Technological innovation and operational effectiveness: their role in achieving performance improvements

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Santa, Ricardo, Hyland, Paul, & Ferrer, Mario (2014) Technological inno-vation and operational effectiveness : their role in achieving performanceimprovements. Production Planning & Control, 25(12), pp. 969-979.

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Technological innovation and operational effectiveness: Their role in achieving

performance improvements

Abstract

The purpose of this paper is to examine the role of the alignment between technological

innovation effectiveness and operational effectiveness after the implementation of

enterprise information systems (EIS), and the impact of this alignment on the

improvement in operational performance. Confirmatory factor analysis (CFA) was used

to examine structural relationships between the set of observed variables and the set of

continuous latent variables. The findings from this research suggest that the dimensions

stemming from technological innovation effectiveness such as system quality,

information quality, service quality, user satisfaction and the performance objectives

stemming from operational effectiveness such as cost, quality, reliability, flexibility and

speed, are important and significantly well correlated factors. These factors promote the

alignment between technological innovation effectiveness and operational effectiveness

and should be the focus for managers in achieving effective implementation of

technological innovations. In addition, there is a significant and direct influence of this

alignment on the improvement of operational performance. The principal limitation of

this study is that the findings are based on investigation of small sample size.

Keywords: Improvement in operational performance; information systems alignment;

operational effectiveness; system effectiveness; technological innovation

2

1. Introduction

Organisations today are faced with competitive pressures to improve efficiency and

productivity. They need to respond to market changes through the continual

improvement of their paradigms, products, practices, processes and systems or services,

as improvement in performance derives in large measure from innovation (Ifandoudas

and Chapman, 2006; Tidd and Bessant, 2009). Accordingly, many service organisations

are investing substantial resources in technological innovation such as enterprise

information systems (EIS) to reengineer their processes, but the extent to which these

innovations assist organisations to improve the operational performance still need to be

explored (Armbruster et al., 2008; Mabert et al., 2003). According to Rosenbusch et al.

(2005), dedicating more resources to innovation process outcomes leads to a greater

increase in performance than dedicating more resources to innovation process inputs

(e.g. R&D spending). This argument emphasises the importance of the appropriate

management of the innovation process. Therefore, being aware of the importance of

innovation and subsequently dedicating substantial resources to the innovation task

might not be sufficient, as the operational performance might not meet the expected

outcomes (Olson et al., 2005).

It is important to gain a better understanding of stakeholders‟ expectations in

regards to the operational performance, and how a firm‟s innovation in the

implementation of technological innovations such EIS can improve operational

effectiveness, because such understanding can enhance an organisation‟s competitive

advantage (Slack et al., 2009). The competitive context that today‟s companies are

operating in requires many of them to adopt practices aimed at helping them to evaluate

the extent to which they are complying with their objectives and improved effectiveness

(Alfaro et al., 2007). Improving operational effectiveness involves determining key

3

performance objectives and establishing benchmarks. Furthermore, some organisations

are failing to benefit from the implementation of technological innovations because they

either do not measure performance or what they do measure is inappropriate (White,

1996). Effectiveness needs to be measured also from the technological perspective, as

organisations need to better understand if the EIS they have implemented has

contributed to achieving the expected organisational goals and benefits.

There is diversity in the multitude of approaches to measure operational

performance and the number of different measures that can be found. It is difficult to

identify a comprehensive body of literature in which a discussion of innovation

measurement issues might be located; therefore, representing this diversity within a

synthesized framework is a challenging task (Rosenbusch et al., 2011). Additionally,

while research on innovation is growing, studies identifying dimensions that impact

technological and operational innovation and effectiveness in firms are limited, and

consequently the understanding of why and how some organisations adopt innovative

technologies in the quest for performance improvements is incomplete (Fagerberg et al.,

2005; Naranjo-Gil, 2009; Yu, 2009). It is possibly a consequence of this fragmentation

that empirical studies have found many organisations tend to focus only on the

measurement of innovation inputs and outputs in terms of spend, speed to market and

numbers of new products, and ignore the processes in between (Samson and

Terziovskib, 1999). Adams et al. (2011) identified gaps in measurement theory and

practice and pointed the way toward the development of a comprehensive set of

innovation management measures. Adams et al. (2011) also concluded that there has

been a concentration on financial measurement of inputs, and less emphasis on

measuring other aspects of the category.

4

The dualism between the formulation and implementation of EIS creates a need

to investigate the alignment between technological innovation effectiveness and

operational effectiveness that needs to exist in any organisation after the implementation

of an EIS. Therefore, this research addresses the question „Does the alignment between

technological innovation effectiveness and operational effectiveness positively impact

improvements in operational performance?‟ In addressing this question, this research

uses a quantitative approach, based on the results of a survey of employees in

organisations from the service sector in Australia that have recently implemented EIS.

2. Operational effectiveness

The debate about whether any difference exists between manufacturing and service

operations addressed by several researchers such as Morris and Johnston (1987) helps to

conclude that there is no difference per se between manufacturing and service

operations. Additionally, the debate between the two types of operations “is spurious”

(Morris and Johnston, 1987) . Further more, Prajogo, D.(2005) pointed out that there is

no significant difference in the level of most of Total Quality Management (TQM)

practices and quality performance between the Manufacturing and Service sector.

