DEBRE BIRHAN UNIVERSITY COLLEGE OF BUSINES AND …

I

DEBRE BIRHAN UNIVERSITY

COLLEGE OF BUSINES AND ECONOMICS

DEPARTMENT OF LOGISTICS AND SUPPLY CHAIN MANAGEMENT

THE EFFECT OF GREEN LOGISTICS PRACTICE ON THE PERFORMANCE

OF LARGE MANUFACTURING FIRMS LOCATED IN DEBRE BIRHAN

TOWN

MA THESIS

BY: Nigatu Mekasha

JUNE, 2020

DEBRE BIREHAN, ETHIOPIA

II

DEBRE BIRHAN UNIVERSITY

COLLAGE OF BUSINESS AND ECONOMICS

SCHOOL OF POST GRADUATE STUDIES

DEPARTMENT OF LOGISTICS AND SUPPLY CHAIN MANAGEMENT

THE EFFECT OF GREEN LOGISTICS PRACTICES ON PERFORMANCE

OF LARGE MANUFACTURING FIRMS PERFORMANCE LOCATED IN

DEBRE BIRHAN TOWN.

A Thesis Submitted to Debre Birhan University College of business and

economics in Partial Fulfillments of the Requirements for the Degree of

Master of Art in Logistics and Supply Chain Management.

By: Nigatu Mekasha

Under supervision of: Nurezman Jibril (Ph.D.)

June, 2020

Debre Birehan, Ethiopia

III

DEBRE BIREHAN UNIVERSITY

SCHOOL OF GRADUATE STUDIES

P.O. Box: 445, Debre Birehan, Ethiopia

APPROVAL SHEET FOR SUBMITTING FOR FINAL THESIS

As members of the Board of Examining the Final MA thesis open defense, we certify that we

have read and evaluated the thesis prepared by Nigatu Mekasha under the titled on ―the effect of

green logistics practice on the performance of large manufacturing firms located in Debre

Birehan town‖ and recommend that the thesis be accepted as fulfilling the thesis requirement for

the Degree of Master of Art in Logistics and Supply Chain Management.

________________________ ______________________ _____________________

Chairperson Signature Date

____________________ _________________________ _____________________

Internal Examiner Signature Date

______________________ __________________________ _____________________

External Examiner Signature Date

Final Approval and Acceptance

_____________________________ ____________________ __________________

Department PGC Signature Date

______________________________ ____________________ __________________

Dean of College Signature Date

IV

STATEMENT OF THE AUTHOR

I am Mr. Nigatu Mekasha, hereby declare and confirm that the thesis entitled on ―the effect of

green logistics practice on the performance of large manufacturing firms located in Debre

Birehan town‖ is my own work conducted under the supervision of Nurezman Jibril (Ph.D.). I

have followed all the ethical principles of scholarship in the preparation, data collection, data

analysis and completion of this thesis. All academic matter that is included in the thesis has been

given recognition through citation. I have adequately cited and referenced all the original

sources. I also declare that I have adhered to all principles of academic honesty and integrity and

I have not misrepresented, fabricated, or falsified any idea / data source in my submission. This

thesis is submitted in partial fulfillment of the requirement for a degree of masters in logistics

and supply chain management from the Post Graduate Studies at Debre Birehan University. I

further declare that this thesis has not been submitted to any other institution anywhere for the

award of any academic degree, diploma or certificate.

Researcher Name

Nigatu Mekasha _______________________ _________________

Signature Date

Department: Logistics and Supply chain Management

College: Business and Economics

V

DECLARATION

This is to certify that this thesis entitled on ―the effect of green logistics practice on the

performance of large manufacturing firms located in Debre Birehan town‖ accepted in partial

fulfillment of the requirement for the award of the degree of master of art in Logistics And

Supply Chain Management by the School of post Graduate Studies, Debre Birehan University

through the college of Business And Economics done by Nigatu Mekasha under my guidance.

The matter embodied in this thesis work has not been submitted earlier for award of any degree

or diploma. The assistance and help received during the course of this investigation have been

acknowledged. Therefore, I recommend that it can be accepted as fulfilling research thesis

requirements.

________________________ _____________________ ______________________

Advisor Signature Date

VI

ACKNOWLEDGEMENTS

A long journey, with many ups and downs, has now ended. I could not have walked alone to

complete this research paper. First and always, I praise and thank God for giving me the physical

and mental strength to complete this research paper and to reach to this point in my life after

passing ups and downs. My greatest appreciation and grateful thanks goes to my advisor

Nurezman Jibril (Ph.D.) for his continues encouragement, inspiration and valuable comments up

to the completion of this study. I would like to express my thankful to Debre Birhan University

for giving me this opportunity to undertake this study and financial support for completion of

this research work.

Further, I am delighted to acknowledge from the deep of my heart to my mother Bizunesh

Asaminew, she sustainably pray for God to see this success. My acknowledgment extends to

Solomon Dubale and all my friends for their patience, understanding and all the encouragement

they gave me when I needed it most. God bless you all

Finally, I would also like to forward my truthful thanks to employees of large manufacturing

who responded to my questionnaires during this research without their support it was impossible

to complete this study.

Thank you all very much again.

Nigatu Mekasha

June, 2020

Debre Berehan, Ethiopia

VII

Table of Contents

Contents page

APPROVAL SHEET FOR SUBMITTING FOR FINAL THESIS ............................................ III

STATEMENT OF THE AUTHOR ......................................................................................... IV

DECLARATION ..................................................................................................................... V

ACKNOWLEDGEMENTS...................................................................................................... VI

LIST OF TABLES ..................................................................................................................... X

LIST OF ACRONYMS ............................................................................................................ XI

ABSTRACT ........................................................................................................................... XII

CHAPTER ONE .........................................................................................................................1

INTRODUCTION TO THE STUDY ..........................................................................................1

1.1.Introduction ................................................................................................................................. 1

1.2.Background of the study ............................................................................................................ 1

1.3.Statement of the problem ........................................................................................................... 2

1.4.1.General objective.........................................................................................................4

1.4.2.Specific objective ........................................................................................................5

1.5.Significance of the study ............................................................................................................ 5

1.6.Scope of the study....................................................................................................................... 5

1.7.Operational Definition ................................................................................................................ 6

1.8.Organization of the study ........................................................................................................... 7

CHAPTER TWO ........................................................................................................................8

REVIEW OF RELATED LITERATURE ...................................................................................8

2.1.Introduction ................................................................................................................................. 8

2.2.Institutional theory ...................................................................................................................... 8

2.3.Concepts and Definition Green Logistics Practices ................................................................. 9

2.4.Green logistics practices........................................................................................................... 10

2.4.1.Green purchasing practices ........................................................................................ 11

2.4.2.Green Manufacturing Practices .................................................................................. 12

VIII

2.4.3.Reverse Logistics Practices ....................................................................................... 13

2.4.4.Environmental Practice and Regulatory ..................................................................... 14

2.5.Manufacturing Firm‘s Performance ........................................................................................ 16

2.5.1.Operational Performance ........................................................................................... 17

2.5.2.Financial performance ............................................................................................... 18

2.6.Empirical Review ..................................................................................................................... 19

2.7.Research framework and hypothesis ....................................................................................... 20

2.7.1.Conceptual Framework.............................................................................................. 20

2.7.2.GLP and Large Manufacturing Firms‘ Performance .................................................. 21

2.8.Gap in Literature ....................................................................................................................... 24

CHAPTER THREE ................................................................................................................... 26

METHODOLOGY OF THE STUDY ....................................................................................... 26

3.1.Introduction ............................................................................................................................... 26

3.2.Research Design ....................................................................................................................... 26

3.3.Research Approach ................................................................................................................... 26

3.4.Target Population ..................................................................................................................... 27

3.5.Sampling Technique and Sample Size .................................................................................... 28

3.6.Types and Source of Data ........................................................................................................ 30

3.7.Method of Data Collection ....................................................................................................... 30

3.8.Validity and Reliability of the Research ................................................................................. 31

3.8.1.Validity ..................................................................................................................... 31

3.8.2.Reliability of the Research ......................................................................................... 32

3.9. METHOD OF DATA ANALYSIS ..................................................................................... 32

3.9.1.Descriptive Statistical Analysis ................................................................................. 32

3.9.2.Inferential statistical .................................................................................................. 33

3.10.Ethical Consideration ............................................................................................................. 34

IX

CHAPTER FOUR ..................................................................................................................... 35

DATA PRESENTATION, ANALYSIS &INTERPRETATION ................................................ 35

4.1.Introduction ............................................................................................................................... 35

4.2.Response Rate ........................................................................................................................... 35

4.3.Respondents General Information ........................................................................................... 36

4.4.Reliability Test .......................................................................................................................... 38

4.5.GLP in Large Manufacturing Firms ........................................................................................ 39

4.5.1.Green purchasing practices ........................................................................................ 39

4.5.2.Green manufacturing practices .................................................................................. 40

4.5.3.Reverse logistics practice .......................................................................................... 41

4.5.4.Environmental practices and Regulation .................................................................... 42

4.6.Large manufacturing firms performance ................................................................................ 42

4.7.Correlation Analysis ................................................................................................................. 44

4.7.1.Correlation between GLP and operational performance ............................................. 45

4.7.2.Correlation between GLP and financial performance ................................................. 46

4.8.Regression Analysis ................................................................................................................. 48

4.9.Summary of results ................................................................................................................... 57

CHAPTER FIVE ...................................................................................................................... 58

SUMMARY, CONCLUSION & RECOMMENDATION ......................................................... 58

5.1.Introduction ............................................................................................................................... 58

5.2.Summary of finding .................................................................................................................. 58

5.3.Conclusion ................................................................................................................................ 60

5.4.Recommendation ...................................................................................................................... 61

5.5.Limitation and suggestion for future studies .......................................................................... 62

REFERENCES ......................................................................................................................... 63

APPENDIX ―B‖‖ ..................................................................................................................... 72

APPENDIX ―B‖........................................................................................................................ 76

X

LIST OF TABLES

Table 1; List of large manufacturing firms................................................................................. 27

Table 2: Number of employees under each department .............................................................. 29

Table 3: Sample size allocation from each stratum .................................................................... 30

Table 4: Cronbach alpha reliability test ..................................................................................... 32

Table 6: Response rate .............................................................................................................. 36

Table 7: Demographic profile of respondents ............................................................................ 36

Table 5: Reliability of questionnaire dimension ......................................................................... 38

Table 8: Green purchasing practice ........................................................................................... 39

Table 9: Green manufacturing practices .................................................................................... 40

Table 10: Reverse logistics practices ......................................................................................... 41

Table 11: Environmental practices and regulation ..................................................................... 42

Table 12: Operational performance of large manufacturing firms .............................................. 43

Table 13: Financial performance of large manufacturing firms .................................................. 44

Table 14: Correlation between GLP and Operational performance ............................................ 45

Table 15: Correlation between GLP and Financial performance ................................................ 47

Table 16: Regression analysis model summary between GLP and OP ....................................... 50

Table 17: ANOVA model fit ..................................................................................................... 51

Table 18: Regression coefficients .............................................................................................. 51

Table 19: Regression coefficients .............................................................................................. 53

Table 20: ANOVA model fit ..................................................................................................... 54

Table 21: Regression coefficients .............................................................................................. 55

Table 22: summary of result ...................................................................................................... 57

XI

LIST OF ACRONYMS

DBBF Debre Berehan Blanket Factory

DBWB Debre Birehan Wood Processing

EMS Environmental Management system

EPR Enviromental Practices and Regulations

FP Finacial Performance

GLP Green Logistics Practices

GM Green Manufacturing

GP Green Purchasing

ISO International Standard Organization

LMF Large Manufacturing Firms‘

OP

PLMF

Operational Performances.

Performance of large manufacturing firms

RL Reverse Logistices

SPSS Statistical Package for Social Sciences

XII

ABSTRACT

Green logistics practices are integration of environmental thinking into the supply chain

management which covers all phases of product life cycle. The purpose of this study was to

explain the effect of green logistics practices on performance of large manufacturing firms in

Debre Birehan town. To achieve the aim of this study, explanatory research design and

quantitative research approach employed. The population of the study were employees of four

large manufacturing firms which selected by simple random sampling, and employees who work

in these factories grouped in four departments via stratified random sample. The sample size

from each department determined through stratified proportional sampling, and sample units

from each department have been selected randomly. A total of 260 sample units were selected

and questionnaires have distributed. Accordingly, 243 (93.5 %.) of questionnaires were correctly

filled, returned and applied for analysis. Besides, SPSS version 23 was used for analyze,

interpret and present the data captured via questionnaire through descriptive and inferential

analysis method: means, standard deviation, correlation and regression analysis were used.

Hence, the descriptive analysis result shown, green purchasing, green manufacturing and

environmental practices, and regulation practiced occasionally, but reverse logistics practiced

very often in the case companies. Furthermore, Pearson correlation result confirmed existence

of statistically significant positive association between the set of green logistics practices and

performance of large manufacturing firms. Lastly, the regression result suggested that dimension

of green logistics practices have statistically significant predicting power on performance of

large manufacturing firms. Therefore, this study recommended, firms to ensure global

competitiveness, working collaboratively with suppliers, customers and government regarding to

green logistics practices.

Keywords: Green Logistics Practices, performance of large manufacturing firms

1

CHAPTER ONE

INTRODUCTION TO THE STUDY

1.1. Introduction

This chapter addressed the introduction part of the research. It basically included background of

the study, a statement of the problem, objective of the study, scope of study, significance of the

study, operational definition and Organization of the study were discussed.

1.2. Background of the study

Green logistics practices defined as the process of fulfilling the needs of present demand without

compromising future generations to meet their own needs, it means that making balance,

resilience, and interconnects current operations with the ecosystem to satisfy its needs without

compromising future generation needs citing by (Di Pillo et al., 2017). In other word, green

logistics practices (GLP) are an integration of environmental thinking into business operation

that covers all phases of products‘ life cycle starting from the design, production, distribution and

disposal of the products (Santos, et al., 2019).

Business activities such as; sourcing, production, and other logistics practices are believed to be

responsible for most of the environmental problems, especially; large manufacturing companies

are to a great extent responsible for environmental degradation, while it plays a significant role in

economic development. In this regard, the focus of government, public association, stakeholders,

researchers, regulators, and customers has been on the large manufacturing industries and forces

them to transform their activities in green practices in order to enhance environmental and social

welfare and stability (Kumar Piaralal et al., 2015). Accordingly, the increment of government,

public associations, and customers emphasizes on green operation lead the manufacturing firms

to give high attention to the environmental damage created by their operation such as: sourcing,

production, distribution and other logistics operations (Sari & Yanginlar, 2015). Therefore,

manufacturing companies use green logistics practices as strategy to enhance their performance

and reduce environmental damages. While different manufacturer in the world have executed

some sort of green logistics practices and the pressure and degree of implementation are not the

same for all firms. Means that, some firms implement green logistics practices by understanding

2

its role in terms of economical, operational, social, and environmental importance, some other

manufacturers implement green logistics practices meeting only regulatory requirements (Qu et

al., 2017).

Therefore, the environmental policy of Ethiopia was approved on April 2, 1997, by the Council

of Ministers to force manufacturers to transform their daily operation in the green, who doesn‘t

implement green logistics practices by understanding its role. To meet only regulatory

requirements, the Environmental Policy of Ethiopia has incorporated the concept of sustainable

development and its design to improve and enhance the health and quality of life of all

Ethiopians and to promote ecological economic and social development through the sound

management of natural, human-made and cultural resources and the environment as a whole to

meet the needs of the present generation without compromising the ability of future generations

to meet their own needs (National Report of Ethiopia, 2012) as cited by (Mesfin, 2016).

1.3. Statement of the problem

Nowadays, environmental concern was raised globally by public associations, government, and

customers, and they force companies to reduce environmental impacts from their daily

operations, on the other hand, companies‘ stakeholders find themselves under growing pressure

of operational and financial performances. Therefore, business stakeholders are obligated to seek

the practices and ways which decrease their environmental degradation at the same time

increasing financial and operational performance to ensure their competitiveness in the global

market (Van Rensberg, 2015).

