Taking an Environmental Perspective on Supply Chain Management A Study on the German Automobile Industry

Transcription

1 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 University Supervisor University Co-Reader Dr. Erwin van der Laan Department of Decision and Information Sciences Dr. Fabian Sting Department of Management of Technology and Innovation

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3 Taking an Environmental Perspective 3

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5 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. 5

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7 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

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9 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 mediumsized 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 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. 9

10 (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 nonexistence 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. 10

11 TABLE OF CONTENTS ACKNOWLEDGEMENTS... 7 EXECUTIVE SUMMARY... 9 TABLE OF CONTENTS LIST OF TABLES AND FIGURES INTRODUCTION Problem Introduction Research Contribution Filling the Gap Research Outline THEORETICAL BACKGROUND Theoretical Background Resource Dependence Theory Resource Dependence Theory Achieving Organizational Performance Resource Dependence Theory and Supply Chain Management Resource Dependence Theory and Green Supply Chain Management Small- and Medium-Sized Suppliers and Green Supply Chain Management The Automotive Industry Institutional Theory Institutional Theory Explained The Evolution of Green Awareness - An Institutional Theory Perspective HYPOTHESES DEVELOPMENT GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall Business Performance Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business Performance, Environmental Performance, and Economic Performance Relational Efficiency, GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance Moderating Effect of Market Pressure Mediating Effect of Employee Job Satisfaction, Operational Efficiency and Relational Efficiency The Conceptual Model RESEARCH METHODS AND DATA Developing the Questionnaire Constructs and Items Population and Data Sources Data Collection Procedure and Response Rate Sample Selection Selection of Key Informants Questionnaire Design and Distribution Invalid Respondents and Missing Data

12 4.3.5 Final Response Rate DATA ANALYSIS Characterization - Responding Firms Awareness and Adoption of Environmental Management Standards (EMSs) Correlation Matrix Validity, Reliability and Goodness-of-Fit of the Research Model (Original Model) Step 1 Assessing Validity of the Constructs Step 2 - Assessing Reliability of the Constructs Step 3 - Goodness-of-Fit of the Research Model Validity, Reliability and Goodness-of-Fit of the Research Model (Modified Model) Step 1 Assessing Validity of the Constructs Step 2 - Assessing Reliability of the Constructs Step 3 - Goodness-of-Fit of the Research Model HYPOTHESES TESTING AND DISCUSSIONS OF QUANTITATIVE DATA Original Model Direct Effects Mediation Analysis Moderation Analysis Modified Model Direct Effects Mediation Analysis Moderation Analysis Robustness of the Original Model SUMMARY AND IMPLICATIONS Main Findings and Managerial Implications Limitations and Future Research Directions Conclusions LIST OF REFERENCES APPENDIX Appendix 1 List of Questionnaire Items and the respective Measurement Scales Appendix 2 Survey Questionnaire

13 LIST OF TABLES AND FIGURES List of Tables Table 1: Summary of constructs, their definitions and the most important literature identified Table 2: Summary description of hypotheses to be investigated Table 3: Characteristics of responding firms Table 4: Awareness and adoption of Environmental Management Standards Table 5: Correlations between theoretical constructs Table 6: Validity and reliability table (original model) Table 7: Factor correlation matrix with the square root of the AVE on the diagonal (original model) Table 8: Summary of validity and reliability measurement results (original model) Table 9: Defining internal consistency using cronbach s alpha Table 10: Summary of cronbach s alpha and item-to-total correlations measurement results (original model) Table 11: Statistics of first- and second-order models (original model) Table 12: Fit indices for the mediators, moderator and dependent concepts (original model) Table 13: Validity and reliability table (modified model) Table 14: Factor correlation matrix with the square root of the AVE on the diagonal (modified model) Table 15: Summary of validity and reliability measurement results (modified model) Table 16: Defining internal consistency using cronbach s alpha Table 17: Summary of cronbach s alpha and item-to-total correlations measurement results (modified model) Table 18: Statistics of first- and second-order models (modified model) Table 19: Statistics of first- and second-order models after performing model fit (modified model) Table 20: Summary of validity and reliability measurement results after performing model fit (modified model) Table 21: Summary of cronbach s alpha and item-to-total correlations measurement results after performing model fit (modified model) Table 22: Fit indices for the mediators, moderator and dependent concepts (modified model) Table 23: Summary of hypotheses test results and comparison to results found by Lee et al. (2012) Table 24: Summary of mediation analysis results (original model) Table 25: Summary of moderation analysis results (original model) Table 26: Results of path analysis and hypotheses tests (original model) Table 27: Summary of mediation analysis results (modified model) Table 28: Summary of moderation analysis results (modified model) Table 29: Results of path analysis and hypotheses tests (modified model) List of Figures Figure 1: Hypotheses development: GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance Figure 2: Hypotheses development: Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall Business Performance Figure 3: Hypotheses development: Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business Performance, Environmental Performance, and Economic Performance Figure 4: Hypotheses development: Relational Efficiency, GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance Figure 5: Hypothesized structural model Figure 6: Path diagram of the first-order measurement model (original model) Figure 7: Path diagram of the second-order measurement model (original model) Figure 8: Path diagrams of the measurement models (original model) Figure 9: Path diagram of the first-order measurement model (modified model)