Additionally, Prajogo (2005), shown that TQM construct based on the Malcolm

Baldrige National Quality Award (MBNQA) criteria is valid across both industry

sectors, and its relationship with quality performance also indicates insignificant

difference between the two sectors.

Olson, Slater and Hult (2005), focused their study on manufacturing and service

firms operating in 20 different two-digit Standard Industrial Classification code

industries, not only to provide a reasonably similar context for respondents but also to

5

be broad enough for the results to be generalizable. Other research such as Enz (2012)

pointed out that service innovation rest on both creating something new, and on

coproducing it. One clear feature of service innovation is that it is characterized as

having a greater organizational dimension than innovations in manufacturing. For our

research project we argue that not all service organizations are differentiators and in our

case the researched organisations tend to be suppliers of commodity services so do not

fit his conception of service innovation. As a consequence of the insignificant difference

between the operations between service and manufacturing, this article based its

theoretical background from both manufacturing and service theory.

.

In the public and private service sectors, the changing environment has driven

organisations into delivering greater flexibility, quality of services and reconfiguration

and transformation of their processes while cutting costs at the same time (Ben-Rajeb et

al., 2008; Teece et al., 1997). These factors are prompting organisations to seek to

operate more efficiently through innovation and to ensure they have effective

operational processes (Ben-Rajeb et al., 2008; Hill, 2005; Slack et al., 2009). This quest

for effectiveness involves the need to deliver value-adding products or services of

exceptional quality, on time, and at a competitive price. Organisations attempting to

meet these objectives need to pay attention to their operational effectiveness as this is a

primary driver of business performance in order to remain competitive (Ben-Rajeb et

al., 2008; Slack et al., 2009; Wheelwright and Bowen, 1996).

Operational effectiveness refers to the ability to establish processes, based on

core capabilities within the organisations, that encourage them to exceed customer‟s

expectations (Evans and Lindsay, 2011; Porter, 1996). Operational effectiveness

6

involves improving and measuring process performance by leading and controlling the

operations within the firm. A better use of resources through these core processes

enables the organisation to eliminate waste and reduce costs, adapt more appropriate

technological innovation, and therefore perform better than competitors (Porter, 1996).

By studying how a firm performs the primary and supporting activities for service

delivery, a firm can determine how it might add value at every stage of the service

delivery process, and seek ways to continuously improve while meeting its operational

performance objectives (Porter, 1990; Rosenbusch et al., 2011). The five performance

dimensions or objectives an organisation seeks to fulfil to attain operational

effectiveness include cost, quality, reliability flexibility and speed (Hill, 2005).

Operational effectiveness deals with meeting cost budgets (Hill, 2005).

Furthermore, improving cost performance means that organisations need to identify the

inefficiencies and waste in processes such as procurement, product or service design,

and the performance of staff (Russell and Taylor, 2008). However, it is not just another

financial measure as the emphasis is on identifying improvement opportunities and not

only costing areas of failure (Prajogo and Goh, 2007). Continuous improvement is

achieved by the proper disaggregation of the cost components that impact the total cost

performance of the organisation (Slack et al., 2009). The measurement of costs allows

quality related activities to be expressed in the language of management (Prajogo and

Goh, 2007). Consequently, prevention and appraisal costs (cost of conformance) are

considered investments, while failure costs (cost of non-conformance) are considered as

losses (Prajogo and Goh, 2007).

Quality has emerged as a strategic entity making quality management a

necessity for overall operational effectiveness and global competence (Desai, 2008).

There are different definitions of quality portrayed in the literature to fit different

7

circumstances (Corbett, 2008; Reeves and Bednar, 1994). For example, the

manufacturing literature refers to quality as the conformance to standards (Elshennawy,

2004; Heizer and Render, 2006). In addition, quality is viewed as a consistent provision

of products and services that satisfy customers, rather than only minimising defects and

conforming to specifications without any clear market-orientated continuous

improvement (Russell and Taylor, 2008). Improving on quality provides organisations

with the opportunity to bridge the gap between what they are able to offer and what

customers demand (Hill, 2005). There are, however, two extremes to the problem of

measuring quality. At one end, the use of too many indicators leads to a loss of control

through bureaucratic and complex structures. At the other end lies a lack of knowledge

or awareness of quality due to the absence of measurement or the measurement of the

wrong things (Prajogo and Goh, 2007). These two positions are detrimental for

continuous improvement efforts with the aim of gaining a competitive edge or achieving

performance excellence.

The third operational performance objective is reliability, which suggests that an

organisation‟s processes consistently perform as expected over time. That is, customers

are satisfied by organisations that provide services that do not fail over a period of time

or with services that are delivered as agreed (Corbett, 1992; Porter, 1996). For systems,

reliability can best be described as the likelihood that a system will not fail to perform

its function as designed within a given time horizon and environmental conditions (Kuo

and Zuo, 2003). When customers are evaluating the characteristics of a product, they

may find that it performs differently from its intended purpose or malfunctions after a

period of time (Wild, 2000). Thus reliability is essential in the effectiveness of

operations and is closely related to the satisfaction of customers with the use of services

or products.