Hence, manufacturing firms in developed countries such as Japan, the United States, and

Germany adopt green logistics practices as a strategy to ensure competitiveness in the global

market by minimizing the environmental impact of their daily operation at the same time to

enhance operational and financial performance (Zhu et al., 2010), whereas, manufacturing

industries that established and operate in Ethiopia aren‘t adopt green logistics practices well,

because of firms didn‘t have the commitment, awareness, resistance to advance technology

adoption, poor organizational culture and capabilities in the adoption of green practices, lack of

environmental awareness of the supplier and less awareness of customer about green practices. It

means that, firms aren‘t using it as a strategy to minimize the environmental impact of their daily

3

operation at the same time to increase operational and financial performance. They aren‘t also

ensuring competitiveness in the world market (Mignot, 2017).

However, there was less level of practicing green logistics practices by manufacturing firms in

Ethiopia and not use it as a strategy to ensuring global competitiveness through minimize

environmental effects as well as increasing operational and financial performances

simultaneously, it was difficult to generalize and raise that as a reason to be out of the global

competition. Because, the previous studies finding which conducted to address the effect of GLP

on the operational and financial performance of firms‘ shown different results, means there was

inconsistent research findings as stated follow.

According to Zhu et al., (2010), green purchasing (GP), and green manufacturing (GM), were

positively related and affected financial performance but negatively correlated and affected

operational performance. On the other hand, according to Khan, & Qianli, (2017), and

Jayarathna & Lasantha, (2018), conclude that green purchasing had negative effects on financial

performance (FP). Whereas, according to, Islam et al. (2017), green purchasing had a positive

relationship and effects on both financial and operational performance, it was also supported by

(Kipkorir & Wanyoike, 2015). In addition, According to Mukonzo (2017), green manufacturing

had a positive correlation and effects on operational performance.

According to Naila, (2013), environmental practices and regulation had been the insignificant

relationship with financial performance, while, Ong et al (2014), and Iwata & Okada (2010),

conclude that environmental practices and regulation (EPR) had negative effects on financial

performance, but this result of the study also opposed by Bartolacci & Zigiotti, (2015), Di Pillo

et al., (2017), and Manrique & Marti-Ballester, (2017), revealed that environmental practices and

regulation had positive correlation and effects on financial performance.

As the current knowledge of the researcher, there exists a lack of research that explores the

effects of green logistics practices on the performance of large manufacturing firms (LMF) in

Ethiopia. Zellalem, (2016), was conducted research entitled on ―green supply chain management

practices in Ethiopian in the case of the tannery industry‖. He founded that, organizational

commitment, green purchasing, green marketing, and investment recovery, eco-design, and

4

environmental practice was positively affected environmental and operational performance, but

negatively affected social and financial performance.

In previous, there were number of studies conducted to address the relationship between green

logistics practice and performance of firms. Yet, the findings from those studies have been

inconsistent. Consequently, experts can‘t give clear answer as to what green logistics practices

would be beneficial to follow, and the conflicting results are difficult to generalize; therefore,

further investigation regarding to the effect of green logistics practices on performance of firms

were vital to reach on generalizability and to be sure what green logistics practices would be

beneficial. It makes inspiration and motives to do this study in addition to lack of research in

Ethiopian which explores the effect of green logistics practices on the operational and the

financial performance.

At the end of this study, Researcher has believed this study enhances knowledge and

understanding of green practices; fills a gap with inconsistency studies outcomes and draws a

clearer picture of the relationship between GLP and firm‘s performance by answering the

following research questions.

What is the effect of GP on the operational performance of LMF?

What is the effect of GP on the financial performance of LMF?

What is the effect of GM on the operational performance of LMF?

What is the effect of GM on the financial performance of LMF?

What is the effect of RL on the operational performance of LMF?

What is th e effect of RL on the financial performance of LMF?

What is the effect of EPR on the operational performance of LMF?

What is the effect of EPR on the financial performance of LMF?

1.4. Objectives of the study

1.4.1. General objective

The main objective of this study was examining the effects of green logistics practices on the

performance of large manufacturing firms‘ that located in Debre Birhan town.

5

1.4.2. Specific objective

To examine the effect of GP on the operational performance of LMF.

To examine the effect of GP on the financial performance of LMF.

To examine the effect of GM on the operational performance of LMF

To examine the effect of GM on the financial performance of LMF.

To examine the effect of RL on the operational performance of LMF.

To examine the effect of RL on the financial performance of LMF.

To examine the effect of EPR on the operational performance of LMF.

To examine the effect of EPR on the financial performance of LMF.

1.5. Significance of the study

The findings of this study help manufacturing firms to understand the role that a set of green

logistics plays in their organizational performance. By understanding the role of different green

logistics practices, they will be able to prioritize and effectively implement it and solving the

challenges that they face to improve economic advantage and operational performance.

The findings of this study also laying the ground for further research in the field of logistics and

supply chain management, especially to explain the effect of green logistics practice adoption on

organizational performance in different institution.

The key policy-makers within the government could also use the findings of this study to set the

right policies that encourage adoption of green logistics practice for manufacturing companies.

1.6. Scope of the study

This study was limited in Debre Birhan town on large manufacturing companies. Even if, the

researcher believes that the problem was studied exhaustively, the researcher was compelled to

limit this study only on large manufacturing firms in Debre Birhan town, because it is hard and

uncontrollable to perform the study in all small, medium and large manufacturing firms

concurrently, and also difficult to conduct this study by considering the large manufacturing

firms all over in Ethiopia, because of time constraints. That is why the researcher obligated to

restrict in large manufacturing firms placed in Debre Birhan town.

6

Moreover, this study was focused on effect green logistics practice on performance of large

manufacturing firms. Green logistics practices are so many and have different impact on

manufacturer performance; even performance might be measure in different metrics. The

researcher only tries to analyze the impact of green purchasing, green manufacturing, reverse

logistics practice and environmental practices and regulations on operational and financial

performance. However. the researcher believe that the study would cover all green logistics

practice effects on all performance metrics of firms, but the researcher was bounded on some

green logistics practice and performance metrics because of time and lack of information or

literature especially in Ethiopian.

1.7. Operational Definition

The main task of this study was test the cause and effect relationship between green logistics

practices and the performance of large manufacturing firms. Therefore, the variables which used

in this study are explained as follows.

o Green purchasing (GP): It is the purchase of environmentally friendly products and

services, the selection of contractors and the setting of environmental requirements in a

contract.

o Green manufacturing (GM): It is a manufacturing mode designed to minimize the

environmental impact in the manufacturing processes by reducing waste and pollution

o Reverse logistics (RL): It is the process of planning, implementing, and controlling the

efficient, cost effective flow of raw materials, in-process inventory, finished goods, and

related information from the dawn-stream to the upper-stream for the purpose of

recapturing, creating value or proper disposal

o Environmental practices regulation (EPR): It is the general rules or specific actions

imposed by administrative agencies that interfere directly with the market allocation

mechanism or indirectly by altering consumer and firm demand and supply decisions

o Operational Performance: it is the degree to which quality, speed, dependability,

flexibility and cost are fulfilled at any point in time of production and delivery of

products and services. It measures by outcomes of a firm‘s processes such as

productivity, reliability and production cycle turn which affect the overall business

performance measures such as customer satisfaction and market share

7

o Financial performance: It focuses mainly on its profitability, revenue growth, increase

in market share, and increase in productivity of firms.

1.8. Organization of the study

This research paper was categorized in to five chapters. The first chapter was the introductory

part which addresses background of the study, statement of the problem, objectives of the study,

significance of the study and scope of the study. The second chapter deals with the review of

related literature where theoretical and empirical evidences were explored from different

publications. The third chapter presents the research design and methodology which focused on

research design, research approach, and target population, sampling techniques, sample size,

sources and instruments of data collection, and finally method of data analysis were discussed.

The fourth chapter was deals about the presentation of results and discussion that is concerned

with the summarization and interpretation of the research findings. Finally, in chapter five,

summary of findings, conclusions, recommendations, limitations and suggestion for future

research were discussed.

8

CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.1. Introduction

This chapter undertakes a review of the available literature on the field of green logistics starting

with the theoretical review, the green logistics practices, performance of large manufacturing

namely; operational performance and financial performance, empirical review, conceptual

framework and hypotheses and finally, gap in literature were discussed.

2.2. Institutional theory

Institutional theory can explain why companies engage in actions contradicting the efficiency

arguments of traditional economic understood when attempting to conform to social norms and

stakeholder's interests. The institutional theory implies that a strong motivating force behind the

firm operation is socially based and proposes that an organization is sure to satisfy its social and

stakeholders simultaneously (Vlachos, 2016).

A key belief in institutional theory is that organizational isomorphism increases organizational

legitimacy. Isomorphism is a key concept in institutional theory which can be defined as the

actions of the organizations that are desirable, proper, or appropriate within the socially

constructed system of norms, values, and beliefs (Khor, 2013). It is the main factor that leads

organizations to adopt similar structures, strategies, and processes regarding the social or

environmental issue. There are three types of mechanisms towards institutional isomorphism:

coercive, mimetic, and normative (Sarkis & Cordeiro, 2001).

Coercive isomorphism is found when customers and government forced companies to

incorporate their operation practice from social, environmental and economic aspects to serve its

own interest as well as the social Conforming to coercive isomorphism makes companies to be

perceived as more legitimate (Zhu et al., 2010).

Mimetic isomorphism occurs due to firms facing uncertainty, at that time firms are trying to

imitate the models, structure, strategy, and process of other firms that they perceived as

successful and legitimacy. Learning from others' best operational practices, benchmarking, and

9

supply chain mimesis produce ‗standard responses to uncertainty‘ which reduce the risk of

unexpected outcomes (Greenstone, 2002).

Normative isomorphism occurs due to professionalization, members of an occupation define the

qualifications, ethics, and methods of their work to establish greater legitimacy for their

occupation, creating homogeneity and legitimacy over time (Zhu et al., 2010).

Therefore, the manufacturing firms forced to implement green logistics practices, such as green

purchasing, green manufacturing, reverse logistics, and environmental practices by one or more

isomorphism to protect the environment and increase the industry competitiveness at the same

time (Ramanathan et al., 2017). In differ from traditional economics view, companies had to

sacrifice part of their profits to reduce externalize like pollution, strict environmental regulation

and adoption of green logistics practices. In return, leads to improve an innovation effect on

companies such as: discovery and introduction of cleaner technologies that help for improving

operation, economic, social and environmental performance simultaneously (Ambec et al.,

(2013), but innovation require higher investment cost, so making production processes and

products more efficient is essential to achieve minimum total cost goals and saving sufficient or

enough amount money for compensating both compliance costs directly attributed to new

regulations and the innovation costs (Romero et al., 2018).

2.3. Concepts and Definition Green Logistics Practices

Green logistics practices means integrating environmental thinking into the business operation

that covers all phases of products‘ life cycle starting from design, production, distribution, and

disposal of the product at the end of the product life cycle (Santos, et al., 2019). The World

Commission on Environment and Development (WCED 1987) defined that green logistics

practices are fulfilling the needs of present demand without compromising future generations to

meet their own needs. It means that making balance, flexibility, and interconnects current

operations with the ecosystem to satisfy its needs without compromising future generation needs

citing by (Di Pillo et al., 2017).

Even though, manufacturing sectors especially large manufacturing sectors play a significant

role in economic development, it also brings badness to environmental sustainability in the long

run. In this regard, adoption of green practices in manufacturing firms emphasizes the waste

10

reduction for better environmental performance. Moreover, it directly leads towards cost

reduction, improving the efficiency of operations and performance (Khan, 2019). It also reducing

the damage of the environment caused by the business operation and maximizing resource

utilization in the cycle of logistics activities, with the aim to move toward sustainable

development. It is a component of both the environmental associated economy and adaptive

economic development, which have important roles in the national green economy strategy (Qu

et al., 2017).

Therefore, it is important for large manufacturing firms to ensure that their products conform to

sustainable designs, production, and the ability of the product to be reused or recycled through

the adoption of green logistics practices and certain environmentally friendly policies as well as

manufacturers should able to ensure that suppliers meet their environmental objectives and

implement reverse logistics practices that include product returns and re-manufacturing,

recovery, recycling, reuse, and redistribution. It is further declared that these practices apply to

final products, their components, and packaging material to improve operational, financial, and

environmental performances at the same time (Mogeni, 2016).

Previous studies indicate that a significant correlation exists between green logistics practices

and companies' profitability. Businesses having higher scores on environmental criteria realize

stronger financial returns from the overall market, whereas companies with poor scores have

weaker returns (Sheikh, 2014). Manufacturing firms invest in green logistics practices, because

going in to green helps businesses develop new market opportunities and increase their

competitive advantage and effective green practices help firms to achieve greater efficiency,

establish and strengthen their core competencies enhance their green image, all of these may

eventually combine to contribute to firm profitability (Elshawarby, 2018).

2.4. Green logistics practices

Different studies use different dimensions to measure green logistics practices (GLP). (Sari &

Yanginlar, 2015), measured GLP through reverse logistics practices, green distribution and

marketing, green purchasing, and manufacturing practices.

According to Mogeni, (2016), GLP measured through the dimension of eco-design (Product re-

manufacturing and Recyclability), Green Purchasing (Waste Control and Compliance to

11

regulations), Reverse Logistics (Backward distribution and Lead time) and Responsive

Packaging (Size of packaging and Use of agile materials). This study was measured GLP by

green purchasing practices, green manufacturing practices, reverse logistics practices and

environmental practices and regulation adopted from (Sari & Yanginlar, 2015 and Mogeni,

2016),

2.4.1. Green purchasing practices

Green procurement is one of the pollution prevention principles and activities. It also known as

green or environmental purchasing, it focuses on; the purchase of environmentally friendly

products and services, and setting environmental requirements for the selection of contractors,

suppliers and sign a contract. It makes compression of price, technology, quality with the

environmental impact of the product, service, or contract (Kipkorir & Wanyoike, 2015). Green

Procurement also called sustainable procurement (SP) which is one of the emerging issues in

procurement. It requires taking public and environmental factors into consideration together with

financial factors in making procurement decisions, and involves looking beyond the traditional

economic restrictions and making decisions based on the whole life cost, the associated risks,

measures of success, and implications on society and the environment (Nderitu & Ngugi, 2014).

Green procurement programs are simply purchasing green products or services; renewable

energy or recycled products or more involved such as setting of environmental requirements for

suppliers and contractors. Green products or services utilize fewer inputs, designed to last longer

and minimize their impact on the environment. In addition, green products and services have less

of an influence on human health and may have advanced safety standards. Whilst certain green

goods or services may have a greater cost, they save money over the life of the product or service

(Kipkorir & Wanyoike, 2015).

Due to this, environmental and social issues are increasingly becoming important in managing

any business and the increasing awareness of social and political leaders have contributed to

green purchasing practices, which are now considered an important aspect of corporate

management that can empower organizations to advance their stated goals. In response to the

sustainable Development, sustainable procurement (SP) has become an important agenda for

governments seeking to demonstrate sustainable development. Studies also prove that

12

sustainable procurement practices can alter markets, save money, increase financial capability,

upturn the competitiveness, safeguard natural resources, and foster job creation, in return which

will contribute to sustainable development. The strategic role of green purchasing practices and

supplying use as a device for sustainable development has been strengthened recently (Islam et

al., 2017).

According to Large & Gimenez Thomsen, (2011), summarize that green purchasing can improve

a firm's economic position, by falling disposal and liability costs, saving resources, and

enlightening an organization's public image, but the two most highly rated difficulties to

operating green purchasing was cost and revenue. In the process of employing green

procurement, the enterprise is assured to increase investment, training staff costs, and the

communication costs with suppliers, etc., which hence causes the loss of other investment

opportunities.

2.4.2. Green Manufacturing Practices

Green Manufacturing has emerged in the last few years and covers phases of the product‘s life

cycle starting from design, production, and distribution phases to the use of products by the end-

users and its disposal at the end of the product‘s life cycle (Kalhari et al., 2018). It is used for

describing practices that do not damage the environment during any part of the work which

includes recycling, conservation, waste reduction management, environmental protection,

regulatory compliance, pollution control, and allied issues (Mukonzo, 2017). It serves as a means

to minimize waste and pollution for all industries, and slows down the depletion of natural

resources as well as lowers the extensive amounts of waste that enter landfills. Moreover, it

emphasizes reducing parts, rationalizing materials, and reusing components to build products

more efficiently (Hami et al., 2016).

