THE EFFECT OF SUPPLY CHAIN INTEGRATION ON THE ENVIRONMENTAL AND SOCIAL PERFORMANCE
-BASED ON GERMAN ELECTRICAL AND ELECTRONIC EQUIPMENT MANUFACTURERS-
Authors: Cennet Eskitürk, 870530-T367, firstname.lastname@example.org Mandy Gädeke, 880622-T464, email@example.com André Willing, 841124-T733, firstname.lastname@example.org Tutor: Åsa Gustavsson
Examiner: Helena Forslund Semester: Spring 2015
Subject: Business Process and Supply Chain Management Level: Master Thesis
This master thesis required hard work and commitment and could not have been completed without support and constructive feedback. Hence, we would like to acknowledge the efforts of those who supported us during the development of this thesis.
First of all, we would like to thank our tutor Dr. Åsa Gustavsson for her constructive feedback, guidance, support and encouragement during the entire process. Also, we would like to thank our examiner Dr. Helena Forslund, whose help and involvement was particularly valuable during the entire writing process.
Furthermore, we are grateful for our opposition group, whose critical feedback helped us improving our manuscript.
Moreover, we would like to express our thanks to all respondents of the questionnaire for participating and contributing with valuable information, which helped us to complete our thesis.
Last but not least, we want to express our gratitude to our families in Germany for their support, encouragement and reassuring pep talks during our entire study in Sweden.
Thank you all very much!
Spring semester, May 2015, Växjö
____________________ ____________________ ____________________
Cennet Eskitürk Mandy Gädeke André Willing
In the past years supply chain integration has become focus of interest, due to the discussion in the literature, that a positive direct relationship exists between higher supply chain integration and higher performance. Additionally, the environmental and social performance of supply chains gained much interest based on the new sustainability focus in the 21st century.
The production of electrical and electronic equipment is one of the fastest growing global manufacturing activities. However, due to technological advancements, the quick obsolescence of electronics result in increased generation of waste of electrical and electronic equipment. Germany is one of the leading electrical and electronic equipment markets within Europe and is strongly affected by the EU directives and regulations, which aim not only to protect human health but also to improve the environmental performance of the electrical and electronic equipment operators in particular. Therefore, the environmental and social performance of German electrical and electronic equipment manufacturers is increasingly gaining importance.
Respectively, the purpose of this study is to investigate what effect the depth of upstream supply chain integration has on the environmental and social performance of German electrical and electronic equipment manufacturers.
This study is based on the quantitative research method. The required empirical data is generated through an online questionnaire, which has been sent to German electrical and electronic equipment manufacturers. An initial operationalization of upstream supply chain integration depth and environmental and social performance is used as a foundation for the questionnaire.
It can be concluded that German electrical and electronic equipment manufacturers seem to have a coordinative upstream supply chain integration and presumably have a
moderate environmental and social performance. Furthermore, it can be assumed that the upstream supply chain integration depth has an effect on the environmental and social performance of German electrical and electronic equipment manufacturers.
Supply Chain Integration Depth, SC Integration, Sustainability, Environmental Performance, Social Performance, Electrical and Electronic Equipment Manufacturers, EEE Industry, German EEE Industry
Table of Content
Acknowledgement ... 2
Abstract ... 3
Table of Content ... 5
Table of Figures ... 8
Table of Tables ... 9
Table of Abbreviations ... 10
1 Introduction ... 11
1.1 Background ... 11
1.1.1 Supply Chain Integration ... 11
1.1.2 Environmental and Social Responsibility ... 14
1.1.3 German Electrical and Electronic Equipment Industry ... 15
1.2 Problem Discussion ... 17
1.3 Research Gap ... 20
1.4 Delimitations ... 21
1.5 Research Questions ... 22
1.6 Purpose ... 22
1.7 Structure ... 22
2 Methodology ... 24
2.1 Scientific Perspective ... 24
2.1.1 Scientific Perspective of the Thesis ... 26
2.2 Scientific Method ... 26
2.2.1 Scientific Method of the Thesis ... 28
2.3 Research Method ... 28
2.3.1 Research Method of the Thesis ... 30
2.4 Data Collection ... 30
2.4.1 Data Collection of the Thesis ... 32
2.5 Questionnaire Design ... 32