8

The fourth operational performance objective concerns being flexible, which

includes an organisation‟s ability and the extent to which it can adjust (what it does,

how it does and when it does) to changes to respond to customers (Slack, 1991). As an

example, large fast-food franchises which are designed to offer high volume and low

cost products may not be able to offer the flexibility required to offer full menu options

to its customers as they do not customise to specific customer needs (Samson and

Singh, 2008). Flexibility includes the capacity to produce a wider range of services and

products, respond to any seasonal demand factors, meet shorter lead times, and cope

with customers‟ specification changes during the process (Hill, 2005).

Finally, improving on speed prompts an organisation to be able to shorten the

time between the service request and delivery of the service, with the frequency and at

the times requested by customers (Hill, 2005). In today‟s competitive environment, time

is a valuable tool; thus businesses that are able to respond faster than their competitors

are more likely to gain a competitive advantage. Manufacturers are discovering the

advantages of time-based competition (Russell and Taylor, 2008). Competing on speed,

however, requires an organisation characterised by fast moves, fast adaptation and tight

linkages (Russell and Taylor, 2008). At the same instance, the speed with which an

organisation can provide new products or service development is an important

capability because the environment is constantly changing (Tidd and Bessant, 2009).

3. Technological innovation effectiveness

Maintaining or improving the level of performance has been recognised as one of the

critical issues that organisations are struggling with. Thus they adopt innovations that

are allegedly better able to accomplish this goal (Hernandez and Jimenez, 2008 ;

Herring and Roy, 2007). It has been recognised that technological innovations are useful

9

in the improvement of performance of a business faction and that investments in new

technology will increase a firm‟s efficiency and effectiveness (Badescu and Garces-

Ayerbe, 2009; Damanpour, 1987; Hernandez and Jimenez, 2008 ).

DeLone and McLean (1992, 2003) define the effectiveness of an implemented

information system as the extent to which the system adds to the achievement of

organisational goals and benefits. The organisations that pay more attention to the

achievement of operational effectiveness rather than the enterprise information system

effectiveness alone are more likely to get the greatest benefits from their investment and

to achieve improvements in operational performance (Davenport, 1998). There is,

however, a great concern due to the high rate of failures of implemented technological

innovations such as enterprise information systems (Davenport, 1998).

As stated by Jamieson and Hyland (2004), there is a very high rate of failure in

the implementation of large innovative technological projects as they do not succeed in

delivering the promised outcomes. Furthermore, Jamieson and Hyland (2004) argue that

it is difficult to know the real failure rate, and it could be larger than that reported.

Gómez and Carnero (2011) reported that the failure rate in maintenance of software

implementation can be as high as 70% in some industries, with a successful

implementation of only 20% on Computerised Maintenance Management System. As a

consequence, it is important to gain a comprehensive set of measures that facilitates the

proper identification of the improvements in performance after the implementation of

technological innovations such as enterprise information systems.

To measure the dependent variable information system success (IS success), the

DeLone and McLean (2003) model identified six dimensions: system quality,

information quality, service quality, user use and user satisfaction, individual impact

and organisational impact. In the DeLone and McLean (2003) success model, system

10

quality measures the efficacy of the technical component of the enterprise information

systems; in other words, the preferred characteristics that users want from the system

based on the assessment of the productivity of the technological innovation.

Information quality is the measurement of the production from the enterprise

information systems. Information quality is measured by the users, when the attributes

of the information and the way it is presented satisfy their needs, also known as

semantic success (DeLone and McLean, 1992). Information quality is also seen as the

degree to which the information produced by the enterprise information system has

characteristics of high quality of content, accuracy, precision, currency, reliability,

timeliness, completeness, relevance and format required as perceived by the end user

(DeLone and McLean, 2003; Negash et al., 2003; Nielsen, 2005).

Service quality is the level of service received by the users of enterprise

information systems and the manner in which the service is provided by the IS/IT

department, as it influences the degree of satisfaction with an enterprise information

system (DeLone and McLean, 2003; Pitt et al., 1995). According to Moad (1989), the

quality of the IS/IT department‟s service as perceived by the user is a key indicator of

EIS success. The IS/IT department‟s ability to supply installation assistance, product

knowledge, software training, support and online help is a factor that will have an

impact on the relationship between IS/IT and users (Pitt et al., 1995). Thus, this

relationship should have an impact on the effectiveness of the day to day operations of

users, and therefore have an impact on the operational performance of the organisation.

System use is defined as the utilisation and interaction of the enterprise

information system by the users or stakeholders in the organisation (Straub et al., 1995).

Use and user satisfaction measure and analyse the successful conformance to

specifications in the view of the user in addition to the effectiveness and successful

11

utilisation and interaction of the user with the enterprise information system. The

satisfaction rate is positively correlated to the improvement of the job performance

(DeLone and McLean, 2003).