In general, green Manufacturing includes the whole practices connected with environmental

concerns that boundlessly incorporate eco-friendly manufacturing processes of goods. It involves

transforming raw materials into finished goods that leave less environmental hazards but with

high efficiency (Soubihia et al., 2015). In accordance with the reality of the manufacturing

system, green production plans, and adopts the production technology program and process route

with fewer resources and energy consumption leads to minimizing environmental pollution. The

13

standards to reach on green manufacturing are zero safety problems, zero health threats on the

operators and product users, zero environmental pollution, waste recycling, and waste disposal

during the production process as much as possible (Alshura, & Awawdeh, 2016). It should

strategically reduce a percentage of all costs including costs of sourcing for raw materials,

production, and supply chain costs; maintenance, replacement, and any other costs associated

with green products (Ngniatedema & Li, 2000).

According to Mukonzo, (2017), green manufacturing strategy is a means to create harmonious

conditions between commerce and their environments. It focuses on value creation by producing

more with fewer resources through adopting green manufacturing strategies, the outcomes of

these strategies should be no pollution, defects, downtime, and inventories. Therefore,

manufacturers should develop green manufacturing practices as strategies to overcome these

challenges through: green technology innovation; learning and environmental technology

innovation, continuous improvement to environmental health hazards. As a result, considering

the views of stakeholders would also be a critical issue.

Adopt proactive strategy in supply chain management play vital roles to enhance the

environmental performance at the same time financial performances and operational

performance of supply chain management. Therefore, it's essential for manufacturers to create

cooperative efforts with both the first-and the second-tier suppliers to ascertain green systems

and comply with environmental regulations in manufacturing components and parts (Onyinkwa

& Ochiri, 2016).

2.4.3. Reverse Logistics Practices

Reverse logistics is defined as, the process of planning, executing and controlling the efficient

and effective flow of materials, work in process inventory, finished goods, and associated

information from the point of consumption to the point of source for the purpose of recollecting

worth or of proper discarding (Vlachos, 2016). Any items may be returned from point of

consumption to the origin due to damage, periodic inventory, restock, salvage, recalls, and

additional inventory. It involves re-use, recovery of products and waste disposal, hence reducing

the negative effects on the environment will be attained (Turrisi et al., 2013).

14

In a practical business environment, products are returned because of manufacturing returns,

commercial returns, recalled products, warranty returns, service returns, end-of-use returns, and

end-of-life returns. These products are returned due to reasons such as; poor packaging and

quality issues. unsatisfactory quality, installation or usage problems, warranty claims, faulty

order processing, retail overstock, end of product life cycle or product replacement, and

manufacture recall (Afum & Zhuo, 2019).

Reverse Logistics programs are growing for financial, corporate social responsibility and legal

requirements, the flexibility of information, distribution respond to customer needs and to reduce

response times, supporting a variety of delivery requirements and to reduce costs (Ramirez &

Morales, 2011).

Reverse logistics is more necessary for the large manufacturing firms to face the uncertainty in

their activities that is increasingly high. In this case, it increases the need for flexibility of

information distribution because it helps to reduce this uncertainty. It also allows manufacturing

sectors to improve the availability of options, reducing uncertainty, and improving decision-

making. In reverse logistics programs information systems used to improve data processing

operations that facilitate or help you make better decisions, reducing response times and

improving the flexibility of information distribution (Adebambo & Adebayo, 2014).

In general, the adoption and implementation of reverse logistics are necessary to achieve the

goals of sustainable development which focus on both environmental and economic goals, that

means, practicing reverse logistics can help reduce waste and increase profit through an effective

re-use and recovery option in manufacturing firms (Abdullah & Yaakub, 2014). Furthermore, the

increase in awareness of environmental issues and the benefit of re-use and recovery options

places more pressure on firms to create a better reverse logistics strategy (Salim, 2016).

2.4.4. Environmental Practice and Regulatory

Regulation consists of legislation and rules issued by the administrative agencies. There are two

types of regulation are recognized: social and economic. Social regulation encompasses non-

economic activities across manufacturing, while economic regulation is designed at specific

businesses. For example, the Environmental Protection Agency conducts social regulation while

state utility commissions conduct economic regulation (Ramanathan et al., 2017).

15

On the other hand, environmental regulations are the overall rules and specific actions imposed

by administrative agencies so as to control pollution and manage natural resources with the

purpose of protecting the environment and internalizing externalities, including direct and

indirect involvements (Stavropoulos et al., 2018). Here, environmental regulations are classified

into two: flexible and inflexible. Flexible regulations are innovation-friendly encouraging firms

to develop appropriate new processes/products to meet regulatory requirements, whereas

inflexible regulations prescribe specific processes/products to achieve a particular outcome.

Flexible regulations have a higher level of market governance while inflexible regulations are

dominated by elements of hierarchical governance (Ramanathan et al., 2017).

Environmental problems are regularly caused by the undesirable externalities of economic

activities, which mean that economic actors add external costs to society through pollution

without paying the equivalent social costs. In the absence of regulation, individuals tend to

damage the environment at their own advantage. Consequently, environmental problems cannot

be resolved by simple market mechanisms: most countries implement environmental procedures.

Thus, strengthening environmental protection and reinforcing environmental regulations have

become key issues, especially in the manufacturing firms (Stavropoulos et al., 2018).

In the growing awareness of the importance of the environment condition, governments set rules

and legislations concerning the implementation of green logistics to reduce wastes and to ensure

safeguard the environment. The pressure that comes from the regulations and legislations

considered to be one of the most crucial reasons for practicing green practices. The governments

can be as a driver for the companies to make their activities green in the following aspects:

The government needs to take a crucial part in the creation of regulation and companies‘

engagement in them

The government should create good circumstances for the growth of innovative ideas in

the most important areas of green logistics

The government needs to launch some educational programs that will increase the

environmental consciousness of ordinary citizens.

The governments should offer good financial incentives for decreasing emissions and

improving energy efficiency.

16

Lastly, governments can apply lower taxes for the companies that are practicing green

ways of doing business (Peng, Tu, & Wei, 2018).

Usually, environmental regulation targets to improve public welfares through regulation (for

example in the form of reduced pollution) by requiring firms to adopt sustainable practices,

while firms attempt to maximize private benefits (for example, in the form of reduced

consumption of energy/raw material) that positively impacts their bottom line (Ramanathan et

al., 2017). Even though, some industries face strict regulation that constricts or eliminates many

activities, while other industries face far fewer regulations (Journal et al., 2017).

In general, Environmental issues are components of corporate social responsibility (CSR) aspects

covering environmental implications of a company‘s operations, products and facilities, such as:

eliminating waste and emissions; maximizing efficiency and productivity of resources; and

minimizing practices that might adversely affect the enjoyment of a country‘s resources by

future generations (Rubashikina et al., 2015).

2.5. Manufacturing Firm’s Performance

The generic performance objectives can be aggregated into composite measures, like customer

pleasure, overall service level and operational agility; or by means of measures like achieving

market targets, financial, operations, overall strategic objectives, and even environmental

objectives. Comprehensive performance measures have greater strategic relevance in the overall

performance of the business (Laari, 2016).

There is no common attributes to measure the performance of companies. Different scholars‘

measure firms performances in different parameters. As the study of Sari & Yanginlar, (2015),

measure firms' performance through operational performances, economic performances, and

environmental performances. According to Laosirihongthong et al. (2014), measure the

performance of the organization by environmental performance, economic performances, and

intangible performance. In this study manufacturing firm performance was measured on the basis

of operational performance and financial performances.

17

2.5.1. Operational Performance

Operational performance defined as the measurable outcomes of a firm‘s processes such as;

productivity, reliability, flexibility, speed, and production cycle time of all functional areas such

as; procurement, human resources, marketing, operations, finance and strategy which affect the

overall business performance measures such as customer satisfaction, profit and market share

(Santos et al., 2019). According to Turrisi et al., (2013), operational performance is the degree to

which; quality, speed, dependability, flexibility, and cost are fulfilled at any point in the time of

production and delivery of products and services. Quality can be looked at from different

dimensions like customer complaints, wastage, claims on warranty, malfunction, satisfaction

levels, and environmental impact. The development of quality policy systems that encourage

green culture and commitment to quality improvement is vital to ensuring product reliability,

durability, functionality, and environmental compliance. This is achieved through total quality

management and adoption of green manufacturing strategies that are highly reliable in meeting

consumers green needs (Ho, Wang, & Shieh, 2016).

Moreover, operations with high internal dependability are more effective. Dependability saves

time by ensuring that manufacturing resources allocated are used effectively and efficiently. Poor

use of time would be converted into the extra cost. The main benefit of speed delivery in

manufacturing depends on how operations are enhanced. Response to outside customers is

significantly improved through the quick decision making and the flow of materials and

information (Chiu & Hsieh, 2016).

On the other hand, Flexibility enables a company to offer a broad variety of products to its

clients. It is an important concept for manufacturing firms. There are a lot of dynamics

associated with manufacturing operations especially due to changes in customers' needs and a lot

of innovation in green production technology like the use of robots to eradicate exposure of

employees to health hazards. In this case, the manufacturer must be flexible in order to meet

changes (Rha, 2010).

Finally, Cost management is a universal operational objective for all manufacturing plants

without compromising the levels of quality, speed, dependability, and flexibility. The cost can be

18

achieved by developing a strategy that is inclusive of total quality management and

environmental impact (Chien & Shih, 2007).

2.5.2. Financial performance

Financial performance is a subjective measure of how a firm can use its assets properly and

generate income. The term also used as an over-all measure of a firm's overall financial health

over a given period. It helps to identify how well a firm generates incomes and manages its

assets, liabilities, and the financial interests of its stakeholders. The level of performance of the

industry over a stated period of time expressed in terms of overall profits and losses during that

time. Evaluating the financial performance of business allows decision-makers to judge the

results of business strategies and activities in objective monetary terms, and also it uses to

compare similar firms across the same industry or to compare industries or sectors in

aggregate(Khan, 2019).

Financial performance is one of the determinants for the firm‘s sustainability. Many organization

usually focuses on its profitability, revenue growth, increase in market share, and increase in

productivity by searching the way of improving the general level of profitability, decrease the

level of production costs, reduce penalties cost, decrease in the costs of raw materials or

components and decrease in packaging costs (Laosirihongthong, Tan & Adebanjo 2014).

Now a day, balancing between financial performance & environmental performance has become

progressively significant for organizations for facing competitive, regulatory, and community

pressures. With these increasing forces, firms have to adopt certain strategies, practices, and

processes to face competition (Jayarathna & Lasantha, 2018). That means, the companies must

design the best strategy that helps to attain financial performance as well as environmental

performance simultaneously, for that purpose practicing green logistics practices are important,

Even though, the adoption of green logistics practices are costly in investment and purchasing

environmentally friendly materials, operational and training cost, it has a positive effect on

minimization of the overall cost (cost of energy consumption, cost of waste treatment and

discharge and avoid penalties in case of environmental accidents) and opens new market by

ensuring sustainable approach (Sari & Yanginlar, 2015).

19

2.6. Empirical Review

This section of the literature review includes global studies from prior researchers about the

relationship between green logistics practices and organizational performance.

Green logistics practices are implemented through the growth of customer awareness and

compliance pressure about environmental issues. Due to this, manufacturing firms are facing

more and more heavy pressure to reduce hazardous chemicals and emissions by implementing

green practices throughout the SC (Mohamed et al., 2015). Companies‘ activities are

significantly connected with both environmental performance and economic performance to be

competent in a long market environment (Jayeola, 2015, Manrique & Marti- Ballester, 2017). On

the other hand, environmental performances have been viewed as a drain on company profit-

ability, because implementing green practices requires heavy investments in technology,

processes, and employee training to adopt green logistics practices during manufacturing goods

and services (Ong et al., 2014).

A number of studies have been done to know the relationship between green logistics practices

adoption and organizational performance. According to Mohamed, (2012), Sari & Yanginlar,

(2015) that successful implementation of Green Logistics practices (GLP) such as; green

purchasing, cooperation with customers, Eco design and reverse logistics will lead to improved

environmental and financial performance which support improved organizational performance.

On the other hand, different researches conducted by diverse researchers on the relationship

between green logistics (GLP) and firms‘ performance in different periods of time show that

negative relationships. According to Zhu et al., (2010), GLP has a negative influence on

operational performance. According to Iwata & Okada (2010), Khan, & Qianli, (2017) and

Jayarathna & Lasantha, (2018), found that Environmental practice and regulations (EPR), green

purchasing (GP) has a negative impact on financial performance.

In the next section, the details of previous research finding which conducted about green

logistics effects on the performance of companies were presented.

20

2.7. Research framework and hypothesis

Under this section, the conceptual model of the study and hypotheses that were developed to be

tested were discussed. As a memo, the study was proposed to find out the effect of green

logistics practices on performance of large manufacturing firms (LMF) in Debre Birhan town.

2.7.1. Conceptual Framework

A conceptual framework is a visual or written product that described either graphically or in

narrative forms and that demonstrates the core things to be studied, concepts, or variables and the

supposed association among them (Wilson et al., 2015) as cited by (Tsegaye Habitye 2018).

The below figure shows, the conceptual framework of this study that developed based on the

research questions, works of literature and assumed relationship. It developed by considering

different practices of green logistics practices namely; green purchasing practices, green

manufacturing practices, reverse logistics practices and environmental practices and regulations

used as the independent variable and measurement of large manufacturing firms‘ performance,

namely; operational performance and financial performance used as the dependent variables.

Figure1: conceptual framework

Independent variables

H1a

H1b H2a

H2b

H3a H4a

H3b

H4b

Source: researcher, (2020), by reviewing deferent literature

Green purchasing (GP)

Green manufacturing (GM)

Environmental practice and

regulation (EPR)

Reverse logistics (RL)

a) Operational

performance(OP)

b) Financial

performance (FP)

Green

logistics

practices

Dependent variables

Perfor

mance

of large

manufa

cturing

firms‘

21

2.7.2. GLP and Large Manufacturing Firms’ Performance

Green logistics practices integrate environmental thinking into the supply chain management and

emerged in the last few years and cover all phases of products‘ life cycle starting from design,

production, distribution, and disposal of the product at the end of the product life cycle (Santos,

et al., 2019). On the other way, green logistics refers to reducing the damage to the environment

caused by business activity and maximizing resource utilization in the cycle of operation, with

the aim to move toward sustainable development. It is a component of both the environmental

symbiotic economy and adaptive economic development, which have important roles in the

national green economy strategy (Qu et al., 2017).

Performance measurement is the process of quantifying action, where management means the

process of quantification, and the performance of the operation is assumed to derive from actions

taken by its management. As a prerequisite for achieving competitiveness, organizations must

have some kind of performance measurement. These performance measurements also may have

an effect on the management of whether to take some actions or adopt certain practices

(Jayarathna & Lasantha, 2018).

Results in different studies on the relationship between green logistics practice and firms‘

performance have indicated inconsistent outcomes. The details are discussed in the next section

2.7.2.1. GLP and operational performances

According to Soubihia et al., (2015) and Santos et al., (2019), Green logistics practices (GLP)

have a positive influence on operational performance in the manufacturing industries. Green

practices are implemented to answer to regulatory or social pressures and may get operational

benefits and it enhances customer satisfaction with respect to delivery and quality by adapting to

changes in demand, as well as reducing inventory levels. According to, Zhu et al., (2010), Green

logistics practices (GLP) have a negative influence on operational performance because of lack

of external cooperation and diffusion with supplier and customers may seriously impede on

operational performance improvements. Due to this, green purchasing practices and green

manufacturing practices were negatively correlated and affected operational performance. While,

according to, Mukonzo (2017), green manufacturing had a positive correlation and effects on

operational performance. Green manufacturing practices lead to enhanced operational

22

performance. If the Firms adopt green manufacturing strategies are able to produce at minimal

cost and have less health environmental impact that would enhance the long-term global

competitive environment. Therefore it is recommended that manufacturing firms should adopt

green manufacturing practices as this will enhance their operational performance thus making

them more competitive.

According to Kipkorir & Wanyoike (2015), Islam et al, (2017) and Mogeni, (2016), studies

shown that green purchasing practices had a positive relationship and effects on operational

performance. Purchasing recyclable products, acquisition of bio-gradable products, and

purchasing of low energy consumption enhance operation performance (Kipkorir & Wanyoike,

2015). Moreover, green procurement practices are positively and significantly related to reduced

use of natural resources; increased product quality; enhanced company image, foster innovation,

enhance competitiveness, attracting foreign direct investment, meeting strategic goals; and

improved working environment, compliance, efficiency, and transparency. further evidence

shown that improvement in an organization‘s internal quality and operational process,

innovativeness, efficiency, and transparency, social responsiveness, and environmental issues are

highly influenced by green purchasing practices (Islam et al., 2017).

