic growth in

Pakistan in the last few years (Government of Pakistan, 2017b).

China-Pakistan Economic Corridor (CPEC) project is expected to change the business

dynamics of Pakistan as it will enhance the geographical linkages through improved road,

rail and air transportation system, people-to-people contact, academic and cultural

understanding, trade and businesses, energy production, and regional integration and

harmony (Government of Pakistan, 2017a). CPEC project promises a future of economic

connectivity and regional cooperation having far reaching positive implications: by

cultivating a more systematic, up-graded and need-based interaction for socio-economic,

industrial, energy and trade development (Rizvi, 2015); by building a strong,

interconnected, and integrated nation where all segments of population benefit equally

12

from stability and growth (Hussain, 2016); and by providing an economic support to a

long-time ally with strategic hedge and facilitating trade (Ritzinger, 2015). The

interaction of Chinese engineers, technicians and labor with their counterpart Pakistanis

will enhance the skills of the local workforce. This will also impact the strategic practices

and management of local business in the backdrop of international competition.

On the other hand, in the “ease of doing business ranking” index, Pakistan is at 144th

number among 190 economies. On the basis of certain key parameters, this index ranks

the economies or countries against each other. These parameters are: the level of

conduciveness of regulatory environment to business operations; and level of protections

of property rights. Countries with top twenty ranking (1 to 20) have friendlier and simpler

regulatory environments for businesses with strong property rights protection. Pakistan is

behind India (ranked 130), Egypt (122), Indonesia (91), China (78), and Turkey (69) in

this ranking whereas Bangladesh is ranked below at 176 (World Bank, 2017). The major

obstacles for business in Pakistan are non-availability of electricity, political instability,

and tax administration (World Bank, 2013). Similarly, Pakistan falls in the lowest band of

innovation as its innovation index is 91/125. The interest of businessmen is being shifted

away from entrepreneurial activities because of increasing cost of doing business (Haque,

et al., 2007). According to them, the absence of innovation and the non-dynamism of

business are the two major problems for lacking entrepreneurial activities. The reasons

for lack of innovation include the inheritance of family business in majority cases and the

imitation of business models. Similarly, the mindset of the businessmen is to remain

local. Particularly, small businesses rarely attempt to move even across the cities or local

boundaries. Hence, internationalization conglomerates are seldom phenomenon.

Businessmen in Pakistan need to invest in brand management and brand development to

give a significant boost to entrepreneurial activities. According to Haque (2007), the

causes for the poor business atmosphere are:

Lack of Expert Skills and Research

o Business in Pakistan generally resist the development of professional

management because they are largely owner-operated

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o Rather than depending on professional management, the growth in

business is traditionally dependent on policy favors

Poor Legal Framework

o Because of weaknesses in legal and judicial systems, mistrust of

professional managers and misuse of business information harms the

professional growth of the businesses

o There is lack of faith and confidence because of the incompetence of the

legal system for enforcement of even the fundamental business rights of

property and contracts

The other obstacles for business growth in Pakistan include the lack of trust and social

capital, and financing constraints etc.

The large corporations in Pakistan are less than 10% in terms of number of business

enterprises which is dominated by SMEs (more than 90%). But the contribution of large

corporations towards GDP and exports is more than 60%. Large industries and business

have advantage of accessing funds from financial markets over small enterprises (SMEs).

Similarly, large corporations have the advantage of having good management and

technical skills, large assets base, distributed ownership, relevant and appropriate

knowledge base, focus corporate governance, international financial support in case of

MNCs, and backing of the Government particularly in case of public sector enterprises

(BYCO Petrolium, 2015).

On cultural dimensions, Pakistan scores high on “high power distance”, “low

individualism”, “high masculinity”, “high uncertainty avoidance”, and “no long-term

horizon” (Hofstede, 2017; Shah & Amjad, 2011). When we link these cultural

dimensions of Pakistan with the management concepts and practices, it is found that in

such circumstances, people take care of group problems and show loyalty with long-term

group commitment. Employer/employee relationships and hiring and promotion decision

are perceived in moral terms. People in such situations resist innovation having pessimist

attitude. For continuous motivation, they consider security as an important element.

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1.3 Strategic Management in Pakistan

For any business to be effective and sustainable, the choice of strategy is considered to be

the most fundamental ingredient. The creation of market perception for product and

service value, market and customer focus, and competitive advantage requires the

selection of an appropriate and suitable business strategy. The studies on strategy-

performance relationships in Pakistani firms are not very large. However, there are many

studies in which this relationship is investigated directly or through moderation and

mediation of some contingent variables. These studies investigated many aspects of

strategic management such as strategic orientations, strategy formulation and

implementation, marketing strategies, strategic management concepts and practices,

strategic change behavior, capital structure, working capital policies and their

relationships with organizational performance.

Hassan et al., (2013) investigated the direct impact of marketing strategy creativity and

marketing strategy implementation effectiveness on organizational performance and

checked the mediating and moderating relationship of strategy and environmental

uncertainty. Miles and Snow’s strategic types (prospectors, analyzers, differentiated

defenders, low cost defenders and reactors) were operationalized through survey

instrument from service and manufacturing companies in Pakistan. Applying Miles and

Snow typology, Khan et al. (2016) analyzed the effect of strategy formulation and

strategy content on organizational performance for firms in private sector where only

defending, prospecting and reacting strategic types were considered for analysis. Afza &

Ahmed (2017) examined the capital structure and performance relationship for non-

financial firms using 8 years archived data taking business strategy as a moderating

variable. Michael Porter’s strategic types: cost leadership, product differentiation, hybrid

strategy (combination of cost leadership and product differentiation), and stuck in the

middle (no strategy) were operationalized through financial measures. Afzal (2009) also

applied Porter’s generic strategies: low cost leadership, differentiation, and product

market scope to investigate strategy-performance relationship where marketing practices

are taken as moderating variable.

15

Besides application of strategic typologies, some other studies also studied the strategic

management concepts in Pakistan. For example, Afza et al. (2008) investigated the

relationship between strategic diversification and firm performance. Nazir & Afza (2009)

analyzed the relationship of working capital management policies (aggressive versus

conservative approaches) and firm performance. Malik & Kotabe (2009) studied the

relationship of dynamic capability development mechanism (“organizational learning,

reverse engineering, and manufacturing flexibility“) with organizational performance for

emerging market manufacturing firms in India and Pakistan.

Keeping in view the above facts, the challenges for strategic managers and policy makers

are manifold in Pakistan. The key characteristics of Pakistani business environment

include the rich natural resources and cultural heritage, young growing population,

turbulent business environment, geostrategic and geopolitical importance, uncertain and

unstable environment etc. Hence, it is expected that the organizational performance,

strategic choices, and the behavior of the strategic orientation of the firms for Miles and

Snow typology assumptions may not be similar as are found in developed countries.

1.4 Motivation of the Study

One of the central question in strategy-performance relationship research is why firms

succeed or fail in a given environment? To find out the causes of success or failure of the

firms such questions are investigated further: how firms choose strategy and how they

behave over the time to create a fit? Why firms differ in their strategic fit and in

structure? Why firms differ in performance? These questions provide the motivation for

an extensive investigation of listed firms of Pakistan Strategy-performance context. The

research purpose include: firstly, to extend the scope of prior research within the strategic

management field by investigating strategy-performance relationship keeping in view the

contingent factors of firm size and industry. Here, strategy is conceptualized in terms of

pure and hybrid strategies. Similarly, the identification of strategic behavior over the time

is missing in strategy-performance relationship research. This research fills this gap by

measuring the strategic transition and find out the consistent, flexible, and reactor

behavior over the time and then investigating the strategy-performance relationship;

16

secondly, the research uses the archived financial data of listed companies in Pakistan

which is very rarely used for measuring strategic orientation of the firms. The objective

here is to reach a better understanding of management’s strategic approaches and

behaviors so that a proper structure and resource fit can be created for better

performance; thirdly, the research analyses the multi-industry data and single industry

(textile industry) for comparing the similarities and differences between the results so that

the findings can be generalized; and fourthly, there are methodological limitations for

classification of strategic types and strategic behavior when archived financial data is

used. This research does the refinements in methodology by presenting step-by-step

procedure for operationalization and classification of strategic types and behavior.

The context of the research is Pakistan which is a low per capita income country

depending historically on agricultural sector. The trend is now changing and the

dependence has been shifting towards manufacturing and service sectors. The factors

such as law and order situations, global economic competition and challenges, R&D and

innovation pressures, quality in production design, service delivery, and infrastructure are

affecting the business competition and practices in the country. In terms of “ease of doing

business” index, Pakistan is ranked at a lower number, because of lack of expertise and

skills and poor legal framework. CPEC is also changing the business environment and

state of competition in Pakistan.

Keeping in view this background, all listed firms on Pakistan Stock Exchange (PSE) are

selected for this study because: first, they represent the whole economy comprising the 12

economic groups as classified by the State Bank of Pakistan; secondly, they represent

both public and private sector; thirdly, the large corporations in Pakistan contribute more

than 60% towards the GDP and exports; fourthly, the personal interests of the researcher

is to study strategy through financial data that reflect the realized strategy of the

management to provide the foundation for researcher in the field of strategic

management.

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1.5 Problem Statement

The increasing emphasis of firms for hybridization or combination of pure strategies to

have sustainable competitive advantage has raised the question whether strategic

hybridization gives superior performance than strategic purity? Similarly, there is an

inconclusive debate in contemporary literature about the superiority of strategic

consistency over strategic flexibility and vice versa. Even though, the strategic typologies

are widely used for investigating strategy-performance relationship, there are

methodological limitations to operationalize the strategic orientation and strategic

behavior of the firms into pure versus hybrid strategic types and consistent versus flexible

and reactors behavior respectively. To address these issues, a renewed emphasis is,

therefore, needed for typology-driven classification of strategic orientation and behavior

coupled with methodological refinements to investigate the relationship of strategy and

performance along with contingent impact of size of the firm and the industry in which a

firm operates. Drawing upon the perspective of contingency theory of strategy, the aim of

this research is to address these issues.

1.6 Objectives of the Study

To make refinements in methodology that will help in identification of strategic types

through archived data to categorize the strategic types into pure, hybrid, consistent,

flexible, and inconsistent (reactors) types and then to empirically test the relationship of

these strategic types with organizational performance. The impact of contingent factors

such as firm size and industry is also investigated. Following are the specific objectives

of the study:

1. To develop a scoring methodology using archived financial data to operationalize

the strategic orientation. The method will help in classifying the firms in strategic

groups following pure, hybrid, consistent, flexible, and reactors.

2. To investigate the differences in performance among strategic groups/types across

firm sizes and industries

3. To investigate the relationships of:

a. Pure, hybrid, and reactor strategy with performance

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b. Consistent, flexible, and reactor strategy with performance

c. Firm size and Industry with performance

d. Contingent (interactive) effect of firm size and industry on strategy and

organizational performance

4. To have an in depth study of one industry and compare the similarities and

differences in results with multi-industry analysis

1.7 The Research Questions

Based on the problem statement and the research objectives, the study investigated the

following research questions:

1. What is the strategic orientation and strategic behavior of listed firms in Pakistan?

Specifically, to know:

a. Whether firms pursuing pure strategies or hybrid strategies?

b. Is there consistency in their strategic stance over the time or they adapt

flexibility or have inconsistent behavior?

2. Does there exists a significant difference in the performance based on their

strategic groups/types or strategic behavior of the firms? Specifically;

a. Is the performance of viable strategies similar or differ significantly?

b. Dose there exist a significant difference between the performance of viable

strategies and reactors?

c. Is hybrid strategy superior to pure strategy?

d. Is consistency in strategy performs better than flexibility?

3. Is the effect of contingent factors (firm size and industry) on organizational

strategy and performance is significant?

4. Does the results from single industry analysis are similar or different from multi-

industry analysis? Can the findings are generalizable or not?

1.8 Research Methodology

The data of 307 joint stock firms representing 12 industries (economic groups), listed at

Pakistan Stock Exchange -PSE (Formerly Karachi Stock Exchange –KSE) for seven

19

years (2007-2013) is used for analysis. The data source is the publication of Central Bank

(State Bank of Pakistan) “Financial Statement Analysis of Companies (Non-Financial)

Listed at Karachi Stock Exchange”. Strategy is considered as independent variable,

organizational financial performance as dependent variable while firm size and industry

are taken as contingent variables. Descriptive Statistics, ANOVA, and Regression

techniques are applied to analyze the data and to interpret the results. SAS (9.3) software

is used for data management and analysis.

1.9 Contribution of the Study

The proposed scoring methodology will help the researchers in identification of

multiple strategic groups based on varying characteristics of the firms. A standard

scale is developed for classification of pure and hybrid strategic types on a

continuum. Secondly, a mechanism is developed for identification of strategic

behavior of the firms over the time to classify the firms into consistent, flexible,

and inconsistent (reactors) types. In this context, this study is the pioneering work

in a longitudinal research. The empirical findings validate the theoretical

underpinning associated with these strategic groups. The methodology can be

replicated where strategic groups are to be operationalized through typologies.

The original work of Miles and Snow suggests four mutually exclusive and static

strategic types. In actual, the firms hybridize the pure strategies by combining the

characteristics of pure strategies. Also, when viewed through the lenses of

strategic fit, they are better presented as the changing behavior over the time. This

means that an organization reconfigures its processes and deploys resources in

reply to changes in its internal and external environment. Doing so, the

organization reposition itself into one of the viable strategic types. The

investigation of strategic transition over time and their classification into

consistent, flexible, and specifically reactors is another contribution of the study.

The study presents a detailed comparative analysis of single-industry and multi-

industries in the same settings. The applicability of the methodology in both

settings and the conformity of most of the results provide sufficient evidence to

generalize the findings.

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1.10 Structure of the Thesis

This study is organized into six chapters. The second chapter presents a theoretical

review of literature. This include the historical development of strategic management

field, the concept of strategy and its various dimensions, strategic groups and typologies,

pure and hybrid strategies, strategic consistency and flexibility, Miles and Snow strategic

typology, contingency theory, strategy and organizational performance with special

relevance to Miles and Snow typology and contingency theory. In the third chapter, the

empirical literature review where Miles and Snow typology is applied for strategy-

performance relationships. The chapter provides the updated information about the

research settings, strategy and performance relationships, methodologies used for

analysis, and distribution pattern of strategic types. Hypotheses are also developed in this

chapter. The fourth chapter covers the research design, strategy and performance

measures, variables of interest, detail description of scoring method, research model and

estimation methodology. The fifth chapter presents the results, analysis and discussions

while sixth and last chapter represents conclusion including implications of the study and

the opportunities for the future research. The industry-wise firms’ strategic orientation

and strategic behavior over the time, the results of textile industry, and the list of

publications is given in the annexures.

21

Chapter 2

Theoretical Literature Review

22

2.1 Introduction

The chapter presents the theoretical foundation and the scope of the topic. It starts

with a brief on historical development of strategic management field. The concept of

strategy in its various forms (intended and realized; pure and hybrid; consistent and

flexible) and strategic levels in organization (corporate strategy, business strategy, and

functional strategy) is discussed next. The contemporary debate on strategic purity

versus strategic hybridization and strategic consistency versus strategic flexibility is

highlighted in subsequent sections. Next, the idea of strategic groups and strategic

typologies in general and the typology of Miles and Snow in particular is presented in

greater length followed by the literature on contingency theory. A review of literature

regarding the strategy-performance relationship with special focus on Miles and Snow

typology and the perspective of contingency theory concludes the chapter.

The literature presented in this chapter is important because it discusses the broader

domain of the thesis and then narrowed down the concept to the main focus that is

related to the strategy-performance relationship in the background of contingency

theory. Here the concept of strategy is narrowed to the realized strategy

operationalized using Miles and Snow typology. Performance is referred to the

financial measures and the contingent factors are identified as firm size and industry.

Hence, the chapter provides the foundation to the next chapter: empirical literature

review which focused on the core domain of the thesis.

2.2 Strategic Management –A Historical Perspective

Strategic management has been a multidisciplinary area of research. The theories and

concepts are borrowed from other disciplines such as: psychology, economics,

philosophy, sociology, political science, and systems science etc. The developments

in the concepts and theories of strategic management includes the industrial

organizational view, resource-based and knowledge-based views of the firm,

competitive and behavioral strategy, competitive dynamics, global strategy,

innovation and technology strategy, institutional and nonmarket strategies,

stakeholder theory, strategic leadership and change, and strategy processes etc.

(Durand, Grant, & Madsen, 2017).

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The evolution in the field of strategic management has been impressive. There has

been an inspiring growth in this field particularly in the variety of topics and the

diversity of methods employed for research in a relatively small duration of time.

Furrer & Thomas (2008) and Hoskisson et al. (1999) did a systematic review of the

evolution of strategic management field. According to them, the development in

strategic management is divided into three periods: (1) the founders or the precursors;

(2) the birth in the 1960s; and (3) the transition towards a research orientation in the

1970s and afterwards. The precursors (the writers of the era before 1960s) explored

and suggested the definition and the role of management along with the possibilities

for strategic choice. For example, Taylor (1947) initiated the concept of a “science of

work”; Barnard (1938) studied the “roles of managers”; Simon (1947) analyzed the

administration and developed a framework for that; and Selznick (1957), introduced

the idea of “distinctive competence” etc. According to Koch (2011), the roots of the

strategy go back to Alfred Salon’s reorganization of General Motors in 1921 while

Peter Drucker in his book “Concept of the Corporation” published in 1944 suggested

that goal setting and organization design or structure are the key features of the most

successful companies. Drucker was the first person to propose that creating and

satisfying the customer needs is the basic purpose of a business.

The pace of development of strategic management field grew to powerful adolescence

in 1960s. In 1960, the article “Marketing Myopia” published in HBR by Theodore

Levitt was one of the first attempts to look at corporate strategy. The developments

during this period can be attributed to the three ground-breaking works by Alfred

Chandler’s “Strategy and Structure” (1962), Igor Ansoff’s “Corporate Strategy”

(1965), and Kenneth Andrews’s books “Business Policy: Text and Cases” (1965) and

“The Concept of Corporate Strategy” (1971). According to Chandler (1962), structure

follows strategy and therefore, organizations should develop their strategies before

designing organizational structure. He defined strategy as the “setting of long term

goals and objectives, the determination of courses of action, and the allocation of

resources to achieve the objectives”. Ansoff’s (1965) work presumes that

maximization of profitability is the ultimate purpose of an organization. His

framework provides a number of checklists and charts for developing objectives,

evaluating synergy between firms’ business and functions, assessing the

organization’s competency profile, deciding how to diversify and expand the

24

businesses, product-market position, and resource allocation etc. Kenneth Andrews

presented one of the most applied models of strategy, one component of which is

SWOT analysis. Based on SWOT analysis, strategy is chosen with particular

emphasis on the fit between external opportunities and firms’ own competences

(internal strengths). The organizational structure is then modified to facilitate the

implementation of the strategy.

Another important development was the foundation of Boston Consulting Group

(BCG) in 1963 by Bruce Henderson. BCG developed two very widely used strategic

management tools: an experience curve and Growth-Share matrix. The focus of the

research shifted from a “deterministic -one-best-way approach” to a more contingency

based perspective where firms adapt themselves to their environments effectively.

The orientation of these studies was towards managerial aspects with an emphasis on

normative recommendation rather than on analysis and pragmatism. The research in

these developments was dominantly based on “in-depth case studies” where the

results of such studies were hardly generalizable (Furrer & Thomas, 2008; Hoskisson

et al., 1999; Koch, 2011).

In response to generalizability issue because of case study research, a shift towards

research orientation focus started in 1970s. During this time period a dichotomous

development took place between two sets of research perspectives. One was

ontological (philosophy concerned with the nature of being) and the other was

epistemological (philosophy concerned with the knowledge of being) perspective.

One perspective was a “process approach” consisting descriptive studies explaining

how strategies are formulated and implemented. In this context, the research is based

on the investigation of organization’s actual decision-making. This leads to a more

realistic conceptions of process, in which strategies are arrived at indirectly and, to

some extent, unintentionally. The major contributions during this time, for example,

were “The nature of Managerial Work” by Henry Mintzberg (1973), and Igor

Ansoff’s (1979) “Strategic Management”, “Logical Incrementalism” by Quinn (1980)

and Mintzberg’s (1978, 1985) “Emergent Strategy” (Furrer & Thomas, 2008; Koch,

2011). The other perspective was the research exploring strategy-performance

relationship. Here, departing from case study research, studies were based on large-

scale data and statistical procedure were applied for deductive analysis and

25

hypotheses testing. The theoretical bases were dominated by industrial organization

(IO) view where Porter (1979, 1980, and 1985) has made the most influential

contributions specifically in understanding the structure of an industry in which the

firms are in. Porter argued that the profitability of the organization is influenced by

the structural characteristics of the industry in which the firm is placed besides firm’s

relative competitive position. External environment was assumed to have more impact

on profitability than the internal factors. Hence, the main focus of strategic

management research during this time was on the relationship of external

environment with organizational performance (Furrer & Thomas, 2008; Hoskisson et

al., 1999; Koch, 2011).

From late 1980s, the focus of research on strategy switched from the focus of external

environment of the firms (e.g. industry structure) as a unit of analysis to the internal

resources of the firms such as capabilities, and structure. The field of strategic

management witnessed the attention of two major streams of research attracted during

this time period. These streams are: the concept of “transaction costs economics

(TCE)” by Williamson (1975, 1985) and a well-known “agency theory” by Jensen

and Meckling (1976) and Fama (1980). TCE has three major contributions in this

regard. One, it provided a theoretical foundation to the management of large

organizations that how to adapt a multidivisional structure in their organizational

setup. The view argued for diversification of the firms’ geographical presence and

emphasized the relationship of multidivisional structure with the organizational

performance. Second, TCE concept clarify the working of hybrid forms of

organization such as joint ventures and strategic alliances. Finally, TCE view provide

guidance for choosing global modes of business and how to enter in the global

markets. Agency theory, on the other hand, clarifies that the interests of share-holders

and managers may diverge and suggested for separation of ownership and control.

Agency theory has been applied in strategic management covering diverse topics such

as corporate governance, diversification, and innovation etc. (Furrer & Thomas, 2008;

Hoskisson et al., 1999).

Parallel to these developments, a resource-based view (RBV) rose to the lime lights.

RBV highlights the importance of internal resources, instead of external resources, for

competitive advantage. The emphasis of the RBV is on the relationship of

26

organization’s internal resources, capabilities, and performance. RBV theorizes a firm

as a bundle of unique resources which are valuable, rare, non-imitable, and hard to

substitute. These internal resources are more important than external factors for

competitive advantage. These resources become the strengths and weaknesses of the

firms. Barney (1991) and Grant (1991b) provided other important developments in

strategy theory during this time. The research focus once again shifted to the RBV

theory (Furrer & Thomas, 2008; Hoskisson et al., 1999).

Late 1980s also saw the development of competency based theories of corporate

diversification (Hamel & Prahalad, 1989). In their ground-breaking article “Strategic

Intent”, Gary Hamel and Prahalad argued that proportionately higher commitment and

ambition to change are the hallmark of successful organizations. They further argued

in their article “The Core Competence of the Corporation”, that the real key to

competence was a firms’ distinctive skills, collective learning abilities, and

technologies (Koch, 2011). The other sources of competitive advantage are customer

trust, corporate culture, brand image, management skills, technology, and

information-based invisible assets etc. These resources are durable and time-

consuming to accumulate. They can be simultaneously applied in multiple ways.

These resources effects the productivity in a sense that they are both inputs and

outputs of business activities. The approach of corporate diversification developed by

Prahalad and his colleagues, postulate that value creation through diversification can

be increased by sharing the less tangible assets across businesses. They also

highlighted the potential role of this sharing in creating value through diversification

(Furrer & Thomas, 2008).

Along with these developments, the concept of strategic typologies emerged from

1970’s which opened a new dimension of empirical research in strategic management.

For instance, Miles & Snow (1978) presented four “strategic types” (“Defenders,

Analyzers, Prospectors, and Reactors”), Porter (1980) offered his set of "Generic

Strategies" (“Cost Leadership, Differentiation, and Focus”); Miller (1990) followed

with his “high-performance Gestalts" (“Craftsman, Builder, Pioneer, and Salesman”);

and Treacy & Wiersema’s (1995) three strategic types “Operational Excellence,

Product Leadership, and Customer Intimacy” etc. Specifically, the typology of Miles

and Snow has been applied widely and has gone through the frequent tests of validity

27

in a broad range of backgrounds such as hospitals, colleges, banking, manufacturing,

and life insurance etc from both public and private sectors. The typology has provided

a conceptual framework for examining a host of relationships. For example,

researchers have studied how the strategic types differ in their administrative practices

(including environmental scanning, power and influence processes, organizational

structures, and reward systems); in their functional profiles and policies (for example,

vertical integration, sales force management, marketing and advertising, R&D

intensity, and fixed-asset configurations etc.); and in their effectiveness under diverse

environmental conditions. Researchers who have undertaken these studies come from

different academic fields, including strategy, organizational theory, human resource

management, operations management, marketing, and accounting etc. Clearly, the

typology have had a substantial influence on the research trajectories of several fields

in the administrative sciences (Hambrick, 2003). The typology is considered as uniqe

in a senses as it views the organization as an integrated and complete social system

that interact with the ever changing environment (Daniel Rajaratnam & Chonko,

1995).

The contemporary discussion in strategic management research is about the

superiority of strategic hybridization over strategic purity and vice versa. The initial

concept that pure strategy leads to higher performance is challenged by the

contemporary scholars who argue for the adaption of hybrid form of strategy for

superior performance. The evidence in favour of this shift is increasing with

accelerating pace (Salavou, 2015). Similarly, there is an inconclusive debate in the

contemporary literature regarding the supremacy of strategic consistency over

strategic flexibility and vice versa? The supporters of the strategic consistency argue

that organizations with consistency in their strategic stance over the longer period of

time produce better results (Fehre et al., 2016; Lamberg, Tikkanen, & Nokelainen,

2009; Miles & Snow, 1978; Parnell & Lester, 2003; Porter, 1980; Sanchez, 1995). On

the other hand, it is argued that flexibility in strategic choice is the necessary

condition for improved performance (Herhausen & Morgan, 2014; Ouakouak &

Ammar, 2015; Parnell, 2005).

This overview of historical development of strategic management demonstrates how

research in this area grew remarkably and consistently. According to Furrer &

28

Thomas (2008) and Koch (2011), the sustainability of the growing development in the

field of strategic management without delivering great value was not possible and the

value of strategy has increased, is increasing, and shows no sign of diminishing.

2.3 What is Strategy?

The origin of strategy is the Greek word “strategos” which means the “art of the

general”. Chandler (1962) defined strategy as “the determination of the long-term

goals and objectives and the adoption of courses of actions and allocation of resources

necessary for carrying out these goals”. Ansoff (1965) viewed strategy as the decision

rules and guidelines required by an organization for its profitable growth. According

to Porter & Roach (1996), “strategy is the creation of a unique and valuable position

involving a different set of activities. It is the trade-offs in competing by choosing

what not to do, and involves creating a fit among activities of an organization”.

Mintzberg (1978) defined strategy as a pattern in a stream of decisions (Mintzberg,

1978). According to Waterman et al (1980), strategy means the actions that a

company plans in response to or in an anticipation of changes in its external

environment, its competitors, and its customers. It is the way a company intends to

improve its position. The position can be achieved through competitive advantage by

following, for example, a low-cost production or delivery mechanism, by achieving

sales and service dominance, and by providing better value to the customers etc. It is

an organization’s way of saying: Here is how we will create unique value.

The concept of strategy was introduced in the academics by faculty of Harvard

Business School during the 1950s. Their view of strategy is normative in a sense that

strategy is an imaginative act of integrating number of complex decisions. Chandler's

(1962) was the first person who employed descriptive concept for defining strategy.

According to him, strategy is the mechanism used for plotting a new direction. He

argues that there is a substantial impact of strategy on organizational structure and

hence on performance. This idea of strategy comprises the elements of both ends

(goals and objectives) and means (allocation of resources and courses of action).

Mintzberg (1978) has introduced the concept of intended, emergent and realized

strategies. These developments in the concept of strategy enabled the researchers to

think beyond the normative and the abstract aspects of strategy. It enables them to

move toward those decisions which involve organizational ends and means necessary

29

to achieve those ends (Snow & Hambrick, 1980). Mintzberg (1978) summarizes the

developments in the definition of strategy and concludes that such definitions treat

strategy as unambiguous, consciously and purposefully developed, and formulated

earlier for application of particular decisions in future. Mintzberg improved the

conception of strategy by introducing the notion of deliberate and emergent strategy.

Deliberate strategy is related with the effective implementation of the intended

strategy. Emergent strategy originates because of the interaction of the organization’s

internal and external environments. This is different from what was intended by the

managers. The first kind is generally applied to predictable and stable conditions,

while the second one (emergent strategy) emerges from a firm’s quick response to the

uncertainties of the environment.

Focusing on RBV, Lin et al., (2014), postulates that the strategy is a mechanism that

ensures a sustainable competitive advantage. This advantage is achieved by the

development of key capabilities through investing in the resources needed for superior

performance in the long run. But, strategy is successful only if it supports an

organization to develop and possess critical capabilities and resources. The

inseparability of organization and its external environment is the fundamental concern

for strategy thinkers because the organizations use strategy to cope with the change

taking place in the environment. Resultantly, the substantial part of strategy remains

un-programmed, un-structured, and non-repetitive.

Strategy formulation involves both conceptual and analytical exercises. There are

ample support in favor of both views as some authors stress more for analytical

dimension while others affirm that the heart of strategy making is the conceptual work

(Chaffee, 1985). It is, therefore, understandable that strategy scholars should search

and propose numerous configurational schemes that will help to bring order to an

otherwise complex array of business choices (Hambrick, 2003).

2.4 Intended versus Realized Strategy

It is difficult to conclude whether an organization follows a certain type of strategy or

not?. Another issue relating to this difficulty is that a course of action or mechanism

that an organization has formulated but not implemented is actually a strategy? In

contrast to this, is a set of specific pattern being applied for making important

30

decisions, and which was not planned earlier at the formulation stage, is really a

strategy? This difference between what was intended and what is realized may be of

critical importance for some researchers and may not be for others. Mintzberg (1978)

presented this difference in a framework for conceptualization of intended, deliberate,

emergent, and realized strategies (Figure 2.1).

Figure 2.1: The concept of intended versus realized strategy (Source: Mintzberg,

1978)

The framework highlight three key points:

1. There are realized strategies that are actually intended. These types of

strategies are called deliberate strategies.

2. There are cases where intended strategies are not realized. This non-realization

may occur because of impractical expectations, miscalculations while

analyzing environment, and inability to make adjustments in strategy during

implementation. Such types of strategies may be termed as unrealized

strategies.

3. There are instances where realized strategies were not intended. This may

happen due to the fact that no strategy was intended at all or because intended

strategy got displaced along the way. Such types of strategies may be called as

emergent strategies.

It is not easy to determine the intended strategy of an organization because

management rarely think of a strategy in the same language as categorized by the

literature. For instance, the typology of Miles and Snow classifies an organization

based on its orientations toward product-market development. Based on level and

Intended Strategy

Deliberate Strategy

Realized Strategy

Unrealized Strategy

Emergent Strategy

31

intensity of this orientation, an organization is classified into one of the four

strategic types (defenders, analyzers, prospectors, and reactors). Typically,

managers do not consider their organizations as being one of these strategic types.

Instead, management imagines and strive for being first, the best, biggest in size

or market share, lowest in price, and highest quality provider etc. Therefore,

researchers should have a clear understanding whether their purpose is served by

observing realized strategy or by investigating the intended strategies (Snow and

Hambrick, 1980). A strategy (intended or realized) guides the organization's

ongoing alignment with its environment and shapes internal policies and

procedures. This perspective for empirically viewing business-level strategy is

available in the Miles and Snow (1978) typology. The key questions, therefore,

are: How does the industry environment affect the effectiveness of different

strategic types? How do the strategic types differ in their functional tendencies?

(Hambrick, 1983). These questions are still valid and therefore need investigation.

2.5 Strategic Purity versus Strategic Hybridization

The strategy-performance relationship has been critically and widely examined by

applying the strategic typologies of Miles and Snow and Michael Porter. These

typologies classify organizations into one of the distinct strategic types. All

configurational theories (e.g. Miles and Snow) initially identify a finite number of

ideal types. Such type of grouping of strategic types are considered as pure strategies.

Firms those are able to hybridize or make combination of several factors related to

pure strategies (e.g. “low cost and differentiation” or “defenders and prospectors”)

may have competitive advantage. According to Parnell (2011) and Doty & Glick

(1994), hybrid strategies are combination of ideal or pure strategic types depicting a

behaviour that combines or integrate more than one pure strategic choices for

differentiation to achieve efficiency (Pertusa-Ortega, Molina-AzorÃn, & Claver-

Cortes, 2009). Hybridization of ideal types can result either in a finite or an infinite

set of hybrid types. Typically, hybrid types are supposed to be effective when

organizations respond simultaneously to conflicting contingencies (Doty & Glick,

1994).

The traditional view was that superior performance is linked with strategic purity

while deviation core strategy (such as having a “stuck-in the middle” strategy) leads

32

to undesirable performance (Porter, 1980). But, in practice, firms combine the aspects

of pure strategies instead of perusing the pure form of strategies created by theory.

This hybridization of pure strategies reflect reality because through hybridization

firms can adapt many combinations of pure strategies. This phenomenon is

independent of the industry in which a firm falls. Although, there is ample support in

favour of strategic purity, the debate in extant literature is that does strategic purity

still is a better choice for superior performance or not? (Pertusa-Ortega et al., 2010;

Salavou, 2013, 2015; Thornhill & White, 2007). The other terms used for hybrid

strategies in the literature are “mixed”, “integrated”, or “combination of strategies”

etc.

2.6 Strategic Behaviour

Organizational behavior refers to organizational members' work-related activities.

Behavioral perspective postulate that different strategies require different behaviors.

The link between strategy and behavior is useful because it provides a clear

explanation of why behavior should be linked to strategy. There are many examples

of strategic behaviors such as customer-oriented behavior; competitor-oriented

behavior; innovation-oriented behavior; and internal or cost-oriented behavior (Olson,

Slater, & Hult, 2005). Similarly, Strategic groups provide empirical evidence of the

presence of a number of patterns of strategic behavior of the firms. The comparison of

differences and similarities in strategic groups in a given industry, helps to clarify the

strategic features accompanied with performance (Zamani et al. 2013). According to

Slater, Olson, & Finnegan (2011), the success of business strategy is dependent on an

appropriate strategic behavior that helps organizations to create a supportive

organizational culture which is a source of competitive advantage as it enables the

organization to execute its strategy in a more effective and efficient manner.

In this study, we examine strategic behaviors that have the potential to influence

organizational performance. These include consistency-oriented behavior; flexibility-

oriented behavior; and inconsistency or reaction-oriented behavior. The consistent

strategic behavior means that the organizations are stick to the core business strategy

for a longer period of time and exploit the business opportunities with cautious and

incremental growth by maintaining their products, customers, markets, and core

technology intact. The flexibility-oriented strategic behavior focus on broad and

33

continuous growth by aggressively exploring and exploiting the new opportunities for

product and market development (Fehre et al., 2016; Lamberg et al., 2009; Moss et

al., 2014). In the process, firms following this behavior change their strategic

orientation for adjustment to get the benefits of innovation and first movers’

advantages. However, their shift in strategic stance is well thought and infrequent.

The inconsistent or reactor behavior means that such firms do not have any defined

strategy and they react to the changes in the environment in an inconsistent way.

Doing so they cannot reap the benefits of a well thought strategic moves (Ingram,

Kraśnicka, Wronka-pośpiech, Gtod, & Gtod, 2016; Miles & Snow, 1978; Parnell et

al., 2015; Snow & David J. Ketchen, 2014). Consistent and flexible strategic

behaviors are expected to have positive impact on organizational performance while

the inconsistent or reactor behavior is expected to have a negative impact on

performance or have worse performance than the consistent and flexible behaviors. In

this context, literature on strategic consistency and strategic flexibility is discussed

below while the literature on reactor will be discussed under Miles and Snow

typology (section 2.10.1.4)

2.6.1 Strategic Consistency versus Strategic Flexibility

Strategic consistency is the intentional continuity of the management to stick with the

past strategic choice (Moss et al., 2014). The consistency in strategic choice becomes

the constituent element (Fehre et al., 2016). Strategic consistency is the firm’s actions

that necessitate adaptation in response to changes in the business environment and

ensuring continuity to the history of the firms. In a dynamic environment, maintaining

strategic consistency means adjustment in strategic stance in response to the most

efficient change in competitive actions to accommodate new and intentionally

identified strategic direction and goals. While in stable environment this would

usually mean constant and unwavering competitive behaviour over time (Lamberg et

al., 2009). This helps managers in selecting a strategic course of action for a longer

duration of time. Doing so, the organizations develop expertise, enjoy the benefits of

specialization, portray a clear customer image, and foster a culture of learning

organization. Consistency in strategic approach provides an opportunity to deal with

imperfect information in a rapidly changing environment. Consistency ensures the

accumulation of competitive advantages because of the activities that support each

34

other rather than cancelling them out. Strategic consistency makes it easier to

communicate to employees, shareholders, and customers. Because of single-

mindedness, it improves implementation as well (Porter & Roach, 1996).

Organizational value resides in the development of consistent stakeholders

relationship by developing routines, specialization, and core capabilities (Miles et al.,

1978; Porter, 1980). Past research shows a positive relationship strategic consistency

with organizational performance (Fehre et al., 2015; Lamberg et al., 2009).

However, in a fast changing environment with a dynamic and competitive nature of

businesses, a long-term orientation toward strategic consistency may be less

rewarding since current business environments demands adaptability, flexibility, and

speed. Flexibility in strategic choices leads to improved performance due to the firms’

ability to make adjustments in direction and speed of strategic activities (Anikeeff &

Sriram, 1995; Lamberg et al., 2009; Moss et al., 2014). An organization adapts

flexibility to avoid being outdated and unresponsiveness to the demands of new

products, technology, or market approaches (Parnell, 2005).

Strategic flexibility is an organization's ability to respond or react quickly to the

changing competitive circumstances (Herhausen & Morgan, 2014). It makes an

organization capable to respond effectively in a dynamic, complex, an unpredictable

circumstances (Sanchez, 1995). Flexibility in strategy is the capacity of a firm that

enable it to change, adjust, and exploit opportunities resulted from environmental and

evolutional changes (Ouakouak & Ammar, 2015). Flexibility enables an organization

to redeploy its resources so that it can cope with the barriers to exit in weakening

industries (Harrigan, 1980). Strategic flexibility enables an organization to manage

economic and political risks. This is done by responding quickly (in a proactive or

reactive manner) to market opportunities and threats that makes it possible to resort to

"surprise management." With a diverse collection of strategic choices, strategic

flexibility enables firms to manage effectively in the uncertain and "fast-occurring"

markets (Tansuhaj & Grewal and Patriya, 2001). The crucial antecedents of strategic

flexibility include technological orientation, strategic planning, and inter-functional

cooperation (Herhausen & Morgan, 2014). According to Sanchez (1995), the concept

of strategic flexibility is divided into resource flexibility and coordination flexibility.

Resource flexibility refers to the ability of a firm in adjusting and responding for

35

deployment of product-creating resources while the flexibility in using these available

resources is referred to as coordination flexibility. Likewise, March (1991) proposes

the flexibility in a dichotomous manners as offensive flexibility and/or defensive

strategic flexibility. The objective or aim of the offensive strategic flexibility is to

create and grab an initiative while defensive strategic flexibility safeguards the

environmental eventualities and unforeseen competitive moves. Miles and Snow

(1978) explained that flexibility in strategic stance is expected to increase the

effectiveness of communication and plans. Along with adapted product offering and

marketing mix, strategic flexibility should enhance organizational performance.

Contrary to the positive influence of strategic flexibility on performance, it is

expected to have a negative impact on organizational performance when there is no

need of responding environmental eventualities. The adaption of flexible strategy is

only beneficial when the resulting gains are likely to be more than the standardized or

consistent strategy (Tansuhaj & Grewal and Patriya, 2001).

Table 2.1: Characteristics of Consistent and Flexible Strategy

Strategic Questions Strategic Consistency

Viewpoint

Strategic Flexibility

Viewpoint

“How can

organizations address

rapid environmental

change?”

“How important are

first mover

advantages?”

“Should organizations

change strategies if

there are substantial

changes in the

resources they

control?”

“Should the

organization change

strategies if

performance declines?”

Uncertainty can be reduced by

adapting a consistency in

strategic approach

Less important – first mover

advantages is not guaranteed

as it is not necessary that

advantage secured be

maintained or not

Not necessary – buyers may

get confused even if such

changes utilize resources

more effectively

Necessarily not– the changes

in strategy brings higher cost

leading to decline in

performance

Make adjustments and

changes in strategic

approach according to the

demands of the

environment

Important – to capitalize

the first-mover advantage,

firms must maintain

flexibility in their strategic

stances

Probably so – buyers

expect changes. Hence

change in strategy be made

to align with changes in

organizational resources

Probably so –maintaining

strategic flexibility can

exploit the situations to

improve performance Source: (Parnell, 2005)

36

In real situations, strategic change is incremental as the firms are stuck to a successful

and predictable courses of action. Since, the environments are dynamics, the end

results are not always predictable. Therefore, a reasonably sound argument can be

established for most organizations to be flexible even when performance is not an

issue (Tansuhaj & Grewal and Patriya, 2001). Similarly, managers are pushed to

endorse flexibility and renewal in strategic stance to increase profitability when

organizations perform poorly by opting traditional strategies. On the other hand,

industry experts stress the managers to return to their core strategy when strategic

flexibility fail to produce the desired results. Based on the above discussion, it is easy

to be agreed with both stances as there is convincing and appealing arguments having

empirical and intuitive backing in favour of both strategic options (Parnell, 2005).

Parnell (2005) listed the broad strategic questions and response perspective in terms

of strategic consistency and flexibility (Table 2.1).

2.7 Strategy Levels

Strategic management deals with three levels of strategy. Corporate-level strategy

answers the question “In what industry or industries will we compete?” Business-

level strategy answers the question “How will we compete in each of our chosen

businesses?” The task of functional-level strategy is figuring out “How will each of

the organization’s functional areas support our business and corporate level

strategies?” To get the maximum out of the available resources, appropriate strategies

are designed and selected for three organizational levels: “corporate-level”, “business-

level”, and “functional-level”. Once a strategy is selected, it must be implemented.

Formulation without implementation is incomplete and once it is implemented, it

must be evaluated. And if it is not up to the required results, it must be modified or

even changed. The strategy levels are depicted in figure 2.2.

2.7.1 Corporate-Level Strategy

Andrews (1971), defined corporate strategy as "the pattern of major objectives,

purposes, or goals and essential policies and plans for achieving those goals stated in

such a way as to define what business the company is in or is to be in and the kind of

company it is or is to be". Corporate-level strategy is concerned primarily with

answering the question of what set of businesses should we be in. Corporate-level

37

strategy of an organization deals with the distribution of firm assets, employment,

capital-budget, and selection of market or industry. Most firms (generally with local

presence) have simple corporate-level strategies as they compete in only one industry.

The large firms with cross-boundaries presence e.g. Fortune 500 firms, typically

participate in several industries (Beard & Dess, 1981). Corporate strategy relates to

the choice of what products to produce and in which market to operate. Besides

product/market selection, corporate strategy deals with the goals and objectives of the

firm, the markets and the environment in which it will exist (Hatten, Schendel, &

Cooper, 1978).

Research on corporate-level strategy started mainly after the path-breaking work of

Alfred Chandler (1962) and others notably Richard Rumelt (1974). Chandler

presented the historic review of major U.S. corporations, tracing their archetypal

evolution of a firm from single product-market to vertically integrated organization to

a multi-business firm in scope. He argued that how these shifts in strategy influenced

the changes in structure. The examples of changes in structure refereed to the changes

from the functional form of structure to the divisional form. Rumelt extended

Chandler's ideas, primarily by developing a more refined system for classifying

diversification strategies. Basing his categories on the firm's overall degree of

diversification and the "relatedness" (in terms of product-market similarities) of the

firm's array of businesses, he categorizes the term corporate-strategy as single

business, dominant-vertical, related-constrained, related-linked, and unrelated. The

simple and compelling classification systems developed by Chandler and Rumelt

facilitated the researchers to investigate corporate-level strategy. The major job at

corporate level is to evaluate the relative attractiveness of businesses in the portfolio

of an organization (Hambrick, 2003).

2.7.2 Business-Level Strategy

Business strategy deals with competition within the same industry in which the firm is

doing business. It is defined in terms of variation in the relevant characteristics of the

firm in comparison to the competing firms’ success or failure in a given industry.

Hofer and Schendel (1978) provided a concise definition: “At the business level,

strategy focuses on how to compete in a particular industry or product-market

38

segment”. Competitive advantage and distinct competencies are generally the most

important components of business strategy (Beard & Dess, 1981). Porter called this as

the competitive strategy and characterized it as the relative positional or competitive

advantage of an organization that leads to outperform its competitors (Porter 1985).

Each organization applies a specific competitive strategy because its selection is

effected by the market structure and the economic environment (Bayraktar,

Hancerliogullari, Cetinguc, & Calisir, 2017). According to Kim & Mauborgne (2009),

the ultimate test of corporate strategy is whether it creates value for shareholders or

not. At corporate level strategy development, managers always begin with

environment scanning or analysis to find out opportunities and threats along with

assessing the internal strengths and weaknesses. With these external and internal

analyses findings, business strategy is developed to create a distinctive strategic

position where they can perform better than their competitors by building a

competitive advantage. To have a competitive advantage, an organization tries to

differentiate itself from others. This differentiation is based on differences in services,

price, and quality.

The main contribution in business strategy is based on typological research by

Michael Porter (1978); Miles and Snow (1978); Danny Miller (1990); March (1991);

and Treacy and Wiersema (1995). The theoretical insights of these authors stimulated

a large stream of subsequent research in the academic fields of strategic management,

organizational theory and behavior, HRM and organizational performance,

operational management, marketing and accounting etc (Bentley, Omer, & Sharp,

2013; Hambrick, 2003). According to Hambrick (2003), Miles and Snow bestowed

researchers interested in business-level strategy with their famous typology. They

helped to manifest the concept of strategic “equi-finality –the idea that there is more

than one way to prosper” within a particular industry or environment. They argue that

there are a number of patterns or groupings that organizations can choose in order to

achieve their goals and objectives. According to (Ketchen, 2003), the way Miles and

Snow examined business-level strategy is their greatest contribution.

The organization aligns its value chain according to the focus tailored at corporate and

business level strategies. This value chain is created by formulating and implementing

the strategies for manufacturing, marketing, and human resource management. On the

39

basis of these strategies, financial strategies are developed for making financing and

investing decisions.

2.7.3 Functional-Level Strategy

Functional-level strategy is the goal-directed decisions and actions of the organization

at its functional units. These decisions are usually for the short term, generally based

on yearly business plans. Each functional unit of the organization has a strategy for

achieving its own mission for helping the organization reach its overall vision.

Typical functional units include: production/operations or manufacturing; R&D,

marketing; HRMD (Human Resource Management and Development); financial

/accounting; and management information systems etc. These functional units are

different for different types of organizations. For example, universities have different

functional units than a manufacturing or an engineering firm. Functional-level

strategy utilizes unique resources, capabilities, competencies, and work activities that

are the source of organization’s business /competitive strategies. Functional strategies

support the business strategies (Coulter, 1998).

It is important to strategically manage the basic functional strategies to achieve

competitive advantage for organizations. Otherwise, the resources, capabilities, and

core competencies found in various organizations’ functions won’t be effectively

developed into any sustainable competitive advantage. The strategies must be

implemented once the strategic choices are made in each of the functional areas.

Strategy implementation involves five aspects: processes; budget; structure; and

culture. While strategy implementation at this level involves the specific use of these

aspects, it also involves the coordination of the functional units. The evaluation of

functional strategy involves specific performance measures for each of the functional

areas.

Functional strategy should be coordinated with business level and corporate level

strategies to get the required results, because choices made at business and corporate

levels do affect and are influenced by the implementation of functional strategies.

Since functional strategies play an important role in executing the vision, mission, and

goals of the organizations, it is necessary that this must be coordinated with other

40

strategy levels. Functional strategy must be changed or modified so that it can

accommodate changes in business-level and corporate-level strategies.

Figure 2.2: Strategy Levels

Source: Coulter (1998)

2.8 Organizational and Environmental Contingencies

It is generally accepted fact that context and contingencies matters. Therefore, the

relationship of strategy and performance is influenced by the organizational,

structural, and environment contingencies (Desarbo et al., 2005; Herhausen &

Morgan, 2014; Lamberg et al., 2009; Pleshko et al., 2014; Thornhill & White, 2007).

There are many contingency factors but the most important contingency factors

include the firm size and the industry in which an organization competes. The

strategic choice varies with the changes in firm size. For example, small sized firms

contain less resources and less complexities compared with larger firms. Similarly,

the idiosyncrasies and dynamics are different across industries. Therefore the

response of the firms’ management to a given situation will be different for firms with

different size and different industry (Moss et al., 2014). Firms, as they grow and

mature in size, become increasingly formalized, structured, and routinized. Therefore,

institutionalized processes in response to environmental shifts constraint the role of

strategy and managers. Hence, sometimes the role of industry and firm size is more

significant for performance than the strategies (Thomas & Ramaswamy, 1996).

However, the management holds a control on resources and has the capability to cope

Corportae Level Strategy (CLS)

Business Level Strategy (BLS)

Functional Level Strategy (FLS)

41

with the environmental changes. This enables them to select an appropriate course of

action despite the limitation imposed by the contingencies. Also, the performance of

an organization is closely related with the performance of its industry which is

affected by number of components including cost of input, level of pricing, and

diversification of products etc.

