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SCM-Thesis-12

Master Thesis

Taking an Environmental Perspective on Supply

Chain Management – A Study on the German

Automobile Industry

Master Thesis to obtain the Master of Science in Supply Chain Management

from the Rotterdam School of Management, Erasmus University

Date

July 2013

Author Michelle Engert

Student Number 329909

University Supervisor

Dr. Erwin van der Laan

Department of Decision and Information Sciences

University Co-Reader Dr. Fabian Sting

Department of Management of Technology and

Innovation

Master Thesis 2013 Supply Chain Management-Taking an Environmental Perspective Michelle Engert 329909

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Taking an Environmental Perspective


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The copyright of the Master thesis rests with the author. The author is responsible for its

contents. RSM is only responsible for the educational coaching and cannot be held liable for the

content.


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ACKNOWLEDGEMENTS

The accompanying thesis – Taking an Environmental Perspective on Supply Chain Management: A Study

on the German Automobile Industry – was written to obtain the Master of Science Degree in Supply

Chain Management from the Rotterdam School of Management, Erasmus University.

After nearly 6 months the time has come to say thank you. First and foremost, I wish to thank my thesis

coach Professor Dr. Erwin van der Laan from the Department of Decision and Information Sciences at the

Rotterdam School of Management for his guidance, supervision and help throughout this thesis project.

One could not wish for a more patient and friendly supervisor. Furthermore, I would also like to thank my

thesis co-reader Professor Dr. Fabian Sting from the Department of Management of Technology and

Innovation at the Rotterdam School of Management. Lastly, a special thank you goes out to the

participants who have willingly shared their valuable time and responded to my survey questionnaire.

Thank you.

Michelle Engert

July 2013


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EXECUTIVE SUMMARY

This research has taken an environmental perspective on Supply Chain Management and has,

furthermore, made an attempt to address the, in previous literature, scantly discussed relationship between

resource dependence and Green Supply Chain Management (GSCM) performance. Having drawn on

knowledge from the Resource Dependence Theory as well as the Institutional Theory the relationship

between green supply chain practices and organizational performance has been explored.

This study is based on the study performed by Lee et al. (2012) who have investigated the relationship

between GSCM practice implementation and firm performance on small- and medium-sized suppliers in

the electronics industry in Korea. In contrast, this study was conducted focusing on small- and medium-

sized suppliers in the automotive industry in Germany. As stated by Oliver (1991) the external pressures

referred to by the Resource Dependence and Institutional Theories originate from the organizations

stakeholders. For Korean firms these pressures are originating from buying firms in the EU and for

German firms, which are regarded as early adopters of ISO 14001 standards (Welch et al., 2002), these

pressures are assumed to be insignificant.

Even though there is still enormous potential for further investigations, especially in regards to

investigating whether a moderating effect exists between GSCM Practice Implementation and Business

Performance, the results of this study reveal a number of interesting insights into the topic of Green

Supply Chain Management and organizational performance.

(1) Firstly, the most anticipated finding of there being a significant, direct relationship between GSCM

Practice Implementation and Overall Business Performance was weakly supported. (2) Furthermore, this

study also made a distinction between Environmental and Economic Performance and found supporting

evidence for the existence of a significant, direct relationship between GSCM Practice Implementation

and Environmental and Economic Performance. (3) The study results also reveal that organizations

should not only focus on achieving Overall Business Performance outcomes but should also recognize the

potential that increasing Employee Job Satisfaction, Operational Efficiency and Relational Efficiency

bring with it when trying to improve an organizations Environmental and Economic Performance. (4)

Additionally, when conducting the study on German suppliers it was found that improvements in all three

mediators (Employee Job Satisfaction, Operational Efficiency and Relational Efficiency) yielded stronger

improvements in Overall Business Performance as compared to the results found by Lee et al. (2012). (5)

In terms of the Relational Efficiency it can be concluded that the implementation of GSCM practices

helps a supplying firm improve its Relational Efficiency with its buying firms. This ability of a supplying

firm to build trust and credibility in the relationship with the buying firm by means of collaboration and

information sharing will eventually have a positive effect on Business Performance. More specifically,

the increased transparency and openness in business processes has a strong impact on Overall Business

Performance and Environmental Performance and a weak but still significant impact on Economic

Performance. The existence of a relationship between Relational Efficiency and Environmental and

Economic Performance provides new insights for managers who wish to increase their performance gains

by means of increased collaboration and trust with their supply chain partners. This study revealed that

performance gains are not only to be expected in regards to asset utilization and competitive position but

also in terms of a decrease of waste discharge and a reduction in water usage as well as waste disposal.


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(6) Lastly, it was found that even though German enterprises are operating in a rather mature environment

in regards to green supply chain initiatives, in comparison to companies located in Korea, there is still

enormous potential for increasing operations‘/ supply chain managers‘ awareness of differing

Environmental Management Standards.

In summary, it can be said that this study provides enormous potential for future research especially in

regards to investigating whether a moderating effect exists between GSCM Practice Implementation and

Business Performance. To what degree does market pressure, when differentiating between companies

that experience more pressure and ones that experience less pressure, have an impact on the Overall,

Environmental and Economic Performance? However, even though conclusions on the existence or non-

existence of a moderating effect could not be drawn this study has made a contributing attempt in

determining differences between countries with differing GSCM Practice Implementation maturity and in

differentiating between Economic and Environmental Firm Performance outcomes.

A drawback of this study is that it remains questionable if the findings can be generalized in consideration

of the low response rate. Nevertheless, this thesis has managed to identify several possible improvements

that can be made to the methodological approach and which will undoubtedly enable future research on

the topic to yield more generalizable and accurate results. The main recommendation for future research

is to conduct the study on a larger sample and to continuously refine the survey instrument. As measuring

GSCM Practice Implementation is a rather new discipline the development of good measurement tools

provides enormous potential for further research.


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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................................................................ 7

EXECUTIVE SUMMARY ......................................................................................................................................... 9

TABLE OF CONTENTS .......................................................................................................................................... 11

LIST OF TABLES AND FIGURES......................................................................................................................... 13

1. INTRODUCTION ............................................................................................................................................ 15

1.1 Problem Introduction ............................................................................................................................................. 15

1.2 Research Contribution – Filling the Gap ............................................................................................................... 16

1.3 Research Outline .................................................................................................................................................... 17

2. THEORETICAL BACKGROUND ................................................................................................................. 19

2.1 Theoretical Background – Resource Dependence Theory.............................................................................. 19

2.1.1 Resource Dependence Theory – Achieving Organizational Performance ..................................................... 19

2.1.2 Resource Dependence Theory and Supply Chain Management ..................................................................... 20

2.1.3 Resource Dependence Theory and Green Supply Chain Management .......................................................... 21

2.2 Small- and Medium-Sized Suppliers and Green Supply Chain Management ....................................................... 22

2.3 The Automotive Industry ....................................................................................................................................... 23

2.4 Institutional Theory ............................................................................................................................................... 24

2.4.1 Institutional Theory Explained ....................................................................................................................... 24

2.4.2 The Evolution of Green Awareness - An Institutional Theory Perspective ................................................... 24

3. HYPOTHESES DEVELOPMENT ................................................................................................................. 26

3.1 GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic

Performance ................................................................................................................................................................. 26

3.2 Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall Business Performance

..................................................................................................................................................................................... 27

3.3 Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business Performance,

Environmental Performance, and Economic Performance .......................................................................................... 29

3.4 Relational Efficiency, GSCM Implementation, Overall Business Performance, Environmental Performance, and

Economic Performance ................................................................................................................................................ 30

3.5 Moderating Effect of Market Pressure ................................................................................................................... 32

3.6 Mediating Effect of Employee Job Satisfaction, Operational Efficiency and Relational Efficiency ..................... 33

3.7 The Conceptual Model .......................................................................................................................................... 34

4. RESEARCH METHODS AND DATA ........................................................................................................... 38

4.1 Developing the Questionnaire – Constructs and Items .......................................................................................... 38

4.2 Population and Data Sources ................................................................................................................................. 39

4.3 Data Collection Procedure and Response Rate ...................................................................................................... 40

4.3.1 Sample Selection ............................................................................................................................................ 40

4.3.2 Selection of Key Informants ........................................................................................................................... 41

4.3.3 Questionnaire Design and Distribution .......................................................................................................... 41

4.3.4 Invalid Respondents and Missing Data .......................................................................................................... 42


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4.3.5 Final Response Rate ....................................................................................................................................... 42

5. DATA ANALYSIS ............................................................................................................................................ 44

5.1 Characterization - Responding Firms .................................................................................................................... 44

5.2 Awareness and Adoption of Environmental Management Standards (EMSs) ...................................................... 45

5.3 Correlation Matrix ................................................................................................................................................. 46

5.4 Validity, Reliability and Goodness-of-Fit of the Research Model (Original Model) ............................................ 46

5.4.1 Step 1 – Assessing Validity of the Constructs ................................................................................................ 47

5.4.2 Step 2 - Assessing Reliability of the Constructs ............................................................................................. 52

5.4.3 Step 3 - Goodness-of-Fit of the Research Model ........................................................................................... 53

5.5 Validity, Reliability and Goodness-of-Fit of the Research Model (Modified Model) ........................................... 62

5.5.1 Step 1 – Assessing Validity of the Constructs ................................................................................................ 62

5.5.2 Step 2 - Assessing Reliability of the Constructs ............................................................................................. 65

5.5.3 Step 3 - Goodness-of-Fit of the Research Model ........................................................................................... 66

6. HYPOTHESES TESTING AND DISCUSSIONS OF QUANTITATIVE DATA ....................................... 76

6.1 Original Model ...................................................................................................................................................... 76

6.1.1 Direct Effects ................................................................................................................................................. 76

6.1.2 Mediation Analysis ........................................................................................................................................ 77

6.1.3 Moderation Analysis ...................................................................................................................................... 79

6.2 Modified Model ..................................................................................................................................................... 82

6.2.1 Direct Effects ................................................................................................................................................. 82

6.2.2 Mediation Analysis ........................................................................................................................................ 82

6.2.3 Moderation Analysis ...................................................................................................................................... 84

6.3 Robustness of the Original Model ......................................................................................................................... 87

7. SUMMARY AND IMPLICATIONS .............................................................................................................. 88

7.1 Main Findings and Managerial Implications ......................................................................................................... 88

7.2 Limitations and Future Research Directions .......................................................................................................... 90

7.3 Conclusions ........................................................................................................................................................... 91

LIST OF REFERENCES .......................................................................................................................................... 93

APPENDIX .............................................................................................................................................................. 111

Appendix 1 – List of Questionnaire Items and the respective Measurement Scales ................................................. 111

Appendix 2 – Survey Questionnaire .......................................................................................................................... 114


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LIST OF TABLES AND FIGURES

List of Tables

Table 1: Summary of constructs, their definitions and the most important literature identified .................................. 36

Table 2: Summary description of hypotheses to be investigated ................................................................................. 37

Table 3: Characteristics of responding firms ............................................................................................................... 45

Table 4: Awareness and adoption of Environmental Management Standards ............................................................. 46

Table 5: Correlations between theoretical constructs .................................................................................................. 46

Table 6: Validity and reliability table (original model) ............................................................................................... 50

Table 7: Factor correlation matrix with the square root of the AVE on the diagonal (original model) ....................... 50

Table 8: Summary of validity and reliability measurement results (original model) .................................................. 51

Table 9: Defining internal consistency using cronbach‘s alpha................................................................................... 52

Table 10: Summary of cronbach‘s alpha and item-to-total correlations measurement results (original model) ......... 53

Table 11: Statistics of first- and second-order models (original model) ...................................................................... 55

Table 12: Fit indices for the mediators, moderator and dependent concepts (original model) .................................... 60

Table 13: Validity and reliability table (modified model) ........................................................................................... 63

Table 14: Factor correlation matrix with the square root of the AVE on the diagonal (modified model) ................... 64

Table 15: Summary of validity and reliability measurement results (modified model) .............................................. 65

Table 16: Defining internal consistency using cronbach‘s alpha................................................................................. 65

Table 17: Summary of cronbach‘s alpha and item-to-total correlations measurement results (modified model)........ 66

Table 18: Statistics of first- and second-order models (modified model) .................................................................... 67

Table 19: Statistics of first- and second-order models after performing model fit (modified model) ......................... 68

Table 20: Summary of validity and reliability measurement results after performing model fit (modified model) .... 69

Table 21: Summary of cronbach‘s alpha and item-to-total correlations measurement results after performing model

fit (modified model) ............................................................................................................................................ 70

Table 22: Fit indices for the mediators, moderator and dependent concepts (modified model) .................................. 74

Table 23: Summary of hypotheses test results and comparison to results found by Lee et al. (2012)......................... 77

Table 24: Summary of mediation analysis results (original model) ............................................................................ 79

Table 25: Summary of moderation analysis results (original model) .......................................................................... 79

Table 26: Results of path analysis and hypotheses tests (original model) ................................................................... 80

Table 27: Summary of mediation analysis results (modified model) .......................................................................... 84

Table 28: Summary of moderation analysis results (modified model) ........................................................................ 84

Table 29: Results of path analysis and hypotheses tests (modified model) ................................................................. 85

List of Figures

Figure 1: Hypotheses development: GSCM Implementation, Overall Business Performance, Environmental

Performance, and Economic Performance .......................................................................................................... 26

Figure 2: Hypotheses development: Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and

Overall Business Performance ............................................................................................................................ 27

Figure 3: Hypotheses development: Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall

Business Performance, Environmental Performance, and Economic Performance ............................................ 29

Figure 4: Hypotheses development: Relational Efficiency, GSCM Implementation, Overall Business Performance,

Environmental Performance, and Economic Performance ................................................................................. 30

Figure 5: Hypothesized structural model ..................................................................................................................... 35

Figure 6: Path diagram of the first-order measurement model (original model) ......................................................... 57

Figure 7: Path diagram of the second-order measurement model (original model) ..................................................... 58

Figure 8: Path diagrams of the measurement models (original model) ....................................................................... 61

Figure 9: Path diagram of the first-order measurement model (modified model) ....................................................... 71


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Figure 10: Path diagram of the second-order measurement model (modified model) ................................................. 72

Figure 11: Path diagrams of measurement models (modified model) ......................................................................... 75

Figure 12: Hypothesized structural model results (original model) ............................................................................. 81

Figure 13: Hypothesized structural model results (modified model) ........................................................................... 86


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Taking an Environmental Perspective on Supply Chain Management

– A Study on the German Automobile Industry

Note: This is a replication (Lee, S., Kim, S. and Choi, D. (2012), “Green supply chain management and organizational performance”, Industrial

Management and Data Systems, Vol. 112, No. 8, pp. 1148-1180).

1. INTRODUCTION

The first chapter of this thesis will provide an introduction to the general research topic and will elaborate

on how this study aims to contribute to the findings of other researchers on the subject. The chapter will

conclude with an outline of how the research report is structured.

1.1 Problem Introduction

Globalization pressures, increasing competitiveness and advances in information technology are the key

contributors to the shortening of product life cycles. The fast pace of globalization continues to promote

not only challenges and uncertainties but also opportunities. Companies that have the ability to respond

rabidly to the dynamic needs of new consumer segments and growing markets are more apt to stay

competitive and continue to remain industry leaders.

In the past two to three decades, environmental issues have received increasing attention, particularly in

the West, where the negative side-effects of industrial production are more than ever threatening

conditions for future generations. Ever more severe natural disasters, global warming, the reduction of the

stratospheric ozone layer as well as an increase in the pollution of the earth‘s oceans, rivers and air have

prompted the need for local, regional, national and international change.

Environmental legislations and agreements in combination with a continuous strive to remain competitive

have pressed organizations worldwide to recognize the importance of adopting environmentally friendly

practices. The importance placed on environmental practices by competitors, governments and the market

have fostered the adoption of corporate environmental management practices which has become a rather

mature discipline over the years. Numerous companies have understood the importance of reducing their

environmental impact and have incorporated environmental considerations into their day-to-day business

activities. However, as stated by Lee and Klassen (2008) as well as Lee (2009) it is of the utmost

importance for multinationals to engage their upstream suppliers in adopting environmentally friendly

practices which will contribute to the entire supply chain‘s competitive ability. Furthermore, engaging

upstream and downstream supply chain partners in green supply chain management initiatives has gained

importance when taking into account the fact that customers and other stakeholders are not always

inclined to make a distinction between a company and its supplying firms (Bacallan, 2000).

Legislations are being imposed on a global basis with the European Union being a forerunner. The

European Union and the USA have recognized the importance of proper environmental management

(Guimares and Sato, 1996). However, at present quite a few countries in Asia have made a noteworthy

step towards becoming ISO 14001 (requirements for Environmental Management Systems) accredited.


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Asian countries are following the lead primarily because of the rising environmental awareness associated

with increasing pressures and to remain competitive in global trading.

China as the forerunner followed by Japan, Italy and Korea currently outperforms both the USA and

European countries which are falling behind the remarkable pace set by developing nations in regards to

green initiatives. (Welch et al., 2002)

As can be derived from the in 2011 conducted ―ISO Survey of Management System Standard

Certification‖ a total of 267,457 ISO 14001 certificates were in issuance in 158 countries in the year 2011

(ISO, 2011). In terms of the regional share East Asia and the Pacific are, with 51.3% of the total number

in issuance, the forerunners closely followed by Europe with 39.9%. With a noteworthy distance to

Europe, North America is on third place with 2.8%. It should also be noted that East Asia and the Pacific

managed to overtake Europe for the first time in the year 2005.

Bansal and Roth (2000) in their qualitative study on the motivations for becoming more ecologically

responsive have found there to be three forces that induce companies to adopt environmentally sound

practices. These are regulatory, market and social pressures. Numerous researchers have recognized the

importance of regulatory pressures in pushing companies to be environmentally responsible (Newton and

Harte, 1997; Lawrence and Morell, 1995). Firms not only seek to avoid penalties or fines by complying

with legislations but also opt to remain competitive by actively engaging in environmental activities to

stay ahead of changes in regulations (Rondinelli and Vastag, 1996; Clark, 1999 cited in Vastag, 2004).

Market pressures originating from customers and suppliers as well as social pressures from the general

public and environmental activists also contribute to an increase in environmental awareness amongst

firms (Starik and Rands, 1995; Lawrence and Morell, 1995).

As stated by Welch et al. (2002) early adopters of ISO 14001 standards in Japan are likely to be larger,

greener and most of all less motivated by competitive, media or regulatory pressures. In comparison, later

adopters were found to have been more pressured by competitive, regulatory and media forces and were

likely to be smaller and less green.

Lee et al. (2012) expect that as pressures from external stakeholders such as governments and large

buying firms, which continuously extend the environmental requirements set for supplying firms, increase

the employees of the supplying firms are assumed to become increasingly dissatisfied and resistant to

change.

1.2 Research Contribution – Filling the Gap

From the amount of previous literature, addressing the relationship between resource dependence and

Green Supply Chain Management (GSCM) performance, it can be concluded that this topic is fertile for

investigation. Thus, the subsequent research, drawing on knowledge from the Resource Dependence

Theory as well as the Institutional Theory, will explore the relationship between green supply chain

practices and organizational performance. Scott (1992) (cited in Rowley, 1997) stated that both the

Resource Dependence Theory and the Institutional Theory emphasize the importance of managing

external demands and expectations as well as being responsive to these pressures in order to survive.

Furthermore, Oliver (1991) concluded that the external pressures referred to by the Resource Dependence

and Institutional Theories originate from the organizations stakeholders. The stakeholders to a business

are actors which have the control over scarce resources and who have the power to enforce institutional

values and guidelines. An organizations survival is dependent on its ability and the degree to which it is

able to satisfy its stakeholders (Brenner and Cochran, 1991).


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In this study, the independent concept (GSCM Implementation) and dependent concepts (Overall

Business Performance, Environmental Performance and Economic Performance) will be related by means

of three organizational variables, namely: Employee Job Satisfaction, Operational Efficiency and

Relational Efficiency which are assumed to have a mediating effect on the dependent concepts.

Furthermore, it will be investigated in how far Market Pressure moderates the relationship between

GSCM Implementation and Business Performance.

The study will be conducted from the supplier‘s point of view focusing on small- and medium-sized

suppliers in the automotive industry in Germany. German suppliers are considered to be operating in a

rather mature environment in regards to green supply chain initiatives in comparison to companies located

in Korea. As stated by Welch et al. (2002) early adopters of ISO 14001 standards might be more inclined

to adopt environmental practices and late adopters are assumed to require an increasing amount of

external pressure to adopt green initiatives. It is, thus, expected that German enterprises are less pressured

by regulatory, media or competitive forces and in turn staff members of these suppliers are assumed to be

more satisfied. This increased employee satisfaction, in turn, is assumed to more positively influence

business performance than is the case with companies operating in Korea, which are assumed to be more

pressured in becoming environmentally friendly. Furthermore, early adopters of ISO standards are

assumed to have the resources and motivation to pursue the adoption of environmental initiatives. This, in

turn, will be beneficial in the long-run as the early adoption of GSCM practices enables companies to

better position themselves for survival (Welch et al., 2002). However, it should be noted that followers

face less risk as they are able to learn from early adopters and, thus, are able to make more informed

choices as to which initiatives to adopt and which have not proven to be beneficial in the past. Lastly, it is

expected that the relationship between GSCM Implementation and Environmental Performance will be

stronger in European firms facing higher market pressures as opposed to firms facing lower

environmental pressures from buying firms. In contrast, however, economic performance is assumed to be

lower in European firms facing higher market pressures than in firms experiencing lower pressures.

In conclusion, a more significant indirect relationship between Green Supply Chain Practice

Implementation and Overall Business Performance is expected when conducting the study on German

Enterprises as opposed to Korean suppliers. This study is expected to provide new insights as to whether

business performance will increase or decrease as pressures from large customer firms begin to cease.

More explicitly, the following study will make a contribution to previous literature in making a

distinction between environmental and economic performance improvements and assesses the effects

market pressures have on early and late adopters in the industry. The study will differentiate between

German suppliers that are facing higher environmental pressures and companies that are facing lower

environmental pressures to determine the effects on business performance.

Additionally, the study will also provide new insights as to the relationship between relational efficiency

and environmental and economic performance.

1.3 Research Outline

This research report is structured as follows. Subsequent to this introductory chapter in which the problem

to be investigated and the research objectives were outlined the study proceeds with a detailed description

of the theoretical background. This description will provide the basis for Chapter 3 which elaborates on

the hypotheses development process and is rounded off with a depiction of the hypothesized conceptual


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model. Chapter 4 will provide a short description of how the questionnaire was developed and distributed.

Additionally, characteristics of the population to be studied will be identified subsequent to which the

chapter provides a description of how the final sample was obtained. In Chapter 5 a more detailed

description of the final sample characteristics will be provided and the gathered data will be analyzed by

means of various statistical methods. In Chapter 6 the hypotheses will be tested and the quantitative

outcomes will be discussed. The final chapter of this thesis, Chapter 7, will provide a summary of the

main findings, describe the study‘s limitations and will offer avenues for future research.


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2. THEORETICAL BACKGROUND

Chapter 2 will provide an outline of this study‘s theoretical background. First off, the literature review

will delve into the topic of the Resource Dependence Theory drawing a relationship to organizational

performance, supply chain management and green supply chain initiatives. Additionally, two further

important topics dealt with in this research paper are elaborated on, namely: small- and medium-sized

firms and the automotive industry. Subsequently, this chapter will also draw on literature about

Institutional Theory which will prove to be of use in building the conceptual model.

2.1 Theoretical Background – Resource Dependence Theory

The first section will delve into the topic of the Resource Dependence Theory drawing a link to

organizational performance, supply chain management and green supply chain initiatives.

2.1.1 Resource Dependence Theory – Achieving Organizational Performance

Resource Dependence Theory Explained

According to Pfeffer and Salancik (1978) (cited in Lee et al., 2012) Resource Dependence Theory (RDT)

proposes that dependence and collaboration are to be seen as key characteristics of member firms in a

supply chain which strive to increase their performance gains. Firms are said to be interdependent when

not one actor is in control of achieving a desired outcome but each member firm requires another actors‘

resources in order to sustain growth (Handfield, 1993). The aim should be to achieve higher performance

outcomes in the long-term as opposed to pursuing short-term benefits to the detriment of others. The

Resource Dependence Theory assumes that firms cannot be entirely self-sufficient with regards to

strategically vital resources. They are reliant on external resources and need to cautiously manage these

resources in order to remain competitive (Heide, 1994). Cook (1977), furthermore, states that these

developed dependencies facilitate the degree of influence partners have on one another‘s business

practices.