2.5.1 Questionnaire Design of the Thesis ... 35
2.6 Population and Sample ... 35
2.6.1 Population and Sample of the Thesis ... 37
2.7 Non-Response ... 38
2.7.1 Non-Response of the Thesis ... 39
2.8 Data Analysis ... 39
2.8.1 Data Analysis of the Thesis ... 41
2.9 Scientific Credibility ... 41
2.9.1 Scientific Credibility of the Thesis ... 44
2.10 Research Ethics ... 45
2.10.1Research Ethics of the Thesis ... 46
2.11 Synopsis ... 47
3 Literature Review... 48
3.1 Depth of Upstream Supply Chain Integration ... 48
3.1.1 Cooperative Supply Chain Integration ... 49
3.1.2 Coordinative Supply Chain Integration ... 50
3.1.3 Collaborative Supply Chain Integration ... 51
3.2 Environmental and Social Performance ... 53
3.2.1 Environmental Performance ... 53
3.2.2 Social Performance ... 56
3.3 The Effect of SC Integration on the Environmental and Social Performance... 59
3.4 Operationalization of SC Integration and Environmental and Social Performance ... 60
3.4.1 Operationalization of upstream SC integration depth ... 60
3.4.2 Operationalization of Environmental Performance ... 61
3.4.3 Operationalization of Social Performance ... 62
4 Hypothesis Development ... 64
4.1 Hypothesis 1 ... 64
4.2 Hypothesis 2 ... 65
4.3 Hypothesis 3 ... 67
5 Empirical Findings ... 70
5.1 Data Editing ... 70
5.2 Quantitative Data ... 72
5.3 Reliability Test ... 72
5.4 Descriptive Statistics ... 73
5.4.1 Mean, Standard Deviation and Median – SC Integration Depth ... 73
5.4.2 Mean, Standard Deviation and Median – Environmental Performance ... 75
5.4.3 Mean, Standard Deviation and Median – Social Performance ... 76
5.4.4 Mean, Standard Deviation and Median – Summarized Statistics ... 78
5.5 Correlation Test ... 79
5.5.1 Spearman Correlation ... 79
5.5.2 Pearson Correlation ... 80
6 Analysis and Discussion ... 82
6.1 Analysis of Research Question 1 ... 82
6.2 Analysis of Research Question 2 ... 83
6.3 Analysis of Research Question 3 ... 86
7 Conclusion and Further Research ... 90
7.1 Answers to the Research Question ... 90
7.2 Theoretical and Practical Contribution ... 92
7.3 Implications ... 93
7.4 Limitations ... 94
7.5 Further Research... 94
References ... 96
Appendix 1: Introduction Letter of Questionnaire ... 108
Appendix 2: Questionnaire ... 110
Appendix 3: Descriptive Statistics ... 118
Table of Figures
Figure 1: Width and depth of SC integration ... 13
Figure 2: Upstream SC integration depth and environmental and social performance ... 20
Figure 3: Structure of the thesis... 23
Figure 4: Types of questionnaires ... 34
Figure 5: Procedure of drawing a sample ... 37
Figure 6: Synopsis of the thesis... 47
Figure 7: Hypothesized effect of upstream SC integration depth on environmental performance ... 68
Figure 8: Hypothesized effect of upstream SC integration depth on social performance ... 69
Table of Tables
Table 1: Cooperation, coordination, collaboration ... 49
Table 2: Environmental performances ... 54
Table 3: Social performances ... 56
Table 4: Operationalization of upstream SC integration depth ... 61
Table 5: Operationalization of environmental performance ... 62
Table 6: Operationalization of social performances ... 63
Table 7: Hypothesized integration of German EEE manufacturers ... 65
Table 8: Hypothesized environmental performance of German EEE manufacturers.... 66
Table 9: Hypothesized social performance of German EEE manufacturers ... 67
Table 10: Questionnaire responses ... 72
Table 11: Cronbach's alpha test ... 72
Table 12: Descriptive statistics of upstream SC integration depth ... 74
Table 13: Descriptive statistics of environmental performance ... 76
Table 14: Descriptive statistics of social performance ... 77
Table 15: Summarized descriptive statistics ... 78
Table 16: Spearman correlation of summarized categories ... 80
Table 17: Pearson correlation of summarized categories ... 81
Table of Abbreviations
EEE Electrical and Electronics Equipment E&E Electrical and Electronics
EU European Union
IT Information Technology MRS Market Research Society SC Supply Chain
WEEE Waste of Electrical and Electronics Equipment
The introduction chapter provides an overview of the current discussed phenomenon sustainability e.g. environmental and social responsibility in supply chains by defining the relevant terms and bringing them into context. While the problem discussion elucidates the significance for German EEE manufacturer to engage their supply chains with sustainability, it also problematizes the effect of upstream supply chain integration depth on the environmental and social performance. The research gap provides the foundation for this study and illustrates its contribution to the literature.
Followed by the delimitations, research questions, and the purpose, the introduction chapter ends with an illustration that presents the structural character of this study.