Mintzberg (1979) names a number of important contingency factors such as age of the

firm, firm size, technical system, regulation, complexity, stability, diversity of the

environment, and firm ownership etc. He considered size, technology and

environment as the most influential contingency factors (Matyusz, 2012). Similarly,

Porter (1980) identified three crucial contingency variables: degree of industry

concentration, stage of product life cycle, and exposure to international competition

(Hambrick & Lei, 1985). Organizational strategy is also considered as an important

contingent factor because strategy concerns patterns of behavior used by firms in

adjusting to their context, with implications for the practices organizations adopt in

order to deal with competitive challenges (Lucianetti, 2018; Miles and Snow, 1978;

Snow and Hrebiniak, 1980). Delery & Doty (1996) and Matyusz (2012) considered

industry as an important contingent variable that need to be included while conducting

multi-industry analysis. Based on these arguments, we considered firm size and

industry as the two internal and external contingent factors besides strategy.

2.9 Strategic Groups and Typologies

Strategic groups emerge within most industries based on some common

characteristics. To create a competitive advantage, groups are formed on the basis of

organizations following a similar strategic choice (Lin et al., 2014). The analysis at

strategic group level contributes towards the understanding of strategy-performance

relationships. Within groups and across groups analyses helps to clarify the nature and

characteristics of strategic groups that contribute towards higher performance in a

given industry. The extensive research on strategic groups started in 1970s. The focus

of the research has been to know what types of groups exist in a given industry or set

of industries, what are the characteristics of distinct groups, how they influence the

performance of an organization, and what are the implications for such groupings

(Short, Jr., Palmer, & Hult, 2007). Earlier research on strategic groups focused only

on the implication for organizational performance. Later on, the dimensions of the

42

research has been extended to more broad topics such as to examine behavioral

distinctions of the firms, investigating how membership in a group explains

competitive position, how firms behave strategically, and what are the competitive

patterns (Parnell, 2011; Zamani, et all., 2013).

In literature, there are generally three streams of research on strategic group: the

presence of strategic groups; characteristics of strategic groups; and the linkages of

strategic groups with firm performance. For the first stream, there are many empirical

studies providing support that strategic groups exist. These studies are carried out in

varying nature of industries. These include brewing industry, pharmaceuticals,

computer equipment, banking sector, and insurance companies etc. The second stream

of research focuses on the dynamics and changing behavior of strategic group

membership and stability over time. The third wave of research examines strategy-

performance linkages to find out the similarities and differences in performance

among strategic groups. Subsequently, research is extended to investigate the effect of

contingent factors such as firm size and industry on the performance of strategic

groups (Murthi, Rasheed, & Goll, 2013).

Typologies are classification schemes that provide a mechanism for ordering,

comparing, and clustering the organizations into categorical types. Strategic

typologies provide frameworks for grouping organizations, make available the

description about the complexities of groups, and explain the results. Conceptually

driven, typologies are set of interrelated ideal strategic types that meet three criteria.

First, the strategic types or groups contain clearly defined constructs or paradigms that

can be quantified. Second, the relationships among the strategic types or clusters are

expressed in detail. Third, propositions, predictions, or assumptions accompanying

with the typology are testable and subject to rejection (Doty & Glick, 1994).

Typologies are one of the most commendable contributions in management. They

helps not only in organizing complex set of causal relationships but also provide key

tool to the theorist to reduce complexity existed in multiple causal relationships

(Delbridge & Fiss, 2013). A typology is worth value if its strategic types are

comprehensive and mutually exclusive. This means that the strategic types can be

measured in a valid and reliable manner. Also, the typology provides a clearly

articulated theoretical foundation while explaining the ideal strategic types. Ideally, a

43

typology should represent heterogeneity among strategic groups. This heterogeneity

should exist with respect to their capabilities, effectiveness, and their resulting

performance (Snow and Ketchen, 2014).

The literature on strategic management provides number of typologies for studying

business strategy. These typologies define how organizations compete and flourish in

their market environments. The most famous and applied work on strategic groups

and typologies is done by Chandler (1962), Rumelt (1974), Miles and Snow (1978),

Porter (1980), Danny Miller (1990), Treacy and Wiersema (1995) etc. Some other

examples of typologies in the realm of strategic management are: “strategic context”

by (Mintzberg, 1978); “views on strategy” by (Hamel & Prahalad, 1994); and

“strategic decision making types” by (Ansoff, 1965) etc. Among these, the most

influential typologies are presented in (Table 2.2). These typologies have had the

profound effect on the subsequent research on strategic management and related

fields. However, the typologies of Porter and Miles and Snow are the most widely

applied.

Porter (1980) proposed three types of generic strategies (“Cost Leadership;

Differentiation; and Focus”). Firms compete on the basis of the characteristics

attached to each strategic type for competitive advantage. Choosing the cost

leadership strategy, a firm intends to lead the market by offering low cost products in

its industry. Differentiation strategy is opted by an organization to be distinct in its

industry on the basis of key features such as product quality, design, brand name, and

improved service etc. In focus strategy, a firm creates a market niche and select either

the differentiation or a cost leadership strategy. This typology advocates for exploring

the proper fit between the strategic types and characteristics of an industry

environment which is composed of the forces that drive industry competition.

Although the typology of Porter is widely used, it has some limitations. For example,

relatively little explanation is provided regarding the structure of organization,

processes and programs that are necessary requirements for effective implementation

of each strategy. Such limitations provide little room for explanation if one, for

example, wants to explain the kinds of strategic actions undertaken by a firm while

deploying certain resources and capabilities (Walker & Ruekert, 1987).

44

Table 2.2: Prevalent Strategic Typologies

Authors Strategic Types

Chandler (1964) “Single product-market; Vertically integrated;

Multi-business”

Rumelt (1974) “Single-business; Dominant-vertical; Related-

Constrained; Related-linked; and Unrelated”

Miles & Snow (1978) “Prospectors; Analyzers; Defenders; Reactors”

Porter (1980) “Differentiation; Cost Leadership; Focus”

Miller (1992) “Salesman; Pioneer; Builder; Craftsman”

Treacy & Wiersema (1995) “Customer Intimacy; Operational Excellence;

Product Leadership”

Source: Author

The typology of Miles and Snow addresses the above limitation. Their typology

focusses and explain the nature of structure and the processes an organization takes to

adjust in the external environment where it exists. This typology provides a

theoretical framework of co-alignment or “fit” of external environment with

interrelating strategy, structure and process of an organization (Smith et al., 1989).

The typology underlines the importance of both internal and external fit and provides

the theoretical framework that how firms should adapt distinct and relatively lasting

patterns of strategic behavior to co-align the organization with its environment

(Mcdaniel & Kolari, 1987b). The typology is unique in a sense that it views the

organization as an integrated and complete system interacting with its dynamic

environment (Smith et al., 1986). The typology has put forth a substantial impact in

the field of strategic management. Because of its strong theoretical foundation and

ability to be generalized, the typology has been widely accepted, richly described, and

meets the requirements of being both comprehensive and parsimonious (Conant et al.,

1990; Desarbo et al., 2005; Parnell et al., 2015; Parnell & Wright, 1993; Shortell &

Zajac, 1990; Snow & David J. Ketchen, 2014; Snow & Hrebiniak, 1980; Zahra &

Pearce II, 1990; Zamani et al., 2013). The description of Miles and Snow typology is

given below in greater detail.

45

2.10 Miles and Snow Typology

By 1978, the research on corporate-level strategy had made notable progress,

primarily due to the path-breaking work of Alfred Chandler (1962) and Rumelt

(1974). Miles and Snow (1978) bestowed a similar gift to researchers interested in

business-level strategy. Prior to their work, two opposing camps dominated scholarly

discourse about business-level strategy, but neither approach was generating much

intellectual or practical progress. On the one side were the "Situationalists," who saw

the design and implementation of business-level strategy as a situational art. No two

strategic settings are the same, and therefore strategies cannot be described in any

general way. Such a philosophy, of course, spelled trouble for scholars who were

interested in generalizability, theory, and prediction, and it was of little use for

managers who wanted to learn from the successes and failures of other firms. On the

other side were the "universalists." Members of this camp believe that there were

universal laws of strategy. For example, market share and superior product quality is

always a good thing etc. While the situationalists abhorred generalization, the

Universalists refused to acknowledge context or contingency. Miles and Snow entered

this conflict, and greatly helped to resolve it, by taking the middle ground (Hambrick,

2003).

Miles and Snow’s (1978) seminal work “Organizational Strategy, Structure, and

Process” has created a great level of interest and debate among scholars in the field of

organizational design and management. The framework of Miles and Snow has two

major components: adaptive cycle and strategic typology. According to Zahra &

Pearce II (1990), an “adaptive cycle” presents a broad functioning of organizational

behavior. The framework of Miles and Snow provides the means of conceptualizing

the major elements of strategic adaptation process. It also guides for visualizing the

relationships among these elements for an effective organization. The adaptive cycle

represents the methods taken by businesses with divergent viewpoints on the

environmental competition to address three fundamental problems relating to

entrepreneurial activities, administrative decisions, and engineering needs. The

solution of the entrepreneurial problems required the understanding and defining the

market-product domain. Engineering problems address the organizations’ technical

systems while administrative problems deals with the issues relating to the structure

46

and processes of organizations. The second part of Miles and Snow framework

identifies the existence of four strategic types known as defenders, analyzers,

prospectors, and reactors within a given industry. The fundamental difference among

these strategic types is the responsiveness in relation to the change in the domain of

an organization. The third premise of the framework is that viable strategic types

(defenders, analyzers and prospectors) can produce better performance if they are

properly understood and properly implemented. The corollary of this premise is that

the viable strategies outperform the non-viable strategy -reactors.

The adaptive cycle of Miles and Snow framework portrays business as continuously

cycling through the sets of decisions on three fronts: how to deal with entrepreneurial

problems, engineering problems, and the administrative problems. For example, if the

management of an organization while making the decisions in the entrepreneurial

domain (defining and selecting the product line for example) represent the

prospecting behavior, the decisions in the engineering and administration domains

will also be prospector-oriented. The cycle of such decision making will move on the

entrepreneurial domain again the cycle will continue. The same process will be for

analyzers and defender strategies. With enough cycles and insight, the strategic

orientation of a given business will be an effective, comprehensively aligned

prospector, analyzer, or defender. And if a business lacks the required insight, and if it

fails to get benefits of the alignment opportunities provided by the adaptive cycle, it

will lose the direction and will become an unrelated, poorly performing reactor. Of

course, the hyper-aligned business will face a major challenge if it needs to change its

strategy. But the adaptive-cycle concepts are even useful for assessing what is

required to change from one strategic profile to another. Miles and Snow’s ideas

provide great practical help to executives who attempt to implement strategies that are

needed to support the new strategic direction (Hambrick, 2003). Their typology

includes description of strategy, structure and process in detail along with the

managerial characteristics as depicted in the management theories. It explains how

strategies are identified through the study of the internally consistent set of attributes

including technology, domain definition, and administration. Hence, the typology has

theoretical foundations and offers a balanced view of the competitive strategies that

an organization opts (Thomas & Ramaswamy, 1996).

47

The typology has been applied widely to investigate a host of causal relationships. For

example, studies have investigated how the strategic orientation differs in selecting

administrative practices such as environmental scanning, organizational structures,

and reward systems etc.; in opting their functional profiles and policies - vertical

integration, R&D intensity, fixed assets configuration, sales force management

practices, and advertising etc.; and in their performance under various environmental

conditions etc. (Hambrick, 2003).

2.10.1 Miles and Snow’s Strategic Types

2.10.1.1 Prospector

Prospectors are innovative companies and they continuously seek to identify and

exploit the opportunities for the development of new products and markets.

Prospector’s budget is marketing oriented with the focus on research and development

(R&D). This innovation oriented focus of prospectors demands the development of

multiple technologies for creating a diverse product mix. The flexibility in adapting

technology allows prospectors to respond quickly to the rapid change. However, there

is a related cost as there is always a risk of not achieving the maximum efficiency in

their production and distribution processes. The effectiveness of prospectors depends

on product and market development. The growth for prospector may not be steady

rather it may occur in spurts. The administration of prospectors is decentralized for

deployment and coordination of resources among organizational units and projects.

Hence their structure is of organic nature which requires marketing experts in the top

management teams, R&D experts, planning broad horizon, low degree of

formalization, decentralized control, and horizontal as well as vertical communication

channels. Prospectors maintain a low degree of mechanization or routinization and

avoid lengthy commitments to a single technological process. They do this by

utilizing the knowledge and skills of their employees (Hambrick, 2003; Miles &

Snow, 1978; Robbins, Stephen P., Barnwell, 2006; Snow & David J. Ketchen, 2014;

Ven et al., 2013).

Strategically, prospectors respond with emphasis on new product development, new

market development, accelerating growth, and promoting existing brands to overcome

financial crunch. Known to be innovators and market leaders, prospectors concentrate

48

on offering the most innovative products or services. In search of new opportunities,

prospectors continuously examine and exploit their product and service related

markets. To improve their marketing management and enhance their R&D

capabilities, prospectors are always willing to invest more in latest technologies and

new markets (Evans & Green, 2000; Habib & Hasan, 2017; Ingram et al., 2016; Lin et

al., 2014; Ostos, Hinderer, & Bravo, 2017). For prospectors, innovation is the key

component of competitive advantage. Typically, prospectors offer a large range of

latest products targeted at a diverse market segments by monitoring a wide range of

environmental conditions. Flexibility in adapting technology is a vital aspect of this

strategy. Consequently, they rarely attain the level of efficiency that is required to

maximize the economies of scale. Prospector firms adopt decentralized structures and

control systems that facilitate change (Thomas & Ramaswamy, 1996). Prospectors

constantly seek new opportunities and stress product development and follow more

sophisticated and formal planning approaches (Koseoglu, Topaloglu, Parnell, &

Lester, 2013; Parnell, 2010; Parnell et al., 2015; Zahra & Pearce II, 1990).

2.10.1.2 Defender

Defenders firms focus on efficiency. They achieve this efficiency with narrow market

focus and through greater emphasis on production and distribution of goods and

services. Rather than pursuing new product and market opportunities, defenders

develop closely related products and services, which minimize their new product

development efforts. In contrast to prospectors’ efforts to “protect” the marketing and

R&D functions, defenders “protect” the finance and production functions. The growth

of defenders is steady, cautious, and incremental through market penetration. To

ensure efficiency, defenders maintain strict centralized organizational control.

Defenders invest heavily with special focus on “single core” cost-efficient technology

and continual improvement leading to high degree of routinization and mechanization

for achieving production, distribution, and technological efficiency. To maintain strict

control, they follow the mechanistic structure. The major characteristics of the

mechanistic organization include a team of production and cost-control specialists in

the top-management, less analysis of the environment for exploring new

opportunities, focused and intensive planning for cost reduction and efficiency

enhancement issues, extensive division of labor with formal structure form,

49

hierarchical communication channels, and centralized control etc. (Hambrick, 2003;

Miles & Snow, 1978; Robbins, Stephen P., Barnwell, 2006; Snow & David J.

Ketchen, 2014; Ven et al., 2013).

Defenders normally nurture growth by deploying their resources on current markets

instead of engaging in new product development. Defenders, in contrast to

prospectors, try to create market niches by improving their efficiency and

effectiveness and by bringing cost down. Their managers are production personnel

and focus on an established and stable market. They strive to effectively implement

formal procedures and standardized technical processes for quality production (Evans

& Green, 2000; Habib & Hasan, 2017; Ingram et al., 2016; Lin et al., 2014; Ostos et

al., 2017). Defenders stress on creation of a stable and narrow domain with a mix of

limited customers and products by making aggressive efforts to protect their domain

from the competitors (Miles and Snow, 1978). Defenders are inflexible in choosing

technology. They strive for cost-efficiency and integrate vertically to control costs.

Their emphasis on efficiency tends to narrow the range of environmental domains.

Formal work cultures and centralized organizational structures are the key attributes

for controlling operating costs. Research and development efforts of defenders are

related to process improvements instead of product development through innovation

(Thomas & Ramaswamy, 1996). Defenders maintain a narrow domain by controlling

secure (often premium) niches in their industries. They stress for efficiency in

operations, following informal planning methods (Koseoglu et al., 2013; Parnell,

2010; Parnell et al., 2015; Zahra & Pearce II, 1990).

2.10.1.3 Analyzers

Analyzers balance out the risks associated with prospectors and defenders strategies.

They take the middle position and enjoy the advantages of both prospectors and

defenders by combining their capabilities. Similar to prospectors, analyzers pay

attention to innovation and simultaneously focus on establishing products, as

defenders do in several stable businesses. Production, marketing, and R&D

capabilities are essential for implementing an analyzer strategy (Lin et al., 2014). The

analyzer strategy places a heavy emphasis on both marketing and engineering aspects.

Analyzers use the mixture of both the stable and entrepreneurial markets (Evans &

Green, 2000; Habib & Hasan, 2017; Ingram et al., 2016; Ostos et al., 2017). The

50

analyzers pursue hybrid strategies that exhibit some features of both prospectors and

defenders (Zahra & Pearce II, 1990). In rapidly evolving environments, analyzers

operate like prospectors while in the stable domains they adapt the defenders’

approach. They adopt dual core technologies that have both stable and flexible

components. Analyzers follow matrix structures which include the benefits of both

functional specialization and centralized control (Koseoglu et al., 2013; Parnell, 2010;

Parnell et al., 2015; Thomas & Ramaswamy, 1996; Zahra & Pearce II, 1990).

2.10.1.4 Reactors

The reactor is the residual strategy. Reactors exhibit an inconsistent and unstable

pattern of adjustment to its environment. They lack a clearly defined pattern of

response mechanism and their response to the changing environment is not

predictable. Reactors do not have long-term goals and have no patterns of decision.

They do not develop any functional capability for achieving competitive advantage.

As a consequence, this type exists in a state of perpetual instability, responding

inappropriately to environmental change and uncertainty. They behave reluctantly to

aggressive situation which leads to poor performance. Unless an organization exists in

a protected and highly regulated environment, they cannot continue to behave as a

reactor indefinitely. Hence, they must move toward one of the consistent and stable

strategies of defender, analyser, or prospector (Miles & Snow, 1978). Managers of

reactors are unable to pursue successful organizational strategies because their plans

are ambiguous and unfocused (Evans & Green, 2000). Reactors occasionally make

adjustments in their strategic stance until they are forced by the environment to react.

They lack effective control mechanisms on both internal and external environments.

Reactors do not possess the necessary capabilities to consistently peruse a strategy for

a longer period of time (Lin et al., 2014) and are viewed as dysfunctional

organizational type following informal planning methods (Ingram et al., 2016;

Koseoglu et al., 2013; Parnell et al., 2015; Zahra & Pearce II, 1990).

Conceptualization of reactor strategy varies in the literature. They are characterized

by extreme lethargic organizations behaviour (organizational inertia) by some

researchers (Conant et al., 1990) lacking a consistent strategic approach and respond

simply to the occasional environmental pressures (Mcdaniel & Kolari, 1987b). But

there is dearth of literature which clarifies the behaviour and time frame showing the

51

inconsistency of reactors in their strategic approach. It is also possible that the

behaviour of reactors may vary during varying time horizons to exhibit the

characteristics of a prospectors, defenders, and analyser. But according to Blackmore

& Nesbitt (2013), the operationalization of reactor strategy is a tough job as it is

difficult to identify the inconsistency in the behaviour of strategic orientation in a

single point in time particularly objective data is used. Hence, reactor strategy is

frequently omitted from studies.

The most insightful and lasting contribution of Miles and Snow lies in their

development of a classification scheme for firms based on the fact how they respond

to the adaptive challenge. Their scheme is based on a view of organizations as

“integrated whole in dynamic interaction with their environments”, in contrast to most

work on strategic and organizational “archetypes” that tend to be grounded in static

analysis of organizational snapshots. Another great contribution lies in how they

examined the organizations at business-level strategy. In this sense, the typology

offers understandings for what type of structure to adopt; what functional strategies to

pursue; how to improve the firm’s position in the industry; and how to make strategic

decisions (Ketchen, 2003). According to Smith, Guthrie, & Chen (1989) the typology

is generalizable across industrial settings in comparison to other typologies.

The characteristics of strategic types of Miles and Snow are common with other

famous typologies. For example, the characteristics of defender strategy are aligned

with “Cost Leadership” strategy of Porter (1980) and March’s “Exploitation”, and

“Operational Excellence” by Treacy & Wiersema (1995). Similarly, prospector

strategy is similar with Porter’s “Product Differentiation”, March’s “Exploration”,

and “Product Leadership” of Treacy & Wiersema (Bentley et al., 2013). Therefore,

the application of Miles and Snow typology does not provide an isolated outcomes,

rather they are widely conceptualized in the literature by the gurus of the subject.

2.11 Contingency Theory

One difficulty in studying business strategy is relating to the fact that the contexts of

the business where an organization operates are seldom identical i.e. they have some

distinct features which distinguish them from one another. To overcome this problem,

there are three approaches which are used to study business strategy. These

52

approaches are: “the situation-specific approach”; “universal approach”; and

“contingency approach”. in situation-specific view, strategy is seen as an purposeful

alignment of internal strengths and weaknesses, environmental opportunities and

threats, and the capabilities and values of management (Andrews, 1971). Supporters

of this view claim that researchers and analysts cannot draw a conclusion about the

strategic stance of a firm unless they comprehend the unique context of that

organization. Studies based on this approach tend to do case studies. There are

empirical evidences where it was demonstrated that strategic generalization is risky

beyond one or two firms (Hatten et al., 1978). Contrary to the situation-specific view,

there exists universal view approach. This approach profess that there are universal

laws of strategy. These universal laws exist and to some extent hold true in all

settings. For example, the Boston Consulting Group’s “universally observable view”

and “The Profit Impact of Market Strategies (PIMS)” program draws popularity to the

concepts implying its universal applicability. For example, BCG matrix is universally

applicable in each settings to each multi-unit (SBUs) organization. Such laws suggest

that there is only one universally accepted sound strategy and only one grand type of

competitive setting (Hambrick & David Lei, 1985). Contingency theories state that

the appropriateness of different strategies depends on the competitive settings of

businesses. Such theories differ from the universal view and state that context matters

by stressing that "it all depends." On the other hand, they differ from the situation-

specific view by arguing that there are categories of backgrounds for which

generalizations for strategic orientations can be made. Organizational and strategy

scholars can make their greatest contributions through the contingency view which

requires to have a basis on which to divide competitive settings into discrete classes

(Hambrick & Lei, 1985; Hofer, 1975).

The idea of contingency theory began in the academic discussion during 1950s and

1960s which was later utilized into organization theory. Contingency approach

challenged the traditional management belief: “finding the one best way to organize”.

Keeping intact the notions of subsystems, contingency theory attracted the attentions

of the management theorists and obtain general acceptance (Donaldson, 1996; Hatch

& Cunliffe, 2006). Contingency theory appears with the core idea "there is no one

best way". Rather firms constitute the organizational designs based on their internal

and external environments to achieve efficiency. This means that an organization will

53

be more efficient when its internal designs and structure meets the demands of

external environment. Contingency theorists assume that the performance of an

organization is dependent on the fit created between organizational characteristics and

internal and external contingencies. These contingencies include organizational

strategy, size, industry, technology and environment etc. (Chandler, 1962; Daft, 2015;

Donaldson, 2001; Madanoglu et al., 2014).

The important contingency factors include firm size and the industry of its business.

For example, smaller firms lack the resources which larger firms have. Similarly, the

peculiarities and dynamics of one industry are different from other industries. As a

firm grows in size and matures in age, it gradually becomes structured, formalized,

and routinized. Similarly, the industry can significantly constrain the managerial

influence the strategic decisions as the dynamics of an industry influence the

organizations to design and implement strategy for achievement of superior

performance in a proactive manner. Consequently, environmental shifts force the

management to reshape the institutionalized processes. Therefore, these contingencies

are important determinants of organizational performance (Hambrick & Lei, 1985;

Jennings et al., 2003; Donaldson, 2001; Moss et al., 2014; Thomas & Ramaswamy,

1996).

Almost all theories of strategy (corporate or business) are contingency-based. The

popularity of contingency theory research is due to its fundamental assumption that

“there is no one best way” to create a fit because there are many ways of organizing to

get organizational effectiveness. The reason of applying contingency theory

assumption to the strategy context is due to the fact that the concept of strategic

choice is embedded in creating the match between organizational resources and its

environment (Andrews, 1971; Chandler, 1962). The contingency approach to strategy

suggests that selection of optimal strategy is based on creating the right fit for a

certain set of organization’s internal and external conditions. The fit is represented by

the existence of an interactive relationship between two variables predicted by a third

variable. In accordance with this paradigm, the studies that focus on the contingent

relationship between an independent or contextual variable (for example strategic

orientation, firm size, and industry etc) and a dependent variable (for example

organizational performance) across different contexts are considered as legitimate

54

contingency based studies (Ginsberg & Venkatraman, 1985; Donaldson, 2001;

Pleshko et al., 2014).

The selection of a strategic choice is an important contingency for many

organizational decisions. These decisions involve the design for organizational

structure, deployment of human, financial, and physical resources, and management

systems. Selection of strategy influencing performance emphasizes the formulation

perspective of strategic management. The implementation perspective of strategy

influence the organizational context (such as structure) or influence performance

through organizational context. Because of interdependent nature of formulation and

implementation, they should be considered jointly within a managerial perspective.

However, strategy generally influence performance directly across different contexts

(Ginsberg & Venkatraman, 1985). Hofer (1983) highlighted the contingency theory

within a system model composed of input, process, and output. The input component

refers to the environmental dimensions. This include environmental uncertainty,

market structure, and product life cycle. The process component represent the

organizational dimensions and is composed of organizational systems and structure.

The output represent the outcomes, results, productivity, and organizational

performance.

Another aspect of contingency approach is that it views organizations as social

system. The continuous coordination within subsystems is achieved through the

implementation of management policies and practices (strategy). The interaction of

the resultant fit of coordination helps in achieving organizational goals and objectives

(Olson et al., 2005). Based on this argument, the performance of an organization is

dependent on the congruence of its elements (internal fit) and its alignment with the

external environment (external fit) (Wilden et al., 2013). Hence, to reach optimal

outcome (organizational performance or effectiveness), an organization should have a

functional (marketing, finance, HRM, R&D etc.) fit with environment, strategy, and

structure (Luoma, 2015). The contingency based research discloses that most of the

studies exhibit a statistically significant relationship between the moderating variable

and strategy/performance measures. It is also recommended that contingency based

research should first examine the contingency relationships within a single industry

55

followed by generalizations across the industries. This will help in advancing the

theory of organizational strategy (Ginsberg & Venkatraman, 1985).

The underlying idea for typologies is that organizations strive for internal consistency

and coherence which produce configurations. The configuration to which an

organization belongs has an impact on many aspects of organizational activity. These

different configurations are systematically related to variations in organizational

characteristics such as size and technology, and to environmental variations such as

uncertainty and complexity. The configuration perspective sees organizations in a

holistic way. Here, organizations are conceived as both a set of subsystems as well as

distinguished from components. These subcomponents are coherently related to each

other represented by an overall pattern called a work system. These configurations are

often named as modes, ideal types, and archetypes etc. Such configurations are

represented by conceptually developed typologies or captured by empirically derived

taxonomies. Contingencies pressurize the development of new legitimized

organizational configurations (archetypes). This configurational concept is aligned

with the contingency theory where the performance of an organization is maximized

by creating a fit and by minimizing the misfit (Amitabh & Gupta 2010; Van de Ven et

al. 2013). Strategic group research mainly through strategic typologies are used to

refine theory and test contingency models emphasizing how to create a fit between

strategy and other environmental and organizational variables (Parnell et al., 2015;

Parnell & Wright, 1993). The framework of Miles and Snow has been integrated into

contingency research in organizational theory and it has been extended by the

configurrists to other organizational processes as well (Farjoun, 2002).

2.12 Business Strategy and Organizational Performance

The assessment of organizational performance has been the subject of extensive and

increasing empirical and conceptual investigation. There are three levels on which the

focus of the analysis in research on performance has been. These levels are: strategic

group level; firm level; and industry level (Short et al., 2007). Luoma (2015)

discussed strategy-performance relationship in detail. He highlighted three important

dimensions of research on performance. These dimensions are: theoretical

dimensions; empirical dimensions; and managerial dimensions. In theoretical

reasoning, better performance is referred as the time tested outcome of a strategy

56

execution. This is a well-established argument. In empirical dimension, researchers

investigated the impact of strategy on performance through the quantitative analysis.

The importance of managerial dimension of performance is apparent from a number

of studies where it was investigated that how managers can improve the performance

of an organization. Luma (2015) identified three more aspects of performance as are

discussed in the literature on strategy. The first aspect of the performance is that it is

treated as the ultimate goal of management. This concept of ultimate goal or an end in

itself is investigated performance of managers or teams, businesses or corporate

performance etc. Second aspect of performance is its measurement perspective to

identify proper performance measures and indicators along with the some sort of

quantifying outcomes for an organization. The third aspect highlights performance as

a question of scope. It integrates all the previous aspects and comprise the totality of

performance.

Organizational performance is concerned with efficiency and effectiveness of the

conversion processes at each level of the organization. Performance is the extent to

which an organization is conditioned by to achieve its objectives. It is the degree to

which an organization is able to control its environment for desired objectives.

Performance is a multidimensional phenomenon that is not easy to comprehend and

measure. The concept of performance is different for different respondents. The

difference in the concept and meaning of any organizational outcome (performance)

is according to whose viewpoint is taken. For example, customers and stockholders

treat performance differently. The meaning of performance is different when the time

period observed is taken into account. The criteria of performance may give different

meanings, and so on. Similarly, there are number of ways to reach at the

organizational effectiveness. To achieve the desired results, an organization may

pursue a different strategy and course of action than its competitors (Snow &

Hrebiniak, 1980).

One difficulty with studying performance is its measurement which is not

straightforward. This difficulty is due to the fact that there is no universally accepted

single measure of organizational performance. For example, stakeholders use

financial performance measures such as revenue, earning per share (EPS), and stock

price as well as non-financial measures such as satisfaction level of customer,

57

employees, employee compensation, and relations with suppliers relative to the

competitors to assess overall performance of an organization. The evaluation of the

firm performance can be either objective or subjective (Bayraktar et al., 2017). There

is a disagreement about the criteria and indicators of performance that is who should

set the criteria of assessment and what characteristics and variables are pertinent to

investigate organizational performance? One of the most frequently offered

explanations for the disagreement is about the differences in the concepts of

performance. For example, the performance indicators such as supervisor appraisals,

self-perceptions, and other similar measures are known as "Soft" performance

measures whereas indicators such as production, sales, gross profit, commissions, and

services rendered are “Hard” measures of performance (Dalton et al., 1980).

Long-term and sustainable higher performance is linked with well thought strategy

(Lin et al., 2014). In strategy-performance relationship research, performance is

generally treated as an aggregate outcome of an organization (Luoma, 2015). The

fundamental domain that strategy addresses is the origin of differences in

performance among close competitors (Durand et al., 2017). In a study of 2,125

strategy articles between 1986-2005, performance was the most prominent used key

word (Furrer & Thomas 2008). Similarly, among 421 articles published in Strategic

Management Journal between 2009 and 2013, 46% dependent variables were relating

to performance dominated by financial performance with 37.5% (Durand et al., 2017).

The organizations use strategy by adapting novel combinations of circumstances to

deal with changing environments for sustained competitive advantage, generally

reflected in organizational performance. Rregarding performance measurement, it is

measured by many indicators. For example, performance in term of growth is

measured through growth in assets, employees, sales, and revenues etc. For

profitability, performance is generally measured by ROA, ROE, ROS, ROCE, and

earnings per share (EPS) etc.

Strategic group research argues that organizations are grouped into similar

competitive approaches that they adapt where some approaches produce better results

than others. This variation in performance may be due to the mobility barriers as the

availability of opportunities is different and is not equally available across industries.

This is because of the fact that potential for profit is better than others in some

58

industry segments. The variation in performance arises may be due to the reason that

an organization occupying one niche market may change its strategic stance or may

be tempted to expand its operations to exploit opportunities available in other

segments of the industry. But shifting to a new market segment or to a different

strategic group can be risky because there can be a substantial investment cost in

developing the required skill set and the products. On the other hand, the perceived

opportunities may not be available for longer period of time and disappear in short

time period. Because of the risk that expected gain will be less than the cost incurred,

organizations are generally stuck to their core stance and opt for not changing their

strategic group. Therefore, strategic groups that occupy rewarding segments of an

industry should outperform those strategic groups which are in less lucrative

segments. Similarly, members of less rewarding groups are hesitant to enter into more

fertile and rewarding strategic groups because of the fear that their decision to this

shift may not produce the desired results (Short et al., 2007).

Organizational performance in a strategy-performance relationship is generally taken

as dependent variable. Both hard or objective and soft or subjective measures of

performance have been used to investigate this relationship. The number and nature of

performance measures varies across studies. In objective measures, organizational

performance is measured in terms of profitability by measures such as ROA, ROI,

CFOI, ROS, ROCE, Annual stock return, Operating Income, and Market share etc.

Here, performance is also measured in term of growth by measures such as Assets

growth, Sales growth, Revenue or earning growth, Profit growth, and Employee

growth etc. On the other hand, in subjective measures, the performance of the

organization is generally determined by the measures such as competitive position,

general profitability, customer satisfaction, service quality etc. In this study, four

objective measures: ROA, ROS, ROE, and ROCE are used for measuring

organizational performance.

2.12.1 Miles and Snow’s Strategic Types and Organizational Performance

Miles and Snow assume that viable strategic types (defenders, analyzers and

prospectors) are expected to give equal performance in a given situation because they

are well articulated strategies and respond consistently to the change occurring in the

59

environment. They also assume that these three viable strategies will outperform

reactors because reactors respond inconsistently and inappropriately to the

environmental uncertainty and change that results in poor performance (Miles &

Snow, 1978). Hambrick (1983) pointed out that original model of Miles and Snow

does not predict that, under what circumstances, which of the strategic types would

give the highest performance. Rather 'performance' was not clearly defined in original

model. Therefore, more research was needed at that time on strategy-performance

relationship. However, subsequent empirical research have generally supported the

assumptions that the three viable strategic types would perform equally well (or

insignificant difference in performance) in the long-run and will outperform reactors

(Conant et al., 1990; Jennings et al., 2003; Parnell, 2010; Snow & Hambrick, 1980;

Woodside & Sullivan, 1999). At the same time, there are evidences where the

difference reported about the performance of viable strategies is significant

(Blackmore & Nesbitt, 2013; Hambrick, 1983; Parnell et al., 2015; Parnell & Wright,

1993; Smith et al., 1986).

2.12.2 Strategy and Performance: A Contingency Theory Perspective

Strategy is the major factor that shapes performance. At corporate level, strategy

guides for role of each part of the business. It determines the nature of the business for

organizations. At business level, strategy guides the managers for the development of

products and services in a way to achieve competitive advantage. Also, strategy is

thought to be the exclusive factor that affects structure for organizational

effectiveness. Weber (1946), Chandler (1962), Rumelt (1974), Miles and Snow

(1978), Portor (1980), and many others made a great contribution to strategy-

structure-performance (SSP) relationship.

In essence, a contingency theory proposes that the performance of an organization is

the result of the fit between the organization’s internal arrangements and its external

context. The performance can be improved by creating the external fit between the

organization’s environmental demands and its internal structural design. Similarly, the

performance can be enhanced by creating a fit among the key internal components of

organizational design that include organizational strategy, systems, structure, and

culture. In other words, the relationship between organizational strategy and

60

performance is moderated by organizational internal and external environments. And,

when a relationship is moderated by a third variable it falls in the domain of

contingency theory (Ostos et al., 2017; Ven et al., 2013).

The most influential research in this regard was pursued by Alfred Chandler (1962).

He observed that, companies often have one single product at the beginning of

operation with centralized decision making by the senior management. They follow

formal structures having low complexity. The structure is divisionalized when

companies evolve to multi products. Complexity increased both in structure and

processes as they are looking for growth and product diversification. Again strategy

plays its role in designing the appropriate structure that leads to the better

performance. Chandler’s argument is well established that organizational structure

follows organizational strategy. This means that strategy brings variation in

organization structure (Robbins et al., 2006) which in returns effect performance of

the organization. Rumelt (1974) examined the performance implications while

extending strategy-structure-performance argument. For example, Rumelt found that

alignment of diversification strategy with divisional structure of an organization

affects performance in a positive way. Specifically, organizations attained the highest

performance that follow controlled diversity as a strategic choice and adopted

divisional structures whereas organizations performed poorly that followed unrelated

diversification strategy.

Seeing differently, Organizations can be distinguished on the basis of their organic

and mechanistic nature of design. These two types of organizations presents the

opposite direction of a continuum. The organizations with organic design continually

adjusts and redefines their tasks. They follow both vertical and horizontal

communication styles and avoid formal role definitions, procedures, and formal rules.

On the other hand the mechanistic organizations (machines) have a bureaucratic style

with highly differentiated, clear (and rigid) definitions of roles, strong hierarchies

following vertical communication style and control. The mechanistic organization

performs well under conditions of certainty while organic firms perform better in high

levels of uncertainty. This means that there is no one optimum type of organization

and management which is the underlying concept of contingency theory (Ven et al.,

2013).

61

Miles and Snow typology is built on Chandler's foundations. The typology proposes

different organizational structure for each strategic type. Organizations adapting

defender strategies follow functional structures. On the other hand prospectors’ type

organizations follow divisional structures. Analyzer is the balancing strategy. They

use matrix structures to balance the functional and divisional form of structures. In

this sense, Miles and Snow launched the "configurational view" of strategy

(Hambrick 2003). The focus of configurational view is on creating fit of

organizational characteristics to complement each other. This strategy-structure-

performance paradigm holds that when the strategy and organizational structure are

congruent, the performance of that organization is likely to be higher than the

conditions where they do not match with each other (Wasserman, 2008). The

defenders are expected to maintain an environment where stable form of

organizations can flourish. They produce a limited set of products catering for the

needs of narrow market segment. Working aggressively on cost reduction and high

quality product development with a focus on single core technology are the hallmark

characteristics of defender strategy. To maintain strict control, they follow the

mechanistic structure. The major characteristics of the mechanistic organization

include a team of production and cost-control specialists in the top-management, less

analysis of the environment for exploring new opportunities, focused and intensive

planning for cost reduction and efficiency enhancement issues, extensive division of

labor with formal structure form, hierarchical communication channels, and

centralized control etc.

In contrast, the key point of prospector strategy is innovation and attempt. Prospectors

emphasize on new opportunities with large scale investigation through R&D, superior

product idea and product development. They are risk takers and not scared of

uncertainty. Rather, they can meet the demands of progressing society and settle

themselves in dynamic environment. The administration of prospectors is

decentralized for deployment and coordination of resources among organizational

units and projects. Hence their structure is of organic nature which requires marketing

experts in the top management teams, R&D experts, planning broad horizon, low

degree of formalization, decentralized control, and horizontal as well as vertical

communication channels. The third strategic type is analyzers which follows a unique

combination of the strengths of both prospectors and defenders. In order to eliminate

62

risks and obtain profit maximization, analyzers use their innovative ideas and

experience. They create the balance between the steady growth and flexibility to

achieve higher performance. This means that analyzers can have high level of

standardized products with routine manufacturing processes meanwhile they are

always ready to develop new products. This strategic type follows matrix

organizational structure. Product managers generally have more influence than

functional managers. Intensive planning, centralized control mechanism in the

functional division and decentralized control in the production units are the key

features. The residual strategic type is reactors. This type describes the organizations

which fail to respond the market appropriately and perform poorly. Thus,

configuration for each strategic type is made up of contextual, structural, and strategic

factors (Hambrick, 2003; Miles & Snow, 1978; Robbins, Stephen P., Barnwell, 2006;

Snow & David J. Ketchen, 2014; Ven et al., 2013).

The argument of Miles and Snow is an extension to the strategy-structure-

performance paradigm of Chandler (1962, and Rumelt (1974). Here structure and

hence performance of an organization is contingent upon the strategic choice. Miles

and Snow believe that the complexity of the adjustment process can be penetrated by

searching for patterns in the behavior of organizations. One of the key component of

their work is that management's strategic choices shape the organization's structure

and process. Hence strategy act as a contingency facto in this relationship.

The above discussion on contingency theory clearly indicates that the structure and

performance of an organization are contingent upon the strategy, firm size, and

industrial sector in which an organization operates besides other internal and external

contingencies. The application of contingency theory is, therefore, highly legitimate

in the study of strategy and performance relationship.

2.13 Summary

The logical developments in the domain of the topic is covered in this chapter. It starts

with a brief introduction on the purpose of the organization and the characteristics of

organizational effectiveness followed by a logical and analytical commentary on the

historical development of the field of strategic management. It highlights the swings

of the research’s focus from one area to another in theoretical and empirical research.

63

The development in the concept of strategy along with its various forms (intended,

realized, and emergent), behavior (consistent, flexible, pure, and hybrid), and its

levels in organization (corporate, business, and functional) is discussed. The strategy-

performance relationship is generally investigated by strategic groups and strategic

typologies. Therefore, the concept of strategic group in general and the typology of

Miles and Snow in particular are discussed in detail. Specifically, the theoretical

foundations and the subsequent developments based on empirical research on Miles

and Snow strategic types (defenders, analyzers, prospectors, and reactors) is presented

in length followed by discussion on contingency theory, its application and relevance

with strategy-performance relationship and with Miles and Snow typology. The last

section explains the business strategy and organizational performance in the context

and relevance of Miles and Snow typology and its application under contingency

theory.

The theoretical literature review suggest that the field of strategic management is

rapidly changing with the advent of technology and globalization. New challenges are

providing the opportunities for researchers and academicians to investigate strategy-

performance relationships and propose new models for future needs. The debate on

strategy-performance relationship in the context of strategic purity, hybridization,

consistency, flexibility, and their comparative relationship with organizational

performance is an area where intellectual investigation is needed both theoretically

and empirically.

64

Chapter 3

Empirical Literature Review

65

3.1 Introduction

In strategic management research, the relationship of strategy and performance hold key

importance. To investigate this relationship, strategic typologies are applied in a large number of

empirical studies. Among these strategic typologies, the typology of Miles and Snow has been

one of the most applied typology for operationalization of strategic types. The first section of this

chapter provides the critical review of the application of Miles and Snow typology. It provides

the information regarding the industries and countries of the research, presence and distribution

of strategic types, data types, and methodologies used for measuring and classification of

strategic types. In the second section, the review of the studies is presented where strategy-

performance relationship is investigated. Here, information regarding performance measures,

sample size, strategic types, and findings of the studies are presented. Methodological

developments for the Miles & Snow typology over the time is presented in next section followed

by hypotheses development section.

3.2. Evidence on Industries and Countries Studied, Distribution Patterns of

Strategic Types, and Methodologies Applied

The application of strategic typology of Miles and Snow is widespread. This is evidenced from

the summary of literature presented in Table 3.1. The sound foundation of the typology is

evidenced from its application by researchers in a number of industries such as:

Financial Sector such as: banks ((Mcdaniel & Kolari, 1987b); “saving and

loans”(Jennings & Seaman, 1994); “insurance, mutual funds, brokerage” (Jennings et al.,

2003) etc.

Non-financial Sector like: manufacturing (Evans & Green, 2000; Ingram et al., 2016;

Olson et al., 2005; F. Slater et al., 2011; Smith et al., 1986; Snow & Hambrick, 1980);

“electronics, chemical, plastic, semi-conductors” (Thomas & Ramaswamy, 1996) etc.

Service sector: transportation (Daniel Rajaratnam & Chonko, 1995; F. Slater et al., 2011;

Snow & Hrebiniak, 1980); hospitals, hotels/lodging (Jennings et al., 2003) etc.

66

Public sector organizations: “colleges, hospitals, local governments, nursing homes,

schools, state owned enterprises etc.” (Conant et al., 1990; Hambrick, 1982; Jennings et

al., 2003; Shortell & Zajac, 1990) etc; and

Other areas: “construction, churches, and retailing” etc.

One shortcoming of the applicability of Miles and Snow typology is its limited research in

developing countries. The mainstream research has been in USA. Other countries include UK,

Australia, China, and some other developed countries. Very few mainstream researches are from

developing countries like Pakistan. Since, the level of uncertainty of the external environment

and the inefficiency in strategy formulation and its implementation in developing countries is

quite different; it is needed to have rigorous research in developing countries for the validity and

applicability of the typology.

The percentage distribution of defenders, analyzers, prospectors, and reactors is different for

different studies. This means that there is no standard pattern of strategic orientation even among

the similar industries of a similar condition. Analyzers dominates in majority of the studies. The

reason for this trend can be due to the tendency of high neutral response by respondents in a

questionnaire based survey methods used for perceived data collection. Another reason can be

the fact that subjective cut-off points are used by different researchers when ranking techniques

are used for classification of strategic types. Hence, there is a need to set some criteria or limit

that can be applied as a standard for distribution of strategic types. One third of the studies of our

sample did not identify all four strategic types. When archived data is used, generally two

extreme strategies are operationalized repressing defender on one side of the continuum and the

prospector on the other side of the continuum. For example Hambrick (1981; 1982; and 1983)

studied hospitals, colleges, and insurance firms and categorized the firms in defenders and

prospectors types. Similarly, Thomas & Ramaswamy (1996) categorized electronic, chemical,

and petroleum refining firms into two extreme categories. Liang et al (2009) also classified

manufacturing firms into defenders and prospectors while using archived data.

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Table 3.1: Empirical studies on Miles and Snow Typology: Snapshot

Reference

DT M

C

P

N (%)

A

N (%)

D

N (%)

R

N (%)

Industries Studied

Snow & Hrebiniak (1980)

A

Q

ST

US 17 (31)

32 (19)

18 (32)

8 (31)

5 (9)

9 (8)

8 (14)

5 (19)

22 (40)

36 (33)

19 (34)

3 (12)

9 (16)

26 (24)

8 (14)

7 (27)

“Automotive, Air

Transportation, Plastic,

Semiconductors”

Hambrick (1981; 1982) A

Q

EO

ST

US

20 (40)

29 (54)

31 (51)

-

-

-

30 (60)

25 (46)

30 (49)

-

-

-

“Hospitals, College,

Insurance”

Hambrick (1983) A RC US

EU

25 (30)

66 (55)

31 (7)

79 (41)

-

-

-

-

59 (70)

53 (45)

425 (93)

112 (59)

-

-

-

-

“Growth non-innovative,

Growth Innovative, Mature

non-innovative, Mature

innovative”

Smith et al. (1986) Q, I SR US 11 (25) 19 (42) 10(22) 5(11) “Electronic Manufacturing”

Zahra (1987) Q ST US 28 (42) 7 (11) 21 (32) 10 (15) Hospitals

Mcdaniel & Kolari (1987) Q ST US 67 (22) 155 (51) 57 (19) 22 (8) Banks

Smith et al (1989) Q SR US 10 (22) 19 (42) 11 (25) 5 (11) Electronics

Conant et al. (1990) Q ST US 48 (32) 61 (41) 30 (21) 9 (6) HMOs

Shortell & Zajac (1990) Q

A

I

SR

ST

US

35 (7)

96 (18)

228 (45)

315 (62)

196 (38)

31 (6)

58 (10)

71 (14)

“Hospitals

Time 1

Time 2”

Jennings & Seaman (1994) Q

I

ST US

28 (28) 19 (19) 52 (53) - “Savings and Loans”

Daniel Rajaratnam & Chonko

(1995)

Q ST US

89 (22) 181 (45) 87 (22) 43 (11) “Six Service Industries”

Thomas & Ramaswamy (1996) A CA US

135 (50) - 134 (50) - “Electronics, Chemical,

Petroleum Refining”

Evans & Green (2000) A SR US

24 (25) - 28 (29) 45 (46) “Manufacturing, Service,

Wholesale, Recreation, Food

Service”

68

Jennings et al. (2003) Q ST US

89 (22) 181 (45) 87 (22) 43 (11) “Banking, Brokerage,

Hospitals, Hotel/ Lodging,

Insurance, Transportation”

Desarbo et al. (2005) Q MR

US

JP

CH

234 (33) 220 (31) 168(24) 87 (12) “Companies listed at

Ward Business Directory

World Marketing Directory”

Olson et al. (2005) Q ST US 63 (28) 45 (20) 107 (47) 12 (5) “Manufacturing and Services

Firms”

Liang et al. (2009) A CA

EO

US

45 (73) - 17 (27) - Manufacturing Firms

Slater et al. (2011) Q ST US 67 (31) 48 (22) 96(44) 6 (3) “Manufacturing and Services

Firms”

Blackmore & Nesbitt (2012) A CA AU 317 (18) 301 (17) 402 (23) 753 (42) SMEs

Sarac et al. (2014) A CA TU 25 (13) 131 (72) 27 (15) - Joint Stock Companies

Parnell et al. (2015) Q ST US

CH

61 (37)

30 (17)

39 (23)

62 (35)

38 (23)

62 (35)

28 (17)

21 (12)

SMEs

(Ingram et al., 2016) Q,I ST PL 12 (15) 20 (25) 34 (42) 15 (18) Manufacturing Firms

Source: Author

Notes: “C=Country; US=United States; EU=Europeans Union; JP=Japan; CH=China; AU=Australia; TU=Turkey; PL=Poland

A=Analyzers; D=Defenders; P=Prospectors; R=Reactors;

DT=Data Type; Q=Questionnaire; A =Archived; I=Interview

M= Method for identification of Strategic Type; EO=Expert Opinion; RC=Relative Comparison; SR=Score Ranking; ST=Self Typing; CA=Cluster Analysis;

MR=Majority Response”

69

In some studies analyzers were operationalized as the middle or balancing strategy while

defender and prospector strategies are taken at the extreme ends of the continuum (Bentley et

al., 2013; Jennings & Seaman, 1994). The operationalization of reactor strategy is generally

neglected in such studies mainly due to the difficulty in its identification and operationalization.