Therefore, Green purchasing as a method of lean operation has been a recent issue all over the

world and has been tested enterprise-wide to attain the goal of increasing operational

performances through waste minimization, compliance to regulations, and customer & supplier

involvement along the value chain (Mogeni, 2016).

As discussed in the above, the previous research conducted on the effect of green logistics

practice on operational performance was revealed that different results. Depend on that, the

researcher hypothesized the hypothesis as follow:

H1a: GP has not statistical significant effect on the OP of Large Manufacturing Firms.

H2a: GM has not statistical significant effect on the OP of Large Manufacturing Firms.

H3a: RL has not statistical significant effect on the OP of Large Manufacturing Firms.

H4a: EPR has not statistical significant effect on the OP of Large Manufacturing Firms.

23

2.7.2.2. GLP and financial performances

According to Mohamed, (2012), result indicates that green logistics practices (GLP) have a

positive influence on financial performance. The adoption of GLP has greatly benefited for the

most manufacturing firms especially to minimization of waste, and hence leading to an increase

in demand for the green products; thereby profit maximization will achieve. Andrushchak et al.,

(2018), suggest that Companies must adopt green logistics practices in order to attain financial

benefits; tax reduction, subsidies and reducing penalties cost and competitive advantage resulting

in new markets possibilities and product innovations; environmental standards, following which

result in positive customer‘s perception.

According to Sari & Yanginlar, (2015), Youssef, (2016) and Kalhari et al., (2018), studies

found that Green Manufacturing (GM), Green Purchasing (GP), and Reverse Logistics Practices

(RLP) have a positive impact on financial performance. Green manufacturing can help to

decrease waste and harmful emissions and work towards preserving resources that are limited

and non-renewable. Shareholders could identify which green manufacturing practices improve

financial performance vastly and how to develop existing green manufacturing practices in order

to maximize their financial performance. In addition, according to Zhu et al., (2010), Kipkorir &

Wanyoike (2015), and Islam et al. (2017), green purchasing practices were positively related to

financial performance. But according to Khan, & Qianli, (2017) and Jayarathna & Lasantha,

(2018), Green purchasing has a negative impact on the financial performance because green

materials are much more expensive than non-green materials. Due to this firms obligated to add

extra cost to acquire green material, products and equipment.

According to Bartolacci & Zigiotti, (2015), environmental practices and regulation had positive

correlation and effects on financial performance, means that, revenues can be positively

impacted when customers prefer the products of environmentally friendly firms resulting in

increased market share than less environmentally-oriented competitors. Further, it is possible that

environmental management may be necessary to maintain markets in the long-run (Di Pillo et al.,

2017, Manrique & Marti-Ballester, 2017). Moreover, costs can be lowered when firms invest in

environmental management systems that lead to decreasing environmental risk and liability. As

the same as proactively managing environmental regulation, may create barriers and first-mover

advantages that are difficult for competitors to imitate (Jayeola, 2015).

24

According to Iwata & Okada, (2010), found that Environmental practices and regulation has a

negative impact on financial performance, because the cost to dispose of waste is higher due to

more strict regulations; due to this, industries often confront both more risks of failure to comply

with laws, and lawsuits. Ong et al., (2014), study result indicated compliances that are required

by environmental laws and regulations, increase the cost of companies and thus decrease their

profit. When companies are mitigating the environmental influence of products and services,

they may need a lot of research and development, which will subsequently raise expenses, and

diminish the return on the assets and equities.

According to Naila, (2013), the study on manufacturing companies in Tanzania, shown

environmental practices and regulation had been an insignificant relationship with financial

performance.

As discussed in the above, the previous research conducted on the effect of green logistics

practice on financial performance was revealed that different results. Based on that the researcher

hypothesized the hypothesis as follow:

H1b: GP has not statistical significant effect on the FP of Large Manufacturing Firms.

H2b: GM has not statistical significant effect on the FP of Large Manufacturing Firms.

H3b: RL has not statistical significant effect on the FP of Large Manufacturing Firms.

H4b: EPR has not statistical significant effect on the FP of Large Manufacturing Firms.

2.8. Gap in Literature

Green logistics refers to reducing the damage to the environment caused by the business

operation and maximizing resource utilization in the cycle of logistics activities, with the aim to

move toward sustainable development. It is a component of both the environmental associated

economy and adaptive economic development, which have important roles in the national green

economy strategy (Qu et al., 2017). There are a number of studies which have been done

previously to address the relationship between green logistics practices and firms performance

in different countries at different period of time are available such as: Zhu et al., (2010); Iwata &

Okada (2010), Naila, (2013), Ong et al (2014), Bartolacci & Zigiotti (2015), Kipkorir &

Wanyoike (2015), Jayeola (2015), Mogeni, (2016), Islam et al (2017), Khan, & Qianli (2017),

25

Mukonzo (2017), Di Pillo et al., 2017, Manrique & Marti-Ballester, (2017), Jayarathna &

Lasantha, (2018) and Kalhari et al., (2018).

An increasing number of studies have addressed the relationship between green logistics practice

and firms performance. Yet, the findings from these studies have been inconsistent; therefore,

experts unable to give clear answer as to what green logistics practices would be beneficial to

follow. Based on this, it is difficult to generalized and determine which green logistics practices

are more importance for a given organization. Therefore, further investigation is critical in

different contexts. Accordingly, this research proposed to examine the effect of green logistics

practices on the performance of large manufacturing firms in Debre Birehan town. At the end of

this study, the researcher has believed this study has a lot of advantages for firms and experts‘,

the most important ones are enhancing knowledge and understanding of green logistics practices,

fills a gap of inconsistency studies results and draws a clear picture of the relationship between

GLP and firm‘s performance by rejecting and supporting the given hypothesis. As the best

knowledge of the researcher, it will be the first empirical study in this field from the perspective

of the Ethiopian large manufacturing industry.

26

CHAPTER THREE

METHODOLOGY OF THE STUDY

3.1. Introduction

This chapter deals about the research methodology used to do this study. According to Kothari

(2004), Research methodology is a way to scientifically solve the research problem. It may be

understood as a discipline of studying how research is done scientifically.

Here, this chapter describes the research design, research approach, target population, sampling

techniques and sample size, types and source of data, method of data collection, validity and

reliability of instruments, method of data analysis and finally, ethical considerations of this study

were discussed.

3.2. Research Design

According to Uma & Roger, (2010), explains research design as the framework or plan for a

study or used as a guide in collecting and analyzing data. It is the blueprint that is followed in

finalizing a study. It looks like the architect's blueprint for a house. However, there is no single

perfect design for conducting research; there are different classifications of research design. The

most useful classification is based on the objectives of the research: Exploratory, Descriptive and

Explanatory (Creswell, 2014; Kothari, 2004).

As presented in chapter one, the main objective of this study was examining the effect of GLP on

the performance of LMF, or testing the hypothesis to support or reject. Hence, to attain the

general objective of this study, explanatory types of research design is appropriate. It used to

explain how green logistics practices affect the performance of large manufacturing firms in

Debre Birehan.

3.3. Research Approach

In social sciences, there are two primary approaches to conduct this research work and generate

knowledge. They are quantitative research approach and qualitative research approach.

Qualitative research approach is based on the interpretation of researcher and often depends on

27

words and descriptions to create a deeper understanding of specific area interviews and

observations are an example of qualitative analysis while the quantitative research approach is

based on numerical and statistical data, and it is a convenient approach to manage a large amount

of data which can be measured in a numerical way (Kothari, 2004). The goal of the quantitative

approach is testing hypotheses. Therefore, the researcher used a quantitative research approach in

this study to test hypotheses, or answer basic research questions which stated in chapter one to

realize the general objective.

3.4. Target Population

The target population is the total number of subjects targeted by the study, or the group of

elements to which the researcher wants to make a conclusion (Creswell, 2014). Accordingly, the

target area for this study was Large Manufacturing Firms/LMF in Debre Berhan town. The

following were LMF in Debre Berhan town

Table 1; List of large manufacturing firms

No List of LMF Types of manufacturing

1 Amayra Garment

2 Dashin Beer Factory Beer factory

3 Debre Berehan Blanket Blanket factory

4 Debre Berehan wood processing Wood processing

5 EMMY edible oil Oil factory

6 Etal aluminum Aluminum factory

7 Juniper Glass factory

8 Habesha Beer Factory Beer factory

9 Kedir Seid plc. Flour factory

10 R.Z.X. Comfort Blanket

11 MEM Canned water factory

12 Tera plc. Cosmetics

13 Vairo garden Furniture

14 Wedera Flour Flour factory

Sources: Debre Birehan town investment commission bureau (2020)

28

3.5. Sampling Technique and Sample Size

According to Kothari, (2004), when the field of inquiry is large or vast, considerations of time

and cost lead to a selection of respondents that means; selection of a few respondents. The

selected respondents should be as representative of the total population as possible in order to

increase the appropriateness of the study output. The selected respondents create what is

technically called a sample, the selection process is called sampling technique and the number of

items to be selected from the total population to constitute a sample is called a sample size.

Therefore, from the target population, Debre Birehan blanket factory, Debre Birehan wood

processing factory, Etal aluminum factory and MEM candle spring water factory were selected

as a sample frame via simple random sampling technique.

Then, the permanent employees of Debre Birehan blanket factory, Debre Birehan wood

processing factory, Etal aluminum factory and MEM candle spring water factory were classified

into four main strata namely: purchasing/procurement, production/manufacturing, accounting

and finance, and marketing/sales via stratified random sampling technique.

The researcher used a stratified random sampling method to classifying the population in

different categories (strata). Because, a sample drawn from the population does not found to

representatives sample size from each group, the stratified sampling technique is generally

applied to obtain a representative sample size from each groups and able to acquire relevant

information from concerned body. Accordingly, the population was divided into several

subpopulations that were individually more homogeneous than the total population (the different

sub-populations are called ‗strata‘) and then employees selected from each stratum to constitute a

sample.

After stratifying, the sample size from each stratum or departments namely; purchasing,

production, finance and marketing were determined through proportional stratified sampling

technique in order to get representative sample size from each departments. Finally, sample units

from each department were selected via simple random sampling technique.

29

Table 2: Number of employees under each department

Firms Number of employees under department of:

Procurement

/purchasing

department

Production/manufact

uring department

Accounting

and finance

department

Marketing/sales

department

Total

DBBF 8 230 9 10 257

DBWP 7 104 10 8 129

Etal aluminum factory 14 260 12 17 303

MEM canned water

factory

4 37 5 11 57

Total 33 631 36 46 746

Sources: each companies HRM

The sample size should neither be extremely large nor too lesser. It should be optimum. An

optimum sample is one that fulfills the requirements of efficiency, Reliability, Flexibility, and

Representativeness (Kothari, 2004). The total number of population for this study was 746

(N=746) permanent employees, working in selected large manufacturing firms located in Debre

Birehan town. Considering all permanent employees was impossible because of complexity,

time, and cost constraints. The sample size was calculated using a formula called Slovin‘s (1992)

as cited by (Tsegaye, 2018). This formula was used for determine sample size; the reason for

using this sample size formula was researchers adopt this formula which was similar in this

research study.

n=

n=

=260

Where: n = number of samples

N = Population of the study

e = possible error term=0.05

Accordingly, with estimated error terms of 5%, it yield (95%) confidence interval

30

Table 3: Sample size allocation from each stratum

Where:

N= total population

n= sample size

NP, NPM, NA and NM= total population of each department in each firm

np, npm, na and nm= sample allocated from each department in each firm

3.6. Types and Source of Data

The researcher used primary source of data for the entire analysis of this study. Therefore, the

information was collected through questionnaire from the selected sample of respondents and the

data collected from the respondents through questionnaires was used as primary data.

3.7. Method of Data Collection

A Quantitative approach was used to make statistical generalizations about the effect of green

logistics practice on the performance of large manufacturing firms. This enables the person who

is carrying out the investigation to make a statement concerning the sample population by

gathering material from the sources that were selected (Kothari, 2004).

Firms Number of employees under department of;

Procurement/purchasi

ng

Production

/manufacturing

Accounting

& finance

Marketing/sales

NP Sample assigned

np= (NP/N)*n

NPM Sample assigned

npm= (NPM/N)*n

NA Sample assigned

na= (NA/N)*n

NM Sample assigned

nm= (NM/N)*n

Total

DBBF 8 3 230 80 9 3 10 3 89

DBWP 7 2 104 36 10 4 8 3 45

Etal Aluminum 14 5 260 91 12 4 17 6 106

MEM canned

water factory

4 1 37 13 5 2 11 4 20

Total 33 11 631 220 36 13 46 16 260

31

In this study, structured questionnaires were used to collect quantitative data from respondents.

Here, the questionnaires were adapted from Mafini, & Loury-Okoumba, (2018), and

Laosirihongthong, Tan, & Adebanjo, (2014). Structured questionnaires were used to collect the

data from the selected permanent employees of selected LMF who works under four departments

namely: purchasing, production, finance and marketing to obtain appropriate information.

Moreover, the questionnaires were sent in person to the concerned person to answer the

questions and return it.

The questionnaires were chosen to collect data, because, it would enable the researcher to reach a

number of respondents within a limited period of time and it is convenient to ensure the privacy

of respondents and also close-ended questionnaires enable to cover more ground within a limited

time frame, particularly for respondents who would have severe time constraints.

Moreover, the questionnaires have three sections; first of all, section one- deals about the

demographic characteristics of the respondents, then, section two- deals about Green logistics

practices, finally, section- three deals about the Large manufacturing firms performance namely;

operational and financial performance.

3.8. Validity and Reliability of the Research

3.8.1. Validity

Validity gives details of how well the collected data covers the actual area of investigation. It

basically means measure what is intended to be measured. A measure is valid if it measures what

it is supposed to measure (Uma & Roger 2010).

According to Kindy et al. (2016), as cited by Tsegaye (2018), content validity is the extent to

which the items in an instrument cover the entire range of the significant aspects of the area

being investigated. It is the degree to which the measurement device, therefore, the measuring

questions in the questionnaires, provides sufficient coverage of the research investigative

questions. Hence, to maintain the validity of the instruments in this study, the questionnaires

were adopted from previous researches conducted by Laosirihongthong, Tan & Adebanjo

(2014), and Mafini & Okoumba (2018), and invite experts to evaluate it.

32

3.8.2. Reliability of the Research

Reliability refers to the capability of an instrument to produce consistent measurements. When

the researcher gather a similar set of information more than once using a similar instrument and

get the same or similar results under the same or similar conditions, an instrument is considered

to be reliable (Kumar, 2011).

The most popular method of testing for internal consistency in is Cronbach‘s coefficient alpha.

Cronbach‘s alpha coefficient typically ranges between 0 and 1. Gliem, (2003) as cited by

Tsegaye, (2018), and provide the following rules of thumb: if ―α > 0 .9 – Excellent, α > 0.8 –

Good, α > 0.7 – Tolerable, α > 0.6 – Doubtful, and α > 0.5 – Poor, and α < 0.5 – Unacceptable.

Table 4: Cronbach alpha reliability test

No Instrument dimension Cronbach‘s

alpha

No. of items Reliability

range

1 Green purchasing 0.856 5 Good

2 Green manufacturing 0.83 5 Good

3 Reverse logistics 0.8 5 Good

4 Environmental practice and regulation 0.859 4 Good

5 Operational performance 0.78 6 Good

6 Financial performance 0.886 4 Good

Source: Laosirihongthong, Tan & Adebanjo (2014) and Mafini & Okoumba (2018).

3.9. Method of Data Analysis

After all the data were collected through questionnaires, its completeness is verified, coded, and

entered the computer using SPSS. Means that the data was analyzed by using application

software packages named as Statistical Package for Social Sciences (SPSS) version 23 through

descriptive and inferential statistics.

3.9.1. Descriptive Statistical Analysis

Descriptive statistics summarizes and describes quantitative information in the form of frequency

distributions and measures of central tendency (mean and standard deviation). Frequencies and

percentages were used to analyze general information about respondents, the mean and standard

33

deviation was used to describe aspects of green logistics practices, and performance of large

manufacturing firms (PLMF). The mean was preferred as it considers the precise score of each

case thus it incorporates more information than the median which only states scores relative

position. The standard deviation, on the other hand, used to measure variation. The outcomes

were presented by using tables accompanied by explanations.

3.9.2. Inferential statistical

In Inferential statistical analysis, correlation and multiple linear regression analysis were used to

determine the relationship between the dependent variable (green logistics practice) and

dependent variable (performance of large manufacturing firms) and to test the effect of green

logistics practices on large manufacturing firms‘ performance respectively. Finally, the results

were presented using tables and every table was accompanied by result interpretation.