14 Figure 10: Path diagram of the second-order measurement model (modified model) Figure 11: Path diagrams of measurement models (modified model) Figure 12: Hypothesized structural model results (original model) Figure 13: Hypothesized structural model results (modified model)

15 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 ). 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 (requirements for Environmental Management Systems) accredited. 15

16 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 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 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 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). 16

17 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 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 17

18 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. 18

19 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 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: 19

20 (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 knowhow 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) 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 20

21 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 endcustomer. 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 leadtimes 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) 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. 21

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 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 per cent of all European businesses are smalland medium-sized enterprises the majority of which, with 92.2 per cent, are characterized as microbusinesses 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 22

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 smalland 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 23

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 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 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 24

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. 25

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 GSCM Implementation Overall Business Performance Environmental Performance Economic Performance Figure 1: Hypotheses development: GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance 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 26

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 GSCM Implementation Employee Job Satisfaction Overall Business Performance Operational Efficiency Figure 2: Hypotheses development: Employee Job Satisfaction, GSCM Implementation, Operational Efficiency, and Overall Business Performance 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. 27

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. Therefore, the following hypotheses are being proposed: 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. 28

29 3.3 Operational Efficiency, GSCM Implementation, Relational Efficiency, Overall Business Performance, Environmental Performance, and Economic Performance Independent Concept Mediators Dependent Concepts GSCM Implementation Operational Efficiency Overall Business Performance Environmental Performance Economic Performance Relational Efficiency 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 29

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. Therefore, the following hypotheses are being proposed: 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 GSCM Implementation Relational Efficiency Overall Business Performance Environmental Performance Economic Performance Figure 4: Hypotheses development: Relational Efficiency, GSCM Implementation, Overall Business Performance, Environmental Performance, and Economic Performance 30

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 sociallybonded 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). 31

32 Therefore, the following hypotheses are being proposed: 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 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 32

33 would be reluctant to incorporate innovative environmental practices which would be beneficial in improving the economic situation. Therefore, the following hypotheses are being proposed: 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. 33

34 Therefore, the following hypotheses are being proposed: 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. 34

35 The Conceptual Model Independent Concept Mediators and Moderator Dependent Concepts GSCM Implementation H2a + H3a + Employee Job Satisfaction Operational Efficiency H2c + H3b, H3c, H3d + H2b + Overall Business Performance Environmental Performance Economic Performance H4a + Relational Efficiency H3e + H4b, H4c, H4d + H5a, H5b, H5c + H1a, H1b, H1c + Market Pressure Figure 5: Hypothesized structural model

36 Moderator Mediators Dependent Concepts Independent Concept Master Thesis 2013 Supply Chain Management-Taking an Environmental Perspective Michelle Engert 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 GSCM Practice Adopting green supply chain practices such as internal Zhu et al. (2008) Implementation environmental management (IEM), green purchasing (GP), cooperation with customers (CC), and eco-design (ECO) will enable the company to achieve environmental sustainability. Overall Business Performance Environmental 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. 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. (2008), Zacharia et al. (2009), Zhou et al. (2008) Zhu et al. (2007), Zhu et al. (2008) Economic Performance - Positive Employee Job Satisfaction Operational Efficiency Relational Efficiency Market Pressure 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. 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. 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. 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. 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. Table 2: Summary of constructs, their definitions and the most important literature identified Rao and Holt (2005), Zhu and Sarkis (2007), Zhu et al. (2007), Zhu et al. (2008) Zhou et al. (2008), Homburg and Stock (2004), Patterson et al. (2004) Koufteros et al. (2002), Rusinko (2007), Zhu et al. (2008), Zacharia et al. (2009) Zacharia et al. (2009), Kim et al. (2011), Pfeffer and Salancik (1978) (cited in Lee et al., 2012) Zhu and Sarkis (2007), DiMaggio and Powell (1983)