Contrary to the general research evidences, Evans & Green, (2000), took reactor strategy as the

balancing one.

Different methodologies are used for measuring and classifying the strategic types. For example,

more than 70% in our sample of studies are based on questionnaire using either paragraph

approach or self-typing approach (where top management of the selected firms were asked to

select the type of their firm from the four anonymous strategic types). Other methodologies

include interviews and archived data. Few studies (only 32%) used archival data for measuring

strategy. Cluster analysis, and some ranking techniques (quintiles, percentiles, scoring etc) are

used for identification and classification of strategic types. There is a lack of standardized

method of identifying strategic types specifically, when some type of scoring method is applied.

Hence this is the area where more work is needed to refine the methodology.

3.3 Strategy-Performance Relationships

The relationship of strategy and performance has been widely investigated theoretically as well

empirically. For empirical research, the strategy-performance linkage is operationalized by a

number of measures representing strategy and performance. The focus of this section is to review

the literature on this relationship where Miles and Snow typology is applied for

operationalization of strategic orientation. The summary of the studies in this regard is presented

in Table 3.2

One of the major assumption of Miles and Snow typology is that “viable strategies (prospectors,

analyzers, and defenders) perform equally well in the long-run”. That is the difference in their

performance is insignificant. The earlier research findings support this assumption in large scale

70

(Conant et al., 1990; Jennings et al., 2003; Parnell, 2010; Snow & Hambrick, 1980; Woodside &

Sullivan, 1999). On the other hand, there are sufficient evidences where this assumption of equal

performance is violated. For instances, the studies of Blackmore & Nesbitt (2013), Hambrick

(1983), Parnell et al. (2015), and Zamani et al. (2013) etc. found significant variation among the

performance of viable strategies. There are many reasons for these differences. One reason for

these differences is the different nature and scope of performance measures. The environmental

dynamics and contexts may also lead to this variation in performance. The example for these

variations are numerous. For instance, defenders outperformed prospectors when performance is

measured in term of current profitability while in the same study prospectors outperformed

defenders on the basis of market share (Hambrick, 1983). Similarly, when the performance was

compared on the basis of sales growth, prospectors showed highest growth in sales. In the same

study analyzers outperformed others on profitability measured by ROA (Parnell & Wright,

1993). Similar results were found in other studies as well (Sarac et al., 2014; Zamani et al., 2013)

etc. The differences in performance were also found when performance of viable strategies was

compared across countries (Parnell et al., 2012; Parnell et al., 2015).

The second part of Miles and Snow assumption is that reactor performs poorly and below viable

strategies. The support for this assumptions is also overwhelming. However, in some cases

reactors also outperformed even viable strategies. Snow & Hrebiniak (1980) found that reactors

performed better in highly regulated industry. Blackmore & Nesbitt (2013) also found that

reactor showed high profitability in terms of ROA. This shows that industry also impact the

strategic choice and in some industries one type of strategy may not performed well. The

performance of reactors in some situations and for some performance measures support the

understanding of Zahra & Pearce (1990) who argued that pre-assumed inferiority of reactors to

other viable strategies is questionable. According to Conant et al. (1990), the reactors can also

exploit the situations because they have the potential to improve incrementally.

Another interesting finding is the fact that viable strategies performed negatively in many

studies, although, they outperformed reactors. For example, the performance of prospectors and

analyzers were negative in Turkey and USA (Parnell et al., 2012; Parnell et al., 2015). Similarly,

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in terms of growth in business and overall organizational performance measures, the

performance of defenders was found as negative (Zamani et al., 2013). Exploring the

contingency effect it was found that firm size affected performance significantly (Blackmore &

Nesbitt, 2013; Jennings et al., 2003). In some cases it was it was insignificant as well (Ingram et

al., 2016). The results for industry effect were mix. For example, in one study, the contingent

effect of industry on performance was found significant (Blackmore & Nesbitt, 2013) while in

another study this effect was found insignificant (Sarac et al., 2014). The studies support the

higher performance when there exist strategic clarity in the minds of management (Parnell et al.,

2015). Similarly, the findings were in favor of better performance when firms go for strategic

hybridization (Zamani et al., 2013).

Based on the above review, there is a need to further investigate the relationship in a different

country and environmental context, with refined methodology for identification and

classification of strategic types including reactors.

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Table 3.2: Research Evidence on Strategy-Performance Relationships

Reference Settings M DT Performance Sample

Size

Strategy Findings

(Snow &

Hrebiniak,

1980)

MI ST Q ROA 207 D,A,P,R Uneven distribution. All strategic types are

present. Defenders are highest followed by

prospectors, analyzers and reactors. Except for

highly regulated industry, viable strategies

outperform reactors.

(Hambrick,

1983)

MI

RC A ROI

CFOI

1230 D, P Only two strategies are operationalized. Defenders

better than prospectors in terms of profitability

and cash flows. Prospectors showed higher market

share gains in innovative industries.

(Smith et al.,

1986,

1989)

SI SM Q

I

Sales Growth;

Profit; ROA;

Overall

45 D,A,P,R The distribution of strategic types is uneven.

Analyzers outnumbered others. Performance

difference is significant. The performance of

viable strategies is better than reactors. The effect

of firm size on organizational performance is

significant

(Conant et al.,

1990)

SI ST Q General

profitability; ROI

148 D,A,P,R All strategic types are operationalized. Uneven

distribution of strategic types. Analyzers are

highest followed by prospectors and defenders.

Viable strategies performed equally well and

outperformed reactors

(Parnell &

Wright, 1993)

MI ST Q Revenue Growth;

ROA

104 D,A,P,R Uneven distribution with prospectors are

dominating followed by analyzers and reactors.

All viable strategies outperformed reactors.

Prospectors showed higher growth in sales while

analyzers showed higher profitability (ROA).

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Support for combination or hybrid strategies.

(Daniel

Rajaratnam &

Chonko, 1995)

MI

ST Q Earnings growth

rate; Sales Growth

rate; ROI

ROS

410 D,A,P,R Presence of all strategic types with uneven

distribution. Analyzers outnumbered others.

Viable strategies performed equally well and

outperformed reactors.

(Woodside &

Sullivan,

1999)

MI ST Q General

Profitability; ROI

93 D,A,P,R Uneven distribution. Defenders are highest

followed by analyzers and prospectors. Viable

strategies outperformed reactors.

(Jennings et

al., 2003)

MI ST Q Earnings growth

rate; Sales Growth

rate; ROI

ROS

410 P,A,D,R Uneven distribution. Analyzers dominate

followed by prospectors. Viable strategies

performed equally well and outperformed reactors

(Jusoh &

Parnell, 2008)

MI ST Q Operating Income

Sales Growth

Sales Revenue

ROI, Cash Flow

120 P,A,D,R

Country effect influence performance. Malaysian

firms perceive strategy differently. More focused

on the use of financial measures of performance.

Difficulties in application of western developed

measurement scale in eastern context.

(Parnell, 2010) MI ST Q Sales Growth;

Growth in Profit;

Market Share

ROA; ROE; ROS;

Overall

Performance;

Competitive

Position

277 P,A,D,R There is an uneven distribution of strategic types.

Prospector strategy is the dominating choice.

Viable strategies outperformed reactors and

“Stuck in the middle” strategies. High strategic

clarity firms outperformed firms with low

strategic clarity

(Parnell,

Koseoglu, et

al., 2012)

MI ST Q Sales Growth;

Growth in Profit;

Market Share

511 P,A,D,R Uneven distribution in both China and Turkey. In

China, Analyzers are dominating followed by

defenders and reactors while in Turkey, defenders

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ROA; ROE; ROS;

Overall

Performance;

Competitive

Position

dominates followed by analyzers and prospectors.

Viable strategies outperformed reactors in both

countries. Defenders performed above all in China

while analyzers in Turkey. In china, prospectors

and reactors showed negative performance while

in Turkey, analyzers and reactors performed

negatively.

(Blackmore &

Nesbitt, 2013)

MI

(SMEs)

CA A ROE; ROA;

Growth in

Employment;

Growth in Sales;

Growth in Assets

1772 P,A,D,R Uneven distribution. Difference is significant for

ROA, growth in employment, and growth in

assets while insignificant difference for ROE and

growth in sales. Firm size and industry effect is

significant. Reactors showed Higher performance

for ROA

(Zamani et al.,

2013)

MI ST Q ROI

Profitability

Market Share

Growth

Overall

Performance

129 P,D,R Prospectors and defenders outperformed reactors.

Prospectors showed highest than rivals in terms of

market share. Defenders performed negatively in

growth measure and overall performance. Hybrid

or Combination strategy performed better than the

pure strategy

(Sarac et al.,

2014)

MI CA A ROA 190 P,A,D Uneven distribution. Analyzers dominate

followed by defenders and prospectors. Strategies

performed equally as the difference is

insignificant. Insignificant impact of strategy,

size, and industry on performance. the contingent

impact of strategy and size is significant for

performance

75

(Parnell et al.,

2015)

MI

ST Q Sales Growth;

Growth in Profit;

Market Share

ROA; ROE; ROS;

Overall

Performance;

Competitive

Position

342 P,A,D,R Uneven distribution of strategic types in both

USA and China. In USA, prospectors dominates

followed by analyzers while in China, analyzers

dominates followed by prospectors. Viable

strategies outperformed reactors. Strategic clarity

brings better results. In USA, prospectors

performed above all while defenders in China.

Poor performance of analyzers in USA and

prospectors in China. Small outperformed

medium sized in both countries

(Ingram et al.,

2016)

SI I General

Profitability;

Competitiveness:

a subjective

measure

81 P,A,D,R Uneven distribution where defenders dominates.

Viable strategies outperformed reactors.

Contextual effect is insignificant

Source: Author

“Settings: MI=Multi-Industry; SI=Single Industry

M=Method for Strategy Classification: ST=Self-Typing; RC=Relative Classification; RS=Rank Score; CA=Cluster Analysis

DT=Data Type: Q=Questionnaire; A=Archival; I=Interview

Strategy: P, A, D, R=Prospectors, Analyzers, Defenders, and Reactor”

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3.4 Methodological Development for Application of Miles and Snow

Typology

One of the reasons of longevity of Miles and Snow typology is the continuous

development in the methodology, specifically for measuring and classifying the strategic

types. There are some major contributions in this regards. Some of the key

methodological developments and/or refinements are briefly presented below.

Snow & Hambrick (1980) conducted multiple studies of 200 firms in 10 industries to

explore the strategic behavior of organizations. The individual studies employed different

methods such as intensive interviews, mailed questionnaires, surveys and combination of

interviews and questionnaires. The purpose of their research was to address the

theoretical and methodological problems encountered in the attempts to arrive at valid

and reliable measures of organizational strategies. Specifically, they tried to find the

answers to the issues:

What constitutes the change in strategy instead of strategic adjustment?

Is there a difference between intended and realized strategy?

How do these different forms of strategies arise?

How can the investigator recognize which type of strategy is being observed?

What are some of the problems that can occur when industries and organizations

are being considered for inclusion in a research sample?

They suggested four distinct measures of strategies: (1) the Investigator inference

method in which the researchers use available information to assess the organizational

strategy, (2) the Self-typing method, where organization's managers (usually top

managers) are asked to select the best suited organization's strategy from the alternatives,

(3) the External assessment method, where an external expert panel assesses the

organizational strategy, and (4) the Objective indicators method, where proxies are used

to measure the strategic stance of an organization through archived data.

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Three conclusions were drawn from the above study. First, the validity of strategy

measures can be enhanced if researchers rely on multiple sources of information. Second,

the opportunities for strengthening the connection between concepts and measures of

strategy are great. For example, the measures for classifying the intended and realized

strategies can be identified and used. Third, studies of organizational strategies across

industries for comparative analysis can be more useful. The subsequent research on Miles

and Snow typology is overwhelmingly based on one of the four methods explained

above.

Smith et al. (1986) classified the strategic types by scaling the questionnaire and a

content evaluation of each cluster. Questions were arranged on a continuum scaled from

0-8. Miles and Snow's four strategies were identified by summing the scores to the

questions asked within each cluster. Prospector firms were classified based on the highest

summed score of a cluster. The next cluster in score ranking was classified as following

an analyzer strategy followed by the defender strategy cluster. Reactor strategy cluster

was identified as the residual cluster or the cluster with the lowest score.

Segev (1989), systematically compared, analyzed and evaluated two widely used

business-level strategies of Porter (1980) and Miles and Snow (1978). Following a pilot

study and a review of literature on strategic, 31 strategic variables were evaluated on

scale of 1-7 for each strategy within a given typology and a strategic profile was

developed for each strategy. The analysis indicated similarities and differences between

two typologies. A synthesis of two typologies is also suggested. One important outcome

of the study is the list of strategic variables. The analysis shows that Defender strategy is

closest to Porter’s Cost-Focus; Prospector is nearer to Differentiation; Analyzer is to

Differentiation and Cost-Focus; and Reactor is closest to the Stuck-in-the-Middle

strategy. The results show high ranks of Prospector for market share; rate of growth,

number of technologies used; and product/market breadth. Analyzers ranks high for

quality; price level; active marketing; rate of growth; and proactive management style.

Defenders rank high in quality; centralization; organization age; proactive managerial

style and mechanism. Defenders are low risk strategies whereas prospectors are high risk

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strategies for short time. However, the risk profile in the long run becomes identical for

all strategy types.

Conant, Mokwa, & Varadarajan (1990) developed and empirically tested a new multi-

item scale for operationalizing Miles and Snow's typology. The methodology is widely

used by researchers because it possess significant managerial and research potential. The

scale is theoretically anchored, easily administered, and possesses diagnostic value to

both strategists and their organizations.

Thomas & Ramaswamy (1996), operationalized the strategic types by using archival data

from annual reports and other published documents for analysis of realized strategy using

5 years averages. The ratios for marketing expenditure to sales, R&D expenditure to

sales, production expenditure to sales, and capital intensity measured by total assets per

employee were used to operationalize the strategic types along with firm age, firm size,

and industry are used as the contextual/contingency variables. Similarly, Ittner, Larcker,

& Rajan (1997), conducted analysis using cross-sectional latent variable regression

analysis. Strategy was measured using four variables: (1) the ratio of research and

development to sales, (2) market to book ratio, (3) the ratio of employees to sales, and (4)

the introduction of new product and services. These indicator ratios for each year are

averaged over 5 years. The subsequent studies, where archived financial data is used,

generally follow these proxies for measuring strategy.

Evans & Green (2000) used four proxy variables for measuring strategy. These proxies

measure the cost efficiency, the breadth of product and market mix, projected growth of

sales; and projected change in marketing expenditure. For classification of strategic

types, scoring method was used. The ranking for each firm was calculated based on the

scoring points representing the firms from strongest to weakest on each of four strategy

variables. Organizations were grouped on the basis of ranking using quintiles on a scale

of 1-5. The final scores were averaged. The firms which received mean score of less than

2 were classified as defenders. On the other side of the continuum, the prospector firms

were those who received mean score of greater than 4 while reactors were identified as

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the firms having mean score within the range of 2 & 4. This is the betrayal of the general

rule where the balancing strategy is termed as analyzers.

Desarbo et al (2005) re-examined the typology of Miles and Snow and its

interrelationships with several theoretically relevant variables. This includes the SBU

strategic capabilities at SBU level, environmental uncertainty, and performance. Using

survey data, they used a modified multi-objective classification methodology to

empirically derive an alternative quantitative typology. They supported the Miles and

Snow typology for its durability and excellence to its inborn parsimony, industry

independent nature, and its correspondence with actual strategic postures of firms across

multiple industries and countries. Their methodology facilitates the selection of strategic

groups through data and provides the deeper insights about the actual strategic stance of

the firms and their relationship with performance across industries and across countries.

They argued that different strategic groups may emerge in different contexts when the

grouping is made empirically.

Bentley, Omer, & Sharp (2013) developed a scoring methodology by extending the

earlier concepts. The strategies (prospectors, analyzers, and defenders) are classified

based on the outcomes of six strategy measures. These measures are: R&D expenses to

sales ratio; number of employees to sales ratio; historical growth rate of total sales;

marketing expenses to sales ratio; employee fluctuations measured by standard deviation

of total employees; and capital intensity measured by net power, plant, and equipment

(PPE) to total assets ratio respectively. All variables are computed using a rolling average

of the preceding five years. This is consistent with Ittner et al. (1997). All of the six

variables were ranked individually using quintiles per industry and year. The variables

are ranked based on the hierarchy of quintiles. The variables with the highest quintiles

(i.e. top 20%) are ranked with a score of 5. The next 20% quintiles are given a score of 4,

and so on. The lowest score was 1, allotted to the least quintiles. For each company-year,

the the ranking scores were summed across the six variables. Resultantly, a firm could

receive a maximum score of 30 and a minimum score of 6 on a continuum of 6-30. The

firms were classified as prospectors having their scores within 24-30, the analyzers with

the scores 13-23, while defenders with the scores lying between 6 -12.

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Lin, Tsai, & Wu (2014) designed a strategic game model and developed a framework for

making the collaboration partner choice decisions using Miles and Snow typology. The

model helps in the selection of a collaboration strategy. The differences between strategic

types are based on various patterns of distinctive resources and capabilities in terms of

three functions of management activities. The categorization of strategies is based on the

following criteria

Figure 3.1: Criteria for Classification of Strategic Types (Lin et al, 2014)

The developments in methodology for conceptualization, identification, and classification

of strategic types are impressive. Specifically, the methodologies used for measuring

intended strategy are matured and standardized. These methodologies such as self-typing

or paragraph methods developed by Snow and Hambrick (1980) and modified by Conant

et al (1990) are being repeatedly used by the researchers. On the other hand, the

methodologies developed for measuring realized strategy are still being modified. This is

specifically true when some type of scoring method is used. Also, there are almost non-

existence of any mechanism where the strategic behaviour or the transitions of strategic

orientation over the time is classified. Hence, there is a need for developing a standard

procedure that can be applied independent of context. This research fills this gap and

proposed a refined scoring methodology for conceptualization, identification, and

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classification of strategic groups based on their strategic orientation and their strategic

behaviour over the time.

3.5 Strategic Management in Pakistan

The studies on strategy-performance relationships in Pakistani firms are not very large.

However, there are some studies in which this relationship is investigated directly or

through moderation and mediation of some contingent variables. These studies

investigated many aspects of strategic management such as strategic orientations, strategy

formulation and implementation, marketing strategies, strategic management concepts

and practices, strategic diversification, strategic change behavior, capital structure,

working capital policies and their relationships with organizational performance. A brief

explanation of these studies is presented below.

Khan et al., (2016) analyzed the strategy-performance relationship for private sector

organizations in Pakistan. Strategy formulation and strategy contents were used as the

independent variables where rational planning, logical instrumentalism and strategy

process absence were measured for strategy formulation and strategy content were

represented by the viable strategic types of Miles and Snow typology. The results show

that rational planning has a positive relationship while logical incrementalism and

strategy absence has negative relationship with performance. The relationship of

prospector, defenders and reactors strategies was positive and significant with

organizational performance. Hassan et al (2013) investigated the direct impact of

marketing strategy with performance. The study investigates a mediating role of the

effectiveness of marketing strategy implementation on the relationship between strategic

creativity and performance. The moderating role of environmental uncertainty is also

investigated to the above relationship. The strategic types used for this study were

classifies as prospectors, analyzers, differentiated defenders, low cost defenders and

reactors. A survey method is used to collect data from service and manufacturing

companies of Pakistan. Creativity in strategy and effective implementation leads to

higher performance as evidenced by the findings of this study. Within strategic types,

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marketing strategic creativity and marketing strategy implementation effectiveness has

significant relationship with analyzers strategy only (Hassan et al., 2013).

Using Michael Porter (1980) typology, Afza & Ahmed (2017) examined the moderating

role of business strategy. The relationship between capital structure and firm performance

was investigated using the 8 years (2006-2013) data of 333 non-financial firms of

Pakistan. The results show that strategy has significant impact on the performance

measured by accounting and market performance measures. The relationship of unclear

or stuck in the middle strategy was negative with performance as expected. Furthermore,

the outcomes of cost leadership strategy show that debt financing is imperative both for

the accounting and market performance. The benefits of debt decreases along with

significant performance loss when firms follow the hybrid strategy, product

differentiation strategy, and stuck in the middle (unclear) strategy (Afza & Ahmed,

2017). Investigating the marketing aspects of strategy, Afzal (2009) found that marketing

practice regulates the relationship between marketing capabilities & business strategy and

organizational performance. The results show that adaption of marketing practices is

moderated by the market they serve. Strategy was measured by using Michael Porter’s

generic strategic types: Low Cost Leadership, Differentiation and Product Market Scope.

The results suggest that Marketing Capabilities and Strategy frame exist in the business

environment of Pakistan but there is a weak relationship with firm performance. Among

the various capabilities, marketing research capabilities have the highest impact on

performance. Comparing the impact of strategic types, it was found that low cost

leadership strategy is more successful than differentiated strategy in Pakistani

environment (Afzal, 2009).

There are more studies where different aspects of organizational strategies are

investigated. For example, Arif et al (2012), carried out a study to identify the nature of

strategic management concepts and practices in the construction firms in Pakistan.

Primary data from client, consultant, contractor and project management firms were

collected. It was found that there is a lack of commitment in the areas of strategy

evaluation, R&D, and the use of management information system as a decision tools

although, the importance of the strategic planning is felt at the top level. Also, operational

83

aspects of strategic management have been found in line with the strategic management

requirements, however, the effectiveness of quality control and inventory control

procedures are not up to the standards (Arif et al., 2012). Diversification is one of the

strategic choices and it continues to be an important strategy for corporate growth and

better organizational performance. Afza et al. (2008) investigated the relationship

between diversification and a firm’s financial performance for Pakistani firms where the

financial performance in terms of risk and return has been analyzed. The research found

that the performance of non-diversified firms is better than the diversified firms.

Interestingly, low risk is accompanied by high return for non-diversified firms while

diversified firms are more risky even their return is lower. Hence, they recommend that

the managers should be careful while selecting the degree of diversification (Afza et al.,

2008).

A qualitative study was carried out by Malik (2014) in a Public sector organization of

higher education to analyze the strategic change behavior. The behavior of top

management support, entrepreneurial spirit in organization; and project champion were

measures. Significant evidence of the presence of all three components of strategic

behavior were found to follow a successful change initiatives. Malik & Kotabe (2009)

developed and tested a model of the dynamic capability development mechanisms,

linking with performance, for Emerging Market manufacturing Firms (EMMF) in India

(54 firms) and Pakistan (39 firms) mainly involved in exports. Results showed that

organizational capabilities, organizational learning and government support has

significant impact on performance (Malik & Kotabe, 2009).

Analyzing the traditional relationship between working capital management policies and

firm performance, Nazir & Afza (2009) found that there is a negative relationship

between the profitability of the firms and degree of aggressiveness of working capital

investment and financing policies. The reason for this relationship may be the

inconsistent and volatile economic conditions of Pakistan (Nazir & Afza, 2009).

Keeping in the view the above literature survey, it is found that although there are many

studies on strategy-performance relationship in Pakistan, there is no study where the

performance of pure versus hybrid strategies and strategic consistency versus strategic

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flexibility is compared along with the contingent effect of firm size and industry in one

settings. The mechanism of identifying the reactor strategy from financial archived data

is also an addition to the existing studies.

3.6 Hypotheses Development

One of the premises is that all strategic types do exist in an economy and similar pattern

may be followed in a given industry as well. However, because of the dynamics of

industries and the type of competition and external environment, this may not hold true.

The research evidence (see table 3.1) also supports the uneven distribution of the

strategic types both in single-industry and in multi-industry settings. Therefore, we state

the hypothesis (H1) as:

H1: There is a significant difference among the distribution of strategic types

within a given industry and overall in the economy.

One of the basic assumption of Miles and Snow (1978) is that viable strategies

(defenders, analyzers, and prospectors) produce better performance in the long run if they

stick to them for a longer period of time. This is because of the fact that these viable

strategies are well articulated and respond consistently to the environmental change. The

second part of the assumption is that all viable strategies will outperform the reactor firms

because the behaviour of the reactor organization is inconsistent and inappropriate to the

changes occurring in the environment. There is an overwhelming support in favor of

these assumptions (Conant et al., 1990; Jennings et al., 2003; Parnell, 2011b; Woodside

& Sullivan, 1999). In these cases, there is statistically insignificant difference among the

performance of viable strategies and they outperformed reactors. However, there is

sufficient evidences where viable strategic types performed significantly different

(Blackmore & Nesbitt, 2013; Jusoh & Parnell, 2008; Koseoglu et al., 2013; Parnell et al.,

2015; Smith et al., 1989; Zamani et al., 2013).

There are many reasons for these inconsistencies in performance. The differences in

performance among viable strategies may be due to the fact that different studies used

performance measures which are different in nature and scope from each other. For

85

instance, for current profitability measure, defenders performed than prospectors while in

another study prospectors performed better than defenders when performance was

measured as market share of the firms (Hambrick, 1983; Zamani et al., 2013). Similarly,

in terms of growth, prospectors performed better than analyzers when profitability was

measured by ROA (Parnell & Wright, 1993) etc. The variation of performance within

viable strategies may be due the differences in the environmental contexts of the studies.

For example, the variation in performance is found for cross-country studies (Parnell,

Koseoglu, et al., 2012; J. A. Parnell et al., 2015). That is why, Desarbo et al, (2005),

suggested for more rigours in research for investigating strategy-performance relationship

to find out whether the typology of Miles and Snow is universally applicable or is context

dependent. On the basis of above findings, we state the following hypotheses:

H2: There is an insignificant difference in the performance of viable strategies

H2a: Viable strategies outperform reactors

In practice, organizations hybridize the strategies because of certain problems and

difficulties related to the pursuance of pure strategies. For example, strategic purity may

ignore some important needs of the customers because of their stickiness to the specified

boundaries leading to some critical gaps in product development and offers. Second, pure

strategies may be easily imitated by the competitors whereas hybrid strategy is difficult to

imitate because it combines several factors related to pure strategies. Resultantly, hybrid

strategies may realize higher performance. Third, the competitors invent new challenges

because of changing behaviour of market, customer needs, and evolvement of

consumers’ tastes etc. This change makes pure strategies more vulnerable due to their

inherent rigidity to stick with the core. Firms reduce their resilience and adaptability

when they focus on a single strength. Keeping in view the above issues linked with pure

strategies, firms prefer to adapt the hybrid strategies. In this way, firms with hybrid

strategies can satisfy the customers in a better way. This is because of the fact that by

hybridizing their strategy, they are difficult to be imitated. They can be more flexible in

adapting the ongoing change (Pertusa-Ortega et al., 2009). Hybridization is therefore, not

only a viable option but it can also be more profitable choice as well (Claver-Cortés,

86

Pertusa-Ortega, & Molina-Azorín, 2012). There are number of challenges for firms to

remain competitive and successful. These challenges include: competition from foreign

firms; imports export liberalization policies; varying supply and demand conditions; and

technological advancements etc. Hybridization of strategy is expected to deal well with

these challenges and can lead the organization towards higher performance (Madanoglu

et al., 2014; Manev et al., 2015; Proff, 2000; Salavou, 2013). These arguments provide

the basis for the following hypothesis:

H3: Hybrid strategies are superior to the pure strategies both in adaptation and in

performance

The proponents of strategic consistency argue that strategic consistency is adapted by the

firms due to number of advantages. First, firms stick to the existing strategy and avoid

flexibility to avoid uncertainty. In this way they can beat the challenges of rapid changes.

Second, shift in strategy may require considerable expenses of capital. For example, a

shift from a growth oriented and innovative prospector or balancing analyser strategy to a

defender strategy may require investments in sophisticated production equipment to

lower production costs for effective implementation of a defender strategy (Miles &

Snow, 1978). Similarly, a shift from a defender or analyser strategy to a prospector

strategy may require expenditure to develop or improve R&D facilities. Third, quality

and price conscious consumer may get confused by rapid changes in firms’ strategic

stance. For instance, a shift from low-cost strategy to adapt differentiation strategy by a

firm may confuse its price-conscious customers. The customers may switch to other firms

and develop their relationship to another low cost strategy holder firm. At the same time,

the customers willing to purchase differentiated products and to pay a premium price may

not respond quickly to the low-cost products because of ignorance of the strategic move

of the organization. Fourth, sustaining and maintaining the success in new product and

service is always a challenging job for organizations because competitors are ready to

distort the consumer perception and divert their attention towards themselves to reap the

benefits of initial strategic change (Fehre et al., 2016; Moss et al., 2014; Parnell & Lester,

2003).

87

On the other hand, the proponents of strategic flexibility have their own arguments. First,

a flexibility in strategy may return when organizations are able to create fit between

strategic choice and organization’s internal and external environment (Parnell, 1997).

Second, by adapting flexibility an organization can benefits from first-mover advantages.

This move can help an organization to exploit scarce resources and increase the

knowledge base. This will lead to the long-term competitive advantage for the

organization. Third, when there is a shift in adapting of new resource base, the shift

towards strategic change adaptability becomes necessary. Fourth, when performance of

an organization is not picking up, strategic change become necessity (Parnell, 2005;

Parnell & Lester, 2003). Alternatively, flexibility can create problems as well. Flexibility

or rapid change in strategic stance may put the existence of the firms at risk. This may

lead to an imbalance situation between capabilities of an organization and recent strategic

change actions causing increasing costs and decline in competitive market position

(Lamberg et al., 2009).

Based on the above arguments, it can be concluded that given the highly competitive and

ever changing nature of business environments characterised by flexibility, adaptability,

and speed a strategic orientation toward consistency may not be a natural choice. Firms

with high adaptability would be able to exploit the given situation with flexibility in their

stance in a quick and speedy way in response to competitive environmental changes to

achieve higher performance (Lamberg et al., 2009; Moss et al., 2014). Therefore, for an

organization to remain competitive, flexibility is inevitable so that it should remain

committed to make improvements in products and services, market approaches, and

adaption of latest technology (Parnell, 2005).

One of the differences between the viable strategy and reactor strategy is the behaviour in

response to the market changes. The reactor strategy, according to Miles and Snow,

behave inconsistently while viable strategies remain consistent over the time to exploit

the situation based on their well thought strategy. That is why it is assumed that reactors

perform below the consistent strategy. Based on the arguments in favour of both strategic

consistency and strategic flexibility, we hypothesize as under:

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H4: Strategic consistency and strategic flexibility performed equally well and

outperform reactors

The performance of the viable strategies is expected to have positive impact on

organizational performance. However, in many case viable strategies performed even

negatively showing losses although they outperformed reactors. For instance, in one

study in China, the performance of prospectors was negative. In the same way analyzers

showed losses in USA and Turkey (Parnell et al., 2015). Similarly, there are evidences

where viable strategies performed negatively such as performance of defenders was

found negative in terms of growth measures and in terms of overall performance (Zamani

et al., 2013). To test for the positive relationship of strategy with performance, the

following hypothesis is stated:

H5: Strategy has a positive relationship with performance

There are many contingent factors such as firm size and industry in which the

organization is placed in can influence the performance significantly. Firm size is one of

the widely used contingent variables (Smith et al., 1986). The performance and the choice

of strategy vary for small, medium, and large size because the structure, availability of

resources, and skill set varies with the variation in firm size. For example, larger

organizations have more resources available to pursue a variety of opportunities than

small firms. Large firms benefit by economies of scale but at the same time a growth in a

firm size exposes the firm to higher agency costs which rise the agency conflicts and

escalates the differences in the interests of contracting parties. Characteristics like the

ability to make use of scale economies, diverse capabilities, and formalization of

procedures help larger firms to produce better results than the smaller firms in terms of

financial performance. On the other hand, small sized firms are likely to get benefits from

the spirit of corporate entrepreneurship for growth (S. Shah, Tahir, Anwar, & Ahmad,

2016). Also, the strategy formulation process differs between small and large firms, with

smaller firms showing a preference for simplistic models. Smaller firms tend to abandon

their strategic planning process because they operate in less complex environments

(Ouakouak & Ammar, 2015; Parnell, 2008).

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From above discussion, it is expected that firm size has a significant impact on

organizational performance. There are evidence for and against of this expectation. To

see the contingent impact of firm size on performance, the following hypothesis is stated:

H6: The Firm size has a significant impact on firm performance

Similarly, the industry, in which an organization operates, is one of the most often

mentioned influencing variables on performance in strategy-performance relationship

studies. According to Thomas and Ramswamy (1996), industry can significantly limit

managerial influence. The evolving structure of an industry due to market forces,

constrains the managers to proactively design or implement strategy in order to achieve

superior organizational performance. Although a multi-industry sample is considered to

enhance the external validity of findings, there is a substantial body of literature which

contends that industry factors can severely affect statistical results and interpretations.

Because of the industry dynamics, the profitability varies significantly across industries,

making the industry a significant predictor of firm profitability (Jennings et al., 2003;

Madanoglu et al., 2014; Thornhill & White 2007). Based on these arguments, the

hypothesis for testing the influence of industry on organizational performance is stated as

below:

H7: The Industry has a significant impact on firm performance

A firm should have a functional fit among the elements of its environment, strategy, and

structure to achieve competitive advantage (Luoma, 2015). In view of Miles and Snow,

strategy is relatively binding because it constrains the firms in its responses to the

environment. According to them, each of their three viable strategies can be found in any

industry. Each strategy can perform equally well if properly implemented. However, the

evidence from prior studies shows that the performance of an organization varies across

the firm size (Blackmore & Nesbitt, 2013; Jennings et al., 2003; Smith et al., 1989).

Similarly, the influence of industry was significant showing significant variation in the

performance due to industry impact (Blackmore & Nesbitt, 2013). To test the contingent

impact of both firm size and industry, the following hypothesis is formulated:

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While Hambrick (1983) pointed out that the generic character of the Miles and Snow

typology ignores environmental and industry peculiarities, there are strong evidences that

the interaction between strategy, size, and industry significantly affect the organizational

performance. This means that the performance of viable strategic types varies for

different firm size and in different industries (Sarac et al., 2014; Smith et al., 1986, 1989).

In this context, Shortell & Zajac (1990) stressed for further investigation of Miles and

Snow’s conception of generic strategies tendency for their equal viability across

environmental contexts and time. Therefore, to test the contingent impact of firm size and

industry on strategy-performance relationship, we set the following hypotheses:

H8: The performance of strategic types varies with the change in industry and firm

size

Contingency approach views organizations as social systems. The continuous

coordination within sub-systems is achieved through the implementation of management

policies. The interaction of the resultant fit of coordination helps in achieving

organizational objectives and goals (Olson et al., 2005). Therefore, the performance of an

organization is dependent on the congruence of its internal fit (strategy and structure) and

its alignment with the external or environmental fit (Luoma, 2015; Wilden et al., 2013).

Hence, strategy, firm size, and industry when combined together have significant impact

on organizational performance. Therefore, the following hypothesis is stated for this

purpose:

H9: Combined together, strategy, size, and industry has a significant impact on firm

performance

The performance of an organization is influenced by the structural, organizational, and

environmental contingencies. The most important contingencies include firm size,

strategy, and industry. A specific strategic type force the management to modify the

structure which in turn influence the performance. Also, when a firm grows in size, it

gradually becomes structured, formalized, and routinized. At the same time industry can

significantly constraints the managerial influence for making strategic decisions due to

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the dynamics and peculiarities prevailing in a specific industry (Jennings et al., 2003; Lex

Donaldson, 2001; Moss et al., 2014; Thomas & Ramaswamy, 1996). To test the

interactive impact of different combinations of strategy, size, and industry, the hypothesis

is stated as below:

H10: Interaction for possible combinations of strategy, size, and industry has a

significant impact on performance

When firms grow in size their structure becomes more formal and their processes are

routinized. Therefore, institutionalized processes in response to environmental shifts

minimize the role of managers and strategy. Similarly, the industry can significantly

constrain the managerial influence the strategic decisions of the managers. Also, the

performance of an organization is closely related with the performance of its industry

which is mostly affected by some structural components such as input costs, price level,

and product diversification etc. Hence, firm size and industry membership is sometimes

more significant drivers of performance than the strategy. However, the management

holds a control on resources and has the capability to cope with the environmental

changes. This enables them to select an appropriate course of action despite the limitation

imposed by firm size and the industry. Thus, strategic behaviour and resources of the

firms continue to play a significant part in shaping organizational fortune and hence

influencing performance despite the constraints of the environment (Jennings et al., 2003;

Moss et al., 2014; Thomas & Ramaswamy, 1996). Based on these arguments, we state

that strategy is the better predictor of the performance than firm size and industry. This is

tested through the following hypothesis:

H11: Strategy is a better predictor of performance than size and industry

3.7 Summary

The empirical review of the literature provides sufficient support in favor of applying the

Miles and Snow framework for current study. Also, there are number of opportunities

where more research can be done. The strong support for this typology is supported by

the increasing studies being done using this typology and its application in a variety of

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industries and also in cross-country studies. These studies used data from a single

industry at a time as well as from multi-industry data. Similarly, research is done in a

single country as well as in cross-country settings. Generally, prospectors and defenders

strategies are operationalized in the studies where archived data (depicting realized

strategy) is used treating analyzers as the balancing strategy. Reactor strategy is mostly

ignored in these studies mainly due to methodological limitations. Similarly, little or no

research is available that investigate the behavior of firms’ strategic stance over the time

to check their consistency, flexibility, and inconsistency (reactor strategy). The increasing

demand for hybridization of strategy to have sustainable performance also demands the

separate and comparative study of pure versus hybrid strategies. The refinement in

methodology is also needed especially for having a standardized procedure to

operationalize and classify the firms into certain theoretically supported strategic groups.

The srategy-performance linkage applying Miles and Snow typology with archived

financial data is an under researched area in developing countries including Asia.

According to Parnell et al. (2015), the environments in developing countries are

fundamentally different as they lack many of essential resource, infrastructure and control

systems representing more risks and uncertainty than the developed countries although,

they represent attractive markets at the same time. Also, they have different demand

features and level of stability than their counterpart developed economies. Hence, it can

be expected that underlying assumptions of Miles and Snow typology regarding

performance and strategic behavior of the firms may be different from western developed

countries. Therefore, there is a great significance of doing research in environment like

Pakistan where the systems are not well established and supportive for designing and

implementing specific strategies on sustainable basis.

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Chapter 4

Research Methodology

94

4.1 Introduction

This chapter presents the research paradigm, research design and methodology adapted

for this research. Research paradigm discusses the ontology and epistemology of the

positivists approach used for this research. Followed by the research design, selection of

sample size and collection of relevant strategic, performance, and contingent variables; a

detailed scoring methodology for operationalization of strategic orientation of the firms

using SAS codes; the categorization of strategic groups based on Miles and Snow’s

strategic types, strategic purity and hybridization, and strategic consistency and

flexibility; and explanation of the methodology for descriptive analysis and for ANOVA

and regression analysis.

4.2 Research Paradigm

A research paradigm is a systematic set of beliefs that dictates what researchers in a

particular discipline should study, how research should be conducted and how results

should be interpreted (Lincoln and Guba 1985; Bryman and Bell 2011). A research

paradigm consists of three elements: ontological, epistemological and methodological

question (Guba and Lincoln 1994). Ontology is concerned with the nature of reality and

existence and what can be known about it (Guba and Lincoln 1994; Thomas 2004;

Easterby-Smith et al. 2012). The ontological paradigm concerns with the essence of the

phenomena which is under research or investigation. Here, the fundamental questions

faced by the social researchers are “what is the nature of reality” or “what the nature of

the knowable is?” On the other hand, epistemology questions what might represent

knowledge or evidence of the entities or ‘reality’ that one wishes to investigate (Mason

2002). Epistemology concerns the relationship between the researcher and what can be

known (Guba and Lincoln 1994) and therefore provides assumptions guiding the

knowledge inquiry (Easterby-Smith 2012). Finally, methodology is a combination of

techniques used by the researcher in finding out what is believed or to be known (Guba

and Lincoln 1994) and stipulates how the research could be practically done (Bo Zhang,

2011). Hence, the selection of the appropriate research methodology is constrained by the

ontological and epistemological choice of the researcher (Mariyani Ahmad Husairi,

2014)

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There are many research paradigms but two most widely applied paradigms are positivist

and interpretivist paradigms. In positivistic approaches, the study of human nature and

behaviors is carried similar to the research conducted in the pure sciences (Collis &

Hussey, 2003). Positivists make efforts to create a knowledge of a reality existence of

which is beyond the mind of human. Positivists consider that experiences of human about

the world is reflected as an objective reality. On the other hand, the interpretivist

paradigm was developed as a reaction to this approach by social scientists. They argue

that the scientific models are not applicable to the social sciences. According to Burr

(1995), interpretivist view human knowledge is reflected by specific goals, experience,

culture, and history of those things which were created by humans within social world

framework (SEBAA, 2010).

This study is an empirical investigation of causal relationship of strategy with

performance and the interactive effect of firm size and industry with strategy on

performance. The approach is deductive as hypotheses are developed on the basis of

existing literature and tested using secondary data. The research is, therefore, fall under

the positivist research paradigm which is explained in detail below.

4.2.1 Positivist Approach

The positivist viewpoint assumes that the individual should be free of subjectivity,

exempt of feelings, sentiments, or intuition. According to Gill and Johnson (1991, p. 32)

“the processes by which theories and hypotheses are tested are often rated as more

important than the source of such theories and the essence of positivism is the

identification of causal relationships”. According to Saunders et al (1997), a positivist

approach: deduct the hypothesis from the theory; express these hypothesis for

operationalization; tests the operational hypotheses; and examines the outcome. Besides,

if necessary, positivist approach modifies the theory in the light of the findings or

outcomes. Hence, a positivist approach is a highly structured methodology that facilitates

the replication of the methodology in other settings. There is clarity of research questions

and the methods that are used to test the models. Large sample size is with generally

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quantitative data analysis techniques are used (Susana C. S. F. Rodrigues, 2002). The

positivistic approach emphasizes interdependence and causality, reductionism and

generalization.

The objectives of the common research in social science is to identify causal relationship

and their explanations to generate principles that explain consistency in human behavior

(Easterby-Smith et al., 1996) (Susana C. S. F. Rodrigues, 2002). Here, rather than human

interests and desires, the object of the research is defined by objective criteria. The

advocacy of the paradigm is to apply natural science methods to investigate and study the

social science reality. This is done by explaining and predicting the happening in the

social world by finding the causal relationships between constituent elements of the

research phenomena (Burrel and Morgan, 1994). Generally, five broad principles are

highlighted by Bryman & Bell (2007) for positivist paradigm.

1. The knowledge is said to be genuine only if that can be confirmed by the sense.

2. The purpose of theory is to generate hypotheses for explanations of laws to be

assessed.

3. Knowledge is based on the collection of facts that provide the basis for laws.

4. Scientific research must be conducted in a way that is value free.

5. Creating a clear distinction between normative statements and the scientific

statements with the belief that the scientific statements are the true domain of the

researchers

In positivist approach, data collection is relatively easy, clear theoretical foundation, the

control of researcher on the research process and the results are relatively easy to

compare with the findings of other studies.

This research follows deductive approach as it developed the research model and

hypotheses based on theory. The basic approach followed in this study is that of theory

testing through empirical research. A set of testable hypotheses have been formulated on

the basis of theoretical underpinnings and the findings of previous studies. These

hypotheses have been tested and conclusion are derived by using relevant statistical tools

and techniques in order to assess and model the relationships. The longitudinal archived

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financial data of 307 joint stock companies listed at Pakistan Stock Exchange is used for

this purpose. The study operationalizes the concepts of Miles and Snow strategic types.

The strategies are further classified as pure and hybrid, consistent and flexible, and

reactors using measurable constructs. Firm size (small, medium, and large) and industry

(twelve economic groups) are used as contingent factors along with strategy. A multi-

industry and single industry analyses is done for generalization of the findings.

4.3 Research Design

There are number of methods used to measure the strategic orientation of an organization.

Snow and Hambrick (1980) introduced four main approaches which are most widely

applied:

1. Investigator inference method: In this method, the researcher uses all of the

available information about the strategic orientation of an organization and

assesses its strategic choice

2. Self-typing method: In this approach, the organization's management (usually top

management) is asked to categorize the organization's strategy from among

alternatives

3. External assessment method: Here, an expert panel is asked to assess the

strategic orientation of a given organization

4. Objective indicators method: It involves proxies for measuring parameters that

provide information about the strategic orientation of an organization

The first two methods are used to identify the intended strategy of the firm while the last

two methods are used for knowing the realized strategy. Since, this study investigates the

realized strategy of the selected firms, the first two methods are excluded from

consideration. Comparing the advantages and disadvantages of the last two methods, we

excluded the external assessment method because objective indicator method has certain

advantages over it. These advantages are aligned with the objectives of this study. The

advantages of objective indicator methods include:

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The objective indicators method allows differentiation between strategic changes and

strategic adjustments if the data is available for sufficient time period (usually five

years or longer).

The objective indicators method is comparatively appropriate for identifying realized

strategies because it controls for perceptual and interpretive bias.

This method allows large, heterogeneous samples.

Snow and Hambrick (1980) provided a detail of advantages and disadvantages of all four

methods mentioned above. Keeping in view the number of advantages and the nature and

availability of data for sufficient time period, this study uses the objective indicators for

measuring strategy and performance.

4.4 Strategy and Performance Variables, Sample size, Tools and

Techniques

The information regarding the research where objective measures are used is summarized

in Table 4.1. It represent the list of the strategy and performance variables. It also

provides the information about the sample size of the studies, industry in which the

research is carried out, research methods used for the research, and tools and techniques

used to carry out the research where objective measure are applied.

The variables used for measuring and operationalizing strategy are related to the aspects

of strategic orientation and they are used to find: “the marketing and R&D focus”;

“growth and production capability”; “capital intensity”; “cost efficiency”; and

“diversification of the firms” etc. The common indicators used for measuring financial

performance are: ROA, ROE, ROS, and Growth Rates. Other financial performance

measures include “Return on Capital Employed –ROCE”, “Cash Flow on Investment -

CFOI” “Earning Per Share –EPS”, and “Annual Stock Return –ASR” etc. whereas

customer satisfaction and service quality are used as non-financial performance

measures.

Researchers used varying number of measures for operationalization of strategy and

performance. For example, Luoma (2015) used multiple performance measures based on

multiple categories such as: Profitability (Operating Margin, Profit Margin, Return on

99

Capital Employed, and Return on Total Capital); Growth (change of net sales); Solvency

(Equity Ratio, Net Gearing, Relative Indebtness); Liquidity (Quick Ratio, Current Ratio);

Cash Management and activity measures (Working Capital, Inventory to Sales ratio,

Sales Receivable Turnover, Accounts Payable Turnover). Hambrick et al. (1982) used

ROI, Cash flow on Investment (CFOI), Sales Growth, Return per Risk, and Market share

change as performance measures. Ittner et al. (1997) used both financial and non-

financial performance measures in the same study. For financial measures, they used

Sales, EPS, and ROA while for non-financial measures, customer satisfaction and service

quality were used. Blackmore & Nesbitt (2013) used ROA, ROE, Growth in

employment, Growth in sales, and Growth in assets in one study.

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Table 4.1: Summary of the Strategy and Performance Variables where archived data is used

Source Strategy and Performance Variables Dataset and Research Techniques

(Hambrick, 1983) Strategy Measures

Entrepreneurial Attributes:

Product R&D/Sales; Marketing Expense/Sales; Relative Integration

Forward

Engineering Attributes

Gross Fixed assets/Employees; Relative Integration Backward;

Relative Compensation rates; Relative direct costs; Process

R&D/Total R&D; Value Added/Employees; Capacity Utilization;

Competitive Devices; Relative Price; Relative Service; Relative

Quality

Performance Measures

ROI, Cash Flow on Investment (CFOI), Share Growth

Data: 4 years (1978-82) average data

were drawn from the Profit Impact of

Market Strategies (PIMS) database.

Strategy Classification: Percent Scores

Analysis: Univariate t-tests and

multivariate regressions (with dummy

variables)

(Shortell &

Zajac, 1990)

Strategy Measures

The number of diversified services offered (home health care,

outpatient diagnostic services, geriatric screening, health promotion,

and sports medicine); the number of these diversified services added

in the past two years; the number of these services planned; and the

number of high-technology services offered; ratio of outpatients to

inpatient services


Market Share, Market Growth

Data: Data of two pints in time (1984-

85 and 1986-87) were collected for 574

hospitals. The source of data is

American Hospital Association

Strategy Classification: Factor

Analysis,

Analysis: ANOVA, Correlation

(Thomas &

Ramaswamy,

1996)

Strategy Measures

Marketing expenditure - A ratio of marketing expenditure to total

sales; Research and development expenditure - A ratio of research

and development expenditure to total sales; Production expenditure -

A ratio of cost of goods sold to total sales; Asset intensity - A ratio

of total assets per employee was used to measure asset intensity.