The impact on individuals is the influence that information from the enterprise

information system has on the attitude or behaviour of the stakeholders in regards to the

job performance. It includes the personal improvements and also the overall

consequences on the performance of the department or business unit, in relation to what

effect the information from the enterprise information systems has on management

decisions. This impact occurs when the information is received and interpreted by the

users, and applied to their jobs (DeLone and McLean, 2003; Nielsen, 2005).

Awareness of the impact on the organisation derives from the investigation of

the effect of the implemented enterprise information systems on the performance and

improvement of the operations of the enterprise (DeLone and McLean, 2003; Nielsen,

2005; Rai et al., 2002). According to Saarinen (1996), organisational impact stands for

the benefits of the investment in the technological innovation.

4. Performance measurement

Performance measurement has gained wide attention as a necessary complement to

quality management and continuous improvement, even though the scope was

significantly expanded to cover issues including effectiveness and efficiency, success

and failure (Hyland et al., 2004). It is important to have a clear set of dimensions and

key performance objectives to properly measure the outcomes from a significant

investment in financial resources on technological innovations that are not always

implemented in a way that satisfies the needs and requirements of the stakeholders.

Traditional measures such as accounting systems have been used to determine the

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performance of organisations. These financial measures are focused solely on data such

as profit, return on investment and cash flow. The problem with these traditional

measures is that they do not reflect the competitive requirements that organisations must

focus on in a very dynamic and challenging market. The effectiveness of an

implemented technological innovation should be measured in terms of the benefits

gained in the improvements on the operational effectiveness instead of the effectiveness

of the technological innovation only.

The findings from Rosenbusch et al. (2005) show that innovation has a positive

effect on the performance. Being aware of the importance of innovation and

subsequently dedicating substantial resources to technological innovations might not be

sufficient, as the expected performance implication might not be substantiated (Olson et

al., 2005). Consequently, EIS effectiveness should be measured in terms of the real

operational benefits rather than through the achievement of information systems

outcomes only. Accordingly, it is important to link the five operational performance

objectives with technological innovation effectiveness dimensions: system quality,

information quality, service quality, and user satisfaction. Thus, the main purpose of this

research is to build on and extend the existing literature and to put forward a theoretical

framework that examines the following propositions:

Proposition 1. There is a predictive relationship between technological

innovation effectiveness, operational effectiveness and improvement in

operational performance;

Proposition 2. An alignment between technological innovation effectiveness

and operational effectiveness is necessary to improve operational performance.

13

5. Research method

This research was undertaken with an exploratory purpose as the alignment between

technological innovation effectiveness and operational effectiveness and its impact on

the improvement of operational performance has had little previous empirical

investigation. An exploratory study is undertaken when there is a lack of understanding

of the problem which leads to an unstructured problem design.

This research is related to current public service industry problems such as ineffective

technology implementation in Australia. Similarly, the work addressed the sometime

overlooked links between traditional quality, more contemporary information systems

special projects such as innovation-based improvement projects. For this purpose,

quantitative data were gathered through a self-administered mail questionnaire directed

to large service organisations which had recently implemented an enterprise information

system in Australia.

The questionnaire was administered to managers, engineers (technologists), and

administrative and operational staff as, according to Orlikowski and Gash (1994) and

Schein (1996), different actors in an organisation have different assumptions,

expectations, knowledge and perceptions of technological innovation. In the process of

constructing measures of key variables and refining the survey instrument, four pilot

tests were conducted. These pilot tests enabled the introduction of a number of revisions

to be carried out to improve the survey instrument between the initial draft and the final

instrument.

The final questionnaire was divided into six sections; however, the three first

sections of the survey instrument are not part of this article. The fourth section

(Technological innovation effectiveness) had nineteen questions selected from three

previous studies mentioned in the DeLone and McLean (2003) ten-year update as an

14

appropriate empirical test and validation of the DeLone and McLean information

system success model. The studies used to develop the section related to technological

innovation effectiveness were: Seddon and Kiew‟s (1994), which surveyed 104 users of

a recently implemented university accounting system; Rai et al.‟s (2002) which

surveyed 274 users of a university student information system; and from Pitt et al.

(1995), who administered their questionnaire in three service organisations in three

different countries to test the validity of „quality of service‟ as a measure of information

system effectiveness. Rai et al. (2002) believed that there is a danger that information

system researchers will mismeasure information system effectiveness if they do not

include in their assessment package a measure of information system service quality.

They conclude that the effectiveness of an information system unit can be partially

assessed by its capacity to provide quality service to its users. This supports the decision

to include service quality measures in the questionnaire used in this study.

In the fifth section of the questionnaire, twenty questions were prepared relating

to operational effectiveness, drawn from the literature review. Through this study it is

proposed that the effectiveness of a technological innovation cannot be thoroughly

measured without a comprehensive consideration of the operations of the organisation.

It is essential to bring the dimensions of operational effectiveness into the technological

innovation context to have a better representation of the real effectiveness of the

implementation of the technological innovation. The final section addressed questions

related to the improvement in operational performance, based on the literature review.