1.1.1 Supply Chain Integration
Companies, regardless their size, do not exist isolated instead they exist in interdependent relationships (Håkansson & Snehota, 1995). The interdependency of the relationship derives from the opportunity of performing activities jointly and thus utilizing resources, which would not be possible to accomplish in isolation (Håkansson
& Snehota, 1995; Schoenherr & Swink, 2012). Therefore, the accomplishments of companies depend on the relationship they have with each other (Håkansson &
Snehota, 1995). The recognition of this causality caused scholars as well as practitioners to focus exhaustingly on supply chain (SC) integration (Schoenherr &
Introduction Methodolog y
Analysis and Discussion
Conclusion and Further Research
The term SC integration evolved as a prime topic among practitioners and academics over the past years (Pagell, 2004; Glenn Richey et al., 2009; Chen, Daugherty &
Landry, 2009; Mackelprang et al., 2014). Due to its many definitions and attempts of characterisation, there are various differing perceptions of SC integration in the according literature (Schoenherr & Swink, 2012; Glenn Richey et al., 2009). For instance, some studies focus on a single-sided integration e.g. only with suppliers (Danese, 2013), or only with customers (Germain & Iyer, 2006), while others focus on the integration with both, suppliers and customers (Vachon & Klassen, 2006).
However, the study ”Arcs of integration: an international study of supply chain strategy”
by Frohlich and Westbrook (2001) had a significant influence in the SC literature, as it was referred to in numerous studies (Cousins & Menguc, 2006; Fabbe-Costes & Jahre, 2008; Schoenherr & Swink, 2012; Danese, 2013). Based on the combination options of the arcs (1) direction (supplier and/or customer) and (2) extent (degree of integration) Frohlich & Wetbrook (2001) defined five strategies ranging from inward-facing to outward-facing.
Wiengarten & Longoni (2015) adapted Frohlich & Wetsbrook’s (2001) arcs of integration and extended it by the dimensions width and depth as illustrated in Figure 1.
The width dimension indicates the span of integration along the SC, e.g. upstream and downstream (Wiengarten & Longoni, 2015). The depth dimension refers to the scope of shared activities with SC partners (Frohlich & Westbrook 2001, cited by Wiengarten &
Longoni, 2015). Furthermore, there are authors who relate the depth dimension to coordinative and collaborative practices (Vereecke & Muylle, 2006; Ahmed & Pagell, 2012; van der Vaart et al., 2012, cited by Wiengarten & Longoni, 2015). The literature defines these terms as following; coordinative practices are of tactical nature and involve the sharing of information in order to manage material flows and activities, whereas collaborative practices are of strategic nature and involve joint improvements in products and processes (Van der Vaart et al., 2012; Botta-Genoulaz et al., 2013).
Furthermore, Ashby, Leat & Hudson-Smith (2012) stress out that coordination is
perceived as a weak form of integration compared to collaboration, which represents the optimum form of integration.
The nucleus of both concepts however, is that they acknowledge a positive direct relationship between higher SC integration and higher performance (Frohlich &
Westbrook 2001; Wiengarten & Longoni, 2015). This causality, however, is highly discussed in the literature and opinions are divided whether or not a positive direct relationship exists between higher SC integration and higher performance (Ho, Au &
Newton, 2002; Van der Vaart & van Donk, 2008; Mackelprang et al., 2014).
Figure 1: Width and depth of SC integration, adopted from Wiengarten & Longoni (2014, p. 141)
1.1.2 Environmental and Social Responsibility
In the past two decades there has been a remarkable shift in the business landscape, as companies increasingly engaged their long-term and short-term decision-making with the aspects of sustainability (Ahi & Searcy, 2015). Sustainability started to gain importance in the 1980s, reinforced through local, national, and international policy- makers as well as policy entrepreneurs in non-governmental organizations (Agyeman, 2013). A major contribution herein was the report ”Our Common Future” released in 1987 by the World Commission on Environment and Development (Ibid) which defined sustainability as meeting the requirements of today without impairing the possibility of future generations to meet their requirements (WCED, 1987). Since then, a growing number of publications have dealt with the subject and although the research majority approaches sustainability in similar manners, a clear consensus about how to operationalize sustainability is still missing (Metz, 2001; Baumgartner & Ebner, 2010;
Basile, Hershauer & McNall, 2011; Agyeman, 2013; Fahy & Rau, 2013).
Sustainability has become an issue that progressively concerns SCs (Azevedo et al., 2012; Ashby, Leat & Hudson-Smith, 2012; Ahi & Searcy, 2015). Therefore, sustainable supply chain is a term that frequently resurfaces in the related literature. Seuring &
Müller (2008, p. 1700) define sustainable supply chain as “the management of material, information, and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental, and social, into account which are derived from customer and stakeholder requirements.”.