ROS (Sales), ROA, ROE

Data: 3 years average data (1987-89) of

83 firms from Fortune 500 belonging to

electronic, chemical and petroleum

industries which earn 70% of sales from

single industry. The source of data is

COMPUSTAT

Strategy Classification: Cluster

Analysis

101

Analysis: ANOVA

(D. Ittner et al.,

1997)

Strategy Measures

The ratio of research and development expenditures to sales; Market

to book ratio; Employees to sales Ratio; The number of new

products and services introduction


Financial (Sales, EPS, ROA etc); Non-financial (Customer

satisfaction, Service quality, etc)

Data: Two years data (1993-94) of 317

firms having chemicals (27), machinery

(23), electrical and gas services (27),

and commercial banks (21 firms) etc of

different size

Strategy Classification: Weights

Scores

Analysis: Partial Least Square Method

(PLS) using Structural Model

(Evans & Green,

2000)

Strategy Measures

Cost Efficiency: Projected (Total Expense-historical total

expense)/Sales; Product Mix Breadths: Number of Product Lines and

Services; Projected Sales Growth: (Forecasted Sales-Historical

Sales)/Historical Sales; Projected Change in the Marketing

Expenditure: (Forecasted Marketing Expense-Historical Marketing

Expense)/Sales


ROS, Growth (Firm Size)

Data: 97 firms (manufacturing =32,

Services=43, Wholesale=6,

Recreation=2, and Food Service=14)

Strategy Classification: Scoring

Method

Analysis: MDA and ANOVA

(Balsam,

Fernando, &

Tripathy, 2011)

Strategy Measures

SG&A/Sales: Ratio of selling, general and administrative expenses

to net sales; R&D/Sales; Sales/COGS; Sales/CAPEX: Ratio of net

sales to Capital expenditure on property plant and equipment;

Sales/P&E: Ratio of sales to net book value of plant and equipment;

Employee/Assets: Ratio of employees to total assets


ROA, Annual Stock Return

Data: 1658 firms having 15 years data

(1992-2006) using Five Year averages

Strategy Classification: CFA

Analysis: Correlation, Regression

(Blackmore &

Nesbitt, 2013)

Strategy Measures

New product/service development; Change in markets targeted;

Change in advertising; Change in distribution; Change in production

Data: Longitudinal data of 1773 SMEs

from database of surveys conducted

from 1994-95 through 1997-98.

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technology; Comparison of performance; Formal business planning


ROE, ROA, Growth in employment; Growth in Sales, Growth in

assets;

Strategy Classification: K-Means

Cluster Analysis

Analysis: ANOVA

(Bentley et al.,

2013)

Strategy Measures

The ratio of research and development to sales; the ratio of

employees to sales; a historical growth measure total sales; the ratio

of marketing (SG&A) to sales; a measure of employee fluctuations

(standard deviation of total employees); and a measure of capital

intensity respectively


ROA, Growth

Data: 17 years (1993-2009) for strategy

types construction. The source of data is

COMPUSTAT

Strategy Classification: Scoring

Method

Analysis: Logistic Regression,

ANOVA

(Sarac et al.,

2014)

Strategy Measures

Same as used by Bentley (2013) explained above


ROA, Growth

Data: 6 years data (2006-2011) of 190

listed firms at Istambul Stock Exchange

categorized as small, medium and large

on the basis of employees (<50, 50-250

and >250 respectively).

Strategy Classification: Cluster

Analysis

Analysis: ANOVA, Regression

(Lin et al., 2014) Strategy Measures

Marketing and R&D Capability

R&D to Sales Ratio; Marketing Expense to Sales ration

Production Capability

COGS to Sales Ratio


ROIC, ROS, Capital Turnover

Data: 10 years average data of 35

semiconductor firms. Source of data is

COMUSTAT

Strategy Classification: Strategic game

model using multiple-objective

programming in lingo Software

Analysis: Multiple-Objective

Programming

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4.5 Sample and Data

A sound, stable and robust industrial base, represented by non-financial corporate sector,

is an important segment of a country’s economy and is, therefore, essential for economic

wellbeing of a country. In Pakistan, non-financial corporate sector is divided into 12

diversified nature of businesses/economic groups including: “Textile; Food; Chemical,

Chemical products, and Pharmaceuticals; Other Manufacturing; Non-metallic Mineral

Products; Motor Vehicles, Trailers and Auto parts; Fuel and Energy; Information;

Communication and Transport Services; Coke and Refined Petroleum products; Paper,

Paper board and Products; Electrical Machinery and Apparatus; Other Services

activities”.

The study uses seven years (2007-13) multi-industry data of joint stock companies in

Pakistan. The total number of firms listed on Pakistan Stock Exchange (PSE) formerly

known as Karachi Stock Exchange (KSE) at the end of year 2013 were 396 including

both public and private firms. The final list of 307 firms was based on the following

selection criteria:

1. A firm to be included for analysis must be listed for year 2013. This means that

the age of the firm must not be less than 7 years i.e. it must have remained listed

for the last seven consecutive years

2. The value of sales of a selected firm should not be zero for any years within study

time period i.e. for all 7 years of interest (from 2007-2013).

3. The firms with outlier numbers for one of the selected variables are also excluded

to have representative results

Based on the above selection criteria, a total of 33 firms (8% of total firms) were

excluded because of age factor (condition one above). On the basis of non-zero sale

criteria, 43 firms (about 11%) were excluded because of second condition while 13 firms

(3%) were dropped from the study because of third condition. Hence, the final list include

307 firms fulfilling the above selection criteria which is 78% of the total population. The

data is taken from the Central Bank of Pakistan’s (State Bank of Pakistan) publication

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“Financial Statement Analysis of Companies (Non-Financial) Listed at KSE (now

Pakistan Stock Exchange –PSE)”.

Table 4.2: Distribution of firms according to Industry (Economic Groups)

Economic Group or Industry

Code

No of

Firms

%age

1. Textile and Allied Sector

Spinning, weaving, finishing of textile

Made-up textile articles

Other textiles

A

A1

A2

A3

119

106

5

8

38.73

34.53

1.63

2.61

2. Food and Allied Sectors

Sugar

Other food products n.e.s.

B

B1

B2

39

28

11

12.70

9.12

3.58

3. Chemicals: (Chemical products and pharmaceuticals) C 32 10.42

4. Other Manufacturing D 26 8.47

5. Other non-metallic mineral products

Cement

Mineral products

E

E1

E2

21

15

6

6.84

4.89

1.95

6. Motor vehicles, trailers, and auto parts F 20 6.51

7. Fuel and Energy G 11 3.51

8. Information, communication and transport services H 9 2.93

9. Coke and refined petroleum products I 9 2.93

10. Paper, paperboard and paper products J 6 1.95

11. Electrical machinery and apparatus K 8 2.61

12. Other services activities L 7 2.28

Total 307 100

The Textile sector represent the highest number of firms and constitutes 39% of the total

firms followed by 13% from Food sector and 10% from Chemical, Chemical products,

and Pharmaceuticals industry. The other sectors’ individual presentation is less than 10%.

The industry-wise number and percentage representation is presented in Table 4.2.

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4.6 Measures of Strategy, Performance, and Contingent Variables

4.6.1 Measures of Strategy: Independent Variables

In literature, different researchers used different set of objective measures to assess the

types of strategy and performance. Based on these research evidences and depending on

the availability of data, this study uses following four variables to measure the strategic

types;

1. MESR: Marketing Expenses to Sales Ratio. The marketing expenses are calculate by

adding the selling, administration, and general expenses. These expense shows the

intention of the management toward growth and innovation to differentiate the

products and services. Therefore, the ratio measures the strategic orientation of the

firms towards innovation (Bentley et al., 2013; Thomas & Ramaswamy, 1996)

2. COGSR: This is the ratio of Cost of Goods Sold (COGS) to Sales. COGS ratio is

used to measure internal efficiency (Thomas & Ramaswamy, 1996) as well as

production efficiency (Lin et al, 2014). According to Thomas & Ramaswamy (1996),

COGSR is the standardized measure for internal efficiency represented by cost

reduction and process improvement. These characteristics are represented by defender

strategy with centralized structure and standardized processes. On the other hand,

prospectors focus on product improvement with decentralized structure and non-

standardized production process which limit their ability to reduce cost.

3. CASGR: The ratio is abbreviated for Compound Annual Sales Growth Rate

(CASGR). It shows the historical growth pattern of sales of a firm and is applied for

measuring the growth orientation of the firm (Slater & Zwirlein, 1996). CASGR is

calculated as:

CASGR=(𝑬𝒏𝒅𝒊𝒏𝒈 𝑽𝒂𝒍𝒖𝒆

𝑩𝒆𝒈𝒊𝒏𝒏𝒊𝒏𝒈 𝑽𝒂𝒍𝒖𝒆)

(𝟏

# 𝒐𝒇 𝒚𝒆𝒂𝒓𝒔)

− 𝟏

4. CIR: Capital Intensity Ratio: It is generally calculated as the ratio of net of

equipment, plant, and property divided by total assets (Bentley et al., 2013). A slight

106

modification is made in this ratio for this study. We take fixed assets which include

net property, plant and equipment because of availability of data in this form. The

ratio is used to measures the technological orientation of a firm.

A summary of the measures of strategy, their focus and orientation, implications, and

indicators for prospectors and defenders is presented in Table 4.3.

Table 4.3: 0Strategy measures, their implications, and indicators

Strategy Measure Implications for Prospector and

Defenders

Indicators

1. MESR

“Company’s focus on

exploiting new products

and services. It leads to

marketing efficiency”

It covers the entrepreneurial dimension.

The expected expenditure for

prospectors on marketing is greater than

defenders

High score for

Prospectors

2. COGSR

“Company’s emphasis

on internal efficiency

leads to production

efficiency”

Covers the entrepreneurial dimension.

Defenders are expected to have lower

production costs as their emphasis is on

internal and production efficiency.

High score for

Prospectors

3. CASGR

“Company’s historical

growth or investment

opportunities”

The ratio covers both the entrepreneurial

dimension as well as the administrative

perspective. Prospectors are expected to

have high growth rate

High score for

Prospectors

4. CIR

“Company’s

commitment to

technological

efficiency”

This measure covers the engineering

dimension. It shows the commitment of

the organizations towards the

technological efficiency. Defenders are

expected to have higher ratio

High score for

Defender

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4.6.2 Measuring Performance: Dependent Variables

Accounting and financial data is often the most used source for measuring strategic

performance when objective data collection methods are used. Within these measures,

most frequently used accounting-based performance indicators are: Return on Assets

(ROA), Return on Equity (ROE), Return on Sales (ROS) or Gross Profit, Net Profit

Margin (NPM) etc. In contrast, the market-based performance indicators use market data

as a source for performance measures. The market-based method is based on an ‘outside-

in’ approach to evaluate performance. “Market Share”, “Tobin’s Q”, “Jensen’s Alpha”,

and “Sharp Measure” etc. are the most commonly used market based performance

indicators (Eikelenboom, 2005).

Four performance measures (dependent variables) are used for this study. These measures

shows different aspects of profitability generated through the application of assets

(ROA), return generated in response to sales (ROS), return based on owners’ equity

(ROE), and return based on the overall capital employed (ROCE). The definitions and

calculation of these measures are based on the State Bank of Pakistan’s publication

“Financial Statement Analysis of Listed Companies” (State Bank of Pakistan, 2014) The

rationale behind the choice of multiple financial performance measures is to avoid

subjectivity in the data and to avoid a restricted and narrow view of performance

(Salavou, 2015). These measures are briefly explained below:

Return on Assets (ROA): ROA gives an idea about the efficiency of the management in

utilizing of an organization’s assets to generate profits. ROA is an indicator of how

profitable a company is relative to its total assets. Calculated by dividing a company's net

profit before tax (NPBT) by its total assets, ROA is displayed as a percentage. The assets

of the company are comprised of both current and non-current assets. The higher the

ROA, the better the performance is. The ROA is useful when the profitability of an

organization or a group of organizations is compared with the performance of another

firm or group of firms in the same industry or across industries.

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Return on Equity (ROE): ROE measures a firm’s profitability in response to the money

invested by the owners or the shareholders of the firm. ROE is calculated as a ratio of net

profit before tax (NPBT) to Average Shareholder's Equity and is expressed as a

percentage. The ROE is useful when the profitability of an organization or a group of

organizations is compared with the performance of another firm or group of firms within

the same industry.

Return on Sales (ROS): ROS is a profitability ratio widely used to measure a firm’s

operational efficiency. It is also known as a firm's "Operating Profit Margin". This

measure helps management in knowing how much profit is being produced by per rupee

of sales. ROS is used to compare a company's performance over the time to look for

trends. It is also used to compare the performance of the firm with its competitors with in

the same industry. An increasing trends in ROS is the indicator of growth and efficiency.

ROS is calculated as the ratio of NPBT (Net Profit Before Tax) to Sales.

Return on Capital Employed (ROCE): ROCE, usually expressed in percentage

terms, measures the returns that a business generates from its employed capital.

Employed Capital is equal to a company's Equity plus Non-current liabilities (or Total

Assets − Current Liabilities). In other words, it is the long-term funds that a firm has

generated for business operations. ROCE indicates the efficiency and profitability of a

company's capital investments. To be efficient and to create shareholders’ value, ROCE

should always be higher than the rate at which the company borrows its capital. It is

calculated as the ratio of NPBT to Average Capital Employed.

4.6.3 Measuring Contingency Variables

The performance of an organization is the outcome of the fit that a firm creates between

certain organizational characteristics and the environmental context. The performance is

affected by the internal characteristics of an organization (e.g. firm size) as well the

environmental changes in technology, consumer behavior, and regulation etc. For

example, larger firms can outperform smaller firms as they can exploit the market

conditions due to economies of scale and bargaining power. Similarly, small firms can

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perform better while exploring new ideas and switching quickly to the changes in the

market and technology. Also, when a firm grows in size, it becomes increasingly formal,

structured, and routinized making the adjustment in strategic orientation more complex

than the smaller firms. Looking at the external environmental influence, the industry can

significantly affect and influence the managerial choice because the ever changing

dynamics of an industry forces the management to make adjustments in designing or

implementing strategy proactively to achieve superior performance. Consequently,

environmental shifts force the management to reshape the institutionalized processes.

These arguments support the fact that industry membership and size of the firm are the

important and key determinants of organizational performance (Jennings et al., 2003;

Madanoglu et al., 2014;Thomas & Ramaswamy 1996; Sarac et al. 2014). Based on their

importance and influence on performance and strategy, firm size and industry are used as

the contingent variables for this study. These variables are briefly explained below:

Firm size –Size of the firm is one of the most important and widely used contingent

variables in strategy-performance studies. The performance and the choice of strategy

vary for small, medium, and large size because the structure, availability of resources,

and skill set varies with the variation in firm size. Strategy formulation process differs

between the firm sizes. For example, smaller firms showing a preference for simplistic

models (Parnell, 2008) while large firms are likely to adopt more comprehensive and

formal strategic processes because of their complex structures (Ouakouak & Ammar,

2015). There are many ways to divide the firms into sizes. Total assets, sales, and number

of employees are generally used for this purpose. For example, Smith et. al. (1989)

divided the number of employees into thirds to find out the cut-points for small, medium,

and large firms. Similarly, for this study total assets, based on the ranking of the total

assets of the firm, are divided into thirds, for categorizing the firms as small, medium and

large. The firms with the total assets falls in the lowest 33% are taken as small firms, the

next 33% as medium and the highest third as the large firms.

Industry– Industry is mostly used as the influencing variables on performance for multi-

industry analysis. The industry can significantly affect and influence the managerial

choice because the ever changing dynamics of an industry forces the management to

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make adjustments in designing or implementing strategy proactively to achieve superior

performance. because of the peculiarities and distinguishing features of each industry,

performance varies significantly across industries, making the profitability of the industry

a significant predictor of firm performance (Jennings et al., 2003; Madanoglu et al., 2014;

Thomas & Ramaswamy, 1996; Thornhill & White, 2007). Keeping in view the

importance of industry in predicting the strategy and performance of an organization, it is

also taken a as contingency variable along with firm size.

4.7 Identification of Strategic Types

The strategic classification, when archived data is used, is based on cluster analysis and

scoring methods in majority research. Out of these two, cluster analysis is applied most of

the time. Although, this study applies scoring method, a brief on cluster analysis

technique is also provided.

4.7.1 Cluster Analysis

Cluster analysis is one of the widely used statistical techniques for the classification of

groups or clusters after sorting the observations into similar sets. It is often used in

strategy research to derive strategic classifications or taxonomies. It has been found to be

one of the most effective method for revealing known group structures in a data set.

Cluster Analysis became popular after its application by Hatten in a series of research

during 1974-1978. Cluster Analyses takes a sample of elements (e.g. organization) and

group them in a way that the variance or standard deviation among the groups is

minimized while it is maximized between the groups. However, according to Ketchen &

Shook, (1996), the application of cluster analysis is a multifaceted challenge because to

determine the quality of the clusters, it requires several methodological choices. Research

reveals that the implementation of cluster analysis has been often less than ideal because

of certain limitations. For example, extensive reliance on the researcher’s judgment;

lacking an underlying theoretical bases; the potential of not only to offer inaccurate

depiction of the grouping in a sample but also to impose grouping where none exists; and

it ignores time effect etc. (Ketchen & Shook, 1996).

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To overcome these limitations and to provide researchers an alternative and easy to use

methodology, we propose the scoring methodology, discussed below, to identify the

strategic orientation of the firms.

4.7.2 Conceptual Development for Scoring Method

Literature review suggests that scoring method is one of the options available for

classification of strategic types. In this method, composite ranking score of proxies used

for operationalization and classification of strategic orientation of the firms into strategic

groups. One of the shortcoming of the scoring method used earlier is that there is no

standardized way to reach at the final classification of the firms. Different researchers

used different steps. Another, issue with this method is that not all four strategic types are

identified through this method. For instance, Conant et al. (1990), identified the

limitations of identifying only two strategy types as defenders and prospectors which are

placed on the opposite ends of a continuum while analyzer strategy is placed in the

middle treating it as a balancing strategy. Limited efforts were made to solve this

problem. Another shortcoming is the exclusion of reactor strategy from studies where

objective indicator method is used for classification of strategic types. Since reactors can

change their archetypal posture with incremental improvement in their strategic practices.

By doing so they can exploit market conditions and sustain their performance as well.

Therefore, the investigation of the reactor behavior and the classification of reactor

strategy to investigate their relationship with performance in comparison to viable

strategies an important aspect of this study.

Extension in the number of strategic types is another area of improvement because the

existing strategic types only discuss the pure strategies while in real situation firms make

some type of adjustments and make a combination of pure strategies to hybridize the

strategic choice. Specifically at the prospectors and defenders strategies can be divided

into pure and hybrid strategies. The concept of hybridization of pure strategies has been

discussed in literature but not extensively. Hambrick, (1981) used the terms “pure

defenders”, “pure prospectors” and “extreme defender/prospector” to differentiate the

strategic types. Later, Hambrick (1983) introduced the term defender-like and prospector-

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like. The firms were labeled as “defender-like” that lags its industry in new product sales.

On the other side firms were labeled as “prospector-like” which surpasses its industry in

new product sales. Other researchers also used new terms for different strategic types

such as “pure defenders/prospectors” and “mixed strategies” (Valos and Felix, 2003) and

“low-cost defenders” and “differentiated defenders” (Slater et al., 2011) are used

subsequently. Madanoglu et al. (2014), classified the hybrid strategic types and named

them as “Prospector-Analyzers (PAs)” and “Analyzers-Defenders (DAs)”.

Taking into account the discussion above, hybrid strategies can be operationalized and

proposed for future investigation based on Miles and Snow typology. According to the

typology, defenders and prospectors are treated as pure strategies and are placed at the

extreme ends of the continuum when some type of archived data is used for

operationalization of strategic orientation. Doing so, a large area on the continuum is

either left for analyzers strategy or the extreme ends are extended towards the centre to

expand the range for prospectors and defenders making the selection somewhat

subjective or judgmental. This study argue that instead of expanding the boundaries of

pure strategies or the analyser strategy, the firms that lie between the upper limits of

analyzers and lower limits of pure prospector on a continuum may be called as

“Prospector-Analyzer-Like (PA-Like)” strategies on one side of the continuum while the

firms lying between the lower limits of analyzers and upper limits of pure defenders may

be classified as “Defender-Analyzer-Like (DA-Like)” strategies. This will separate the

pure defenders and pure prospectors from those firms which somehow hybridize the

strategies. Hence, in this study, the strategic types are extended to defender, DA-Like,

analyzers, PA-Like, prospectors and reactors.

The scoring method is developed for classification of strategic types based on the ranking

of the scores based on theoretical support. The ranking is generally based on quintiles or

percentiles. One problem with the ranking is the cut-off points because different

researchers used different cut-off points. Some of the examples are presented here. Smith

et al. (1986), used the ranking scale of 0-8 on the continuum. He used cluster analysis for

classification of strategic types. The clusters with highest score was categorized as

prospectors, followed by analyzers, and defenders. The cluster with lowest score was

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classified as reactor. Evans & Green (2000) applied four measures “cost efficiency”,

“product mix breadth”, “projected sales growth”, and “projected change in marketing

expenditures” to classify the strategic types. Firms were given ranking scores on a scale

of strongest to weakest based on the values of above mentioned four strategy variables.

Firms were divided into quintiles groups where every firm received a score between 1

and 5 (1 = lowest; 5= highest) in such a way that firms in the lowest quintile (last 20%)

received a score of 1; the next 20% quintile group received a score of 2, and so forth. At

the end, scores were averaged. The firms with a mean score of greater than 4 were

categorized as prospectors while those with mean score less than 2 were classified as

defenders. The remaining firms having the mean score between 2 and 4 were treated as

reactor. In another study Bentley et al. (2013) used six proxies for measuring strategic

orientation of the firms. Discrete ranking scores were calculated on a scale of 6-30 on a

continuum. Firms were categorized into respective category as: defenders with score

between 6 and 12 while analyzers were categorized based on the score between 24 and

30. The remaining firms with scores in the range of 13-23 were termed as analyzers.

For this study, business strategy was operationalized on the basis of ranking scores

calculated from the values of four ratios used as proxies for measuring strategy (“MESR,

COGSR, CASGR, and CIR”). Quintiles are used for final scores. The highest quintiles

(top 20%) were given a score of 4 while observations in the lowest quintiles (bottom

20%) were given a score of 0 for three measures MESR, COGSR, CASGR. To

standardize the scoring, reverse ranking is calculated for capital intensity variable (CIR).

The scores are summed over the four measures. The maximum score a firm can receive is

16 and the minimum possible score is 0 making the continuum scale of 0-16. The pure

strategies are expected lie towards the extreme ends of the continuum while the rest of

the firms constituting the middle of the continuum. Specifically, the strategic types are

classified as: “Pure Defenders (0–3)”; “DA-Like (4-6)”; “Analyzers (7-9)”; “PA-Like

(10-12)”; and “Pure Prospectors (13–16)”. Because of the inconsistent nature of reactors

(as they change their stance from one strategic approach to another over the time), special

treatment was given to judge their behavior over the time (Figure 4.2).

114

Pure Pure

Defenders DA-Like Analyzers PA-Like Prospectors

0-3 4-6 7-9 10-12 13-16

Reactors

Figure 4.1: Strategy continuum and reactors’ domain

For identification of reactor strategy and to find out the inconsistency in strategic

orientation over the time, strategy scores were calculated at four points in time. To find

out the long-term orientation of the firms, scores were calculated based on the average

data for all seven years (2007-13). The calculation for transition during short-to-medium

term strategic orientation of the firms is calculated on the basis of preceding five year

averages. Hence, a total of three point in time scores were calculated for the years 2011,

2012, and 2013 respectively. The procedure helped in identification of consistent,

flexible, and inconsistent or reactor strategies. The strategies other than reactors are

viable strategies divided into pure or hybrid and consistent or flexible strategic groups.

The criteria for a firm to follow a viable category is that it must follow the same strategy

in at least three times out of four. Otherwise the firm is marked as a reactor firm. The

process exposed the firms’ behavior over the time and helped in classification of the

firms in a better way. For example, a firm in a long term fall under one of the viable

strategies but the behavior of the firms during short-to-medium term period or transition

varies. This variation or inconsistency identifies the reactor strategy. Hence, many firms

fall into one of the viable strategic category in a long-term but actually behave like

reactor strategy during transition period.

4.8 Step-by-Step Process to Calculate the Strategy Types using SAS

Codes

This section presents a step-by-step procedure using a self-generated raw panel data to

explain the coding steps, procedures, and the outcomes to facilitate the researchers how

to identify the strategic orientation of firms from a given data set of their interest. The

115

software used for this purpose is SAS – statistical analysis software, used in many fields

of research. SAS can read data files created by other statistical packages such as data files

created by SPSS®, Excel®, Minitab®, Stata®, Systat, and others. These data files can be

incorporated or imported into a SAS dataset. SAS is versatile and powerful enough to

meet researchers’ needs in data analyses. It is flexible, with a variety of input and output

formats and numerous procedures for descriptive, inferential, and forecasting of statistical

analyses. It includes a wide range of analysis procedures to help researchers navigate

through data (SAS Inc, 2017).

4.8.1 SAS Data Set

A raw data set of 7 years (for example for years 2011-17), representing the characteristics

of original dataset, is prepared for the step-by-step procedure and explanation. The data

contains the information of 18 firms from 5 industries with four strategy variables, assets

for measuring size, and one performance variable. The composite score calculated

through the steps explained below for strategy variables (V1, V2, V3, and V4 for

simplicity) treated as independent variables. Sector and size are considered as contingent

variables and ROA as dependent variable (other performance variables are excluded for

simplicity). The purpose of the study is to prepare a base line for strategy-performance

relationship using different typologies, especially Miles and Snow typology, and to

investigate the impact of contingent factors on this relationship. The following code

generates the data set for this exercise:

Data test.practice;

input Sector Firms Years V1 V2 V3 V4 Asset ROA;

Datalines;

1 1 2011 12 23 45 30 200 0.09

1 1 2012 11 22 50 31 225 0.21

1 1 2013 11 25 33 32 250 0.08

1 1 2014 10 30 45 33 250 0.17

1 1 2015 12 27 34 34 245 0.22

116

1 1 2016 12 30 40 35 252 0.09

1 1 2017 13 33 45 36 250 0.25

1 2 2011 32 45 21 37 155 0.21

…………

…………

5 3 2014 24 27 25 104 120 0.2

5 3 2015 24 25 30 105 100 0.24

5 3 2016 34 35 25 106 130 0.26

5 3 2017 12 24 24 107 125 0.1

Run;

The data in above format prepared in any other format or software (for example, SPSS,

Excel etc) can be directly imported either through GUI procedure or through import

procedure or through “Infile” option in data command. The complete raw data set and the

step-by-step procedure (explained below for classification of strategic types into pure,

hybrid, consistent, flexible, and reactors) is appended in Annexure: A2.

4.8.2 Average Calculation

The researchers use averages (simple or moving/rolling) to calculate proxies for strategy

and to smooth the variations of a time series data due to seasonal or other variations. For

moving/rolling averages, one of the most suitable SAS procedures is PROC EXPAND

(Premal P. Vora, 2008). For this study, PROC SQL is used to calculate the simple

averages, rounded off to 2 decimal points, for each firm within an industry.

4.8.3 Rank Calculation

The ranking is done based on the theoretical foundations for each selected variable. For

example, in our raw data set variables: V1, V2, V3, and V4 refer to the variables selected

for measuring strategic orientation (please refer to the variables MESR, COGSR,

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CASGR, CIR in section 4.5.1 and Table 4.3 above). As evidenced from the previous

research, it is supposed that prospectors score high for V1, V2, and V3 and low score for

V4. Therefore, reverse ranking is calculated for V4. PROC RANK procedure of SAS

(Bilenas, Morgan, & Bank, 2009) facilitates to rank variables according to their demand.

For this purpose, quintiles are used to divide the data into five bins. The next step is to

calculate, within sectors, the ranking of the four strategy variables. The ranking is done

for size calculation as well to categorize the firms into small, medium, and large.

4.8.4 Categorization of Firms

The next step is to categorize the firms according to their strategic orientation and

according to the size of the firm. The outcome of the codes written in above sections,

produce the data set having averaged values for strategy and performance variables,

ranking for strategy variables and for assets, total score of strategy variables and

categorization of the firms according to respective strategic type and size are presented

below (Table 4.4).

Table 4.4: Ranking, Scores, and classification of Strategic types and Firm Size

Ob

serva

tion

s

Secto

r

Firm

s

V1

V2

V3

V4

Asset

RO

A

R1

R2

R3

R4

To

tal S

core

Asset R

an

k

Stra

tegy

Size

1 1 1 11.571 27.143 41.714 33.000 238.857 0.159 1 1 2 3 7 2 Analyzers Large

2 1 2 39.143 43.286 21.571 40.000 138.286 0.293 3 3 1 1 8 1 Analyzers Medium


4 2 1 26.000 50.000 35.000 37.000 142.143 0.149 3 3 3 2 11 2 PA-Like Large

5 2 2 19.000 30.143 23.857 41.571 132.429 0.184 1 2 1 1 5 1 DA-Like Medium

6 2 3 25.429 29.714 26.714 33.000 145.000 0.224 2 1 2 3 8 2 Analyzers Large

7 3 1 23.286 34.286 25.571 38.143 129.143 0.203 1 1 0 3 5 1 DA-Like Medium

8 3 2 30.286 36.857 27.571 34.000 124.429 0.246 3 3 2 4 12 1 PA-Like Medium

9 3 3 30.286 33.000 27.286 41.000 123.714 0.244 3 0 2 2 7 0 Analyzers Small

10 3 4 68.857 60.143 54.857 48.000 115.571 0.203 4 4 4 2 14 0 Prospectors Small


118

Ob

serva

tion

s

Secto

r

Firm

s

V1

V2

V3

V4

Asset

RO

A

R1

R2

R3

R4

To

tal S

core

Asset R

an

k

Stra

tegy

Size

12 3 6 18.143 36.429 26.429 62.000 123.714 0.244 0 2 1 0 3 0 Defenders Small

13 4 1 25.143 48.000 51.000 69.000 267.571 0.159 1 3 3 3 10 2 PA-Like Large



16 5 1 21.429 50.714 33.857 90.000 142.143 0.149 2 3 3 3 11 2 PA-Like Large

17 5 2 19.000 30.143 22.143 97.000 107.429 0.184 1 2 1 2 6 0 DA-Like Small

18 5 3 25.429 29.714 26.571 104.00 114.286 0.224 3 1 2 1 7 0 Analyzers Small

V1…V4 are averages of strategy variables and R1…R4 are ranks for V1…V4 respectively

4.8.5 Comparison of Strategies Overtime and Identification of Consistent,

Flexible and Reactor Strategy

Zahra Shaker A. & Pearce (1990), urged the researchers to study the strategic change and

transitional characteristics of strategic types over the time as studying shifts among

strategic types in this way will make it possible to examine parallel changes in strategic

process. Specifically, to check: are there predictable paths of strategic change (e.g. a

defender becomes analyzer); and is a certain strategic transition is more conducive than

others to effective firm performance? Another important question is the identification of

reactor strategy using archival data. According to Miles and Snow (1978), reactors

respond to the challenges of the adaptive cycle in uneven and transient ways; they tend to

be short-term oriented and environmentally dependent. Conant et al. (1990) labeled

reactors as ‘less stable’ and 'inconsistent'. Reactors are unassertive and varied in strategic

orientation and follow consistently inconsistent behavior pattern. Blackmore & Nesbitt

(2012), asserted that reactors could exhibit the behavioral characteristics of defenders,

analyzers and prospectors.

For this purpose, the procedure adopted for identification of strategic types using average

data for all seven years is repeated for classification of strategic orientation for multiple

points in time. For this study, strategic orientation at three points in time (2014, 2015, and

119

2016) is identified using preceding 5 years average data and compared with overall

strategic stance of the firms based on 7 years averages. The identification of the behavior

of the firms over the time is one of the basic objective of the study. This identification

process helped in not only the identification of reactor strategy (which is generally

ignored in such studies) but it also helped in finding the strategic behavior during the

transition period of the firms in terms of strategic consistency and strategic flexibility.

These grouping will help in studying the relationship of these strategic groups and firm

performance. The resultant outcome of the strategic orientation of the firms over the time

is presented in Table 4.5.

Table 4.5: Strategic Orientations of the firms over time

Strategic Orientation Over Time

Final

Classification* Obs Sector Firms

Strategy at

Time 1

Strategy at

Time 2

Strategy at

Time 3

Strategy

Overall

1 1 1 Analyzers Analyzers Analyzers Analyzers Analyzers


3 1 3 PA-Like+ Analyzers Analyzers Analyzers Analyzers

4 2 1 PA-Like PA-Like PA-Like PA-Like PA-Like

5 2 2 DA-Like+ DA-Like DA-Like DA-Like DA-Like

6 2 3 Analyzers PA-Like Analyzers Analyzers Analyzers

7 3 1 DA-Like Analyzers Analyzers DA-Like Reactor


9 3 3 Analyzers DA-Like Analyzers Analyzers Analyzers

10 3 4 Prospectors Prospectors Prospectors Prospectors Prospectors

11 3 5 DA-Like Analyzers Analyzers Analyzers Analyzers

12 3 6 DA-Like Defenders Defenders Defenders Defenders

13 4 1 Analyzers PA-Like Analyzers PA-Like Reactors

14 4 2 DA-Like Analyzers Analyzers Analyzers Analyzers



17 5 2 DA-Like DA-Like DA-Like DA-Like DA-Like


*Final grading is based on the rule as: a strategic type is considered viable if it occurs at least 3 out of 4 times; else

“Reactors”; +PA-Like=Prospector-Analyzers-Like, DA-Like=Defender-Analyzers Like

120

The identification of consistent, flexible and the reactor strategy is possible by

monitoring the movement of strategic orientation over time. For example, a strategy type

is considered as consistent if it remains same for all four time periods (see observation

number 1,2,4,5,8,10, and 15-18). A firm is set to follow the flexible strategy if it changes

its strategy only once for adjustment i.e it occurs for at least three times out of four (see

observation number 3,6,9,11,12 and 14). Otherwise, the firm is considered as inconsistent

or reactor firm (see observation number 7 and 13).

4.9 Data Analysis Techniques

4.9.1 Descriptive Statistics

The descriptive statistics such as number of observations, minimum, maximum, mean,

median, standard deviation, quartiles, quintiles, skewness, kurtosis, confidence interval,

etc can be calculated by using a number of SAS procedures. Procedures like “PROC

SUMMARY”, “PROC MEANS” and “PROC UNIVARIATE” etc provide descriptive

information by using their respective options and formats. Generally, these procedures

are used to find out the Univariate statistics. Bivariate statistics can be obtained by using

“PROC FREQ”, a very powerful procedure to get information of contingency tables

(cross classification), Chi-Square etc. PROC FREQ is well suited to dealing with nominal

or ordinal data. It is useful for tabulating frequencies of occurrences in each category,

while simultaneously converting frequencies into proportions.

4.9.2 ANOVA and Regression Analysis

Analysis of Variance (ANOVA) and regression analysis require special treatment and

attention for categorical variables which are essentially presented in nominal scale. Chi-

Square is used to find out the proportionate variation among the distribution of different

categories. ANOVA is used to test the variation among the group of categories for any

variable of interest. Regular OLS or least Square Dummy Variable (LSDV) regression

models are employed to estimate the coefficients when dependent variable is continuous

and one or more independent variables are categorical. For a categorical variable such as

strategy, firm size, and industry in this study, with k categories, k-1 dummy variables are

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used for a common intercept and k dummy variables are used without a common

intercept. This is done to avoid dummy variable trap - a situation of perfect collinearity.

For investigating joint, interactive or contingent effect of two or more variables,

interactive dummies are used. The models without common intercept make the

interpretation of coefficients easier and straightforward. But, there are issues with the

interpretation of goodness of fit measures (F-statistics and R2 values). On the other side,

the interpretation of the estimates is complex for the models where intercepts are used.

But, the benefit of such modes is that they provide the correct measures of goodness of fit

(Damodar Gujarati, 2011; Park, 2009)). Keeping in view the advantage of using models

with common intercept, they are used for this study. For comparing the performance of

strategic types, the reactor strategy is used as the reference or the benchmark strategy.

Similarly, the reference categories for firm size and industry are also selected to

investigate the contingent effects. Univariate models are employed for investigating the

strategy-performance, size-performance, industry-performance relationships. Multivariate

models are used for combined and interaction impact of strategy, size, and industry on

performance. Hierarchical regression models are used for measuring the effect of

different combination of variables.

The application of regression methods often uses interaction effects (also called

moderator effects). The term “interaction effect” describes a situation in which the effect

of an independent variable on the dependent is conditional upon the value of another

variable, usually termed as moderator variable. Conceptualizing an interaction effect

involves three variables: the dependent variable, the “focal” independent variable and the

moderator variable. The effect of the focal independent variable on the dependent

variable is said to be moderated by the moderator variable if it effects the direction and or

strength of the relation between the independent and dependent variables (Baron &

Kenny, 1986; Gravelle, 2012; Hayes, Glynn, & Huge, 2012). Statistical interactions can

take many forms. One form is when the effect of one predictor is modified according to

the value of another predictor. In this case, although variables alone have no effect but

when they interact then there is a measurable effect. Or it may be that either variable

alone produces an effect but having both present does not increase the effect. More

122

commonly, the magnitude of the effect may be only somewhat different (but statistically

significantly different) for combinations of two (or more) variables than one would

expect from the effect of each variable alone (Pasta, 2011). Applying this concept to this

study means that the relationship between performance (dependent variable) and strategy

(independent variable) is contingent upon (or moderated by) the firm size and industry

(moderator variable) and to see weather strategy interacts with firm size and industry and

weather their interaction has any impact on performance or not?

In ANOVA, a basic moderator effect can be represented as an interaction between a focal

independent variable and a factor that specifies the appropriate conditions for its

operation. The relationship of focal independent variable and dependent variable has

three paths when moderation effect is tested: the impact of noise intensity as a focal

variable, the impact of controllability as a moderator and the interaction or product of

these two. Moderation implies that the causal relationship between the two variables

changes as a function of the moderator variable and the statistical analysis must measure

and test the differential effect of the independent variable on the dependent variable as a

function of a moderator. The moderator variables are typically introduced when there is

an unexpectedly weak or inconsistent relation between a predictor and a criterion

variable (Baron & Kenny, 1986; Hayes et al., 2012).

4.10 Conceptual Model

The conceptual framework represented research model is given in figure 4.2 where,

strategy is an independent categorical variable representing strategic types categorized as

Defender, Defender-Analyzer-Like (DA-Like), Analyzer, Prospector-Analyzer-Like (PA-

Like), Prospector, and Reactor based on extension to the Miles and Snow strategic types

concept. When strategic behaviors are analyzed, then strategy is represented by

Consistent, Flexible, and Reactor strategic types. Firm Size and Industry are used as the

categorical contextual or contingent variables. Firm size is divided into small, medium,

and large based on the total assets of the firms. Industry represents the twelve (12)

economic groups in which the firms are divided. The performance is a continuous

dependent variable and is represented by ROA, ROE, ROS, and ROCE.

123

Figure 4.2: Conceptual Model

Where,

Direct Effect = , Contingent Effect =

Source: Adapted from (Ouakouak & Ammar, 2015; Smith et al., 1989; Thomas & Ramaswamy, 1996)

Univariate analysis is done where strategy, size and industry are taken as independent

variables and performance as dependent variable (hypotheses 4-6 presented in chapter 3).

The Univariate models to test these hypotheses are:

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝜀𝑖 (1)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒊𝒛𝒆𝑖 + 𝜀𝑖 (2)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑖 + 𝜀𝑖 (3)

Where,

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖= ROA, ROS, ROE, and ROCE

𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖= Defenders, DA-Like, Analyzers, PA-Like, Prospectors, and Reactors,

Consistency and Flexibility

𝑺𝒊𝒛𝒆𝑖 = Large, Medium, and Small

Size

Strategy

Industry

Performance

124

𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑖= 12 economic groups (Industries) (Textile, Food, Cement, ……, Others)

To find the combined effect of strategy, size, and industry, and their possible

combinations (interactions), multivariate regression models are applied. The main

hypotheses in this regards test whether the performance is explained by the strategy, size,

and industry when combined in a model together. Further, in order to establish a robust

analysis, the study also tests through hierarchical models whether the observed

relationship between the performance and strategy, size, and industry varies with the

inclusion of additional proxy variables or not? The multivariate analysis is done to test

hypotheses 7-9 using following models:

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖 (1)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽4𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑺𝒊𝒛𝒆𝑗 + 𝜀𝑖 (2)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽4𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖

(3)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽4𝑺𝒊𝒛𝒆𝑖 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖 (4)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽4𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗

𝑺𝒊𝒛𝒆𝑗 𝛽5𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖 (5)

𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆𝑖 = 𝛽0 + 𝛽1𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 + 𝛽2𝑺𝒊𝒛𝒆𝑗 + 𝛽3𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽4𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑺𝒊𝒛𝒆𝑗 +

𝛽5𝑺𝒊𝒛𝒆𝑗 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖 (6)


𝛽5𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽6𝑺𝒊𝒛𝒆𝑗 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖 (7)


𝛽5𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽6 𝑺𝒊𝒛𝒆𝑗 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝛽7𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚𝑖 ∗ 𝑺𝒊𝒛𝒆𝑗 ∗ 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝑘 + 𝜀𝑖

(8)

Model 8 is a complete model whereas models 1-7 are reduced models with an

incremental interaction term over preceding model.

125

4.11 Ethical Issues and their Resolution

4.11.1 Ethical Issues

Ethical considerations have become a cornerstone for conducting effective and

meaningful research because of its unprecedented scrutiny in recent times. As such, the

ethical behavior of individual researchers is under unprecedented scrutiny (Best & Kahn,

2006). There is huge amount of data that is collected through the management

information systems, surveys, and other data collection methods. Secondary data analysis

refers to the use of existing data to find answer to a question that was different from the

original work. It can be analyzed to generate new hypothesis along with critical research

questions. Permission for further use of data and analysis is implied if the data is freely

available on the Internet, books or other public fora. In this case, the ownership of the

original data must be acknowledged (Tripathy, 2013).

The ethical challenges related to the general research include: potentially difficult

considerations surround the purpose and value of the research; benefits and harm to

participants; privacy; informed consent; and confidentiality (Golder et al., 2017). Even if

you are not using human participants in your research, there is still the question of

honesty in the way you collect, analyze and interpret data. By explaining exactly how you

arrived at your conclusions you can avoid accusations of false reasoning (Walliman,

2011). According to Walliman (2011), there are two aspects of ethical issues in research:

1. Honesty and integrity of the researcher

2. The treatment of the researcher with the respondents of the data in terms of

informed consent, confidentiality, anonymity and courtesy.

These issues are resolved through the ethical committee within the organizations where

research is being carried out. The issue of honesty related to the claim of intellectual

ownership; plagiarism; non-acknowledgement of cited work; discrepancies and

biasedness in description of results and analysis; data set and its interpretations;

declaration of theoretical or epistemological perspective of the research etc. (Walliman,

2012).

126

4.11.2 Resolution of Ethical Issues

Author’s Declaration and undertaking: A declaration and undertaking by the

author is an integral part of the thesis where it is stated that the work is original

and solely done by the author and it has not been submitted elsewhere.

Intellectual ownership and plagiarism: A “Plagiarism Free Certificate” duly

signed by the author (student) and counter signed by the supervisor, head of the

department, chairman and Dean of the faculty is also part of the thesis. The

document certifies that the work is genuine and is plagiarism free.

Acknowledgement and citation: Obviously, in any field of research, one

cannot rely entirely on his own ideas, concepts and theories. Therefore, the

sources (journals, books, conferences, internet etc.) from where the ideas,

theories, and related material is used are properly acknowledged and cited

with full publication details provided at the end as references so that the

referred source can be identified. The assistance provided during data

collection, analysis, and thesis compilation is properly acknowledged in

the beginning of the thesis.

Responsibility and accountability of the researcher

Apart from correct attribution, honesty is essential in the substance of

what you write. For this purpose, the theoretical perspective and paradigm

is explained in methodology chapter in detail along with the descriptions

about the data set, variables, measures, tools and techniques. The results

are also presented in detail and extra information is provided in annexure

as well.

Data and interpretations

Personal judgment and subjective biasedness is avoided by clearly

referring for and against opinions from the literature in discussion part of

the research to maintain scientific objectivity as much as possible.

127

4.12 Summary

The chapter described in detail the methodology adopted for the study. It includes the

data collection procedure and the criteria of the selection of the firms for final analysis.

The variables used to measure the strategic orientation, performance, and contingencies,

along with the reason for their selection, are explained. The detail description of

conceptual and theoretical development of scoring method for categorization of strategic

types and a step-by-step SAS coding and the output is presented for data management

and analysis. The techniques for data analysis, the conceptual framework, and the models

for Univariate and multivariate analyses are also explained.

128

Chapter 5

Results and Discussion

129

5.1 Introduction

The chapter comprises two major parts. The results in the first part include the

categorization of firms in strategic groups as: strategic types according to Miles and

Snow typology; classification of firms according to strategic purity and hybridization;

strategic consistency and flexibility; firm size wise and industry wise etc. The

performance of the strategic groups according to various classification is also presented

in this part. The second part consist of the results relating to the testing of hypotheses.

Descriptive statistics, contingency tables, results of Chi-square, ANOVA, Univariate and

multivariate analysis are presented. The analysis of the single industry (Textile) is done to

find the commonality and differences in the findings of single industry versus multi-

industry analysis. The also chapter contains a synthesis of the results and discusses the

consistencies/inconsistencies of the findings with earlier research and the possible

reasons for these findings.

5.2 Identification of Strategic Types

Scoring method is used to categorize the strategic types according to the groupings based

on Miles and Snow typology. Other strategic groups are made on the basis of strategic

purity and hybridization and strategic consistency and strategic flexibility, and reactors.

As discussed in methodology, the criteria for a firm to follow a viable category is that it

must follow the same strategy in at least three times out of four. Otherwise the firm is

marked as a reactor firm. For example, a firm in a long term fall under one of the viable

strategies but the behavior of the firms during short-to-medium term period or transition

varies. This variation or inconsistency identifies the reactor strategy. To understand this

process, the example for categorization and behavior of firms for a selected industry –

“Other Non-Metallic Mineral Products” with two sub-sectors “Cement” and

“Mineral Products” is presented in Table 5.1. The last column of the table shows the

final classification of the strategy types according to our set criteria.

130

Table 5.1: Identification of strategic types and their transition over the time

Sr

#

Industry

Name of the Firm

Strategic Orientation Overtime

Final

Category

2011

2012

2013

Overall

1 Cement Power Cement Ltd. Prospector PA-Like Analyzer Prospector Reactor

2 Cement Attock Cement Pakistan Ltd. DA-Like DA-Like DA-Like DA-Like DA-Like

3 Cement Bestway Cement Ltd. Analyzer Analyzer Analyzer Analyzer Analyzer

4 Cement Cherat Cement Co. Ltd. DA-Like DA-Like DA-Like DA-Like DA-Like

5 Cement D.G. Khan Cement Co. Ltd. PA-Like Analyzer Analyzer Analyzer Analyzer

6 Cement Dandot Cement Co. Ltd. Analyzer Analyzer Analyzer Analyzer Analyzer

7 Cement Dewan Cement Ltd. (Pakland

Cement Ltd.)

DA-Like Analyzer PA-Like DA-Like Reactor

8 Cement Fauji Cement Co. Ltd. DA-Like Analyzer Analyzer Analyzer Analyzer

9 Cement Fecto Cement Ltd. DA-Like DA-Like DA-Like DA-Like DA-Like

10 Cement Kohat Cement Co. Ltd. Analyzer Analyzer Analyzer Analyzer Analyzer

11 Cement Lafarge Pak. Cement Ltd.

(Pakistan Cement Ltd.)

PA-Like Analyzer Analyzer Analyzer Analyzer

12 Cement Lucky Cement Ltd. Analyzers PA-Like Analyzer Analyzer Analyzer

13 Cement Maple Leaf Cement Factory

Ltd.

PA-Like Analyzer DA-Like PA-Like Reactor

14 Cement Mustehkam Cement Ltd. PA-Like PA-Like PA-Like PA-Like PA-Like

15 Cement Pioneer Cement Ltd. DA-Like DA-Like Analyzer DA-Like DA-Like

16 Mineral

Products

Balochistan Glass Ltd. Analyzer Analyzer Analyzer Analyzer Analyzer

17 Mineral

Products

Frontier Ceramics Ltd. Prospector Prospector Analyzer Prospector Prospector

18 Mineral

Products

Ghani Glass Ltd. Analyzer Analyzer Analyzer Analyzer Analyzer

19 Mineral

Products

Karam Ceramics Ltd. DA-Like Defenders Defender Defender Defender

20 Mineral

Products

Shabbir Tiles And Ceramics

Ltd.

PA-Like Analyzer Analyzer Analyzers Analyzer

21 Mineral

Products

Tariq Glass Industries Ltd. DA-Like Analyzer PA-Like Analyzer Reactor

*Other Non-Metallic Mineral Products

The strategic types mentioned in columns 4, 5, and 6 show the transitions of the firms’

strategic stance over the time. The rows in black color (row # 2, 3, 4, 6, 9, 10, 14, 16, and

18) represent the consistent strategic orientation whereas blue color (row # 5, 8, 11, 12,

131

15, 17, 19, and 20) show flexible (less consistent) while red color ((row # 1, 7, 13, and

21) represent inconsistent or reactor strategy. The second last column (Column 7 from

left to right) shows the firms’ long-term strategic orientation. This long-term orientation

may be different from the final classification which is based on the strategic

behavior/transition over the time. For example, our final classification of reactor strategy

turns out to be prospector, DA-Like, PA-Like, and Analyzer respectively when it was

calculated by taking the overall average. Hence, a viable strategy in a long-term may

behave like reactor strategy during transition period. The detail for all firms and

industries is given in Appendix A2.

Based on the above criteria, Table 5.2 represents the overall classification of the firms in

terms of their strategic orientation and according to their behavior over the time. From

the results, it is found that firms in Pakistan predominantly adapt hybrid strategies instead

of pure strategies, as the proportion of pure defenders and pure prospectors are only 2%.