Uncertainty Explained

Taking this definition of Resource Dependence Theory one step further, Bordonaba-Juste and Cambra-

Fierro (2009) assert that to improve a firms performance, in the highly dynamic environment with

increasing globalization in which they are operating, it is of essence to adapt to the surroundings, while

those that are unsuccessful in doing so are condemned to fail. Considering the ongoing rise in

environmental complexity and dynamism firms are experiencing increasing uncertainty and managing

this uncertainty will be one of the main challenges facing companies all over the globe in the years to

come.

Literature on uncertainty presents a range of definitions of the concept; however, the most appropriate

characterization of uncertainty in the context of this study is the circumstance in which a company is

lacking sufficient knowledge and information in decision making (Duncan, 1972; Lawrence and Lorsch,

1967). Furthermore, uncertainty is characterized by the inability to know the out-come of a decision

beforehand and the inability to assign probabilities to how environmental factors will affect business

success (Duncan, 1972). The environment can be segmented into a total of four dimensions of uncertainty

(Jabnoun et al., 2003), namely:


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(1) Macro-environmental uncertainty: This dimension relates to a firms general environment

encompassing regulatory, political and economic conditions.

(2) Competitive uncertainty: This uncertainty type refers to a firm‘s inability to characterize their

competition in terms of their strategies, their competitive position and their prospective course of action.

(3) Market (and demand) uncertainty: The market is ever changing and this turbulence is making it ever

more difficult for firms to predict future demand and supply conditions.

(4) Technology uncertainty: This dimension relates to the change in the industry‘s technological know-

how and expertise.

The Resource Dependence Theory postulates that firms manage dependence and seek to reduce

uncertainty by means of creating formal linkages (Ulrich and Barney, 1984) such as negotiating and

arriving at agreements with collaborating firms (Koberg and Ungson, 1987; Cai and Yang, 2008). In

addition, it is also important to establish and maintain semiformal ties with other firms (Ulrich and

Barney, 1984) to facilitate the creation of a socially-bonded and trust-based relationship.

Linking Organizational Performance and Resource Dependence Theory

Drawing the link between Resource Dependence Theory and organizational performance it can be

concluded that with the increase in uncertainty and the ever faster changing environment a single firm is

hard pressed to acquire all resources it necessitates to develop and uphold its existing competitive

advantages whilst building new ones (Dyer and Singh, 1998). Thus, creating customer and supplier

linkages will contribute to reducing the uncertainty faced by firms in their operating environment (Carter

and Rogers, 2008). Additionally, integrating complementary resources can lead to the realization of

unique synergy which will in turn facilitate the creation of competitive advantages and thereby contribute

to increased firm performance (Harrison et al., 2001). Harrison et al. (2001) further state that

complementary resources are also advantageous in facilitating learning and expediting the development of

new capabilities. The resource bundle developed through interdependencies will provide the partnering

firms with capabilities that are superior to the ones they would have been able to build on their own.

These interdependencies enable firms to obtain sustainable competitive advantage and, in turn, enable the

improvement of the organizations performance (Sambharya and Banerji, 2006; Paulraj and Chen, 2007).

2.1.2 Resource Dependence Theory and Supply Chain Management

Having elaborated on the relationship between the Resource Dependence Theory and an organizations

performance the following paragraphs will now draw the link between the Resource Dependence Theory

and the entire supply chain.

Supply Chain Management

The focus of the term Supply Chain Management has shifted throughout the years. The traditional supply

chain was characterized by organizations which feared dependence and were more inclined to make use

of the business processes and facilities which were in their possession as opposed to collaborating with

the members of their supply chain (Thomas and Griffin, 1996). According to Ketchen and Hult (2007)

having others become dependent on an organization could be advantageous in being superior. Harland

(1996) in his study identified and addressed a range of differing definitions of Supply Chain


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Management. He stated that the term Supply Chain Management comprised of the functions of

purchasing, manufacturing and distributing the product.

Globalization and ever faster changing customer demands have increased the complexity of managing

supply chains. Thus, the definition of Supply Chain Management has shifted to account for rapidly

changing markets and an increasing globalization.

Hervani et al. (2005) define Supply Chain Management as the coordination and management of a network

of interconnected businesses and activities with the goal of providing a product or service to the end-

customer. A supply chain structure comprises of suppliers, distributors, manufacturers, wholesalers,

retailers and customers. Supply Chain Management is considered to be a critical business function

encompassing all activities which are related to the transformation and flow of goods from the sourcing of

raw materials and parts, the manufacturing and assembly of products, as well as the storage, distribution

and the delivery to the end-customer.

Linking Supply Chain Management and Resource Dependence Theory

Today, as never before, companies all over the globe are designing ever more efficient supply chains

which can withstand the complexities of globalization and the indefinite and unpredictable uncertainties it

brings with it. The terms outsourcing and offshoring have in recent years become increasingly important.

Companies source internationally to benefit from a reduction in production and service costs, increased

revenues and better reliability (Ferdows, 1997). MacCormack et al. (1994) have identified additional

benefits such as better access to overseas markets and close proximity to customers and suppliers which

facilitates organizational learning and improves reliability, respectively.

Having elaborated on the benefits of outsourcing and offshoring it is also important to bear in mind the

risks and challenges companies are encountering on a day to day basis in a world comprising of invisible

boundaries. MacCarthy and Atthirawong (2003) state that global supply chains in comparison to domestic

supply chains are harder to manage. This is mainly due to the geographical distance, an increase in lead-

times and the difference in language, culture and skills. Additionally, global supply chains are

characterized by risks and challenges which have an effect on every single member of the chain.

Economic and political instability, currency exchange rates and risks relating to changes in the regulatory

environment have made the supply chain more vulnerable to disruptions.

To counteract these strategic weaknesses that supply chains are facing the Resource Dependence Theory

stipulates that inter-organizational linkages and relationships will enable firms to achieve sustainable

growth. The theory emphasizes the need for buyer-supplier relationships which focus on cooperation and

organization in order to jointly benefit (Kanter, 1994).

2.1.3 Resource Dependence Theory and Green Supply Chain Management

Green Supply Chain Management

Increasing levels of pollution, the escalating deterioration of the environment and diminishing raw

material resources have contributed to an increase in environmental awareness. Institutional forces such

as regulatory requirements and consumer pressures are also drivers of change. Businesses have realized

the importance of integrating environmentally sound practices not only at an organizational level but

throughout the entire supply chain.


22

Srivastava (2007) defines Green Supply Chain Management as the integration of environmental

awareness into supply chain related processes. By means of collecting and classifying previous literature

on the topic Srivastava (2007) was able to define the scope of Green Supply Chain Management as

ranging from green material choice and sourcing, to product design, manufacturing processes, product

delivery and end-of-life management. Drawing from past literature Zhu and Sarkis (2004) have developed

four factors for Green Supply Chain Management practices, namely:

(1) Internal environmental management: This factor describes the company‘s internal activities aimed at

becoming more environmentally-friendly. The dimension addresses the degree of commitment received

from top management as well as the company‘s obtainment of environmental compliance programs such

as ISO 14001 certification.

(2) External Green Supply Chain Management: This dimension encompasses the external relationships. It

deals with the purchasing of eco-friendly products and with the building of relationships with customers

and suppliers to become more environmentally sound.

(3) Investment recovery: Investment recovery deals with the sale of used materials and scrap as well as

the selling of excess inventory materials.

(4) Eco-design: This factor includes the design of products for recycling, reuse or recovery.

Linking Green Supply Chain Management and Resource Dependence Theory

Recovering materials and designing products in an economically friendly way has increased the need for

inter-organizational collaboration to an ever greater extent to realize potential gains and to, in turn,

achieve improved overall performance objectives (Zhu et al., 2010; Zhu and Sarkis, 2004; Zhu et al.,

2005; Shang et al., 2010).

In the context of Green Supply Chain Management, adopting Green Supply Chain Management related

practices, for example green purchasing and customer cooperation, does not only require the internal

adoption of environmental practices but the cooperation of the entire supply chain in becoming more

environmentally sound. González et al. (2008) have found that given the superiority of larger firms over

their smaller supply chain partners the large firms will opt for environmentally friendly practices to be

adopted by the smaller supplying firms. Thus, a diffusion of environmentally responsive practices

throughout the entire supply chain will take place. This diffusion can, however, only take place if firms

recognize the need of forming partnerships. As stated by the Resource Dependence Theory, partnerships

are indispensable if individual firms are lacking the required resources to achieve the desired outcomes.

2.2 Small- and Medium-Sized Suppliers and Green Supply Chain Management

Large, multinational enterprises are dominating the headlines with new international expansion strategies,

multi-billion Euro takeovers or bankruptcies. However, according to the European Commission (2012)

SMEs play the most important role in the economy. 99.8 per cent of all European businesses are small-

and medium-sized enterprises the majority of which, with 92.2 per cent, are characterized as micro-

businesses with less than 10 employees. Approximately 6.5 per cent of SMEs in the EU are small

enterprises which employ between 10 and 49 workers and the medium-sized enterprises account for 1.1

per cent (employing 50 to 249 people). Lastly, large businesses with a minimum of 250 employees make


23

up merely 0.2 per cent of the total number of firms in the European Union. Thus, SMEs can be considered

as the back-bone of the European economy being mainly atone for advances in innovation and Resource

and Development as well as contributing to the prosperity and expansion of the economy. In the private

sector these businesses provide employment for two out of three workers and account for more than 50%

of the total value-added created by firms in the European Union. (European Commission, 2012)

Thus, it comes as no surprise that the involvement of small- and medium-sized suppliers is a critical first

task to take when endeavoring to achieve environmental change (Holt et al., 2001).

For companies to reap the greatest benefit from their environmental management practices it is of the

utmost importance to integrate both upstream and downstream members of the supply chain into their

environmental initiatives. Considering the suppliers‘ deciding role in improving the overall performance

of a supply chain (Sarkar and Mohapatra, 2006) it has become increasingly important for manufacturers

to collaborate with their upstream supply chain partners to enable the development of a competitive

advantage (Sheth and Sharma, 1997; Cannon and Homburg, 2001).

Lee (2008) has identified two drivers for firms and governments to include small- and medium-sized

enterprises in the environmental initiatives undertaken to make the entire supply chain more

environmentally friendly. The first reason for extending the environmental responsibility also to small-

and medium-sized suppliers is the risk of disruption. Suppliers that are not aware or are not concerned

with complying with the latest environmental standards can cause both excessive financial and

reputational damage to a buying firm. The second driver deals with the number of SMEs making up a

supply chain‘s base or a country‘s industrial base.

Micro, small- and medium-sized enterprises, however, often lack the resources, strategy, environmental

know-how and awareness to improve their processes to contribute to the entire supply chain becoming

more environmentally friendly (Pimenova and Vorst, 2003). These enterprises, thus, often have

difficulties implementing the requirements set by their larger buying firms and in consequence hinder

their customer firms from achieving their greening objectives (Lee and Klassen, 2008).

Concluding, it is important, as stated by the Resource Dependence Theory, to build strong relationships

amongst supply chain partners and to share the resources required to achieve the set goals. Partnerships

and the integration of complementary resources are a requisite to facilitate the development of new

capabilities and thereby contribute to the creation of competitive advantage.

2.3 The Automotive Industry

In the past three decades the automotive industry underwent several major developments. In the 1980s,

the industry experienced a turnaround in management practices with an increasing focus on quality

management and lean manufacturing (Oliver et al., 1996). The 1990s were characterized by a rapid

increase in globalization which brought about the expansion of the industry (Okada, 2004) as well as an

increase in customization, customer expectations and requirements.

The automotive supply chain, with its worldwide scope, provides a unique case for exploration in regards

to the implementation of GSCM practices and the resulting performance gains. The auto industry has a

pervasive global environmental impact and has been driven by competitive, regulatory and economic

reasons to adopt green supply chain initiatives. Furthermore, this industry is one of a small number of


24

global industries in which customers have prescribed minimum environmental performance standards

with which suppliers have to comply. Companies have recognized the need for ensuring that their

upstream supply chain partners comply with environmental standards. It has been stated by Orsato and

Wells (2007) that, from an economic viewpoint, suppliers provide up to 80 per cent of the value,

consisting of the materials and parts, required to manufacture the end-product.

According to the European Automobile Manufacturers‘ Association (ACEA) (2012) Europe with a yearly

production quantity of more than 17 million vehicles (passenger cars, vans, buses and trucks), which is 24

per cent of the world total, is the worldwide forerunner in vehicle production. Germany is Europe‘s

largest car manufacturer closely followed by France which is the world‘s fifth largest. The top four

manufacturers worldwide are the United States, Japan, Germany and China (ACEA, 2013). The European

auto industry comprises of a few global lead manufacturers such as BMW, Daimler and Renault which

are supplied by a large number of small- and medium-sized enterprises. Goodyear, Bridgestone and

Magna are three of the largest auto industry suppliers in Europe. Suppliers in supporting sectors such as

electronics and electrical engineering, metal manufacturing and plastics and glass production are also

highly concentrated in the European area.

2.4 Institutional Theory

The fourth section of this chapter will delve into Institutional Theory and elaborate on the role of

institutional pressures in firms‘ willingness to adopt green supply chain practices.

2.4.1 Institutional Theory Explained

Institutional Theory tries to explain how external pressures such as market, regulatory and competitive

forces influence a company to implement a specific organizational practice or structure (Hirsch, 1975; Lai

et al., 2006). DiMaggio and Powell (1983) state that within Institutional Theory three isomorphic

processes exist, namely: coercive, normative, and mimetic. Coercive isomorphic drivers mainly originate

from parties who are in power such as governmental agencies. Governments are in the position to

coercively influence organizations by means of fines and trade barriers (Rivera, 2004). Normative

isomorphic pressures on the other hand mainly originate from consumers. Enterprises are driven to

conform so as to be perceived as operating legitimately. The third driver, namely mimetic isomorphism

takes place when organizations endeavor to become as successful as their industry competitors by

mimicking or imitating their actions (Aerts et al., 2006).

Drawing the link to GSCM practice implementation, Jennings and Zandbergen (1995) as well as

Lounsbury (1997) state that the Institutional Theory provides an important theoretical background to

assess in how far external pressures impact a company‘s adoption of green supply chain practices.

Kilbourne et al. (2002) have identified there to be a relationship between coercive forces and the

implementation of environmental change initiatives. Furthermore, normative pressures exerted from

consumers, foreign and domestic, have also driven companies to adopt environmentally-friendly practices

(Ball and Craig, 2010). Lastly, mimetic isomorphism was also found to play a contributing role in green

practice implementation.

2.4.2 The Evolution of Green Awareness - An Institutional Theory Perspective

The environmental revolution has changed the way companies do business. The 1960s and 1970s were

characterized by corporations denying the fact that their business activities were negatively impacting the


25

environment. A series of ecological problems initiated organizations to rethink the way they do business.

Today, almost four decades later, going green is an imperative not many companies can afford to ignore.

The corporations of the twenty-first century have accepted their responsibility towards the environment

and they are in a better state than ever having the motivation, the resources and the know-how to act and

achieve sustainability.

According to Lee and Rhee (2007) Korea‘s concern for the environment emerged two decades ago when

the country experienced first-hand environmental impacts. Up until the 1990s Korea‘s number one

priority had been economic growth (Lin and Sheu, 2012). The Korean economy grew at a tremendously

fast rate for three decades until environmental events and accidents increased the country‘s awareness for

the environment. Furthermore, almost simultaneously, Korean companies experienced an increase in

external pressures especially from the European Union which has implemented laws and regulations to

reduce the environmental impact of a firm‘s entire supply chain.

In conclusion, the European Union can be seen as a forerunner when it comes to establishing and

implementing laws and regulations to pressure businesses in increasing their environmental awareness.

Thus, the EU is a rather mature continent as compared to Korea which has only just recently started to be

concerned about the country‘s environmental impact.


26

3. HYPOTHESES DEVELOPMENT

Chapter 3 outlines the hypotheses development process and draws on additional literature from

Economics and Business Administration, Marketing and Supply Chain Management. The chapter

concludes with a conceptual model, a summarizing table of the concepts and their respective definitions

as well as a table summarizing the hypotheses.

3.1 GSCM Implementation, Overall Business Performance, Environmental Performance, and

Economic Performance

Independent Concept Dependent Concepts

Figure 1: Hypotheses development: GSCM Implementation, Overall Business Performance, Environmental Performance, and


Chien and Shih (2007) have studied the effects of implementing green initiatives on firm performance at

manufacturing firms operating in the electrical and electronics industry in Taiwan. The authors were able

to conclude a positive relationship between implementing green supply chain practices and the

environmental and financial performance of firms. More specifically, the increase in the environmental

performance of a firm will inevitably yield an increase in market share and corporate profits. Rao (2002)

has found there to be a relationship between green supply chain management, competitiveness and

economic performance. Becoming more environmentally committed, for instance, by means of employing

waste and emission reduction initiatives in combination with cutting costs and enhancing product quality

and production efficiency to remain competitive, firms can achieve economic performance gains. Porter

and van der Linde (1995) also support the existence of a relationship between cost savings or product

value increases and the degree of company competitiveness.

However, as stated by Aragón-Correra and Sharma (2003) temporarily implementing a proactive

environmental approach does not guarantee the obtainment of a competitive advantage. A continuous

development is required in terms of innovations and constant learning to maintain and improve the

competitive position (Ulhøi and Madsen 2003).

The empirical research of Zhu and Sarkis (2004) provides evidence for there to be a noteworthy

relationship between GSCM implementation and the environmental and economic performance of a firm.

In a more recent study Zhu et al. (2007) were able to show that apart from there being a significant

relationship between GSCM implementation and a firm‘s environmental and economic performance there

is also an association to be made to the operational performance outcomes of a company. Operational

performance encompasses the quality of products, the capacity and inventory levels.

It is also important to mention that for firms to effectively benefit from GSCM practice adoption Chen

(2008) as well as Yang et al. (2009) have identified the need for successfully integrating and managing

internal (for instance top management commitment) as well as external (e.g. collaborating with suppliers

and customers) green supply chain practices. Moreover, Geffen and Rothenberg (2000) (cited in Zhu et

GSCM

Implementation

Overall Business Performance

Environmental Performance



27

al., 2005), Vachon (2007) and Seuring and Mueller (2008) were able to conclude that the performance

gains reaped from implementing GSCM practices are both financial and non-financial in nature.

Furthermore, it should be noted that the adoption of green supply chain practices is linked to the firm‘s

image of being socially responsible (Montiel, 2008; Cruz and Pedrozo, 2009). As a matter of fact,

McGuire et al. (1988) studied the relationship between perceptions of a firms‘ social responsibility and

the corresponding financial performance. The authors found there to be a positive association between

corporate social responsibility and a firm‘s business performance evaluated in terms of accounting and

stock-market-based measures.

Therefore, the following hypotheses are being proposed:

H1a: The implementation of GSCM practices is positively related to the overall business performance at

the firm level.

H1b: The implementation of GSCM practices is positively related to the environmental performance at

the firm level.

H1c: The implementation of GSCM practices is positively related to the economic performance at the

firm level.

3.2 Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall

Business Performance

Independent Concept Mediators Dependent Concept

Figure 2: Hypotheses development: Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall


Valentine and Fleischman (2008) in their exploratory study on ethics programs, the perceived corporate

social responsibility and the corresponding employee job satisfaction have found there to be a positive

relationship between ethics programs and employee work attitudes mediated by corporate social

responsibility. The authors suggest management to place an increasing focus on the organizations ethics

codes, culture and corporate social responsibility to increase the positive beliefs about the company.

GSCM

Implementation


Employee Job

Satisfaction

Operational

Efficiency


28

Somers (2001) found there to be a positive relationship between ethics code awareness and accountants‘

organizational commitment. Valentine and Barnett (2003) also concluded that sales managers‘

organizational commitment was higher in firms that had an ethics code as opposed to firms that did not.

The presence and the employee‘s awareness of the existence of an ethics code was found to be positively

related to job satisfaction.

Mansoor et al. (2011), in their study examining the impact of job stress on employees‘ work satisfaction,

concluded there to be a negative relationship between job stress and the employees‘ resulting satisfaction.

These results are in line with the conclusions drawn by Keller (1975). Mansoor et al. (2011) categorize

job stress into stress resulting from the workload, from role conflict and job stress resulting from the

physical environment. The physical environment encompasses not only the noise, temperature, lighting

and the air circulation but also the exposure of employees to dangerous and toxic substances. According

to Davey et al. (2001) job stress is experienced mainly due to organizational aspects such as a lack of

support and commitment from top managers, long working hours or conflicts at the work place.

Job satisfaction is characterized as the extent to which employees derive pleasure from their jobs

comprising of both cognitive and affective factors (Hulin and Judge, 2003, p. 259 cited in Scott and

Judge, 2006). Edwards et al. (2008) take this definition of job satisfaction one step further and found there

to be a positive relationship between job satisfaction and task performance. Researchers in the last few

decades have found numerous explanations for a relationship to exist between employee work satisfaction

and job performance (Schleicher et al., 2004; Locke, 1976). Social Cognitive Theories state there to be a

link between employee attitude toward the job and the resulting behavior on the job which will be

reflected in job performance (Kraus, 1995). Furthermore, Expectancy-Based Theories draw a link

between the expected outcomes of a particular performance and the attitudes one has toward the job

(Naylor et al., 1980 cited in Edwards et al., 2008).

Homburg and Stock (2004), in their dyadic analysis on the link between salespeople‘s job satisfaction and

the satisfaction of the company‘s customers, were able to conclude that an increase in employee

satisfaction is positively related to customer satisfaction. A link between employee satisfaction and

financial performance which is mediated through the constructs of customer loyalty, customer satisfaction

and employee loyalty is suggested by the service profit chain model (Heskett et al., 1997).

Furthermore, Patterson et al. (2004) found supporting evidence for the existence of a relationship between

company climate and company productivity mediated by average job satisfaction level. The concept of

company climate was measured by assessing the perceptions employees had of the organizations‘ guiding

principles and practices. The authors stated there to be a link between company climate and company

performance which is mediated by employee job satisfaction.


H2a: The implementation of GSCM practices is positively related to employee job satisfaction.

H2b: Employee job satisfaction is positively related to overall business performance at the firm level.

H2c: Employee job satisfaction is positively related to the operational efficiency of the firm that

implemented GSCM practices.


29

3.3 Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business

Performance, Environmental Performance, and Economic Performance

Independent Concept Mediators Dependent Concepts

Figure 3: Hypotheses development: Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business

Performance, Environmental Performance, and Economic Performance

Operational outcomes, as stated by Zacharia et al. (2009), receive a great deal of attention from supply

chain members. The parties aim to reduce costs, improve product quality and customer service as well as

attempt to reduce the required cycle time (Koufteros at al., 2002).

Lin and Sheu (2012) surveyed U.S. and Taiwanese manufacturing plants operating in the electric and

electronics industry to examine the impact of Institutional Theory on GSCM practice adoption and how

the implementation of green initiatives in turn influences the supply chain‘s performance. The findings

prove the existence of a positive relationship between implementing green supply chain practices and the

manufacturer‘s operational efficiency.

Yang et al. (2010), furthermore, have identified there to be a positive relationship between the degree of

supplier partnership collaboration and the development of proactive environmental management

programs. The study further posits that this increase in adopting green supply chain practices positively

influences the competitive advantage by means of product quality enhancements, cost savings and

increased innovativeness. Evidence for the existence of a correlation between GSCM practice adoption

and a reduction in costs and cycle time was also provided by the firm interview responses obtained by

Lippman (2001).

Szwilski (2000) stated that an environmental management system, which is characterized to be an

information and environmental policy management mechanism, will enable the industry to facilitate the

improvement of an individual organization‘s operational performance.

In regards to the link between operational efficiency and environmental performance it can be said that

only a limited number of studies have explored this relationship. Slack et al. (2009) (cited in Ramanathan

and Akanni, 2010) posit that lean principles such as waste reductions, continuous improvements and

stakeholder involvements positively influence a company‘s environmental performance. Porter and van

der Linde (1995) underline this statement made by Slack et al. (2009) and conclude that an increase in a

firm‘s operational efficiency will inevitably lead to a reduction in waste and scrap. Toffel and Lee (2009)

state there to be a relationship between environmental performance indicators and the process efficiency

GSCM

Implementation Overall Business Performance



Operational

Efficiency

Relational

Efficiency


30

programs (programs such as lean manufacturing and six-sigma quality improvements). In conclusion, it

can be said that organizations characterized by higher operational efficiency are, as opposed to

organizations with lower operational efficiency, probably able to reduce waste more efficiently.