Sustainability is regarded as a multidimensional concept that is based on three pillars of economy, environment, and social (Kannegiesser & Günther, 2014; Neumüller et al., 2015). One central concept that is frequently mentioned in the sustainability literature is the triple bottom line (Seuring & Müller, 2008; Ahi & Searcy, 2015). The triple bottom line engages the traditional performance dimension economy with the environmental
and social dimensions (Seuring & Müller, 2008; Diabat, Kannan & Mathiyazhagan, 2014). Another concept that is often referred to in the same context with the triple bottom line is corporate social responsibility (Henriques, 2004; Meixel & Luoma, 2015).
Due to the terms frequently used interchangeably in the literature, a discussion aroused about the definition and relation of these terms (Van Marrewijk, 2003;
Henriques, 2004; Meixel & Luoma, 2015). The triple bottom line is a rather new concept that aims to support manager to improve their performance in all three dimensions (economy, environment and social) with the promise of increasing the overall performance of companies (Meixel & Luoma, 2015). Corporate social responsibility, however is more ancient (Meixel & Luoma, 2015) dating back to the 1950s (Williams, 2013). Corporate social responsibility can be defined as a concept that utilizes companies to integrate social and environmental issues in their business operations and their interactions with stakeholders (European Commission, 2001).
Both concepts focus on incorporating the environmental and social dimension into companies in order to address the environmental and social responsibility of companies. Based on these similarities it is needless to regard the triple bottom line and corporate social responsibility as separate and detached from each other.
1.1.3 German Electrical and Electronic Equipment Industry
Electrical and Electronic Equipment (EEE) refer to equipment which depends on electric currents or electromagnetic field in order to function (ElektroG, 2005). The production of EEE is not only the largest and fastest growing but is also considered as the most pollution causing industry worldwide (Achilias et al., 2009; Darby & Obara, 2005; Fraige et al., 2012).
Germany is considered as the leading country in Europe and the German EEE industry is ranked number five globally (Deutsche Bank Research, 2009). According to ZVEI, Die Elektroindustrie (2015a; 2015b) the German EEE industry accounted in 2014 for a general turnover of about 171.9bn €, remaining 2,9 % above the pre-year level. In 2014
Germany reached an export record of 165.5bn Euro (IXPOS, 2015; ZVEI, Die Elektroindustrie, 2015b). Expectations for 2015 are that the turnover of the EEE industry will increase to approximately 174.5bn € (ZVEI, Die Elektroindustrie, 2015b).
Thus, 26 % of the EEE companies expect the business to grow further. Accordingly, 35
% of the companies consider their situation as good, while 55 % evaluate their situation as stable and 10 % as bad (ZVEI, Die Elektroindustrie, 2015b).
The production within the German E&E market increased between 1995 and 2010 annually by 3,5 % and the productivity increased noticeably between 1995 and 2008 by annually 6 % whereas other EU countries such as France, Great Britain and Italy reduced their productivity (ZVEI, Die Elektroindustrie, 2011). The German EEE market accounts for 10 % of the total German industrial production and for 3 % of the gross domestic product (GDP), which makes it the second largest industry in Germany (Ibid).
Since two decades, the EEE industry faces high environmental challenges caused by the increased environmental awareness (Shu-Hung et al., 2012). The EEE industry is recognized as a major waste stream worldwide and the fastest growing waste stream within the EU (Darby & Obara, 2005; Georgiadis & Besiou, 2009; Pérez-Belis, Bovea &
Simó, 2014). The total EEE industry accounts for an environmental impact of 10 – 20
% worldwide (depletion of resources, greenhouse gases, air acidification, dust and contamination) whereas in Germany the total amount of produced EEE waste is about 950,000 tons per year (Georgiadis & Besiou, 2009; Quariguasi Frota Neto et al., 2010).
Consequently the German EEE industry has to cope with several EU directives and German regulations posed on EEE manufacturers (Shu-Hung et al., 2012; Vere Association, 2014).
1.2 Problem Discussion
(1) What depth of upstream SC integration do German EEE manufacturers have?
Due to the fact that markets, technologies and the competition landscape are constantly changing (Kropp et al., 2006), companies have been faced to re-evaluate their strategic, organizational and operational structures in order to remain competitive (Kobayashi, Tamaki & Komoda, 2003; Danese, 2013). Furthermore, competition today takes place between entire SCs, rather than between individual companies (Lambert &
Cooper, 2000; Andersson & Larsson, 2006; Seebacher & Winkler, 2013). Motivated by that, SC integration gained much attention (Pagell, 2004; Glenn Richey et al., 2009;
Chen, Daugherty & Landry, 2009; Mackelprang et al., 2014) due to the assumptions in the literature about a positive direct relationship between SC integration and higher performance (Frohlich & Westbrook, 2001; Wiengarten & Longoni, 2015).