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: H3c: H3d: Operational efficiency is positively related to overall business performance at the firm level. Operational efficiency is positively related to environmental performance at the firm level. 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: 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. 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 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. 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 37

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 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 38

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 39

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 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 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 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 (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 40

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 (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) 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 and 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 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 (n.a.) and to distribute the link to the survey questionnaire via . The was not individualized but written in the respondents native language (German). Furthermore, a description of the project was provided, a detailed explanation on 41

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 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) 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) 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. 42

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. 43

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 employees, 11 (17.5%) have employees, and 7 (11.1%) employee 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. 44

45 Respondents job title Employee in charge Middle manager Senior executive Top executive Total Respondents work experience in the industry (in years) Less than More than 15 Total Firm size (no. of full-time employees) Less than More than 500 Total Industry classification of the buying firms (multiple answers are possible) Automobile Electronics Telecommunication Retail Total Firm s primary business goal in the supply chain First-tier supplier to major firms Second-tier supplier Supplier to government Other Total Table 4: Characteristics of responding firms Frequency Percentage Value 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 series and 57 (90.5%) of the firms also adopted ISO (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 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. 45

46 Awareness Adoption EMSs Yes No Yes No ISO 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 GP Green Purchasing ** CC Customer Cooperation ** ** ECO Eco-Design ** ** SAT Employee Job Satisfaction ** OE Operational Efficiency * * RE Relational Efficiency ** ** OBP Overall Business Performance * EP Environmental Performance ** ECP Economic Performance ** Table 6: Correlations between theoretical constructs ** ** 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 ** ** * ** ** ** ** ** ** ** ** * ** * ** ** * * Notes: ** Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed) ** 1.00

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 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 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 (R 2 = =0.239), CC1 (R 2 = =0.236), SAT4 (R 2 = =0.219), SAT5 (R 2 = =0.242), ECP1 (R 2 = =0.207) and ECP2 (R 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. 47

48 According to Hooper et al. (2008) one can also assess the R 2 value of the items. Items with an R 2 value below 0.20 should be removed from the analysis as a low R 2 value indicates a high level of error. From Table 8 it can be inferred that all R 2 values except the R 2 value for ECP2 (R 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 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. 48

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. 49

50 Validity and Reliability Table Factors CR AVE MSV ASV EP Environmental Performance IEM Internal Environmental Management OBP Overall Business Performance CC Customer Cooperation ECO Eco-Design GP Green Purchasing SAT Employee Job Satisfaction OE Operational Efficiency RE Relational Efficiency ECP Economic Performance Table 7: Validity and reliability table (original model) Factor Correlation Matrix with the Square Root of the AVE on the Diagonal Factors EP IEM OBP CC ECO GP SAT OE RE ECP EP Environmental Performance IEM Internal Environmental Management OBP Overall Business Performance CC Customer Cooperation ECO Eco-Design GP Green Purchasing SAT Employee Job Satisfaction OE Operational Efficiency RE Relational Efficiency ECP Economic Performance Table 8: Factor correlation matrix with the square root of the AVE on the diagonal (original model) 50

51 Summary of Validity and Reliability Measurement Results (Original Model) Factors Internal Environmental Management Green Purchasing Cooperation with Customers Eco-Design Employee Job Satisfaction Operational Efficiency Relational Efficiency Market Pressure Overall Business Performance Environmental Performance Economic Performance Item Number IEM1 IEM2 IEM3 IEM4 IEM5 GP1 GP2 GP3 GP4 CC1 CC2 CC3 CC4 ECO1 ECO2 ECO3 ECO4 ECO5 SAT1 SAT2 SAT3 SAT4 SAT5 OE1 OE2 OE3 OE4 OE5 OE6 RE1 RE2 RE3 RE4 RE5 RE6 MP1 MP2 OBP1 OBP2 OBP3 OBP4 EP1 EP2 EP3 EP4 EP5 EP6 ECP1 ECP2 ECP3 ECP4 ECP5 ECP6 ECP7 ECP8 ECP9 Standardized Loading R Average Variance Extracted (AVE) Table 9: Summary of validity and reliability measurement results (original model) Discriminant Validity Construct/ Composite Reliability _a _a Critical Ratio (t-value) _a (p=0.003) (p=0.001) (p=0.002) _a _a _a _a _a _a _a (p=0.008) Notes: _a a parameter fixed at 1.0 in the measurement model.; all critical ratios (t-values), unless otherwise stated, are significant at p<

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 ) (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 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). 52

53 Factors Number of Items Mean SD Cronbach s alpha Internal Environmental Management Green Purchasing Cooperation with Customers Eco-Design Employee Job Satisfaction Operational Efficiency Relational Efficiency Market Pressure Overall Business Performance Environmental Performance Economic Performance Table 11: Summary of cronbach s alpha and item-to-total correlations measurement results (original model) Range of corrected itemto-total correlations 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 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. 53