Cohen et al. (1995) concluded that firms which are able to more efficiently manufacture their products

pollute less. Efficiencies in manufacturing are said to result in improved resource efficiencies and are also

associated with a reduction in operating and environmental compliance costs (Berman et al., 1999). Thus,

it can be posited that firms managing to achieve environmental performance improvements also tend to be

able to achieve improved financial performance.

In regards to the effect of operational efficiency on relational efficiency, Goffin et al. (2006) in their

empirical study were able to conclude that an improvement in the operational performance of firms

supports the building of trust. Firms that accomplish their performance goals are more inclined to trust

their collaboration partner. This can be underlined by the fact that firms managing to increase their

performance gains as a result of close collaboration with another entity are under the impression that the

partnering firm made a considerable, valuable contribution to the successful outcome. This in turn

increases the reliability and credibility of the partnering firm.


H3a: The implementation of GSCM practices is positively related to the operational efficiency of the firm.

H3b: Operational efficiency is positively related to overall business performance at the firm level.

H3c: Operational efficiency is positively related to environmental performance at the firm level.

H3d: Operational efficiency is positively related to economic performance at the firm level.

H3e: The improved supplier’s operational efficiency has a positive impact on the relational efficiency

between the supplier and the large customer firm.

3.4 Relational Efficiency, GSCM Implementation, Overall Business Performance, Environmental

Performance, and Economic Performance

Independent Concept Mediator Dependent Concepts

Figure 4: Hypotheses development: Relational Efficiency, GSCM Implementation, Overall Business Performance,

Environmental Performance, and Economic Performance

GSCM

Implementation




Relational

Efficiency


31

Global supply chains as compared to their domestic counterparts are increasingly challenged by

differences in economies (e.g. tax and exchange rates, inflation and transfer prices) (Nelson and

Toledano, 1979), infrastructures, political factors, local cultures and competitive environments (Schmidt

and Wilhelm, 2000). The available modes of transport, the number of intermediaries and the quality of

documentation (Mentzer and Samli, 1981) as well as differences in politics such as the law, governmental

regulations and sanctions are contributing to increasing the risks, in terms of uncertainty and variability,

faced in managing these boundary spanning supply chains.

Drawing the link to Resource Dependence Theory, Ulrich and Barney (1984) stated that firms attempt to

reduce the environmental uncertainty encountered in managing global supply chains by creating formal

and informal linkages. These long-lasting inter-firm relationships facilitate the creation of a socially-

bonded and trust-based relationship which in turn increases the respective performance gains of both

supplying and buying firms (Dyer and Nobeoka, 2000).

Trust has been acknowledged to play a very significant role in any supply chain relationship (Doney and

Cannon, 1997; Monczka et al., 1998). A relationship of trust is characterized by having confidence in a

partner and being able to rely on the fact that the party will not behave opportunistically (Jap, 2001).

Thus, it can be concluded that an increase in the extent of trust between the parties increases the degree of

commitment (Gundlach et al., 1995).

The Transaction-Cost Framework first described by Williamson (1975) is also important to consider. The

original explanatory framework focuses on the amount of cost and effort that is required for two entities

to complete a transaction. Buyers and suppliers aim to minimize the costs associated with completing the

activity (Lai et al., 2005). David and Han (2004) and Grover and Malhotra (2003) have utilized the

framework to assess in how far the increase of transaction costs and the decrease of uncertainty benefit

the performance gains achieved by the transacting entities.

Kim et al. (2011) in their empirical study on 125 companies in South Korea have found there to be a

positive relationship between GSCM orientation and firm performance. The relationship has been found

to be mediated by the supply chain partners‘ trust and the degree of information sharing. The authors

concluded that trust among corroborating partners facilitates the amount of information sharing in terms

of general product and risk information which in turn yields an improvement in firm performance.

Zacharia et al. (2009) have found there to be a positive relationship between the degree of inter-firm

collaboration and business performance which is mediated by operational and relational outcomes.

Operational outcomes, which are mainly focused on in supply chain collaboration, are the reduction of

cost, improving quality and the value delivered to the customer as well as reducing the cycle time

(Koufteros et al., 2002). Relational outcomes are relationship specific and characterized by effectiveness,

trust and credibility between suppliers and buying firms.

Drawing the connection to GSCM implementation, it is suggested that making a joint effort to implement

GSCM practices between manufacturers and suppliers will yield an improvement in the resulting

performance gains for both parties (Liao, 2010).


32


H4a: The implementation of GSCM practices has a positive influence on the relational efficiency between

the supplier and the large customer firm.

H4b: Relational efficiency between the supplier and the large customer firm is positively related to the

supplier’s overall business performance.

H4c: Relational efficiency between the supplier and the large customer firm is positively related to the

supplier’s environmental performance.

H4d: Relational efficiency between the supplier and the large customer firm is positively related to the

supplier’s economic performance.

3.5 Moderating Effect of Market Pressure

As stated previously, there are three major external pressures which influence a company to implement

green supply chain practices. These pressures are, namely: market, regulatory and competitive forces

(Hirsch, 1975; Lai et al., 2006). DiMaggio and Powell (1983) state that within Institutional Theory three

isomorphic processes exist, namely: coercive, normative, and mimetic. The current study focuses on

normative isomorphic drivers which mainly originate from consumers. Enterprises are driven to conform

so as to be perceived as operating legitimately. More specifically, the study will focus on how market

pressures, in this study characterized as exports and sales to foreign customers, moderate the relationship

between Green Supply Chain Practice Implementation and Business Performance (Overall Business

Performance, Environmental Performance and Economic Performance) (Zhu and Sarkis, 2007).

Supporting evidence for non-market and market pressures to moderate the relationship between

environmental practices and organizational performance was given by Hoffman and Ventresca (1999).

Manufacturers are forced to improve their environmental performance by means of market pressures

originating from customers and suppliers as well as non-market pressures arising from the general public,

regulators and environmental activists who also contribute to an increase in environmental awareness

amongst firms (Starik and Rands, 1995; Lawrence and Morell, 1995; Zhu and Sarkis, 2007).

As was stated by Welch et al. (2002) early adopters of ISO 14001 standards are likely to be larger,

greener and most of all less motivated by competitive, media or regulatory pressures. Later adopters were

found to have been more pressured by competitive, regulatory and media forces and were likely to be

smaller and less green. German suppliers are considered to be operating in a rather mature environment in

regards to green supply chain initiatives in comparison to companies located in Korea.

It is, thus, expected that German enterprises are less pressured by regulatory, media or competitive forces

and this, in turn, is assumed to more positively influence the relationship between GSCM practice

implementation and economic performance than is the case with companies operating in Korea, which are

assumed to be more pressured in becoming environmentally friendly. Companies that are more pressured

are assumed to experience increased environmental performance but at the same time are said to

experience a decrease in the economic performance (Zhu and Sarkis, 2007).

However, it should be noted that according to Kagan et al. (2003) market pressures are a necessity for

organizations to experience performance improvements. In the absence of market pressures companies


33

would be reluctant to incorporate innovative environmental practices which would be beneficial in

improving the economic situation.


H5a: The positive relationship between GSCM practice implementation and overall business

performance is weaker in German suppliers facing higher environmental pressure from buying firms

(market pressures) than in German suppliers facing lower environmental pressure from buying firms.

H5b: The positive relationship between GSCM practice implementation and environmental performance

is stronger in German suppliers facing higher environmental pressure from buying firms (market

pressures) than in German suppliers facing lower environmental pressure from buying firms.

H5c: The positive relationship between GSCM practice implementation and economic performance is

weaker in German suppliers facing higher environmental pressure from buying firms (market pressures)

than in German suppliers facing lower environmental pressure from buying firms.

3.6 Mediating Effect of Employee Job Satisfaction, Operational Efficiency and Relational Efficiency

As was stated previously, Homburg and Stock (2004), in their dyadic analysis on the link between

salespeople‘s job satisfaction and the satisfaction of the company‘s customers, were able to conclude that

an increase in employee satisfaction is positively related to customer satisfaction. A link between

employee satisfaction and financial performance which is mediated through the constructs of customer

loyalty, customer satisfaction and employee loyalty is suggested by the service profit chain model

(Heskett et al., 1997).

Furthermore, Patterson et al. (2004) found supporting evidence for the existence of a relationship between

company climate and company productivity mediated by average job satisfaction level. The concept of

company climate was measured by assessing the perceptions employees had of the organizations guiding

principles and practices.

In regards to the mediating effect of operational efficiency Lin and Sheu‘s (2012) findings, as was

previously stated, proved there to be a positive relationship between GSCM practice implementation and

operational efficiency. Yang et al. (2010), furthermore, were able to conclude that operational efficiency

mediates the relationship between GSCM practice adoption and company performance.

Kim et al. (2011) in their empirical study have found there to be a positive relationship between adopting

environmentally friendly practices and firm performance. The relationship is mediated by the supply

chain partners‘ trust and the degree of information sharing. The authors concluded that trust among

corroborating partners facilitates the amount of information sharing in terms of general product and risk

information which in turn yields an improvement in firm performance.

Furthermore, Zacharia et al. (2009) were able to conclude that operational and relational outcomes

mediate the positive relationship between the degree of inter-firm collaboration and business

performance.


34


H6a: Employee job satisfaction and operational efficiency in the supplier firm, and relational efficiency

between the supplier and the large buyer firm mediate the relationship between GSCM practice

implementation and the supplier’s overall business performance.

H6b: Operational efficiency in the supplier firm and relational efficiency between the supplier and the

large buyer firm mediate the relationship between GSCM practice implementation and the supplier’s

environmental performance.

H6c: Operational efficiency in the supplier firm and relational efficiency between the supplier and the

large buyer firm mediate the relationship between GSCM practice implementation and the supplier’s

economic performance.

3.7 The Conceptual Model

This section, more specifically this chapter, concludes with the conceptual model depicted on the next

page (Figure 5). Furthermore, Table 1 and Table 2 provide a summary of the constructs and hypotheses,

respectively.

The Conceptual Model

Independent Concept Mediators and Moderator Dependent Concepts

GSCM

Implementation




Employee Job

Satisfaction

Operational

Efficiency

Relational

Efficiency

Market

Pressure

H2a

+

H4a

+

H3a

+

H3e

+

H2c

+

H2b

+

H4b, H4c, H4d

+

H5a, H5b, H5c

+

H1a, H1b, H1c

+

H3b, H3c, H3d

+

Figure 5: Hypothesized structural model


Construct Definitions

The following table provides a summary of the constructs, their respective definition (as made use of in

this study) and the most important literature identified in the theoretical background.

Construct Definition Literature Base

Ind

epen

den

t

Co

nce

pt

GSCM Practice

Implementation

Adopting green supply chain practices such as internal

environmental management (IEM), green purchasing

(GP), cooperation with customers (CC), and eco-design

(ECO) will enable the company to achieve environmental

sustainability.

Zhu et al. (2008)

Dep

end

ent

Co

nce

pts

Overall Business

Performance

The non-financial and financial performance of a

company resulting from the implementation of GSCM

practices as well as an improvement in the operational

and relational efficiency and the employee job

satisfaction.

Zhu et al. (2008), Zacharia

et al. (2009), Zhou et al.

(2008)


The environmental performance in terms of a reduction in

air emissions, waste water or solid waste resulting from

the implementation of GSCM practices as well as an

improvement in the operational and relational efficiency.

Zhu et al. (2007), Zhu et al.

(2008)


- Positive

Positive economic performance is achieved when benefits

are gained through implementing green supply chain

practices. Benefits are here described as a decrease in fee

for waste discharge, for waste treatment or for example a

decrease in the cost for energy consumption.

Rao and Holt (2005), Zhu

and Sarkis (2007), Zhu et

al. (2007), Zhu et al. (2008)

Med

iato

rs

Employee Job Satisfaction The extent to which employees like their jobs related to

working climate and the relationship with supervisors

which is expected to result in improved performance

outcomes.

Zhou et al. (2008),

Homburg and Stock (2004),

Patterson et al. (2004)

Operational Efficiency The supplying firm‘s ability to have greater success in

achieving cost and cycle time reductions, improve service

or value delivery to the customer and the ability to

improve product quality.

Koufteros et al. (2002),

Rusinko (2007), Zhu et al.

(2008), Zacharia et al.

(2009)

Relational Efficiency The supplying firm‘s ability to build trust and credibility

in the relationship with buying firms by means of

collaboration and information sharing which increases the

transparency and openness in business processes.

Zacharia et al. (2009), Kim

et al. (2011), Pfeffer and

Salancik (1978) (cited in

Lee et al., 2012)

Mo

der

ato

r

Market Pressure Organizations experience both formal an informal

pressures from downstream customers and consumers.

More specifically, this paper defines market pressure in

terms of two items, namely: exports and sales to foreign

customers.

Zhu and Sarkis (2007),

DiMaggio and Powell

(1983)

Table 2: Summary of constructs, their definitions and the most important literature identified


37

Hypotheses Summary

Table 2 provides a summary description of the hypotheses to be investigated in this study.

Hypotheses Summary

H1 H1a:

The implementation of GSCM practices is positively related to the overall business performance at the firm

level.

H1b:

The implementation of GSCM practices is positively related to the environmental performance at the firm level.

H1c:

The implementation of GSCM practices is positively related to the economic performance at the firm level.

H2 H2a: The implementation of GSCM practices is positively related to employee job satisfaction.

H2b:

Employee job satisfaction is positively related to overall business performance at the firm level.

H2c:

Employee job satisfaction is positively related to the operational efficiency of the firm that implemented GSCM

practices.

H3 H3a:

The implementation of GSCM practices is positively related to the operational efficiency of the firm.

H3b:

Operational efficiency is positively related to overall business performance at the firm level.

H3c:

Operational efficiency is positively related to environmental performance at the firm level.

H3d: Operational efficiency is positively related to economic performance at the firm level.

H3e:

The improved supplier‘s operational efficiency has a positive impact on the relational efficiency between the

supplier and the large customer firm.

H4 H4a:

The implementation of GSCM practices has a positive influence on the relational efficiency between the supplier

and the large customer firm.

H4b:

Relational efficiency between the supplier and the large customer firm is positively related to the supplier‘s

overall business performance.

H4c:


environmental performance.

H4d:


economic performance.

H5 H5a: The positive relationship between GSCM practice implementation and overall business performance is weaker in

German suppliers facing higher environmental pressure from buying firms (market pressures) than in German

suppliers facing lower environmental pressure from buying firms.

H5b: The positive relationship between GSCM practice implementation and environmental performance is stronger in



H5c: The positive relationship between GSCM practice implementation and economic performance is weaker in



H6 H6a: Employee job satisfaction and operational efficiency in the supplier firm, and relational efficiency between the

supplier and the large buyer firm mediate the relationship between GSCM practice implementation and the

supplier‘s overall business performance.

H6b: Operational efficiency in the supplier firm and relational efficiency between the supplier and the large buyer firm

mediate the relationship between GSCM practice implementation and the supplier‘s environmental performance.

H6c: Operational efficiency in the supplier firm and relational efficiency between the supplier and the large buyer firm

mediate the relationship between GSCM practice implementation and the supplier‘s economic performance.

Table 3: Summary description of hypotheses to be investigated


38

4. RESEARCH METHODS AND DATA

Hereunder, a brief overview of this study‘s research methods is provided. The chapter starts off with a

short description of how the questionnaire was developed after which the population to be studied and the

data sources are elaborated on. The final section will provide a detailed explanation on how the sample

was selected.

4.1 Developing the Questionnaire – Constructs and Items

In reference to the study performed by Lee et al. (2012) this study will also make use of a survey

questionnaire to obtain the required data. However, as stated by Dul and Hak (2008), the preferred

research strategy to test causal relationships is to conduct experiments. Lee et al. (2012), however, made

use of a single-method, namely a survey, which is according to Dul and Hak (2008) the second-best

preferred strategy to test a probabilistic relation. It should be mentioned that, in the actual practice of

business research and in this specific situation, conducting an experiment would not have been reasonable

as manipulating the independent concept (GSCM Practice Implementation) would have proven to be

somewhat difficult. Thus, it can be concluded that the research strategy chosen by Lee et al. (2012) is

indeed the best alternative to conducting an experiment and hence this research will also administer a

survey questionnaire to obtain the required data. A drawback of this approach is the fact that making use

of a single research method at the same moment in time will most likely lead to information bias as well

as common method variance (Dul and Hak, 2008).

In accordance with Lee et al. (2012), the independent concept will be measured by means of four

dimensions, namely: Internal Environmental Management (IEM), Green Purchasing (GP), Cooperation

with Customers (CC), and Eco-Design (ECO). Lee et al. (2012) have adopted these four GSCM practices

from the five factors identified by Zhu and Sarkis (2006) who additionally made use of the construct

Investment Recovery (IR).

The study will look at GSCM Practice Implementation from three different perspectives:

(1)Internal Perspective – internal environmental management: This dimension describes how the

company is internally improving processes and practices to become more environmentally friendly. This

includes the support received for green initiatives from top management as well as establishing

environmental compliance programs and obtaining ISO 14001 certification.

(2)External Perspective – external relationships: Encompasses the cooperation with customers and

suppliers as well as the purchasing of eco-friendly products.

(3)Interacting with the entire supply chain – product design: This dimension includes the design of

products for recycling, reuse or recovery.

The measurement items for the four dimensions of GSCM Practice Implementation were adopted from

Lee et al. (2012) who in turn adopted the measurement items from Zhu and Sarkis (2006). Lee et al.

(2012) made modifications to the measurement items Internal Environmental Management and Green

Purchasing. Furthermore, for the constructs Cooperation with Customers and Eco-Design the authors


39

chose to make use of additional items mentioned by Zhou et al. (2008), Chen (2005), Hsu and Hu (2008),

Matos and Hall (2007), and Rusinko (2007).

The four dimensions, namely: Internal Environmental Management (IEM), Green Purchasing (GP),

Cooperation with Customers (CC), and Eco-Design (ECO) will be measured by 5, 4, 4, and 5 items,

respectively, on a 5-point scale (1: not considering it, 2: planning to consider it, 3: considering it

currently, 4: initiating implementation, and 5: currently implementing).

The three mediators (Employee Job Satisfaction, Operational Efficiency and Relational Efficiency) will

be measured by 5 items, 6 items and 6 items, respectively, on a 5-point scale (1: strongly disagree, 2:

disagree, 3: neutral, 4: agree, and 5: strongly agree). (Lee et al., 2012; Zhu and Sarkis, 2007)

The dependent concept Overall Business Performance will be measured by 4 items on a 5-point scale (1:

strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree) (Lee et al., 2012; Zhu and

Sarkis, 2007). The remaining two dependent concepts, namely: Environmental Performance and

Economic Performance will be measured by 6 and 9 items, respectively, on a 5-point scale (1: not at all,

2: a little bit, 3: to some degree, 4: relatively significant, and 5: significant).

Lastly, the measurement items for the moderator, Market Pressure, were adopted from Zhu and Sarkis

(2007). The moderator will be measured by means of 2 items on a 5-point scale (1: not at all important, 2:

not important, 3: not thinking about it, 4: important, and 5: extremely important).

Please refer to Appendix 1 for item descriptions and please refer to Appendix 2 for the complete

questionnaire.

4.2 Population and Data Sources

The population to be studied consists of operations/supply chain managers of small- and medium-sized

auto mobile enterprises in Germany.

The population of interest has the following three characteristics:

- small- and medium-sized suppliers;

- only in the automotive industry; and

- only German firms.

Operations/ supply chain managers were chosen as the population to be studied as they, according to

Walton et al. (1998), are in the position and have the influence to enable the company to obtain a

competitive advantage by means of adopting green practices. The operations and supply chain managers

are responsible for supplier selection, evaluation and activities such as purchasing which ideally positions

them to impact the environmental friendliness of the company. Furthermore, Stock (1998) (cited in

Srivastava, 2007) stated that 95 per cent of total costs in recycling are attributable to logistics activities.

Thus, it can be noted that Supply Chain Management plays a deciding role in becoming more

environmentally friendly and operations- and supply chain-managers have the power to change company

processes and activities. Gupta (1995) in his study takes an operations point of view and discusses the

impact that environmental management has on the production and operations of a company. The author

was able to conclude that operations managers are required to play a proactive role in the development


40

and implementation of green practice management systems. Handfield et al. (2005), in their paper

addressing the issue of environmental supply chain strategy development, state that the increase in the

adoption of environmental practices has raised awareness about the importance of adopting the right

supply chain strategy in order to manage these new challenges faced by companies all over the world.

This study will focus on small- and medium-sized suppliers as these often lack the resources,

environmental know-how, awareness and strategy to improve their processes to contribute to the entire

supply chain becoming more environmentally friendly (Pimenova and Vorst, 2003). Lee and Klassen

(2008) concluded that the small- and medium-sized enterprises encounter difficulties in implementing the

requirements set by their larger buying firms and, thus, are often considered as a bottleneck in enabling

the customer firms to implement green initiatives. Furthermore, SMEs can account for 70 per cent of total

industrial pollution when regarded as a sector (Hillary, 2004). These enterprises make up 99.8 per cent of

all firms in the EU, they are said to account for 65 per cent of the business revenue generated and 66 per

cent of all employed workers work for a SME (Ilomaki and Melanen, 2001).

The second characteristic of the population of interest is that the firms are operating in the automotive

industry. This industry was selected because of its worldwide scope and its pervasive global

environmental impact. Additionally, as stated by Orsato and Wells (2007) suppliers to the auto mobile

industry provide up to 80 per cent of the value required to manufacture the end-product and, thus,

contribute significantly to the environmental footprint of the entire supply chain. Lastly, the sample was

only taken from the auto mobile industry to control for any potential confounding variables. Market

conditions and environmental regulations differ from one industry to another and by means of studying

solely one industry these variations affecting the independent variable are minimized.

Lastly, the decision was made to perform the study on European suppliers, more specifically German

suppliers, as Europe is the worldwide forerunner in vehicle production and is consequently also home to a

large number of enterprises supplying Europe‘s auto mobile manufacturers. It is expected that, as Europe

is one of the early adopters of ISO 14001 standards, West-European firms are less motivated by media,

competitive and regulatory forces. Consequently, it is assumed that external stakeholders do not exert as

much pressure on suppliers as do governments and buying firms in countries which are considered to be

late adopters of ISO 14001 standards (Welch et al., 2002). This in turn would lead to employees being

more satisfied and would in turn result in increased business performance gains.

4.3 Data Collection Procedure and Response Rate

This last section, which is split into five subsections, outlines the data collection process and provides a

detailed description of how the final respondent number was obtained.

4.3.1 Sample Selection

The survey questionnaire was administered to a subset of the population to be studied, namely

operations/supply chain managers of small- and medium-sized auto mobile enterprises in Germany. The

list of firms in the auto mobile industry was made available by www.marklines.com (2012). The database

enlisted a total of 1071 firms. As a second step the database had to be verified before using the firms as

the target sample. To this end the firm contact details were verified by means of the company websites.

Additionally, if available, the number of full-time employees working at the respective firm was obtained

from the company website. A total of 483 firms were found to not meet the criteria of being a SME thus

the total number of firms which were assumed to meet the expected characteristics were a total of 588. It

should here be noted that the majority of the company websites failed to mention the number of full-time

http://www.marklines.com/


41

employees working at the respective firm, thus, these firms were also taken into account and assumed to

be part of the population of interest. It should also be noted that www.marklines.com (2012) fails to

explicitly state how the 1071 supplying firms in the auto mobile industry were selected from the total

number of instances in the population (the sampling frame).

According to Dul and Hak (2008) the preferred option to guarantee that each instance has the same chance

of being selected is probability sampling (randomly sampling instances from the population).

4.3.2 Selection of Key Informants

As has been stated earlier operations/ supply chain managers were chosen as the population to be studied

as they, according to Walton et al. (1998), are in the position and have the influence to enable the

company to obtain a competitive advantage by means of adopting green practices. More specifically, as

recommended by Mitchell (1994), the survey questionnaire was targeted at the qualified employee of the

supplying firm who has the knowledge and means to implement GSCM practices in the firms supply

chain. Thus, the key informant selected for this study was either the manager or owner of the firm or any

other individual who is actively involved in supply chain issues relating to the respective firm. According

to Mitchell (1994) these high-ranked informants are considered to be more reliable and enable the

standardization of information across differing firms. Sub-sections 4.3.3 and 4.3.4 outline the approach

that was taken to guarantee that only responses from employees dealing with supply chain related

activities were obtained.