According to Wiengarten & Longoni (2015) SC integration has two dimensions, width and depth. While the width of SC integration refers to the integration with SC partners, the depth of SC integration considers the extent of shared activities with SC partners (Frohlich & Westbrook, 2001, cited by Wiengarten & Longoni, 2015). Wiengarten &
Longoni (2015) associate coordination and collaboration with the depth of SC integration, while Ashby, Leat & Hudson-Smith (2012) identify cooperation as the weakest form of an inter-organisational relationship (Spekman, Kamauffr & Myhr, 1998;
Keast, Brown & Mandell, 2007). Among these SC integration depths, collaboration in particular is claimed to generate significant benefits, such as increased responsiveness to demand changes (Mentzer et al., 2000). Nevertheless, Childerhouse & Towill (2011) claim that collaboration works better in theory than it does in practice, due to the contraction that several SC partners could act as one company.
(2) What environmental and social performance do German EEE manufacturers have?
The world is facing critical changes in climate, water level and soil condition (Kumar &
Imam, 2013; Parry, 2007; Martin, 2010). Since these changes tend to have serious
impacts on humans (Parry, 2007; Martin, 2010) an increased concern about the environment occurred in the past decades (Chuang & Yang; 2013; Barde & Pearce, 2013). As a result, a growing number of stakeholders are concerned with the environmental and social effects of business processes (Klassen & Vereecke, 2012) and pressure SCs to integrate sustainability aspects in their processes (Wong, Wong &
Boon-itt, 2015; Zhang & Awasthi, 2014; Neumüller et al., 2015; Foerstl et al., 2015;
Sayogo et al., 2014).
In Europe the production of EEE is one of the fastest growing global manufacturing activities (Babu, Parande & Basha, 2007). However, the life cycle of such electronics are characterized as short, due to technological advancements causing customers to purchase newer models (Directive, E. C., 2012; Sachs, 2006, cited by Ji, Gunasekaran
& Yang, 2014). The quick obsolescence of electronics does not only result in increased consumption of EEE but also in increased generation of waste of electrical and electronic equipment (WEEE) (Alavi et al., 2014; Ji, Gunasekaran & Yang, 2014). For instance, the EU is expected to generate over 12 million tons of WEEE by 2020 (European Commission, 2015). One of the leading EEE markets within Europe is Germany, which generated a general turnover of about 171,9bn € in the previous year (Deutsche Bank Research, 2009; IXPOS, 2015; ZVEI, Die Elektroindustrie, 2015b).
Considering the alarming forecasts concerning the increasing WEEE, the European Union reacted by legislating the Directive 2012/19/EU with the objective to “preserve, protect and improve the quality of the environment, to protect human health and to utilise natural resources“ (Directive, E. C., 2012, p. 39). It is also stressed out in the Directive 2012/19/EU that the objective herein is to achieve an improvement in the environmental performance of the EEE operators in particular (Ibid). Performance measurements support companies to identify gaps between the present and desired performance and monitor the progress towards filling those gaps (Mani et al., 2014).
Furthermore, corresponding performance indicators do not only quantify information but also allow to understand and compare the information (Mani et al., 2014) leading to
valuable feedback that positively affects the learning curve of companies (Gates &
Germain, 2010). Therefore, performance indicators are frequently used by companies to set targets (Ibid). However, besides the indicator, performance measurements should include also the according metric (Ibid). Thus, according to Mani et al. (2014) a challenge derives from identifying those performance indicators that can be directly related to manufacturing metrics.
Furthermore, there is the question whether to approach sustainable performance measurements collectively or separately (environment, social, and economy) and which is considered most suitable for companies (Gates & Germain, 2010). Therefore, insight can be gained by revealing which environmental and social performance measurements German EEE manufacturers use in order to engage their business operations with sustainability practices (Ibid).
(3) What effect has the depth of upstream SC integration on the environmental and social performance of German EEE manufacturers?
The relationship between SC integration and performance has been researched excessively by numerous authors such as Frohlich & Wetbrook (2001), Min & Mentzer (2004), Vachon & Klassen (2006), Germain & Iyer (2006), Glenn Richey et al. (2009), Schoenherr & Swink (2012), Danese (2013), and Mackelprang et al. (2014). However, while some authors acknowledge a positive direct relationship between SC integration and higher performance (Frohlich & Wetbrook, 2001; Germain & Iyer, 2006; Glenn Richey et al., 2009), other authors’ findings could not confirm such a relationship (Koufteros et al., 2010; Gimenez & Ventura, 2005, cited by Mackelprang et al., 2014).