3.9.2.1. Correlation Analysis

According to Koutsoyiannis, (1977), as cited by Tsegaye (2018), Correlation used to determine

the degree of the relationship existing between two or more variables. The correlation coefficient

(r) is a measure of the degree of co-variability of the variables. Therefore, Pearson correlation

was used to show the relationship of variables namely: Green purchasing (GP), Green

manufacturing (GM), Reverse logistics practices (RLP), and Environmental practices and

regulation (EPR) with the operational and financial performance of large manufacturing firms.

The number r, called the linear correlation coefficient, measures the strength and the direction of

a linear association between the set of green logistics practices and metrics of large

manufacturing firms‘ performance. As a statistical estimate, r is inevitability subject to some

error and would be testing its reliability by conducting some test of significance. While

computing a correlation, the level of significance should be set at 95% with an alpha value of

0.05).

34

3.9.2.2. Multiple Regression Analysis

Regression analysis was concerned with the study of the dependence of one variable on one or

more other variables or with a view of knowing the mean or average value of the former

(explanatory variables) used to estimating and/or predicting the values of the latter that is

dependent variable (Uma & Roger, 2010).

The multiple regression analysis was used to determine whether the set of green logistics

practices would have an influence on large manufacturing firms‘ performance. The study uses

the following multiple regression model to establish the statistical significance of the

independent variables on the dependent variables.

Y1 = β0 + β1X1 + β2X 2 + β3X3 + β4X 4 +

Where; Y1 = Large manufacturing firms‘ performance

X1 = Green purchasing

X2 = Green manufacturing

X3 = Reverse logistics practices

X4 = Environmental practices and regulation

In the model, β0 = Constant, represent the value of large manufacturing performance, if

coefficient of green logistics practices were zero, β1 to β4 = Regression coefficients represent the

mean change in the performance of large manufacturing firms‘ for one unit of change in the set

of green logistics practices, and ꞓ = Error term which captures the unexplained variation in the

model.

3.10. Ethical Consideration

Ethics are the norms or values for behavior that distinguish between right and wrong. It helps to

determine the difference between acceptable and intolerable behaviors. Ethics is particularly

significant components throughout the research procedures and if failed to be taken into account,

it can lead to misinterpretation or even invalid conclusions. Hence, in this paper did not go under

any form of bias or change, and the researcher respected the code address issues such as honesty,

objectivity, respect for intellectual property, social responsibility, confidentiality, non-

discrimination.

35

CHAPTER FOUR

DATA PRESENTATION, ANALYSIS &INTERPRETATION

4.1. Introduction

As discussed in the previous chapters the study attempted to explain the cause and effects

relationship between green logistics practice and performance of large manufacturing firms

(PLMF) located in Debre Birehan town. This chapter presents the result of statistical analysis of

the data obtained from the respondents and the research finding, focuses on answering the

research questions stated in chapter one. Accordingly the results of this study, the researcher

gives interpretation.

First of all, this chapter includes the analysis part of the research such as the response rate,

general characteristics of the respondents, reliability analysis, level of green logistics practicing

in case companies and performance of large manufacturing firms presented by descriptive

statistics. Secondly, the degree of association between green logistics practices and performance

of large manufacturing firms were measured and presented via correlation analysis were

presented. Finally, the result of multiple regressions to show the cause and effects relationship

between green logistics practices namely: green purchasing, green manufacturing, reverse

logistics and environmental practices and regulation on performance of large manufacturing

firms namely; operational performance and financial performances.

4.2. Response Rate

Questionnaires were distributed and collected from respondents in person. Here, out of the total

260 questionnaires distributed to the selected respondents; purchasing, accounting, production,

and marketing staff workers of Debre Berhan wood processing, MEM canned water factory,

Debre Berhan blanket factory, and Etal aluminum factory, only 243 were correctly filled and

returned to the researcher. The remaining 17 questionnaires; 8 were not returned at all, and 9

were not correctly filled. Therefore, the overall response rate was 93.5%.

36

Table 5: Response rate

Response rate Filled and

returned

Not

returned

Not correctly

filled

Total

Frequency 243 8 9 260

Percentage 93.5 3% 3.5% 100%

Source own survey, 2020

4.3. Respondents General Information

In this section, the general information of respondents; gender, level of education, work unit, and

work experience were presented. Here, Gender was assessed to understand the involvement of

both genders in the study as the same as the level of education was important to imply that the

respondents were well educated and had the ability to understand and respond to the issues

sought by the study. In addition, the work unit was required to infer that the respondents were

able to understand different set of green logistics practices sought by the research, and finally,

Work experience was used to ensure aspects of familiarity and experience of the respondents in

matters of green logistics practices.

Table 6: Demographic profile of respondents

Characteristics Descriptions Frequency Percent

Gender of respondents Male 138 56.8

Female 105 43.2

Total 243 100.0

Age of respondents 18-25 41 16.9

25-35 102 42

35-45 79 32.5

45 and above 21 8.6

Total 243 100.0

Educational status

Diploma 82 33.7

Degree 149 61.3

Master 12 5

PhD and above 0 0

37

Total 243 100.0

Department of respondents

Procurement 10 4.1

Production 208 85.6

Sales and marketing 14 5.8

Accounting and Finance 11 4.5

Total 243 100.0

Less than five year 52 21.5

5-10 107 44

Working experience 10-15 73 30

15 and above 11 4.5

Total 243 100.0

Source: own survey, 2020

The above table shows that 43.2% (105) of the respondents were females, while males accounted

for 56.8% (138). This indicates that almost both genders were fairly involved in the study, and

42% (102 respondents) were between the ages of 25-35, 32.5% (79 respondents) were between

the ages of 35-45. 16.9% (41 respondents) were between the age of 18-25 and 8.6% (21

respondents) were at the age of greater than 45. It implies that large manufacturing had hot

manpower.

In addition, the above table depicts that the majority (61.3%) of the respondents had a degree

level of education followed by 33.7% of the respondents who had a diploma and finally 4.9%

who had the master level of education. This indicates that the respondents had sufficient levels of

education to understand and respond to the issues sought by the study. It also shown that 85.6%

of the respondents were from the production work unit followed by marketing work unit (5.8%),

accounting and finance work unit (4.5%), and procurement work unit (4.1%) respectively. This

implies that the respondents were able to understand the different green logistics practices sought

by the research based on the different work units they belong to.

As the above table shown that majority of the respondents 107(44%) had a work experience of

between 6 to 10 years followed by 73(30.0%) respondents had a work experience of between 11

to 15 years, 52 (21.5%) respondents a work experience of between 0 to 5 and 11(4.5%)

respondent had above 16 years of work experience. This shows that majority of the respondents

38

had served for a considerable period of time which implies that they were in a position to give

credit information relating to the study.

4.4. Reliability Test

In this study, the data was collected through questionnaire data gathering tools. Therefore, in

order to evaluate the internal consistency of the item or data collection instruments, Cronbach‘s

Alpha was used.

Cronbach‘s Alpha is an indicator of the degree of internal consistency of scales. The higher the

coefficient the higher degree of consistency denotes; >0.9 Excellent, >0.8-Good, >0.7-

Acceptable, >0.6Quesstionable, >0.5-Poor, <0.5-Unacceptable as cited by (Tsegaye, 2018).

Therefore, as shown in the table below, the result of the reliability test revealed that the items in

the questionnaire exhibited Cronbach Alpha rate more than enough to be called consistent or

acceptable.

Table 7: Reliability of questionnaire dimension

Green Logistics Dimension Cronbach‘s Alpha No. of item

Green purchasing .767 5

Green manufacturing .815 5

Reverse logistics practice .780 5

Environmental practices and regulation .730 4

Operational performance .715 6

Financial performance .777 4

Source: own survey, 2020

As the above table show that all the items used to measure the dimensions of this particular study

scored calculated alpha values that range from the lowest value of .715 to the highest value of

.815.

39

4.5. GLP in Large Manufacturing Firms

The respondents were asked to indicate the level of practicing set of green logistics practices.

Here, the green logistics practices were green purchasing, green manufacturing, reverse logistics,

environmental practices, and regulation. Therefore, in order to determine level of practicing the

stated green logistics practices, five-point Likert scale were used; 1- Never practiced; 2- rarely

practiced; 3- occasionally practiced; 4- Very often practiced; 5 -Always practiced. Moreover,

analysis of the data was done using means and standard deviations, the recorded means were

interpreted as follows: 1-1.49 = Never practiced; 1.5-2.49 = rarely practiced; 2.5-3.49 =

occasionally practiced; 3.5-4.49 = Very often practiced; 4.5-5.0 =Always practiced (Lady, 2016),

as cited by (Tsegaye, 2018)

4.5.1. Green purchasing practices

In this part, the study determined the level of practicing green purchasing practices by large firms

located in Debre Birehan town. The respondents were asked to rank the green purchasing

practices that they have practiced.

Table 8: Green purchasing practice

Green purchasing N Mean Std. Deviation

Ensure suppliers meet their environmental objectives 243 3.7 .939

Requires suppliers to have certified EMS like ISO 14001 243 3.62 .921

Ensure purchased materials contain green attributes 243 3.35 .986

Evaluates suppliers on specific environmental criteria 243 3.24 .975

Requires suppliers to develop and maintain an EMS 243 3.30 1.019

Overall mean 243 3.44 .6399

Source: own survey, 2020

The above table depicts, an overall mean and standard deviation of (M=3.44, SD= .6399) was

recorded, that indicate green purchasing was occasionally practiced by large manufacturing firms

in Debre Birehan.

40

In addition, the above table depicts that Ensure suppliers meet their environmental objectives

(M=3.70, SD=.939), and Requires suppliers to have certified EMS like ISO 14001 (M=3.62,

SD=.921) were very often practiced in large manufacturing firms followed by Ensure purchased

materials contain green attributes (M=3.35, SD=.986), Requires suppliers to develop and

maintain an EMS (M=3.30, SD=1.019) and Evaluates suppliers on specific environmental

criteria (M=3.24, SD=.975) were occasionally practiced respectively by selected case large

manufacturing firms.

4.5.2. Green manufacturing practices

In this section, the study sought to disclose the level of practicing green manufacturing practices

by large manufacturing firms located in Debre Birehan town. The results are shown in the below

table.

Table 9: Green manufacturing practices

Green manufacturing N Mean Std. Deviation

Cross-functional cooperation for environmental improvements 243 3.40 1.099

Total quality of environmental management 243 3.70 .939

Environmental compliance and auditing programs 243 3.38 .998

ISO14000 series certification 243 3.5 1.002

Environmental management systems 243 3.32 1.026

Overall mean 243 3.45 .7686

Source: own survey, 2020

As shown from the above table, an overall mean and standard deviation of (M=3.45, SD= .7686)

was recorded, which indicates green manufacturing practices were occasionally practiced by

large manufacturing which selected in this study.

In addition, the above table indicates that total quality of environmental management and

ISO14000 series certification was very often practiced with a relatively highest mean (M=3.7,

SD=.939) and (M=3.5, SD=1.002) respectively followed by Cross-functional cooperation for

41

environmental improvements (M=3.4, SD=1.099), Environmental compliance and auditing

programs (M=3.38, SD=.998) and Environmental management systems (M=3.32, SD=1.026)

was occasionally practiced respectively by large manufacturing firms in Debre Birehan town.

4.5.3. Reverse logistics practice

Under this section of discussion, the level of practicing reverse logistics practices by large

manufacturing firms located in Debre Birehan town were discussed as follow..

Table 10: Reverse logistics practices

Reverse logistics N Mean Std. Deviation

Accepting product returns from customers 243 3.44 1.048

Recalling products with quality problems 243 3.58 .999

Returning products to suppliers 243 3.56 1.094

Recycling scrap and used items 243 3.39 .983

Repairing, recondition and remanufacture component parts

from returned, defective, or damaged products

243 3.54 1.037

Overall mean 243 3.5 .7530

Source: own survey, 2020

The above table depict, an overall mean and standard deviation of (M=3.5, SD= .7530) was

recorded, which indicates reverse logistics practices were very often practiced in large

manufacturing firms which located in Debre Berhan town.

As the above table indicated, Recalling products with quality problems (M=3.58, SD=.999),

Returning products to suppliers (M=3.56, SD=1.094) and Repairing, recondition and

remanufacture component parts from returned, defective, or damaged products (M=3.54,

SD=1.037) were practiced very often followed by Accepting product returns from customers

(M=3.44, SD=1.048) and Recycling scrap and used items (M=3.39 SD=.983) were occasionally

practiced within selected large manufacturing firms.

42

4.5.4. Environmental practices and Regulation

In this part of the study, the extent of practicing environmental practice and level of obeying for

regulation by the selected case companies presented as follow.

Table 11: Environmental practices and regulation

Environmental practice and regulation N Mean Std. Deviation

Adopt green logistics initiatives to avoid threat of legislations 243 3.17 1.144

Strict environmental standards to comply with 243 3.30 1.038

Frequent government inspections in firm 243 3.31 1.103

Government imposed many environmental regulations 243 3.54 1.069

Overall mean 243 3.33 .8091

Source: own survey, 2020

From the above table, an overall mean and standard deviation of (M=3.33, SD=.80913) was

recorded, which indicates environmental practices and regulation were occasionally practiced by

the large manufacturing firm located in Debre Birehan town.

Moreover, the table depicts that Government imposed many environmental regulations (M=3.54,

SD=1.069), frequent government inspection in the firm (M=3.31, SD=1.103), Strict

environmental standards to comply with (M=3.30, SD=1.038) and Adopt green logistics

initiatives to avoid the threat of legislations (M=3.17, SD=1.144) were practiced occasionally by

selected case manufacturing firms.

4.6. Large manufacturing firms performance

In the same way, the respondents were also asked to indicate the status of the performance of

their firms. As a performance measurement: operational performance and financial performance

were used as metrics of large manufacturing firms‘ performances for this study. Here, five-point

Likert scale with 1- Not at all; 2- small extent; 3- moderate extent; 4- great extent; 5 –very great

extent was used to rate the operational and financial performances of large manufacturing firms

located in Debre Birehan town

43

Based on the findings of the above Table, the overall mean and standard deviation (M=3.59,

SD=.5943) was recorded, which shows the operational performance of large manufacturing

which locate at Debre Berhan was categorized under great extent.

Moreover, the above table illustrates that Increase the number of goods delivered on was

relatively high with a mean of time (M=3.70, SD=939), followed by Increase product quality

(M=3.68. SD=.845), Decrease inventory levels (M=3.65, SD=.826), Decrease scrap rate

(M=3.63, SD=.947), and Increase product line (M=3.62, SD=.921) were great extent practiced

respectively. Only Improved capacity utilization (M=3.59, SD=.942) was groped under moderate

extent in large manufacturing firms which locate at Debre Berhan town.

In addition, analysis of the data was done by using means and standard deviations and the

means recorded were interpreted as follows: 1-1.49 = Not at all; 1.5-2.49 = Small Extent;

2.5-3.49 = Moderate Extent; 3.5-4.49 = Great Extent; 4.5-5.0 = Very great extent (Tsegaye,

2018).

Table 12: Operational performance of large manufacturing firms

Operational performance N Mean Std. Deviation

Increase the amount of goods delivered on time 243 3.70 .939

Decrease inventory levels 243 3.65 .826

Decrease scrap rate 243 3.63 .947

Increase product quality 243 3.68 .845

Increase product line 243 3.62 .921

Improved capacity utilization 243 3.28 1.055

Overall mean 243 3.59 .5943

44

Table 13: Financial performance of large manufacturing firms

Financial performance N Mean Std. Deviation

Improvement in general level of profitability 243 3.58 .856

Decrease in the level of production costs 243 3.57 1 .007

Decrease the costs of raw materials or components 243 3.6 .892

Decrease in packing cost 243 3.53 .963

Overall mean 243 3.56 .7209

Source: own survey, 2020

As shown from the above table, an overall mean and standard deviation was (M=3.56,

SD=.7209) was recorded, that show, financial performance of large manufacturing classify to a

great extent level.

Moreover, the above table indicates that Decrease the costs of raw materials or components was

great extent with a relatively high mean (M=3.6, SD=.892), in large manufacturing firms

followed by Improvement in the general level of profitability (M=3.58, SD=.856), Decrease in

the level of production costs (M=3.57, SD=1.007) and Decrease in packing cost (M=3.53,

SD=.963) respectively were practice in great extent in large manufacturing firms which locate at

Debre Berhan town.