The difference between the fifth section (Operational Effectiveness) and the

sixth section (Improvements of Operational Performance) is based in the way questions

were designed and the purpose of the questions. The fifth section of the questionnaire

aimed at exploring which performance objectives in the view of all three cultures were

15

perceived as being met when implementing technological innovations such as enterprise

information systems. This fifth section also investigated how the implementation of the

enterprise information system contributed to the improvement of the work unit‟s

process via the fulfilment of the operational objectives. Questions for the sixth section

were aimed at understanding the perception of the respondents of the operational

effectiveness across the organisation after the implementation of the enterprise

information system.

Of the 450 surveys distributed among the service organisations from the service

sector that had implemented EIS recently, 144 were returned (32% response). Each

returned questionnaire was reviewed for completeness and, of the 144, six were

considered unusable due to large amounts of missing data, lack of involvement of the

respondent in the use of EIS, or the impossibility of identifying the role of the

respondent (manager, engineer or operator-user).

The fourth section (technological innovation effectiveness) reported a

Cronbach‟s Alpha coefficient of 0.859. The fifth section (operational effectiveness)

reported a Cronbach‟s Alpha coefficient of 0.936. This high coefficient supported the

argument for bringing the dimensions of operational effectiveness into the technological

innovation effectiveness context to have a more comprehensive understanding of the

real effectiveness of the EIS. The last section (improvement in operational performance)

reported a Cronbach's Alpha coefficient of 0.862. These Cronbach‟s Alpha coefficients

indicated a high level of internal consistency within these measures as the generally

accepted lower limit is 0.7, though some studies allow 0.6; for example, Hair et al.

(2010).

6. Results

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6.1. Confirmatory factor analysis

As the main purpose of the study was to examine the alignment between technological

innovation effectiveness and operational effectiveness and their influence in the

improvement in operational performance, the next step in the data analysis was to

perform a confirmatory factor analysis (CFA). Confirmatory factor analysis was chosen

instead of other classical validation techniques such as exploratory factor analysis

(EFA) as EFA has a number of significant shortcomings. Among other issues, EFA can

produce distorted factor loadings and incorrect conclusions regarding the number of

factors, also the solution obtained is only one of an infinite number of solutions (Segars

and Grover, 1993).

Confirmatory factor analysis was used to study the relationships between the set

of observed variables and the set of continuous latent variables. The overall fit of a

measurement model is determined by a CFA (Cooksey, 2007; Hair et al., 2010). In the

CFA, all factor loadings are freed (i.e. estimated); items are allowed to load on only one

construct (i.e. no cross loading); and latent constructs are allowed to correlate

(equivalent to oblique rotation in exploratory factor analysis) (Figure 1). The input

covariance matrix generated from the model‟s 12 measurement variables contains 45

sample moments. There are six regression weights, three covariances and 12 variances,

for a total of 21 parameters to be estimated. The model therefore has 24 degrees of

freedom.

Insert Figure 1 about here

The chi-square goodness-of-fit test shows that the model did not fit the data well, X2 (N

= 138, df = 24) = 80.29, p < .05. Although the model did not fit well by the chi-square

17

test, the baseline comparisons fit indices of the NFI, RFI, IFI, TLI and CFI are close to

or exceed 0.90 (Table1). This suggests that the hypothesised model fit the observed

variance-covariance matrix well relative to null or independence model. The only

possible improvement in fit for these two models ranges from 0.053 to 0.109.

Insert Table 1 about here

The estimates were analysed for the measurement model. The unstandardised

regression weights were all significant by the critical ratio test (> 1.96, p < .05). The

standardised regression weights range from 0.718 to 0.903. These values indicate that

the nine measurement variables are significantly represented by their respective latent

constructs. Explained variances (Squared Multiple Correlations) and residual variances

for correlations ranged from 0.516 to 0.865 (Table 2). The residual (unexplained

variances) were from 13.5% to 49.4%.

Insert Table 2 about here

The study now turns to examining the hypothesised structure model. The chi-

square value for the models (Figure 2) was X2 (N = 138, df = 24) = 80.29, p < .05. The

chi-square per degree of freedom was 3.34. The baseline comparisons fit indices of NFI,

RFI, IFI, TLI and CFI for the model were close to the suggested cut off value 0.90. This

suggests that the hypothesised model fit the observed variance-covariance matrix

reasonably well relative to null or independence model.

Insert Figure 2 about here

18

Regression weights (Table 3), Standardised regression weights, and Squared

Multiple Correlations: Of the coefficients associated with the paths linking the model‟s

exogenous and endogenous variables, four are significant by the critical ratio test (±

1.96, p < .05). Support was found for Propositions 1 and 2. These significance levels

show that there is a relationship between system effectiveness, operational effectiveness

and improvement in operational performance. Additionally, the significance levels

support Proposition 2, that an alignment between technological innovation effectiveness

and operational effectiveness is necessary to improve operational performance. The

impact of operational effectiveness and technological innovation effectiveness are

related directly and significantly to the improved operational performance. The greater

the perception on the increase of operational effectiveness the greater the improved

operational performance (b = 0.66). Likewise, the greater the perception on the increase

of technological innovation effectiveness the greater the improved operational

performance (b = 0.54).