Nevertheless, approaching SC integration from the two separate dimensions width and depth, it has been acknowledged that a greater width of SC integration is beneficial for the operational performance of SCs (Wiengarten & Longoni, 2015). However, the same acknowledgement does not exist for the depth of SC integration, as it has been neglected in the literature so far (Ibid). Furthermore, Vachon & Klassen (2008) stress
out that despite its conceptual correctness, considering simultaneously upstream and downstream SC can disguise significant research contribution. Therefore, this study investigates what depth of upstream SC integration German EEE manufacturers have.
Considering that stakeholders are increasingly concerned with the environmental and social performance of SCs (Klassen & Vereecke, 2012), this study investigates the depth of upstream SC integration of German EEE manufacturers in relation to their environmental and social performance (Figure 2). According to Wiengarten & Longoni (2015), SC integration may affect the environmental and social sustainability performance of companies. However, this assumption has yet to be proven. Thus, the investigation about the effect of SC integration depth on the environmental and social performance would not only contribute to the literature but also provide empirical evidence to Wiengarten & Longoni’s (2015) assumption in this regard.
Figure 2: Upstream SC integration depth and environmental and social performance, own source
1.3 Research Gap
Despite the accumulating knowledge based on SC integration (Winter & Knemeyer, 2013; Wiengarten & Longoni, 2015; Blome, Paulraj & Schuetz, 2014), there are still aspects of SC integration that require further investigation. For instance, Wiengarten &
Longoni (2015) point out that previous research about SC integration has mainly focused on the width of integration e.g. spread of integration along the SC, yet have neglected the depth of SC integration.
Furthermore, given the rather new emphasises on sustainability (Fahy & Rau, 2013;
Meixel & Luoma, 2015; Ahi & Searcy, 2015), the burgeoning research field of
sustainable supply chain management needs to be developed further (Ashby, Leat &
Hudson-Smith, 2012). In this sense further research is needed, specifically in measuring environmental and social aspects in SCs, as Cuthbertson & Piotrowicz (2008) and Ahi & Searcy (2015) conclude. Cuthbertson & Piotrowicz (2008) stress out that there are plenty of theoretical approaches considering SC performance measurements, however there is a lack of empirical research in this regard. While performance measurements can refer to both, upstream and downstream SC, Vachon
& Klassen (2008) point out that despite its conceptual correctness, considering simultaneously upstream and downstream SC can disguise significant research contribution.
Considering these research gaps, this study contributes empirically by investigating the effect of SC integration depth on the environmental and social performance of German EEE manufacturers, with emphasis on the upstream SC.
This study is mainly delimited to investigate German EEE manufacturers, which operate in Germany and/or on the global market. In order to approach German EEE manufacturers, the company database ORBIS is used and the criterion >50 employees and provided email address was added. Based on these criterions, this study will investigate the depth of upstream SC integration and the environmental and social performance of German EEE manufacturers. As this study investigates the depth of upstream SC integration the customers are not considered in particular. Moreover, from chapter 3.4 onwards, the operationalization of the upstream SC integration depth is strictly based on McNamara’s (2012) classification of cooperation, coordination and collaboration. Similarly, the operationalization of the environmental performance is based on Kocmanová & Šimberová’s (2014) classification whereas the social performance is based on the classification of Hubbard (2009). The empirical data is collected via emailing directed towards managers of German EEE manufacturers.
1.5 Research Questions
The authors have developed the following research questions:
(1) What depth of upstream SC integration do German EEE manufacturers have?
(2) What environmental and social performance do German EEE manufacturers have?
(3) What effect has the depth of upstream SC integration on the environmental and social performance of German EEE manufacturers?
The purpose of this study is to investigate what effect the depth of upstream SC integration has on the environmental and social performance of manufacturers. Based on German EEE manufacturers, this study provides empirical evidence in order to contribute to the on-going discussion, whether or not a positive direct relationship exists between SC integration and improved performance.
As presented in Figure 3, chapter 1 of this study gives background information on the subjects upstream SC integration depth, environmental and social responsibility, and German EEE manufacturers. This is then followed by chapter 2, which explains the research choices made for this thesis and also frames the questionnaire design for the empirical investigation. Chapter 3 elucidates the concepts of upstream SC integration depth and identifies common environmental and social performances mentioned in the literature. Chapter 3 ends with an operationalization of the concepts discussed. In chapter 4, hypotheses are developed in accordance to the research questions. The following chapter 5 presents the empirical findings, which have been generated through the questionnaire. In chapter 6 the empirical findings are brought into context with the
literature through analysis and discussion. Finally, in chapter 7, the findings of this study are summarized followed by further research suggestions.