It should here be noted that a limitation of this study is the fact that information was only gathered from

one source within each firm. According to Podsakoff et al. (2003) making use of multiple respondents per

firm would be advantageous in reducing common method variance.

4.3.3 Questionnaire Design and Distribution

The choice was made to gather data by means of a self-administered electronic mail survey. The main

advantages/ strengths of this method are anonymity, free expression and confidentiality (Bush and Hair,

1985; Davis, 2000 cited in Saleh, 2006). Furthermore, according to Sutton (2001) (cited in Saleh, 2006)

electronic mail surveys facilitate adequate record keeping and enable the generation of uniform data from

different respondents. Moreover, it can also be noted that this method in comparison to other methods is

not extensively costly. In consideration of these advantages this study made use of an electronic mail

survey for its ability to collect data in a very short period of time from diversely scattered sources.

As has been stated by Greer et al. (2000) industrial populations, characterized as respondents who receive

survey questionnaires at their place of employment, are less likely to respond to survey questionnaires

than consumer groups. The difference in response rates is mainly due to the fact that industrial

populations are preoccupied with work, because of company rules and policies as well as confidentiality

and anonymity issues. Greer et al. (2000) found that a recipients‘ willingness to respond to a

questionnaire did not to a great extent depend on pre-notifications and follow-ups. Decisions on whether

to cooperate or not were more based on the trade-off between perceived costs and benefits to be obtained

from participating in a mail questionnaire. Thus, the following factors were taken into account during the

design of the questionnaire to ensure the obtainment of an adequate number of responses. The choice was

made to make use of the online survey website www.thesistools.com (n.a.) and to distribute the link to the

survey questionnaire via e-mail. The e-mail was not individualized but written in the respondents‘ native

language (German). Furthermore, a description of the project was provided, a detailed explanation on


42

how to fill in the questionnaire and the benefits (incentives) to be obtained. The actual survey

questionnaire started off with a cover letter describing the project, the fact that all information is

confidential and anonymous, and instructions on the procedure were provided. Additionally, respondents

were given an incentive to answer the questionnaire. Respondents were given the opportunity to insert

their e-mail address upon completion of the questionnaire and would receive a copy of the Executive

Summary or Master Thesis subsequent to its completion. In regards to the design of the questionnaire it

can be said that great care was taken to make it as user-friendly as possible with page numbers indicating

the current page position and the number of pages that still needed to be completed. Furthermore, all

questions relating to the topic under investigation were fixed alternative questions (structure of questions),

required a qualitative response (nature of response), sought for an individual‘s opinion (information

sought) and were assessed by means of non-comparative scales (measurement scales). Moreover, to

minimize the possibility of context effects, in which one or more questions has an influence on how an

individual interprets subsequent questions, the choice was made to ask the questions relating to the

dependent concepts (Overall Business Performance, Environmental Performance and Economic

Performance) first and to ask questions relating to GSCM Practice Implementation towards the end to

decrease the potential for prior questions to influence how respondents answer the remainder of the

questions (Tourangeau, 1999). Additionally, the questions which requested more personal information

from the respondent were situated at the end of the survey questionnaire. This, according to Babbie

(2001) and Dillman (2000), enables respondents after having read the cover letter to proceed straight onto

answering the main questions of the survey. On the very last page of the survey questionnaire respondents

were thanked for having completed the survey.

The questionnaire was sent out on the 28 May 2013 and one week subsequent to having distributed the

survey questionnaire telephone follow-up calls were made (4 June 2013). As the questionnaire was

answered anonymously it was not possible to know which firms had replied and which had not thus the

decision was made to make use of simple random sampling when deciding on which firms to contact via

telephone call. This sampling technique is an unbiased surveying technique and guarantees that each

individual has the same probability of being selected at any moment in time (Podsakoff et al., 2003).

4.3.4 Invalid Respondents and Missing Data

In consideration of the fact that this research utilized a self-administered questionnaire the occurrence of

response error (Hyman et al., 1954) had to be taken into account as there was no control over how the

questionnaire was completed. Hence, in order to perform the statistical analysis it was important to

remove data that was invalid or respondents who had not completed the entire survey. A total of 28

people responded in the first round. However, a total of 4 responses had to be deleted and were not taken

into account as these respondents had failed to complete the entire questionnaire. In the second round

telephone follow-up calls were made and a total of 41 people responded. Two responses had to be deleted

due to insufficient data. It should be noted that all respondents that answered the entire questionnaire

(N=63) answered that they were involved in supply chain related activities (Question 16 of the

questionnaire; Appendix 2).

4.3.5 Final Response Rate

After excluding invalid respondents 63 firms remained for data analysis. Chapter 5 will provide a detailed

description of the responding firms‘ characteristics.


43

The final number of responses is the first major limitation identified in this study. 63 responses were

received from small- and medium-sized German automobile suppliers which corresponds with a response

rate of 10.71% (=63/588). Thus, it can be concluded that when comparing the sample (N=588) to all

responses (N=63) it is probable that the total responses are not representative for the population implying

non-response bias. In consequence, making generalizations from the sample to the population is not

possible/ advisable. Thus, in future research case study research (comparative case study) is encouraged

to verify the findings.


44

5. DATA ANALYSIS

Chapter 5 is divided into five sections each of which has several subsections. The first two sections will

provide a detailed description of the characteristics of the responding firms and also delve into the topic

of Environmental Management Standards adoption. The data analysis will be conducted in Sections 5.3,

5.4 and 5.5 by means of the statistical software programs Statistical Package for Social Science (SPSS)

version 17.0 and Structural Equation Modeling (Amos) version 21.0 using maximum likelihood

estimates. Section 5.4 will determine validity, reliability and goodness-of-fit of the original research

model. In Section 5.5 the same analysis is performed, however, in this last section the model will be

adjusted to more accurately fit the data and to minimize validity and reliability issues.

Note: The model constructs are reflective. Furthermore, making use of Structural Equation Modeling is

deemed most appropriate for this study as, according to Gefen et al. (2000), this method has great

potential for advancing theory development and by utilizing Structural Equation Modeling the researcher

is also able to simultaneously assess numerous interrelated dependence relationships. Moreover, Hair et

al. (1998) (cited by Sridharan, 2010) noted that this modeling technique permits the incorporation of

latent variables representing unobserved concepts while at the same time accounting for measurement

errors.

5.1 Characterization - Responding Firms

Table 3 provides a summary of the responding firms‘ characteristics. From the table it can be derived that

92.1% of the respondents are middle managers which implies that GSCM Practice Implementation is a

supply chain issue mainly dealt with by higher-level management. It should, however, be noted that

respondents might have found the terms employee in charge and middle manager to be interchangeable

especially when considering that the targeted firms are small- and medium-sized companies which cannot

be compared to large hierarchical firms. The question about the respondents‘ work experience in the

industry does not show any clear pattern. 34.9% of respondents have been working in the industry for

more than 15 years and 55.5% of the respondents answered that they have been working in the industry

for a maximum of 10 years.

Even though the European Union classifies large businesses to have a minimum of 250 employees Lee et

al. (2012) took all responding firms up to a maximum employee number of 500 into account. Thus, this

study will also make the cutoff at 500 and from Table 3 it can be derived that all responding firms were

small- and medium-sized. The distribution is as follows: 31 (49.2%) of the firms that responded have less

than 50 employees, 12 (22.2%) have 50-100 employees, 11 (17.5%) have 101-200 employees, and 7

(11.1%) employee 201-500 workers. In regards to the industry classification of the buying firms, all

respondents answered that their buying firms were in the automobile industry and 22 firms also said that

their buying firms operate in the electronics industry.

The last question asked respondents to indicate what their firm‘s primary business goal in the supply

chain is. The majority of the SMEs (65.1%) reported that they are first-tier suppliers to major firms.

28.6% of the firms indicated that they are second-tier suppliers.


45

Frequency Percentage Value

Respondents’ job title

Employee in charge

Middle manager

Senior executive

Top executive

Total

5

58

0

0

63

7.9

92.1

0.0

0.0

100.0

Respondents’ work experience in the industry (in years)

Less than 5

5-10

11-15

More than 15

Total

16

19

6

22

63

25.4

30.2

9.5

34.9

100.0

Firm size (no. of full-time employees)

Less than 50

50-100

101-200

201-300

301-400

401-500

More than 500

Total

31

14

11

5

2

0

0

63

49.2

22.2

17.5

7.9

3.2

0.0

0.0

100.0

Industry classification of the buying firms (multiple answers are possible)

Automobile

Electronics

Telecommunication

Retail

Total

63

22

6

3

63

100.0

Firm’s primary business goal in the supply chain

First-tier supplier to major firms

Second-tier supplier

Supplier to government

Other

Total

41

18

0

4

63

65.1

28.6

0.0

6.3

100.0

Table 4: Characteristics of responding firms

5.2 Awareness and Adoption of Environmental Management Standards (EMSs)

The administered survey questionnaire not only asked respondents to provide information about their firm

but also to answer questions in regards to their awareness and degree of adoption of Environmental

Management Standards. From Table 4 it can be concluded that all responding firms were aware of the

existence of the ISO 14000 series and 57 (90.5%) of the firms also adopted ISO 14000. 18 (28.6%) of the

operations/ supply chain managers are aware of the existence of Environmental, Health and Safety (EHS)

programs but the majority (71.4%) have never heard of such a program. In terms of the adoption rate, 12

(19%) firms have adopted EHS programs. Another interesting finding is that 52 (82.5%) of the managers

that responded have never heard of Life Cycle Analysis (LCA) and of those who have heard of LCA only

8 (12.7%) have adopted LCA. The survey results reveal that the most widely adopted EMS is ISO 14000

series – 57 out of 63 firms (90.5%) have implemented it, however, the results also show that there is

limited use of other EMS‘s. This comes as a surprise as this study is based on the assumption that

Germany operating in a mature industry would be seen as a forerunner in EMS adoption. In conclusion it

can be said that in contrast to initial assumptions even in a quite mature country in terms of environmental

regulations there is definite informational potential.


46

Awareness Adoption EMSs Yes No Yes No

ISO 14000 Series 63 (100.0%) 0 (0.0%) 57 (90.5%) 6 (9.5%)

Electronic Product Environmental Assessment Tool 8 (12.7%) 55 (87.3%) 6 (9.5%) 57 (90.5%)

European EMAS 7 (11.1%) 56 (88.9%) 0 (0.0%) 63 (100.0%)

EU Eco-Label Award Scheme 2 (3.2%) 61 (96.8%) 0 (0.0%) 63 (100.0%)

EHS Programmers 18 (28.6%) 45 (71.4%) 12 (19.0%) 51 (81%)

LCA 11 (17.5%) 52 (82.5%) 8 (12.7%) 55 (87.3%)

Total Quality Environmental Management 3 (4.8%) 60 (95.2%) 2 (3.2%) 61 (96.8%)

Table 5: Awareness and adoption of Environmental Management Standards

5.3 Correlation Matrix

Table 5 depicts the means (M) and standard deviations (SD) of all the constructs included in the analysis

with the exception of Market Pressure as well as the bivariate Spearman‘s rho correlation results between

them. The constructs were derived by averaging the corresponding scale items. Cohen‘s (1988) (cited in

Sridharan et al., 2010) rule of thumb states that correlations with a value less than 0.2 can be considered

as weak, whereas correlations between 0.2 and 0.5 are regarded to be moderate. Correlations with a value

greater than 0.5 are, according to Cohen (1988) (cited in Sridharan et al., 2010), considered to be strong.

The results show significant relationships among Internal Environmental Management, Green Purchasing,

Customer Cooperation and Eco-Design with Environmental Performance and Economic Performance.

Only Green Purchasing and Eco-Design were found to have a significant relationship with Overall

Business Performance. The correlations between GSCM practices and the three dependent performance

constructs are in the expected direction. Furthermore, there are no excessive correlations between the

constructs in the model. According to Field (2005) multicollinearity is avoided when the correlations

between the constructs do not exceed a value of 0.9.

Factors M SD IEM GP CC ECO SAT OE RE OBP EP ECP

IEM Internal Environmental

Management

3.01 0.74 1.00

GP Green Purchasing

2.76 0.72 0.667

**

1.00

CC Customer Cooperation

2.49 0.70 0.530

**

0.628

**

1.00

ECO Eco-Design

2.70 0.67 0.560 **

0.656 **

0.532 **

1.00

SAT Employee Job

Satisfaction

3.11 0.55 0.219 0.328

**

0.200 0.497

**

1.00

OE Operational Efficiency

2.98 0.60 0.248

*

0.285

*

0.060 0.189 0.258

*

1.00

RE Relational Efficiency

3.51 0.69 0.421 **

0.423 **

0.055 0.193 0.351 **

0.681 **

1.00

OBP Overall Business

Performance

3.41 0.70 0.179 0.278

*

0.186 0.370

**

0.384

**

0.465

**

0.465

**

1.00

EP Environmental Performance

3.02 0.66 0.457 **

0.355 **

0.339 **

0.509 **

0.213 0.306 *

0.351 **

0.265 *

1.00

ECP Economic Performance

3.04 0.65 0.625

**

0.559

**

0.452

**

0.672

**

0.305

*

0.150 0.202 0.268

*

0.443

**

1.00

Table 6: Correlations between theoretical constructs

Notes: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed)

5.4 Validity, Reliability and Goodness-of-Fit of the Research Model (Original Model)

In the following the measurement properties of the constructs will be assessed making use of reliability

and item-to-total correlation analysis, after which a confirmatory factor analysis (CFA) is performed to

examine the goodness-of-fit of the research models proposed by Zhu et al. (2008). According to Hooper

et al. (2008) it is foremost, before determining model fit, of importance to assess the fit of each construct


47

and its respective items individually. It should here be noted that Section 5.4 will solely identify validity

and reliability issues but no changes to the constructs will be made to improve validity and reliability.

This is of importance to enable a better comparison of this study‘s outcomes to the study performed by

Lee et al. (2012). In Section 5.5 the same analysis is performed again, however, the model will be

adjusted to account for validity and reliability issues.

5.4.1 Step 1 – Assessing Validity of the Constructs

Firstly, before performing reliability estimation it is of importance to determine if the constructs are valid.

Validity can be defined as the degree to which a measuring procedure captures the specific concept that

the researcher aims to measure. Construct validity can be defined as the extent to which a specific number

of variables really represent the theoretical latent construct they are expected to measure (Said, 2011).

Construct validity is made up of four components, namely: face validity, convergent validity, discriminant

validity, and nomological validity.

Face Validity is a subjective determination of whether a measure (the content of the items) appears to

measure what it is supposed to measure. (Schwab, 2005)

Convergent Validity is the extent to which indicators of a construct that theoretically should be related are

indeed related (converge) and thus share a high proportion of variance. (Schwab, 2005)

Discriminant Validity is the extent to which two constructs that are theoretically assumed to be unrelated

are indeed unrelated (truly distinct from each other). (Schwab, 2005)

Nomological Validity is present if a construct correlates as expected within a system of related constructs.

(Schwab, 2005)

In order to establish construct validity using confirmatory factor analysis two components have been

made use of, namely: (1) convergent validity and (2) discriminant validity. Convergent validity will be

examined by means of factor loadings, average variance extracted (AVE) and construct/ composite

reliability. Note: construct/ composite reliability will be determined in Sub-Section 5.4.2.

1. Convergent Validity

Standardized Factor Loadings and t-Values

To determine convergent validity it is foremost of importance to assess the standardized loadings

(standardized regression weights). Standardized loadings characterize the degree of correlation amongst

each observed variable (indicator) and the corresponding factor (latent construct). According to Johnson

et al. (2001) and Nunnally (1978) (cited in Abdul-Halim, 2009) all loadings should be at least 0.5 and

preferably 0.7 or higher (Chin et al., 1995). When assessing the standardized loadings it can be inferred

that a total of six items score slightly lower than 0.5 (Table 8). More specifically, a great deal of variance

in each observed variable is accounted for with the exception of the following variables: IEM5

(R2=0.489

2=0.239), CC1 (R

2=0.486

2=0.236), SAT4 (R

2=0.468

2=0.219), SAT5 (R

2=0.492

2=0.242), ECP1

(R2=0.455

2=0.207) and ECP2 (R

2=0.433

2=0.187). Thus, all loadings except these five are significant

(p<0.05) as is required for convergent validity. Even though this study uses the threshold of 0.5

recommended by Johnson et al. (2001) and Nunnally (1978) (cited in Abdul-Halim, 2009) it should also

be mentioned that other researcher such as Lee and Crompton (1992) as well as Saris et al. (2009, p. 571)

set the threshold at 0.4 stating that any loading above 0.4 still indicates a reasonable and sufficient fit.

However, as the sample size in this study is quite low higher loadings are preferable.


48

According to Hooper et al. (2008) one can also assess the R2 value of the items. Items with an R

2 value

below 0.20 should be removed from the analysis as a low R2

value indicates a high level of error. From

Table 8 it can be inferred that all R2 values except the R

2 value for ECP2 (R

2=0.433

2=0.187) are

acceptable.

Furthermore, it is of importance to examine the statistical significance through t-values (Dunn et al.,

1994). The t-values are referred to as critical ratio (C.R.) in the Amos text output file. The critical ratio is

the parameter estimate divided by its standard error. According to Segar (1997) and Byrne (2001)

statistical significance is implied when the t-value is greater than 1.96 or smaller than negative 1.96. The

evidence of there being a relationship between the observed indicators and their latent factor is stronger if

the factor loadings or coefficients are, when compared to their standard errors, larger (Bollen, 1989 and

Koufteros, 1999). In conclusion, it can be inferred from Table 8 that all t-values (critical ratio‘s) for the

individual paths are significantly related to their underlying construct.

Average Variance Extracted

To further draw conclusions about the degree of convergent validity achieved the average variance

extracted (AVE) was also determined. The average variance extracted summarizes the convergence

among a set of items making up a construct. This measure draws a relationship between the amount of

variance that is captured by the construct and the variance arising from measurement error (Fornell and

Larcker, 1981). Amos software is not able to calculate these values, thus, in the following the average

variance extracted (AVE) will be calculated manually.

The AVE is calculated by means of the following formula (Fornell and Larcker, 1981):

AVE= Average Variance Extracted= Variance Extracted

= 𝑠𝑢𝑚 𝑜𝑓 (𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔𝑠 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 )

𝑠𝑢𝑚 𝑜𝑓 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔𝑠 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 + (𝑠𝑢𝑚 𝑜𝑓 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑒𝑟𝑟𝑜𝑟𝑠 )

𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝐸𝑟𝑟𝑜𝑟 = 1 − (𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔)2

Alternatively:

AVE= Average Variance Extracted= Variance Extracted

= 𝑠𝑢𝑚 𝑜𝑓 (𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔𝑠 𝑠𝑞𝑢𝑎𝑟𝑒𝑑 )

𝑛

As can be derived from Table 8 the average variance extracted (AVE) of the majority of constructs is

greater than 0.5 (Fornell and Larcker, 1981), thus exhibiting convergence validity. Five constructs

(Cooperation with Customers, Employee Job Satisfaction, Operational Efficiency, Environmental

Performance and Economic Performance) score slightly beneath 0.5 indicating that on average the error

remaining in the items is larger than the variance that is actually explained by the latent factor structure.


49

2. Discriminant Validity

As has been stated above discriminant validity is the extent to which two constructs that are theoretically

assumed to be unrelated are indeed unrelated (truly distinct from each other and uncorrelated). Variables

should relate more strongly to the factor they are actually supposed to measure than to another factor. In

essence, variables should load significantly on only one factor. Table 8 provides the calculated

discriminant validity values for each factor.

Discriminant Validity values can be obtained by utilizing the following formula (Said et al., 2011):

DV= 𝐴𝑉𝐸

According to Fornell and Larcker (1981) the existence of discriminant validity can be determined by

comparing the AVE estimates to the squared correlation coefficients between two latent constructs.

Discriminant validity is said to exist if the items measuring one construct share more common variance

than this particular construct shares with any other construct. In essence the AVE estimates for each

individual construct should be greater than the squared correlation coefficient between two constructs

(Hair et al., 2010) (cited in Gaskin, 2012a). More specifically, the thresholds for discriminant validity are

MSV<AVE and ASV<AVE. Having made use of the Sats Tools Package developed by Gaskin (2012b)

Table 6 and Table 7 were obtained. Table 6 provides a summary of the values for composite reliability

(CR), average variance extracted (AVE), maximum shared squared variance (MSV) and the average

shared squared variance (ASV).

Maximum Shared Squared Variance (MSV) is the maximum correlation (squared covariance) with

another factor. (Hair et al., 2010 cited in Gaskin, 2012a)

Average Shared Squared Variance (ASV) is the average of all correlations with other variables. (Hair et

al., 2010 cited in Gaskin, 2012a)

Table 7 is a factor correlation matrix depicting the square root of the average variance extracted (the

discriminant validity values) on the diagonal. All underlined values show validity issues. More

specifically, the AVE for IEM, CC, ECO, GP, OE and ECP is smaller than the MSV (Table 6) implying

validity concerns. Furthermore, the square root of the AVE (Table 7 and Table 8) for IEM, CC, ECO, GP,

OE and ECP is less than the absolute value of the correlations with another factor.


50

Validity and Reliability Table

Table 7: Validity and reliability table (original model)

Factor Correlation Matrix with the Square Root of the AVE on the Diagonal

Table 8: Factor correlation matrix with the square root of the AVE on the diagonal (original model)

Factors CR AVE MSV ASV

EP Environmental Performance 0.845 0.477 0.430 0.232

IEM Internal Environmental Management 0.875 0.592 0.682 0.269

OBP Overall Business Performance 0.884 0.655 0.286 0.150

CC Customer Cooperation 0.737 0.414 0.694 0.263

ECO Eco-Design 0.833 0.502 0.594 0.360

GP Green Purchasing 0.784 0.479 0.694 0.384

SAT Employee Job Satisfaction 0.792 0.443 0.428 0.196

OE Operational Efficiency 0.831 0.452 0.672 0.170

RE Relational Efficiency 0.926 0.676 0.672 0.210

ECP Economic Performance 0.882 0.466 0.599 0.328

Factors EP IEM OBP CC ECO GP SAT OE RE ECP

EP Environmental Performance 0.691

IEM Internal Environmental Management 0.499 0.770

OBP Overall Business Performance 0.405 0.187 0.810


ECO Eco-Design 0.656 0.590 0.438 0.725 0.709

GP Green Purchasing 0.494 0.826 0.316 0.833 0.764 0.692

SAT Employee Job Satisfaction 0.305 0.220 0.447 0.367 0.654 0.485 0.665

OE Operational Efficiency 0.395 0.229 0.466 0.058 0.250 0.336 0.376 0.672

RE Relational Efficiency 0.458 0.379 0.535 0.101 0.271 0.453 0.436 0.820 0.822

ECP Economic Performance 0.590 0.671 0.331 0.590 0.771 0.774 0.540 0.332 0.316 0.683


51

Summary of Validity and Reliability Measurement Results (Original Model) Factors Item

Number

Standardized

Loading

R2 Average

Variance

Extracted

(AVE)

Discriminant

Validity

Construct/

Composite

Reliability

Critical Ratio

(t-Value)

Internal

Environmental Management

IEM1

IEM2 IEM3

IEM4

IEM5

0.881

0.952 0.705

0.703

0.489

0.776

0.906 0.497

0.494

0.239

0.583 0.764 0.875 _a

10.553 6.623

6.602

4.085

Green Purchasing GP1

GP2

GP3 GP4

0.611

0.850

0.767 0.573

0.373

0.722

0.588 0.328

0.503 0.709 0.784 _a

4.541

4.457 3.654

Cooperation with

Customers

CC1

CC2

CC3 CC4

0.486

0.584

0.795 0.684

0.236

0.341

0.632 0.468

0.419 0.647 0.737 _a

2.951 (p=0.003)

3.210 (p=0.001) 3.162 (p=0.002)

Eco-Design ECO1

ECO2 ECO3

ECO4

ECO5

0.784

0.807 0.715

0.684

0.509

0.615

0.651 0.511

0.468

0.259

0.501 0.708 0.833 _a

6.090 5.456

5.203

3.800

Employee Job Satisfaction

SAT1 SAT2

SAT3

SAT4 SAT5

0.848 0.800

0.611

0.468 0.492

0.719 0.640

0.373

0.219 0.242

0.439 0.663 0.792 _a 5.963

4.682

3.511 3.701

Operational

Efficiency

OE1

OE2 OE3

OE4

OE5 OE6

0.686

0.710 0.548

0.776

0.558 0.733

0.471

0.504 0.300

0.602

0.311 0.537

0.454 0.674 0.831 _a

4.789 3.813

5.129

3.876 4.914

Relational

Efficiency

RE1

RE2 RE3

RE4

RE5 RE6

0.790

0.810 0.837

0.904

0.756 0.829

0.624

0.656 0.701

0.817

0.572 0.687

0.676 0.822 0.926 _a

7.087 7.403

8.203

6.480 7.305

Market Pressure MP1

MP2

0.889

0.536

0.790

0.287

0.539 0.734 0.557 -


OBP1 OBP2

OBP3

OBP4

0.759 0.794

0.873

0.807

0.576 0.630

0.762

0.651

0.655 0.809 0.884 _a 6.254

6.840

6.362


EP1 EP2

EP3

EP4 EP5

EP6

0.676 0.686

0.761

0.751 0.612

0.649

0.457 0.471

0.579

0.564 0.375

0.421

0.478 0.691 0.845 _a 4.632

5.029

4.983 4.194

4.416


ECP1 ECP2

ECP3

ECP4 ECP5

ECP6

ECP7 ECP8

ECP9

0.455 0.433

0.671

0.667 0.730

0.918

0.681 0.659

0.795

0.207 0.187

0.450

0.445 0.533

0.843

0.464 0.434

0.632

0.466 0.683 0.882 _a 2.644 (p=0.008)

3.355

3.348 3.476

3.746

3.377 3.330

3.588

Table 9: Summary of validity and reliability measurement results (original model)

Notes: _a a parameter fixed at 1.0 in the measurement model.; all critical ratios (t-values), unless otherwise stated, are significant at p<0.001


52

5.4.2 Step 2 - Assessing Reliability of the Constructs

After having assessed the validity of the constructs it is of importance to determine the reliability.