Insert Table 3 about here

The unidirectional arrows (without origin) pointing to latent factor of improved

operational performance represent unexplained (residual) variance for this factor. Thus,

using the squared multiple correlation table, 21.2% of the variation in improved

operational performance is unexplained. Alternatively, 79.8% of the variance is

accounted for by the joint influence of the technological innovation effectiveness and

operational effectiveness. This finding confirms that it is not possible for the studied

organisations to gain performance improvements after the implementation of

19

technological innovations focusing only on the technology without considering the

performance objectives stemming from operational effectiveness.

7. Conclusion

The research question „Does the alignment between Technological Innovation

Effectiveness and Operational Effectiveness positively impact the Improvement in

Operational Performance?‟ has been confirmed by this study. This research also found

that the three performance objectives stemming from operational effectiveness, quality,

speed and cost, and the three dimensions stemming from technological innovation

effectiveness, service quality, information quality and system quality, are important

when trying to achieve improvements in operational performance in an aligned

approach. It is expected that giving priority to these dimensions or performance

objectives in the implementation of enterprise information systems will assist

organisations to enhance operational performance and gain a competitive advantage.

The three performance objectives - quality, speed and cost - identified in the

CFA analysis of this study demonstrated that in the quest for effectiveness through the

implementation of technological innovations, it is essential that these technologies

encourage the delivery of value-adding products or services of exceptional quality, on

time, and at a competitive price, as stated by Slack et al. (2009). The fact that quality

has emerged as one of the main constructs to measure operational effectiveness

demonstrates the strategic role it plays. Therefore, quality management is a necessity for

overall operational effectiveness and global competence as stated by Desai (2007).

Furthermore, the EIS must have a focus on these three performance objectives to make

its implementation successful.

20

The CFA analysis also highlighted quality of the service as an important and

reliable dimension to measure technological innovation effectiveness, which confirms

the argument from Rai et al. (2002), that service quality is a key indicator of EIS

implementation success. Organisations need high quality information as decisions about

innovation are made based on information, so one of the problems in continuously

innovating organisations is that, although they implement EIS systems, these do not

lead to improved operational effectiveness.

In testing proposition 1, this research has demonstrated that the linkages between

technology innovation effectiveness (system effectiveness) dimensions and operational

effectiveness performance objectives are strongly and significantly correlated, showing

the proposed alignment. In our opinion, the high positive correlations of technology

innovation effectiveness with operational effectiveness dimensions provide strong

empirical support to include the stated operational effectiveness dimensions or

performance objectives in the measurement of technological innovation implementation

success. Furthermore, these new dimensions will assist organisations to more accurately

measure the impact of the technological innovation implementation on the business

processes and operations of the organisation. Furthermore, these new dimensions will

assist organisations to more accurately measure the impact of the technology

implementation on the business processes and operations of the organisation. Likewise,

in testing hypothesis 2, the SEM results demonstrated that there is a predictive

relationship between technology innovation effectiveness and operational effectiveness

in the implementation of enterprise information systems. This predictive relationship

will lead organisations to improve the operational performance and gain a competitive

advantage. In addition, for academics this predictive relationship is important because

the literature has not discussed it in a comprehensive way.

21

This research is related to current industry problems and addressed the sometime

overlooked links between traditional quality, more contemporary information systems

and special projects such as innovation-based improvement projects, Therefore

organisations must be more conscious about the practical implications of an

implemented enterprise information system on the processes and operations of the

organisation. Our results confirmed that organisations that combined technical and

operational objectives increased their performance (Naranjo-Gil, 2009). Furthermore,

this study provides general support for the alignment between technological innovation

effectiveness and operational effectives, by showing that both types of innovations -

technological and operational processes - must fit well with each other to facilitate

organisations to perform optimally.

22

References

Alfaro, J., Ortiz, A. and Poler, A. (2007) 'Performance measurement system for

business processes', Production Planning & Control: The Management of

Operations, Vol 18, No. 8, pp 641-654.

Armbruster, H., Bikfalvi, A., Kinkel, S. and Gunter, L. (2008) 'Organizational

innovation: the challenge of measuring non-technical innovation in large-scale

surveys', Technovation, Vol 28, No. 2, pp 644–657.

Badescu, M. and Garces-Ayerbe, C. (2009) 'The impact of information technologies on

firm productivity: empirical evidence from Spain', Technovation, Vol 29, No. 4,

pp 122-129.

Ben-Rajeb, H., Morel-Guimaraes, L., Boly, V. and Assielou, N.G. (2008) 'Measuring

innovation best practices: Improvement of aninnovation index integrating

threshold and synergy effects', Technovation, Vol 28, No. 12, pp 839-854.

Cooksey, R. (2007) Illustrating statistical procedures for business, behavioural &

social science research, Tilde University Press.

Corbett, L.M. (1992) 'Delivery windows - a new view on improving manufacturing

flexibility and on-time delivery performance', Production and Inventory

Management Journal, Vol 33, No. 3, pp 74-79.

Corbett, L.M. (2008) 'Quality Management in Operations', in D Samsonand PJ Singh

(eds), Operations Management: An Integrated Apporach, Cambridge,

Melbourne, 78-89.