Figure 3: Structure of the thesis, own source
This chapter contains the methodology used in this study by describing and explaining the research approach, the goals of the research, the data sources used and the design in accordance with the credibility. Furthermore, this chapter includes the research ethics that have been applied in this study. The subchapters first present the theoretical frame e.g. describing the different methods before determining the relevant method used in this thesis.
2.1 Scientific Perspective
The scientific perspective of business research facilitates the researcher to formulate, conduct and analyse a research (Hair et al., 2013). These are the required steps for a reasonable decision-making for one in designing hypotheses and theories and for another in collecting data from adequate sources. Thus, the researcher is able to correctly assess whether the developed theories lead to concrete empirical results (Hair et al, 2003). The two traditions, positivism and hermeneutics are generally considered by researchers (i.e. Prasad, 2005; Bryman & Bell, 2011).
Positivism is an epistemological research approach, which incorporates the philosophy of knowing (Trochim & Donnelly, 2001). Positivism assumes an objective reality, which is perceivable and measurable (Ibid). Significant knowledge is objectively collected through positive verification of observations, thus it is often found in quantitative researches (Ibid). Literature presents experiments, scientific tests, surveys or statistics as valid forms of data collection in a positivistic research approach (Collins, 2010;
Introduction Methodolog y
Analysis and Discussion
Conclusion and Further Research
Trochim & Donnelly, 2001; Neuman, 2003). The necessary information is analysed in order to support or reject predetermined hypothesizes, hence, positivism incorporates deductive rather than inductive conclusions (Crowther & Lancaster, 2008) in order to explain assessable laws (Bryman & Bell, 2011). Collins (2010, p. 38) defines positivism
“as a philosophy [which] is in accordance with the empiricist view that knowledge stems from human experience. It has an atomistic, ontological view of the world as comprising discrete, observable elements and events that interact in an observable, determined and regular manner.”
Prasad (2005, p. 30) defines hermeneutics as “a form of textual interpretation, concerned mainly with the methodical analysis of different forms of texts”. Thereby, special effort is taken to discover the real meanings of texts, which due to complexity, personal world-view or social location of the original authors, are often not immediately recognizable (Ibid). In contrast to positivism and its premise of an objective and measurable reality, hermeneutic is considered as a tradition, in which a prejudice-free interpretation is considered as impossible (Gadamer, 1960, cited by Prasad, 2005).
A vital instrument for hermeneutic text analysis is the hermeneutic circle, which is used to close the gap between the (obvious) text and its hidden meaning (Prasad, 2005).
Through repetitive analyses the researcher identifies the interconnection between the text (genre) specific context and its individual elements (Arnold & Fisher, 1994, cited by Prasad, 2005). Prasad (2005) elucidates the concept of the hermeneutic circle and states that “a comprehension of words and sentences is enhanced by a grasp of the literature genre, in which they are composed, while an understanding of the larger genre itself is predicated on an understanding of its minute elements or parts” (Prasad, 2005, p. 35).
2.1.1 Scientific Perspective of the Thesis
For this study a positivistic perspective is chosen since, the necessary information is collected with the purpose to test a predetermined hypothesis in order to explain assessable laws. Significant knowledge is objectively collected, through positive verification of observations. Therefore, the inherent reality is objective, which means it can be perceived and measured. In this context, the theory about the effect of the upstream SC integration depth on the environmental and social performance can be considered as an objective reality, which is why the theory can be perceived and measured.
2.2 Scientific Method
The scientific method is concerned with the required procedure for a researcher to conduct a scientific research (Ethridge, 2004). The inductive and deductive methods are generally applied to guide researchers in perceiving the empirical reality and the corresponding results (Arbnor & Bjerke, 1997; Ghauri & Grønhaug, 2005).
The method of induction bases its research on empirical evidence (Ghauri &
Grønhaug, 2005) and researchers using this method establish an inductive relationship between theory and the research subject (Bryman & Bell, 2011). Saunders, Lewis &
Thornhill (2009) explain that the inductive research methods require a preliminary data collection, often obtained through observations or personal interviews. Based thereupon, researchers discover patterns or regularities wherefrom tentative hypothesizes are drawn (Trochim & Donnelly, 2001). These are further explored and controlled in order to induce significant theories related to the research subject (Ibid).