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 = (χ 2 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 = χ 2 df 54

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 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 χ 2 statistics of at 129 degrees of freedom implying that χ 2 /df=1.619 for the first-order model and the χ 2 statistics of at 131 degrees of freedom implying that χ 2 /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 χ df χ 2 /df (cmin/df) <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 >0.05 (Barrett, 2007) CFI great; >0.90 traditional; >0.08 sometimes permissible (Hair et al., 2010, p. 654) >0.90 (Hu and Bentler, 1999) SRMR (Hu and Bentler, 1999) RMSEA <0.08 good; 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 55

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. 2 χ diff = χ M1 χ M2 χ diff = = df diff = df M1 df M2 df diff = = 2 M1 denotes the smaller model which in comparison to the larger model M2 has more degrees of 2 freedom (fewer parameters). The value for χ diff = is distributed with df diff = 2 and checking the significance by means of a χ 2 -table it can be concluded that the null hypothesis is not rejected. The results show that the difference between the χ 2 statistics for the first- and second-order models is which is smaller than where the degree of freedom is 2 at p Thus, it can be concluded that there is no statistically significant difference between the smaller model (the first-order model) and the higherorder 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. 56

57 Path Diagram of the Measurement Model (First-Order-Model) IEM1 IEM2 IEM3 IEM4 IEM IEM GP1 GP2 GP3 GP GP CC1 CC2 CC3 CC CC ECO1 ECO2 ECO3 ECO4 ECO ECO Figure 6: Path diagram of the first-order measurement model (original model) 57

58 Path Diagram of the Measurement Model (Second-Order-Model) IEM1 IEM2 IEM3 IEM4 IEM IEM GP1 GP2 GP3 GP GP 0.99 GSCM CC1 CC2 CC3 CC CC ECO1 ECO2 ECO3 ECO4 ECO ECO 0.79 Figure 7: Path diagram of the second-order measurement model (original model) 58

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 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 χ 2 statistic, even though almost all χ 2 /df ratios of the constructs were greater than 2.0 Medsker et al. (1994) state that any χ 2 /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

60 Model χ 2 df p χ 2 /df CFI SRMR RMSEA GSCM Practice Implementation First-Order GSCM Practice Implementation Second- Order Employee Job Satisfaction Scale Operational Efficiency Scale Relational Efficiency Scale Market Pressure Scale Overall Business Performance Scale Environmental Performance Scale Economic Performance Scale 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. 60

61 Path Diagrams of the Measurement Models SAT1 SAT2 SAT3 SAT4 SAT SAT OE1 OE2 OE3 OE4 OE5 OE OE RE1 RE2 RE3 RE4 RE5 RE RE MP1 MP MP OBP1 OBP2 OBP3 OBP OBP EP1 EP2 EP3 EP4 EP5 EP EP ECP1 ECP2 ECP3 ECP4 ECP5 ECP6 ECP7 ECP8 ECP ECP Figure 8: Path diagrams of the measurement models (original model) 61

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) 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 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). 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= =0.239), CC1 (R2= =0.236), SAT4 (R2= =0.219), SAT5 (R2= =0.242), ECP1 (R2= =0.207) and ECP2 (R2= =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. Average Variance Extracted 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 62

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 Factors CR AVE MSV ASV EP Environmental Performance IEM Internal Environmental Management OBP Overall Business Performance CC Customer Cooperation ECO Eco-Design GP Green Purchasing SAT Employee Job Satisfaction OE Operational Efficiency RE Relational Efficiency ECP Economic Performance Table 14: Validity and reliability table (modified model) 63

64 Factor Correlation Matrix with the Square Root of the AVE on the Diagonal Factors EP IEM OBP CC ECO GP SAT OE RE ECP EP Environmental Performance IEM Internal Environmental Management OBP Overall Business Performance CC Customer Cooperation ECO Eco-Design GP Green Purchasing SAT Employee Job Satisfaction OE Operational Efficiency RE Relational Efficiency ECP Economic Performance 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 Internal Environmental Management Green Purchasing Cooperation with Customers Eco-Design Employee Job Satisfaction Operational Efficiency Relational Efficiency Market Pressure Overall Business Performance Environmental Performance Item Number IEM1 IEM2 IEM3 IEM4 GP1 GP2 GP3 GP4 CC2 CC3 CC4 ECO1 ECO2 ECO3 ECO4 ECO5 SAT1 SAT2 SAT3 SAT5 OE1 OE2 OE4 OE6 RE1 RE2 RE3 RE4 RE5 RE6 MP1 MP2 OBP1 OBP2 OBP3 OBP4 EP1 EP2 EP3 EP4 Standardized Loading R Average Variance Extracted (AVE) Discriminant Validity Construct/ Composite Reliability _a _a _a _a _a _a _a _a _a Critical Ratio (t-value) 64