Within hybrid strategies, analyzers dominate with 56% presence followed by DA-Like

(23%), and PA-Like (19%).

Table 5.2: Classification of Strategic Types –Overall (Long-Term Orientation)

Viable Strategic Types

Total

(Long-term

Orientation)

Pure

Defenders

DA-Like Analyzers PA-Like Pure

Prospectors

1

<1%

71

23%

171

56%

59

19%

5

2%

307

100%

The above data does not portray the presence of the reactors because based on the

calculation in this way can only categorize the firms into one of the group mentioned

alongside the continuum. Since, the domain of the reactor strategy represents the whole

continuum where other viable strategies can fall during the short time or transition period,

it need further calculations. For example, there are 71 firms identified as following DA-

Like strategy in the long-term orientation (based on the calculation of 7 years average

data). Further investigation of medium-to-long term strategic orientation showed that out

132

of 71 firms, 36 firms did not change their strategic stance and remained consistent

throughout the study period. During the same time period, 27 firms did adjusted

themselves and showed flexibility in their strategic stance but readapted the core strategy

again while 8 firms showed inconsistent behavior and changed their strategic stance

frequently without having a set pattern. These firms are categorized as reactors whereas

the firms with consistent and flexible strategic orientation are termed as the viable

strategic types. The detail of this behavior of the firms is presented and reflected in Table

5.3 and Figure 5.1 respectively.

Table 5.3: Categorization of Strategic Types and Strategic Behavior

Strategic

Types

Consistent

(A)

Flexible

(B)

Viable

Strategies

(C=A+B)

Reactors

(D)

Total

(Long-Term

Orientation)

(C+D)

Defenders - 1 1 - 1

DA-Like 36 (51%) 27 (38%) 63(89%) 8 (11%) 71

Analyzers 70 (41%) 74 (43%) 144 (84%) 27 (16%) 171

PA-Like 24 (41%) 20 (34%) 44 (75%) 15 (25%) 59

Prospectors 1 (20%) 2 (40%) 3 (60%) 2 (40%) 5

Total 131 124 255 52 307

The above results are reflected in figure below:

Strategic Types Pure Pure

Defenders DA-Like Analyzers PA-Like Prospectors

Long-Term 1 71 171 59 5

Viable Strategy 1 63 144 44 3

Reactors - 8 27 15 2

Figure 5.1: Strategy continuum and reactor domain

133

Based on these criteria, we found that overall, 131 (43%) firms are consistent in their

strategic choice and 124 (40%) firms are flexible while 52 (17%) are reactors. Comparing

with overall strategic orientation, we found that 51% of DA-Like are consistent, 38% are

flexible, and 11% are reactors. Similarly the distribution of other strategic types is

reflected in above table. Within viable strategies, 57% of DA-Like, 49% of analyzers,

55% of PA-Like and 33% of prospectors are consistent firms, while rest of the are

flexible respectively.

5.3 Strategic Types, Firm Size and Industry

The results for distribution of strategic types according to firm size are presented in

Table 5.4.

Table 5.4: Strategic Types and Firm Size

Strategic Types Firm Size

Small Medium Large Total

Defenders 1 - - 1

DA-Like 23 19 21 63

Analyzers 46 53 45 144

PA-Like 15 14 15 44

Prospectors 2 1 - 3

Reactors 15 16 21 52

Total 102 103 102 307

Within strategic types, highest number of small firms are perusing DA-Like strategy

followed by large and medium size firms. PA-Like strategy is followed by equal number

of small and large firms followed by medium sized firms. The balancing strategy i.e.

analyzers are highest in number for medium in size firms followed by small and medium

sized firms respectively. Majority of the reactors strategy firms are large followed by

medium and small firms. The difference between the numbers of firms distributed across

134

firm size for each strategy is not very large although they represent a clear pattern for

different strategic types across the firm size.

The distribution of strategic consistency, flexibility, and reactors according to firm size

are presented in Table 5.5.

Table 5.5: Strategic Behavior and Firm Size

Strategic Types Firm Size

Small Medium Large Total

Consistent 44 43 44 131

Flexible 43 44 37 124

Reactors 15 16 21 52

Total 102 103 102 307

The strategic behaviour of the firms across size reveals that consistent firms are equally

distributed across the firm size whereas flexible firms are equally distributed among

small and medium sized firms leaving lesser number for large firms.

Within industries, the highest number of firms (39%) is from “textile sector” followed by

“food sector” (13%), and “chemical and pharmaceutical products industry” (10%). These

three industries represent 62% of the total sample size. Hence, the results pattern of

performance and other statistics in these industries will influence the rest of the results.

The distribution of strategic types among industries is presented in Table 5.6.

135

Table 5.6: Strategic Types: Overall and Industry wise Distribution

Industry

Strategic Types

D* DAL A PAL P R Total

Textile

“Spinning, Weaving ,

Finishing of Textile; Made

up Textile articles; and

Other Textiles”

- 21

(17.7)+

62

(52.1)

17

(14.3)

19

(16.0)

119

(100)

Food

“Sugar, and Other food

Products”

- 6

(15.4)

18

(46.2)

6

(15.4)

1

(2.6)

8

(20.5)

39

(100)

Chemicals, chemical

products and

pharmaceuticals

- 8

(25.0)

12

(50.0)

6

(25.0)

-

-

6

(25.0)

32

Other Manufacturing - 10

(38.5)

8

30.8)

6

(23.1)

-

-

2

(7.7)

26

(100)

Other non-metallic

mineral products

“Cement and other mineral

products”

1

(4.8)

4

(19.1)

10

(47.6)

1

(4.8)

1

(4.8)

4

(19.1)

21

(100)

Motor vehicles, trailers,

and auto parts

- 4

(20.0)

11

(55.0)

2

(20.0)

1

(10.0)

2

(20.0)

20

(100)

Fuel and Energy - 2

(18.2)

6

(54.6)

1

(9.1)

- 2

(18.2)

11

(100)

Information,

communication and

transport services

- 4

(44.4)

2

(22.2)

1

(11.1)

- 2

(22.2)

9

(100)

Coke and refined

petroleum products

- 2

(22.2)

2

(22.2)

2

(22.2)

- 3

(33.3)

9

(100)

Paper, paperboard and

paper products

- - 4

(66.7)

- - 2

(33.3)

6

(100)

Electrical machinery and

apparatus

- 1

(12.5)

5

(62.5)

1

(12.5)

- 1

(12.5)

8

(100)

Other services activities - 1

(14.3)

4

(56.9)

1

(14.3)

- 1

(14.3)

7

(100)

Total 1

(0.3)

63

(20.5)

144

(46.9)

44

(14.3)

3

(1.0)

52

(16.9)

307

(100)

“*D=defenders; DAL=DA-Like; A=analyzers; PAL=PA-Like, P=Prospectors; R=reactors”

+Figures in parentheses are percentage distribution across industries

The analyzers strategy is dominating in ten out of twelve industries. In two industries

DA-Like strategy is followed by the firms more than others strategies. Only one industry

(i.e. “other non-metallic mineral products”) represent all six types of strategic types

followed by presence of five strategic types in two industries i.e. “food industry” and

“motor vehicle and auto parts” industry. The rest of the industries present strategic types

136

that mostly used hybrid strategies or the reactor strategy. Reactors are present in each

industry and the highest number of reactors is in textile sector while in term of

percentages other sectors e.g. food, chemical and pharmaceutical represent significant

presence. The results for strategic behavior of the firms across industries are presented in

Table 5.7.

Table 5.7: Strategic Behavior: Industry wise Distribution

Industry

Strategic Behavior

Consistent Flexible Reactors Total

Textile

“Spinning, Weaving , Finishing of Textile;

Made up Textile articles; and Other

Textiles”

58

(48.7)

42

(35.3)

19

(16.0)

119

(100)

Food

“Sugar, and Other food Products”

11

(28.2)

20

(51.3)

8

(20.5)

39

(100)

Chemicals, chemical products and

pharmaceuticals

11

(34.4)

15

(46.9)

6

(18.8)

32

(100)

Other Manufacturing n.e.s. 5

(19.2)

19

(73.1)

2

(7.7)

26

(100)

Other non-metallic mineral products

“Cement and other mineral products”

9

(42.9)

8

(38.1)

4

(19.0)

21

(100)

Motor vehicles, trailers, and auto parts 12

(60.0)

6

(30.0)

2

(10.0)

20

(100)

Fuel and Energy 7

(63.6)

2

(18.2)

2

(18.2)

11

(100)

Information, communication and

transport services

5

(55.6)

2

(22.2)

2

(22.2)

9

(100)

Coke and refined petroleum products 2

(22.2)

4

(44.4)

3

(33.3)

9

(100)

Paper, paperboard and paper products 2

(33.3)

2

(33.3)

2

(33.3)

6

(100)

Electrical machinery and apparatus 3

(37.5)

4

(50.0)

1

(12.5)

8

(100)

Other services activities 6

(85.7)

-

-

1

(14.3)

7

(100)

Total 131

(42.7)

124

(40.4)

52

(16.9)

307

(100)

The strategic behavior of the firms also varies across the industries. For example, the

firms adapting consistency in their strategic behavior are more in numbers than flexibility

in “textile sector”, “cement & other mineral products”, “motor vehicles”, “fuel and

energy”, “information & communication”, and “other services activities” industries

137

respectively whereas firms with strategic flexibility are more in numbers than consistency

in “food sector”, “chemical & pharmaceuticals”, “other manufacturing”, “coke and

refined petroleum products”, and “electrical machinery & apparatus” industries

respectively.

5.4 Strategy and Performance

The results (Table 5.8) show that DA-Like strategy outperform all other strategies in all

performance measures. Analyzer strategy also performed better than the remaining

strategies for all performance measures. Both DA-Like and analyzers performed above

the overall performance. The performance of reactors, PA-Like, and prospectors is below

overall average performance for all performance measures while defenders performed

below average in three out of four performance measures (other than ROS). The

performance of prospectors is negative for all performance measures.

Table 5.8: Strategic Types and Performance -Overall

P

Strategic Types Overall

Performance Defender

s

DA-Like Analyzer PA-Like Prospectors Reactors

ROA

ROE

ROS

ROCE

(1)

1.80

5.14

1.72

2.91

(63)

9.54

13.93

7.19

9.77

(144)

6.00

13.68

2.69

9.12

(44)

3.88

3.17

-8.16

1.79

(3)

-3.93

-8.04

-10.83

-9.11

(52)

2.67

4.14

-4.94

3.10

(307)

5.75

10.37

0.63

6.97

P=Performance; Bold=Highest; Underline=Least (across rows)

Comparing the performance of viable strategies with reactors, it is found that all viable

strategies outperformed reactors in at least one performance measure except for

prospectors, Specifically, DA-Like and analyzers outperformed reactors in all four

measures while defenders in terms two measures (ROE and ROS) and PA-Like in one

measure (ROA). Interestingly, reactors outperformed PA-Like in three measures (ROE,

ROS, and ROCE) while they performed better than defenders two measures (ROA and

ROCE). The presence and performance of reactors is according to our expectation

138

keeping in view the uncertain, unstable, and ever changing political and economic

conditions in Pakistan.

The comparative position of strategic types for all four performance measures is depicted

in the graph

Figure 5.2: Strategy-wise Performance

5.5 Strategic Behavior and Performance

The results showing the comparative position of consistent, flexible, and inconsistent or

reactor are presented in Table 5.9. These results show that that, firms following both

strategic consistency and strategic flexibility performed better than reactors for all

performance measures. They also performed above overall averages. The performance of

reactors is below overall average performance as well as below consistent and flexible

strategies respectively. Comparing firms adapting specific strategy, it is evidenced that

flexibility brought better results for three (ROA, ROS, and ROCE) out of four measures

while consistency produce better results than flexibility in one measure (ROE). However,

the difference in performance is not very large.

-12.00

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Defenders DA-Like Analyzers PA-Like Prospectors Reactors Overall

ROA ROE ROS ROCE

139

Table 5.9: Strategic Behavior and Performance –Overall

Performance

Strategic Behavior

Overall Average Consistent

(131)

Flexible

(124)

Reactors

( 52)

ROA 6.64 7.00 2.67 5.75

ROE 11.79 11.47 4.14 10.37

ROS 1.50 2.04 -4.94 0.63

ROCE 7.63 7.93 3.10 6.97

Bold=Highest; Underlined=Least (See Horizontal: Column 2-4)

The reason for better performance of firms with flexibility in their strategic stance may be

due to the fact that such firms adjust and realign their strategies according to the changes

in external environment for the short term and then sticking to the core strategy. The

results for comparative position of strategic consistency and strategic flexibility within

strategic types are presented in Table 5.10. Within viable strategic types, PA-Like

performed better when they stuck to strategic consistency while flexibility performed

better for analyzers and prospectors respectively. The results are mix for DA-Like as both

strategic flexibility and consistency performed higher than other for two measures each.

For analyzers flexibility in strategy brought better results for all performance measures.

Consistency produced better results for PA-Like strategy in all performance measures. As

per expectation and the nature of prospectors, flexible strategy outperformed consistent

strategy in three performance measures (except for ROS).

Table 5.10: Strategic Behavior and Performance –Strategy-wise

P

Defenders DA-Like Analyzer PA-Like Prospectors

C F C F C F C F C F

ROA

ROE

ROS

ROCE

-

-

-

-

-

(1)

1.80

5.14

1.72

2.91

(36)

9.12

15.83

7.00

10.07

(27)

10.11

11.40

7.46

9.37

(70)

5.98

12.86

0.44

6.53

(74)

6.02

14.46

4.81

11.57

(24)

5.44

3.86

-3.2

8.22

(20)

2.00

2.33

-14.07

-5.92

(1)

-7.45

-17.74

-8.22

-17.16

(2)

-2.18

-3.19

-12.14

-5.08

P=Performance; C=Consistent; F=Flexible; (n) = number of firms

140

5.6 Strategy, Firm Size, and Organizational Performance

The distribution of strategic types into various groups and their performance according to

firm size is presented in Table 5.11. Among pure strategies, there is only one defender

which has small size while out of three prospectors, two are small and one is medium.

For large firms, hybrid strategies (DA-Like, analyzers, and PA-Like) outperformed

reactor strategy in all four measures. Comparing the performance among hybrid

strategies, it is found that DA-Like performed above others in two measures (ROA and

ROE) while analyzers performed better than others in other two measures (ROS and

ROCE). The results for medium firms show that DA-Like strategy outperformed reactors

in all performance measures while they performed better than others in two measures

(ROS and ROCE). Here, analyzers outperformed reactors in three performance measures

(ROA, ROE, and ROCE). PA-Like strategy in medium sized firms outperformed reactors

in three measures (ROA, ROS, and ROCE) while outperformed others in term of ROA.

Table 5.11: Strategic Types and Performance: Firm Size wise

Firm

Size

Strategic Types Overall

Size

Averages D

DAL

A

PAL

P

R

Small

ROA

ROE

ROS

ROCE

(1)

1.80

5.14

1.72

2.91

(23)

9.76 6.53

5.17 4.95

(46)

3.13

6.96

-4.46

3.88

(15)

-0.04

-2.29

-26.35

-2.66

(2)

-3.49

-8.37

-7.39

-10.20

(15)

2.20

14.31 -16.12

5.14

(102)

3.88

6.27

-7.22

3.06

Medium

ROA

ROE

ROS

ROCE

- (19)

5.67

9.96

4.68

8.27

(53)

4.89.

12.62 1.04

7.80

(14)

6.55 -2.82

1.65

6.31

(1)

-4.82

-7.37

-17.71

-6.92

(16)

3.67

-1.65

1.60

5.23

(103)

4.98

7.62

1.70

7.14

Large

ROA

ROE

ROS

ROCE

- (21)

12.81

25.63 11.68

16.41

(45)

10.25

21.80

11.93

16.03

(15)

5.30

14.21

0.86

2.04

(21)

2.26

1.29

-1.94

0.02

(102)

8.40

17.25

7.40

10.76

Overall

ROA

ROE

ROS

ROCE

(1)

1.80

5.14

1.72

2.91

(63)

9.54

13.93

7.19

9.77

(144)

6.00

13.68

2.69

9.12

(44)

3.88

3.17

-8.16

1.79

(3)

-3.93

-8.04

-10.83

-9.11

(52)

2.67

4.14

-4.94

3.10

(307)

5.75

10.37

0.63

6.97

“D=Defenders; DAL=DA-Like; A= Analyzers; P=Prospectors; PAL=PA-Like; R=Reactors”

Bold=Highest; Underline= Least

141

The performance of prospectors remain poor across firm size. The trend of performance

is different for small firms. For small firms the performance of DA-Like and reactors is

better than other strategies. Surprisingly, small sized reactors performed better than the

viable strategies in two performance measures (ROE and ROCE) while DA-Like

performed above other for the other two performance measures (ROA and ROS). The

performance of prospecting strategies (PA-Like and prospectors) is the lowest among all

strategies.

Figure 5.3: Firm Size-wise Performance

Comparing the performance of small, medium, and large sized firms with overall

performance, irrespective of strategic types, it is evidenced that large sized firms

performed better than small and medium in all four performance measures followed by

medium and small firms. This gives the clear indication that the size of the firm does

matter in Pakistani environment.

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Small Medium Large Overall

ROA ROEROS ROCE

142

5.7 Firm Size, Strategic Behavior, and Organizational Performance

The results for the performance of strategic behavior represented by strategic consistency,

strategic flexibility and reactors within firm size are presented in Table 5.12. Overall,

both strategic consistency and strategic flexibility outperformed reactor behavior in all

performance measures. Here, strategic flexibility performed above all in three measures

while strategic consistency performed better than others in one measure. For large sized

firms, reactors are again outperformed by both strategic consistency and strategic

flexibility in all performance measures. Large firms with consistency in their strategic

approach performed better than flexible firms in terms of ROA and ROCE while firms

with flexibility in strategy outperformed consistent firms in ROE and ROS.

Table 5.12: Firm Size, Strategic Behavior, and Performance

Firm

Size

Strategic Behavior

Overall Average

(Size) Consistent

(131)

Flexible

(124)

Reactors

( 52)

Small

ROA

ROE

ROS

ROCE

(44)

3.77

4.49

-5.86

0.88

(43)

4.57

5.29

-5.51

4.56

(15)

2.19

14.31

-16.12

5.14

(102)

3.88

6.27

-7.22

3.06

Medium

ROA

ROE

ROS

ROCE

(43)

5.94

10.79

1.17

7.99

(44)

4.51

7.89

2.25

7.01

(16)

3.67

-1.65

1.60

5.23

(103)

4.98

7.62

1.70

7.14

Large

ROA

ROE

ROS

ROCE

(44)

10.19

20.08

9.18

14.03

(37)

9.76

22.94

10.58

12.95

(21)

2.26

1.29

-1.94

0.02

(102)

8.40

17.25

7.40

10.76

Bold=Highest; Underlined=Least (See Horizontal: Column 2-4)

Like overall results, the difference between the performance of the firms with clear

strategic approach (consistency or flexibility) is small while the difference in comparison

with the reactors is large. The performance of medium sized reactors is almost similar to

the large sized reactors. Here again, reactors were outperformed by the firms with

strategic flexibility in all four measures followed by consistent firms in three measures.

143

However, the performance of firms perusing consistency in their strategic behavior

outperformed the firms with flexible approach in three out of four measures. The results

are quite different for small sized firms where firms with flexible behavior performed

better followed by reactors. Here, the flexibility in strategic behavior outperformed others

in terms of ROA, and ROS while reactors performed better than others in terms of ROE

and ROCE. However, reactors were outperformed by others in terms of ROA and ROS.

Small sized firms with consistent behavior performed poorly for ROE and ROCE

measures. These results reveal that firms with conscious approach in their strategic

choice brings the fruits in terms of better performance whereas inconsistent or reactor

behavior may get some benefits but not all the time and not across the firm size.

5.8 Strategy, Industry, and Performance

The results for comparative position of strategy, industry, and performance are presented

in Table 5.13. The performance of four industries: “Food industry (sugar and other food

products)”, “Chemical, chemical products, and pharmaceuticals”, “Motor Vehicles,

trailers, and auto parts”, and “Coke and refined petroleum products” industries was found

above overall averages for all performance measures. Three industries i.e. “Other

manufacturing”, “Information, communication, & transport services”, and “Paper,

paperboard, and paper products” performed above overall average in three measures. The

performance of the largest industry i.e. “Textile” along with “cement and other non-

mineral products” industry performed below overall averages for all measures. Since,

these two industries represent 46% of overall sample size, the high performing industries

listed above becomes invisible if not analyzed separately.

The results for the performance of strategic types across the industries also provide

interesting and useful information. For example, the performance of DA-Like firms is

reported above the overall averages but when it is investigated across industries it was

found that there is a great variation in their performance from industry to industry. For

instance, in “Food”, and “Other non-metallic mineral products” industries, DA-Like

outperformed others for all measures while in “Textile”, “Other manufacturing”, and

“Coke and refined petroleum products” they performed better than others in two

144

measures only. The performance of analyzers also varies across industries. They

outperformed others in “Fuel and Energy” and “Paper paperboard and paper products”

for all performance measures while they performed better than others in three

performance measures in one industry (“Other services activities”) and by two measures

in two industries: “Textile”, “Chemical, chemical products, and pharmaceuticals”.

Although, the performance of PA-Like firms is below expectation in aggregate terms,

they performed better in two industries i.e. “Motor vehicles, trailers, and auto parts”, and

“Information, communication, and transport services” industries for three out of four

performance measures followed by in two measures in one industry (“Coke and refined

petroleum products”). Reactors also performed better than others in “Chemical, chemical

products, and pharmaceuticals” industry for two measures. The results also indicate that

although, the performance of DA-Like and analyzers strategies remain well across

industries, the performance of DA-Like strategy was better in large and medium sized

industries while the performance of analyzers strategy was better in medium to-small

scale industries.

The results clearly indicate that there is no best strategy applicable for all industries. The

nature, competition, and other dynamics demand different strategic stance in different

industries.

145

Figure 5.4: Industry-wise Performance

5.9 Industry, Strategic Behavior, and Performance

The industry-wise comparative position of performance measures for consistent, flexible

and reactor strategies are presented in Table 5.14. The findings show that both

consistency and flexibility in strategic behavior outperformed reactor behavior.

Specifically, consistent and flexible strategies outperformed reactor strategy in ten out of

twelve industries. Within viable strategic behavior, consistent firms perform better than

firms with flexible strategy in most of the industries, although the difference is not very

large. The performance of consistent strategy is better than others in five industries in all

four performance measures.

-8

-4

0

4

8

12

16

20

24

28

32ROA ROE ROS ROCE

146

Table 5.13: Strategic Types and Performance –Industry-wise

Industry (Economic Groups)

Strategic Types

P D

(N=1)

DA-

Like

(N=63)

A

(N=144)

PA-

Like

(N=44)

P

(N=3)

R

(N=52) I A

Textile

“Spinning, Weaving , Finishing of

Textile; Made up Textile articles; and

Other Textiles”

4.53

7.20

2.23

5.40

2.80

8.23

-1.50

5.47

0.56

-9.19

-3.65

-11.35

0.95

-10.22

-12.73

3.23

2.49

2.62 b

-2.94

2.70

ROA

ROE

ROS

ROCE

Food


12.01

20.82

7.59

16.51

6.66

16.80

2.77

8.00

4.46

6.44

-5.16

9.19

-4.82

-7.37

-17.71

-6.92

4.51

10.92

1.64

8.95

6.41

14.00

1.54

9.31

ROA

ROE

ROS

ROCE

Chemicals, chemical products and

pharmaceuticals

13.15

24.27

10.85

19.49

14.26

27.68

14.15

21.78

3.96

-5.85

-21.84

1.47

13.81

28.80

9.82

23.59

11.97

20.75

5.76

17.44a

ROA

ROE

ROS

ROCE

Other Manufacturing

12.24

6.15

4.62

3.36

9.00

25.88

2.68

15.89

6.26

19.75

-16.75

6.76

-2.81

55.94

-8.80

3.62

8.70

19.19

-1.94

8.02

ROA

ROE

ROS

ROCE

Other non-metallic mineral products


1.80

5.14

1.72

2.91

8.93

14.33

7.19

12.02

2.40

4.32

-7.42

0.63

-0.75

-2.37

-7.06

-1.16

0.47

0.99

-6.56

-3.24

0.29

-2.27

-4.90

0.70

2.97

4.54

-3.66 b

2.65

ROA

ROE

ROS

ROCE

Motor vehicles, trailers, and auto parts

11.38

16.41

8.16

15.72

8.60

16.43

1.94

10.37

15.33

32.57

5.91

30.11

-7.45

-17.74

-8.22

-17.16

12.07

23.99

5.47

26.27

9.37

17.09

3.42

13.63

ROA

ROE

ROS

ROCE

Fuel and Energy

0.32

2.43

-5.59

-41.04

12.47

27.60

16.53

17.11

-7.35

-7.94

-46.77

-7.87

2.70

-31.95

-0.37

3.34

6.68

8.97

3.68

3.25

ROA

ROE

ROS

ROCE

Information, communication and

transport services

14.33

26.80

24.10

20.00

12.60

17.38

6.73

17.11

23.30

28.10

40.84

27.47

-9.51

43.34

-22.92

-55.69

9.64

28.53 a

11.65

3.63

ROA

ROE

ROS

ROCE

Coke and refined petroleum products

29.67

47.58

31.90

45.45

16.42

26.65

27.59

23.73

16.17

50.03

3.38

48.55

-3.24

-18.56

-3.52

-26.44

12.76 a

21.43

12.80 a

17.36

ROA

ROE

ROS

ROCE

Paper, paperboard and paper products

8.22

9.23

17.44

9.12

4.22

7.42

2.31

8.09

6.89

8.63

12.40

8.78

ROA

ROE

ROS

ROCE

Electrical machinery and apparatus

5.86

13.25

4.71

11.28

4.12

15.91

2.63

13.61

-3.99

-7.41

-21.18

-6.30

-4.06

17.36

-19.23

-8.28

2.30 b

12.84

-2.82

8.10

ROA

ROE

ROS

ROCE

Other services activities

2.15

2.48

12.66

2.42

5.60

6.00

10.21

4.02

-1.18

-2.17

-19.04

-2.12

1.21

-0.77

11.05

0.26

3.51

3.36

6.50

2.38 b

ROA

ROE

ROS

ROCE

Overall Averages

1.80

5.14

1.72

2.91

9.54

13.93

7.19

9.77

6.00

13.68

2.69

9.12

3.88

3.17

-8.16

1.79

-3.93

-8.04

-10.83

-9.11

2.67

4.14

-4.94

3.10

5.75

10.37

0.63

6.97

ROA

ROE

ROS

ROCE

D=Defenders; A=Analyzers; P=Prospectors; IA=Industry Averages: a=highest, b=lowest (see column 8 vertically);

Performance: Bold=Highest; Underline= Lowest; (see horizontally columns 2-7)

147

Table 5.14: Industry, Strategic Behavior, and Performance

Industry

Strategic

Behavior

Performance

ROA ROE ROS ROCE

1. Textile

“Spinning, Weaving , Finishing of Textile;

Made up Textile articles; and Other

Textiles”

C 3.41 7.04 -2.42 3.99

F 1.92 2.31 0.75 0.67

R 0.95 -10.22 -12.73 3.23

2. Food


C 10.05 15.38 1.86 8.03

F 5.17 14.47 1.31 10.15

R 4.51 10.92 1.64 8.95

3. Chemicals, chemical products and

pharmaceuticals

C 13.09 21.67 12.18 18.79

F 10.40 16.86 -0.56 14.63

R 13.81 28.80 9.82 23.59

4. Other Manufacturing n.e.s. C 8.83 16.45 3.95 14.95

F 9.88 16.04 -2.77 6.66

R -2.81 55.94 -8.80 3.62

5. Other non-metallic mineral

products


C 3.48 5.27 -10.53 1.88

F 3.75 7.11 4.69 4.49

R 0.30 -2.27 -4.90 0.70

6. Motor vehicles, trailers, and auto

parts

C 8.47 14.77 1.67 9.47

F 10.29 19.43 6.25 17.71

R 12.07 23.99 5.47 26.27

7. Fuel and Energy C 7.12 16.74 1.81 -1.10

F 9.13 22.67 14.27 18.39

R 2.70 -31.95 -0.37 3.34

8. Information, communication and

transport services

C 14.79 21.20 17.81 20.06

F 15.93 32.03 30.81 20.68

R -9.51 43.34 -22.92 -55.69

9. Coke and refined petroleum

products

C 35.39 62.14 32.29 59.58

F 13.44 31.06 15.29 29.10

R -3.24 -18.55 -3.52 -26.44

10. Paper, paperboard and paper

products

C 10.25 14.03 27.60 11.55

F 6.19 4.44 7.29 6.70

R 4.22 7.42 2.31 8.09

11. Electrical machinery and apparatus C 0.79 7.48 -6.32 5.58

F 5.02 15.73 3.90 14.08

R -4.06 17.36 -19.23 -8.28

12. Other services activities C 3.90 4.05 5.74 2.73

F - - - -

R 1.21 -0.77 11.05 0.26

Overall C 6.64 11.79 1.50 7.63

F 7.00 11.47 2.04 7.93

R 2.67 4.14 -4.94 3.10

Note: C= Consistent; F= Flexible; R= Reactors; Performance: Bold=Highest; Underline=

Lowest (Vertical Comparison for each Industry)

148

Firms following flexible strategy performed better than their counterparts in five

industries when performance was measured in terms of ROA and ROCE. They

performed better than others in two industries in terms of ROE while in six industries in

when performance measure was ROS. The performance of reactor strategy was also good

in many cases. For example they performed above other in terms of ROE in four

industries while for ROA it was better than others in two industries and for ROCE it was

in one industry.

5.10 Hypotheses Testing

This section provides the detailed results for hypotheses (H1 to H11).

5.10.1 Proportionate Distribution of Strategic Types

One of the premises is that all strategic types do exist in an economy and similar pattern

may be followed in a given industry as well. This may not hold true because of the

dynamics of industries and the type of competition and external environment for each

industry. Therefore, we stated the hypothesis (H1) as:

H1: There is a significant difference among the distribution of strategic types

within a given industry and overall in an economy.

To test that the strategic types are distributed in equal proportion within an economy and

within an industry, the Chi-square test is applied (Table 5.15).

Overall, the difference among the proportionate distribution of strategic types in economy

is statistically significant (χ2=266.71, p-value<0.0001). The results for difference within

industries indicate that there are insignificant differences in eight out of twelve industries

while there is statistically significant difference in four industries. The significant

difference in overall economy may be due to the difference exists in the two major

industries i.e. textile and food industries constituting 51% of the total firms. Therefore,

our first hypothesis is accepted for overall results along with four industries (“textile,

149

food, other non-metallic mineral products, and motor vehicles, trailers, and auto parts”).

The insignificant difference among eight industries leads us to reject the hypotheses

which mean that strategic types are evenly distributed within the given industry. This

shows a mix support for our hypothesis.

Table 5.15: Test for equal proportion of strategic types across industries

Industry

Strategy

χ2 –Value(p-value)

1. Textiles 46.88 (<0.0001)

2. Food 20.10 0.0005)

3. Chemicals, Chemical Products and Pharmaceuticals 3.00 0.3916)

4. Other Manufacturing 5.38 (0.1457)

5. Other Non-metallic Mineral Products 17.57 (0.0035)

6. Motor Vehicles, Trailers, and Auto-parts 16.50 (0.0024)

7. Fuel and Energy 5.36 (0.1470)

8. Information, Communication and Transport Services 2.11 (0.5497)

9. Coke and Refined Petroleum Products 0.33 (0.9536)

10. Paper, Paperboard and Paper Products 0.67 (0.4142)

11. Electrical Machinery and Apparatus 6.00 (0.1116)

12. Other Services Activities 3.86 (0.2773)

Overall 266.71 (<0.0001)

5.10.2 Analysis of Variance (ANOVA)

5.10.2.1 Performance Comparison among Viable Strategies

One of the main assumptions of the Miles and Snow typology is that viable strategies

perform equally over the long term and they outperform reactor strategy. Therefore, the

performance of strategic types in various combinations is analysed based on the

following hypotheses.

150

H2: There is an insignificant difference among the performance of viable

strategies

H2a: Viable strategies outperform reactors strategy

For comparison of performance of pure and hybrid strategies we stated the following

hypothesis

H3: Hybrid strategies are superior to the Pure strategies

To test the significance of differences in performance means among the viable strategies,

we used the one-way ANOVA. The models were run to test the differences in

performance means for all measures among viable strategies keeping prospectors as the

referenced strategy. The summary of the models with F-statistics and parameter estimates

with t-statistics are presented in Table 5.16a to Table 5.16d.

The model fits for ROE and ROCE have insignificant p-value which indicates that the

difference in the performance of viable strategies is insignificant while the model fit for

ROA and ROS are significant at 5% and 1% confidence level respectively indicating that

at least one strategy performs significantly different from others. The parameter estimates

for ROA show that except prospectors, all strategies have positive impact on

performance. The impact of DA-Like is significant at 5% confidence level. Although

statistically insignificant, the impact of analyzers is closer to DA-Like followed by PA-

Like and defenders. The parameter estimates for ROE, ROS, and ROCE also show a

positive impact of all strategies except for prospectors but the influence on performance

is insignificant. To know that which strategy is performing significantly different from

others, we run post hoc Tukey's Honest Significant Difference (HSD) test. The test is run

for ROA and ROS only because the F-statistics showed significant values indicating that

at least one of the strategies is performing differently. The results of pair wise

comparison for ROA show that there is insignificant difference among the performance

of strategies. However, the Adjustment for Multiple Comparisons: Tukey-Kramer through

Least Square Means (LSMEANS) shows significant difference (represented by blue line)

for the PA-Like strategy from other strategies (Figure 5.2).

151

Table 5.16a: Test for the difference of performance means (ROA) among viable

strategies

DF Sum of Squares Mean Square F Value Pr > F

Model 4 1266.59129 316.64782 2.86 0.0243*

Error 250 27718.61837 110.87447

Total 254 28985.20966

Parameters Estimate Standard Error t Value Pr > |t|

Intercept -3.93200000 6.07932215 -0.65 0.5184

Strategy Analyzers 9.93481944 6.14232199 1.62 0.1070

Strategy DA-Like 13.47530159 6.22238460 2.17 0.0313*

Strategy Defenders 5.73200000 12.15864430 0.47 0.6377

Strategy PA-Like 7.80877273 6.28315464 1.24 0.2151

Strategy Prospectors 0.00000000 . . .

Table 5.16b: Test for the difference of performance means (ROE) among viable

strategies


Model 4 5295.6957 1323.9239 1.87 0.1167

Error 250 177250.4366 709.0017

Total 254 182546.1322


Intercept -8.03966667 15.37315568 -0.52 0.6015


Strategy DA-Like 21.97204762 15.73492648 1.40 0.1638


Strategy PA-Like 11.20473485 15.88859942 0.71 0.4813


152

Table 5.16c: Test for the difference of performance means (ROS) among viable

strategies


Model 4 6792.0860 1698.0215 3.50 0.0084**

Error 250 121339.1980 485.3568

Total 254 128131.2840


Intercept -10.82966667 12.71949674 -0.85 0.3953


Strategy DA-Like 18.02301587 13.01881997 1.38 0.1675


Strategy PA-Like 2.66550758 13.14596644 0.20 0.8395


Table 5.16d: Test for the difference of performance means (ROCE) among viable

strategies


Model 4 2963.6139 740.9035 1.34 0.2566

Error 250 138525.5937 554.1024

Total 254 141489.2075


Intercept -9.10666667 13.59046694 -0.67 0.5034


Strategy DA-Like 18.87800000 13.91028639 1.36 0.1760


Strategy PA-Like 10.90157576 14.04613923 0.78 0.4384


**,*=Significant at 1% and 5% respectively

153

Figure 5.5: Adjustment for Multiple Comparisons: Tukey-Kramer

5.10.2.2 Pair-wise Differences in Performance

To investigate the pair wise differences in performance among the strategic types, we run

the Tukey’s Studentized test for one of the performance measure (ROS) where the overall

model fit results were statistically significant. The results (Table 5.17) show that there is

significant difference in the performance of DA-Like and analyzer strategies from PA-

Like at alpha=5% while all other pair wise difference is insignificant. These results

provide the partial support to our hypothesis (H2), that all viable strategies perform

equally well.

154

Table 5.17: Tukey's Studentized Range (Honest Significant Difference -HSD) Test for

ROS

Source DF

Alpha 0.05

Error Degrees of Freedom 250

Error Mean Square 485.3568

Critical Value of Studentized

Range

3.88596

Comparisons significant at the 0.05 level are indicated by ***.

Strategy

Comparison

Difference Between

Means

Simultaneous 95% Confidence Limits

DA-Like - Analyzers 4.508 -4.636 13.652

DA-Like - Defenders 5.469 -55.545 66.484

DA-Like - PA-Like 15.358 3.464 27.251 ***

DA-Like - Prospectors 18.023 -17.750 53.796

Analyzers - DA-Like -4.508 -13.652 4.636

Analyzers - Defenders 0.961 -59.785 61.707

Analyzers - PA-Like 10.849 0.422 21.277 ***

Analyzers -

Prospectors

13.515 -21.798 48.827

Defenders - DA-Like -5.469 -66.484 55.545

Defenders - Analyzers -0.961 -61.707 59.785

Defenders - PA-Like 9.888 -51.332 71.108

Defenders -

Prospectors

12.554 -57.347 82.455

PA-Like - DA-Like -15.358 -27.251 -3.464 ***

PA-Like - Analyzers -10.849 -21.277 -0.422 ***

PA-Like - Defenders -9.888 -71.108 51.332

PA-Like - Prospectors 2.666 -33.457 38.788

Prospectors - DA-Like -18.023 -53.796 17.750

Prospectors -

Analyzers

-13.515 -48.827 21.798

Prospectors -

Defenders

-12.554 -82.455 57.347

Prospectors - PA-Like -2.666 -38.788 33.457

***=Significant at 1%

155

5.10.2.3 Performance Comparison between Viable Strategies and Reactors

To test the sub-hypotheses (H2a), the models were run including reactor strategy which is

taken as the referenced strategy for comparison. The summary of the results are presented

in Table 5.18a to Table 5.18d. The results for model fit show significant variation for

two performance measures (ROA and ROS) while the variation in performance is

insignificant for ROE and ROCE. For ROA, DA-Like and analyzers performed

significantly higher than reactors. PA-Like also performed better than reactor but the

difference is insignificant. However, prospectors performed below reactors. For ROE, the

performance of DA-Like and analyzers is again significantly higher than reactors. Here,

defenders also performed better than reactors but the difference is insignificant.

Prospectors performed below reactors for this measure. In terms of ROS, DA-Like,

analyzers, defenders, and PA-Like performed better than reactors where DA-Like and

analyzers performed significantly above than reactors. The results for ROCE are

insignificant for all strategies. Only DA-Like and analyzers performed better than

reactors. The other strategies performed below reactors. These results partially support

our sub-hypothesis (H2a).

Table 5.18a: Test for the difference of performance means (ROA) among strategies

including reactors


Model 5 1859.07003 371.81401 3.55 0.0039***

Error 301 31551.23234 104.82137

Total 306 33410.30236


Intercept 2.673634615 1.41978716 1.88 0.0606

Strategy Analyzers 3.329184829 1.65641835 2.01 0.0453**

Strategy DA-Like 6.869666972 1.91823535 3.58 0.0004***

Strategy Defenders -0.873634615 10.33620653 -0.08 0.9327

Strategy PA-Like 1.203138112 2.09716462 0.57 0.5666

Strategy Prospectors -6.605634615 6.07916542 -1.09 0.2781

Strategy Rectors 0.000000000 . . .

***,**=Significant at 1% and 5% respectively

156

Table 5.18b: Test for the difference of performance means (ROE) among strategies

including reactors


Model 5 7723.7433 1544.7487 1.72 0.1298

Error 301 270373.7462 898.2516

Total 306 278097.4895


Intercept 4.14230769 4.15620863 1.00 0.3197

Strategy Analyzers 9.53940064 4.84891007 1.97 0.0501*

Strategy DA-Like 9.79007326 5.61533907 1.74 0.0823*


Strategy PA-Like -0.97723951 6.13912700 -0.16 0.8736



Table 5.18c: Test for the difference of performance means (ROS) among strategies

including reactors


Model 5 8733.5495 1746.7099 3.46 0.0047***

Error 301 151884.1562 504.5985

Total 306 160617.7057


Intercept -4.94032692 3.11509515 -1.59 0.1138

Strategy Analyzers 7.62542415 3.63427768 2.10 0.0367**

Strategy DA-Like 12.13367613 4.20871931 2.88 0.0042***


Strategy PA-Like -3.22383217 4.60130048 -0.70 0.4841



157

Table 5.18d: Test for the difference of performance means (ROCE) among strategies

including reactors


Model 5 3907.8252 781.5650 1.38 0.2314

Error 301 170423.0155 566.1894

Total 306 174330.8407


Intercept 3.10248077 3.29973606 0.94 0.3479


Strategy DA-Like 6.66885256 4.45818256 1.50 0.1357

Strategy Defenders -0.19348077 24.02244114 -0.01 0.9936

Strategy PA-Like -1.30757168 4.87403318 -0.27 0.7887



***,**,*=Significant at 1% , 5%, and 10% respectively

5.10.2.4 Performance Comparison of Pure, Hybrid, and Reactors

We consider DA-Like, analyzers, and PA-Like as hybrid strategies and defenders and

prospectors as the pure strategies. For comparison purpose, one of the pure strategy

(prospectors) is taken as reference. The results show that DA-Like and analyzers

outperformed both defenders and prospectors in terms of all performance measures. PA-

Like also outperformed prospectors in all four measures while they outperformed

defenders in terms of ROA. Defenders as pure strategy, however, performed better than

PA-Like in terms of ROE, ROS, and ROCE. The overall results overwhelmingly support

our hypotheses (H3) that hybrid strategies are superior to pure strategies.

5.10.2.5 Performance Comparison of Consistent, Flexible, and Reactors

Based on the arguments for strategic consistency and strategic flexibility, we stated the

following hypothesis to test for the difference in performance in comparison to the

reactor strategy.

H4: Strategic consistency and strategic flexibility are expected to perform equally

well and will outperform reactors

158

To test this, we categorize the firms in three groups: consistent; flexible, and reactors.

The comparison of their performance is done through ANOVA and post hoc tests. The

reactor strategy is taken as benchmark. The results are presented in Tables 5.19a to

Table 5.19d.

The overall model (F) showed significant results for ROA only which means that

although both consistent and flexible strategies outperformed reactors, the difference in

performance among consistent, flexible, and reactor strategies is statistically insignificant

for ROE, ROS, and ROCE as far as the overall model fit is concerned. However, the

parameter estimates show that both flexible and consistent strategies have significant

difference in performance in comparison to reactor strategy. To see the combined effect

of consistent and flexible with reactors, we grouped these two types together and run the

model again. The results were statistically significant for ROA and ROS. These findings

are supported by the post hoc test results as well. Hence our hypothesis (H4) is

supported.

Table 5.19a: Test for the difference of performance means (ROA) among Strategic

Behaviours


Model 2 611.38654 305.69327 2.83 0.0604*

Error 304 32798.91582 107.89117

Total 306 33410.30236


Intercept 2.673634615 1.44042709 1.86 0.0644

Consistent 3.968609659 1.70247670 2.33 0.0204**

Flexible 3.423800868 1.71607700 2.00 0.0469**

Reactors 0.000000000 . . .

***,**,*=Significant at 1% , 5%, and 10% respectively

159

Table 5.19b: Test for the difference of performance means (ROE) among Strategic

Behaviours


Model 2 2434.5617 1217.2809 1.34 0.2628

Error 304 275662.9278 906.7859

Total 306 278097.4895


Intercept 4.142307692 4.17590604 0.99 0.3220

Consistent 7.653165590 4.93560750 1.55 0.1220

Flexible 7.333385856 4.97503580 1.47 0.1415

Reactors 0.000000000 . . .

Table 5.19c: Test for the difference of performance means (ROS) among Strategic

Behaviours


Model 2 1960.3915 980.1957 1.88 0.1546

Error 304 158657.3143 521.8991

Total 306 160617.7057


Intercept -4.940326923 3.16804678 -1.56 0.1199

Consistent 6.439365091 3.74439350 1.72 0.0865*

Flexible 6.984464020 3.77430573 1.85 0.0652*

Reactors 0.000000000 . . .

160

Table 5.19d: Test for the difference of performance means (ROCE) among Strategic

Behaviours


Model 2 949.9370 474.9685 0.83 0.4358

Error 304 173380.9037 570.3319

Total 306 174330.8407


Intercept 3.102480769 3.31178525 0.94 0.3496

Consistent 4.529755872 3.91428158 1.16 0.2481

Flexible 4.829559553 3.94555098 1.22 0.2219

0.000000000 . . .

**,*=Significant at 5% and 10% respectively

5.10.3 The Impact of Strategy, Firm Size, and Industry on Performance:

Univariate Analysis

To test the following hypotheses (H5 to H8), Univariate regression models are used

where performance as taken as dependent variable and strategy, size, and industry as

independent variables respectively.

H5: Strategy has a positive relationship with performance

H6: The Firm size has a significant impact on firm performance

H7: The Industry has a significant impact on firm performance

The summary of the results are presented in the following sub-sections for strategy-

performance, size-performance, and industry-performance.

5.10.3.1 Strategy-Performance Relationship

The summary results for strategy-performance relationship are presented in Tables 5.20

to 5.23. Strategy is explained in terms of strategic types (Defenders, DA-Like, Analyzers,

161

PA-Like and Prospectors) and in terms of strategic behaviour (Consistent, Flexible, and

Reactors). These results are based on the earlier ANOVA section (Tables 5.18a to 5.18d

and Tables 5.19a to 5.19d) discussed in previous section.

The results for strategy-performance relationship (Tables 5.20 & 5.21) show significant

model fit for ROA and ROS while insignificant for ROE and ROCE. This shows that at

least one of the strategic types performed significantly different from others for ROA and

ROS which is evidenced from parameter estimates that DA-Like and analyzers

performed significantly different. There is insignificant difference among other strategic

types. The impact of analyzers and DA-Like strategies is positive for all measures and

significant for three measures (except ROCE). The impact of defenders is positive and

insignificant for two performance measures (ROE and ROS) as well as negative and

insignificant for ROA and ROCE. The PA-Like have positive impact for ROA only and

negative for the rest. Prospectors have negative impact on performance for all measures.

The impact of both PA-Like and prospectors is insignificant. Reactors have negative

impact for ROS.

Table 5.20: The results for goodness of fit test (Strategy=Performance)

Measure Root MSE Mean F-

Value

Pr>F

ROA 10.24 5.75 3.55 0.0039***

ROE 29.97 10.37 1.72 0.1298

ROS 22.46 0.63 3.46 0.0047***

ROCE 23.79 6.99 1.38 0.2314

162

Table 5.21: Summary of parameter estimates and their significance

(Strategy=Performance)

Parameter

ROA ROE ROS ROCE

Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t|

Intercept 2.67 1.88

0.0606*

4.12 1.00

0.3197

-4.94 -1.59

0.1138

3.10 0.94

0.3479

Analyzers 3.33 2.01

0.0453 **

9.54 1.97

0.0501 *

7.63 2.10

0.0367 **

6.02 1.56

0.1191

DA-Like 6.87 3.58

0.0004 ***

9.79 1.74

0.0823 *

12.13 2.88

0.0042 ***

6.67 1.50

0.1357

Defenders -0.87 -0.08

0.9327

1.00 0.03

0.9736

6.66 0.29

0.7691

-0.19 -0.01

0.9936

PA-Like 1.20 0.57

0.5666

-0.98 -0.16

0.8736

-3.22 -0.70

0.4841

-1.31 -0.27

0.7887

Prospectors -6.61 -1.09

0.2781

-12.18 -0.68

0.4942

-5.89 -.44

0.6591

-12.21 -0.86

0.3882

Reactors 0.00 . . . . . . .

***,**,*=Significant at alpha=1%; 5%, and 10% respectively

The comparative position of strategy-performance relationship where strategy is

measured in terms of consistent, flexible, and reactors is presented in Tables 5.22 &

5.23. The variation in performance is statistically significant for ROA only. However, the

parameter estimates showed that strategic consistency and flexibility are significantly

different from reactors in terms of ROA and ROE. This shows that both consistency and

flexibility have positive impact on performance for all four performance measures while

the impact is significant for two measures. Reactors have negative impact for ROS. These

results indicate that the impact of change in strategy on firms’ performance varies with

the type of performance measures. The results for strategy-performance relationship

provide major support for fifth hypothesis (H5).

163

Table 5.22: The results for goodness of fit test (Strategic Behaviour =Performance)


Value

Pr>F

ROA 10.39 5.75 2.83 0.0604***

ROE 30.11 10.37 1.34 0.2628

ROS 22.85 0.63 1.88 0.1546

ROCE 23.88 6.99 0.83 0.4358

Table 5.23: Summary of parameter estimates and their significance (Strategic

Behaviour=Performance)

Parameter

ROA ROE ROS ROCE

Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t|

Intercept 2.67 1.86

0.0644

4.94 0.99

0.3220

-4.94 -1.56

0.1199

3.10 0.94

0.3496

Strategic

Consistency

3.97 2.33

0.0204 **

7.65 1.55

0.1220

6.44 1.72

0.0865 *

3.91 1.16

0.2481

Strategic

Flexibility

3.42 2.00

0.0469 **

7.33 1.47

0.1415

6.98 1.85

0.0652 *

3.95 1.22

0.2219

Reactors 0.00 . . . . . . .