4.7. Correlation Analysis

Correlation analysis used to determine the degree of the relationship existing between the set of

GLP and PLMF. The correlation coefficient (r) is a measure of the degree of co-variability of the

variables. The value of ‗r‘ relies on between ± 1.

The sign of the correlation coefficient determines the relationship of set of GLP from operational

performance and financial performances of LMF were positive or negative. If Positive values of

‗r‘ indicate that set of GLP has been a positive correlation from operational performance and

financial performances of LMF, whereas negative values of ‗r‘ indicate that set of GLP has been

a negative correlation from operational performance and financial performances of LMF. A zero

45

value of ‗r‘ indicates that there is no association between the GLP and from operational

performance and financial performances of LMF.

In addition, the degree of the correlation coefficient defines the strength of the correlation. When

r = (+) 1, it indicates a perfect positive correlation and when it is (–) 1, it indicates a perfect

negative correlation. The value of ‗r‘ nearer to +1 or –1 indicates a high degree of correlation

between the two variables (Kothari, 2004). A result between 0.1 and 0.3 indicates weak

relationship, whereas a result between 0.4 and 0.6, and 0.7 and 0.9 imply respectively moderate

and strong relationships among variables as cited by (Mesfin, 2016).

4.7.1. Correlation between GLP and operational performance

Here, the researcher carried out a correlation analysis to test the relationship between set of GLP

and operational performance. Green logistics practices which included in this study were: green

purchasing practices, green manufacturing practices, reverse logistics practices, and

environmental practices and regulation. Therefore, the findings for this analysis were shown in

the following correlation matrix table as follow:

Table 14: Correlation between GLP and Operational performance

Green logistics practices GP GM RL EP OP

Green Purchasing Pearson Correlation 1 .440**

.323**

.423**

.612**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Green Manufacturing Pearson Correlation .440**

1 .575**

.368**

.736**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Reverse Logistics

Pearson Correlation .323**

.575**

1 .229**

.589**

Sig. (2-tailed)

N

.000 .000 .000 .000

243 243 243 243 243

Environmental

Practices &regulation

Pearson Correlation

Sig. (2-tailed)

N

.423**

.368**

.229**

1 .430**

.000 .000 .000 .000

243 243 243 243 243

Operational

performances

Pearson Correlation .612**

.736**

.589**

.430**

1

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

**. Correlation is significant at the 0.01 level (2-tailed).

Source: SPSS output survey, 2020

46

From the above Pearson correlation coefficient analysis table14: the set of green logistics

practice mentioned as independent variables in the model and operational performance of large

manufacturing firms have been a positive relationship.

In details, the result of this study shown that Green manufacturing practices (GM), have a strong

positive correlation with operational performance with Pearson correlation coefficient value

r=.736, p<0.01. Whereas, Green purchasing practices (GP), reverse logistics practices (RL) and

environmental practices and regulations have a moderate positive correlation with operational

performance with the Pearson correlation coefficient of r=.612, p<0.01, r=.589, p<0.01 and .430,

p<0.01 respectively.

In general, the correlation analysis shows that there was a strong and moderately positive and

statistically significant relationship between set of GLP mentioned in the model and the

operational performance of large manufacturing firms Debre Birehan town.

Accordingly, Thus finding was consistent with the findings of Soubihia et al., (2015), and Santos

et al., (2019), that conclude; Green logistics practices (GLP) are adopted to respond to regulatory

or social pressures and may bring operational benefits to the manufacturing industries. It

enhances customer satisfaction with respect to delivery and quality by adapting to changes in

demand, as well as reducing inventory levels. In addition, this study were support Kipkorir &

Wanyoike, (2015), Islam et al, (2017), and Mogeni, (2016), studies finding that conclude: green

logistics practices enhance organization‘s internal quality and operational process,

innovativeness, efficiency, and transparency, social responsiveness, and environmental issues are

highly influenced by green purchasing practices, and finally, Mukonzo (2017), conclude that

green manufacturing strategies are able to produce at minimal cost and have less health

environmental impact that would enhance the long-term global competitive environment.

4.7.2. Correlation between GLP and financial performance

As the same way, the researcher also carried out a correlation analysis to test the relationship

between GLP and financial performance of LMF. Green logistics practices which included in

this study were: green purchasing practices, green manufacturing practices, reverse logistics

practices, and environmental practices and regulation. Therefore, the findings for this analysis

were shown as follow:

47

Table 15: Correlation between GLP and Financial performance

Green logistics practices GP GM RL EP FP

Green Purchasing Pearson Correlation 1 .440**

.323**

.423**

.530**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Green Manufacturing Pearson Correlation .440**

1 .575**

.368**

.698**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Reverse Logistics Pearson Correlation .323**

.575**

1 .229**

.649**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Environmental Practices

and regulation

Pearson Correlation .423**

.368**

.229**

1 .404**

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

Financial Performances Pearson Correlation .530**

.698**

.649**

.404**

1

Sig. (2-tailed) .000 .000 .000 .000

N 243 243 243 243 243

**. Correlation is significant at the 0.01 level (2-tailed).

Source: SPSS output survey, 2020

From the above Pearson correlation coefficient analysis table 15, a set of green logistics practice

in the model, and financial performance of large manufacturing firms have been a positive

relationship.

In specifics, the result indicated that green manufacturing practices (GM) have a nearest strong

positive association with financial performance with the Pearson correlation coefficient value of

r=698, p<.001. Whereas, reverse logistics practices (RL), green purchasing practice (GP) and

environmental practices and regulations(EPR) have moderate relationship with financial

performance of large manufacturing firms with the Pearson correlation coefficient value of

r=649,p<0.01, r=.530, p<0.01 and r=.404, p<0.01 respectively.

Generally, the correlation analysis shows that there was a strong and moderately positive and

statistically significant relationship between the green logistics dimension stated in the model

and the financial performance of large manufacturing firms.

48

In this regard, the finding was consistent with the findings of Mohamed, (2012) and

Andrushchak et al., (2018), conclude that Green Logistics Practices (GLP) has greatly benefited

most manufacturing firms especially minimization of waste and hence leading to increase in

demand for the product; thereby profit maximization, tax reduction and financial support will be

improved; economic benefits meaning cost reduction and profit increase. Costs can be lowered

when firms invest in environmental management systems that lead to decreasing environmental

risk and liability (Jayeola, 2015).The competitive advantage resulting in new markets

possibilities and product innovations; environmental standards, following which result in

positive customer‘s perception.

4.8. Regression Analysis

In order to determine how the dimensions of GLP predict the PLMF, multiple linear regression

analysis was conducted. Regression analysis is a statistical method to deal with the formulation

of a mathematical model depicting relationship amongst variables which can be used for the

purpose of prediction of the value of the dependent variable, given the value of the independent

(Kothari,2004). Therefore via the multiple linear regressions analysis efforts were made to

determine the predictive power of the green logistics practices, namely :green purchasing

practice, green manufacturing practice, reverse logistics practices, and environmental practice

and regulation) on performances of large manufacturing firms, namely: operational performance

and financial performance.

Before carrying out multiple regression analysis, the researcher has checked the required

assumptions that the data must meet to make the analysis reliable and valid. The following

assumptions of multiple linear regressions were tested using SPSS version 23.

Linearity assumption: Linearity assumption was tested by producing scatterplots of the

relationship between each independent variable and each dependent variable. By visually looking

at the scatterplot produced by SPSS, the relationship between each independent variable and

each dependent variable found to be linear as shown in appendix B.

Multicollinearity assumption: Multicollinearity is a statistical phenomenon in which there

exists a perfect or exact relationship between the predictor variables. When there is a perfect or

exact association between the predictor variables, it is hard to come up with reliable estimates of

49

their separate coefficients. It will result in incorrect conclusions about the relationship between

the outcome variable (LMF performances) and the predictor variable (GLP). The most widely

applicable method of detecting the multicollinearity is Tolerance and Variance Inflation Factor

and it is very accurate in determining the problem of multicollinearity. The common thumb rule

is if any of the VIF values exceed 5 or 10, it implies that the associated regression coefficients

are poorly estimated because of multicollinearity. Accordingly, Multicollinearity diagnostics

were conducted using SPSS and VIF values found to be less than the values stated in the rule of

thumb which shows that multicollinearity was not a problem as shown in appendix B.

Normality assumption: Multiple regressions assume that variables have normal distributions.

This means that errors are normally distributed and that a plot of the values of the residuals will

approximate a normal curve. Two common methods to check normality assumptions include

using a histogram (with a superimposed normal curve) and a Normal P-P Plot. It can be

concluded that normality is guaranteed as the histogram generated is normally distributed and the

P-P plot follows the diagonal reference line as shown in appendix B.

Homoscedasticity assumption: The assumption of homoscedasticity refers to the equal variance

of errors across all levels of the independent variables. This means that errors are spread out

consistently between the variables. This is evident when the variance around the regression line

is the same for all values of the predictor variable. Homoscedasticity can be checked by a visual

examination of a plot of the standardized residuals by the regression standardized predicted

value. If possible, residuals are randomly distributed around zero (the horizontal line) providing

even distribution. Heteroscedasticity is indicated when the scatter is not even; fan and butterfly

shapes are common patterns of the violation. To assess homoscedasticity, the researcher created

a scatterplot of standardized residuals versus standardized predicted values using SPSS and

found that heteroscedasticity was not a major problem as shown in appendix B.

After the data was checked for the above required multiple regression assumptions and

confirmed that it has met all these assumptions, multiple regression analysis was carried out to

determine how well the regression model fits the data (model summary), independent variables

statistically significantly predict the dependent variable (ANOVA) and statistical significance of

each of the independent variables (regression coefficients).

50

4.8.1. Regression Analysis between GLP and operational performance

This regression analysis was directed to know by how much the set of GLP explains the

operational performance LMF.

Table 16: Regression analysis model summary between GLP and OP

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .825a .680 .675 .33879

a. Predictors: (Constant), Green purchasing, Green Manufacturing, Reverse

Logistics, Environmental practices and regulation

b. Dependent Variable: Operational performance

Source; SPSS output survey, 2020

As indicated in the above model summary table (table 16), The "R" column represents the value

of R, the multiple correlation coefficient. R value of 0.825 indicates a positive strong correlation

between a set of green logistics practices mentioned in the model and operational performance

which shows a good level of prediction.

In addition, the "R Square" column represents the R Square value (also called the coefficient of

determination), which is the proportion of variance on the operational performance that can be

explained by the set of green logistics practices. As shown from the table, R Square value of .680

indicates that 68% of the variation on the operational performance of large manufacturing firms

can be explained by the set of green logistics practices included in the model.

However, R-squared measures the proportion of the variation on the operational performances

explained by set of green logistics practices, irrespective of how well they are correlated to the

operational performances. Conversely, adjusted R-squared provides an adjustment to the R-

squared statistic such that; the set of green logistics practices have a correlation to operational

performance increases adjusted R-squared and any set of green logistics practices without a

correlation will make adjusted R-squared decrease.

Therefore, adjusted R-squared is more preferred than R-squared to ensure reliability of

prediction, According to adjusted R-squared, the variation of operational performance explained

by the combined effect of all the green logistics practices stated in the model is 67.5%.

51

Table 17: ANOVA model fit

Model Sum of Squares Df Mean Square F Sig.

1

Regression 58.180 4 14.545 126.722 .000b

Residual 27.317 238 .115

Total 85.497 242

a. Dependent Variable: Operational performance

b. Predictors: (Constant), Green purchasing , Green manufacturing, Reverse

logistics, Environmental practices and regulation

Source: SPSS output survey, 2020

The F-ratio in the above ANOVA table (table 17), tests whether the overall regression model is a

good fit for the data or not. Therefore, the above table shows that the set of green logistics

practices mentioned in the model have statistically significant to predict the operational

performance of large manufacturing firms, because there is high value of F = 176.722,

especially, p < .001. accordingly, the researcher conclude that the regression model is a good fit

of the data).

Table 18: Regression coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig. B Std. Error Beta

1

(Constant) .609 .142 4.283 .000

Green Purchasing .289 .040 .311 7.209 .000

Green Manufacturing .344 .037 .445 9.205 .000

Reverse Logistics .168 .035 .213 4.728 .000

Environmental Practice

and regulation .064 .030 .087 2.088 .038

a. Dependent Variable: Operational performance

Source: SPSS output survey, 2020

To memorize, the researcher was developed null hypotheses in chapter two, mean that; the set of

green logistics practices mentioned in the model have no explanatory power on operational

performance of LMF. This means the entire coefficients of green logistics practices mentioned in

52

the model are zero or none of the green logistics practices mentioned in the model help to predict

the operational performance of large manufacturing firms.

But here, based on the above tables, the researcher has very strong evidence to reject the null

hypotheses (H1a, H2a, H3a, and H4a), and accept the alternative hypotheses, since the p-value is

statically significant, (less than .05), and conclude that green logistics practices mentioned in the

model, namely; green purchasing, green manufacturing, reverse logistics, and environmental

practices and regulations have statistically significant to predict the operational performance of

large manufacturing firms located in Debre Birhan town.

Standardized Coefficients β

The standardized coefficients are useful to know which of the green logistics dimension has

more impact on the operational performance of large manufacturing firms. It used for comparing

the impact of green logistics practices mentioned in the model on the operational performance of

large manufacturing firms.

As indicated in the above regression coefficients table, green manufacturing practices have the

highest standardized coefficient (.445) followed by green purchasing practices (.311). This

revealed that green manufacturing practices have a higher relative effect on the operational

performance of LMF than green purchasing practices. Reverse logistics practice (.213) and

environmental practices and regulations (.087), have been ranked 3rd and 4th respectively in

their relative effect on the operational performance of LMF.

Unstandardized Coefficients β

The unstandardized coefficient denotes the mean or average change in the operational

performance of large manufacturing firms with a unit change in set of green logistics practices

stated in the model as independent variables.

The regression equation between green logistics practices and operational performance can be

written as follows:

OP=.609+.289GP+.344GM+.168RL+.064EPR+ .33879

53

Where; OP= Operational performance

GP= Green purchasing

GM= Green manufacturing

RL= Reverse logistics

EPR= Environmental practices and regulation

The constant value (β0 = .609) demonstrations that the operational performance of large

manufacturing firms would be .609, if coefficients of green logistics practices which mentioned

in the model were zero. On the other hand, a beta coefficient of .289 indicates that, a unit change

in green purchasing practice leads to a change in the operational performance by .289, a unit

change in green manufacturing practice leads to a .344 increments in the operational

performance , a unit change in reverse logistics practice leads to a .168 increments in the

operational performance, finally, a unit change in environmental practice and regulation leads to

a .064 increments in the operational performance of large manufacturing firms performance.

4.8.2. Regression Analysis between GLP and financial performance

Here, the regression analysis shows that how much the set green logistics practices mentioned in

the model explains the financial performances of large manufacturing firms located in Debre

Birehan town.

Table 19: Regression coefficients

Source: SPSS output survey, 2020

As indicated in the above model summary table (table 19), the value of R, the multiple

correlation coefficient. R value of 0.797 indicates a positive strong correlation between a set of

green logistics practices which mentioned in the model and financial performance of large

manufacturing firms which shows a good level of prediction.

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .797a .635 .629 .43934

a. Predictors: (Constant), Green purchasing, Green Manufacturing, Reverse

Logistics, Environmental practices and regulation

b. Dependent Variable: Financial performance

54

Moreover, The R Square value (also called the coefficient of determination), which is the

proportion of variance in the dependent variable that can be explained by the independent

variables. As shown from the table, R Square value of .635 indicates that 63.5% of the variation

in the financial performance of large manufacturing firms can be explained by set of green

logistics practices in mentioned in the model.

However, R-squared measures the proportion of the variation in the financial performance of

large manufacturing firms explained by the set of green logistics practices mentioned in the

model, irrespective of how well they are correlated to the financial performance. On the other

hand, adjusted R-squared provides an adjustment to the R-squared statistic such that; the set

green logistics practices that has a correlation to financial performance of case companies

increases adjusted R-squared and any green logistics practices without a correlation will make

adjusted R-squared decrease.

Therefore, adjusted R-squared is more preferred for goodness of prediction than R-squared,

According to adjusted R-squared, the variation of financial performance explained by the

combined effect of all the predictor variables mentioned in the model is 62.9%.

Table 20: ANOVA model fit

Model Sum of Squares Df Mean Square F Sig.