Damanpour, F. (1987) 'The Adoption of Technological, Administrative, and Ancillary

Innovations.I mpactof Organizational Factors', Journal of Management, Vol 13,

No. 4, pp 675–688.

Davenport, T. (1998) 'Putting the enterprise into the enterprise system', Harvard

Business Review, Vol 76, No. 4, pp 121-131.

DeLone, W. and McLean, E. (1992) 'Information systems success: The quest for the

dependent variable', Information Systems Research, Vol 3, No. 1, pp 60-95.

DeLone, W. and McLean, E. (2003) 'The DeLone and McLean model of information

system success: A ten-year update', Journal of Management Information

Systems, Vol 19, No. 4, pp 9-30.

Desai, D.A. (2008) 'Cost of quality in small- and medium-sized enterprises: case of an

Indian engineering company', Production Planning & Control, Vol 19, No. 1, pp

25-34.

Elshennawy, A. (2004) 'Quality in the New Age and the Body of Knowledge for

Quality Engineers', Total Quality Management, Vol 15, No. 5-6, pp 603-614.

Enz, C. (2012) 'Strategies for the Implementation of Service Innovations', Cornell

Hospitality Quarterly, Vol 53, No. 3, pp 187-195.

Evans, J. and Lindsay, W. (2011) The Management and Control of Quality, South-

Western Cengage Learning, New Jersey.

Fagerberg, J., Mowery, D.C. and Nelson, R.R. (2005) The Oxford Hand Book

Innovation, Oxford University Press, Oxford, UK.

Gómez, A. and Carnero, M.C. (2011) 'Selection of a Computerised Maintenance

Management System: a case study in a regional health service', Production

Planning & Control: The Management of Operations, Vol 22, No. 4, pp 426-

436.

Hair, J., Black, W.C., Babin, B. and Anderson, R.E. (2010) Multivariate data analysis:

A global perspective Seventh edn, Pearson, Upper Saddle River, New Jersey.

23

Heizer, J. and Render, B. (2006) Operations Management, 8th edn, Pearson-Prentice

Hall, New Jersey.

Hernandez, B. and Jimenez, J., Mart´ın, M.J (2008 ) 'Extending the technology

acceptance model to include the ITdecision-maker: a study of business

management software', Technovation, Vol 28, No. 3, pp 112-121.

Herring, H. and Roy, R. (2007) 'Technologivcal innovation, energy efficient design and

the rebound effect', Technovation, Vol 27, No. 4, pp 194-203.

Hill, T. (2005) Operations management, Palgrave Macmillan, New York, NY.

Hyland, P., Gieskes, J. and Sloan, T. (2004) 'Performance measurement and product

innovation', paper presented to CINet - Continuous Innovation Network,

Sydney, Australia, 22-25 September.

Ifandoudas, P. and Chapman, R. (2006) 'Strategic ICT implementation within SMEs for

business Improvement: The COSTWORTH project', paper presented to 6th

International CINet Conference, Brighton, United Kingdom, 8-12 September.

Jamieson, K. and Hyland, P. (2004) 'IS failure: Just too much information?', paper

presented to 8th World Multi-Conference on Systemics, Cybernetics and

Informatics, Orlando, U.S.A., 18-21 July.

Kuo, W. and Zuo, M. (2003) Optimal Reliability Modeling: Principles and application,,

John Wiley & Sons, Hoboken.

Mabert, V.A., Soni, A. and Venkataramanan, M.A. (2003) 'Enterprise resource

planning. Managing the implementation process', European Journal of

Operation Research, Vol 146, No. 2, pp 302-314.

Moad, J. (1989) 'Asking users to judge IS', Datamation, Vol 35, No. 21, pp 93-100.

Morris, B. and Johnston, R. (1987) 'Dealing with Inherent Variability: The Difference

Between Manufacturing and Service?', International Journal of Operations &

Production Management, Vol 7, No. 4, pp 13 - 22.

Naranjo-Gil, D. (2009) 'The influence of environmental and organizational factors on

innovation adoptions: Consequences for performance in public sector

organizations ', Technovation, Vol 29, No. 12, pp 810-818.

Negash, S., Ryan, T. and Igbariab, M. (2003) 'Quality and effectiveness in Web-based

customer support systems ', Information & Management, Vol 40, No. 8, pp 757-

768.

Nielsen, J. (2005) 'Critical success factors for implementing ERP system', in L Von

Hellens, S Nielsenand J Beekhuyzen (eds), Qualitative Case Studies on

Implementation of Enterprise Wide Systems, Idea Group Inc., London, 211-231.

Olson, E.M., Slater, S.F. and Hult, T.M. (2005) 'The performance implications of fit

among business strategy, marketing, organization structure, and strategic

behaviour', Journal of Marketing, Vol 69, No., pp 49-65.

Orlikowski, W. and Gash, D. (1994) 'Technological frames: making sense of

information technology in organizations', ACM Transactions on Information

Systems (TOIS, Vol 12, No. 2, pp 174-207.