***,**,*=Significant at alpha=1%; 5%, and 10% respectively

5.10.3.2 Size-Performance Relationship

The results of Univariate regression models for the relationship of firm size and

performance are presented in Table 5.24 & Table 5.25. The results for goodness of fit

(F-value) showed a significant influence of size on ROA and ROS at 1%, on ROE at 5%,

and on ROCE at 10% respectively. Keeping small sized firms as the reference category,

the parameter estimates show that the impact of large firms is positive and significant for

all performance measures. Medium size firms have positive and significant impact on

164

performance for ROS only. Therefore, hypothesis that firm size has significant impact on

performance (H6) is fully supported.

Table 5.24: The results for goodness of fit test (Size=Performance)


Value

Pr>F

ROA 10.30 5.75 5.36 0.0052***

ROE 29.84 10.37 4.11 0.0173**

ROS 22.18 0.63 11.26 <0.0001***

ROCE 23.74 6.99 2.69 0.0698*

***,**,*=Significant at 1%, 5%, and 10% respectively

Table 5.25: Summary of parameter estimates and their significance (Size=Performance)

Parameter

ROA ROE ROS ROCE

Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t| Estimate

t Value

Pr > |t|

Intercept 3.88 3.80

0.0002

6.27 2.12

0.0347

-7.22

-3.29

0.0011

3.06

1.30

0.1942

Large 4.53 3.14

0.0019***

10.92 2.63

0.0090***

14.62 4.71

<0.0001**

7.70 2.32

0.0212**

Medium 1.1

0.76

0.4457

1.35 0.32

0.7463

8.92 2.88

0.0043***

4.08 1.23

0.2190

Small 0.00 - - - - - - -

***,**, *=significant at 1% ,5% , and 10% respectively

5.10.3.3 Industry-Performance Relationship

The Univariate regression model was run for industry-performance relationship and the

results are presented in Tables 5.26 and 5.27. The overall model fit is significant for

165

ROA and ROE at 1% and 5% respectively while there are insignificant results for ROS

and ROCE.

Table 5.26: The results for goodness of fit test (Industry=Performance)


Value

Pr>F

ROA 10.00 5.75 3.54 0.0001**

ROE 29.62 10.37 2.00 0.0286*

ROS 22.83 0.63 1.19 0.2957

ROCE 23.69 6.99 1.43 0.1588

**,*=significant at 1% and 5% respectively

Table 5.27: Results for parameter estimates and their significance

(Industry=Performance)

Parameter

ROA ROE ROS ROCE Estim

ate t Value

Estim

ate t Value

Estim

ate t Value

Estim

ate t Value

Intercept 3.51 0.93 3.36 0.30 6.50 0.75 2.38 0.27

Textiles -1.02 -0.26 -0.75 -0.06 -9.44 -1.06 0.32 0.03

Food 2.90 0.71 10.64 0.87 -4.97 -0.53 6.93 0.71

Chemicals, chemical products

and pharmaceuticals

8.45 2.02** 17.39 1.41 -0.74 -0.08 15.36 1.55

Other Manufacturing n.e.s. 5.19 1.22 15.83 1.25 -8.44 -0.87 5.64 0.56

Other non-metallic mineral

products

-0.54 -0.12 1.17 0.09 -10.16 -1.02 0.27 0.03

Motor vehicles, trailers, and

auto parts

5.86 1.33 13.73 1.06 -3.08 -0.31 11.25 1.08

Fuel and Energy 3.17 0.66 5.60 0.39 -2.82 -0.26 0.87 0.08

Information, communication

and transport services

6.13 1.22 25.17 1.69* 5.15 0.45 0.99 0.08

Coke and refined petroleum

products

9.24 1.83* 18.06 1.21 6.30 0.55 14.98 1.25

Paper, paperboard and paper

products

3.37 0.61 5.26 0.32 5.90 0.46 6.40 0.49

Electrical machinery and

apparatus

-1.21 -0.23 9.48 0.62 -9.32 -0.79 5.72 0.47

Other services activities 0 - - - - - - -

***,**,*=significant at 1%, 5% and 10% respectively

166

The parameter estimates show that, within industries, the impact of 10 out of 12

industries is positive for ROA where the impact of “ Chemical, chemical products, and

Pharmaceuticals” and “ Coke and refined petroleum products” is positively significant.

Here, the impact of Textile and Electrical machinery apparatus is negative on

performance. Overall, the impact of majority of industries on performance is positive but

insignificant. The relationship of textile with performance is negative for all measures

followed by “Other non-metallic mineral products” and “Electrical machinery apparatus”

in two measures. Therefore, hypothesis (H7) is partially supported

5.10.4 The impact of Strategy, Size, and Industry on Performance:

Multivariate Analysis

Since, the performance is influenced by strategy, size, and industry, as we found in

preceding sections, it is imperative to check the combined effect of these three variables

on performance. For this purpose, we state the following hypotheses:

H8: Combined together, strategy, size, and industry has a significant impact on

firm performance

H9: Interaction for possible combinations of strategy, size, and industry has a

significant impact on performance

H10: Strategy is a better predictor of performance than size and industry

The multivariate models were run to test the above hypotheses by investigating the joint

impact of strategy, size, and industry on performance. Interaction terms are added as

incremental variable to explore the robustness of the model for sensitivity analysis. A

total of eight multivariate regression models were run to see the impact of additive and

interactive variables on performance. To avoid space consumption, only p-values and R2

values for all regression models are presented for all performance measures in Table

5.28. According to the results, the combined impact of strategy, size, and industry

showed a significant influence over performance as was expected. The contingent or the

interactive impact is also significant foe overall model fit for all performance measures.

167

While comparing the results for performance measures, it is found that the impact is

significant in all (eight) models for ROA. The impact is statistically significant for ROS

and ROCE in six models and for ROE in four models respectively. Strategy is proved to

be the better predictor of performance in three performance measures when a combined

impact of strategy, size and industry was modeled. Firm size is followed by strategy

while industry has the weakest influence compared to strategy and firm size. The results

support our hypotheses (H8, H9, and H10). The values of R2 also showed visible

improvement when hierarchical regression models are run. This is seen in the increment

in its value from reduced models to the complete models as the explanatory power

improved from 19% to 53%, 12% to 36%, 17% to 35%, and 9% to 41% respectively for

four performance measures (ROA, ROE, ROS, and ROCE) respectively. The results also

show that the combine impact of strategy, size, and industry on performance is more than

their individual influence.

The multivariate model for interaction of strategy, size, and industry were run. The

results for the interaction of strategy and size reveal that large analyzers and large PA-

Like firms outperformed medium and small firms in their respective categories. Large

DA-Like firms performed better than small and medium sized DA-Like firms followed

by the small DA-Like firms keeping reactors as benchmark. For strategy and industry

interaction, analyzers and DA-Like performed well in “Information, Communication, and

Transportation Services” and “Coke, and Refined Petroleum Products” industries while

prospectors in “Other Non-metallic Mineral Products” in comparison to reactors who

performed well in “Motor Vehichle, Trailors, and Auto parts” and “Chemicals, Chemical

Products, and Pharmaceutical Products”. The results for interaction of size and strategy

show that large firms performed well in “Fuel and Energy” and “Chemicals, Chemical

Products, and Pharmaceutical Products” while medium firms in “Motor Vehicles,

Trailers, and Auto parts” and “Chemicals, Chemical Products, and Pharmaceutical

Products” in comparison to small firms who performed well in “Information,

Communication, and Transportation Services” and “Other Non-metallic Mineral

Products”.

168

Table 5.28: Results of multivariate analysis for goodness of fit (p-values)

Source M1 M2 M3 M4 M5 M6 M7 M8

Mo

del

ROA

ROE

ROS

ROCE

<.0001 a

0.0027 a

<.0001 a

0.0445 b

<.000 a

0.0031 a

<.000 a

0.0990 c

<.000 a

0.0178 b

0.0080 a

0.0003 a

<.000 a

0.1608

0.0012 a

0.1673

<.000 a

0.0157 b

0.0099 a

0.0014 a

<.000 a

0.1369

0.0013 a

0.2846

<.000 a

0.0886 c

0.1401

0.0055 a

<.0001 a

0.0014 a

0.1506

0.0001 a

Str

ateg

y

ROA

ROE

ROS

ROCE

0.0018 a

0.0440 b

0.0008 a

0.1305

0.0043 a

0.0642 c

0.0010 a

0.1635

0.0033 a

0.7478

0.0339 b

0.0766 c

0.0014 a

0.0607 b

0.0009 a

0.2918

0.0122 b

0.8982

0.0822 c

0.1286

0.0033 a

0.0810c

0.0013 a

0.3238

<.0001 a

0.7044

0.0397 c

0.5095

0.0001 a

0.7311

0.0656 c

0.6668

Size

ROA

ROE

ROS

ROCE

0.0228 b

0.0379

<.0001 a

0.0767 c

0.2084

0.0887 c

0.0065 b

0.4582

0.134

0.0042

0.0001 a

0.0030 a

0.7378

0.6958

0.0932c

0.7272

0.1083

0.0139 b

0.0006 a

0.0245 b

0.9308

0.7461

0.2488

0.8806

0.7067

0.3040

0.0312 c

0.1048

0.5804

0.2873

0.1948

0.1790

Ind

ustry

ROA

ROE

ROS

ROCE

0.0003 a

0.0238

0.2668

0.1044

<.0001 a

0.0139

0.1612

0.1069

0.0013 a

0.0031

0.5550

0.0285 b

<.0001 a

0.0612 b

0.1612

0.1424

0.0004 a

0.0023 b

0.4578

0.0257

<.0001 a

0.0331

0.1685

0.1742

<.0001 a

0.0368

0.3405

0.0510 c

<.0001 a

0.0116

0.4238

0.0453 b

Str

ateg

y*

Size

ROA

ROE

ROS

ROCE

0.0722 c

0.2063

0.0484

0.6244

0.1759

0.2385

0.3626

0.8472

0.2004

0.2619

0.2282

0.7875

0.0152 b

0.7047

0.6433

0.9060

Str

ateg

y*

Ind

ustry

ROA

ROE

ROS

ROCE

0.3027

0.3141

0.7539

0.0013 a

0.4129

0.3323

0.9475

0.0024

0.0257 b

0.1302

0.9722

0.0011 a

Size*

Ind

ustry

ROA

ROE

ROS

ROCE

0.1979

0.9884

0.6156

0.6242

0.3342

0.8675

0.7875

0.7173

0.0044 a

0.7103

0.9973

0.2969

Str

ateg

y*

Size*

Ind

ustry

ROA

ROE

ROS

ROCE

<.0001 a

0.4529

0.8990

0.0184 b

<.0001 a

0.2596

0.5801

0.2374

R2

ROA

ROE

ROS

ROCE

0.19

0.12

0.17

0.09

0.23

0.15

0.21

0.11

0.30

0.23

0.25

0.28

0.26

0.15

0.22

0.15

0.32

0.26

0.27

0.29

0.29

0.18

0.24

0.16

0.52

0.36

0.35

0.41

0.53

0.36

0.35

0.41

a,b,c=Significant at 1%, 5%, and 10% respectively

169

5.11 Textile Industry Analysis and its Comparison with Overall Results

The studies on Miles and snow typology considered single industry as well as multi-

industries for analysis. One of the issues relating to single industry analysis is the

generalization of the findings while the issues with multi-industry analysis include the

differences in the structure and other dynamics relevant to specific industry due to which

the results may not reflect the true picture. To overcome this shortcoming, this study

analyzes the single industry along with multi industry to find the commonalities and

differences in the results so that the generalization of the findings can be suggested. For

this purpose, textile industry is selected for this study as it constitutes around 39% of the

sample size. Besides this, textile sector, compared to any other sector, has backward and

forward linkages with other sectors. For example, textile sector links agriculture with

industry hence contributing about 24 percent of the value added output of the industrial

sector, providing 40 percent of employment for industrial sector and contributing around

55 percent in Pakistan’s total exports, and consumes 40 percent of the banking sector

loans given to the industrial sector. The textile sector has sound production chain

spanning from the growth of cotton to ginning, processing, weaving and finishing

products. On the finished products the production chain include fabrics, home textiles and

apparel products. This production chains have indigenous footings and strong industry

linkages developed by Pakistan’s own industry (Pakistan Economic Survey, 2017; Tahir

& Anuar, 2015). The importance of textile sector along with intra-industry linkages and

influences make it an automatic choice for a single industry analysis.

Textile sector is passing through a stagnation phase over a decade due to a number of

internal and external factors. One of the factors for this stagnation is distorted prices due

to the government’s subsidy given to cotton farmers and other textiles products

producers. The other factors include global recession, marketing constraints, and

increasingly tough buyers’ conditions etc. The price of cotton from Pakistan in

international market decreased by 10 cents per pound due to the consistent resistance to

standardization and grading of cotton bales by ginners and spinners. At the same time,

170

there was a marginal growth of the value-added garments sector. There are many reasons

for this marginal growth. The reasons include the limited product range of finished

goods, the inability of manufacturing units of textile sector to update themselves to the

international requirements, and lesser degree of using manmade fibers. In addition, the

lack of skilled human resource, absence of modern management practices, and low

employment of women in the garment sector have thwarted the efforts for achieving the

high value-addition markets resulting in low exports from the entire textiles industry

supply chain (Government of Pakistan, 2015).

The selection of one industry is consistent with earlier studies (see Table 3.1). Jennings

& Seaman (1994) suggested two reasons for selection of single industry for analysis.

First, there should be enough information available publically to satisfy the measures of

performance. Second, there shoulld be evidence for adation of environmental changes by

the selected industry. The textile industry in Pakistan fulfills these requirements and

hence eligible for in depth analysis. Another reason for doing single industry analysis is

based on the argument by Snow & Hambrick (1980) that the selection of one industry

solve the issue of comparison of measures and other parameters because of the

environmental homogeneity. The main objective of this analysis is to make a comparative

analysis of single industry findings with the multi-industry results so that the common

findings can be generalized for strategy-performance relationships studies. The results for

textile sector analysis are presented in Annexure A3. However, the summary of the

results is presented below under sub-headings.

5.11.1 Strategic Types Distribution

Like overall findings, the dominance of analyzers strategy is also visible in textile sector

representing 52% of the total firms followed by DA-Like firms with 18%, Reactors with

16% and PA-Like with 14% respectively. There is non-existence of pure defenders and

pure prospectors. The percentage of analyzers is more in textile sector than the overall

presence (47%) and DA-Like are less than overall presence (21%). The distribution in

overall economy is almost identical for reactors and PA-Like. The results reveal that

firms in textile sector are mostly small and medium sized (39% each) whereas large firms

171

are only 22%. Within strategic types, DA-Like and reactors are mostly small firms,

analyzers are medium, and PA-Like are large firms. In comparison with overall pattern,

the findings are consistent for DA-Like, analyzers, and PA-Like firms. However, in

contrast to textile sector, the majority of reactors are large in overall findings. Comparing

the behavior of strategic types, it is found that consistent firms are more than the flexible

firms for all viable strategies in textile sector. The pattern is similar for overall findings

except for analyzers where consistent firms are more than the flexible firms.

5.11.2 Strategic Types and Performance

In textile sector, both DA-Like and analyzers outperformed reactors and PA-Like in all

four performance measures. Both of them also performed above industry averages. DA-

Like gave highest performance in terms of ROA and ROS while analyzers performed

above all in terms of ROE and ROCE. Reactors performed below industry averages in

three measures whereas PA-Like performed below industry averages in all four measures.

The performance of PA-Like is negative in three measures whereas that of reactors in two

measures. The results for PA-Like and reactors are similar with overall results. However,

the results for DA-Like and analyzers are slightly different.

5.11.3 Strategic Behavior and Performance

Strategic consistency returns better performance when results are compared for consistent

strategy with flexible and reactors. The performance of consistent strategy was better

than reactors in all four performance measures while flexible strategy firms outperformed

reactor in three performance measures. In contrast to overall results, firms with

consistency in their strategic stance performed better than firms having flexible strategy

in three performance measures whereas for overall results flexible firms perform better

than consistent firms in three measures.

Within strategic types there is no specific pattern as far strategic behavior of firms is

concerned. For example, among DA-Like and PA-Like firms, consistent firms performed

better for all performance measures. Among analyzers, consistent firms performed better

in terms of ROA and ROE and firms with flexible strategy performed better in terms of

172

ROS and ROCE. In comparison with overall results, it is found that for DA-Like,

consistent strategy outperformed flexible in three measures. For analyzers and PA-Like,

consistent performed better in all four measures. Hence the results for PA-Like are

similar for single industry and multi-industry analysis while they are different for other

strategic types.

5.11.4 Strategy, Size and Performance

Within small sized firms, DA-Like outperformed others in all measures while analyzers

in three measures. PA-Like performed poorly along with reactors. The performance of

small PA-Like is similar with overall results whereas small reactors’ performance is

below overall performance of small reactors. Similarly, medium DA-Like and analyzers

outperformed others in all performance measures. DA-Like showed highest performance

in three measures while analyzers in one measure. Reactors and PA-Like showed lowest

performance in two performance measures. The trend is different for large sized firms

where analyzers outperformed others including DA-Like. The performance of large PA-

Like is also improved while large reactors performed poorly. The performance of large

PA-Like and large reactors is aligned with overall performance results for large firms.

Comparing the performance of small, medium, and large sized firms with overall

performance of textile sector, irrespective of strategic types, it is evidenced that large

sized firms performed better than small and medium. These findings are also aligned with

overall results.

5.11.5 Strategic Behavior, Firm Size, and Performance

For medium sized firms, organizations with consistent and flexible strategies

outperformed reactors in all four performance measures. They outperformed large

reactors in three and small reactors in two performance measures. These results reveal

that consistency in strategic behavior brings the fruits in terms of better performance

whereas inconsistent or reactor behavior may get some benefits but not all the time and

not across the firm size. The results of firms following consistent and flexible strategies

173

are aligned with overall results but the pattern of results within firm size is different for

textile sector.

5.11.6 Strategy, Industry, and Performance

The textile sector is further divided into three sub-sectors: “Spinning, weaving, and

finishing of textile”, “made up of textile article”, and “other textile sub-sector”. Spinning,

weaving, and finishing of textile, the largest sub-sector in textile sector, performed better

in terms of ROS and ROCE while “made up of textile article” sub-sector performed

better in terms of ROA and ROE. The “other textile sub-sector” performed poorly. The

results for the performance of strategic types across the sub-sector show that DA-Like

firms performed better than others across the sub-sectors followed by analyzers. Despite

the poor performance of textile sector in comparison of overall performance, the

performance of sub-sector “made up of textile article” in terms of ROE and ROCE is

above the overall average.

5.11.7 Industry, Strategic Behavior, and Performance

The findings show that both consistency and flexibility in strategic behavior

outperformed reactor behavior. However, the performance of consistent strategy in textile

sector is better whereas in overall results, the performance of flexible firms is better.

However, within sub-sectors of the textiles industry, the pattern of the performance for

consistent and flexible firms is varying as in multi-industry analysis. The sub-sectors of

“spinning, weaving, and finishing of textile” and “other textile” consistent firms

outperformed reactors and flexible firms whereas in “made up of textile articles”, flexible

firms outperformed reactors and consistent firms. Reactors, performed poorly across sub-

sectors. Their performance is however better in “other textile” sub-sector.

5.11.8 Strategy-Performance Relationship

To test the hypothesis that viable strategies will perform equally well in single industry as

well, the test for equality of means was applied for viable strategies excluding reactor

strategy. The results suggest that viable strategies performed evenly for ROA and ROS

174

whereas the performance among the viable strategies is significantly different for ROE

and ROCE at 10% and 5% respectively. The parameter estimates show that analyzers and

DA-Like strategies are significantly better than the PA-Like strategy for ROE and ROCE

measures while DA-Like is also significantly better than PA-Like in terms of ROA. The

performance of the viable strategies among firm size is significant for ROS only. The

parameter estimates show that the performance of large size firms is better than small and

medium sized firms. Strategy is the better predictor of the performance as the influence

of strategy on performance is more than the influence of size for three out of four

measures which is in contrast with the results for overall findings where size is the better

predictor than strategy. The comparison of viable strategies with reactor strategy

performance is also made using one-way ANOVA and the effect of size on performance

is also tested. Again, the results show that strategy is the better predictor of the

performance as the influence of strategy on performance is more than the influence of

size for three out of four measures.

The parameter estimates show that all strategic types performed better than reactors in

two performance measures (ROE and ROS) while DA-Like and analyzers strategies

outperformed reactors in all four measures. The performance of reactors is found

negative for ROE and ROS. DA-Like performed significantly well in comparison to the

reactors in three measures while analyzers performed significantly different from reactor

in two measures. Contrary to the expectations, PA-Like performed significantly below

reactors in terms of ROCE. The post hoc test results show significant difference in

performance for the pair of analyzers and PA-Like firms only. Similarly the parameter

estimates for firm size for impact on performance show that large firms outperformed

small and medium sized textile sector firms in all performance measures but the

difference in performance is statistically significant for small size firms in terms of ROS

which is confirmed by the post hoc test results as well.

A two-way ANOVA was done to find the effect of strategy, size, and combined effect on

performance. For this purpose, two models (M3 and M4) were run in addition to models

M1 and M2. In model M3 the effect of strategy and size was tested on performance while

in model M4 the interaction term of strategy and size was added to see whether the

175

interaction has any significant effect on performance or not. Overall effect of strategy and

size on all performance measures is significant. The level of significance is 1% for ROS,

5% for ROA and ROCE while it is 10% for ROE. The model shows that strategy is better

predictor than firm size as the effect of strategy is statistically significant for all

performance measures while the effect of size is significant for only two measures. For

interaction model, the results are significant for only two performance measures (ROE

and ROS) for overall model while strategy has significant effect for only ROE. The effect

of interaction of strategy and size has also significant effect for ROE only while the effect

of size on performance for this model is insignificant for all performance measures.

However, the power of significance (R2) is more for interaction term model than without

interaction.

5.12 Comparative Summary of the Results: Multi-Industry (MI) versus

Single Industry (SI)

The findings from single-industry and multi-industry analysis are compared and

summarized in table 5.29. The comparison is made for each hypothesis (H1 to H11)

along with the major findings and decision about the consistency of the findings with the

prior research findings. The findings suggest that there is similarity in majority cases.

There are some instances, however, where there are dissimilarities in the results. This

summary of the results provides quick overview of the study and is helpful for

generalization of the results.

176

Table 5.29: Comparison for multi-industry and single industry analysis -Summary

Hypotheses

Findings Decision

Multi Industry Single Industry MI SI

H1: “There is a

significant

difference among

the distribution of

strategic types

within a given

industry and overall

in the economy”

The results for overall distribution of strategic

types are significant. Within industries, the

results for textile, food, non-metallic mineral

products, and motor vehicle, trailers, and auto

parts industries are also significant. The results

for other industries are insignificant

The results for proportionate

distribution of all strategic types within

the textile industry are statistically

significant (Chi-square value=46.88, p-

value<0.0001).

Major

Support

Full

Support

H2: “There is an

insignificant

difference among

the performance of

viable strategies”

The overall model results show that the

difference among the performance of viable

strategies is significant for ROA and ROS while

the difference in terms of ROE and ROCE is

insignificant. However, the parameter estimates

show that only DA-Like has performed

significantly different for ROA only while the

post hoc test for pair wise comparison show that

DA-Like/ PA-Like and analyzers/PA-Like pairs

are significantly different from each other for

ROS only.

The overall model results show that the

difference among the performance of

viable strategies is significant for ROA

and ROS only. The parameter

estimates show that analyzers and DA-

Like strategies are significantly better

than the PA-Like strategy for ROE and

ROCE measures while DA-Like is also

significantly different than PA-Like in

terms of ROA.

Partial

Support

Partial

Support

H2a: “Viable

strategies

outperform reactors”

Except prospectors, all viable strategies

outperformed reactors in at least one

performance measure. Specifically, analyzers,

DA-Like, and PA-Like outperformed reactors for

ROA; defenders, analyzers, DA-Like for ROE

and ROS; and analyzers and DA-Like for ROCE.

All strategic types outperformed

reactors for ROE and ROS while DA-

Like and analyzers outperformed

reactors in all measures. The

performance of reactors is negative for

ROE and ROS. DA-Like performed

Major

Support

Major

Support

177

Further, the performance of reactors is below

overall performance averages in all measures.

The parameter estimates show that analyzers and

DA-Like are significantly different from reactors

for all performance measures except ROCE.

significantly higher than reactors in

three while analyzers performed

significantly different from reactor in

two measures. PA-Like performed

significantly below reactors for ROCE.

H3: “Hybrid

strategies are

superior to the Pure

strategies”

Hybrid strategies (DA-Like, and analyzers)

outperformed pure strategies (defenders and

prospectors) in all four performance measures

while another hybrid strategy (PA-Like) also

outperformed prospectors in all measures and

defenders in one measure. However, defenders

performed better than PA-Like in three measures.

All firms are adapting hybrid strategies

as there is no firm with pure strategy.

Hence, there is no comparison of

performance.

Major

Support

NA

H4: “Strategic

Consistency and

Strategic Flexibility

are expected to

perform equally well

and will outperform

the Reactor

strategy”

Strategic consistency and strategic flexibility

outperformed reactors in all performance

measures. The difference between consistent and

flexible strategic behavior is insignificant and

minimal while both of them outperformed

reactors

Both types of firms with consistency

and flexibility in their strategic choice

performed equally well and

outperformed reactors in all

performance measures.

Full

Support

Full

Support

H5: “Strategy has a

positive relationship

with performance”

Analyzers, DA-Like, and defenders have positive

relationship with performance for all

performance measures while PA-Like and

reactors have negative performance for one

measure each. Only prospectors have negative

performance for all four measures.

Analyzers and DA-Like have positive

relationship with performance for all

measures while PA-Like and reactors

have negative performance for two

measures each.

Major

Support

Major

Support

H6: “The Firm size

has a significant

impact on firm

The results for size-performance model fit show

statistically significant values for all performance

measures. The influence of the performance of

large size firms is significantly different from

The results for size-performance

relationship show statistically

significant values for only one

performance measure (ROS). Within

Full

Support

Minor

Support

178

performance”

small firms in all four measures. size, the influence of the large sized

firms is significantly different from

small firms for ROS whereas the

difference is insignificant for other

measures.

H7: “The Industry

has a significant

impact on firm

performance”

The overall influence of industry on two

performance measures (ROA and ROE) is

significant. However, within industries, only two

industries have significant influence for ROA,

and only one industry for ROE. Majority of the

industries have insignificant influence on firm

performance

NA Moderate

Support

NA

H8: “Combined

together, strategy,

size, and industry

has a significant

impact on firm

performance”

The model of goodness of fit showed significant

results for all performance measures. Here, the

impact of size is significant for all four measures.

The impact of strategy is significant for three

measures while the impact of industry is

significant for two measures.

The model of goodness of fit showed

significant results for all performance

measures. The impact of strategy is

significant for all four measures while

it is significant for firm size in only

two measures. Since, there is only one

industry, only the size impact is

combined with strategy

Major

Support

Major

Support

179

H9: “Interaction for

possible

combinations of

strategy, size, and

industry has a

significant impact

on performance”

The overall models with interaction terms are

significant for varying performance measures.

Specifically, the influence of interaction term

strategy*size is significant for ROA in two out of

four models and in one model for ROE. The

results for strategy*industry interaction has

significant results for ROCE in all three models

and in one model for ROA. The influence of

interaction term size*industry has significance

results for only ROA in only one model out of

three. The interaction of strategy*size*industry

has significant results for two models for ROA

and for one model for ROCE.

NA Moderate

Support

NA

H10: “Strategy is a

better predictor of

performance than

size and industry”

The influence of strategy on performance, when

run together with size and industry, is more.

Specifically, the influence of strategy on

performance is significant in all eight models in

terms of ROA and ROS followed by four models

for ROS and for one model for ROCE. The

influence of industry is significant for ROA and

ROE in all eight models and for ROCE in four

models while the influence for ROS is

insignificant for all models. The influence of size

is significant for ROS in six models followed by

four models for ROE and three and one models

in terms of ROCE and ROA respectively. Hence

overall strategy is better predictor of performance

than industry and size.

The influence of strategy on

performance is more than firm size

when both strategy and size are

included in the model with interaction

of both

Full

Support

Full

Support

180

5.13 Discussion

5.13.1 Refinement in Scoring Methodology

The scoring method is used for categorization of strategic types based on the ranking of

the scores theoretically calculated. The ranking is generally calculated on the basis of

quintiles or percentiles. One problem with the ranking is the cut-off points because

different researchers used different cut-off points. Some of the examples are presented

here. Smith et al. (1986), used the ranking scale of 0-8 on the continuum. He used cluster

analysis for classification of strategic types. The clusters with highest score was

categorized as prospectors, followed by analyzers, and defenders. The cluster with lowest

score was classified as reactor. Evans & Green (2000) categorize the strategic types on

the basis of mean ranking scores of quintiles. However, the firms constituting the middle

of the continuum are termed as reactors instead of analyzers. Bentley et al. (2013) also

used quintiles to rank the strategy variables and based on composite score firms are

categorized as prospectors with a range of scores on the higher side of the continuum,

defenders at the lower side and the analyzers as the balancing category. The reactor

strategy was not identified.

Extension in the number of strategic types was another area of improvement because the

existing strategic types only discuss the pure strategies while in real situation firms make

some type of adjustments and make a combination of pure strategies to hybridize the

strategic choice. For this purpose prospectors and defenders strategies were divided into

pure and hybrid strategies. The concept of hybridization of pure strategies has been

discussed in literature (Hambrick, 1981; Madanoglu et al.; 2014; Slater et al., 2011;

Valos & Fix, 2003) but less efforts were made towards the operationalization and

classification of such hybrid strategic types. To overcome these deficiencies, the scoring

methodology was refined that provides the mechanism to identify all strategic types as

proposed by Miles and Snow along with other strategic groups. One of the major

contribution of this study is the development of methodology for identification of

inconsistent behavior –a characteristics that define reactor strategy which is generally

omitted from the studies particularly when archived financial data is used. The other

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contribution of the study in the methodology is the identification of pure versus hybrid

and consistent versus flexible strategic groups. The theoretical basis of these strategic

groups and the finding from empirical analysis in this study provides enough evidence for

the reliability and validity of the methodology.

5.13.2 Presence and Distribution of Strategic Types

The refined scoring methodology is used to categorize the strategic groups on the basis of

Miles and Snow typology; pure versus hybrid strategic types; and consistent versus

flexible strategic types. The distribution of strategic types within industry and across

industries is uneven which is consistent with earlier studies. In this study, among viable

strategies, analyzers is the dominating strategy followed by DA-Like, reactors, and PA-

Like strategies. The existence of pure strategies (prospectors and defenders) is negligible.

The overall distribution of firms following strategic consistency and strategic flexibility

is almost equal. Almost, same pattern was found for strategic consistency and flexibility

with in strategic types.

One of the fundamental premises of the Miles and Snow typology is that each of the

strategy types is considered to exist within an industry or industries (Zahra & Pearce II,

1990). A number of empirical studies validate this premise. The presence of strategic

types is supported by the studies both for multi-industry settings, single industry analysis,

and cross-country studies as reflected in Table 3.2. The dominance of analyzers in our

study is specifically consistent with those studies where archived data is used (Table

3.1). In most of these studies, the percentage of prospectors and defenders is less than the

analyzers. Also, there is a mixed support in the literature for the industry membership

proportions. Different researchers have reported significantly varied distribution of some

or all of the strategy types across different industries (Blackmore & Nesbitt, 2013;

Hambrick, 1981, 1982; James & Hatten, 1995; Snow & Hambrick, 1980; Zahra & Pearce

II, 1990). Hence, our findings are overwhelmingly consistent with the prior research as

far as the presence and distribution of the strategic types are concerned.

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5.13.3 Strategy-Performance Relationship: Pure Versus Hybrid Strategies

There is growing discussion in the extant literature on the supremacy of pure strategy

over hybrid and vice versa. The results of this study show that in Pakistan, firms are

adapting hybrid strategies instead of pure strategies as the presence of pure strategies is

negligible. The results support the arguments of the extant literature that the

organizations are hybridizing or combining the pure strategies for competitive advantage.

The adaption of hybrid strategies is near to reality because by doing so organizations get

many strategic options to adapt irrespective of the industry they are in. The arguments in

favour of hybridization of strategic orientation is being discussed by the contemporary

literature more than before (Pertusa-Ortega et al., 2009; Salavou, 2015; Thornhill &

White, 2007). Theoretically, the adaption of hybrid strategy by the organizations is

because of some demerits associated with pure strategy. These problems are:

1. Sticking to the core and pure specialized strategy leads to ignoring the important

customer needs because of serious shortcomings in product offerings.

2. In contrast to hybrid form of strategy, there are more chances of imitation by the

competitors.

3. Market changes rapidly, customer needs and tastes are evolving quickly, and

competitors are inventing new products and services mechanisms.

The issue list above related with firms following pure strategies put them in a vulnerable

conditions to compete with the firms those are able to combine or hybridize the pure

strategies to gain superior performance through exploiting the market conditions (Claver-

Cortés et al., 2012; Pertusa-Ortega et al., 2009). Hence, to address customer needs in

better way, firms adapt hybrid strategies because their strategy is difficult to imitate and it

offers more flexibility in their approach to meet the changing environmental challenges

One of the research question for this study was whether pure strategy is still superior to

hybrid strategy? Our results do not support this question as the performance of hybrid

strategies better than pure strategies. According to the results, within defending strategies,

hybrid strategy (DA-Like) outperformed pure defenders in all performance measures.

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Similarly, on prospecting side, PA-Like strategy outperformed pure prospectors in all

performance measures. This provides clear evidence that hybrid strategies are superior to

pure strategies. The analyser strategy, which is considered as balancing strategy and

hence is categorized as hybrid or combination strategy also outperformed both defenders

and prospectors in all performance measures.

Our results are in favour of the debate that in the given changing environment, the better

choice for organizations is to go for combination of strategies rather than sticking to the

pure strategy (Parnell et al., 2015; Salavou, 2015). The reason for higher performance of

hybrid strategies in Pakistan can be due to the fact that in uncertain and volatile

environment like ours, firms are more vigilant to combine the strategic options as

suggested by Gabrielsson et al. (2016) that firms that operate in an uncertain and dynamic

environment can realize a hybrid competitive strategy for higher performance. The

performance of DA-Like firms may be due to their relatively internally oriented posture.

This orientation requires the firms to focus on efficiency, price, and quality by offering

superior products, at a higher quality and better prices than their competitors instead of

developing new products and services. The performance of analyzers is linked with their

maintenance of product line along with tracking and adapting changes in production and

manufacturing process through a keen observation of strategies of the competitors.

The poor performance of the prospectors and defenders may be due to the fact that they

may not be able to respond the market changes. Also, they could not perhaps maintain

their agility and flexibility in offering products and services that left them in a

disadvantaged position compared to their counterparts.

5.13.4 Strategy-Performance Relationship: Consistency versus Flexibility

There is a debate in the literature about adaptation of strategic stance by the management

in a given environment. The researchers who are in favour of strategic consistency argue

that the performance of the organizations can be increased if the firms follow the same

strategy for a longer period of time (Fehre et al., 2016; Lamberg et al., 2009; Parnell &

Lester, 2003; Sanchez, 1995). On the other hand, the proponents of strategic flexibility

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claim that it adjustment in strategic stance and to remain flexible for exploiting the

environmental conditions is a necessary condition to remain in the competition and hence

to achieve higher performance (Herhausen & Morgan, 2014; Ouakouak & Ammar, 2015;

Parnell, 2005). Our results does not out rightly support any one of the above argument

rather they provide the support for both arguments. This shows that both consistency and

flexibility can bring better results if the firms are consciously following them with spirit.

The result of this study reveal that, the performance of strategic consistency and strategic

flexibility were above overall average and the difference between consistency and

flexibility was insignificant. As expected, both consistent and flexible strategies

outperformed reactors in all performance measures. However, within viable strategies,

when overall results were compared, it was found that firms following flexible strategies

performed slightly better than the firms adapting consistent strategy in three out of four

measures.

Investigating the performance results for strategic types separately, it was revealed that

there are mix results and support for both strategic consistency (Anikeeff & Sriram, 1995;

Fehre et al., 2016; Lamberg et al., 2009; Pleshko et al., 2014) and flexibility (Herhausen

& Morgan, 2014; Ouakouak & Ammar, 2015; Parnell, 2005). There is variation in the

results for performance due to variation in strategic choice, firm size and industry for

both consistent and flexible strategies. For example, among DA-Like firms, strategic

flexibility performed well when performance was measured by ROA and ROS the

performance of strategic consistency was better in terms for other two measures (ROE

and ROCE). The performance of strategic flexibility were better for analyzers and

prospectors while consistency produced better performance for PA-Like firms. These

findings reflect the arguments by Pleshko et al. (2014) who classified the strategic

groups according to their response to the market changes. According to them, prospectors

and analyzers represent the aggressive behaviour and therefore expected to be more

flexible. On the other hand, defenders reflect less aggressive or consistent behaviour. The

results of this study are somewhat consistent with this argument as strategic flexibility

produced higher performance for analyzers and prospectors’ types. However, the results

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are mix for DA-Like firms as they performed better by adapting both aggressive

(flexible) strategy as well as less aggressive (consistency) in their strategic choices.

Strategic consistency suits large firms as they perform better while adapting strategic

consistency while strategic flexibility produced higher performance from medium size

firms. Analysing industry performance, it was revealed that industries with large market

share and volume such as “Textile” and “Food” industry follow consistency in their

strategic stance while other industries such as “Chemicals, chemical products and

pharmaceuticals”; “Cement”; “Motor vehicle industries” preferred flexibility in their

strategic stance. These findings are aligned with the theory that for large and complex

industries, it is difficult to shift the focus in very quick time because of the complexities

and the cost attached to that shift.

The proponents of strategic consistency argue that the probability of survival of firms

without being consistent with their own history and with the rate and nature of change in

the environment is minimized. Firms operating in a relatively stable environment are

expected to have high level of strategic consistency where firms tend to preserve their

state of rest or uniform action (Lamberg et al., 2009) and a high level of strategic

consistency can signal the existence of strong competitive strategy (Porter, 1980). Firms

may adapt strategic consistency because of four major reasons:

1. Firms stick to the existing strategy to avoid uncertainty and flexibility because

coping with the constant changes in the environmental factors is a challenging job

for strategists.

2. Strategic change or shift may require substantial capital expenditure and other

costs. For example, a shift of focus from a growth oriented and innovative

strategy ( such as prospector) or balancing strategy (analyser or stuck in the

middle strategy) to a cost focus strategy (such as defender) may require huge

investments in sophisticated production equipment to lower costs for effective

implementation of a defender strategy (Miles & Snow, 1978). Similarly, a change

of strategic stance from a defender or analyser strategy to a prospector strategy

may require outlays of capital for R&D activities.

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3. Rapid strategic changes may confuse the price and quality conscious consumers.

Hence, they may shift their relationship with those firms which remain consistent

in providing the quality product or services.

4. Achieving and maintaining the sustainability of the success in new product and

service is a challenging task. To overcome this challenge, firms stick to the core

strategy. In this way they are able to avoid the situation where competitors may

distort consumer perception to gain competitive advantage (Parnell, 2005).

The flexibility in strategic orientation is well adapted in dynamic environments to

exploit market opportunities that lead to improved performance and the firms capable

of coping with their environments quickly and efficiently are more successful

(Ouakouak & Ammar, 2015). Empirical findings are, however, incongruent

(Herhausen & Morgan, 2014). The supporters of strategic flexibility make certain

arguments in their favour as under:

1. An organization adapts flexibility and creates a fit with its internal and

external environment to exploit the opportunities for superior performance

(Parnell, 1997).

2. Flexibility provides the opportunities to reap the first-mover advantage.

Flexibility helps in securing scarce resources, increasing the knowledge base,

and long-term competitive advantage.

3. Because of flexible orientation, organizations modify their strategy to create

unique resources: human resources, physical resources, capital resources etc.

(Barney, 1991).

4. Strategic change becomes inevitable when the required performance is not

being achieved. Therefore, organizations believe that a shift in strategy will

increase the ability of the firm to generate returns, increase market share, and

improve overall performance (Parnell, 2005; Parnell & Lester, 2003).

Flexibility raises problems as well that may put the existence of the firms at risk.

Because rapid changes in strategic stance may lead to unwanted actions by the

stakeholders. The actions contrary to the past behaviour may lead to create an imbalance

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between capabilities and competitive actions causing a quick increase in costs and decline

in competitive position (Lamberg et al., 2009).

5.13.5 Strategy-Performance Relationship: Miles and Snow Typology

Perspective

The assessment of strategy-performance relationship associated with varying strategy

types has reported extensively in studies where Miles and Snow typology is applied. One

of the fundamental assumptions of Miles and Snow typology is that all viable strategies,

if perused for a longer period of time, will perform equally well. The second part of the

assumption is that they are expected to outperform reactors –a non-viable strategy. The

results of this study showed partial support for this assumption in both multi-industry

analysis and single-industry analysis. For multi-industry analysis, the results for overall

model show that the difference among the performance of viable strategies is significant

for two measures (ROA and ROS) while the difference is insignificant for ROE and

ROCE. The post hoc test for pair wise comparison show that pairs of DA-Like/ PA-Like

and analyzers/PA-Like are significantly different from each other for ROS only.

Similarly, the results for overall model in single-industry also show that the insignificant

difference for ROE and ROCE.

Comparing the performance of strategic types in absolute terms show that DA-Like

strategy outperform all strategies in all four performance measures followed by analyzer

strategy. Both types of strategies also performed above the overall performance averages.

The performance of reactors, PA-Like and prospectors is below overall average for all

measures while pure defenders performed below average in three measures. The

performance of pure prospectors is negative for all performance measures. Comparing the

performance with reactors, it is revealed that except for prospectors, all viable strategies

performed better than reactors at least in one performance measure. However, reactors

also performed well in some instances. The presence and performance of reactors is

according to our expectation keeping in view the uncertain, unstable, and ever changing

political and economic conditions in Pakistan. If we exclude the results for prospectors

and defenders, since their numbers are negligible, the findings support our hypotheses.

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The support for Miles and Snow’s this assumption is widespread. However, there are

number of instances, where inconsistent results are also found showing significant

differences in performance of viable strategies (Blackmore & Nesbitt, 2013; Parnell et

al., 2015; Parnell & Wright, 1993; Smith et al., 1989; Zamani et al., 2013). There are

many factors for the significant variation in performance among the viable strategies.

These factors include: the different nature and scope of performance measures; cultural

and environmental contexts; level of market efficiencies and/or deficiencies, level of

market competition, and market and product innovativeness (Blackmore & Nesbitt, 2013;

Snow & Hrebiniak, 1980; Zahra & Pearce, 1990). For example, in a study by Hambrick

(1983), defenders performed better than prospectors in terms of profitability measure

while in the same study prospectors performed better when performance is measured by

market share. In another study, prospectors performed better when performance was

measured as growth in sales while analyzers outperformed others in terms of ROA

(Parnell & Wright, 1993) etc. The country effect also cause the variation in performance

as was found in cross-country analysis. The performance of viable strategies is negative

or showed losses although viable strategies outperformed reactors in such cases (Parnell

et al., 2012; Parnell et al., 2015). These findings support the idea that context matters for

the performance variation among the strategic types. The results for contingent effect of

firm size and industry on performance are also inconclusive. For instance, firm size

influence firm performance significantly (Blackmore & Nesbitt, 2013; Jennings et al.,

2003) whereas Sarac et al. (2014) found it insignificant. Similarly, industry effect was

found significant by Blackmore & Nesbitt (2013) as well as insignificant by Sarac et al.

(2014).

Overall defending (DA-Like and defenders) and analyzing strategies performed better

than prospecting (PA-Like and prospectors) strategies. The poor performance of pure

prospectors and PA-Like can be due the facts supported by Hambrick (1983). He called it

"the liability of newness" and the “cost of innovation” and explained that the factors

responsible of creating these liabilities and costs are: new product development,

production, and their marketing; expansion or modification of plants, machinery, and

equipment; new supply chain establishment; inventory buildups; enhancement of skill set

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of human resources etc. According to Miles and Snow (1978; 2003), many organizations

cannot prosper financially until and unless their markets continually seek new products

and services even if they become very adept at managing continual change. As Hambrick

(1983) argued, very few industries are able to be innovative consistently. They further

argue that pure prospector is relatively temporary and uncommon and hence represent a

very small share. These arguments favor the findings of this research since there are only

3 firms which could be categorized as pure prospectors. When their performance is

compared to other strategic types, it was found less than others as well.

The business environment and context of Pakistan also support our findings. According

to Haque et al. (2007), Pakistan lack entrepreneurial activities because of two problems:

absence of innovation and non-dynamic business environment. Businesses lack

innovation because most of the businessmen seem to be involved in inherited businesses

and they are based on imitation rather than innovation –the hallmark of prospectors. As

for dynamics of the businesses is concerned, the businessmen in Pakistan are content with

their present status as they seldom seem to develop their business to international

conglomerates. The factors responsible for poor performance are: lack of innovation and

non-dynamic business environment; lack of research and expert skills; poor legal

framework; lack of trust and social capital; and constraints in financing.

5.13.6 Strategy-Performance Relationship: Reactor Strategy

Reasonable number of firms in Pakistan are adapting reactor strategy. This is as per our

expectation since the environment in Pakistan is uncertain, unstable, and vibrant. The

operationalization of reactor strategy is not very common in the literature especially

through financial data. Therefore, its identification and the resultant performance is an

important outcome of the study. The underperforming nature of reactors is due to their

inconsistent strategic approach (Miles and Snow, 1978). Reactors often represent the

lethargic and inactive nature of organization (Conant et al., 1990), and “lack a consistent

strategy and simply respond to environmental pressures when forced to do so”, they are

frequently omitted from studies (Mcdaniel & Kolari, 1987) and due to this omission there

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is scarcity of literature that refers to the frequency with which they engage in “reactive

activity” (Blackmore & Nesbitt, 2013).

The performance of reactor strategy is generally below viable, consistent, and flexible

strategies. However, their performance varies across industries. The variation in

performance also due to different nature of performance measures. For example, in this

reactors performed better than other in one industry in terms of ROA, ROE, and ROCE

while they outperformed others in terms of ROE in three industries and in two industries

in terms of ROCE. There are evidences in prior research e.g. Snow & Hrebiniak (1980)

where reactors performed better than others in a highly regulated air transportation

industry. The findings also supported by the arguments of Zahra & Pearce (1990) in

favour of reactors as they suggested that reactor strategy is viable in randomized

/unstable and in easygoing environment where change process is of low degree.

Similarly, Conant et al. (1990) suggested that reactors do not make grand strategy rather

they respond on some situations. Jennings et al. (2003) called reactors a unique type of

strategy. The findings of this study and the performance of reactors support these views.

Keeping in view the characteristics of reactor strategy and prevalent inconsistency in

Pakistani environment, the presence and performance of reactors firms is as per our

expectation.

5.13.7 Strategy-Performance Relationship: The Contingency Effect

The influence of firm size and industry on firm performance is visible supported by the

results of the study. Overall comparative results for the performance of strategic

consistency and strategic flexibility indicate that consistency achieved higher

performance in large and medium sized firms whereas the performance of strategic

flexibility are better in small firms. These findings make sense as it is not an easy job for

large and complex organizations to switch their strategic stance quickly. Hence they stick

to the consistent approach. For small firms, flexibility is the better option to exploit the

situation by quickly adapting to the environment. The performance of reactor strategy in

small firms is the indicator of quick reactions. Comparison of the intra size performance

of strategic groups indicates that large or medium sized firms follow consistency or

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flexibility without compromising performance. Similarly, reactor strategy brought better

results for small firms. Hence, inconsistent or reactor behavior may get some benefits but

not all the time and not across the firm size. The results for goodness of fit (F-value)

showed a significant influence of size on ROA, ROS and on ROCE respectively.

Keeping small sized firms as the reference category, large firms performed significantly

different from medium and small for all performance measures.

There are interesting findings when the results for the performance of strategic types

across the industries were analyzed. Although, the performance of DA-Like firms is

better than all when compared with overall performance but there is variation when it was

investigated across industries. DA-Like outperformed others for all measures in two

industries and for two measures in three industries. Analyzers also outperformed others in

two industries in all measures. Similarly, the performance of PA-Like was better than

others for threes measures in two industries. Reactors also performed better than others in

two performance measures in two industries. DA-Like performed well in large and

medium sized industries while analyzers performed better in medium to-small industries.

Comparing the performance of strategic consistency and strategic flexibility across

industries it was found that firms with strategic consistency performed better than others

in five industries in all four performance measures whereas firms following flexible

strategies performed better than consistency in five industries for ROA and ROCE. In

two industries flexibility performed better in two industries for ROE and in six industries

for ROS. The model fit for industry-performance relationship is significant for ROA and

ROE while there are insignificant results for ROS and ROCE. Overall, the influence of

industry on two performance measures (ROA and ROE) is significant. However, within

industries, only two industries have significant influence for ROA while only one

industry has significant influence for ROE. Rest of the industries have insignificant

influence on firm performance.

The factorial ANOVA were run for combined impact of strategy, size, and industry on

performance along with the interaction or contingent effect on performance. The results

showed significant influence on overall model fit for all performance measures.