Reliability can be defined as the extent to which a measuring procedure, if repeatedly administered, yields

consistent results (Said, 2011). To assess reliability the following will make use of two methods, namely:

cronbach‘s alpha and construct/ composite reliability.

1. Cronbach’s alpha

One of the most commonly used methods is cronbach‘s alpha which measures the intercorrelation of

items. To test the measurement properties of the model constructs reliability and item-to-total correlation

analysis was made use of. The reliability test and item-to-total correlation analysis was performed by

means of the statistical software SPSS and the obtained values have been summarized in Table 10. From

the table it can be inferred that there is a reasonable fit between the data collected and the latent factors.

According to George and Mallery (2003) as well as Kline (1999) the internal consistency using

cronbach‘s alpha can be described as follows:

Cronbach’s alpha Internal Consistency α≥ 0.9 Excellent

0.8≤ α < 0.9 Good

0.7≤ α < 0.8 Acceptable (Survey)

0.6≤ α < 0.7 Questionable

0.5≤ α < 0.6 Poor

α < 0.5 Unacceptable

Table 10: Defining internal consistency using cronbach‘s alpha

The cronbach‘s alpha values are all greater than the suggested value of 0.7 (Nunnally and Bernstein,

1994, pp. 264–265) (cited in Iacobucci and Duhachek, 2003) with the exception of the cronbach‘s alpha

value for the moderator Market Pressure which has a value of 0.645. This value is, however, according to

Malhotra and Birks (2007, p.358) still acceptable. The authors state that an alpha value below 0.6 would

indicate unsatisfactory internal consistency reliability. Furthermore, the rather low value for the

cronbach‘s alpha is assumed to be resulting from the comparatively small number of items. Whereas the

remaining factors are comprised of a minimum of 4 items Market Pressure only consists of 2 items. It

should here be kept in mind that a greater number of items can artificially inflate the value for the

cronbach‘s alpha whereas a small number of items can falsely deflate the value of alpha. The last column

of the table (range of corrected item-to-total correlations) displays the range of the correlation of one item

and the composite score of all the other remaining items. More specifically, it is being determined

whether there is a strong, positive correlation between one item and the combined score of the remaining

items comprising the respective construct. When assessing the item loadings on the factors it can be

concluded that all item scores are internally consistent with the composite scores from the remaining

items of the respective construct (> 0.3) (de Vaus, 2001 cited in Tek and Ruthven, 2003). According to

de Vaus (2001) (cited in Tek and Ruthven, 2003) any score below 0.30 is considered to be a weak

correlation for item-analysis intentions. The item would have to be removed. Furthermore, a value greater

than 0.75 would indicate that the item is responsible for the majority of the correlation and nearly

measuring the whole scale thus implying redundancy (de Vaus, 2001 cited in Tek and Ruthven, 2003).


53

Factors Number of

Items

Mean SD Cronbach’s

alpha

Range of corrected item-

to-total correlations

Internal Environmental

Management

5 3.01 0.74

0.866 0.487-0.842

Green Purchasing 4 2.76 0.72 0.794 0.562-0.680

Cooperation with

Customers

4 2.49 0.70 0.729 0.437-0.618

Eco-Design 5 2.70 0.67 0.822 0.478-0.692

Employee Job

Satisfaction

5 3.11 0.55 0.789 0.42-0.67

Operational Efficiency 6 2.98 0.60 0.819 0.535-0.665

Relational Efficiency 6 3.51 0.69 0.924 0.710-0.862

Market Pressure 2 2.78 0.69 0.645 0.476-0.476

Overall Business

Performance

4 3.41 0.70 0.875 0.708-0.791

Environmental

Performance

6 3.02 0.66 0.844 0.562-0.681

Economic Performance 9 3.04 0.65 0.878 0.443-0.853

Table 11: Summary of cronbach‘s alpha and item-to-total correlations measurement results (original model)

2. Construct/ Composite Reliability

Construct/ composite reliability is a measure of reliability and internal consistency which is based on the

square of the sum of standardized factor loadings of a construct.

Construct reliability is determined by making use of the following formula (Said et al., 2011):

CR= Construct Reliability

= 𝑠𝑞𝑢𝑎𝑟𝑒 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔

𝑠𝑞𝑢𝑎𝑟𝑒 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑙𝑜𝑎𝑑𝑖𝑛𝑔 + (𝑠𝑢𝑚 𝑜𝑓 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑒𝑟𝑟𝑜𝑟𝑠 )

The threshold for construct reliability is 0.8 according to Koufteros (1999). From Table 8 it can be

inferred that almost all constructs show construct reliability. This implies that almost all constructs

capture significantly more of the variance than the variance revealed by the error components. However,

Hair et al. (2010) (cited in Gaskin, 2012a) set the threshold for construct reliability at 0.7 which would

imply that all constructs except the one for Market Pressure show construct reliability.

5.4.3 Step 3 - Goodness-of-Fit of the Research Model

The following section will calculate goodness-of-fit indices for both the first- and second-order

measurement models developed by Zhu et al. (2008) as well as for the mediators, moderator and the

dependent concepts. This will provide information on the extent to which the statistical model represents

a set of observations. By means of goodness-of-fit indices researchers are able to identify discrepancies

between the observed and expected values obtained by utilizing a specific statistical model. (Maydeu-

Olivares and Garcia-Forero, 2010)

Scale Independent Concept - GSM Practice Implementation

Lee et al. (2012) adopted the measurement model for GSCM Practice Implementation from Zhu et al.

(2008) who developed both first- and second-order measurement models for the construct. Lee et al.

(2012) were able to establish validity and reliability for both the first- and second-order models.


54

1. Goodness-of-Fit Indices

In confirmatory factor analysis, as opposed to most statistical methods, model fit is assessed by means of

multiple statistical tests. This is of importance as a single fit index only reflects one specific aspect of

model fit and is thus not able to provide information on overall model fit. The following will determine

how plausible the models are. According to Kline (2005) (cited in Hooper et al., 2008) the following

statistics ought to be reported: chi-squared test, root mean square error of approximation (RMSEA), the

comparative fit index (CFI), and the standardized root mean square residual (SRMR).

Chi-Squared Test is a statistical test utilized to compare data that is expected to be obtained according to a

specific hypothesis with data that is actually obtained/ observed. The chi-square test tests the null

hypothesis which states there to be no significant difference between the expected and the actually

observed results. (Koufteros and Marcoulides, 2006; Hu and Bentler, 1999) Even though the chi-square is

the most commonly used method to determine model fit Hair et al. (2006) (cited in Bigné Alcañiz, 2009)

criticize it to be highly sensitive to sample size. When making use of the chi-square difference test as well

as the chi-squared test it was found that minor differences when using large samples may be found to be

significant while in rather small samples large differences may test as non-significant. Thus, the authors

propose to additionally evaluate the CFI and RMSEA.

Root Mean Square Error of Approximation (RMSEA) is a measure of goodness-of-fit for statistical

models. According to Kaplan (2000, p.111) (cited in Schermelleh-Engel, 2003) the goal is for the

population to have a close fit with the model as opposed to having an exact fit which is said to not be

convenient when dealing with large populations.

The computational formula is as follows:

RMSEA = (𝜒2−𝑑𝑓)

[𝑑𝑓 𝑁−1 ]

Note: N is the sample size and df depicts the degrees of freedom.

Comparative Fit Index (CFI) is also known as the Bentler Comparative Fit Index. Here, the existing

model is compared to a null model also called the independence model in which it is assumed that the

latent variables are uncorrelated. The CFI compares the covariance matrices of the predicted and the

observed model and also compares the covariance matrix of the null model (covariance matrix of 0‘s) to

the observed covariance matrix to determine the percentage value of lack of fit when deciding to use the

new model instead of the null model. The value for CFI ranges from 0 to 1, the latter indicating a good fit.

(Bollen and Long, 1993) It should be noted that Bentler (1989) (cited in Medcof and Hausdorf, 1995) and

Kline (1998) recommend using the CFI when dealing with fewer than 200 respondents as the comparative

fit index is likely to produce biased estimates.

The computational formula is as follows:

𝐶𝐹𝐼 =𝑑 𝑁𝑢𝑙𝑙 𝑀𝑜𝑑𝑒𝑙 − 𝑑(𝑃𝑟𝑜𝑝𝑜𝑠𝑒𝑑 𝑀𝑜𝑑𝑒𝑙)

𝑑(𝑁𝑢𝑙𝑙 𝑀𝑜𝑑𝑒𝑙)

Note: 𝑑 = 𝜒2 − 𝑑f


55

Standardized Root Mean Square Residual (SRMR) is an absolute measure of fit (a value of zero indicates

perfect fit) and depicts the standardized difference between the observed and predicted correlation. The

standardized root mean square residual is a positively biased measure and the bias is said to be greater for

studies which are characterized by a small N and low degrees of freedom. (Hu and Bentler, 1999)

Having made use of the statistical software Amos Table 11 depicts the goodness-of-fit indices for the two

models. The CFI for the first-order model as well as for the second-order model with values of 0.858 and

0.860, respectively, were slightly below the acceptable value of 0.9 (Hu and Bentler, 1999). The SRMR

values were acceptable for both models (≤ 0.09) (Hu and Bentler, 1999) and the χ2 statistics of 208.843

at 129 degrees of freedom implying that χ2/df=1.619 for the first-order model and the χ2 statistics of

210.097 at 131 degrees of freedom implying that χ2/df=1.604 for the second-order model were less than

the benchmark of 2.0 considered optimal by Koufteros and Marcoulides (2006). It can, thus, be concluded

that the items constituting the construct GSCM Practice Implementation are plausible (to a great extent

represent the same theoretical construct/ scales; are unidimensional to a great extent); however, the

models could be adjusted to more accurately fit the data.

It should here be noted that according to Hooper et al. (2008) it is not uncommon to discover that the

proposed model has a poor fit with the data in consideration of the complexity of structural equation

modeling. Even though basing the decision on whether to modify the model or not on modification

indices is not recommended some modifications can, however, considerably improve the obtained results.

Note: the modification analysis will be performed in Section 5.5.

Model Statistics First-Order Second-Order Recommended Value

χ2 208.843 210.097

df 129 131

χ2/df (cmin/df) 1.619 1.604 <2 good (Tabachink and Fidell, 2007 cited in

Hooper et al., 2008 and Koufteros and

Marcoulides, 2006); <5 sometimes permissible

(Wheaton et al. 1977)

p-value for the model 0.000 0.000 >0.05 (Barrett, 2007)

CFI 0.858 0.860 ≥ 0.95 great; >0.90 traditional; >0.08

sometimes permissible (Hair et al., 2010, p.

654)

>0.90 (Hu and Bentler, 1999)

SRMR 0.0863 0.0859 ≤ 0.09 (Hu and Bentler, 1999)

RMSEA 0.100 0.099 <0.08 good; 0.08-0.10 moderate; >0.10 bad

(MacCullum et al., 1996)

<0.07 (Steiger, 2007)

Table 12: Statistics of first- and second-order models (original model)

2. Chi-Square Difference Test

Before making a decision on which model would be best to make use of it is necessary to statistically

compare the fit of the first-order model with the higher-order model. Marsh and Hocevar (1985)

recommend performing the likelihood ratio test also known as the chi-square difference test to evaluate

the efficacy of the two models. This method tests the null hypothesis of there being no significant

difference in fit by determining whether the chi-square difference is significant taking into account the

known degrees of freedom and the chosen significance level. The null hypothesis is rejected if the

difference is significant. As stated previously, a limitation of this test is that it is sensitive to sample size.

In large samples minor differences may be found to be significant while in rather small samples large


56

differences may test as non-significant. To compute the chi-square difference test, the difference of the

chi-square values as well as the difference between the degrees of freedom of both models is taken.

𝜒𝑑𝑖𝑓𝑓2 = 𝜒𝑀1

2 − 𝜒𝑀22 𝜒𝑑𝑖𝑓𝑓

2 = 210.097-208.843 = 1.254

𝑑𝑓𝑑𝑖𝑓𝑓 = 𝑑𝑓𝑀1 − 𝑑𝑓𝑀2 𝑑𝑓𝑑𝑖𝑓𝑓 = 131-129 = 2

M1 denotes the ‗smaller‘ model which in comparison to the ‗larger‘ model M2 has more degrees of

freedom (fewer parameters). The value for χdiff2 = 1.254 is distributed with dfdiff = 2 and checking the

significance by means of a χ2-table it can be concluded that the null hypothesis is not rejected. The results

show that the difference between the χ2 statistics for the first- and second-order models is 1.254 which is

smaller than 5.991 where the degree of freedom is 2 at p≤ 0.05. Thus, it can be concluded that there is no

statistically significant difference between the ‗smaller‘ model (the first-order model) and the higher-

order model. Furthermore, Beltrάn-Martίn et al. (2008) were able to conclude that the existing covariation

between the first-order factors and the observable variables can in its entirety be explained by their

regression onto the second-order factor. Hence, taking this into account and to be able to compare the

results of this study to the study performed by Lee et al. (2012) the decision was made to make use of the

second-order model throughout this study to test the hypothesized relationships.

Figure 6 depicts the path diagram of the first-order model and Figure 7 depicts the second-order factor

measurement model for GSCM Practice Implementation. Latent constructs are shown as an oval. Each

measured variable is associated with an error term which, for simplicity, are not shown in the exhibits.

Two headed connections are a sign of covariance between constructs and one headed connectors reveal

there to be a path from a construct to an indicator variable (a measured variable). Measured variables are

labeled corresponding to those in the questionnaire.


57

Path Diagram of the Measurement Model (First-Order-Model)

IEM

GP

CC

ECO

IEM1

IEM2

IEM3

IEM4

IEM5

ECO1

ECO2

ECO3

ECO4

ECO5

CC1

CC2

CC3

CC4

GP1

GP2

GP3

GP4

0.77

0.88

0.52

0.51

0.26

0.59

0.55

0.44

0.37

0.32

0.49

0.47

0.37

0.58

0.64

0.52

0.51

0.27

0.58

0.80

0.84

0.74

0.64

0.77

0.88

0.94

0.72

0.71

0.51

0.77

0.74

0.66

0.61

0.56

0.70

0.69

0.61

0.80

0.72

0.71

0.52

0.76

Figure 6: Path diagram of the first-order measurement model (original model)


58

Path Diagram of the Measurement Model (Second-Order-Model)

IEM

GP

CC

ECO

IEM1

IEM2

IEM3

IEM4

IEM5

ECO1

ECO2

ECO3

ECO4

ECO5

CC1

CC2

CC3

CC4

GP1

GP2

GP3

GP4

0.77

0.88

0.52

0.51

0.26

0.57

0.57

0.46

0.36

0.33

0.49

0.45

0.37

0.57

0.62

0.53

0.50

0.28

0.94

0.72

0.71

0.51

0.76

0.76

0.68

0.60

0.57

0.70

0.67

0.61

0.79

0.73

0.71

0.53

0.76

0.88

GSCM

0.78

0.99

0.86

0.79

Figure 7: Path diagram of the second-order measurement model (original model)


59

Scale Mediators, Moderator and Dependent Concepts

To assess the construct validity of the mediators, moderator and dependent concepts the

unidimensionality of all seven constructs was determined (Steenkamp and van Trijp, 1991). This is a

necessary step to ensure that the indicator variables of a construct measure the same thing. There are

several methods which can be made use of to test unidimensionality. The most prominent method for

testing unidimensionality is cronbach‘s alpha. Assessing reliability in terms of cronbach‘s alpha has

already been done previously (Sub-Section 5.4.2) thus the following will focus on a second method –

confirmatory factor analysis - to determine whether the model fit indices indicate a good fit to the data.

Confirmatory Factor Analysis in Structural Equation Modeling

Here, the measurement models will be assessed separately from the structural model. For each model the

model fit indices were determined by making use of the statistical software Amos. Table 12 summarizes

the results obtained.

From the table it can be concluded that only one SRMR is not less than 0.09 thus the majority of

constructs satisfy the cutoff point suggested by Hu and Bentler (1999). The CFI‘s range from 0.788 to

1.000, however, the cutoff point suggested by Hu and Bentler (1999) is at a minimum of 0.90 implying

that the constructs Employee Job Satisfaction and Operational Efficiency need further analysis. In terms

of the χ2 statistic, even though almost all χ2/df ratios of the constructs were greater than 2.0 Medsker et

al. (1994) state that any χ2/df ratios which are below 10 can be regarded as indicating a good fit with the

data. In terms of the RMSEA, the majority of the values do not satisfy the upper limit of 0.07 suggested

by Steiger (2007). The value for RMSEA tends to improve as more items are added and is said to

artificially inflate if df and N are low (Kenny et al., 2011 cited in Filippov et al., 2012). Thus, Kenny et al.

(2011) (cited in Filippov et al., 2012) argue to refrain from computing the RMSEA for low df models. It

should here be noted that from the table it can be inferred that the construct Market Pressure with zero

degrees of freedom is just-identified. Identification is defined as the degree to which there is a satisfactory

number of equations enabling the solving for every coefficient (unknown) which has to be estimated. The

model is just-identified when there are zero degrees of freedom and the number of equations is equal to

the number of estimated coefficients. As structural equation models are always over-identified it is

probable that the construct Market Pressure does not have enough items. Thus, it is highly likely that

when performing the moderator analysis no results will be obtained. (Kenny, 2011) The analysis will be

performed in Chapter 6, Section 6.1.3.


60

Model 𝛘𝟐 df p 𝛘𝟐/𝐝𝐟 CFI SRMR RMSEA

GSCM Practice

Implementation First-Order

179.551 125 0.001 1.436 0.903 0.0825 0.084

GSCM Practice

Implementation Second-

Order

155.445 113 0.005 1.376 0.919 0.0842 0.08


Scale

27.140 5 0.000 5.428 0.788 0.1062 0.267

Operational Efficiency

Scale

26.148 9 0.002 2.905 0.867 0.0793 0.175

Relational Efficiency Scale 26.843 9 0.001 2.983 0.935 0.0474 0.179

Market Pressure Scale 0.000 0 - - 1.000 0.000 0.491

Overall Business

Performance Scale

0.945 2 0.624 0.472 1.000 0.0139 0.000


Scale

8.647 9 0.470 0.961 1.000 0.0422 0.000


Scale

52.625 27 0.002 1.949 0.900 0.0885 0.124

Table 13: Fit indices for the mediators, moderator and dependent concepts (original model)

Figure 8 depicts the path diagrams of the measurement models for Employee Job Satisfaction,

Operational Efficiency, Relational Efficiency, Market Pressure and the three dependent concepts (Overall

Business Performance, Environmental Performance and Economic Performance). Latent constructs are

shown as an oval. Each measured variable is associated with an error term which, for simplicity, are not

shown in the exhibits. Two headed connections are a sign of covariance between constructs and one

headed connectors reveal there to be a path from a construct to an indicator variable (a measured

variable). Measured variables are labeled corresponding to those in the questionnaire.


61

Path Diagrams of the Measurement Models

SAT

SAT1

SAT2

SAT3

SAT4

SAT5

0.72

0.64

0.37

0.22

0.24

0.80

0.61

0.47

0.49

0.85 0.47

0.50

0.30

0.60

0.31

0.54

OE1

OE2

OE3

OE4

OE6

OE5

0.71

0.55

0.78

0.56

0.69

0.73

0.62

0.65

0.70

0.82

0.57

0.69

RE1

RE2

RE3

RE4

RE5

RE6

0.81

0.84

0.90

0.76

0.79

0.83

RE MP1

MP2

0.79

0.29 0.54

0.89

OBP1

OBP2

OBP3

OBP4

0.58

0.53

0.76

0.65

0.76

0.79

0.87

0.81

0.46

0.47

0.58

0.56

0.37

0.42

EP1

EP2

EP3

EP4

EP5

EP6

EP

0.68

0.69

0.76

0.75

0.61

0.65

OBP

MP

OE

0.21

0.19

0.45

0.45

0.53

0.84

ECP1

ECP2

ECP3

ECP4

ECP5

ECP6

ECP

0.45

0.43

0.67

0.67

0.73

0.92

0.46

0.43

0.63

ECP7

ECP8

ECP9

0.68

0.66

0.80

Figure 8: Path diagrams of the measurement models (original model)


62

5.5 Validity, Reliability and Goodness-of-Fit of the Research Model (Modified Model)

In the following the measurement properties of the constructs will be assessed making use of reliability

and item-to-total correlation analysis, after which a confirmatory factor analysis (CFA) is performed to

examine the goodness-of-fit of the research models proposed by Zhu et al. (2008). It should here be noted

that Section 5.5 will identify validity and reliability issues and changes will be made to the constructs to

improve validity and reliability. This is of importance to determine the robustness of the model. However,

by making adjustments it will not be possible to compare the outcomes to the study performed by Lee et

al. (2012).

5.5.1 Step 1 – Assessing Validity of the Constructs

Firstly, before performing reliability estimation is of importance to determine if the constructs are valid.

In order to establish construct validity using confirmatory factor analysis two components have been

made use of, namely: (1) convergent validity and (2) discriminant validity. Convergent validity will be

examined by means of factor loadings, average variance extracted (AVE) and construct/ composite

reliability. Note: construct/ composite reliability will be determined in Sub-Section 5.5.2.

1. Convergent Validity


To determine convergent validity it is foremost of importance to assess the standardized loadings

(standardized regression weights). According to Johnson et al. (2001) and Nunnally (1978) (cited in

Abdul-Halim, 2009) all loadings should be at least 0.5 and preferably 0.7 or higher (Chin et al., 1995).

From Table 8 it was previously inferred that a total of five items score slightly lower than 0.5. More

specifically, a great deal of variance in each observed variable is accounted for with the exception of

IEM5 (R2=0.2892=0.239), CC1 (R2=0.486

2=0.236), SAT4 (R2=0.468

2=0.219), SAT5

(R2=0.4922=0.242), ECP1 (R2=0.455

2=0.207) and ECP2 (R2=0.492

2=0.187). Thus, all loadings except

these five are significant (p<0.05) as is required for convergent validity. To increase validity and after

having determined the effects on the remaining items of removing each problematic item individually the

decision was made to remove problematic items (IEM5, CC1, SAT4, OE3, OE5, EP5, ECP1 and ECP2)

which loaded relatively lowly. Table 15 provides a summary of the adjusted constructs and the new

standardized loadings.

Furthermore, it is of importance to examine the statistical significance through t-values (Dunn et al.,

1994). Form Table 15 in can be inferred that all t-values (critical ratio‘s) for the individual paths were

significantly related to their underlying construct.


To further draw conclusions about the degree of convergent validity achieved the average variance

extracted (AVE) was also determined. Amos software is not able to calculate these values, thus, the

average variance extracted (AVE) will be calculated manually.