Pitt, L.F., Watson, R.T. and Kavan, C.V. (1995) 'Service quality: A measure of

information systems effectiveness', MIS Quarterly, Vol 19, No. 2, pp 173-188.

Porter, M. (1990) Competitive advantage: creating and sustaining superior

performance, Free Press, New York.

Porter, M. (1996) 'What is strategy?', Harvard Business Review, Vol 74, No. 6, pp 61-

78.

Prajogo, D.I. (2005) 'The comparative analysis of TQM practices and quality

performance between manufacturing and service firms', International Journal of

Service Industry Management, Vol 16, No. 3, pp 217-228.

24

Prajogo, D.I. and Goh, M. (2007) 'Operations Management activities and operational

performance in service firms', International Journal of Services Technology and

Management, Vol 8, No. 6, pp 478-490.

Rai, A., Lang, S. and Welker, R. (2002) 'Assessing the validity of IS success models:

An empirical test and theoretical analysis', Information System Research, Vol

13, No. 1, pp 50-69.

Reeves, C.A. and Bednar, D.A. (1994) 'Definig Quality: alternatives and implications',

Academy of Management Review, Vol 19, No. 3, pp 419-445.

Rosenbusch, N., Brinckmann, J. and Bausch, A. (2011) 'Is innovation always

beneficial? A meta-analysis of the relationship between innovation and

performance in SMEs', Journal of Business Venturing, Vol 26, No. 2011, pp

441-457.

Russell, R.S. and Taylor, B.W. (2008) Operations management - Quality and

competitiveness in a global environment, 5th edn, John Wiley & Sons, Inc,

London.

Saarinen, T. (1996) 'An expanded instrument for evaluating information system

success', Information & Management, Vol 31, No. 2, pp 103-108.

Samson, D. and Terziovskib, M. (1999) 'The relationship between total quality

management practices and operational performance', Journal of Operations

management, Vol 17, No. 4, pp 393-409.

Samson, D. and Singh, P.J. (2008) Operations Management: An Integrated apporach,

Cambridge University Press, Melbourne.

Schein, E. (1996) 'Three cultures of management: The key to organizational learning',

Sloan Management Review, Vol 38, No. 1, pp 9-20.

Seddon, S. and Kiew, M.-Y. (1994) 'A partial test and development of DeLone and

McLean's model of IS success', paper presented to International Conference on

Information Systems, Atlanta, GA.

Segars, A.H. and Grover, V. (1993) 'Re-examining easy of use and usefulness: A

confirmatory factor analysis', MIS Quaterly, Vol December No., pp 517, 525.

Slack, N. (1991) The manufacturing advantage, Mercury Books, London.

Slack, N., Chambers, S. and Johnston, R. (2009) Operations management, fourth edn,

Prentice Hall/Financial Times., Harlow, England

Straub, D., Limayem, M. and Karahanna-Evaristo, E. (1995) 'Measuring System

Usage: Implications for IS Theory Testing', Management Science, Vol 41, No. 8,

pp 1328-1342.

Teece, D., Pisano, G. and Shuen, A. (1997) 'Dynamic capabilities and strategic

management. ', Strategic Management Journal, Vol 8, No. 7, pp 509-533.

Tidd, J. and Bessant, J. (2009) Managing Innovation John Wiley & Sons, Ltd,

Hoboken, NJ.

Wheelwright, S.C. and Bowen, H. (1996) 'The Challenge of manufacturing advantage.',

Production and Operations Management, Vol 5, No. 1, pp 59-77.

White, G. (1996) 'A survey and taxonomy of strategy-related performance measures for

manufacturing', International Journal of Operations and Production

Management., Vol 16, No. 3, pp 42-61.

Wild, R. (2000) Production and Operations Management, Fifth edn, Continuum,

London and New York.

Yu, C., Tao, Y., (2009) 'Understanding business-level innovation technology adoption',

Technovation, Vol 29, No. 2, pp 92–102.

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Table 1: Baseline comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .927 .891 .948 .921 .947

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

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Table 2: Squared multiple correlations: (Group number 1 - default model)

Estimate

IO3 .516

IO2 .816

IO1 .636

OE3 .779

OE2 .775

OE1 .865

TI3 .816

TI2 .816

TI1 .696

28

Table 3: Regression weights: (Group number 1 - default model)

Estimate P Label

Improved_Operational Performance <-- Operational_Effectiveness .658 *** par_8

Improved_Operational Performance <-- Technological_Innovation_Effectiveness .544 *** par_9

TI1 <-- Technological_Innovation_Effectiveness 1.000

TI2 <-- Technological_Innovation_Effectiveness 1.360 *** par_1

TI3 <-- Technological_Innovation_Effectiveness 1.426 *** par_2

OE1 <-- Operational_Effectiveness 1.000

OE2 <-- Operational_Effectiveness 1.032 *** par_3

OE3 <-- Operational_Effectiveness .937 *** par_4

IO2 <-- Improved_Operational_Performance 1.169 *** par_5

IO3 <-- Improved_Operational_Performance 1.144 *** par_6

IO1 <-- Improved_Operational_Performance 1.000

29

Figure 1: Measurement model

30

Figure 2: Hypothesised structured model