Comparing the influence of strategy, size, and industry on performance, it is proved that

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strategy is the better predictor of performance than size and industry in three out of four

performance measures. The interaction of strategy and size reveal that large analyzers

and large PA-Like firms outperformed medium and small firms while large DA-Like

outperformed small which outperformed medium DA-Like firms keeping reactors as

benchmark. For strategy and industry interaction, analyzers and DA-Like performed well

in “Information, Communication, and Transportation Services” and “Coke, and Refined

Petroleum Products” industries while prospectors in “Other Non-metallic Mineral

Products” in comparison to reactors who performed well in “Motor Vehichle, Trailors,

and Auto parts” and “Chemicals, Chemical Products, and Pharmaceutical Products”. The

results for interaction of size and strategy show that large firms performed well in “Fuel

and Energy” and “Chemicals, Chemical Products, and Pharmaceutical Products” while

medium firms in “Motor Vehicles, Trailers, and Auto parts” and “Chemicals, Chemical

Products, and Pharmaceutical Products” in comparison to small firms who performed

well in “Information, Communication, and Transportation Services” and “Other Non-

metallic Mineral Products”.

These results are also supported by the earlier research where it was argued that context

matters. Hence strategy-performance relationship is also influenced by the environmental

contingencies (Thornhill & White, 2007). As firm size influence the strategic choice

because of changes in structure, design, and other complexities linked with size of the

firm, industry can also limit managerial influence as it constrains the managers to

proactively design or implement strategy for competitive performance. These responses

to environmental shifts reshape the organizational processes minimizing the role of

managers and strategy.

5.14 Summary

The chapter presented the analysis of results, findings and discussion. The results for

distribution of strategies according to their orientation in terms of: viable versus reactors;

hybrid versus pure; and consistent and flexible versus reactors are presented and

analyzed. The contingency tables along with performance for theses strategic groups are

also presented, analyzed and discussed. The comparison of these groups is made for all

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performance measures. These grouping were also presented for each industry and for

small, medium, and large firm size. The hypotheses were tested and the results were

summarized. A detailed analysis of single industry (textile) was also made to find the

commonalities and the differences among the results.

The results for multi-industry analysis show that four out of eleven hypotheses are fully

supported by the results. Four hypotheses have major support while one have moderate

support. Only two hypotheses have minor support. Similarly for textile industry, four out

of eight hypotheses are fully supported by the results and three hypotheses have major

support. Only one hypothesis has minor support. This provides the grounds that there is a

support for the assumptions for Miles and Snow in Pakistani environment. Also, the

behavior of strategic types and their performance is comparable and generalizable for

single industry as well as for multi-industry analysis.

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Chapter 6

Summary and Conclusion

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6.1 Introduction

The chapter presents the conclusions regarding the objectives of the research, research

questions, and summary of the research. It also include the contributions of the research

for theory, researchers, and practitioners in the field of strategic management. The

limitations of the study are also mentioned along with the opportunities for future

research.

6.2 Summary of the Research

The study aimed to investigate the strategy-performance relationship in a multi-industry

setting. Literature on strategy and performance and contingency theory provides the

theoretical foundation of the research. Miles and snow typology framework was used to

operationalize and classify the strategic orientations of the firms into distinct strategic

groups such as pure and hybrid strategies, consistent, flexible, and reactor strategies.

The specific research questions were: What strategic type organizations chose in solving

their entrepreneurial, engineering, and administrative problems in Pakistan? The

subordinate questions include whether firms perusing pure strategies or hybrid strategies?

Is there consistency in their strategic stance over time or they adapt flexibility or have

inconsistent behavior? How the strategic types perform and is there any significant

difference in the performance of the firms adapting viable and reactor strategies? Is

strategic purity and consistency performing better than hybrid and flexible strategy? Why

firms perform higher than other firms although they are in same industry and are under

similar conditions? Is there any significant contingent effect of firm size and industry on

organizational strategy and performance? And what is the current state of literature

regarding strategy-performance relationships, measures, methodologies, and future

research direction?

The specific objectives of the study to address above research questions were: to develop

a detailed scoring methodology using archived financial data to operationalize the

strategic orientation of the firms by applying the typology of Miles and Snow. In

addition, pure, hybrid, consistent, and flexible strategic groups were to be identified for

extended analysis. A detailed analysis of strategic groups/types and their relationship

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with performance across firm sizes and industries is performed. A number of hypotheses

for causal relationships of strategy and performance were tested. The contingent or

interactive influence or impact of firm size and industry on strategy and performance was

also investigated.

In this context, the study examined the proportionate presence of the strategic groups

across firm size and across industries. Extensive comparative analysis of performance of

strategic groups/types in multiple industries having different firm size were also made.

Strategic groups based on Miles and Snow’s strategic types, strategic purity and

hybridization, strategic consistency and flexibility, and reactor strategy are

conceptualized and operationalized through scoring method. The presence and

performance of these strategic groups is compared across the industries and across firm

size. The methodological refinements helped in identifying and classifying the reactor

strategy which is usually left out particularly in archived and longitudinal studies.

The prior studies on Miles and Snow typology considered single industry as well as

multi-industries for analysis. One of the issues relating to single industry analysis is the

generalization of the findings while the issues with multi-industry analysis include the

differences in the structure and other dynamics relevant to specific industry due to which

the results may not reflect the true picture. For this purpose, the study analyzed the single

industry as well as multi industry results to find the commonalities and differences in the

results. Most of the findings for multi-industry analysis and single-industry analysis are

similar. However, there are some differences as well. For example, although analyzers

are dominating in both cases followed by DA-Like firms, the percentage of presence of

analyzers is more in textile industry while the percentage for DA-Like is more for multi-

industry analysis. Another difference is that the influence of firm size in textile sector is

minimum as the results are significant for only one performance measure whereas for

multi-industry, the influence of firm size is significant for all performance measures.

Similarly, large firms in multi-industry analysis performed significantly different from

small and medium sized firms while large firms’ performance is significantly different

from others in only one measure. Also, it was found that all firms in textile industry are

adapting some type of hybrid strategies. The findings provide the sufficient evidence that

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the behavior of strategic types and their performance is comparable and generalizable for

single industry as well as for multi-industry analysis.

6.2.1 Conclusion

The findings indicate that firms in Pakistan adapt hybrid strategies instead of pure

strategies. There are reasonable number of reactor firms as well. Hybrid strategies

performed better than pure strategies. The performance of the defending and analyzing or

balanced strategies is much better than the performance of prospecting strategies. In some

instance, the performance of reactors is also commendable. The firms are equally

distribute for choosing the consistency as well as flexibility in strategic choice i.e. the

number of firms adapting consistency in their strategic choice is almost equal to the firms

opting strategic flexibility. The performance of firms with strategic consistency is not

significantly different from those firms having strategic flexibility. However, both

consistency and flexibility outperformed reactors for all performance measures. Overall,

the performance of flexible strategy firms is slightly higher than the firms with

consistency in strategic choice in three performance measures while the pattern is not the

same within firm size and industries. When individual impact of strategy, size, and

industry on performance was investigated, firm size showed the highest influence on

performance followed by strategy and industry. However, strategy was the best predictor

of performance when all three factors are combined together through interactions.

In contingency predictions, the successful implementation of business strategy relies

heavily on suitable organizational design and structure. Similarly, the firm size influence

the strategic choice because of the incremental complexities linked with the firm as it

grows. Also, the industry peculiarities and dynamics effect the choice of strategic

orientation. Therefore, the choice of a strategic options should be aligned with the

demands of firm size complexities and the industry dynamics in which the firms compete

in order to achieve higher performance. Following these guidelines, this research

investigated the contingent impact of firm size and industry (considered as most

important factors by Mintzberg, 1979) on firm strategy to find out their joint effect on

organizational performance. These contingency predictions were evaluated by

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determining whether size-strategy interaction, industry-strategy interaction, and size-

industry-strategy interaction term significantly increases the level of explained variation

in a hierarchical regression analysis? The results of hierarchical regression models (Table

5.28) for all four performance measures (ROA, ROE, ROS, and ROCE) indicate the

major support for contingency relationships as the value of R2 increased significantly for

all performance measures with the addition of incremental variable in the base model.

This shows that the contingency perspective hold true in this research. The analysis for

single industry (textile industry) and multi-industry is done separately. The findings of

both analyses helped in generalizations of the results and provide the foundations for

further developments of strategy-structure-performance paradigms where strategy is

categorized as pure, hybrid, consistent, flexible, and reactors.

6.2.2 Contributions and Implications of the Study

The study brings benefits both at the theoretical level and at practical level. The

contributions and implications of the research are divided as: contributions to the

literature; contribution to the researchers; and contributions to the practitioners are

presented below:

6.2.2.1 Contributions and Implications for theory

The research extends the contemporary understanding about the typological

research. The research methodology refined for the identification and

operationalization of strategic types can be replicated where strategic groups are

to be studied. Typological classification is beneficial because the systematic

ordering of core elements of a phenomenon provides the building blocks for

future development in theory.

From a theoretical perspective, the research considers organizational strategy as a

contingency factor, which guides for organizational structure for improved

performance. Firm size and industry are other contingent factors that influence

the strategic choice of the management and also impact the performance. A direct

impact of strategy, size of the firm, and industry on performance is tested. Here,

strategy is classified into various categories such as pure strategies: defenders and

199

prospectors; hybrid strategies: DA-Like, Analyzers, and PA-Like; consistent and

flexible; and reactor strategies. These classifications of strategy and their results

for relationship with performance extends the current debate such as: does

strategic purity matters or organizations should adapt hybrid strategy; does

strategic consistency provides superior performance or organizations should

adapt flexible strategy for improved performance; does reactor is can be some

time a viable strategy or it is a residual term representing no strategy etc.

Hypothesis H2 has partial support in both multi-industry and single industry. The

theoretical implications for these findings suggest that different performance

measures may respond differently for certain strategic types. This is evidence

from the fact that the difference among the viable strategic types is significant for

ROA and ROS and insignificant for ROE and ROCE showing a visible

difference of response towards profit generation. The insignificant difference for

performance measures with capital employed whether in terms of equity or

equity plus long-term debt may imply that some firms are employing more equity

or depending more on long-term debt than required level. Similarly, hypothesis

H6 has minor support in single industry while has full support for multi-industry.

The reason for this dissimilarity can be due to the fact that since the overall

industry competition, industry peculiarities, and homogeneity of the firms’

internal and external characteristics of textile industry remain same across firm

sizes. Therefore, the variation in performance does not vary significantly with

the change in size. The impact of size on performance is different when multi-

industry data is analyzed together. Therefore, the interpretation for such

comparative results sometimes can be misleading. Theoretical discussion should

be generated why there is such behavior of one of the key internal contingent

factors.

Hypothesis H8 and H9 states that the relationships of firm size and industry with

performance are contingent on business strategy. These contingency predictions

were evaluated by determining whether size-strategy interaction, industry-

strategy interaction, and size-industry-strategy interaction term significantly

increases the level of explained variation in a hierarchical regression analysis?

200

The results for hierarchical regression models (Table 5.28), indicate the major

support for contingency relationships as the value of R2 increased significantly

for all performance measures with the addition of incremental variable in the

base model. This shows that the contingency perspective hold true in this

research.

6.2.2.2 Contributions and Implications for Researchers

The proposed scoring methodology will help the researchers in identification of

multiple strategic groups based on varying characteristics of the firms. A standard

scale is developed for classification of pure and hybrid strategic types on a

continuum. Secondly, a mechanism is developed for identification of behavior of

the firms’ strategic orientation over the time to classify the firms into consistent,

flexible, and inconsistent (reactors) types. In this context, this study is the

pioneering work in a longitudinal research. The empirical findings validate the

theoretical underpinning associated with these strategic groups.

The original work of Miles and Snow suggests four mutually exclusive and static

strategic types. In actual, the firms hybridize the pure strategies by combining the

characteristics of pure strategies. Also, when viewed through the lenses of

strategic fit, they are better presented as the changing behavior over the time. This

means that an organization reconfigures its processes and deploys resources in

reply to changes in its internal and external environment. Doing so, the

organization reposition itself into one of the viable strategic types. The

investigation of strategic transition over time and their classification into

consistent, flexible, and specifically reactors provides a new dimension to the

researchers to explore and study the strategy-performance relationship in this

context.

The conceptualization and operationalization of strategic orientation and the

investigation of their relationship with organizational performance acknowledges

the argument for the existence of multiple mutually exclusive strategic groups in

the industries. Hence, various strategic groups can be conceptualized if there exist

a theoretical support for such groups.

201

The study presents a detailed comparative analysis of single-industry and multi

industries in the same settings. The applicability of the methodology in both

settings and the conformity of most of the results provide sufficient evidence to

the researchers who study the firms in multiple industries.

6.2.2.3 Contributions and Implications for Practitioners

It is found that there are very few pure defenders and pure prospectors. This

means that firms hybridize the strategies. However, only hybridization does not

guarantee the better performance. Performance is based on creating the right fit.

Managers should understand these contingencies while creating the right fit for

higher performance.

Given the cultural and environmental characteristics and based on the

performance of certain strategic types, manages in Pakistan are recommended to

go for hybrid strategic choice. Doing so, managers should focus on stable and a

narrow product line to compete on the basis of quality, service, price, and

operational excellence. A balance of innovation and core product feature can be

fruitful for organization to generate higher performance. This can be done by

sticking to a limited but quality product line while carefully analyzing the

competitors moves.

Managers should carefully adapt strategies for new product development,

innovation, and growth. However, such initiatives can be carefully taken in

industries such as “motor vehicles, trailers, and auto parts”; “information,

communication, and transport services”; and “coke and refined petroleum

products”. Therefore, the managers should study the dynamics of these industries

while adapting the prospecting strategies.

For higher performance, managers are recommended to adapt consistent strategy

for large industries such as textile and food industries. On the other hand,

performance can be increased by adapting strategic flexibility in small to medium

level industries such as “fuel and energy” sector. These findings also provide

guidance for government and other regulatory bodies for devising policies to get

maximum fruits for economic development. For example, the government and

202

regulators should stick to a long-term policies for textile and food industries

because the strategic behavior of these industries is consistent to their previously

adapted strategic orientation. These industries are unable to reap the benefits if

the policies at government level are rapidly changing. This may be due to their

slow response or due to the heavy cost involved in the shifting from one strategy

to another in shorter period of time.

6.2.4 Limitations of the Study

There are certain limitation of the study. These limitations are:

The research only operationalized the realized strategy based on past financial

data. The intended strategy (current and future) of the management was not

considered. Hence, there are possibilities that the current intension of the

management about the future strategic orientation may not be aligned with the

strategic orientation found based on past financial data.

Only four ratios or proxies are used to measure the strategic orientation of the

firms. This may not fully reflect the real picture of the firms about their strategic

orientation. The use of more proxies, in addition to these four, may more

accurately explain the strategic orientation and behavior of the firms.

Seven year data was used for analysis and for measuring the strategic behavior of

the firms over the time. Extended data for more years can be used for more

robust results

Organizational performance was measured through financial measures only.

Subjective performance measures can be added with the objective measures to

get clearer picture of the strategic orientation and their relationship with

performance.

Presence of very few pure strategies (prospectors and defenders) makes the

generalization somewhat difficult. Therefore, the results of the study may not

reflect the true understanding about pure strategies.

203

6.2.5 Opportunities for Future Research

The opportunities for future research are:

One of the promising area for theoretical development is to have a renewed

emphasis on typology-driven theorizing. Since, typological classification is

beneficial for developing a systematic order of core elements of a phenomena

which provides the building blocks for theory development.

As suggested by Snow and Ketchen (2014), the application of typologies is

needed in both fundamental and emerging subject areas of organizations and

management. Topics related to fundamental organizational involve organization

structure and context. While the emergent management areas include the topics

that involve control and coordination mechanisms. Future research can be to

develop such typologies that can address the topic like governance structure and

industry dynamics.

More work is needed for operationalization of the strategic types into multiple

groups like was done for this study for investigating strategy-performance

relationship. Currently, this relationship is predominantly investigated through the

application of Miles and Snow Porter’s typologies where pure strategies are

studied.

The findings of the current study can be further investigated by exploratory study

of the selected firms or industries. These studies can incorporate the intention of

the management about strategy and performance, organization capabilities,

organization structure and other characteristics.

204

Chapter 7

References

205

Acquaah, M., & Yasai-Ardekani, M. (2008). Does the implementation of a combination

competitive strategy yield incremental performance benefits? A new perspective

from a transition economy in Sub-Saharan Africa. Journal of Business Research,

61(4), 346–354. https://doi.org/10.1016/j.jbusres.2007.06.021

Afza, T., & Ahmed, N. (2017). Capital Structure , Business Strategy and firm ’ s

performance : Evidence from. European Online Journal of Natural and Social

Sciences, 6(2), 302–328.

Afza, T., Slahudin, C., & Nazir, M. S. (2008). Diversification and Corporate

Performance: An Evaluation of Pakistani Firms. South Asian Journal of

Management, 15(3), 7–18. Retrieved from

http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=36788796&site=e

host-live

Afzal, S. (2009). Marketing Capability, Strategy and Business Performance in Emerging

market of Pakistan. IUB Journal of Social Sciences and Humanities, 7(2), 88–102.

Ali Ahmad SEBAA. (2010). The Importance of Aligning Managerial Characteristics to

Functional Strtegy in Public Sector Organizations: An Empirical Study of Dubai

Government. University of Bradford.

Amitabh, M., & Gupta, R. K. (2010). Research in strategy-structure-performance

construct: Review of trends, paradigms and methodologies. Journal of Management

and Organization, 16(5), 744–763.

Andrews, R. (1971). The Concept of Corporate Strategy. Homewood, III: Dow Jones -

Irwin.

Anikeeff, M. A., & Sriram, V. (1995). Strategic consistency and performance: an analysis

of real estate developers. Journal of Managerial Issues, 7(4), 435–448.

Arif, F., Azhar, N., & Bayraktar, M. E. (2012). Strategic Management Concepts and

Practices in Pakistan: A Construction Industry Perspective. In Construction

Research Congress 2012 (pp. 1530–1539).

https://doi.org/10.1061/9780784412329.154

Balsam, S., Fernando, G. D., & Tripathy, A. (2011). The impact of firm strategy on

206

performance measures used in executive compensation. Journal of Business

Research, 64(2), 187–193. https://doi.org/10.1016/j.jbusres.2010.01.006

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of

Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Baron, R. M., & Kenny, D. A. (1986). The Moderator-Mediator Variable Distinction in

Social Psychological Research: Conceptual, Strategic, and Statistical

Considerations. Alcohol and Alcoholism, 51(6), 1173–1182.

https://doi.org/10.1093/alcalc/34.2.197

Bayraktar, C. A., Hancerliogullari, G., Cetinguc, B., & Calisir, F. (2017). Competitive

strategies , innovation , and firm performance : an empirical study in a developing

economy environment. Technology Analysis and Strategic Management, 29(1), 38–

52. https://doi.org/10.1080/09537325.2016.1194973

Beard, D. W., & Dess, G. G. (1981). Corporate-Level Strategy , Business-Level Strategy

, and Firm Performance. The Academy of Management Journal, 24(4), 663–688.

Bentley, K. A., Omer, T. C., & Sharp, N. Y. (2013). Business Strategy, Financial

Reporting Irregularities, and Audit Effort. Contemporary Accounting Research,

30(2), 780–817. https://doi.org/10.1111/j.1911-3846.2012.01174.x

Best, J., & Kahn, J. (2006). Research in Education. New Dehli: Prentice Hall of India Pvt

Ltd.

Bilenas, J. V, Morgan, J. P., & Bank, C. (2009). PROC RANK and PROC

UNIVARIATE to Rank or Decile Variables. Applications Big and Small: NESUG

2009, 1–16.

Blackmore, K., & Nesbitt, K. (2013). Verifying the Miles and Snow strategy types in

Australian small- and medium-size enterprises. Australian Journal of Management,

38(1), 171–190. https://doi.org/10.1177/0312896212444692

Bo Zhang. (2011). Business Strategies , HRM Policies and Organizational Performance :

Evidence from the Peoples Republic of China by. Cardiff University.

Boyd, B. K., Haynes, K. T., Hitt, M. A., Bergh, D. D., & Ketchen, D. J. (2012).

Contingency hypotheses in strategic management research: Use, disuse, or misuse?

207

Journal of Management (Vol. 38). https://doi.org/10.1177/0149206311418662

BYCO Petrolium. (2015). Long Term Business Financing Strategy For A Pakistan

Business. Retrieved from http://www.icap.org.pk/nfo/pdf/BYCO.pdf

Chaffee, E. E. (1985). Three Models of Strategy. Academy of Management Review,

10(1), 89–98. https://doi.org/10.2307/258215

Chandler, A.D., J. (1962). Strategy and Structure. Cambridge, MA: MIT Press.

Claver-Cortés, E., Pertusa-Ortega, E. M., & Molina-Azorín, J. F. (2012). Characteristics

of organizational structure relating to hybrid competitive strategy: Implications for

performance. Journal of Business Research, 65(7), 993–1002.

https://doi.org/10.1016/j.jbusres.2011.04.012

Conant, J. S., Mokwa, M. P., & Varadarajan, P. R. (1990). Strategic Types, Distinctive

Marketing Competencies and Organizational Performance: A Multiple Measures-

Based Study. Strategic Management Journal, 11(5), 365–383.

D. Ittner, Larcker, D. F., & Rajan, M. V. (1997). The Choice Performance Measures in

Annual Bonus Contracts. The Accounting Review, 72(2), 231–255.

Daft, R. L. (2015). Organization Theory and Design (12th ed.). Cengage Learning.

Damodar Gujarati. (2011). Econometrics by Example. Hampshire RG21 6XS: Palgrave

Macmillan.

Daniel Rajaratnam, & Chonko, L. B. (1995). The Effect of Business Strategy Type on

Marketing Organization Design, Product-market Growth Strategy, Relative

Marketing Effort, and Organization Performance. Journal of Marketing Theory and

Practice, 3(3), 60–75.

Danny Miller. (1990). Icarus Paradox: How Exceptional Companies Bring About Their

own Downfall. New York: Harper Business.

Delbridge, R., & Fiss, P. C. (2013). Editors ’ Comments : Styles of Theorizing and the

Social Organization of Knowledge. Academy of Management Review, 38(3), 325–

331.

Delery, J. E., & Doty, D. H. (1996). Modes of Theorizing in Strategic Human Resource

208

Management : Tests of Universalistic , Contingency , and Configurational

Performance Predictions Author ( s ): John E . Delery and D . Harold Doty

Published by : Academy of Management Stable URL : http://www. The Academy of

Management Journal, 39(4), 802–835. https://doi.org/10.2307/256713

Desarbo, W. S., Benedetto, C. A. Di, Song, M., & Sinha, I. (2005). Revisiting the Miles

and Snow Strategic Framework: Uncovering Interrelationships between Strategic

Types , Capabilities , Environmental Uncertainty , and Firm Performance. Strategic

Management Journal, 26(1), 47–74.

DeSarbo, W. S., Grewal, R., & Wang, R. (2009). Dynamic strategic groups: deriving

spatial evolutionary paths. Strategic Management Journal, 30(13), 1420–1439.

https://doi.org/10.1002/smj.788

Donaldson, L. (1996). For Positivist Organization Theory: Proving the Hard Core.

Cambridge, MA: Cambridge University Press.

Doty, D. H., & Glick, W. H. (1994). Typologies as a Unique Form of Theory Building:

Toward Improved Understanding and Modeling. The Academy of Management

Review, 19(2), 230–251.

Durand, R., Grant, R. M., & Madsen, T. L. (2017). The Expanding Domain of Strategic

Management Research and the Quest for Integration. Strategic Management

Journal, 38(1), 4–16. https://doi.org/10.1002/smj

Eikelenboom, B. (2005). Organizational capabilities and bottom line performance :The

relationship between organizational architecture and strategic performance of

business units in Dutch headquartered multinationals. Universiteit Nyenrode.

Evans, J. D., & Green, C. L. (2000). Marketing Strategy, Constituent Influence, and

Resource Allocation: An Application of the Miles and Snow Typology to Closely

Held Firms in Chapter 11 Bankruptcy. Journal of Business Research, 50(2), 225–

231.

Farjoun, M. (2002). Towards an Organic Perspective on Strategy. Strategic Management

Journal, 23(7), 561–594. https://doi.org/10.1002/smj.239

Fehre, K., Kronenwett, D., & Lindsta, H. (2016). Lost in transaction ? The transfer effect

209

of strategic consistency. Business Research, 9(1), 101–131.

https://doi.org/10.1007/s40685-015-0024-1

Fiss, Peer, C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to

Typologies in Organization Research. Academy of Management Journal, 54(2),

393–420.

Furrer, O., & Thomas, H. (2008). The structure and evolution of the strategic

management field : A content analysis of 26 years of strategic management research.

International Journal of Management Reviews, 10(1), 1–23.

https://doi.org/10.1111/j.1468-2370.2007.00217.x

Gabrielsson, M., Seppala, T., & Gabrielsson, P. (2016). Realizing a hybrid competitive

strategy and achieving superior financial performance while internationalizing in the

high-technology market. Industrial Marketing Management, 54, 141–153.

https://doi.org/10.1016/j.indmarman.2015.07.001

Ghoshal, S. (2003). Miles and Snow : Enduring insights for managers. Academy of

Management Executive, 17(4), 109–114.

Ginsberg, A., & Venkatraman, N. (1985). Contingency Perspectives of Organizational

Strategy: A Critical Review of the Empirical Research. Academy of Management

Review, 10(3), 421–434. https://doi.org/10.2307/258125

Golder, S., Ahmad, S., Norman, G., & Booth, A. (2017). Attitudes Toward the Ethics of

Research Using Social Media: A Systematic Review. Journal of Medical Internet

Research, 19(6).

Government of Pakistan. (2015). Pakistan Textile Policy: 2014-19. Islamabad. Retrieved

from http://www.textile.gov.pk/moti/userfiles1/file/Textile Policy 2014-19.pdf

Government of Pakistan. (2017a). China Pakistan Economic Corridor (CPEC). Retrieved

August 6, 2017, from http://cpec.gov.pk/introduction/1

Government of Pakistan. (2017b). Overview of the Economy. Islamabad.

Gravelle, T. B. (2012). Using SAS to Test, Probe and Display Interaction Effects in

Linear Models. In Northeast SAS Users Group Conference (Vol. 2011, pp. 1–16). 40

University Avenue, Suite 200 Toronto, Ontario: PriceMetrix Inc. Retrieved from

210

www.lexjansen.com/nesug/nesug12/sa/sa08.pdf

Habib, A., & Hasan, M. M. (2017). Business strategy, overvalued equities, and stock

price crash risk. Research in International Business and Finance, 39(January), 389–

405. https://doi.org/10.1016/j.ribaf.2016.09.011

Hambrick, D. C. (1981). Strategic Awareness within Top Management Teams. Strategic


Hambrick, D. C. (1982). Environmental Scanning and Organizational Strategy. Strategic


Hambrick, D. C. (1983). Some Tests of the Effectiveness and Functional Attributes of

Miles and Snow’s Strateic Types. The Academy of Management Journal, 26(1), 5–

26.

Hambrick, D. C. (1984). Taxonomic Approaches to Studying Strategy : Some Conceptual

and Methodological Issues. Journal Of Management, 10(1), 27–41.

Hambrick, D. C. (2003). On the Staying Power of Defenders , Analyzers , and

Prospectors. The Academy of Management Executive, 17(4), 115–118.

Hambrick, D. C., & David Lei. (1985). Towards an Empirical Prioritization of

Contingency Variables for Business Strategy. The Academy of Management

Journal, 28(4), 763–788.

Hamel, G., & Prahalad, C. . (1989). Strategic Intent.pdf. Haevard Business Review,

(May-June), 63–76.

Haque, N. ul, Idrees, K., & Ahmed, S. (2007). Entrepreneurship in Pakistan. Islamabad.

Harrigan, K. R. (1980). The effect of exit barriers upon strategic flexibility. Strategic

Management Journal, 1(2), 165–176. https://doi.org/10.1002/smj.4250010206

Hassan Askari Rizvi. (2015). The China-Pakistan Economic Corridor: Regional

Cooperation and Socio-Economic Development. Strategic Studies, 34(4), 1–17.

Hassan, M. U., Qureshi, S. U., Sharif, S., & Mukhtar, A. (2013). Impact of Marketing

Strategy Creativity on Organizational Performance via Marketing Strategy

Implementation Effectiveness : Empirical Evidence from Pakistani Organizations.

211

Middle-East Journal of Scientific Research, 16(2), 264–273.

https://doi.org/10.5829/idosi.mejsr.2013.16.02.11641

Hatch, M. J., & Cunliffe, A. L. (2006). Organization Theory: Modern, Symbolic, and

Post Modern Perspectives. Oxford, NY: Oxford University Press.

Hatten, K. J., Schendel, D. E., & Cooper, A. C. (1978). A Strategic Model of the U.S.

Brewing Industry : 1952-1971. Academy of Management Journal, 21(4), 592–660.

Hayes, A. F., Glynn, C. J., & Huge, M. E. (2012). Cautions Regarding the Interpretation

of Regression Coefficients and Hypothesis Tests in Linear Models with Interactions.

Communication Methods and Measures, 6(1), 1–11.

https://doi.org/10.1080/19312458.2012.651415

Herhausen, D., & Morgan, R. E. (2014). A Meta-Analysis of the Antecedents and

Consequences of Strategic Flexibility. Academy of Management Best Papers

Proceedings, 1–6.

Hofer, C. W. (1975). Toward Contingency Theory of Business Strategy. The Academy of

Management Executive, 18(4), 784–810.

Hofstede, G. (2017). The Hofstede Center: Strategy, Culture, Change. Retrieved March

29, 2017, from http://geert-hofstede.com/pakistan.html

Hoskisson, R. E., Michael A. Hitt, William P. Wan, & Daphne Yiu. (1999). Theory and

research in strategic management: Swings of a pendulum. Journal of Management,

25(3), 417–456. https://doi.org/10.1177/014920639902500307

Ingram, T., Kraśnicka, T., Wronka-pośpiech, M., Gtod, G., & Gtod, W. (2016).

Relationships Between Miles and Snow Strategic Types and Organizational

Performance in Polish Production Companies. Journal of Management and Business

Administration, Central Europe, 24(1), 17–45.

https://doi.org/10.7206/jmba.ce.2450-7814.162

Ishrat Hussain. (2016). China Pakistan Econimic Corridor. Defense Journal, 19(6), 13.

James, W. L., & Hatten, K. J. (1995). Further Evidence on the Validity Self Typing

Paragraph Approach: Miles and Snow Strategic Archetypes in Banking. Strategic


212

Janjua, S. Y., & Sobia, J. (2010). The Change Drivers in Business Context : Evidence

from Pakistan. World Journal of Management, 2(3), 101–114.

Jennings, D. F., Rajaratnam, D., & Lawrence, F. B. (2003). Strategy-Performance

Relationships In Service Firms : A Test For Equifinality. Journal of Managerial

Issues, 15(2), 208–220.

Jennings, D. F., & Seaman, S. L. (1994). High and Low Levels of Organizational

Adaptation: An Empirical Analysis of Strategy, Structure, and Performance.

Strategic Management Journal, 15(6), 459–475.

Jusoh, R., & Parnell, J. a. (2008). Competitive strategy and performance measurement in

the Malaysian context: An exploratory study. Management Decision, 46(1), 5–31.

https://doi.org/10.1108/00251740810846716

Ketchen, D. J. (2003). Introduction : Raymond E . Miles and Charles C . Snow ’ s

Organizational Strategy , Structure , and Process. Academy of Management

Executive, 17(4), 95–96.

Ketchen, D. J., & Shook, J. and C. L. (1996). The Application of Cluster Analysis in

Strategi Management Research: An Analysis and Critique. Strategic Management

Journal, 17(6), 441–458.

Khan, S. N., Zeeshantahir, M., & Zafar, S. (2016). Strategy Formulation , Strategy

Content and Performance : Empirical Evidence From Private Sector. Pakistan

Business Review, 18(2), 357–376.

Kim, W. C., & Mauborgne, R. (2009). How Strategy Shapes Structure. Haevard Business

Review, (September), 1–12.

Koch, R. (2011). Financial Time Guide to Strategy: How to Create, Persue and Deliver a

Winning Strategy (Fourth). Edenburgh Gate, Harlow CM20 2JE: Pearson Education

Ltd.

Koseoglu, M. A., Topaloglu, C., Parnell, J. A., & Lester, D. L. (2013). Linkages among

business strategy , uncertainty and performance in the hospitality industry : Evidence

from an emerging economy. International Journal of Hospitality Management,

34(9), 81–91. https://doi.org/10.1016/j.ijhm.2013.03.001

213

Lamberg, J.-A., Tikkanen, H., & Nokelainen, T. (2009). Competitive Dynamics,

Strategic Consistency, and Oorganizational Survival. Strategic Management

Journal, 30(1), 45–60. https://doi.org/10.1002/smj

Lex Donaldson. (2001). The contingency theory of organizations. Thousand Oaks, CA:

Sage.

Liang, X., Musteen, M., & Datta, D. K. (2009). Strategic Orientation and the Choice of

Foreign Market Entry Mode: An Empirical Examination. Management International

Review, 49(3), 269–290. https://doi.org/10.1007/S11575-009-0143-Z

Lin, C., Tsai, H.-L., & Wu, J.-C. (2014). Collaboration strategy decision-making using

the Miles and Snow typology. Journal of Business Research, 67(9), 1979–1990.

https://doi.org/10.1016/j.jbusres.2013.10.013

Luoma, M. A. (2015). Revisiting the strategy-performance linkage: An application of an

empirically derived typology of strategy content areas. Management Decision,

53(5), 1083–1106.

Madanoglu, M., Okumus, F., & Avci, U. (2014). Building a case against strategic

equifinality: Hybrid Ideal Type Service Organizations in a Developing Country.

Management Decision, 52(6), 1174–1193. https://doi.org/10.1108/MD-03-2013-

0131

Malik, O. R., & Kotabe, M. (2009). Dynamic Capabilities , Government Policies , and

Performance in Firms from Emerging Economies : Evidence from India and

Pakistan. Journal of Management Studies, 46(3), 421–450.

Malik, S. Z. (2014). Strategic Change and Organizational Reforms: Case of a Pakistani

University. Journal of Research and Reflections in Education, 8(1), 34–47.

Manev, I. M., Manolova, T. S., Harkins, J. A., & Gyoshev, B. S. (2015). Are pure or

hybrid strategies right for new ventures in transition economies? International Small

Business Journal, 33(8), 951–973. https://doi.org/10.1177/0266242614550322

March, J. G. (1991). Exploration and Exploitation in Organizational Learning.

Organization Science, 2(1), 71–87.

Mariyani Ahmad Husairi. (2014). Imitative market Entry Strategies: The Role of

214

Strategic Orientation, Resources, Capabilities, and Absorptive Capacity. Cardiff

University.

Martín-Alcázar, F., Romero-Fernández, P. M., & Sánchez-Gardey, G. (2005). Strategic

human resource management: Integrating the universalistic, contingent,

configurational and contextual perspectives. International Journal of Human

Resource Management, 16(5), 633–659.

https://doi.org/10.1080/09585190500082519

Mary k. Coulter. (1998). Strategic Management in Action. New Jersey 074: Prentice-Hall

Inc. Upper Saddle River.

Matyusz, Z. (2012). The effect of contingency factors on the use of manufacturing

practices and operations performance. PhD Thesis. Corvinus University of

Budapest Institute.

Mcdaniel, S. W., & Kolari, J. W. (1987a). Implications Marketing Strategy and of the

Strategic Typology. Journal of Marketing, 51(4), 19–30.

Mcdaniel, S. W., & Kolari, J. W. (1987b). Marketing Strategy Implications of the Miles

and Snow Strategic Typology. Journal of Marketing, 51(4), 19–30.

Micheal Treacy, & Fred Wiersema. (1995). The Discipline of Market Leaders: Chose

Your Customers, Narrow our Focus, Dominate Your Market. Reading, MA:

Addison-Wesley.

Miles, R. E., & Snow, C. C. (1978). Organizational Strategy, Structure and Process.

New York: MacGraw Hill.

Miles, R. E., Snow, C. C., Meyer, A. D., & Coleman Jr, H. J. (1978). Organizational

Strategy, Structure and Process. Academy of Management Review, 3(3), 546–562.

Mintzberg, H. (1978). Patterns in Strategy Formation. Management Science, 24, 934–

948.

Moss, T. W., Payne, G. T., & Moore, C. B. (2014). Strategic Consistency of Exploration

and Exploitation in Family Businesses. Family Business Review, 27(1), 51–71.

https://doi.org/10.1177/0894486513504434

215

Murthi, B. P. ., Rasheed, A. A., & Goll, I. (2013). An Empirical Analysis of Strategic

Groups in the Airline Industry using Latent Class Regressions. Managerial and

Decision Economics, 34(2), 59–73. https://doi.org/10.1002/mde

Nazir, M. S., & Afza, T. (2009). Impact of Aggressive Working Capital Management

Policy on Firms ’ Profitability Impact of Aggressive Working Capital Management

Policy on Firms ’ Profitability. IUP Journal of Applied Finance, 15(8), 19–31.

Olson, E. M., Slater, S. F., & Hult, G. T. M. (2005). The Performance Implication of Fit

among Business Strategy , Marketing Organization Structure and Strategic

Behavior. Journal of Marketing, 69(3), 49–65.

Ostos, J., Hinderer, H., & Bravo, E. (2017). Relationship between the Business

Environment and Business Strategy Types : Evidence in Peruvian Companies.

Universidad & Empresa, 19(32), 61–86.

Ouakouak, M. L., & Ammar, O. (2015). How does strategic flexibility pay off in terms of

financial performance ? International Journal of Business Performance

Management, 16(4), 442–456.

Pakistan Economic Survey. (2017). Islamabad: Finance Division, Government of

Pakistan.

Park, H. M. (2009). Linear Regression Models for Panel Data Using SAS , Stata

,LIMDEP, and SPSS. Bloomington, IN 47408.

Parnell, J. A. (1997). New Evidence in the Generic Strategy and Business Performance

Debate : A Research Note. British Journal of Management, 8(2), 175–181.

Parnell, J. A. (2005). Strategic philosophy and management level. Management Decision,

43(2), 157–170. https://doi.org/10.1108/00251740510581894

Parnell, J. A. (2008). Strategy execution in emerging economies: assessing strategic

diffusion in Mexico and Peru. Management Decision, 46(9), 1277–1298.

https://doi.org/10.1108/00251740810911948

Parnell, J. A. (2010). Strategic clarity, business strategy and performance. Journal of

Strategy and Management, 3(4), 304–324.

https://doi.org/10.1108/17554251011092683

216

Parnell, J. A. (2011a). Competitive strategy orientation in Egypt and Peru. African

Journal of Business Management, 5(14), 5489–5499.

https://doi.org/10.5897/AJBM10.486

Parnell, J. A. (2011b). Strategic capabilities, competitive strategy, and performance

among retailers in Argentina, Peru and the United States. Management Decision,

49(1), 139–155. https://doi.org/10.1108/00251741111094482

Parnell, J. A., Koseoglu, M. A., Long, Z., & Spillan, J. E. (2012). Competitive Strategy,

Uncertainty, and Performance: An Exploratory Assessment of China and Turkey.

Journal of Transnational Management, 17(2), 91–117.

https://doi.org/10.1080/15475778.2012.676957

Parnell, J. A., & Lester, D. L. (2003). Towards a philosophy of strategy: reassessing five

critical dilemmas in strategy formulation and change. Strategic Change, 12(6), 291–

303. https://doi.org/10.1002/jsc.639

Parnell, J. A., Lester, D. L., Long, Z., & Köseoglu, M. A. (2012). How environmental

uncertainty affects the link between business strategy and performance in SMEs:

Evidence from China, Turkey, and the USA. Management Decision, 50(4), 546–

568. https://doi.org/10.1108/00251741211220129

Parnell, J. A., Long, Z., & Lester, D. (2015). Competitive strategy, capabilities and

uncertainty in small and medium sized enterprises (SMEs) in China and the United

States. Management Decision, 53(2), 402–431.

Parnell, J. A., & Wright, P. (1993). Generic Strategy and Performance: an Empirical Test

of the Miles and Snow Typology. British Journal of Management, 4(1), 29–36.

Pasta, D. J. (2011). Those Confounded Interactions : Building and Interpreting a Model

with Many Potential Confounders and Interactions. In SAS Global Forum (pp. 1–

13). San Francisco, CA: SAS Institute Inc. USA.

Pertusa-Ortega, E. M., Molina-AzorÃn, J. F., & Claver-Cortes, E. (2009). Competitive

Strategies and Firm Performance: a Comparative Analysis of Pure, Hybrid and

Stuck-in-the-middle Strategies in Spanish Firms. British Journal of Management,

20(4), 508–523. https://doi.org/10.1111/j.1467-8551.2008.00597.x

217

Pertusa-Ortega, E. M., Molina-Azorín, J. F., & Claver-Cortés, E. (2010). Competitive

strategy, structure and firm performance. Management Decision, 48(8), 1282–1303.

https://doi.org/10.1108/00251741011076799

Pleshko, L. P., Heiens, R. A., & Peev, P. (2014). The impact of strategic consistency on

market share and ROA. International Journal of Bank Marketing, 32(3), 176–193.

https://doi.org/10.1108/IJBM-06-2013-0057

Porter, M. E. (1980). Competitive Strategy. New York: The Free Press.

Porter, M. E., & Roach, S. S. (1996). What is Strategy ? Haevard Business Review,

(November-December), 61–78.

Premal P. Vora. (2008). Easy Rolling Statistics with PROC EXPAND Penn State

Harrisburg , Middletown , PA . SAS Global Forum 2008 Applications Development,

1–6.

Proff, H. (2000). Hybrid strategies as a strategic challenge — the case of the German

automotive industry. Omega, 28(5), 541–553. https://doi.org/10.1016/S0305-

0483(00)00018-9

Review. (2018a). Does strategic purity matter?: How strategic typology affects

organizational performance. Strategic Direction, 34(1), 22–24.

https://doi.org/10.1108/SD-10-2017-0154

Review. (2018b). How to enhance performance effectiveness: Finding the right strategy

type. Strategic Direction, 34(24–6). https://doi.org/10.1108/SD-11-2017-0169

Ritzinger, L. (2015). The China-Pakistan Economic Corridor Regional Dynamics and

China’s Geopolitical Ambitions. The National Bureau of Asian Research Journal,

10(2), 1–4.

Robbins, Stephen P., Barnwell, N. (2006). Organisation Theory: Concepts and Cases

(5th ed.). Pearson Academic.

Rozell, D. E., & Terpstra Elizbeth J. (1993). The Relationship of Staffing Practices to

Organizational Level Measures of Performance. Personnel Psychology, 46, 8–27.

Salavou, H. E. (2013). Hybrid strategies in Greece: a pleasant surprise. European

218

Business Review, 25(3), 301–314. https://doi.org/10.1108/09555341311314834

Salavou, H. E. (2015). Competitive strategies and their shift to the future. European

Business Review, 27(1), 80–99.

Sanchez, R. O. N. (1995). Strategic Competition in Product Flexibility. Strategic

Management Journal, 16(S1), 135–159.

Sarac, M., Ertan, Y., & Yucel, E. (2014). How Do Business Strategies Predict Firm

Performance ? An Investigation On Borsa Istanbul 100 Index. The Journal of

Accounting and Finance, 61(1), 121–134.

SAS Inc. (2017). About SAS. Retrieved April 1, 2016, from

https://www.sas.com/en_us/company-information.html

SBP. (2018). Summary of Foreign Investment in Pakistan. Karachi. Retrieved from

http://www.sbp.org.pk/ecodata/NetinflowSummary.pdf

Segev, E. (1989). A Systematic Comparative Analysis and Synthesis of Two Business-

Level Strategic Typologies. Strategic Management Journal, 10(5), 487–505.

Shah, S. A. M., & Amjad, S. (2011). Cultural Diversity in Pakistan : National vs

Provincial. Mediterranean Journal of Social Sciences, 2(2), 331–344.

Shah, S., Tahir, S. H., Anwar, J., & Ahmad, M. (2016). Does Size Matter in Determining

Firms ’ Performance? A Comparative Analysis of Listed Companies. City

University Research Journal, 06(02), 344–353.

Short, J. C., Jr., D. J. K., Palmer, T. B., & Hult, G. T. M. (2007). Firm Strategic Group,

and Industry Influence on Performance. Strategic Management Journal, 28(2), 147–

167.

Shortell, S. M., & Zajac, E. J. (1990). Perceptual and Archival Measures of Miles and

Snow’s Strategic Types: A Comprehensive Assesment of Reliability and Validity.

The Academy of Management Journal, 33(4), 817–832.

Slater, F., Olson, E. m., & Finnegan, C. (2011). Business strategy , marketing

organization culture , and performance. Marketting Letters, 22(3), 227–242.

https://doi.org/10.1007/sl

219

Slater, S. F., & Zwirlein, T. J. (1996). The Structure of Financial Strategy : Patterns in

Financial Decision Making. Managerial and Decision Economics, 17(3), 253–266.

Smith, K. G., Guthrie, I. P., Chen, M., Gannon, M., Olian, J., & Miller, A. (1986). Miles

and Snow ’ s Typology of Strategy , Organizational Size and Organizational

Performance. Academy of Management Proceedings, 33(1), 45–49.

Smith, K. G., Guthrie, J. P., & Chen, M. (1989). Strategy, Size and Performance.

Organization Studies, 10(1), 63–81.

Snow, C. C., & David J. Ketchen, J. (2014). Typology-Driven Theorizing : A Response

to Delbridge and Fiss. Academy of Management Review, 39(2), 231–233. Retrieved

from AR14

Snow, C. C., & Hambrick, D. C. (1980). Measuring Organizational Strategies : Some

Theoretical and Methodological Problems. Academy of Management Review, 5(4),

527–538.

Snow, C. C., & Hrebiniak, L. G. (1980). Strategy , Distinctive Competence , and

Organizational Performance. Administrative Science Quarterly, 25(2), 317–336.

State Bank of Pakistan. (2014). Financial Statements Analysis of Companies (Non-

Financial) listed at Karachi Stock Exchange. Karachi.

Susana C. S. F. Rodrigues. (2002). Business Strtegy and Organizational Performance: An

Analysis of the Portuguese Industry. University of Wolverhampton.

Tahir, M., & Anuar, M. B. A. (2015). The determinants of working capital management

and firms performance of textile sector in Pakistan. Quality Quantity, 49, 1–14.

Tansuhaj, R., & Grewal and Patriya. (2001). Building Organizational Capabilities for

Managing Economic Crisis : The Role of Market Orientation and Strategic

Flexibility. Jpurnal of Marketing, 65(2), 67–80.

Thomas, A. S., & Ramaswamy, K. (1996). Matching Managers to Strategy: Further Tests

of the Miles and Snow Typology. British Journal of Management, 7(3), 247–261.

https://doi.org/10.1111/j.1467-8551.1996.tb00118.x

Thornhill, S., & White, R. E. (2007). Stratigic Purity: A Multy-Industry Evaluation Of

220

Pure vs. Hybrid Business Strategies. Strategic Management Journal, 28(5), 553–

561. https://doi.org/10.1002/smj.606

Tripathy, J. P. (2013). Secondary data analysis: Ethical issues and challenges. Iranian

Journal of Public Health, 42(12), 1478–1479.

Ven, A. H. Van De, Ganco, M., & Hinings, C. R. (2013). Returning to the Frontier of

Contingency Theory of Organizational and Institutional Designs. The Academy of

Management Annals, 7(1), 393–440. https://doi.org/10.1080/19416520.2013.774981

Walliman, N. (2011). Research Methods (1st ed.). New York, NY 10016: Routledge.

Wasserman, N. (2008). Revisiting the Strategy , Structure , and Performance Paradigm :

The Case of Venture Capital. Organization Science, 19(2), 241–259.

Wilden, Ralf, Gudergan, Siegfried, P., Nielsen, Bo, B., & Lings, I. (2013). Dynamic

Capabilities and Performance : Strategy , Structure and Environment. Long Range

Planning, 46(1–2), 72–96.

Woodside, A. G., & Sullivan, D. P. (1999). Assessing Relationships among Strategic

Types, Distinctive Marketing Competencies, and Organizational Performance.

Journal of Business Research, 45(2), 135–146.

World Bank. (2013). Enterprise Surveys: Pakistan Country Profile 2013. Retrieved July

1, 2016, from http://data.worldbank.org/country/pakistan

World Bank. (2017). Doing Business 2017: Economy Profile 2017 Pakistan. Washington

DC. https://doi.org/10.1596/978-1-4648-0948-4

Zahra, S. A. (1987). Corporate strategic types, environmental perceptions, managerial

philosophies, and goals: An empirical study. Akron Blusiness and Economic Review,

18(2), 64–77.

Zahra, S. A., & Pearce II, J. A. (1990). Research Evidence on Miles and Snow Typology.

Journal Of Management, 16(4), 751–768.

Zamani, S., Parnell, J. A., Labbaf, H., & O’Regan, N. (2013). Strategic Change and

Decision Making in an Emerging Nation: An Exploratory Assesment of Iranian

Manufacturing Firms. Strategic Change, 22(5–6), 355–370.

221

https://doi.org/10.1002/jsc

222

Appendices

223

A1: Step-by-step SAS coding for classification of strategic types and

groups

1. SAS Data Set

A raw data set of 7 years (for example for years 2011-17), representing the characteristics

of original dataset, is prepared for the step-by-step procedure and explanation. The data

contains the information of 18 firms from 5 industries with four strategy variables, assets

for measuring size, and one performance variable. The composite score calculated

through the steps explained below for strategy variables (V1, V2, V3, and V4 for

simplicity) treated as independent variables. Sector and size are considered as contingent

variables and ROA as dependent variable (other performance variables are excluded for

simplicity). The purpose of the study is to prepare a base line for strategy-performance

relationship using different typologies, especially Miles and Snow typology, and to

investigate the impact of contingent factors on this relationship. The following code

generates the data set for this exercise:

Data test.practice;

Input Sector Firms Years V1 V2 V3 V4 Asset ROA;

Datalines;

1 1 2011 12 23 45 30 200 0.09

1 1 2012 11 22 50 31 225 0.21

1 1 2013 11 25 33 32 250 0.08

1 1 2014 10 30 45 33 250 0.17

1 1 2015 12 27 34 34 245 0.22

1 1 2016 12 30 40 35 252 0.09

1 1 2017 13 33 45 36 250 0.25

1 2 2011 32 45 21 37 155 0.21

…………

…………

5 3 2015 24 27 25 104 120 0.2

5 3 2016 24 25 30 105 100 0.24

5 3 2017 34 35 25 106 130 0.26

5 3 2014 12 24 24 107 125 0.1

;

Run;

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The data in above format prepared in any other format or software (for example, SPSS,

Excel etc) can be directly imported either through GUI procedure or through import

procedure or through “Infile” option in data command.