As can be derived from Table 8 the average variance extracted (AVE) estimates of the majority of

constructs are greater than 0.5 (Fornell and Larcker, 1981), thus exhibiting convergence validity. Three

constructs (Cooperation with Customers, Operational Efficiency and Environmental Performance) score

slightly beneath 0.5 indicating that on average the error remaining in the items is larger than the variance

that is actually explained by the latent factor structure. The decision was made not to delete any items

relating to Cooperation with Customers due to the fact that this construct would then only be measured by


63

a total of two items. Items OE3, OE5 and EP5 were, however, deleted and proved to have a positive

impact on the average variance extracted values which now are greater than 0.5 for Operational

Efficiency and Environmental Performance (Table 15).

2. Discriminant Validity

As has been stated previously discriminant validity is the extent to which two constructs that are

theoretically assumed to be unrelated are indeed unrelated (truly distinct from each other and

uncorrelated). Table 15 provides the calculated discriminant validity values for each factor.

The thresholds for discriminant validity are MSV<AVE and ASV<AVE (Hair et al., 2010 cited in

Gaskin, 2012b). Having made use of the Sats Tools Package developed by Gaskin (2012b) Tables 13 and

14 were obtained. Table 13 provides a summary of the values for composite reliability (CR), average

variance extracted (AVE), maximum shared squared variance (MSV) and the average shared squared

variance (ASV). Table 14 is a factor correlation matrix depicting the square root of the average variance

extracted (the discriminant validity values) on the diagonal. All underlined values show validity issues.

More specifically, the AVE for CC, ECO, GP, OE and ECP is smaller than the MSV (Table 13) implying

validity concerns. Furthermore, the square root of the AVE (Table 14 and Table 15) for CC, ECO, GP,

OE and ECP is less than the absolute value of the correlations with another factor.

Validity and Reliability Table

Table 14: Validity and reliability table (modified model)

Factors CR AVE MSV ASV

EP Environmental Performance 0.831 0.497 0.393 0.212

IEM Internal Environmental Management 0.890 0.672 0.645 0.253

OBP Overall Business Performance 0.884 0.655 0.285 0.137


ECO Eco-Design 0.832 0.502 0.612 0.359

GP Green Purchasing 0.787 0.483 0.645 0.366

SAT Employee Job Satisfaction 0.787 0.492 0.423 0.177

OE Operational Efficiency 0.820 0.534 0.546 0.138

RE Relational Efficiency 0.926 0.677 0.546 0.186

ECP Economic Performance 0.892 0.546 0.612 0.320


64

Factor Correlation Matrix with the Square Root of the AVE on the Diagonal

Table 15: Factor correlation matrix with the square root of the AVE on the diagonal (modified model)

Summary of Validity and Reliability Measurement Results (Modified Model) Factors Item

Number

Standardized

Loading

R2 Average

Variance

Extracted

(AVE)

Discriminant

Validity

Construct/

Composite

Reliability

Critical Ratio

(t-value)

Internal

Environmental Management

IEM1

IEM2 IEM3

IEM4

0.874

0.962 0.704

0.692

0.764

0.925 0.496

0.925

0.666 0.816 0.890 _a

10.308 6.600

6.432

Green Purchasing GP1 GP2

GP3

GP4

0.611 0.850

0.767

0.573

0.373 0.722

0.588

0.328

0.503 0.709 0.787 _a 4.541

4.457

3.654

Cooperation with Customers

CC2 CC3

CC4

0.536 0.824

0.693

0.287 0.679

0.480

0.482 0.694 0.723 _a 3.172

3.434

Eco-Design ECO1 ECO2

ECO3

ECO4 ECO5

0.784 0.807

0.715

0.684 0.509

0.615 0.651

0.511

0.468 0.259

0.501 0.708 0.832 _a 6.090

5.456

5.203 3.800

Employee Job

Satisfaction

SAT1

SAT2 SAT3

SAT5

0.863

0.820 0.550

0.493

0.745

0.672 0.303

0.243

0.594 0.771 0.787 _a

5.673 4.154

3.697

Operational

Efficiency

OE1

OE2 OE4

OE6

0.675

0.765 0.779

0.717

0.456

0.585 0.607

0.514

0.540 0.735 0.820 _a

4.870 4.918

4.658

Relational Efficiency

RE1 RE2

RE3

RE4 RE5

RE6

0.790 0.810

0.837

0.904 0.756

0.829

0.624 0.656

0.701

0.817 0.572

0.687

0.676 0.822 0.926 _a 7.087

7.403

8.203 6.480

7.305

Market Pressure MP1

MP2

0.889

0.536

0.790

0.287

0.539 0.734 0.557 -

Overall Business

Performance

OBP1

OBP2

OBP3 OBP4

0.759

0.794

0.873 0.807

0.576

0.630

0.762 0.651

0.655 0.809 0.884 _a

6.254

6.840 6.362

Environmental

Performance

EP1

EP2 EP3

EP4

0.687

0.673 0.759

0.762

0.472

0.453 0.576

0.581

0.500 0.707 0.831 _a

4.548 5.001

5.011

Factors EP IEM OBP CC ECO GP SAT OE RE ECP

EP Environmental Performance 0.705

IEM Internal Environmental Management 0.463 0.820

OBP Overall Business Performance 0.370 0.184 0.809


ECO Eco-Design 0.627 0.578 0.442 0.733 0.708

GP Green Purchasing 0.480 0.803 0.315 0.769 0.767 0.695

SAT Employee Job Satisfaction 0.275 0.204 0.449 0.388 0.650 0.476 0.702

OE Operational Efficiency 0.373 0.229 0.345 0.059 0.279 0.354 0.255 0.731

RE Relational Efficiency 0.415 0.358 0.534 0.067 0.274 0.452 0.398 0.739 0.823

ECP Economic Performance 0.581 0.665 0.323 0.581 0.782 0.764 0.503 0.335 0.313 0.739


65

EP6 0.639 0.408 4.350

Economic

Performance

ECP3

ECP4

ECP5

ECP6 ECP7

ECP8

ECP9

0.659

0.651

0.746

0.901 0.694

0.677

0.814

0.434

0.424

0.557

0.812 0.482

0.458

0.663

0.547 0.740 0.892 _a

4.583

5.149

5.952 4.845

4.739

5.531

Table 16: Summary of validity and reliability measurement results (modified model)


5.5.2 Step 2 - Assessing Reliability of the Constructs

After having assessed the validity of the constructs it is of importance to determine the reliability.

Reliability can be defined as the extent to which a measuring procedure, if repeatedly administered, yields

consistent results (Said, 2011). To assess reliability the following will make use of two methods, namely:

cronbach‘s alpha and construct/ composite reliability.

1. Cronbach’s alpha

One of the most commonly used methods is cronbach‘s alpha which measures the intercorrelation of

items. To test the measurement properties of the model constructs reliability and item-to-total correlation

analysis was made use of. The reliability test and item-to-total correlation analysis was performed by

means of the statistical software SPSS and the obtained values have been summarized in Table 17. From

the table it can be inferred that there is a reasonable fit between the data collected and the latent factors.

According to George and Mallery (2003) as well as Kline (1999) the internal consistency using

cronbach‘s alpha can be described as follows:

Cronbach’s alpha Internal Consistency α≥ 0.9 Excellent

0.8≤ α < 0.9 Good

0.7≤ α < 0.8 Acceptable (Survey)

0.6≤ α < 0.7 Questionable

0.5≤ α < 0.6 Poor

α < 0.5 Unacceptable

Table 17: Defining internal consistency using cronbach‘s alpha

The cronbach‘s alpha values are all greater than the suggested value of 0.7 (Nunnally and Bernstein,

1994, pp. 264–265) (cited in Iacobucci and Duhachek, 2003) with the exception of the cronbach‘s alpha

value for the moderator Market Pressure which has a value of 0.645. This value is, however, according to

Malhotra and Birks (2007, p.358) still acceptable. The authors state that an alpha value below 0.6 would

indicate unsatisfactory internal consistency reliability. Furthermore, the rather low value for the

cronbach‘s alpha is assumed to be resulting from the comparatively small number of items. Whereas the

remaining factors are comprised of a minimum of 3 items Market Pressure only consists of 2 items. It

should here be kept in mind that a greater number of items can artificially inflate the value for the

cronbach‘s alpha whereas a small number of items can falsely deflate the value of alpha. The last column

of the table (range of corrected item-to-total correlations) displays the range of the correlation of one item

and the composite score of all the other remaining items. More specifically, it is being determined

whether there is a strong, positive correlation between one item and the combined score of the remaining

items comprising the respective construct. When assessing the item loadings on the factors it can be

concluded that all item scores are internally consistent with the composite scores from the remaining


66

items of the respective construct (> 0.3) (de Vaus, 2001 cited in Tek and Ruthven, 2003). According to

de Vaus (2001) (cited in Tek and Ruthven, 2003) any score below 0.30 is considered to be a weak

correlation for item-analysis intentions. The item would have to be removed. Furthermore, a value greater

than 0.75 would indicate that the item is responsible for the majority of the correlation and nearly

measuring the whole scale implying redundancy (de Vaus, 2001 cited in Tek and Ruthven, 2003).

Factors Number

of Items


alpha

Change in

Cronbach’s

alpha

Range of corrected

item-to-total

correlations

Internal

Environmental

Management

4 2.96 0.81 0.883

↑

0.674-0.863

Green Purchasing 4 2.76 0.72 0.794 - 0.562-0.680

Cooperation with

Customers

3 2.59 0.77 0.713 ↓

0.453-0.616

Eco-Design 5 2.70 0.67 0.822 - 0.478-0.692

Employee Job

Satisfaction

4 3.23 0.57 0.775 ↓

0.466-0.687

Operational

Efficiency

4 3.08 0.64 0.811 ↓

0.619-0.676

Relational

Efficiency

6 3.51 0.69 0.924 -

0.710-0.862

Market Pressure 2 2.78 0.69 0.645 - 0.476-0.476

Overall Business

Performance

4 3.41 0.70 0.875 -

0.708-0.791

Environmental

Performance

5 3.06 0.67 0.813 ↓

0.571-0.601

Economic

Performance

7 3.16 0.71 0.889 ↑

0.589-0.842

Table 18: Summary of cronbach‘s alpha and item-to-total correlations measurement results (modified model)

2. Construct/ Composite Reliability

Construct/ composite reliability is a measure of reliability and internal consistency which is based on the

square of the sum of standardized factor loadings of a construct.

The threshold for construct reliability is 0.8 according to Koufteros (1999). From Table 17 it can be

inferred that a total of four constructs show construct reliability issues. This implies that the majority with

the exception of these four constructs capture significantly more of the variance than the variance

revealed by the error components. However, Hair et al. (2010) (cited in Gaskin, 2012a) set the threshold

for construct reliability at 0.7 which would imply that all constructs except the one for Market Pressure

show construct reliability.

5.5.3 Step 3 - Goodness-of-Fit of the Research Model

The following section will calculate goodness-of-fit indices for the modified first- and second-order

measurement models which were originally developed by Zhu et al. (2008). Furthermore, the goodness-

of-fit indices for the mediators, moderator and the dependent concepts will be determined. This will

provide information on the extent to which the statistical model represents a set of observations.


67

Scale Independent Concept - GSM Practice Implementation

Lee et al. (2012) adopted the measurement model for GSCM Practice Implementation from Zhu et al.

(2008) who developed both first- and second-order measurement models for the construct. Lee et al.

(2012) were able to establish validity and reliability for both the first- and second-order models.

1. Goodness-of-Fit Indices

In confirmatory factor analysis, as opposed to most statistical methods, model fit is assessed by means of

multiple statistical tests. The following will determine how plausible the models are. According to Kline

(2005) (cited in Hooper et al., 2008) the following statistics ought to be reported: chi-squared test, root

mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root

mean square residual (SRMR).

Having made use of the statistical software Amos Table 18 depicts the goodness-of-fit indices for the two

models. The CFI for the first-order model as well as for the second-order model with values of 0.899 and

0.899, respectively, were slightly below the acceptable value of 0.9 (Hu and Bentler, 1999). The SRMR

values were acceptable for both models (≤ 0.09) (Hu and Bentler, 1999) even though the SRMR of the

first-order model was slightly above the recommended threshold of 0.09. In regards to the χ2 statistics of

148.796 at 98 degrees of freedom implying that χ2/df=1.518 for the first-order model and the χ2 statistics

of 150.756 at 100 degrees of freedom implying that χ2/df=1.508 for the second-order model were less

than the benchmark of 2.0 considered optimal by Koufteros and Marcoulides (2006). It can, thus, be

concluded that the items constituting the construct GSCM Practice Implementation are plausible (to a

great extent represent the same theoretical construct; scales are unidimensional to a great extent),

however, the models can be adjusted to more accurately fit the data.

Model Statistics First-Order Second-Order Recommended Value

χ2 148.796 150.756

df 98 100

χ2/df (cmin/df) 1.518 1.508 <2 good (Tabachink and Fidell, 2007 cited in

Hooper et al., 2008 and Koufteros and

Marcoulides, 2006); <5 sometimes permissible


p-value for the model 0.001 0.001 >0.05 (Barrett, 2007)

CFI 0.899 0.899 ≥ 0.95 great; >0.90 traditional; >0.08

sometimes permissible (Hair et al., 2010, p.

654)


SRMR 0.091 0.0824 ≤ 0.09 (Hu and Bentler, 1999)

RMSEA 0.0822 0.090 <0.08 good; 0.08-0.10 moderate; >0.10 bad


<0.07 (Steiger, 2007)

Table 19: Statistics of first- and second-order models (modified model)


68

2. Performing Model Fit in the Confirmatory Factor Analysis

In the following both models will be adjusted to more accurately fit the obtained data. With the help of

the modification indices and the standardized residual covariance matrix provided by Amos output the

following decisions were made.

Adjustments made to the First-Order Model

To improve model fit the decision was made to delete items IEM3, GP3 and ECO5. Table 19 provides a

summary of the improved values for the goodness-of-fit indices.

Adjustments made to the Second-Order Model

To improve model fit of the second-order model Amos suggested deleting several items. The decision

was made to delete items GP3 and ECO5. This contributed to improving the overall model fitting. Table

19 provides a summary of the improved values for the goodness-of-fit indices.

Model Statistics First-Order Change Second-Order Change Recommended Value

χ2 75.683 93.361

df 59 73

χ2/df (cmin/df) 1.283 ↓ 1.279 ↓ <2 good (Tabachink and Fidell,

2007 cited in Hooper et al.,

2008 and Koufteros and

Marcoulides, 2006); <5

sometimes permissible


p-value for the

model

0.071 ↑ 0.054 ↑ >0.05 (Barrett, 2007)

CFI 0.957 ↑ 0.952 ↑ ≥ 0.95 great; >0.90 traditional;

>0.08 sometimes permissible

(Hair et al., 2010, p. 654)


SRMR 0.074 ↓ 0.078 ↓ ≤ 0.09 (Hu and Bentler, 1999)

RMSEA 0.068 ↓ 0.067 ↓ <0.08 good; 0.08-0.10

moderate; >0.10 bad


<0.07 (Steiger, 2007)

Table 20: Statistics of first- and second-order models after performing model fit (modified model)

3. Chi-Square Difference Test

Before making a decision on which model would be best to make use of it is necessary to statistically

compare the fit of the first-order model with the higher-order model. Marsh and Hocevar (1985)

recommend performing the likelihood ratio test also known as the chi-square difference test to evaluate

the efficacy of the two models. This method tests the null hypothesis of there being no significant

difference in fit by determining whether the chi-square difference is significant taking into account the

known degrees of freedom and the chosen significance level. The null hypothesis is rejected if the

difference is significant. To compute the chi-square difference test, the difference of the chi-square values

as well as the difference between the degrees of freedom of both models is taken.

𝜒𝑑𝑖𝑓𝑓2 = 𝜒𝑀1

2 − 𝜒𝑀22 𝜒𝑑𝑖𝑓𝑓

2 = 93.361-75.683= 17.678

𝑑𝑓𝑑𝑖𝑓𝑓 = 𝑑𝑓𝑀1 − 𝑑𝑓𝑀2 𝑑𝑓𝑑𝑖𝑓𝑓 = 73-59= 14


69

M1 denotes the ‗smaller‘ model which in comparison to the ‗larger‘ model M2 has more degrees of

freedom (fewer parameters). The value for χdiff2 = 17.678 is distributed with dfdiff = 14 and checking the

significance by means of a χ2-table it can be concluded that the null hypothesis is not rejected. The results

show that the difference between the χ2 statistics for the first-and second-order models was 17.678 which

is smaller than 23.685 where the degree of freedom is 14 at p≤ 0.05. Thus, it can be concluded that there

is no statistically significant difference between the ‗smaller‘ model (the first-order model) and the

higher-order model. Furthermore, Beltrάn-Martίn et al. (2008) were able to conclude that the existing

covariation between the first-order factors and the observable variables can in its entirety be explained by

their regression onto the second-order factor. Thus, the decision was made to make use of the second-

order model to test the hypothesized relationships and to better conclude on the robustness of the Original

Model.

4. Validity and Reliability Analysis of Adjusted Constructs of the Second-Order Model

Convergent Validity


From Table 20 it can be inferred that the standardized factor loadings of both adjusted constructs (Green

Purchasing and Eco-Design) are not optimal but better than before the adjustment was made. In regards to

the t-values it can be noted that all are significantly related to their underlying construct.


The average variance extracted (AVE) estimate of Green Purchasing decreased from 0.503 to 0.489 thus

dropping below the minimum threshold of 0.5 (Fornell and Larcker, 1981).

Factors Item

Number

Standardized

Loading

R2 Average

Variance

Extracted (AVE)

Discriminant

Validity

Construct/

Composite

Reliability

Critical Ratio

(t-value)

Internal environmental

management

IEM1 IEM2

IEM3

IEM4

0.874 0.962

0.704

0.692

0.764 0.925

0.496

0.479

0.666 0.816 0.890 _a 10.308

6.600

6.432

Green purchasing GP1

GP2

GP4

0.790

0.687

0.610

0.624

0.472

0.372

0.489 0.699 0.738 _a

3.658

3.571

Cooperation with customers

CC2 CC3

CC4

0.536 0.824

0.693

0.287 0.679

0.480

0.482 0.694 0.725 _a 3.172

3.434

Eco-design ECO1 ECO2

ECO3

ECO4

0.765 0.852

0.677

0.687

0.585 0.726

0.458

0.472

0.560 0.749 0.835 _a 6.058

5.073

5.145

Table 21: Summary of validity and reliability measurement results after performing model fit (modified model)



70

Reliability

Cronbach’s alpha

The cronbach‘s alpha values of the adjusted constructs are still greater than the suggested value of 0.7

(Nunnally and Bernstein, 1994, pp. 264–265 cited in Iacobucci and Duhachek, 2003).

Factors Number of

Items


alpha

Range of corrected item-

to-total correlations


Management

4 11.83 3.22 0.883 0.674-0.863

Green Purchasing 3 8.43 2.212 0.732 0.513-0.605

Cooperation with

Customers

3 7.76 2.32 0.713 0.453-0.616

Eco-Design 4 11.38 2.785 0.832 0.618-0.734

Table 22: Summary of cronbach‘s alpha and item-to-total correlations measurement results after performing model fit (modified

model)

Figure 9 depicts the path diagram of the first-order model and Figure 10 depicts the second-order factor

measurement model for GSCM Practice Implementation. Latent constructs are shown as an oval. Each

measured variable is associated with an error term which, for simplicity, are not shown in the exhibits.

Two headed connections are a sign of covariance between constructs and one headed connectors reveal

there to be a path from a construct to an indicator variable (a measured variable). Measured variables are

labeled corresponding to those in the questionnaire.


71

Path Diagram of the Measurement Model (First-Order-Model)

IEM

GP

CC

ECO

IEM4

IEM1

IEM2

0.49

0.78

0.91

0.53

0.83

0.75

0.76

0.60

0.71

0.70

0.88

0.95

GP4

GP1

GP2

0.37

0.65

0.45 0.61

0.81

0.67

CC4

CC2

CC3

0.42

0.44

0.53 0.65

0.67

0.73

ECO1

ECO2

ECO3

ECO4

0.56

0.71

0.48

0.50

0.75

0.84

0.69

0.71

Figure 9: Path diagram of the first-order measurement model (modified model)


72

Path Diagram of the Measurement Model (Second-Order-Model)

IEM

GP

CC

ECO

GSCM

0.82

0.99

0.79

0.74

IEM1

IEM2

IEM3

IEM4

0.77

0.89

0.53

0.50

0.88

0.95

0.72

0.71

GP4

GP1

GP2

0.35

0.65

0.46 0.60

0.81

0.68

CC4

CC2

CC3

0.43

0.51

0.47 0.65

0.71

0.68

ECO1

ECO2

ECO3

ECO4

0.58

0.68

0.50

0.50

0.76

0.82

0.70

0.71

Figure 10: Path diagram of the second-order measurement model (modified model)


73

Scale Mediators, Moderator and Dependent Concepts

To assess the construct validity of the mediators, moderator and dependent concepts the

unidimensionality of all seven constructs was determined (Steenkamp and van Trijp, 1991). This is a

necessary step to ensure that the indicator variables of a construct measure the same thing. The most

prominent method for testing unidimensionality is cronbach‘s alpha. Assessing reliability in terms of

cronbach‘s alpha has already been done previously (Sub-Section 5.5.2) thus the following will focus on a

second method – confirmatory factor analysis - to determine whether the model fit indices indicate a good

fit to the data.

Confirmatory Factor Analysis in Structural Equation Modeling

Here, the measurement models will be assessed separately from the structural model. For each model the

model fit indices were determined by making use of the statistical software Amos. Table 22 summarizes

the results obtained.

From the table it can be concluded that six SRMR‘s are less than 0.06 and one is only slightly above 0.06

thus the majority of constructs satisfy the cutoff point suggested by Hu and Bentler (1999). The CFI‘s

range from 0.925 to 1.000 implying that all constructs satisfy the cutoff point suggested by Hu and

Bentler (1999) which is at a minimum of 0.90. In terms of the χ2 statistic, even though almost all χ2/df

ratios of the constructs satisfied the benchmark (2.0) suggested by Koufteros and Marcoulides (2006)

Medsker et al. (1994) states that any χ2/df ratios which are below 10 can be regarded as indicating a good

fit with the data. In terms of the RMSEA the majority of values do not satisfy the upper limit of 0.07

suggested by Steiger (2007). The value for RMSEA tends to improve as more items are added and is said

to artificially inflate if df and N are low (Kenny et al., 2011 cited in Filippov et al., 2012). Thus, Kenny et

al. (2011) (cited in Filippov et al., 2012) argue to refrain from computing the RMSEA for low df models.

It should here be noted that from the table it can be inferred that the construct Market Pressure with zero

degrees of freedom is just-identified. Identification is defined as the degree to which there is a satisfactory

number of equations enabling the solving for every coefficient (unknown) which has to be estimated. The

model is just-identified when there are zero degrees of freedom and the number of equations is equal to

the number of estimated coefficients. As structural equation models are always over-identified it is

probable that the construct Market Pressure does not have enough items. Thus, it is highly likely that

when performing the moderator analysis no results will be obtained. (Kenny, 2011) The analysis will be

performed in Chapter 6, Section 6.2.3.


74

Model 𝛘𝟐 df p 𝛘𝟐/𝐝𝐟 CFI SRMR RMSEA

GSCM Practice

Implementation First-Order

75.683 59 0.071 1.283 0.957 0.074 0.068

GSCM Practice

Implementation Second-

Order

93.361 73 0.054 1.279 0.952 0.078 0.067


Scale

0.998 2 0.607 0.499 1.000 0.0278 0.000


Scale

8.578 2 0.014 4.289 0.925 0.0503 0.230

Relational Efficiency Scale 26.843 9 0.001 2.983 0.935 0.0474 0.179

Market Pressure Scale 0.000 0 - - 1.000 0.000 0.491

Overall Business

Performance Scale

0.945 2 0.624 0.472 1.000 0.0139 0.000


Scale

6.645 5 0.248 1.329 0.984 0.0436 0.073


Scale

21.022 14 0.101 1.502 0.968 0.0606 0.090

Table 23: Fit indices for the mediators, moderator and dependent concepts (modified model)

Figure 11 depicts the path diagrams of the measurement models for Employee Job Satisfaction,

Operational Efficiency, Relational Efficiency, Market Pressure and the three dependent concepts (Overall

Business Performance, Environmental Performance and Economic Performance). Latent constructs are

shown as an oval. Each measured variable is associated with an error term which, for simplicity, are not

shown in the exhibits. Two headed connections are a sign of covariance between constructs and one

headed connectors reveal there to be a path from a construct to an indicator variable (a measured

variable). Measured variables are labeled corresponding to those in the questionnaire.