65 Economic Performance EP ECP _a ECP ECP ECP ECP ECP ECP Table 16: Summary of validity and reliability measurement results (modified 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< 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 ) (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 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 65

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 Mean SD Cronbach s alpha Change in Cronbach s alpha Internal Environmental Management Green Purchasing Cooperation with Customers Eco-Design Employee Job Satisfaction Operational Efficiency Relational Efficiency Market Pressure Overall Business Performance Environmental Performance Economic Performance Table 18: Summary of cronbach s alpha and item-to-total correlations measurement results (modified model) Range of corrected item-to-total correlations 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 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 goodnessof-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. 66

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 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 In regards to the χ 2 statistics of at 98 degrees of freedom implying that χ 2 /df=1.518 for the first-order model and the χ 2 statistics of at 100 degrees of freedom implying that χ 2 /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 χ df χ 2 /df (cmin/df) <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 >0.05 (Barrett, 2007) CFI great; >0.90 traditional; >0.08 sometimes permissible (Hair et al., 2010, p. 654) >0.90 (Hu and Bentler, 1999) SRMR (Hu and Bentler, 1999) RMSEA <0.08 good; moderate; >0.10 bad (MacCullum et al., 1996) <0.07 (Steiger, 2007) Table 19: Statistics of first- and second-order models (modified model) 67

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 χ df χ 2 /df (cmin/df) <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 >0.05 (Barrett, 2007) model CFI great; >0.90 traditional; >0.08 sometimes permissible (Hair et al., 2010, p. 654) >0.90 (Hu and Bentler, 1999) SRMR (Hu and Bentler, 1999) RMSEA <0.08 good; moderate; >0.10 bad (MacCullum et al., 1996) <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. 2 χ diff = χ M1 χ M2 χ diff = = df diff = df M1 df M2 df diff = 73-59= 14 68

69 M1 denotes the smaller model which in comparison to the larger model M2 has more degrees of 2 freedom (fewer parameters). The value for χ diff = is distributed with df diff = 14 and checking the significance by means of a χ 2 -table it can be concluded that the null hypothesis is not rejected. The results show that the difference between the χ 2 statistics for the first-and second-order models was which is smaller than where the degree of freedom is 14 at p 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 secondorder 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 Standardized Factor Loadings and t-values 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. Average Variance Extracted The average variance extracted (AVE) estimate of Green Purchasing decreased from to thus dropping below the minimum threshold of 0.5 (Fornell and Larcker, 1981). Factors Internal environmental management Green purchasing Cooperation with customers Item Number IEM1 IEM2 IEM3 IEM4 GP1 GP2 GP4 CC2 CC3 CC4 ECO1 ECO2 ECO3 ECO4 Standardized Loading R Average Variance Extracted (AVE) Discriminant Validity Construct/ Composite Reliability Critical Ratio (t-value) _a _a _a Eco-design _a Table 21: Summary of validity and reliability measurement results after performing model fit (modified 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<

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 cited in Iacobucci and Duhachek, 2003). Factors Number of Items Mean SD Cronbach s alpha Range of corrected itemto-total correlations Internal Environmental Management Green Purchasing Cooperation with Customers Eco-Design 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. 70

71 Path Diagram of the Measurement Model (First-Order-Model) IEM1 IEM2 IEM IEM GP1 GP2 GP GP CC2 CC3 CC CC ECO1 ECO2 ECO3 ECO ECO Figure 9: Path diagram of the first-order measurement model (modified model) 71

72 Path Diagram of the Measurement Model (Second-Order-Model) IEM1 IEM2 IEM3 IEM IEM GP1 GP2 GP GP 0.99 GSCM CC2 CC3 CC CC ECO1 ECO2 ECO3 ECO ECO 0.74 Figure 10: Path diagram of the second-order measurement model (modified model) 72

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 to implying that all constructs satisfy the cutoff point suggested by Hu and Bentler (1999) which is at a minimum of In terms of the χ 2 statistic, even though almost all χ 2 /df ratios of the constructs satisfied the benchmark (2.0) suggested by Koufteros and Marcoulides (2006) Medsker et al. (1994) states that any χ 2 /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