1

Regression 79.844 4 19.961 103.415 .000b

Residual 45.939 238 .193

Total 125.783 242

a. Dependent Variable: Financial Performance

b. Predictors: (Constant), Green purchasing, Green manufacturing ,Reverse logistics,

Environmental practices and regulation

Source: SPSS output survey, 2020

The F-ratio in the above ANOVA table (table, 20), helps to test whether the overall regression

model (the above model summary) is a good fit for the data. According to the above table, the set

of green logistics practices mentioned in the model statistically significant to predict the financial

performance of large manufacturing firms, with the high value of F = 103.415, especially, p <

.001. As a result, this study sum up that the regression model is a good fit of the data.

55

Table 21: Regression coefficient

Source: SPSS output survey, 2020

To remind, null hypotheses were developed in chapter two, mean that; the set of green logistics

practices which mentioned in the model have no explanatory power on financial performance of

LMF. This means the entire coefficients of green logistics practices mentioned in the model as

the independent variables are zero or none of the green logistics practices mentioned in the

model help to predict the financial performance of large manufacturing firms.

However, Based on the above tables, the researcher has very strong evidence to reject the null

hypotheses (H1b, H2b, H3b, and H4b), and accept the alternative hypotheses, since the p-value

is statically significant, (less than .05), and summarize that green logistics practices mentioned

model as independents variable in the model, namely; green purchasing, green manufacturing,

reverse logistics, and environmental practices and regulations have statistically significant to

predict the financial performance of large manufacturing firms located in Debre Birhan town.

Standardized Coefficients β

It used for comparing the impact of green logistics practices mentioned in the model on the

financial performance of large manufacturing firms which locate at Debre Berhan town. As

indicated in the above regression coefficients table, Green manufacturing practices have the

highest standardized coefficient (.370) followed by Reverse logistics practices (.344). This

revealed that Green manufacturing practices have a higher relative effect on financial

Model

Unstandardized

Coefficients

Standardized

Coefficients

T Sig. B Std. Error Beta

1 (Constant) .092 .185 .500 .617

Green Purchasing .241 .052 .214 4.636 .000

Green Manufacturing .347 .048 .370 7.152 .000

Reverse Logistics .330 .046 .344 7.159 .000

Environmental Practice and

regulation .088 .040 .099 2.238 .026

a. Dependent Variable: Financial performance

56

performance than Reverse logistics practices. Green purchasing practices and Environmental

Practice and regulations have been ranked 3rd and 4th respectively in their relative effect on

financial performance.

Unstandardized Coefficients β

The unstandardized coefficient denotes the proportion of variation in financial performance of

large manufacturing firms with a unit change in the green logistics practices mentioned in the

model as independent variables.

The regression equation between green logistics practices and financial performance can be

written as follows:

FP=.092+.241GP+.347GM+.330RL+.088EPR+ .43934

Where; FP= Financial performance

GP= Green purchasing

GM= Green manufacturing

RL= Reverse logistics

EPR= Environmental practices and regulation

The constant value (β0 = .092), shows that the financial performance of large manufacturing

firms would be .092, if green logistics practices mentioned as independent variables in the model

were zero. On the other hand, a beta coefficient of .241 indicates that, a unit change in green

purchasing practice leads to a change in the financial performance of large manufacturing firms

by .241, a unit change in green manufacturing practice leads to a .347 growths in the financial

performance , a unit change in reverse logistics practice leads to a .330 growths in the financial

performance, a unit change in environmental practice and regulation leads to a .088 growths in

the financial performance of large manufacturing firms performance.

57

4.9. Summary of results

Table 22: summary of result

Path Hypothesis Type of hypothesis B P Remark

GP OP H1a Null hypothesis .289 .000** Rejected

GP FP H1b Null hypothesis .241 .000** Rejected

GM OP H2a Null hypothesis .344 .000** Rejected

GM FP H2b Null hypothesis .347 .000** Rejected

RL OP H3a Null hypothesis .168 .000** Rejected

RL FP H3b Null hypothesis .330 .000** Rejected

ERP OP H4a Null hypothesis .064 .038* Rejected

EPR FP H4b Null hypothesis .088 .026* Rejected

[*, ** indicates that significance level at 5% and 1% respectively]

To sum up from the summary result tables, basic research question which stated in chapter one,

or, all null hypotheses which developed in chapter two were rejected. That means; the green

logistics practices namely: green purchasing, green manufacturing, reverse logistics, and

environmental practices and regulation which mentioned in the model as independent variables

have the predicting power on the operational and financial performance of large manufacturing

firms located in Debre Birehan town, especially, in the selected case company which were Debre

Birehan blanket factory, Debre Birehan wood processing factory, MEM candle spring water

factory and Etal aluminum factory.

.

58

CHAPTER FIVE

SUMMARY, CONCLUSION & RECOMMENDATION

5.1. Introduction

In this chapter includes the summary, conclusions, recommendations and suggestion for further

researches were discussed. For clarity purpose, the conclusions are made based on the research

objectives of the study. Based on the findings of the study, recommendations are made to LMF

which founded in Debre Birhan.

5.2. Summary of finding

The result of the study provides insight on green logistics practices on the performance of large

manufacturing firms. The summary of the research finding was presented as follows.

First of all, descriptive statistical analysis, the overall mean score was computed for each

independent variable (the set of green logistics practices mentioned in the model).

The study revealed that green purchasing practices (M=3.44, SD=6399), green

manufacturing practices (M=3.45, SD=7686), and environmental practices and regulation

(M=3.33, SD= 8091) were occasionally practiced in large manufacturing firms. Whereas,

reverse logistics practice (M=3.5, SD= .7530) was very often practiced in large

manufacturing firms.

Then, Pearson correlation coefficient was used to determine the relationship between the set of

green logistics practices mentioned in the model independent variable and the operational and

financial performance of large manufacturing firms which used as dependent variable in this

study. Therefore, this study finding revealed that;

Based on the Pearson correlation analysis result, green purchasing (r=.612, p<0.01),

reverse logistics (r=.589, p<0.01) and environmental practice and regulation (r=.430,

p<0.01) have a moderate positive statistical significant relationship with operational

performances of large manufacturing firms. Whereas, green manufacturing (r=.736,

p<0.01), have a strong Positive statistical significant relationship with operational

performance of large manufacturing firms.

59

As the same as, Green purchasing (r=.530, p<0.01), reverse logistics(r=.649, p<0.01)

and environmental practices and regulation (r=.404, p<0.01) have a moderate positive

statistical significant Correlation with the financial performance of large

manufacturing firms. But, green manufacturing (r=.698, p<0.01) has approximately a

strong positive statistical significant correlation with financial performance of large

manufacturing firms.

Then after, multiple regression analysis between GLP and OP, determine the overall

relationship between set of green logistics practices and operational performance depend on

―R‖ (multiple correlation coefficient). So, ―R‖ value (.825) indicates a strong positive

association between green logistics practices which mentioned in the model and operational

performance of large manufacturing firms. Adjusted R square value from the regression

model summary indicates proportion of variation on operational performance explained by

the whole green logistics practices in the model, therefore (Adjusted R2=.675) means that

67.5% of the total variability in operational performance was explained by the whole green

logistics practices mentioned in the model.

The ANOVA test result revealed, the whole green logistics practice stated in the

model collectively have statistically significant predicted the operational performance

of large manufacturing firms (F = 126.722, p < .001).

The regression analysis revealed that green logistics practices, namely; green

purchasing, green manufacturing, reverse logistics, and environmental practices and

regulation were statistically significant to predict the operational performance of large

manufacturing firms because p-values were less than 0.05.

The regression analysis further revealed that green manufacturing has the highest

impact on operational performance followed by green purchasing, reverse logistics

practice and Environmental practices and regulation.

Finally, multiple regression analysis between GLP and FP, determine the overall relationship

between set of green logistics practices and financial performance depend on ―R‖ (multiple

correlation coefficient). So, ―R‖ value (.797) indicates a strong positive association between

green logistics practices which mentioned in the model and financial performance of large

60

manufacturing firms. Adjusted R square value from the regression model summary indicates

proportion of variation on financial performance explained by the whole green logistics

practices mentioned in the model, therefore (Adjusted R2=.629) means that 62.9% of the total

variability in financial performance was explained by green logistics practices which

mentioned in the model.

The ANOVA test result revealed that green logistics practice statistically and

significantly predict the operational performance of large manufacturing firms (F =

103.415, p<.001).

The regression analysis revealed that green purchasing practices, green manufacturing

practices, reverse logistics practices and environmental practices and regulation were

statistically significant to predict the financial performance of large manufacturing

firms because p-values are less than 0.05

The regression analysis further revealed that green manufacturing has the highest

impact on the financial performance of case companies followed by reverse logistics

green purchasing and environmental practices and regulation.

5.3. Conclusion

This research was conducted on large manufacturing firms located in Debre Birehan with the

leading goal of investigating the impact of green logistics practice on the performance of large

manufacturing firms. Based on the objectives and findings of the study, the following

conclusions are drawn.

From the descriptive statistical analysis result regarding the green logistics practice, the studies

conclude that:

Green purchasing practices, green manufacturing practices and environmental practices

and regulations practiced occasionally in large manufacturing firms located in Debre

Birehan town. Whereas, Reverse logistics practice was very often practiced in large

manufacturing firms located in Debre Birhan town.

From correlation analysis, the relationship between green logistics practices mention in the

model and performance of large manufacturing firms conclude as follow:

61

Green manufacturing practice has a strong positive relationship with operational and

financial performances. Whereas, green purchasing practices, reverse logistics practices

and environmental practices and regulation have a moderate positive relationship with

operational and financial performances of large manufacturing firms located in Debre

Birehan town.

To remind, the specific objectives of this study which stated in chapter one were examining the

effect of green logistics practices namely: green purchasing, green manufacturing, reverse

logistics, and environmental practices and regulation on the operational and financial

performance of large manufacturing firms. Therefore, the finding of this study revealed shown

that:

The green logistics practices namely; green purchasing practices, green manufacturing

practice, reverse logistics practices, and environmental practice and regulation have a

predicting power on the operational and financial performance of large manufacturing

firms located in Debre Birhan town.

Moreover, Green manufacturing practice has relatively higher effects on operational

performance of case companies followed by green purchasing practices, reverse logistics

practices and environmental practices and regulation respectively.

In the same way, Green manufacturing practice has relatively higher effects on financial

performance of case companies followed by reverse logistics practices, green purchasing

practices and environmental practices and regulation respectively.

5.4. Recommendation

Depend on the finding of this study, the researcher recommends as follow;

On the basis of findings and conclusion reached, recommendations forwarded that help the large

manufacturing firms to improve practical implementation of green logistics practices so as to

improve operational and financial performance. The large manufacturing firms, in order to be

competitive in global market, give attention and improve green logistics practices through

working collaboratively with supplier, customer and government. Moreover, large manufacturing

firms recommended that to give priority and focus for a set of green logistics practices based on

importance or effects. As this study confirmed, green manufacturing has relatively high effects

62

on operational performances followed by green purchasing, reverse logistics and environmental

practices and regulation respectively as well as green manufacturing has also relatively higher

effects on financial performances followed by reverse logistics, green purchasing and

environmental practices and regulation respectively. Therefore, the researcher recommends,

large manufacturing firms working collaboratively with suppliers, customers and government to

increase level of practicing green logistics practices and give priority for a set of green logistics

practices accordingly its effect or importance in order to ensure global competitiveness through

enhancing their operational and financial performance.

5.5. Limitation and suggestion for future studies

There were limitations in to this study that should be considered when interpreting the study

results. These limitations are left for future researchers.

First, this study didn‘t include all green logistics practices. The study included only four

green logistics practice namely: green purchasing, green manufacturing, reverse logistics,

and environmental practice and regulation: due to this, it suggests for future studies to

consider other green logistics practices, such as green packing, green distribution, and

green marketing, and also challenges to implement GLP.

Second, this study measure effects of green logistics practices on the performance of

large manufacturing firms, from operational and financial perspectives. It suggests future

studies to consider other performance measurements, such as environmental

performance, social performance.

Third, the study focused on large manufacturing firms. It suggests future studies to

consider small and medium enterprises as well as service provider institutions.

Fourth, because of COVID-19 pandemic the researcher obligated to conduct this study by

using only quantitative research approach method, so, it recommends for the future

researchers to do this research by using mixed research approach.

63

References

Adebambo, O. and Adebayo, T. (2014), Empirical Study of the Effect of Reverse Logistics

Objectives on Economic Performance of Food and Beverages Companies in Nigeria.

International Review of Management and Business Research, Vol. 3(3), pp.1484–1494

Afum, E., & Zhuo, S. (2019). Reverse Logistics and Performance of Bottled and Sachet Water

Manufacturing Firms in Ghana: the Intervening Role of Competitive Advantage. IOSR

Journal of Business and Management (IOSR-JBM), vol. 21(4), pp. 39–49.

https://doi.org/10.9790/487X-2104043949

Agbiowu (2016), Impact of Environmental and Social Costs on Performance of Nigerian

Manufacturing Companies. International Journal of Economics and Finance, vol. 8(9), pp.

173. https://doi.org/10.5539/ijef.v8n9p173

Ambec, S., Cohen, M. A., Elgie, S., & Lanoie, P. (2013). The porter hypothesis at 20: Can

environmental regulation enhance innovation and competitiveness? Review of

Environmental Economics and Policy, vol.7 (1), pp.2–22.

https://doi.org/10.1093/reep/res016

Andrushchak, B., Bohdan, A., Pertti, A., & Assigned, F. T. (2018). Green and Reverse logistics

as the tools for improving environmental sustainability pp.1–80. Retrieved from

https://www.theseus.fi/bitstream/handle/10024/147256/Andrushchak_Bohdan.pdf?sequence

=1

Bartolacci, F. and Zigiotti, E. (2015), Environmental and Economic-Financial Performance in

Waste Management Firms, Management International Conference, pp.389–401

Chien, M. and Shih, L. (2007), an empirical study of the implementation of green supply chain

management practices in the electrical and electronic industry and their relation to

organizational performances. International Journal of Environmental Science and

Technology, vol. 4(3), pp.383–394

Chin, T. A., Tat, H. H., & Sulaiman, Z. (2015). Green supply chain management, environmental

collaboration and sustainability performance. https://doi.org/10.1016/j.procir.2014.07.035

64

Chiu, J. Z., & Hsieh, C. C. (2016), the impact of restaurants’ green supply chain practices on

firm performance. Sustainability (Switzerland), vol. 8(1), pp. 1–14.

https://doi.org/10.3390/su8010042

Cooper, (2006) Business research methods: Cambridge; Cambridge University Press

Creswell, John W. (2014).Research design: qualitative, quantitative, and mixed methods

approaches / John W. Creswell. — 4th ed. SAGE Publications Ltd.

Di Pillo, F., Gastaldi, M., Levialdi, N. and Miliacca, M. (2017), Environmental performance

versus economic-financial performance: Evidence from Italian firms. International Journal

of Energy Economics and Policy, vol. 7(2), pp. 98–108

Elshawarby, M. (2018), The Effect of Environmental and Social Corporate Governance on the

Financial Performance with Special Focus on the Egyptian Private Sector Companies.

Journal of Accounting & Marketing, Vol. 07(02), https://doi.org/10.4172/2168-

9601.1000269

Garson G David. (2012). Testing statistical assumption. Asheboro, NC: Statistical Associates

publishing

Greenstone M. (2002), the Impacts of Environmental Regulations on Industrial Activity:

Evidence. Journal of Political Economy; December, vol. 110(6), pp. 1175–1219.

https://doi.org/10.1086/342808

Hami N., Muhamad, M. R., & Ebrahim, Z. (2016), the impact sustainable manufacturing

practices on sustainability, Journal Technology, vol. 78(1), pp.139–152.

https://doi.org/10.11113/jt.v78.3090

Ho, Y. C., Wang, W. B., & Shieh, W. L. (2016). An empirical study of green management and

performance in Taiwanese electronics firms, Cogent Business and Management, vol. 3(1),

pp. 1–13. https://doi.org/10.1080/23311975.2016.1266787

Islam, M. M., Turki, A., Murad, M. W., & Karim, A. (2017), do sustainable procurement

practices improve organizational performance? Sustainability (Switzerland), vol. 9(12), pp.

1–17. https://doi.org/10.3390/su9122281

65

Jayarathna, B. C. P., & Lasantha, S. A. R. (2018), Impact of GSCM Practices on Financial

Performance: Special Reference to Manufacturing Companies in Sri Lanka, Kelaniya

Journal of Management, vol. 7(1), pp. 40. https://doi.org/10.4038/kjm.v7i1.7553

Jayeola, O. (2015). The Impact of Environmental Sustainability Practice on the Financial

Performance of SMEs : A Study of Some Selected SMEs in Sussex. International Journal of

Business Management and Economic Research (IJBMER), vol. 6(4), pp. 214–230.