75

Path Diagrams of the Measurement Models

SAT

0.62

0.65

0.70

0.82

0.57

0.69

RE1

RE2

RE3

RE4

RE5

RE6

0.81

0.84

0.90

0.76

0.79

0.83

RE MP1

MP2

0.79

0.29 0.54

0.89

OBP1

OBP2

OBP3

OBP4

0.58

0.53

0.76

0.65

0.76

0.79

0.87

0.81

EP OBP

MP

0.43

0.42

0.56

0.81

0.48

ECP3

ECP4

ECP5

ECP6

ECP7

ECP

0.65

0.65

0.75

0.90

0.69

0.46

0.66

ECP8

ECP9

0.68

0.81

SAT1

SAT2

SAT3

SAT5

0.74

0.67

0.30

0.24

0.86

0.82

0.55

0.49

OE1

OE2

OE4

OE6

0.46

0.59

0.61

0.51

0.68

0.77

0.78

0.72

OE

EP1

EP2

EP3

EP4

EP6

0.47

0.45

0.58

0.58

0.41

0.67

0.76

0.76

0.64

0.69

Figure 11: Path diagrams of measurement models (modified model)


76

6. HYPOTHESES TESTING AND DISCUSSIONS OF QUANTITATIVE DATA

Hereunder, the hypotheses developed in Chapter 3 will be tested by means of path analysis. More

specifically, this chapter is divided into three sections. First, the hypotheses will be tested by making use

of the original model without adjustments after which the hypotheses will be tested by utilizing the

modified model to determine the robustness of the results obtained. The robustness verification will be

performed in the last section of this chapter. The first two sections are divided into three subsections,

namely: an analysis of the direct effects, a mediation analysis and a moderation analysis. Each of the first

two sections will conclude with a summarizing table and a conceptual model of the hypotheses, their

unstandardized coefficient (b-value) and if a statistically significant relationship was found. It should here

be noted that due to the low response rate (10.71%) making generalizations from the sample to the

population is not advisable. Thus, the conclusions drawn in the following are made not taking into

account the low response rate.

6.1 Original Model

The results of the original structural model are summarized in Table 26 and Figure 12. This section is

divided into three subsections, namely: direct effects, mediation analysis and moderation analysis.

6.1.1 Direct Effects

In regards to the direct effects, hypotheses H1a to H4d, it can be noted that all hypotheses were supported

except H3a (b=0.291, t=1.771, p=0.077). No direct link between GSCM Practice Implementation and

Operational Efficiency could be found. However, the remaining paths from GSCM Practice

Implementation to the following showed positive significant results:

(1) Employee Job Satisfaction (b=0.493, t=2.671, p=0.008)

(2) Relational Efficiency (b=0.390, t=2.361, p=0.018)

As posited by hypotheses H3b, H3c and H3d, Operational Efficiency has a direct effect on all three

dependent concepts. Furthermore, the test results also revealed that an improved Operational Efficiency in

the supplying firm has a positive impact on the Relational Efficiency between the supplier and the buying

firm (H3e: b=0.818, t=4.347, p<0.001). As a last note it was found that there is a positive, significant

relationship between Relational Efficiency and Overall Business Performance (H4b: b=0.535, t=3.700,

p<0.001), Environmental Performance (H4c: b=0.458, t=3.003, p=0.003) and Economic Performance

(H4d: b=0.316, t=2.016, p=0.044).

Comparison to Results found by Lee et al. (2012)

When comparing the test results found in this study to the study performed by Lee et al. (2012) (Table 23)

the most anticipated finding for there to be a direct relationship between GSCM Practice Implementation

and Overall Business Performance was supported, however, weakly. Furthermore, the stronger indirect

effect between GSCM Practice Implementation and Overall Business Performance, which was expected,

could not be proven to exist when having evaluated the test results. Additionally, even though the

relationship between GSCM Practice Implementation and Employee Job Satisfaction is supported by both

studies this study did not find there to be a stronger effect (b=0.493, t=2.671, p=0.008 compared to

b=0.720, t=15.353, p<0.01). However, in contrast to the study performed by Lee et al. (2012) the

hypothesis stating there to be a relationship between Employee Job Satisfaction and Overall Business


77

Performance was supported. The hypothesized effect of GSCM Practice Implementation on Operational

Performance could not be supported by this study (H3a: b=0.291, t=1.771, p=0.077).

Current Study Lee et al. (2012)

Path (from-to) Effects (Critical

Ratio)

Hypotheses

Test Results

Effects (Critical

Ratio)

Hypotheses

Test Results

H1 H1a:

GSCM ImplementationOverall

Business Performance (direct)

0.324 (1.989) Supported

p-value=0.047;

R2=0.11

0.084 (0.782) Not supported

H2 H2a: GSCM ImplementationEmployee

Job Satisfaction

0.493 (2.671) Supported

p-value=0.008;

R2=0.24

0.720 (15.353) Supported

p-value<0.01

H2b:

Employee Job SatisfactionOverall


0.459 (3.097)

Supported

p-value=0.002;

R2=0.21


H2c:

Employee Job

SatisfactionOperational

Efficiency

0.358 (2.281) Supported

p-value=0.023;

R2=0.13


H3 H3a:

GSCM

ImplementationOperational

Efficiency


p-value=0.077;

R2=0.08

0.444 (3.688) Supported

p-value<0.01

H3b:

Operational EfficiencyOverall


0.468 (2.979) Supported

p-value=0.003;

R2=0.22

0.423 (6.578) Supported

p-value<0.01

H3e:

Operational EfficiencyRelational

Efficiency

0.818 (4.347) Supported

p-value<0.001;

R2=0.67

0.447 (7.886) Supported

p-value<0.01

H4 H4a:

GSCM ImplementationRelational

Efficiency

0.390 (2.361) Supported

p-value=0.018;

R2=0.15

0.410 (6.858) Supported

p-value<0.01

H4b:

Relational EfficiencyOverall


0.535 (3.700) Supported

p-value<0.001;

R2=0.29

0.233 (3.022) Supported

p-value<0.01

H6 H6a: GSCM ImplementationOverall

Business Performance (indirect)

0.036 (0.813) Supported 0.455 (5.407) Supported

p-value<0.01

Table 24: Summary of hypotheses test results and comparison to results found by Lee et al. (2012)

6.1.2 Mediation Analysis

To examine the hypothesized mediating effects between GSCM Practice Implementation and the

dependent concepts: Overall Business Performance, Environmental Performance and Economic

Performance the widely known Baron and Kenny (1986) framework for mediation analysis has been

made use of. Furthermore, mediation tests by means of bootstrapping (number of bootstrap samples:

2000; bc confidence level: 95) have been performed to confirm or reject a relationship found by means of

the Baron and Kenny (1986) approach. To perform the analysis the statistical software Amos has been

made use of. Table 24 provides a summary of the results found.

From the previous analysis it was concluded that GSCM Practice Implementation is directly linked to all

three dependent concepts. The relationships were found to be significant. Furthermore, GSCM Practice

Implementation was found to be positively and significantly correlated with two mediators, namely:

Employee Job Satisfaction and Relational Efficiency. In turn, all three mediators (Employee Job

Satisfaction, Operational Efficiency and Relational Efficiency) were found to be positively and

significantly correlated with all three dependent concepts.


78

In regards to H6a proposing the relationship between GSCM Practice Implementation and Overall

Business Performance to be mediated by Employee Job Satisfaction, Operational Efficiency and

Relational Efficiency it can be said that this hypothesis is supported. Having determined the direct effect

without the mediator and the effect with the mediator it could be concluded that there has been a drop in

strength from b=0.324 to b=0.036 and when including the mediators the path dropped out of significance

implying, according to the Baron and Kenny (1986) approach, that full mediation exists. It should here be

noted that according to Judd and Kenny (1981a, 1981b) full mediation exists if the effect of the

independent variable on the dependent variable is zero when the mediator is included. However, this is a

very rare case, thus, one can here also speak of full mediation. Having calculated the indirect effect by

means of bootstrapping it can be concluded that this analysis confirms there to be a mediating effect to

exist. The existence of this effect is quite intuitive considering the fact that the relationship between

GSCM Practice Implementation and Overall Business Performance was found to be very weak.

H6b, proposing the relationship between GSCM Practice Implementation and Environmental

Performance to be mediated by Operational Efficiency and Relational Efficiency is not supported.

According to the Baron and Kenny (1986) approach the path is partially mediated as there has been a drop

in strength when including the mediator and the path is still significant. However, when having a look at

the indirect effect a non-significant relationship was found implying there to be no mediation which

contradicts the results found when making use of the Baron and Kenny (1986) approach.

H6c, proposing the relationship between GSCM Practice Implementation and Economic Performance to

be mediated by Operational Efficiency and Relational Efficiency is also not supported. The path is

partially mediated as the path is still significant when including the mediator. However, the calculated

indirect effect reveals there to be a non-significant relationship which contradicts the results found when

making use of the Baron and Kenny (1986) approach.

Direct

without

Mediator

(p-value)

Direct with

Mediator

(p-value)

Indirect Hypotheses

Test Results

H6a: GSCM Practice ImplementationEmployee

Job Satisfaction, Operational Efficiency ,

Relational Efficiency Overall Business

Performance

0.324

(0.047)

0.036 (0.813) Two-tailed

significance

0.022mediation

Supported

Additional Analysis

GSCM Practice ImplementationEmployee

Job SatisfactionOverall Business

Performance

0.324

(0.047)

0.049 (0.748) Two-tailed

significance

0.209no

mediation

GSCM Practice ImplementationOperational

EfficiencyOverall Business Performance

0.324

(0.047)

0.053 (0.699) Two-tailed

significance

0.469no

mediation

GSCM Practice ImplementationRelational

EfficiencyOverall Business Performance

0.324

(0.047)

0.042 (0.766) Two-tailed

significance

0.073no

mediation

H6b: GSCM Practice Implementation Operational

Efficiency, Relational

EfficiencyEnvironmental Performance

0.590

(0.005)

0.481 (0.012) Two-tailed

significance

0.116no

mediation

Not Supported


79

Additional Analysis

GSCM Practice Implementation Operational

Efficiency Environmental Performance

0.590

(0.005)

0.496 (0.010) Two-tailed

significance

0.040mediation


EfficiencyEnvironmental Performance

0.590

(0.005)

0.501 (0.012) Two-tailed

significance

0.253no

mediation

H6c: GSCM Practice Implementation Operational

Efficiency, Relational EfficiencyEconomic

Performance

0.818

(0.007)

0.828 (0.007) Two-tailed

significance

0.872no

mediation

Not Supported

Additional Analysis

GSCM Practice ImplementationOperational

EfficiencyEconomic Performance

0.818

(0.007)

0.772 (0.007) Two-tailed

significance

0.077no

mediation



0.818

(0.007)

0.859 (0.008) Two-tailed

significance

0.065no

mediation

Table 25: Summary of mediation analysis results (original model)

6.1.3 Moderation Analysis

Having made use of the statistical software SPSS the two items for the moderator Market Pressure MP1

and MP2 have been combined and the median was calculated (equals 6) in order to recode the resulting

values into different variables, namely 0 (MP_Low) and 1 (MP_High). Having made use of the statistical

software Amos, the regression weights tables for both groups (MP_High and MP_Low) were obtained

and with the help of the Stats Tool Package developed by Gaskin (2012c) Table 25 was obtained. From

the table it can be concluded that not one of the three hypotheses proved to be significant. There is no

significant difference between the values obtained for MP_High and MP_Low. However, it should be

noted that this can very likely be attributable to the rather low number of respondents and the fact that

only two items measure this construct. It can, thus, be concluded that there is potential for further

investigation and further refinement/ extension of the number of measurement items measuring the

moderator Market Pressure.

MP_High MP_Low

Estimate P Estimate P z-score

OBPGSCM 0.829 0.013 1.032 0.412 0.156

EPGSCM 0.567 0.020 2.603 0.212 0.971

ECPGSCM 0.904 0.011 2.064 0.245 0.641

Table 26: Summary of moderation analysis results (original model)

Notes: ** p-value < 0.01; * p-value < 0.05

Path (from-to) Mediator Moderator Direct Effects

(Critical Ratio)

Hypotheses Test Results

H1 H1a:

GSCM ImplementationOverall Business Performance (direct) 0.324 (1.989) Supported

p-value=0.047; R2=0.11

H1b:

GSCM ImplementationEnvironmental Performance (direct) 0.590 (2.801) Supported

p-value=0.005; R2=0.35

H1c:

GSCM ImplementationEconomic Performance (direct) 0.818 (2.707) Supported p-value=0.007; R2=0.67

H2 H2a: GSCM ImplementationEmployee Job Satisfaction 0.493 (2.671) Supported

p-value=0.008; R2=0.24

H2b:

Employee Job SatisfactionOverall Business Performance 0.459 (3.097)

Supported p-value=0.002; R2=0.21

H2c:

Employee Job SatisfactionOperational Efficiency 0.358 (2.281) Supported

p-value=0.023; R2=0.13

H3 H3a:

GSCM ImplementationOperational Efficiency 0.291 (1.771) Not supported

p-value=0.077; R2=0.08

H3b:

Operational EfficiencyOverall Business Performance 0.468 (2.979) Supported p-value=0.003; R2=0.22

H3c:

Operational EfficiencyEnvironmental Performance 0.377 (2.471) Supported

p-value=0.013; R2=0.16

H3d: Operational EfficiencyEconomic Performance 0.347 (2.026) Supported

p-value=0.043; R2=0.12

H3e:

Operational EfficiencyRelational Efficiency 0.818 (4.347) Supported p-value<0.001; R2=0.67

H4 H4a:

GSCM ImplementationRelational Efficiency 0.390 (2.361) Supported

p-value: 0.018; R2=0.15

H4b:

Relational EfficiencyOverall Business Performance 0.535 (3.700) Supported

p-value<0.001; R2=0.29

H4c:

Relational EfficiencyEnvironmental Performance 0.458 (3.003) Supported p-value=0.003; R2=0.21

H4d:

Relational EfficiencyEconomic Performance 0.316 (2.016) Supported

p-value=0.044; R2=0.10

H5 H5a: GSCM practice implementationOverall Business Performance High Market Pressure

Low Market Pressure

Not supported

H5b: GSCM practice implementationEnvironmental Performance High Market Pressure

Low Market Pressure

Not supported

H5c: GSCM practice implementationEconomic Performance High Market Pressure

Low Market Pressure

Not supported

H6 H6a: GSCM ImplementationOverall Business Performance Employee Job Satisfaction, Operational

Efficiency and Relational Efficiency

Supported

H6b: GSCM ImplementationEnvironmental Performance

Operational Efficiency and Relational Efficiency

Not Supported

H6c: GSCM ImplementationEconomic Performance

Operational Efficiency and Relational

Efficiency

Not Supported

Table 27: Results of path analysis and hypotheses tests (original model)

Conceptual Model (Original Model)


GSCM

Implementation




Employee Job

Satisfaction

Operational

Efficiency

Relational

Efficiency

Market

Pressure

H2a

0.493**

H4a

0.390*

H3a

0.291

H3e

0.818**

H2c

0.358*

H2b

0.459**

H4b, H4c, H4d

0.535*, 0.458**, 0.316**

H5a, H5b, H5c

Not supported

H1a, H1b, H1c

0.324*, 0.590**, 0.818**

H3b, H3c, H3d

0.468**, 0.377*, 0.347*

Figure 12: Hypothesized structural model results (original model)



6.2 Modified Model

The results of the modified structural model are summarized in Table 29 and Figure 13. This section is

divided into three subsections, namely: direct effects, mediation analysis and moderation analysis.

6.2.1 Direct Effects

In regards to the direct effects, hypotheses H1a to H4d, it can be noted that all hypotheses were supported

except H2c (b=0.253, t=1.634, p=0.102) and H3a (b=0.291, t=1.771, p=0.077). No direct link between

Employee Job Satisfaction and Operational Efficiency as well as no direct link between GSCM Practice

Implementation and Operational Efficiency could be found. However, the remaining paths from GSCM

Practice Implementation to the following showed positive significant results:

(1) Employee Job Satisfaction (b=0.495, t=3.241, p=0.001)

Note: the strength of the relationship and the significance increased compared to the original

model with no adjustment.

(2) Relational Efficiency (b=0.395, t=2.733, p=0.006)

Note: the strength of the relationship and the significance increased compared to the original

model with no adjustment.

As for H2c it can be concluded that, as opposed to the results found when utilizing the original model,

increased Employee Job Satisfaction does not lead to an increase in Operational Efficiency. Furthermore,

as posited by hypotheses H3b, H3c and H3d, Operational Efficiency has a direct effect on all three

dependent concepts (H3b: b=0.367, t: 2.368, p=0.018; H3c: b=0.371, t=2.265, p=0.024; H3d: b=0.352,

t=2.240, p=0.025). Furthermore, the test results also revealed, as did those making use of the original

model, that an improved Operational Efficiency in the supplying firm has a positive impact on the

Relational Efficiency between the supplier and the buying firm (b=0.744, t=4.106, p<0.001). The effect

is, however, not as strong as it was when having made use of the unadjusted model.

6.2.2 Mediation Analysis

To examine the hypothesized mediating effects between GSCM Practice Implementation and the

dependent concepts: Overall Business Performance, Environmental Performance and Economic

Performance the widely known Baron and Kenny (1986) framework for mediation analysis has been

made use of. Furthermore, mediation tests by means of bootstrapping (number of bootstrap samples:

2000; bc confidence level: 95) have been performed to confirm or reject a relationship found by means of

the Baron and Kenny (1986) approach. To perform the analysis the statistical software Amos has been

made use of. Table 27 provides a summary of the results found.

From the previous analysis it was concluded that GSCM Practice Implementation is directly linked to all

three dependent concepts. The relationships were found to be significant. Furthermore, GSCM Practice

Implementation was found to be positively correlated with two mediators, namely: Employee Job

Satisfaction and Relational Efficiency. In turn, all three mediators (Employee Job Satisfaction,

Operational Efficiency and Relational Efficiency) were found to be positively correlated with all three

dependent concepts.

In regards to H6a proposing the relationship between GSCM Practice Implementation and Overall

Business Performance to be mediated by Employee Job Satisfaction, Operational Efficiency and

Relational Efficiency it can be said that this hypothesis is supported. Having determined the direct effect

without the mediator and the effect with the mediator it could be concluded that there has been a drop in

strength from b=0.333 to b=0.031 and when including the mediators the path dropped out of significance


83

implying, according to the Baron and Kenny (1986) approach, that full mediation exists. It should here be

noted that according to Judd and Kenny (1981a, 1981b) full mediation exists if the effect of the

independent variable on the dependent variable is zero when the mediator is included. However, this is a

very rare case, thus, one can here also speak of full mediation. Having calculated the indirect effect by

means of bootstrapping it can be concluded that this analysis confirms there to be a mediating effect to

exist. The existence of this effect is quite intuitive considering the fact that the relationship between

GSCM Practice Implementation and Overall Business Performance was found to be weak.

H6b, proposing the relationship between GSCM Practice Implementation and Environmental

Performance to be mediated by Operational Efficiency and Relational Efficiency is not supported.

According to the Baron and Kenny (1986) approach the path is partially mediated as there has been a drop

in strength when including the mediator and the path is still significant. However, when having a look at

the indirect effect a non-significant relationship was found implying there to be no mediation which

contradicts the results found when making use of the Baron and Kenny (1986) approach.

H6c, proposing the relationship between GSCM Practice Implementation and Economic Performance to

be mediated by Operational Efficiency and Relational Efficiency is also not supported. The path is

partially mediated as the path is still significant when including the mediator. However, the calculated

indirect effect reveals there to be a non-significant relationship which contradicts the results found when

making use of the Baron and Kenny (1986) approach.

Direct

without

Mediator

Direct with

Mediator

Indirect Hypotheses

Test Results

H6a: GSCM Practice

ImplementationEmployee Job

Satisfaction, Operational Efficiency ,

Relational Efficiency Overall


0.333 (0.025) 0.031 (0.841) Two-tailed

significance

0.006mediation

Supported

Additional Analysis

GSCM Practice

ImplementationEmployee Job

SatisfactionOverall Business

Performance

0.333 (0.025) 0.150 (0.350) Two-tailed

significance

0.015mediation

GSCM Practice


EfficiencyOverall Business

Performance

0.333 (0.025) 0.246 (0.095) Two-tailed

significance

0.018mediation

GSCM Practice

ImplementationRelational

EfficiencyOverall Business

Performance

0.333 (0.025) 0.139 (0.322) Two-tailed

significance

0.001mediation

H6b: GSCM Practice Implementation

Operational Efficiency, Relational

EfficiencyEnvironmental

Performance

0.576 (0.001)

0.477 (0.004) Two-tailed

significance

0.108no

mediation

Not Supported

Additional Analysis

GSCM Practice Implementation



0.576 (0.001)

0.508 (0.002) Two-tailed

significance

0.044mediation

GSCM Practice


EfficiencyEnvironmental

Performance

0.576 (0.001)

0.476 (0.004) Two-tailed

significance

0.034mediation


84

H6c: GSCM Practice Implementation

Operational Efficiency, Relational


0.845 (0.001) 0.821 (0.001) Two-tailed

significance

0.973no

mediation

Not Supported

Additional Analysis

GSCM Practice



0.845 (0.001) 0.812 (0.001) Two-tailed

significance

0.125no

mediation

GSCM Practice



0.845 (0.001) 0.847 (0.001) Two-tailed

significance

0.691no

mediation

Table 28: Summary of mediation analysis results (modified model)

6.2.3 Moderation Analysis

Having made use of the statistical software SPSS the two items for the moderator Market Pressure MP1

and MP2 have been combined and the median was calculated in order to recode the resulting values into

different variables, namely 0 (MP_Low) and 1 (MP_High). Having made use of the statistical software

Amos, the regression weights tables for both groups (MP_High and MP_Low) were obtained and with the

help of the Stats Tool Package developed by Gaskin (2012c) Table 28 was obtained. From the table it can

be concluded that not one of the three hypotheses proved to be significant. There is no significant

difference between the values obtained for MP_High and MP_Low. However, it should be noted that this

can very likely be attributable to the rather low number of respondents and the fact that only two items

measure this construct. It can be, thus, be concluded that there is potential for further investigation and

further refinement/ extension of the number of measurement items measuring the moderator Market

Pressure.