74 Model χ 2 df p χ 2 /df CFI SRMR RMSEA GSCM Practice Implementation First-Order GSCM Practice Implementation Second- Order Employee Job Satisfaction Scale Operational Efficiency Scale Relational Efficiency Scale Market Pressure Scale Overall Business Performance Scale Environmental Performance Scale Economic Performance Scale 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. 74

75 Path Diagrams of the Measurement Models SAT1 SAT2 SAT3 SAT SAT OE1 OE2 OE4 OE OE RE1 RE2 RE3 RE4 RE5 RE RE MP1 MP MP OBP1 OBP2 OBP3 OBP OBP EP1 EP2 EP3 EP4 EP EP ECP3 ECP4 ECP5 ECP6 ECP7 ECP8 ECP ECP Figure 11: Path diagrams of measurement models (modified model) 75

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 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 76

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). Path (from-to) H1 H1a: GSCM Implementation Overall Business Performance (direct) H2 H2a: GSCM Implementation Employee Job Satisfaction H2b: Employee Job Satisfaction Overall Business Performance H2c: Employee Job Satisfaction Operational Efficiency H3 H3a: GSCM Implementation Operational Efficiency H3b: Operational Efficiency Overall Business Performance H3e: Operational Efficiency Relational Efficiency H4 H4a: GSCM Implementation Relational Efficiency H4b: Relational Efficiency Overall Business Performance Effects (Critical Ratio) Current Study Lee et al. (2012) Hypotheses Test Results (1.989) Supported p-value=0.047; R 2 = (2.671) Supported p-value=0.008; R 2 = (3.097) Supported p-value=0.002; R 2 = (2.281) Supported p-value=0.023; R 2 = (1.771) Not supported p-value=0.077; R 2 = (2.979) Supported p-value=0.003; R 2 = (4.347) Supported p-value<0.001; R 2 = (2.361) Supported p-value=0.018; R 2 = (3.700) Supported p-value<0.001; R 2 =0.29 Effects (Critical Ratio) Hypotheses Test Results (0.782) Not supported (15.353) Supported p-value< (1.877) Not supported (0.204) Not supported (3.688) Supported p-value< (6.578) Supported p-value< (7.886) Supported p-value< (6.858) Supported p-value< (3.022) Supported p-value<0.01 H6 H6a: GSCM Implementation Overall Business Performance (indirect) (0.813) Supported (5.407) Supported p-value<0.01 Table 24: Summary of hypotheses test results and comparison to results found by Lee et al. (2012) 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. 77

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. H6a: H6b: GSCM Practice Implementation Employee Job Satisfaction, Operational Efficiency, Relational Efficiency Overall Business Performance Additional Analysis GSCM Practice Implementation Employee Job Satisfaction Overall Business Performance GSCM Practice Implementation Operational Efficiency Overall Business Performance GSCM Practice Implementation Relational Efficiency Overall Business Performance GSCM Practice Implementation Operational Efficiency, Relational Efficiency Environmental Performance Direct without Mediator (p-value) (0.047) (0.047) (0.047) (0.047) (0.005) Direct with Mediator (p-value) Indirect (0.813) Two-tailed significance mediation (0.748) Two-tailed significance no mediation (0.699) Two-tailed significance no mediation (0.766) Two-tailed significance no mediation (0.012) Two-tailed significance no mediation Hypotheses Test Results Supported Not Supported 78

79 H6c: Additional Analysis GSCM Practice Implementation Operational Efficiency Environmental Performance GSCM Practice Implementation Relational Efficiency Environmental Performance GSCM Practice Implementation Operational Efficiency, Relational Efficiency Economic Performance Additional Analysis GSCM Practice Implementation Operational Efficiency Economic Performance GSCM Practice Implementation Relational Efficiency Economic Performance (0.005) (0.005) (0.007) (0.007) (0.007) Table 25: Summary of mediation analysis results (original model) (0.010) Two-tailed significance mediation (0.012) Two-tailed significance no mediation (0.007) Two-tailed significance no mediation (0.007) Two-tailed significance no mediation (0.008) Two-tailed significance no mediation Not Supported 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 OBP GSCM EP GSCM ECP GSCM Table 26: Summary of moderation analysis results (original model) Notes: ** p-value < 0.01; * p-value <