2. Average Calculation

The researchers use averages (simple or moving/rolling) to calculate proxies for strategy

and to smooth the variations of a time series data due to seasonal or other variations. For

moving/rolling averages, one of the most suitable SAS procedures is PROC EXPAND

(Premal P. Vora, 2008). Following code can be used to calculate the 5 years rolling

averages, for non-missing values:

\*Program for Calculating Rolling (Moving Averages)*/

Proc Expand data=test.practice out=test.ma;

convert v1 =v1ma/transformin=(setmiss 0) transformout=(movave 5);

convert v2 =v2ma/transformin=(setmiss 0) transformout=(movave 5);

…………..;

convert roa =roama/transformin=(setmiss 0) transformout=(movave 5);

By sector firms;

Run;

The “By” clause is added to calculate the rolling averages within industry of firms. For

calculating overall moving average, the “BY” clause from the above code is dropped. If

there is an even number of years then centered moving average is used. For this, the key

word “movave” is replaced by “cmovave”. For our purpose, following PROC SQL code

is used to calculate the simple averages, rounded off to 2 decimal points, for each firm

within an industry.

\*Calculation of Simple Averages*/

Proc SQL;

Create Table test.avg as

Select Sector,firms,

round(mean(v1),0.001) as V1,round(mean(v2),0.001) as V2,

225

round(mean(v3),0.001) as V3,round(mean(v4),0.001) as V4,

round(mean(asset),0.001) as Asset,round(mean(ROA),0.001) as ROA

From test.practice

Group by sector, firms;

Quit;

3. Rank Calculation

The ranking is done based on the theoretical foundations for each selected variable. For

example, in our raw data set variables: V1, V2, V3, and V4 refer to the variables selected

for measuring strategic orientation. As evidenced from the previous research, it is

supposed that prospectors score high for V1, V2, and V3 and low score for V4.

Therefore, reverse ranking will be calculated for V4. PROC RANK procedure of SAS

(Bilenas et al., 2009) facilitates to rank variables according to their demand. The

following code generate ranking and reverse ranking. For this purpose, quintiles are used

to divide the data into five bins. The codes for this purpose are written below:

\*Program for Data Sorting, Calculating Ranks and Reverse Ranks, and Merging of

Files*/

Proc Sort Data=test.avg;

By sector firms;

Run;

Proc Rank data=test.avg Out=quantiles Groups=5;

By sector; Var V1 V2 V3; Ranks R1 R2 R3;

Run;

Proc Rank Data=test.avg out=quantiles1 Descending Groups=5;

By sector; Var V4; Ranks R4;

Run;

Proc Rank Data=test.avg out=quantiles2 Groups=3;

Var asset; Ranks RA;

Run;

Proc SQL;

Create Table size as select RA as Size from quantiles2;

Quit;

Proc SQL;

Create Table merge as Select a.sector, a.firms, a.v1, a.v2, a.v3, b.v4, a.asset,a.roa,

a.r1,a.r2,a.r3,b.r4, a.r1+a.r2+a.r3+b.r4 as Strategy

From quantiles a, quantiles1 b

Where a.sector=b.sector and a.firms=b.firms;

Quit;

Data test.main;

Merge test.merge size;

Run;

226

The above coding sorts the data in ascending order. The firms are sorted within sectors.

The next step is to calculate, within sectors, the ranking of first three strategy variables in

ascending order. This is done for standardization of scores across the four strategy

variables. The ranking is done for size calculation as well. The last two steps are used to

merge the required information in one table. The ranking is calculated keeping in view

the firm’s standing in comparison to its industry. For calculating the ranking of a firm in

comparison to the overall economy, the “by sector” clause from the above codes is to be

removed.

4. Categorization of Firms

The next step is to categorize the firms according to their strategic orientation and

according to the size of the firm. The following codes accomplish this task:

\*Program for Categorization of Firms*/

Data test.strategy;

Set test.Main;

If strategy>=13 then Orientation="Prospectors";

Else if strategy<=3 then Orientation="Defenders";

Else if strategy in (4 5 6) then Orientation="DA-Like";

Else if strategy in (7 8 9) then Orientation="Analyzers";

Else Orientation="PA-Like";

If Size=0 then Sz='Small ';

Else if size=1 then Sz='Medium';

Else Sz="Large ";

Run;

The outcome of the codes written in above sections, produce the data set having averaged

values for strategy and performance variables, ranking for strategy variables and for

assets, total score of strategy variables and categorization of the firms according to

respective strategic type and size are presented in Table 4.4.

5. Comparison of Strategies over time and identification of Consistent, Flexible and

Reactor Strategy

For this purpose, the procedure adopted for identification of strategic types using average

data for all seven years is repeated for classification of strategic orientation for multiple

points in time. For this study, strategic orientation at three points in time (2014, 2015, and

2016) is identified using preceding 5 years average data and compared with overall

227

strategic stance of the firms based on 7 years averages. The identification of the behavior

of the firms over the time is an important outcome of the study. This identification

process helped in not only the identification of reactor strategy (which is generally

ignored in such studies) but it also helped in finding the important strategic behavior of

the firms in terms of strategic consistency and strategic flexibility. These grouping will

help in studying the relationship of these strategic groups and firm performance.

Following codes are used for combining this important outcome. The resultant outcome

of the strategic orientation of the firms over the time is presented in Table 4.5.

\*Program for Strategic Orientation Over Time */

PROC SQL;

create table st1 as select Code,EcoGroup,OrgName, Strategy1

from test.spoint1;


from test.spoint2;


from test.spoint3;

create table stAll as select Code,EcoGroup,OrgName, StOverall,

TA, ROA,ROE, ROS,ROCE

from test.Soverall;

QUIT;

DATA test.compare;

merge st1 st2 st3 stAll;

RUN;

DATA test.strategy;

SET test.compare;

s1=strategy1;

s2=strategy2;

s3=strategy3;

s4=stOverall;

if s1=s2 and s2=s3 and s3=s4 then Strategy=s1;

else if s1 ne s2 and (s1=s3 and s1=s4)then Strategy=s1;







228






Else Strategy="Reactors";

run;

PROC SQL;

create table StFinal as select EcoGroup, OrgName, s1,s2,s3,s4,

Strategy

from test.strategy;

QUIT;

6. Data Analysis Techniques

a. Descriptive Statistics

The descriptive statistics such as number of observations, minimum, maximum, mean,

median, standard deviation, quartiles, quintiles, skewness, kurtosis, confidence interval,

etc can be calculated by using a number of SAS procedures. Procedures like “PROC

SUMMARY”, “PROC MEANS” and “PROC UNIVARIATE” etc provide descriptive

information by using their respective options and formats. Generally, these procedures

are used to find out the Univariate statistics. For example, the five-number summary can

be obtained by PROC MEANS using the following code;

\*Program for Summary Statistics*/

ODS graphics on;

ODS listing close;

ODS rtf file="e:\test.rtf";

Proc Means Data=test.main1 Min Mean Max Q1 Median Q3;

Var Strategy Size ROA;

Run;

ODS graphics off;

ODS listing;

ODS rtf close;

The starting lines of the above code control the Output Delivery Syatem (ODS) and are

used to save the outcome of the codes to a separate file for permanent use. This is helpful

in formatting the output tables, graphs and other statistics for using in other documents.

229

Bivariate statistics can be obtained by using “PROC FREQ”, a very power full procedure

to get information of contingency tables (cross classification), Chi-Square etc. PROC

FREQ is well suited to dealing with nominal or ordinal data. It is useful for tabulating

frequencies of occurrences in each category, while simultaneously converting frequencies

into proportions. If we need the contingency table of various strategic types distributed

among small, medium, and large organizations, Pearson Chi-Square, and Fisher Exact

test, the following small code is used to generate this useful information.

\*Program for Frequency/Contingency Tables*/

Proc Freq data=test.main1;

Table sz*st/chisq exact relrisk;

Run;

Quit;

The output of the above code provides the information regarding size and strategy type

classification in cross tabulation form. Similarly, other explanations can be made based

on the output of the above coding.

Following simple codes in SAS provide the necessary and useful insights to solve the

Univariate and multivariate models.

\*Program for strategy effect on performance –One Way ANOVA*/

ODS graphics on;

ODS listing close;

ODS rtf file="e:\anova1.rtf";

PROC GLM data=test.master1;

CLASS strategy;

MODEL roa roe ros roce=strategy/ solution ss3;

RUN;

ODS graphics off;

ODS listing;

ODS rtf close;

\*Program for Effect of Size, Strategy, and Size*Strategy on Performance –Factorial

ANOVA*/

230

ODS graphics on;

ODS listing close;

ODS rtf file="e:\anova2.rtf";

PROC GLM data=test.master1;

CLASS strategy Size Industry;

MODEL roa roe ros roce=strategy|size|industry/ solution ss3;

RUN;

ODS graphics off;

ODS listing;

ODS rtf close;

231

A2: Firms Strategic Orientation: Industry Wise

Industry

Codes

Industry Name

Firm Code and

Name

Transition of the Firms Over the Time to measure the

Strategic Behavior of the firms

Final

Category

Strategic

Behavior

Firm

Size

Secto

r

Su

b

Secto

r

2011 2012 2013 Overall

A A1

Spinning, Weaving,

Finishing of Textiles

330011 - Crescent

Cotton Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340002 - (Colony)

Thal Textile Mills

Ltd. DA-LIKE Analyzers Analyzers DA-LIKE Reactors Reactor Small

A A1

Spinning, Weaving,


340007 - Ahmed

Hassan Textile Mills

Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Medium

A A1

Spinning, Weaving,


340015 - Ali Asghar

Textile Mills Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340017 - Allawasaya

Textile & Finishing

Mills Ltd. Analyzers Analyzers DA-LIKE DA-LIKE Reactors Reactor Small

A A1

Spinning, Weaving,


340018 - Al-Qadir

Textile Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340026 - Apollo

Textile Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340027 - Artistic

Denim Mills Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Large

A A1

Spinning, Weaving,


340029 - Ashfaq

Textile Mills Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Small

A A1 Spinning, Weaving, 340030 - Asim

DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

232

Finishing of Textiles Textile Mills Ltd.

A A1

Spinning, Weaving,


340032 - Ayesha

Textile Mills Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340034 - Azgard

Nine Ltd.(Legler-

Nafees Denim Mills

Ltd.) Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

A A1

Spinning, Weaving,


340035 - Babri


A A1

Spinning, Weaving,


340039 - Bhanero

Textile Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340040 - Bilal Fibres

Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Small

A A1

Spinning, Weaving,


340041 - Blessed

Textiles Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340045 - Chakwal

Spinning Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

A A1

Spinning, Weaving,

Finishing of Textiles 340048 - Chenab Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

A A1

Spinning, Weaving,


340049 - Colony

Mills Ltd. (Colony

Textile Mills Ltd.) Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

A A1

Spinning, Weaving,


340051 - Crescent

Fibers Ltd. (Crescent

Boards Ltd.) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340056 - D.M.

Textile Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

233

A A1

Spinning, Weaving,


340059 - Dar Es

Salaam Textile Mills

Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

A A1

Spinning, Weaving,


340061 - Dawood

Lawrencepur Ltd.

(Dawod Coton Mills) Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

A A1

Spinning, Weaving,


340062 - Dewan

Farooque Spinning

Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340063 - Dewan

Khalid Textile Mills

Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small

A A1

Spinning, Weaving,


340064 - Dewan

Mushtaq Textile

Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340066 - Dewan

Textile Mills Ltd. PA-LIKE Analyzers Analyzers PA-LIKE Reactors Reactor Medium

A A1

Spinning, Weaving,


340069 - Din Textile

Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340070 - Elahi

Cotton Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340073 - Ellcot

Spinning Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340075 - Faisal

Spinning Mills Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340078 - Fateh

Textile Mills Ltd. Analyzers Analyzers PA-LIKE PA-LIKE Reactors Reactor Large

234

A A1

Spinning, Weaving,


340079 - Fatima

Enterprises Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

A A1

Spinning, Weaving,


340081 - Fazal Cloth

Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

A A1

Spinning, Weaving,


340082 - Fazal

Textile Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340084 - Gadoon

Textile Mills Ltd. PA-LIKE Analyzers Analyzers PA-LIKE Reactors Reactor Large

A A1

Spinning, Weaving,


340087 - Ghazi

Fabrics International

Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340088 - Glamour

Textile Mills Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340089 - Globe

Textile Mills (OE)

Ltd. Analyzers Analyzers Analyzers PA-LIKE Analyzers Flexible Small

A A1

Spinning, Weaving,


340092 - Gulistan

Spinning Mills Ltd. Analyzers PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Medium

A A1

Spinning, Weaving,


340093 - Gulistan

Textile Mills Ltd. Analyzers Prospectors PA-LIKE PA-LIKE Reactors Reactor Large

A A1

Spinning, Weaving,


340094 - Gulshan

Spinning Mills Ltd. Analyzers PA-LIKE PA-LIKE Analyzers Reactors Reactor Medium

A A1

Spinning, Weaving,


340100 - Hala

Enterprises Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

A A1

Spinning, Weaving,


340102 - Hamid


A A1 Spinning, Weaving, 340107 - Husein

PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

235

Finishing of Textiles Industries Ltd.

A A1

Spinning, Weaving,


340110 - ICC

Textiles Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

A A1

Spinning, Weaving,


340111 - Ideal

Spinning Mills Ltd. PA-LIKE Analyzers Analyzers DA-LIKE Reactors Reactor Small

A A1

Spinning, Weaving,


340112 - Idrees

Textile Mills Ltd. DA-LIKE DA-LIKE Analyzers Analyzers Reactors Reactor Medium

A A1

Spinning, Weaving,


340113 - Indus

Dyeing &

Manufacturing Co.

Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

A A1

Spinning, Weaving,


340118 - Ishaq

Textile Mills Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340119 - Ishtiaq

Textile Mills Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

A A1

Spinning, Weaving,


340120 - Island

Textile Mills Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Medium

A A1

Spinning, Weaving,


340122 - J.A. Textile

Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340123 - J.K.

Spinning Mills Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340124 - Janana De

Malucho Textile


A A1

Spinning, Weaving,


340125 - Jubilee

Spinning & Weaving

Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

236

A A1

Spinning, Weaving,


340130 - Khalid Siraj

Textile Mills Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

A A1

Spinning, Weaving,


340133 - Kohat

Textile Mills Ltd. DA-LIKE Analyzers Analyzers DA-LIKE Reactors Reactor Small

A A1

Spinning, Weaving,


340139 - Kohinoor

Spinning Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340140 - Kohinoor

Textile Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

A A1

Spinning, Weaving,


340151 - Mahmood

Textile Mills Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Large

A A1

Spinning, Weaving,


340152 - Maqbool

Textile Mills Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340154 - Masood

Textile Mills Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

A A1

Spinning, Weaving,


340157 - Mian

Textile Industries


A A1

Spinning, Weaving,


340166 - N.P.

Spinning Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

A A1

Spinning, Weaving,


340167 - Nadeem

Textile Mills Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340169 - Nagina


A A1

Spinning, Weaving,


340170 - Feroze1888

Mills Ltd. PA-LIKE Analyzers PA-LIKE PA-LIKE PA-LIKE Flexible Large

A A1

Spinning, Weaving,


340177 - Nishat

(Chunian) Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

237

A A1

Spinning, Weaving,


340178 - Nishat

Mills Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

A A1

Spinning, Weaving,


340184 - Olympia

Spinning & Weaving

Mills Ltd. Analyzers DA-LIKE DA-LIKE DA-LIKE DA-LIKE Flexible Medium

A A1

Spinning, Weaving,


340189 - Paramount

Spinning Mills Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340193 - Premium

Textile Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340194 - Prosperity

Weaving Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340196 - Quality

Textile Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340197 - Quetta

Textile Mills Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Large

A A1

Spinning, Weaving,


340201 - Redco

Textiles Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340203 - Reliance

Cotton Spinning

Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340204 - Reliance

Weaving Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

A A1

Spinning, Weaving,


340205 - Resham

Textile Industries


A A1

Spinning, Weaving,


340206 - Ruby

Textile Mills Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

238

A A1

Spinning, Weaving,


340210 - Safa

Textiles Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small

A A1

Spinning, Weaving,


340211 - Saif Textile


A A1

Spinning, Weaving,


340215 - Salfi

Textile Mills Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340216 - Sally


A A1

Spinning, Weaving,


340217 - Salman

Noman Enterprises

Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340218 - Samin

Textiles Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340219 - Sana

Industries Ltd. DA-LIKE DA-LIKE DA-LIKE Analyzers DA-LIKE Flexible Small

A A1

Spinning, Weaving,


340220 - Sapphire

Fibres Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

A A1

Spinning, Weaving,


340221 - Sapphire

Textile Mills Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

A A1

Spinning, Weaving,


340222 - Sargodha

Spinning Mills Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

A A1

Spinning, Weaving,


340223 - Saritow

Spinning Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

A A1

Spinning, Weaving,


340226 - Service

Industries Textiles

Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Small

A A1 Spinning, Weaving, 340227 - Shadab

DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

239

Finishing of Textiles Textile Mills Ltd.

A A1

Spinning, Weaving,


340228 - Shadman

Cotton Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340232 - Shahtaj

Textile Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340233 - Shahzad

Textile Mills Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Medium

A A1

Spinning, Weaving,


340234 - Shams

Textile Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340240 - Sunrays

Textile Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340243 - Suraj

Cotton Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

A A1

Spinning, Weaving,


340247 - Tata Textile

Mills Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340252 - The

Crescent Textile

Mills Ltd. Prospectors PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Large

A A1

Spinning, Weaving,


340254 - Towellers

Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

A A1

Spinning, Weaving,


340264 - Yousaf

Weaving Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

A A1

Spinning, Weaving,


340266 - Zahidjee

Textile Mills Ltd. Analyzers PA-LIKE PA-LIKE Analyzers Reactors Reactor Medium

A A1

Spinning, Weaving,


340271 - Zephyr

Textiles Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

240

A A2 Made-up textile articles

340028 - Aruj

Garment Accessories

Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small


340091 - Gul Ahmed

Textile Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large


340117 -

International

Knitwear Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small


340149 - Liberty

Mills Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Large


340162 - Moonlite

(Pak) Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Small

A A3 Other textiles n.e.s.

340012 - Al-Abid

Silk Mills Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Large


340037 - Bannu

Woollen Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small


340052 - Crescent

Jute Products Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small


340108 - Ibrahim

Fibres Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large


340188 - Pakistan

Synthetics Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Medium


340207 - RuPA-Likei

Polyester Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Medium


340239 - Suhail Jute

Mills Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Small

A A3 Other textiles n.e.s. 340253 - The

National Silk & DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Small

241

Rayon Mills Ltd.

B B1 Sugar

330001 - Abdullah

Shah Ghazi Sugar

Mills Ltd.(Al-Asif

Sugar Mills Ltd.) Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

B B1 Sugar

330002 - Adam

Sugar Mills Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Medium

B B1 Sugar

330003 - Al-Abbas

Sugar Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

B B1 Sugar

330005 - Al-Noor

Sugar Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

B B1 Sugar

330009 - Chashma

Sugar Mills Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

B B1 Sugar

330012 - Dewan

Sugar Mills Ltd. Analyzers Analyzers Analyzers DA-LIKE Analyzers Flexible Large

B B1 Sugar

330013 - Faran Sugar

Mills Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Medium

B B1 Sugar

330015 - Habib -

ADM Ltd.( Habib

Arkady LTD.) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

B B1 Sugar

330016 - Habib

Sugar Mills Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

B B1 Sugar

330018 - Haseeb

Waqas Sugar Mills

Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Medium

B B1 Sugar

330019 - Husein

Sugar Mills Ltd. Analyzers DA-LIKE DA-LIKE DA-LIKE DA-LIKE Flexible Small

242

B B1 Sugar

330020 - JDW Sugar

Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

B B1 Sugar

330021 - Khairpur

Sugar Mills Ltd. DA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Medium

B B1 Sugar

330022 - Kohinoor

Sugar Mills Ltd. PA-LIKE PA-LIKE PA-LIKE Analyzers PA-LIKE Flexible Medium

B B1 Sugar

330023 - Mehran

Sugar Mills Ltd. Analyzers PA-LIKE Analyzers PA-LIKE Reactors Reactor Medium

B B1 Sugar

330025 - Mirpurkhas

Sugar Mills Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

B B1 Sugar

330026 - Mirza

Sugar Mills Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

B B1 Sugar

330027 - Noon Sugar


B B1 Sugar

330028 - Pangrio

Sugar Mills Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

B B1 Sugar

330029 - Sakrand

Sugar Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Medium

B B1 Sugar

330031 - Sanghar

Sugar Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

B B1 Sugar

330032 - Shahmurad

Sugar Mills Ltd. PA-LIKE PA-LIKE DA-LIKE Analyzers Reactors Reactor Medium

B B1 Sugar

330033 - Shahtaj

Sugar Mills Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

B B1 Sugar

330034 - Shakarganj

Mills Ltd. PA-LIKE PA-LIKE PA-LIKE Analyzers PA-LIKE Flexible Large

243

B B1 Sugar

330035 - Sindh

Abadgar'S Sugar

Mills Ltd. Analyzers Analyzers PA-LIKE PA-LIKE Reactors Reactor Small

B B1 Sugar

330036 -

Tandlianwala Sugar

Mills Ltd. Analyzers PA-LIKE Analyzers PA-LIKE Reactors Reactor Large

B B1 Sugar

330038 - The

Premier Sugar Mills

& Distillery Co. Ltd. PA-LIKE Prospectors Prospectors Prospectors Prospectors Flexible Medium

B B1 Sugar

330039 - The Thal

Industries

Corporation Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

B B2 Other food products n.e.s

300010 - Ismail

Industries Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Large


300016 - Mitchell's

Fruit Farms Ltd. Analyzers Analyzers DA-LIKE DA-LIKE Reactors Reactor Small


300019 - Murree

Brewery Co. Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium


300021 - National

Foods Ltd. Analyzers PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Medium


300022 - Nestle

Pakistan Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Large


300023 - Noon

Pakistan Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Small


300027 - Punjab Oil

Mills Ltd. PA-LIKE Analyzers Analyzers PA-LIKE Reactors Reactor Small


300028 - Quice Food

Industries Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small

244


300030 - Rafhan

Maize Products Co.

Ltd. Analyzers Analyzers Analyzers PA-LIKE Analyzers Flexible Large


300031 - S.S. Oil

Mills Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Small


300035 - Shezan

International Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360002 - Abbott

Laboratories

(Pakistan) Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360004 - Bawany Air

Products Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360005 - Berger

Paints Pakistan Ltd. Analyzers PA-LIKE PA-LIKE Analyzers Reactors Reactor Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360006 - Biafo

Industries Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360007 - Linde

Pakistan Ltd.

(Former BOC

Pakistan Ltd.) DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Medium

245

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360008 - Buxly

Paints Ltd. PA-LIKE PA-LIKE Prospectors PA-LIKE PA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360009 - Clariant

Pakistan Ltd. ( Now

Archroma Pakistan

Ltd. ) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360010 - Colgate-

PA-Likemolive

(Pakistan) Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360011 - Data Agro

Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360012 - Dawood

Hercules Chemicals


C C

Chemicals, Chemical

Products and

Pharmaceuticals

360013 - Descon

Chemicals (Pvt) Ltd.

(Nimir Resins Ltd.) PA-LIKE PA-LIKE DA-LIKE PA-LIKE PA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360015 - Dynea

Pakistan Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

246

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360016 - Engro

Corporation Ltd.

(Engro Chemical

Pakistan Ltd.) Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360018 - Fauji

Fertilizer Bin Qasim

Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360019 - Fauji

Fertilizer Co. Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360020 - Ferozsons

Laboratories Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360022 -

Glaxosmithkline

(Pakistan) Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360023 - Highnoon

Laboratories Ltd. Analyzers Analyzers DA-LIKE DA-LIKE Reactors Reactor Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360024 - ICI

Pakistan Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

247

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360025 - Ittehad

Chemicals Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360028 - Leiner Pak

Gelatine Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360029 - Lotte

Pakistan PTA

Ltd.(Pakistan PTA

Ltd.) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360030 - Nimir

Industrial Chemicals

Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360033 - Otsuka

Pakistan Ltd. Analyzers Analyzers DA-LIKE DA-LIKE Reactors Reactor Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360034 - Pakistan

Gum & Chemicals

Ltd. PA-LIKE PA-LIKE PA-LIKE Analyzers PA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360041 - Sanofi-

aventis Pakistan Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Medium

248

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360042 - Sardar

Chemical Industries

Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360043 - Searle

Pakistan Ltd. Analyzers PA-LIKE PA-LIKE Analyzers Reactors Reactor Medium

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360044 - Shaffi

Chemical Industries

Ltd. Prospectors PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360047 - Sitara

Chemical Industries

Ltd. DA-LIKE Defenders DA-LIKE DA-LIKE DA-LIKE Flexible Large

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360051 - Wah Nobel

Chemicals Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Small

C C

Chemicals, Chemical

Products and

Pharmaceuticals

360052 - Wyeth

Pakistan Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

D D

Other Manufacturing

n.e.s.

320001 - Khyber

Tobacco Co. Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

249

D D

Other Manufacturing

n.e.s.

320002 - Philip

Morris (Pakistan)

Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Large

D D

Other Manufacturing

n.e.s.

320003 - Pakistan

Tobacco Co. Ltd. DA-LIKE DA-LIKE DA-LIKE Defenders DA-LIKE Flexible Large

D D

Other Manufacturing

n.e.s.

350001 - Al-Khair

Gadoon Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small

D D

Other Manufacturing

n.e.s.

350002 - Bata


D D

Other Manufacturing

n.e.s.

350003 - Eco Pack

Ltd.( Plastobag Ltd.) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

D D

Other Manufacturing

n.e.s.

350004 - Leather Up

Ltd. Prospectors Prospectors PA-LIKE PA-LIKE Reactors Reactor Small

D D

Other Manufacturing

n.e.s.

350005 - MACPAC

Films Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Small

D D

Other Manufacturing

n.e.s.

350008 - Pak Leather

Crafts Ltd. Analyzers PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Small

D D

Other Manufacturing

n.e.s.

350009 - Service

Industries Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

250

D D

Other Manufacturing

n.e.s.

350011 - Tri-Pack

Films Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

D D

Other Manufacturing

n.e.s.

360045 - Shield

Corporation

Ltd.(Transpak Corp.) Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Small

D D

Other Manufacturing

n.e.s.

360054 - ZIL Ltd.

(Zulfeqar Industries

Ltd.) Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Small

D D

Other Manufacturing

n.e.s.

380013 - Emco

Industries Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

D D

Other Manufacturing

n.e.s.

400011 - Crescent

Steel & Allied

Products Ltd. Analyzers DA-LIKE Analyzers Analyzers Analyzers Flexible Large

D D

Other Manufacturing

n.e.s.

400012 - Dadex

Eternit Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Medium

D D

Other Manufacturing

n.e.s.

400025 - Huffaz

Seamless Pipe

Industries Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Large

D D

Other Manufacturing

n.e.s.

400027 -

International

Industries Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

D D

Other Manufacturing

n.e.s.

400029 - KSB Pumps

Co. Ltd. Analyzers Analyzers Analyzers PA-LIKE Analyzers Flexible Medium

251

D D

Other Manufacturing

n.e.s.

400038 - Pakistan

Engineering Co. Ltd. DA-LIKE DA-LIKE DA-LIKE Analyzers DA-LIKE Flexible Large

D D

Other Manufacturing

n.e.s.

410002 - Siddiqsons

Tin Plate Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Medium

D D

Other Manufacturing

n.e.s.

420005 - Gillette

Pakistan Ltd. PA-LIKE Analyzers PA-LIKE PA-LIKE PA-LIKE Flexible Small

D D

Other Manufacturing

n.e.s.

420006 - Grays Of

Cambridge (Pakistan)

Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Small

D D

Other Manufacturing

n.e.s.

420010 - Treet

Corporation Ltd. PA-LIKE PA-LIKE Analyzers PA-LIKE PA-LIKE Flexible Medium

D D

Other Manufacturing

n.e.s.

460001 - Goodluck

Industries Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Small

D D

Other Manufacturing

n.e.s.

460004 - United

Brands Ltd.(Udl

Industries Ltd.) Prospectors PA-LIKE PA-LIKE Prospectors Reactors Reactor Small

E E1 Cement

380001 - Power

Cement (Former Al-

Abbas Cement

Industries Ltd.) Prospectors PA-LIKE Analyzers Prospectors Reactors Reactor Large

E E1 Cement

380002 - Attock

Cement Pakistan Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

252

E E1 Cement

380004 - Bestway

Cement Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

E E1 Cement

380006 - Cherat

Cement Co. Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

E E1 Cement

380007 - D.G. Khan

Cement Co. Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Large

E E1 Cement

380010 - Dandot

Cement Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

E E1 Cement

380011 - Dewan

Cement Ltd.

(Pakland Cement

Ltd.) DA-LIKE Analyzers PA-LIKE DA-LIKE Reactors Reactor Large

E E1 Cement

380015 - Fauji

Cement Co. Ltd. DA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Large

E E1 Cement

380016 - Fecto

Cement Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium

E E1 Cement

380024 - Kohat

Cement Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

E E1 Cement

380025 - Lafarge

Pak. Cement Ltd.

(Pakistan Cement

Ltd.) PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Large

253

E E1 Cement

380027 - Lucky

Cement Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Large

E E1 Cement

380028 - Maple Leaf

Cement Factory Ltd. PA-LIKE Analyzers DA-LIKE PA-LIKE Reactors Reactor Large

E E1 Cement

380030 - Mustehkam

Cement Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

E E1 Cement

380034 - Pioneer

Cement Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Large

E E2 Mineral products

380003 - Balochistan

Glass Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium


380018 - Frontier

Ceramics Ltd. Prospectors Prospectors Analyzers Prospectors Prospectors Flexible Small


380019 - Ghani Glass



380023 - Karam

Ceramics Ltd. DA-LIKE Defenders Defenders Defenders Defenders Flexible Small


380037 - Shabbir

Tiles And Ceramics

Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

254


380038 - Tariq Glass

Industries Ltd. DA-LIKE Analyzers PA-LIKE Analyzers Reactors Reactor Medium

F F

Motor Vehicles, Trailers

& Autoparts

370001 - Al-Ghazi

Tractors Ltd. DA-LIKE Analyzers DA-LIKE DA-LIKE DA-LIKE Flexible Large

F F


& Autoparts

400003 - Agriauto

Industries Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

F F


& Autoparts

400004 - Atlas

Engineering Ltd.

(Allwin Engineering

Industries Ltd.) DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

F F


& Autoparts

400005 - Atlas

Honda Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

F F


& Autoparts

400007 - Baluchistan

Wheels Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

F F


& Autoparts

400008 - Bela

Automotives Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Small

F F


& Autoparts

400010 - Bolan

Castings Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Small

F F


& Autoparts

400016 - Exide


255

F F


& Autoparts

400018 - The

General Tyre &

Rubber Co. of Pak

Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Large

F F


& Autoparts

400019 - Ghandhara

Industries Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Medium

F F


& Autoparts

400021 - Ghandhara

Nissan Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

F F


& Autoparts

400022 - Ghani

Automobiles

Industries Ltd. Prospectors Prospectors Prospectors Prospectors Prospectors Consistent Small

F F


& Autoparts

400023 - Hinopak

Motors Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

F F


& Autoparts

400024 - Honda

Atlas Cars (Pakistan)

Ltd. DA-LIKE DA-LIKE Analyzers Analyzers Reactors Reactor Large

F F


& Autoparts

400026 - Indus

Motor Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

F F


& Autoparts

400031 - Millat

Tractors Ltd. PA-LIKE PA-LIKE Analyzers Analyzers Reactors Reactor Large

F F


& Autoparts

400036 - Pak Suzuki

Motor Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

256

F F


& Autoparts

400046 - Sazgar

Engineering Works

Ltd. PA-LIKE Analyzers PA-LIKE PA-LIKE PA-LIKE Flexible Small

F F


& Autoparts

400054 -

Transmission

Engineering

Industries Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

F F


& Autoparts

440002 - Atlas

Battery Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

G G Fuel and Energy Sector

440013 - Ideal

Energy Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small


440014 - Japan

Power Generation

Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large


440015 - K-Electric

(formerly KESC) Prospectors Prospectors PA-LIKE PA-LIKE Reactors Reactor Large


440016 - Kohinoor

Energy Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large


440019 - Kot Addu

Power Co. Ltd. DA-LIKE Analyzers DA-LIKE Analyzers Reactors Reactor Large


440021 - Mari Gas

Co. Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large


440023 - Oil & Gas

Development Co.

Ltd. (OGDC) Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large


440031 - Sitara

Energy Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Medium

257


440033 - Sui

Northern Gas

Pipelines Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large


440034 - Sui

Southern Gas Co.



440035 - The Hub

Power Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

H H

Information and

Communication Services

480001 - Pakistan

Int. Container

Terminal Ltd. DA-LIKE Defenders DA-LIKE DA-LIKE DA-LIKE Flexible Large

H H

Information and


480002 - Pakistan

International Airlines

Corporation Ltd. PA-LIKE PA-LIKE DA-LIKE Analyzers Reactors Reactor Large

H H

Information and


480003 - Pakistan

National Shipping

Corporation. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Large

H H

Information and


490002 - Hum

Network Ltd.

(formerly EYE

Television Network) Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small

H H

Information and


490004 - Netsol

Technologies Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Medium

H H

Information and


490006 - Pak

Datacom Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Small

258

H H

Information and


490007 - Pakistan

Telecommunication

Co. Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

H H

Information and


490009 - Telecard


H H

Information and


490013 - Worldcall

Telecom Ltd. Analyzers PA-LIKE PA-LIKE Analyzers Reactors Reactor Large

I I

Coke and Refined

Petroleum Products

440003 - Attock

Petroleum Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Large

I I

Coke and Refined

Petroleum Products

440004 - Attock

Refinery Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

I I

Coke and Refined

Petroleum Products

440007 - Byco

Petruleum (Formerly

Bosicor Pakistan

Ltd.) PA-LIKE Analyzers Analyzers PA-LIKE Reactors Reactor Large

I I

Coke and Refined

Petroleum Products

440022 - National

Refinery Ltd. DA-LIKE DA-LIKE Analyzers DA-LIKE DA-LIKE Flexible Large

I I

Coke and Refined

Petroleum Products

440024 - Pakistan

Oilfields Ltd. Analyzers Analyzers PA-LIKE Analyzers Analyzers Flexible Large

259

I I

Coke and Refined

Petroleum Products

440025 - Pakistan

Petroleum Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Large

I I

Coke and Refined

Petroleum Products

440026 - Pakistan

Refinery Ltd. Analyzers Analyzers PA-LIKE PA-LIKE Reactors Reactor Large

I I

Coke and Refined

Petroleum Products

440027 - Pakistan

State Oil Co. Ltd. Prospectors PA-LIKE PA-LIKE PA-LIKE PA-LIKE Flexible Large

I I

Coke and Refined

Petroleum Products

440030 - Shell

Pakistan Ltd. PA-LIKE PA-LIKE DA-LIKE Analyzers Reactors Reactor Large

J J

Paper, Paperboard and

Products

510005 - Century

Paper & Board Mills

Ltd. Analyzers DA-LIKE DA-LIKE Analyzers Reactors Reactor Large

J J


Products

510006 - Cherat

Packaging Ltd. Analyzers Analyzers PA-LIKE PA-LIKE Reactors Reactor Medium

J J


Products

510009 - Merit

Packaging Ltd. Analyzers PA-LIKE Analyzers Analyzers Analyzers Flexible Small

J J


Products

510011 - Packages


260

J J


Products

510012 - Pakistan

Paper Products Ltd. Analyzers Analyzers DA-LIKE Analyzers Analyzers Flexible Small

J J


Products

510014 - Security

Papers Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

K K

Electrical Machinery and

Apparatus

400002 - Ados

Pakistan Ltd. Analyzers Analyzers Analyzers PA-LIKE Analyzers Flexible Small

K K


Apparatus

400028 - Johnson &

Philips (Pakistan)


K K


Apparatus

400035 - Pak

Elektron Ltd. Analyzers Analyzers Analyzers DA-LIKE Analyzers Flexible Large

K K


Apparatus

400037 - Pakistan

Cables Ltd. Analyzers DA-LIKE DA-LIKE DA-LIKE DA-LIKE Flexible Medium

K K


Apparatus

400039 - Pakistan

Telephone Cables

Ltd. DA-LIKE DA-LIKE Analyzers Analyzers Reactors Reactor Small

K K


Apparatus

400048 - Siemens

(Pakistan)

Engineering Co. Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large

K K


Apparatus

400049 - Singer

Pakistan Ltd. PA-LIKE Analyzers Analyzers Analyzers Analyzers Flexible Medium

261

K K


Apparatus

400052 - The Climax

Engineering Co. Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small

L L Other Services Activities

430001 - Dadabhoy

Construction Tech.

Ltd.(Pak German

Prefabs Ltd.) Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small


430002 - Gammon

Pakistan Ltd. PA-LIKE PA-LIKE PA-LIKE PA-LIKE PA-LIKE Consistent Small


450001 -

Dreamworld Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Small


450002 - Pakistan

Hotels Developers

Ltd. DA-LIKE DA-LIKE DA-LIKE DA-LIKE DA-LIKE Consistent Medium


450003 - Pakistan

Services Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Large


460003 - Pace

(Pakistan) Ltd. DA-LIKE Analyzers PA-LIKE PA-LIKE Reactors Reactor Large


500001 - Shifa

International

Hospitals Ltd. Analyzers Analyzers Analyzers Analyzers Analyzers Consistent Medium

262

A3: Results -Textile Sector

Table A3.1: Distribution of Strategic Types

Sub-Sector Strategy Total

DA-

LIKE

Analyzers PA-

LIKE

Reactors

“Spinning, Weaving ,

Finishing of Textile”

19 55 16 16 106

Made up Textile articles 1 3 0 1 5

Other Textiles 1 4 1 2 8

Overall Textile Sector 21 62 17 19 119

Overall* 63 144 44 52 303

*without defenders and prospectors

Table A3.2: Distribution of Strategic Types among Firm Size

Size Strategy Total

DA-

LIKE

Analyzers PA-

LIKE

Reactors

Small 12 24 2 8 46

Medium 7 27 6 7 47

Large 2 11 9 4 26

Overall 21 62 17 19 119

263

Table A3.3: Distribution of Strategic Types among Firm Size

Category

Strategy Total

DA-

LIKE

Analyzers PA-

LIKE

Reactors

Consistent 14 33 11 - 58

Flexible 7 29 6 - 42

Reactors - - - 19 19

Overall Textile

Industry

21 62 17 19 119

Table A3.4: Strategic Types and Performance

Performance

Strategic Types

Overall

Defenders DA-

LIKE

Analyzer PA-

LIKE

Prospectors Reactors

Textile Sector

ROA

ROE

ROS

ROCE

- (21)

4.53

7.20

2.23

5.40

(62)

2.80

8.23

-1.50

5.47

(17)

0.56

-9.19

-3.65

-11.35

- (19)

0.95

-10.22

-12.73

3.23

(119)

2.49

2.62

-2.94

2.70

Overall

ROA

ROE

ROS

ROCE

(1)

1.80

5.14

1.72

2.91

(63)

9.54

13.93

7.19

9.77

(144)

6.00

13.68

2.69

9.12

(44)

3.88

3.17

-8.16

1.79

(3)

-3.93

-8.04

-10.83

-9.11

(52)

2.67

4.14

-4.94

3.10

(307)

5.75

10.37

0.63

6.97

264

Table A3.5: Strategic Types Consistency and Performance –Overall

Industry

Strategic

Behavior

Total

Firms


ROA ROE ROS ROCE

Textile C 58 3.41 7.04 -2.42 3.99

F 42 1.92 2.31 0.75 0.67

R 52 0.95 -10.22 -12.73 3.23

Overall C 131 6.64 11.79 1.50 7.63

F 124 7.00 11.47 2.04 7.93

R 52 2.67 4.14 -4.94 3.10

C= Consistent; F= Flexible; R= Reactor; Highest=bold; Lowest=Underlined

265

Table A3.6: Strategic Types Consistency and Performance –Strategy-wise

Industry

Defenders DA-LIKE Analyzer PA-LIKE Prospectors

C F C F C F C F C F

Textile

- - (14)

5.68

10.78

2.77

6.83

(7)

2.22

0.03

1.16

2.55

(33)

3.19

10.41

-4.37

4.19

(29)

2.37

5.76

1.76

6.93

(11)

1.18

-7.81

-3.15

-0.21

(6)

-0.59

-11.71

-4.57

-31.76

-

-

Overall

-

(1)

1.80

5.14

1.72

2.91

(36)

9.12

15.83

7.00

10.07

(27)

10.11

11.40

7.46

9.37

(70)

5.98

12.86

0.44

6.53

(74)

6.02

14.46

4.81

11.57

(24)

5.44

3.86

-3.2

8.22

(20)

2.00

2.33

-14.07

-5.92

(1)

-7.45

-17.74

-8.22

-17.16

(2)

-2.18

-3.19

-12.14

-5.08

P=Performance; C=Consistent; F=Flexible

266

Table A3.7: Performance of Strategic Types within Firm Size

Firm Size Strategic Types Overall

Average DA-LIKE Analyzers PA-LIKE Reactors

Small

ROA

ROE

ROS

ROCE

(12)

3.49

4.49

1.13

3.33

(24)

0.44

2.29

-10.12

3.20

(2)

0.44

-1.58

-1.37

-0.03

(8)

1.36

1.72

-27.19

1.32

(46)

1.39 b

2.60

-9.77 b

2.76

Medium

ROA

ROE

ROS

ROCE

(7)

6.08

10.27

2.83

7.99

(27)

3.83

11.78

1.75

6.38

(6)

-3.21

-39.43

-9.97

-16.32

(7)

1.40

-18.81

-11.82

-17.37

(47)

2.91

0.46 b

0.05

2.83 a

Large

ROA

ROE

ROS

ROCE

(2)

3.49

4.49

1.13

3.33

(11)

5.45

12.47

9.30

8.21

(9)

3.09

9.28

0.05

-10.55

(4)

-0.63

-19.06

-4.89

11.98

(26)

3.69a

6.53 a

3.72 a

2.34 b

Textile

ROA

ROE

ROS

ROCE

(21)

4.53

7.20

2.23

5.40

(62)

2.80

8.23

-1.50

5.47

(17)

0.56

-9.19

-3.65

-11.35

(19)

0.95

-10.22

-12.73

3.23

(119)

2.49

2.62

-2.94

2.70

Bold=Highest; Underline= Least; a=Highest in Industry; b= Lowest in Industry

267

Table A3.8: Consistency and Performance with in Firm Size

Firm Size

Strategic Behavior

Performance

ROA ROE ROS ROCE

Small Consistent 1.89 3.29 -8.19 0.55

Flexible 0.57 1.93 -2.53 7.39

Reactors 1.36 1.72 -27.19 1.32

Medium Consistent 3.81 8.49 0.15 5.10

Flexible 2.53 -0.81 0.22 1.41

Reactors 1.40 -18.81 -0.70 0.43

Large Consistent 5.44 11.43 3.81 8.31

Flexible 2.78 10.76 7.85 -12.92

Reactors -0.63 -19.06 -4.89 11.98

Overall (Textile

Industry)

Consistent 3.41 7.04 -2.42 3.99

Flexible 1.92 2.31 0.75 0.67

Reactors 0.95 -10.22 -12.73 3.23

268

Table A3.9: Strategy and Performance –Sub-sectors

Strategic Types

Defenders

(N=1)

DA-

LIKE

(N=63)

Analyzers

(N=144)

PA-

LIKE

(N=44)

Prospectors

(N=3)

Reactors

(N=52)

Textile -Overall

4.53

7.20

2.23

5.40

2.80

8.23

-1.50

5.47

0.56

-9.19

-3.65

-11.35

0.95

-10.22

-12.73

3.23

Spinning,

Weaving ,

Finishing of

Textile

(19)

4.37

6.73

2.05

5.02

(55)

2.51

8.43

0.21

5.37

(16)

1.19

-12.40

-2.82

-3.00

(16)

0.89

-12.54 b

-1.77 a

3.69 a

Made up Textile

articles

(1)

6.05

16.15

4.41

11.34

(3)

6.43

21.24

5.68

15.41

- (1)

4.38a

4.42 a

-64.30

0.81

Other Textiles (1)

5.96

7.23

3.53

6.74

(4)

4.12

-4.30

-30.42

-0.66

(17)

-9.53

42.25

-16.93

144.92

(2)

-0.25b

1.09

-74.67 b

0.78 b

Overall

Averages (All

Industries)

1.80

5.14

1.72

2.91

9.54

13.93

7.19

9.77

6.00

13.68

2.69

9.12

3.88

3.17

-8.16

1.79

-3.93

-8.04

-10.83

-9.11

2.67

4.14

-4.94

3.10

IA=Industry Averages: a=highest, b=lowest; Performance: Bold=Highest; Underline=

Lowest

269

Table A3.10: Industry, Consistency, and Performance

Industry

Strategic Behavior

Performance

ROA ROE ROS ROCE

Textile Overall Consistent 3.41 7.04 -2.42 3.99

Flexible 1.92 2.31 0.75 0.67

Reactors 0.95 -10.22 -12.73 3.23

Textile (Spinning, Weaving ,

Finishing of Textile)

Consistent 3.24 7.41 -0.48 3.87

Flexible 1.85 0.01 0.83 3.72

Reactors 0.89 -10.22 -1.77 3.69

Textile (Made up Textile articles) Consistent 5.40 18.21 4.09 15.43

Flexible 7.26 21.72 6.31 13.36

Reactors 4.38 4.42 -64.3 0.81

Textile (Other Textiles) Consistent 5.06 -6.87 40.88 -1.54

Flexible -0.77 17.63 -4.15 -45.39

Reactors -0.25 1.08 -74.67 0.78

Overall (All Industries) Consistent 6.64 11.79 1.50 7.63

Flexible 7.00 11.47 2.04 7.93

Reactors 2.67 4.14 -4.94 3.10

270

Table A3.11: Performance: mean values, (standard deviation), F-Statistics (p-values)*

Performance Measure DA-LIKE

N=21

Analyzers

N=62

PA-LIKE

N=17

F-Value

(Strategy)

F-Value

(Size)

ROA 4.53

(5.99)

2.81

(5.58)

0.56

(8.19)

1.95

1.85

ROE 7.20

(18.81)

8.23

(23.24)

-9.19

(44.85)

2.80*

0.70

ROS 2.23

(4.58)

-1.50

(22.54)

-3.65

(13.78)

0.50

2.82*

ROCE 5.40

(12.78)

5.47

(15.50)

-11.35

(41.38)

4.26**

0.11

*Excluding Reactor Strategy

Table A3.12: Performance: mean values, (standard deviation), F-Stat and (p-values)

Performance

DA-LIKE

N=21

Analyzers

N=62

PA-LIKE

N=17

Reactors

N=19

F-Value

(Strategy)

F-Value

(Size)

ROA 4.53

(5.99)

2.81

(5.58)

0.56

(8.19)

0.95

(5.34)

1.84

1.36

ROE 7.20

(18.81)

8.23

(23.24)

-9.19

(44.85)

-10.22

(43.89)

2.83**

0.31

ROS 2.23

(4.58)

-1.50

(22.54)

-3.65

(13.78)

-12.73

(37.22)

1.63

3.71**

ROCE 5.40

(12.78)

5.47

(15.50)

-11.35

(41.38)

3.23

(9.83)

3.25**

0.01

271

Table A3.13: Performance:Parameter Estimates for Strategy and Size

Strategy Impact Size Impact

Intercept a DA-

LIKE

Analyzer PA-

LIKE

Intercept b Small Medium

ROA 0.95 3.56* 1.85 -0.40 3.69 -2.29 0.78

ROE -10.22 17.42* 18.45** 1.03 6.53 -3.93 -6.03

ROS -12.73 14.97** 11.23* 9.08 3.72 -13.49** -3.67

ROCE 3.23 2.17 2.24 -14.58* 2.34 0.42 0.50

“Note: a, b=Reactor strategy and large size as the reference/ benchmark for comparison;

*, **= significant at alpha= 10%, and 5% respectively”

272

Table A3.14: Strategy-Size-Performance Relationship

Source P M1 M2 M3 M4

Model ROA

ROE

ROS

ROCE

1.84

2.83 b

1.63

3.25 b

1.36

0.31

3.71 b

0.01

2.95 b

2.41 c

3.65 a

2.66b

1.62

2.07b

1.70 c

1.09

Strategy ROA

ROE

ROS

ROCE

1.84

2.83 b

1.63

3.25 b

3.01 b

3.16 b

2.47 c

3.55 b

2.12

3.14 b

1.41

1.74

Size ROA

ROE

ROS

ROCE

1.36

0.31

3.71 b

0.01

6.04 b

1.13

9.37 a

0.92

0.48

1.39

1.53

0.33

Strategy*Size ROA

ROE

ROS

ROCE

1.01

1.94 c

0.69

0.29

R2

ROA

ROE

ROS

ROCE

0.05

0.07

0.04

0.08

0.02

0.005

0.06

0.00

0.09

0.08

0.11

0.09

0.14

0.18

0.15

0.10

“a, b, c=significant at 1%,5%, and 10% respectively”

Business Strategy and Organizational Performance - Pakistan ...

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