MP_High MP_Low

Estimate P Estimate P z-score

OBPGSCM 0.172 0.409 -0.095 0.809 -0.599

EPGSCM 0.265 0.121 0.598 0.082 0.867

ECPGSCM 0.695 0.001 1.080 0.019 0.761

Table 29: Summary of moderation analysis results (modified model)


Path (from-to) Mediator Moderator Direct Effects

(Critical Ratio)

Hypotheses Test

Results

H1 H1a:

GSCM ImplementationOverall Business Performance (direct) 0.333 (2.238) Supported

p-value=0.025; R2=0.11

H1b:

GSCM ImplementationEnvironmental Performance (direct) 0.576 (3.414) Supported p-value=0.001; R2=0.33

H1c:

GSCM ImplementationEconomic Performance (direct) 0.845 (4.567) Supported

p-value=0.001; R2=0.71

H2 H2a: GSCM ImplementationEmployee Job Satisfaction 0.495 (3.241) Supported

p-value=0.001; R2=0.25

H2b:

Employee Job SatisfactionOverall Business Performance 0.460 (3.106) Supported

p-value=0.002; R2=0.21

H2c:

Employee Job SatisfactionOperational Efficiency 0.253 (1.634) Not Supported

p-value=0.102; R2=0.06

H3 H3a:

GSCM ImplementationOperational Efficiency 0.299 (1.921) Not supported

p-value=0.055; R2=0.00

H3b:

Operational EfficiencyOverall Business Performance 0.367 (2.368) Supported p-value=0.018; R2=0.13

H3c:

Operational EfficiencyEnvironmental Performance 0.371 (2.265) Supported

p-value=0.024; R2=0.14

H3d: Operational EfficiencyEconomic Performance 0.352 (2.240) Supported

p-value=0.025; R2=0.12

H3e:

Operational EfficiencyRelational Efficiency 0.744 (4.106) Supported p-value<0.001; R2=0.55

H4 H4a:

GSCM ImplementationRelational Efficiency 0.395 (2.735) Supported

p-value: 0.006; R2=0.16

H4b:

Relational EfficiencyOverall Business Performance 0.535 (3.700) Supported p-value<0.001; R2=0.29

H4c:

Relational EfficiencyEnvironmental Performance 0.419 (2.774) Supported

p-value=0.006; R2=0.18

H4d:

Relational EfficiencyEconomic Performance 0.313 (2.196) Supported

p-value=0.028; R2=0.10

H5 H5a: GSCM practice implementationOverall Business Performance High Market Pressure Low Market Pressure

Not supported

H5b: GSCM practice implementationEnvironmental Performance High Market Pressure

Low Market Pressure

Not supported

H5c: GSCM practice implementationEconomic Performance High Market Pressure

Low Market Pressure

Not supported

H6 H6a: GSCM ImplementationOverall Business Performance Employee Job Satisfaction, Operational Efficiency and Relational Efficiency

Supported

H6b: GSCM ImplementationEnvironmental Performance


Efficiency

Not Supported

H6c: GSCM ImplementationEconomic Performance


Efficiency

Not Supported

Table 30: Results of path analysis and hypotheses tests (modified model)

Conceptual Model (Modified Model)


GSCM

Implementation




Employee Job

Satisfaction

Operational

Efficiency

Relational

Efficiency

Market

Pressure

H2a

0.495**

H4a

0.395**

H3a

0.299

H3e

0.744**

H2c

0.253

H2b

0.460**

H4b, H4c, H4d

0.535**, 0.419**, 0.313*

H5a, H5b, H5c

Not supported

H1a, H1b, H1c

0.333*, 0.576**, 0.845**

H3b, H3c, H3d

0.367*, 0.371*, 0.352*

Figure 13: Hypothesized structural model results (modified model)



6.3 Robustness of the Original Model

Robustness can be defined as a model‘s, test‘s or system‘s ability to operate without failure and

effectively perform even if the variables or assumptions are altered (Pluemper and Neumayer, 2012). To

determine robustness the following will compare the individual hypotheses tests of the original model and

the modified model to draw conclusions on the robustness of the original model.

When comparing Table 26 to Table 29 the most noticeable difference is that H2c, stating there to be a

positive relationship between Employee Job Satisfaction and Operational Efficiency, has changed from

being supported to not being supported. The p-value has experiences a dramatic increase from p=0.023 to

p=0.102 indicating that the hypothesis is after model adjustment strongly not supported. As for the

remaining hypotheses it can be noted that their hypotheses test result did not change. However, when

comparing the tables it can be inferred that the hypotheses stating there to be a positive relationship

between GSCM Practice Implementation and the three dependent variables are more strongly,

significantly supported when making use of the modified model. The same holds true for the relationship

between GSCM Practice Implementation and the mediators Employee Job Satisfaction and Relational

Efficiency. In terms of the mediator Operational Efficiency it can be said that there is still no support for

the existence of a positive relationship between GSCM Practice Implementation and Operational

Efficiency even though the p-value experiences a decrease from p=0.077 to p=0.055.

Concluding the hypotheses comparison it can be noted that due to the rather slight changes in the path

coefficients and their respective p-values it can be inferred that all hypotheses with the exception of H2c

can be considered to be rather robust when making use of the original model.


88

7. SUMMARY AND IMPLICATIONS

The final chapter of this thesis will provide a summary of the most important findings. Furthermore, a

critical assessment of this study‘s limitations will be provided as well as recommendations for future

research. Last but not least this chapter, more specifically this thesis, will be rounded off with a

concluding summary.

7.1 Main Findings and Managerial Implications

This study which has been conducted from the supplier‘s point of view focusing on small- and medium-

sized suppliers in the automotive industry in Germany aimed at investigating whether it is reasonable to

assume that German enterprises are indeed operating in a more mature industry in terms of GSCM

Practice Implementation. More explicitly, this study‘s main intention was to determine if German

enterprises are less pressured by their buying firms and thus experience an improvement in Overall

Business Performance as well as Employee Job Satisfaction. Unfortunately, the analysis revealed that the

moderator Market Pressure ought to have been measured by more than two items and it was thus not

possible to draw conclusions in regards to the existence or non-existence of a moderating effect.

However, the most anticipated finding of there being a significant, direct relationship between GSCM

Practice Implementation and Overall Business Performance was supported. Furthermore, this study also

made a distinction between Environmental and Economic Performance and found supporting evidence for

the existence of a significant, direct relationship between GSCM Practice Implementation and

Environmental and Economic Performance.

This research makes three major managerial contributions to existing literature which will be elaborated

on in the following.

GSCM Practice Implementation, Employee Job Satisfaction and Overall Business Performance

Firstly, not only was a relationship found between GSCM Practice Implementation and Employee Job

Satisfaction but in contrast to the study performed by Lee et al. (2012) this study also managed to find a

relationship between Employee Job Satisfaction and Overall Business Performance. Even though the

indirect method for testing mediation does not provide supporting evidence for Employee Job Satisfaction

to mediate the relationship between GSCM Practice Implementation and Overall Business Performance

the Baron and Kenny (1986) approach did identify Employee Job Satisfaction to be a mediator. It can thus

be concluded that managers who implement GSCM practices achieve an increase in Employee Job

Satisfaction which positively impacts the firms‘ Operational Efficiency and Overall Business

Performance.

GSCM Practice Implementation and Business Performance (direct)

As a second point it is worthwhile to mention that in contrast to the study performed by Lee et al. (2012)

and supporting the studies performed by Chien and Shih (2007) as well as Zhu and Sarkis (2004) this

study managed to find supporting evidence for there to be a positive, significant relationship not only

between GSM Practice Implementation and Overall Business Performance but also between GSCM

Practice Implementation and Environmental and Economic Performance. In consideration of the rather

strong relationship between GSCM Practice Implementation and Environmental and Economic

Performance managers are advised to not underestimate cost savings and performance gains arising from

implementing green supply chain initiatives. The rather weak relationship between GSCM Practice

Implementation and Overall Business Performance can possibly be attributed to the rather broadly


89

defined measurement items and the fact that the relationship has been found to be mediated to a great

extent by Employee Job Satisfaction, Operational Efficiency and Relational Efficiency.

GSCM Practice Implementation, Relational Efficiency and Business Performance

Thirdly, this study has made one of the first attempts (to the researcher‘s knowledge) to tap into the

domain of Relational Efficiency and Business Performance. Relational Efficiency was found to be

impacted by Operational Efficiency and by the degree of GSCM Practice Implementation.

Furthermore, the study results reveal there to be a positive, significant relationship between Relational

Efficiency and Overall Business Performance, Environmental Performance and Economic Performance. It

can thus be concluded that the implementation of GSCM practices helps a supplying firm to improve its

Relational Efficiency with its buying firms. This ability of a supplying firm to build trust and credibility

in the relationship with the buying firm by means of collaboration and information sharing will eventually

have a positive effect on Business Performance. More specifically, the increased transparency and

openness in business processes has a strong impact on Overall Business Performance and Environmental

Performance and a weak but still significant impact on Economic Performance. This is intuitively

understandable as an increased collaboration between supply chain partners will inevitably facilitate the

optimization of entire supply chain activities and thus result in an overall improvement of the supplying

firm‘s Environmental Performance. This improvement incorporates a decrease in air emissions, a

reduction of solid wastes and for instance a decrease in the consumption of hazardous/harmful/toxic

materials.

The existence of a relationship between Relational Efficiency and Environmental and Economic

Performance provides new insights for managers who wish to increase their performance gains by means

of increased collaboration and trust with their supply chain partners. This study revealed that performance

gains are not only to be expected in regards to asset utilization and competitive position but also in terms

of decreases for waste discharge and a reduction in water usage as well as waste disposal.

Concluding this section, it can be said that this study provides enormous potential for future research

especially in regards to investigating whether a moderating effect exists between GSCM Practice

Implementation and Business Performance. To what degree does market pressure, when differentiating

between companies that experience more pressure and ones that experience less pressure, have an impact

on the Overall, Environmental and Economic Performance? However, even though conclusions on the

existence or non-existence of a moderating effect could not be drawn the results of this study suggest a

number of interesting insights. It was found that even though German enterprises are operating in a rather

mature environment in regards to green supply chain initiatives in comparison to companies located in

Korea there is still enormous potential for increasing operations‘/ supply chain managers‘ awareness of

differing Environmental Management Standards. Furthermore, it was found that GSCM Practice

Implementation is directly related to firm performance. Not only do firms experience a slight

improvement in their Overall Business Performance but the implementation of green practices also,

strongly, increases their Environmental and Economic Performance. It was also found that GSCM

Practice Implementation helps improve the Relational Efficiency with their buying firms, which leads to

enhanced business performance. Lastly, it ought to be mentioned that having achieved an improvement in

Employee Job Satisfaction, Operational Efficiency or Relational Efficiency the German supplying firms

would experience a stronger increase in Overall Business Performance than would Korean firms.


90

7.2 Limitations and Future Research Directions

This study examined the relationship between GSCM Practice Implementation and Business Performance

taking a Resource Dependence and Institutional Theory perspective. In the following the findings of this

study will be critically evaluated in terms of limitations in the methodological approach. Additionally,

directions for future research will be outlined.

The first major limitation of this study is the low response rate. 63 responses were received from small-

and medium-sized German automobile suppliers which corresponds with a response rate of 10.71%

(=63/588). Thus, it can be concluded that when comparing the sample (N=588) to all responses (N=63) it

is probable that the total responses are not representative for the population implying non-response bias.

In consequence, making generalizations from the sample to the population is not possible/ advisable.

Thus, case study research (comparative case study) is encouraged to verify the findings. Even though

conclusions cannot and should not be drawn, the low response rate was not taken into consideration when

evaluating the test results. Furthermore, it should be noted that to achieve adequate power in structural

equation modeling Hair et al. (1995) (cited in Williams and Brown, 2012) recommend a minimum sample

size of one hundred. Even though maximum likelihood estimation (MLE) has been made use of which

has been found to present valid results for sample sizes as small as 50 observations it is not advisable to

use such a small sample size. According to Hair et al. (2006) (cited in Karim et al., 1989), optimally,

structural equation modeling is performed with a sample size of 100 to 400. 200 being the most

recommended sample size (Hair et al., 1998 cited in Goldman et al., 2007; Tabachnick and Fidell, 1996

cited in Goldman et al., 2007). Any sample exceeding 400 would result in the maximum likelihood

estimation (MLE) method becoming too sensitive (detecting almost any difference among the data)

resulting in poor model fit.

A second limitation identified relates to the validity of the measurements. Having made use of a single

source from which information was obtained in combination with the choice to make use of only one

single research method at the same moment in time has most likely lead to information bias as well as

common method variance. Collecting data at different moments in time (obtaining longitudinal data)

would be advisable to increase the validity of the measurements. Additionally, having multiple informants

in each supplying company respond to the questionnaire would be deemed beneficial. Furthermore, the

data have been collected from one country only, namely Germany. This choice facilitated data collection

and enabled controlling for diversity but the results are assumed to have low external validity. To make

the findings more generalizable it would be important to perform comparative studies in different

industries and in other countries. (Podsakoff et al., 2003)

Furthermore, it should be noted that the collected data is somewhat perceptual. To increase the credibility

of the survey questionnaire it would have been advisable to collect ―hard‖ data to enable making

comparisons and checking the validity of the ―soft‖ data collected by means of the scales utilized (Nahm

et al., 2003). This is especially the case when measuring Overall Business Performance, Environmental

and Economic Performance. Company documents would be a more valid measurement as opposed to

asking respondents to indicate how they perceive the organization to be performing. Furthermore, the

items measuring the dependent variables, more specifically the items measuring Overall Business

Performance, can be refined to more precisely measure the construct.

On a fourth and last note it should be mentioned that whereas using similar scale formats is advantageous

in the sense that it simplifies the task of answering the questionnaire according to Podsakoff et al. (2003)


91

it is assumed that the covariation among the constructs may result from the consistency in the scale

properties and may not be associable with the content of the items. Thus, future researchers should also

take this into account when deciding on which scale formats to utilize.

7.3 Conclusions

This research, drawing on the Resource Dependence Theory as well as the Institutional Theory, has taken

an environmental perspective on supply chain management and has investigated the relationship between

green supply chain practices and organizational performance. The study‘s main intention was to

determine if the outcomes obtained differed from those found by Lee et al. (2012).

The study results reveal that organizations should not only focus on achieving Overall Business

Performance outcomes but should also recognize the potential that increasing Employee Job Satisfaction,

Operational Efficiency and Relational Efficiency brings with it when trying to improve an organizations

Environmental and Economic Performance. Furthermore, when conducting the study on German

suppliers it was found that improvements in all three mediators (Employee Job Satisfaction, Operational

Efficiency and Relational Efficiency) yielded stronger improvements in Overall Business Performance as

compared to the results found by Lee et al. (2012).

In conclusion, it remains questionable if the findings can be generalized in consideration of the low

response rate. Nevertheless, this thesis has managed to identify several possible improvements that can be

made to the methodological approach and which will undoubtedly enable future research on the topic to

yield more generalizable and accurate results. The main recommendation for future research is to conduct

the study on a larger sample and to continuously refine the survey instrument. As measuring GSCM

Practice Implementation is a rather new discipline the development of good measurement tools provides

enormous potential for further research.


92


93

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APPENDIX

Appendix 1 – List of Questionnaire Items and the respective Measurement Scales

Measurement Items - GSCM Practice Implementation

Factors Measurement Items


Management (IEM)

IEM1: senior managers‘ commitment on GSCM

IEM2: mid-level managers‘ support for GSCM

IEM3: cross-functional cooperation for environmental

improvements

IEM4: environmental compliance and auditing programs

IEM5: ISO 14001 certification_d

Reference: Zhu et al. (2008)

Green Purchasing (GP) GP1: eco labeling of products

GP2: cooperation with suppliers for environmental objectives

GP3: environmental audit for suppliers‘ internal management_d

GP4: suppliers‘ ISO 14000 certification

Reference: Chen (2005), Zhu et al. (2008)

Cooperation with Customers (CC) CC1: cooperation with customers for eco-design_d

CC2: cooperation with customers for cleaner production

CC3: cooperation with customers for clean packaging

CC4: cooperation with customers for developing environmental

database of products

Reference: Hsu and Hu (2008), Zhu et al. (2008)

Eco-Design (ECO) ECO1: design of products for reduced consumption of material/

energy

ECO2: design of products for reuse, recycle, recovery of material,

component parts

ECO3: design of products to avoid or reduce use of hazardous

products and/ or their manufacturing process

ECO4: design of products for disassembly

ECO5: design of products considering LCA_d

Reference: Matos and Hall (2007), Rusinko (2007), Zhu et al.

(2008)

Measured on a five-point scale: 1=not considering it, 2=planning to consider it, 3=considering it

currently, 4=initiating implementation, and 5=currently implementing

Measurement Items – Mediators


Employee Job Satisfaction (SAT) SAT1: most employees like their jobs in the present operations

SAT2: most employees think their supervisor treats them well

SAT3: most employees in our firm like their jobs more than many

employees of other firms


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SAT4: most employees in our firm do not intend to work for a

different company_d

SAT5: overall, our employees are quite satisfied with their jobs

Reference: Homburg and Stock (2004), Zhou et al. (2008)

Operational Efficiency (OE) OE1: cycle time has been reduced

OE2: overall, costs have been lowered

OE3: overall, products‘ quality has been improved_d

OE4: customer service has been improved

OE5: project duration has been reduced_d

OE6: our firm has delivered greater value to our customers

Reference: Rusinko (2007), Paulraj et al. (2008), Zhu et al. (2008),

Zacharia et al. (2009)

Relational Efficiency (RE) RE1: an increased respect for the skills and capabilities of

customers

RE2: an improved level of honesty

RE3: more open sharing of information with our customers

RE4: a more effective working relationship with our customers

RE5: an enhanced commitment to work with our customers in the

future

RE6: an overall more productive working relationship with our

customers

Reference: Zacharia et al. (2009)

Measured on a five-point scale: 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, and 5=strongly

agree

Measurement Items – Moderator


Market Pressure (MP) MP1: export

MP2: sales to foreign customers

Reference: Zhu and Sarkis (2007)

Measured on a five-point scale: 1=not at all important, 2=not important, 3=not thinking about it,

4=important, and 5=extremely important

Measurement Items - Performance



(OBP)

OBP1: better asset utilization

OBP2: stronger competitive position

OBP3: improved profitability

OBP4: overall improved organizational performance

Reference: Zhu et al. (2008), Zacharia et al. (2009), Matsuno and

Mentzer (2000)

Measured on a five-point scale: 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, and 5=strongly

agree


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Environmental Performance (EP) EP1: reduction of air emissions

EP2: decrease of consumption for hazardous/ harmful/ toxic

materials

EP3: reduction of waste water

EP4: reduction of solid wastes

EP5: decrease of frequency for environmental accidents_d

EP6: overall improved environmental performance

Reference: Zhu et al. (2007), Zhu et al. (2008)

Economic Performance (ECP) ECP1: decrease of fee for waste discharge_d

ECP2: decrease of fee for waste treatment_d

ECP3: decrease of cost for materials purchasing

ECP4: decrease of cost for energy consumption

ECP5: decrease of fine for environmental accidents

ECP6: new market opportunities

ECP7: improved profit margin

ECP8: increased sales

ECP9: overall improved economic performance

Reference: Rao and Holt (2005), Zhu et al. (2007), Zhu et al.

(2008), Zhu and Sarkis (2007), Fuentes-Fuentes et al. (2004)

Measured on a five-point scale: 1=not at all, 2=a little bit, 3=to some degree, 4=relatively significant, and

5=significant

Notes: _d indicates the items that were deleted in the modified model


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Appendix 2 – Survey Questionnaire

Green Supply Chain Practices Research Survey

General Information

Thank you in advance for participating in this online questionnaire.

The purpose of this survey is to explore the effect of a firm‘s green supply chain management (GSCM)

efforts on the broader supply chain network.

Green Supply Chain Management (GSCM) practice implementation is defined as the adoption of

environmentally friendly supply chain management practices including internal environmental

management, green purchasing, cooperation with customers, and eco-design for developing corporate and

operational strategies which will enable the company to achieve environmental sustainability.

Instructions. Please answer all questions to the best of your knowledge. All information is strictly

confidential and will not be shared. The entire questionnaire will take approximately 20 minutes to

complete.

At the completion of the survey you will have the opportunity to enter your e-mail address and receive a

copy of the Executive Summary or Master Thesis subsequent to its completion.

Once again, thank you for your time.

Question 1:

Employee Job Satisfaction (Homburg and Stock, 2004 and Zhou et al., 2008)

Please indicate the extent to which you agree or disagree to have perceived the following during the past

year. (Five-point scale: 1=strongly disagree; 2=disagree; 3=neutral; 4=agree; and 5=strongly agree)

Most employees like their jobs in the present operations.

Most employees think their supervisor treats them well.

Most employees in our firm like their jobs more than many employees of other firms.

Most employees in our firm do not intend to work for a different company.

Overall, our employees are quite satisfied with their jobs.

Question 2:

Operational Efficiency (Rusinko, 2007; Paulraj et al., 2008; Zhu et al., 2008; and Zacharia et al.,

2009)

Please indicate the extent to which you agree or disagree to perceive that your plant has achieved each of

the following during the past year. (Five-point scale: 1=strongly disagree; 2=disagree; 3=neutral;

4=agree; and 5=strongly agree)


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Reduction in cycle time.

Overall, a reduction in costs.

Overall, an improvement in product quality.

Improvement in customer service.

Reduction in project duration.

Increase in value delivered to our customers.

Question 3:

Relational Efficiency (Zacharia et al., 2009)




Increase in respect for the skills and capabilities of customers.

Improvement in the level of honesty.

Increase in open sharing of information with our customers.

Improvement in the effective working relationship with our customers.

Enhanced commitment to work with our customers in the future.

Overall, an improvement in the productive working relationship with our customers.

Question 4:

Overall Business Performance (Zhu et al., 2008; Zacharia et al., 2009; and Matsuno and Mentzer,

2000)




A better asset utilization.

A stronger competitive position.

An improved profitability.

An overall improved organizational performance.

Question 5:

Environmental Performance (Zhu et al., 2007 and Zhu et al., 2008)


the following during the past year. (Five-point scale: 1=not at all; 2=a little bit; 3=to some degree;

4=relatively significant; and 5=significant)

Reduction of air emissions.

Decrease of consumption for hazardous/ harmful/ toxic materials.

Reduction of waste water.

Reduction of solid wastes.

Decrease in frequency for environmental accidents.

Improvement in the enterprise‘s overall environmental performance.

Question 6 and 7:

Economic Performance (Rao and Holt, 2005; Zhu et al., 2007; Zhu et al., 2008; Zhu and Sarkis,

2007; and Fuentes-Fuentes et al., 2004)


116


the following during the past year. (Five-point scale: 1=not at all; 2=a little bit; 3=to some degree;

4=relatively significant; and 5=significant)

Decrease in fee for waste discharge.

Decrease in fee for waste treatment.

Decrease in cost for materials purchasing.

Decrease in cost for energy consumption.

Decrease in fine for environmental accidents.

Increase in new market opportunities.

Improvement in profit margin.

Increase in sales.

Improvement in the enterprise‘s overall economic performance.

Question 8:

Which of the following Environmental Management Systems (EMSs) and programs are you aware of?

(multiple answers are possible)

ISO 14000 series

Electronic product environmental assessment tool

European Eco-Management and Audit Scheme (EMAS)

EU eco-label award scheme

Environment, health and safety (EHS) programs

Life Cycle Analysis (LCA)

Total quality environmental management

None of the above

Question 9:

Which of the following Environmental Management Systems (EMSs) and programs has your company

already adopted? (multiple answers are possible)

ISO 14000 series

Electronic product environmental assessment tool

European Eco-Management and Audit Scheme (EMAS)

EU eco-label award scheme

Environment, health and safety (EHS) programs

Life Cycle Analysis (LCA)

Total quality environmental management

None of the above

Question 10:

Internal Environmental Management (Zhu et al., 2008)

Please indicate the extent to which you perceive that your plant is implementing each of the following.

(Five-point scale: 1=not considering it; 2=planning to consider it; 3=considering it currently; 4=initiating

implementation; and 5=currently implementing)

support for GSCM (green supply chain practices) from senior managers

support for GSCM (green supply chain practices) from mid-level managers

cross-functional cooperation for environmental improvements


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environmental compliance and auditing programs

ISO 14001 certification

Question 11:

Green Purchasing (Chen, 2005 and Zhu et al., 2008)




eco labeling of products

cooperation with suppliers for environmental objectives

environmental audit for suppliers‘ internal management

suppliers‘ ISO 14000 certification

Question 12:

Cooperation with Customers (Hsu and Hu, 2008 and Zhu et al., 2008)




cooperation with customers for eco-design

cooperation with customers for cleaner production

cooperation with customers for clean packaging

cooperation with customers for developing environmental database of products

Question 13:

Eco-Design (Matos and Hall, 2007; Rusinko, 2007 and Zhu et al., 2008)




design of products for reduced consumption of material/ energy

design of products for reuse, recycle, recovery of material, component parts

design of products to avoid or reduce use of hazardous products and/ or their manufacturing

process

design of products for disassembly

design of products considering LCA

Question 14:

How important are the following factors to your company when deciding to implement green supply

chain practices. (Five-point scale: 1=not at all important; 2=not important; 3=not thinking about it;

4=important; and 5=extremely important)

Exporting products. (general export pressures)

Selling to foreign customers. (customer pressures)

Question 15:

Please indicate your job title.

Employee in charge


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Middle manager

Senior executive

Top executive

Question 16:

Please indicate your current job specification.

Supply chain

Logistics

Sales

Production

Manufacturing

Other

Question 17:

How many years of work experience do you have in the industry?

Less than 5

5-10

11-15

More than 15

Question 18:

How many full-time employees work at your firm?

Less than 50

50-100

101-200

201-300

301-400

401-500

More than 500

Question 19:

Please indicate how you would classify the industry of industries of your company‘s buying firms.

(multiple answers are possible)

Automobile

Electronics

Telecommunication

Retail

Question 20:

Please indicate how you would define your firm‘s primary business goal in the supply chain.

First-tier supplier to major firms

Second-tier supplier

Supplier to government

Other


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Question 21:

Please enter your e-mail address if you would like to receive a copy of the Executive Summary or the

Master Thesis subsequent to its completion.

Thank you for your participation.

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