80 Path (from-to) Mediator Moderator Direct Effects Hypotheses Test Results (Critical Ratio) H1 H1a: GSCM Implementation Overall Business Performance (direct) (1.989) Supported p-value=0.047; R 2 =0.11 H1b: GSCM Implementation Environmental Performance (direct) (2.801) Supported p-value=0.005; R 2 =0.35 H1c: GSCM Implementation Economic Performance (direct) (2.707) Supported p-value=0.007; R 2 =0.67 H2 H2a: GSCM Implementation Employee Job Satisfaction (2.671) Supported p-value=0.008; R 2 =0.24 H2b: Employee Job Satisfaction Overall Business Performance (3.097) Supported p-value=0.002; R 2 =0.21 H2c: Employee Job Satisfaction Operational Efficiency (2.281) Supported p-value=0.023; R 2 =0.13 H3 H3a: GSCM Implementation Operational Efficiency (1.771) Not supported p-value=0.077; R 2 =0.08 H3b: Operational Efficiency Overall Business Performance (2.979) Supported p-value=0.003; R 2 =0.22 H3c: Operational Efficiency Environmental Performance (2.471) Supported p-value=0.013; R 2 =0.16 H3d: Operational Efficiency Economic Performance (2.026) Supported p-value=0.043; R 2 =0.12 H3e: Operational Efficiency Relational Efficiency (4.347) Supported p-value<0.001; R 2 =0.67 H4 H4a: GSCM Implementation Relational Efficiency (2.361) Supported p-value: 0.018; R 2 =0.15 H4b: Relational Efficiency Overall Business Performance (3.700) Supported p-value<0.001; R 2 =0.29 H4c: Relational Efficiency Environmental Performance (3.003) Supported p-value=0.003; R 2 =0.21 H4d: Relational Efficiency Economic Performance (2.016) Supported p-value=0.044; R 2 =0.10 H5 H5a: GSCM practice implementation Overall Business Performance High Market Pressure Not supported Low Market Pressure H5b: GSCM practice implementation Environmental Performance High Market Pressure Not supported Low Market Pressure H5c: GSCM practice implementation Economic Performance High Market Pressure Not supported Low Market Pressure H6 H6a: GSCM Implementation Overall Business Performance Employee Job Satisfaction, Operational Supported Efficiency and Relational Efficiency H6b: GSCM Implementation Environmental Performance Operational Efficiency and Relational Not Supported Efficiency H6c: GSCM Implementation Economic Performance Operational Efficiency and Relational Not Supported Efficiency Table 27: Results of path analysis and hypotheses tests (original model)

81 Conceptual Model (Original Model) Independent Concept Mediators and Moderator Dependent Concepts Employee Job Satisfaction GSCM Implementation H2a 0.493** H3a Operational Efficiency H2c 0.358* H2b 0.459** H3b, H3c, H3d 0.468**, 0.377*, 0.347* Overall Business Performance Environmental Performance Economic Performance H4a 0.390* Relational Efficiency H3e 0.818** H4b, H4c, H4d 0.535*, 0.458**, 0.316** H5a, H5b, H5c Not supported H1a, H1b, H1c 0.324*, 0.590**, 0.818** Market Pressure Figure 12: Hypothesized structural model results (original model) Notes: ** p-value < 0.01; * p-value < 0.05

82 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 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 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. H6a: H6b: GSCM Practice Implementation Employee Job Satisfaction, Operational Efficiency, Relational Efficiency Overall Business Performance Additional Analysis GSCM Practice Implementation Employee Job Satisfaction Overall Business Performance GSCM Practice Implementation Operational Efficiency Overall Business Performance GSCM Practice Implementation Relational Efficiency Overall Business Performance GSCM Practice Implementation Operational Efficiency, Relational Efficiency Environmental Performance Additional Analysis GSCM Practice Implementation Operational Efficiency Environmental Performance GSCM Practice Implementation Relational Efficiency Environmental Performance Direct without Mediator Direct with Mediator Indirect (0.025) (0.841) Two-tailed significance mediation (0.025) (0.350) Two-tailed significance mediation (0.025) (0.095) Two-tailed significance mediation (0.025) (0.322) Two-tailed significance mediation (0.001) (0.004) Two-tailed significance no mediation (0.001) (0.002) Two-tailed significance mediation (0.001) (0.004) Two-tailed significance mediation Hypotheses Test Results Supported Not Supported 83

84 H6c: GSCM Practice Implementation Operational Efficiency, Relational Efficiency Economic Performance Additional Analysis GSCM Practice Implementation Operational Efficiency Economic Performance GSCM Practice Implementation Relational Efficiency Economic Performance Table 28: Summary of mediation analysis results (modified model) (0.001) (0.001) Two-tailed significance no mediation (0.001) (0.001) Two-tailed significance no mediation (0.001) (0.001) Two-tailed significance no mediation Not Supported 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 OBP GSCM EP GSCM ECP GSCM Table 29: Summary of moderation analysis results (modified model) Notes: ** p-value < 0.01; * p-value <