Retrieved from www.ijbmer.com

Journal, S., Winter, N., Geiger, S. W., & Hoffman, J. J. (2017), the Impact of the Regulatory

Environment and Corporate Level Diversification on Firm Performance. Published by :

Pittsburg State University Stable URL : http://www.jstor.org/stable/40604211

REFERENCES Li. 10(4), 439–453.

KalhariN.L.H., Chandrasoma, M. M. N., Hansini, S. T., Sandarenu, K. G. N. H., Gangani, U. G.

D., & Gunawardana, K. D. (2018). The Impact of Green Manufacturing Practices on

Perceived Financial Performance of the Listed Manufacturing Companies in Sri Lanka, pp.

1–24.

Khan, S. A. R. (2019), The Effect of Green logistics on Economic growth, Social and

Environmental sustainability: An Empirical study of Developing countries in Asia.

https://doi.org/10.20944/preprints201901.0104.v1

Khan, S. A. R., & Qianli, D. (2017), Impact of green supply chain management practices on

firms‘ performance: an empirical study from the perspective of Pakistan. Environmental

Science and Pollution Research, vol. 24(20), pp. 16829–16844.

https://doi.org/10.1007/s11356-017-9172-5

Khor, K. S. (2013), Relationship between green product design, reverse logistics product

disposition and business performance among electrical and electronic manufacturing firms.

pp. 1–348.

Kipkorir and Wanyoike (2015), Factors Influencing Implementation of Green Procurement in

Multinational Tea Companies in Kericho County, International Journal of Economics,

Commerce and Management United Kingdom, vol. 3(6), pp. 431–446. Retrieved from

66

http://ijecm.co.uk/

Kothari, C.R. (2004), Research methodology: methods and techniques: new age international

publisher

Kumar Ranjit, (2011). Research Methodology: a step-by-step guide for beginners: 3rd edition:

SAGE Publications Ltd

Kumar Piaralal, S., Nair, S. R., Yahiya, N., & Karim, J. A. (2015), An Integrated Model of the

Likelihood and Extent of Adoption of Green Practices in Small and Medium Sized Logistics

Firms. American Journal of Economics, vol. 5(2), pp. 251–258.

https://doi.org/10.5923/c.economics.201501.32

Laari S. (2016). Green Supply Chain Management Practices and Firm Performance : Evidence

from Finland.

Large, R. O., & Gimenez Thomsen, C. (2011), Drivers of green supply management

performance: Evidence from Germany. Journal of Purchasing and Supply Management, vol.

17(3), pp. 176–184. https://doi.org/10.1016/j.pursup.2011.04.006

Manrique, S., & Martí-Ballester, C. P. (2017), Analyzing the effect of corporate environmental

performance on corporate financial performance in developed and developing countries.

Sustainability (Switzerland), vol. 9(11), https://doi.org/10.3390/su9111957

Mafini, & Loury-Okoumba, (2018), Extending green supply chain management activities to

manufacturing small and medium enterprises in a developing economy Journal of Economic

and Management Sciences vol. 21(1), a1996. https://doi.org/ 10.4102/sajems.v21i1.1996.

Mesfin Kora. (2016). Assessment of green supply chain management practices and

organizational performance : the case of ethio telecom: Addis Ababa university school of

commerce department of logistics and supply chain management

Mignot Dessalegn (2017). Barriers in Implementing Green Supply Chain Management in

Manufacturing Industries: A Case Study of Ethiopian Leather Industries: Addis Ababa

University school of commerce department of logistics and supply chain management

67

Mogeni, L. M. (2016). The effect of Green Logistics Practices on Performance of Supply Chains

in Multinational Organizations in Kenya, The International Journal of Business &

Management, vol. 6(4), pp. 189–199.

Mohamed, A. G., Fathi, A. A., Sobhy, S., & Barky, E. (2015). Impact of Reverse Logistics

Applications on Customer Satisfaction, Proceedings of the 2015 International Conference

on Operations Excellence and Service Engineering Orlando, Florida, USA, (2000), pp. 393–

405.

Mohamed, K. (2012). Green supply chain management and performance of manufacturing firms

in Mombasa, Kenya. Retrieved from

http://erepository.uonbi.ac.ke/bitstream/handle/11295/16111/Abstract.PDF?sequence=3

Money, F., Alshura, M. S. K., & Awawdeh, H. Z. Y. (2016), Green Supply Chain Practices as

Determinants of Achieving Green Performance of Extractive Industries in Jordan.

International Journal of Business and Social Science, vol. 7(7), pp. 166–177.

Mukonzo, S. (2017), Green manufacturing and operational performance of a firm: Case of a

cement manufacturing firm in Kenya. International Journal of Business and Social Science,

vol. 8(4), pp. 106–120.

Naila, D. L. (2013), the effect of environmental regulations on financial performance in

Tanzania: A survey of manufacturing companies quoted on the Dar Salaam stock exchange.

International Journal of Economics and Financial Issues, vol. 3(1), pp. 99–112.

Nderitu, K., & Ngugi, K. (2014), Effects of Green Procurement Practices on an Organization

Performance in Manufacturing Industry: Case Study of East African Breweries Limited.

European Journal of Business Management, vol. 22(1), pp. 341–352. Retrieved from

http://www.ejobm.org

Ngniatedema, T., & Li, S. (2000), Green Operations and Organizational Performance.

International Journal of Business and Social Science, vol. 5(3), pp. 50–58.

Nisha, J., & Chen, S. (2017). A study on the relationship of environmental regulations and

68

economic performances; IOP Conference Series: Earth and Environmental Science, vol.

94(1). https://doi.org/10.1088/1755-1315/94/1/012035

Ong, Lee, Teh, & Magsi (2019), Environmental Innovation, Environmental Performance and

Financial Performance: Evidence from Malaysian Environmental Proactive Firms. Journal

of Trade, Economics and Finance, vol. 11(12), pp. 3494.

https://doi.org/10.3390/su11123494

Ong, T. S., Teh, B. H., & Ang, Y. W. (2014), The Impact of Environmental Improvements on the

Financial Performance of Leading Companies Listed in Bursa Malaysia. International

Journal of Trade, Economics and Finance, vol. 5(5), pp. 386–391.

https://doi.org/10.7763/ijtef.2014.v5.403

Onyinkwa, C., & Ochiri, G. (2016), Effects of Green Supply Chain Management Practices on

Competitiveness of Firms in the Food and Beverage Sector in Kenya. European Journal of

Business and Management, vol. 8(14), pp. 15–21.

Peng, B., Tu, Y., & Wei, G. (2018), Can environmental regulations promote corporate

environmental responsibility? Evidence from the empirical study in China, Sustainability

(Switzerland), vol. 10(3), https://doi.org/10.3390/su10030641

Qu, Q., Tang, M., Liu, Q., Song, W., Zhang, F., & Wang, W. (2017). Empirical research on the

core factors of green logistics development. Academy of Strategic Management Journal,

vol. 16(2), pp. 1–10.

Ramanathan, R., He, Q., Black, A., Ghobadian, A., & Gallear, D. (2017), Environmental

regulations, innovation and firm performance: A revisit of the Porter hypothesis. Journal of

Cleaner Production, vol. 155, pp. 79–92. https://doi.org/10.1016/j.jclepro.2016.08.116

Ramírez, A. M., & Morales, V. J. G. (2011), Effect of Reverse Logistics and Flexibility on

Organizational Performance. Economics & Management, 16(August), pp. 873–881.

Retrievedfromhttp://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=61822081

&lang=es&site=ehost-live

Rha, J. S. (2010), the Impact of Green Supply Chain Practices on Supply Chain Performance.

69

Romero, J. A., Freedman, M., & O‘Connor, N. G. (2018), the impact of Environmental

Protection Agency penalties on financial performance, Business Strategy and the

Environment, vol. 27(8), pp. 1733–1740. https://doi.org/10.1002/bse.2239

Rubashikina, Y., Galeotti, M., & Verdolini, E. (2015), Environmental regulation and

competitiveness: Empirical evidence on the Porter Hypothesis from European

manufacturing sectors. Energy Policy, vol. 83, pp. 288–300.

https://doi.org/10.1016/j.enpol.2015.02.014

Salim, K. G. (2016). Effect of Reverse Logistics on Operational Performance of Liquefied

Petroleum Gas Companies in Kenya

Santos, H., Lannelongue, G., & Gonzalez-Benito, J. (2019), integrating green practices into

operational performance: Evidence from Brazilian manufacturers. vol. 11(10), pp. 1–18.

https://doi.org/10.3390/su11102956

Sari, K., & Yanginlar, G. (2015), the impact of green logistics practices on firm performance:

Evidence from Turkish healthcare industry. Proceedings of POMS 26th Annual Conference,

1–6. Retrieved from http://www.pomsmeetings.org/confpapers/060/060-1582.pdf

Sarkis, J., & Cordeiro, J. J. (2001), an empirical evaluation of environmental efficiencies and

firm performance: Pollution prevention versus end-of-pipe practice. European Journal of

Operational Research, vol. 135(1), pp. 102–113. https://doi.org/10.1016/S0377

2217(00)00306-4

Sheikh, S. A. (2014), Effect of Green Operations Practices on Financial Performance of

Commercial Banks in Kenya

Soubihia, D. F., Jabbour, C. J. C., & De Sousa Jabbour, A. B. L. (2015), Green manufacturing:

Relationship between adoption of green operational practices and green performance of

Brazilian ISO 9001-certified firms. International Journal of Precision Engineering and

Manufacturing - Green Technology, vol. 2(1), pp. 95–98. https://doi.org/10.1007/s40684-

015-0012-0

Stavropoulos, S., Wall, R., & Xu, Y. (2018). Environmental regulations and industrial

70

competitiveness: Evidence from China. Applied Economics, vol. 50(12), pp. 1378–1394.

https://doi.org/10.1080/00036846.2017.1363858

Tritos Laosirihonga, Keah-Choon Tan, and Dotun Adebanjoc, (2014), green supply chain

management practices and performance; Asia pacific industrial engineering and

management system.

Tsegaye Habitye (2018), the effect of logistics management practices on organizational

performance: a case of ethio telecom Addis Ababa University School of commerce

department of logistics and supply chain management

Turrisi, M., Bruccoleri, M., & Cannella, S. (2013), Impact of reverse logistics on supply chain

performance. International Journal of Physical Distribution and Logistics Management, vol.

43(7), pp. 564–585. https://doi.org/10.1108/IJPDLM-04-2012-0132

Uma Sekaran & Roger Boygie (2016), Research Methods for Business: A Skill-Building

Approach 5th edition: John Wiley & Sons Ltd.

Uma Sekaran & Roger Boygie (2016), Research Methods for Business: A Skill-Building

Approach 7th edition: John Wiley & Sons Ltd.

Van Rensburg, S. L. J. (2015), framework in green logistics for companies in South Africa. Pp.1–

339, retrieved from http://hdl.handle.net/10500/18750

Vasanth, V., Selvam, M., Lingaraja, K., & Ramkumar, R. R. (2015), Nexus between profitability

and environmental performance of Indian firms: International Journal of Energy Economics

and Policy, vol. 5(2), pp. 433–439.

Vlachos, I. P. (2016), Reverse logistics capabilities and firm performance: the mediating role of

business strategy. International Journal of Logistics Research and Applications, vol. 19(5),

pp. 424–442. https://doi.org/10.1080/13675567.2015.1115471

Yu, Z. (2018), the Impact of Reverse Logistics on Operational Performance. American Journal

of Mechanical and Industrial Engineering, vol. 3(5), pp. 99.

https://doi.org/10.11648/j.ajmie.20180305.14

71

Zelallem Tadesse (2016). Green supply chain management practices in Ethiopian tannery

industry: an empirical study: International Research Journal of Engineering and

Technology: www.irjet.net

Zhu, Q., Geng, Y., Fujita, T., & Hashimoto, S. (2010), Green supply chain management in

leading manufacturers: Case studies in Japanese large companies. Management Research

Review, vol. 33(4), pp. 380–392. https://doi.org/10.1108/01409171011030471

Zikmund, William G, Barry J, Carr, Jon C, & Griffin, Mitch (2009), Business Research Methods

8th edition, New Castle: South-western collage publisher

72

APPENDIX “A”

SCHOOL OF BUSINESS AND ECONOMICS

DEPARTMENT OF LOGISTICS AND SUPPLY CHAIN

MANAGEMENT

POST GRADUATE PROGRM

Questionnaires on “effect of green logistics practices on the performance of large

manufacturing firms located at Debre Berehan town‖

Dear respondents

My name is Nigatu Mekasha, Master (MA) student in department of Logistics and supply chain

management at Debre Berehan University. To fulfill the MA requirement, the student is required

to do thesis. The aim of this questionnaire is to collect data for the thesis entitle on ―Effect of

green logistic practices on Performance of large manufacturing firms which located on Debre

Berehan town”. I would like to assure you that, the information you provide will be used only

for the purpose of achieving academic award. Your involvement is regarded as a great input to

the quality of the research results. Hence, I believe that you will enlarge your support through

providing the data. Your honest and thoughtful response is invaluable. Thanks in advance for

your cooperation.

General Instruction

➢ Please do not write your name or address on the questionnaire.

➢ please put a tick (√) mark in the appropriate box of your answer

➢ Contact address: if you have any question please contact me through the following

addresses: Telephone: 09 19 471210/09 95 95 47 07

Email: [email protected]

73

Section one: Demographic related Information

1. Gender; Male Female

2. Your Age; 18-25 25-35 35-45 above 45

3. Level of education; diploma degree masters PhD

Above PhD

4. In which department are you working?

procurement

Production or fabrication

Sales and marketing

Finance and administration

5. How long have you worked in your organization?

0- 5 years 6-10 years 11-15 years

16 years and above

Section two: Green logistics practice in large manufacturing firms

Questions related with green logistics practices. Please put a tick (√) mark on the

appropriate number to indicate the state of green logistics practice in your firm.

The item scales are five-point scales with 1 = not considering it; 2 = planning to consider

it; 3 = considering it currently; 4 = initiating implementation; 5 = implementing

successfully).

74

Green logistics practices Response categories

1. Green purchasing 1-Never

practiced

2- rarely

practiced

3-

occasionally

4-Very

often

5-Always

practiced

Ensure suppliers meet their environmental

objectives

Requires suppliers to have certified EMS

like ISO 14001

Ensure purchased materials contain green

attributes

Evaluates suppliers on specific

environmental criteria

Requires suppliers to develop and maintain

an EMS

2. Green manufacturing

Cross-functional cooperation for

environmental improvements

Total quality environmental management

Environmental compliance and auditing

programs

ISO14000 series certification

Environmental management systems

3. Reverse logistics

Accepting product returns from customers

Recalling products with quality problems

Returning products to suppliers

Recycling scrap and used items

Repairing, recondition and remanufacture

component parts from returned, defective,

or damaged products

4. Environmental Practices & Regulation

Adopt green logistics initiatives to avoid

threat of legislations

75

Strict environmental standards to comply

with

Frequent government inspections in my

firm

Government imposed many environmental

regulations

Section three: - Performance of Large Manufacturing Firms

Questions related with performances. Please put a tick (√) mark on the appropriate

number to indicate the state of performance in your firm.

The item scales are five-point scales with: 1 = not at all; 2 = a small extent; 3 = moderate

extent; 4 = great extent; 5 = very great extent).

Performances Metrics of large firms

Response categories

A. Operational performance 1-not at all 2-small

extent

3-moderate

extent

4-great

extent

5-very

great

extent

Increase in the amount of goods

delivered on time

Decrease in inventory levels

Decrease in scrap rate

Increase in product quality

Increase in product line

Improved capacity utilization

B. Financial performance

Improvement in general level of

profitability

Decrease in the level of production costs

Decrease in the costs of raw materials or

components

Decrease in packaging costs

76

APPENDIX “B”

Linear Regression Assumptions

1. Linearity of relationship test

77

2. Multicollinearity Test Result

Coefficients’

Model Collinearity Statistics

Tolerance VIF

1 Green Purchasing .721 1.386

Green Manufacturing .574 1.741

Reverse Logistics .664 1.507

Environmental Practices & regulation .780 1.281

78

3. Normality Test

79

80

4. Homoscedasticity Test