85 Path (from-to) Mediator Moderator Direct Effects Hypotheses Test (Critical Ratio) Results H1 H1a: GSCM Implementation Overall Business Performance (direct) (2.238) Supported p-value=0.025; R 2 =0.11 H1b: GSCM Implementation Environmental Performance (direct) (3.414) Supported p-value=0.001; R 2 =0.33 H1c: GSCM Implementation Economic Performance (direct) (4.567) Supported p-value=0.001; R 2 =0.71 H2 H2a: GSCM Implementation Employee Job Satisfaction (3.241) Supported p-value=0.001; R 2 =0.25 H2b: Employee Job Satisfaction Overall Business Performance (3.106) Supported p-value=0.002; R 2 =0.21 H2c: Employee Job Satisfaction Operational Efficiency (1.634) Not Supported p-value=0.102; R 2 =0.06 H3 H3a: GSCM Implementation Operational Efficiency (1.921) Not supported p-value=0.055; R 2 =0.00 H3b: Operational Efficiency Overall Business Performance (2.368) Supported p-value=0.018; R 2 =0.13 H3c: Operational Efficiency Environmental Performance (2.265) Supported p-value=0.024; R 2 =0.14 H3d: Operational Efficiency Economic Performance (2.240) Supported p-value=0.025; R 2 =0.12 H3e: Operational Efficiency Relational Efficiency (4.106) Supported p-value<0.001; R 2 =0.55 H4 H4a: GSCM Implementation Relational Efficiency (2.735) Supported p-value: 0.006; R 2 =0.16 H4b: Relational Efficiency Overall Business Performance (3.700) Supported p-value<0.001; R 2 =0.29 H4c: Relational Efficiency Environmental Performance (2.774) Supported p-value=0.006; R 2 =0.18 H4d: Relational Efficiency Economic Performance (2.196) Supported p-value=0.028; R 2 =0.10 H5 H5a: GSCM practice implementation Overall Business Performance High Market Pressure Not supported Low Market Pressure H5b: GSCM practice implementation Environmental Performance High Market Pressure Not supported Low Market Pressure H5c: GSCM practice implementation Economic Performance High Market Pressure Not supported Low Market Pressure H6 H6a: GSCM Implementation Overall Business Performance Employee Job Satisfaction, Operational Supported Efficiency and Relational Efficiency H6b: GSCM Implementation Environmental Performance Operational Efficiency and Relational Not Supported Efficiency H6c: GSCM Implementation Economic Performance Operational Efficiency and Relational Not Supported Efficiency Table 30: Results of path analysis and hypotheses tests (modified model)

86 Conceptual Model (Modified Model) Independent Concept Mediators and Moderator Dependent Concepts Employee Job Satisfaction GSCM Implementation H2a 0.495** H3a Operational Efficiency H2c H2b 0.460** H3b, H3c, H3d 0.367*, 0.371*, 0.352* Overall Business Performance Environmental Performance Economic Performance H4a 0.395** Relational Efficiency H3e 0.744** H4b, H4c, H4d 0.535**, 0.419**, 0.313* H5a, H5b, H5c Not supported H1a, H1b, H1c 0.333*, 0.576**, 0.845** Market Pressure Figure 13: Hypothesized structural model results (modified model) Notes: ** p-value < 0.01; * p-value < 0.05

87 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= 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 mediumsized 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 88

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. 89

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 smalland 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 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) 90

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. 91

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111 APPENDIX Appendix 1 List of Questionnaire Items and the respective Measurement Scales Measurement Items - GSCM Practice Implementation Factors Measurement Items Internal Environmental IEM1: senior managers commitment on GSCM Management (IEM) IEM2: mid-level managers support for GSCM IEM3: cross-functional cooperation for environmental improvements IEM4: environmental compliance and auditing programs IEM5: ISO 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 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 Factors Employee Job Satisfaction (SAT) Measurement Items 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 111

112 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 Factors Market Pressure (MP) Measurement Items 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 Factors Overall Business Performance (OBP) Measurement Items 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 112

113 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 113

114 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 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) 114

115 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) 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) 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) 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) 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) 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=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) 115

116 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=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 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 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 116

117 environmental compliance and auditing programs ISO certification Question 11: Green Purchasing (Chen, 2005 and 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) eco labeling of products cooperation with suppliers for environmental objectives environmental audit for suppliers internal management suppliers ISO certification Question 12: Cooperation with Customers (Hsu and Hu, 2008 and 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) 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) 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) 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 117

118 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 More than 15 Question 18: How many full-time employees work at your firm? Less than 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 118

119 Question 21: Please enter your 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. 119