Ghauri & Grønhaug (2005) add that inductive research is also used to improve existing theories. The authors explain that new discoveries are used to enhance existing theories, which results in a continuously improved understanding of the study subject
(Ibid). Considering the fact that an inductive research mainly relies on information that is gathered through observations or interviews, Bryman & Bell (2011) state that this research method is mainly associated with qualitative researches. However, Ghauri &
Grønhaug (2005) present that conclusions drawn from an inductive method can never be 100 % accurate, due to the often interpretative information analysis. Therefore, literature argues that the inductive, compared to the deductive, research method is less certain, considering the potential of unreliability and invalidity of the procured data (Saunders, Lewis & Thornhill, 2009).
Deductive reasoning implies a research approach, which starts with the development of hypothesizes according to existing theories (Trochim & Donnelly, 2001). Significant data is mainly obtained through standardized methods, such as experiments, closed questionnaires or mathematical tests, which are highly repeatable due to their precise regulations and definitions (Saunders, Lewis & Thornhill, 2009). The deductive method is the most common research method, which enables a researcher to deduce relationships between theory and the research subject through logical reasoning (Bryman & Bell, 2011). The deductive method requires the researcher to initially develop hypothesizes, based on already existing knowledge. This implies that the researcher has to conduct a preliminary literature review, wherefrom significant information regarding the study subject, is discovered (Bryman & Bell, 2011). The subsequent empirical data collection results in (usually) quantitative, numerical information, which is used to test the initial hypothesizes (Trochim & Donnelly, 2001;
Ghauri & Grønhaug, 2005). The deductive research method is often used for quantitative researches in order to discover correlations between variables, which are significant for testing hypothesizes (Saunders, Lewis & Thornhill, 2009).
2.2.1 Scientific Method of the Thesis
The deductive method is chosen for this study, since the research approach comprises the development of hypothesizes according to existing theories. For this study it is required to deduce relationships between theory and the research subject through logical reasoning. Therefore, the effect of SC integration depth on the environmental and social performance can be deduced through logical reasoning. Furthermore, the previously chosen positivistic perspective incorporates deductive conclusions rather than inductive conclusions.
2.3 Research Method
Research methods are concerned with the systematic, focused and sequential collection of data in order to acquire the inherent information, which is required to answer a specific research question (Ghauri & Grønhaug, 2005). The choice of the research method has a significant impact on the data collection since it prescribes the procedure of the data collection (Ghauri & Grønhaug, 2005; Saunders, Lewis &
Thornhill, 2009; Bryman & Bell, 2011). The two research methods qualitative and quantitative are frequently used and thus considered as the most common (Ghauri &
Grønhaug, 2005, Saunders, Lewis & Thornhill, 2009; Bryman & Bell, 2011). These two methods are often used separately due to their contrasting nature being from different school of thoughts. Nevertheless, both methods are equally valid.
The qualitative method is concerned with the study subjects’ perception of their social world and is frequently used to investigate the behaviour, attitude, opinions, impressions etc. of study subjects (Krishnaswami & Satyaprasad, 2010). Accordingly, the data used in the qualitative method is of descriptive nature (Meyers, 1997) such as words, images and videos (Bryman & Bell, 2007; Saunders, Lewis & Thornhill, 2012).
This kind of data is most commonly collected through, interviews, observations, and
case studies (Ghauri & Grønhaug, 2010; Bryman & Bell, 2011; Saunders, Lewis &
Thornhill, 2012). Due to the nature of data, the qualitative method is referred to as a non-numerical method (Saunders, Lewis & Thornhill, 2012).
The qualitative method applies the inductive method in order to build a relation between the research subject and theory (Bryman & Bell, 2011). This means that the research begins with a specific observation and proceeds towards abstract generalisation and theories (Creswell, 2009). The role of the researcher herein is to make a rational interpretation of the meaning by utilizing his individual skills and experiences in the data analysis (Ghauri & Grønhaug, 2005; Creswell, 2009).
Therefore, the researcher is required to work closely with the subjects of the study (Bryman & Bell, 2011).
The quantitative method is employed to develop general theories through strictly controlled experiments (Burns & Burns, 2008). As suggested by the term itself, data in this method is collected and analysed based on numerical inputs (Ibid). The procedure of the quantitative method is to test pre-formulated ideas and investigate the degree of their theoretical contribution (Bryman and Bell, 2007). The representative set of data is mainly collected through surveys, experiments or statistics (Bryman & Bell, 2007;
Shuttleworth, 2008). In order to analyse the set of data, the quantitative method relies on deduction. Through deduction the relationship between study subject and theory can be investigated by testing existing theories (Bryman & Bell, 2007). Apart from this, it is possible to use structured interviews, which consist of close-ended questions (Saunders, Lewis & Thornhill, 2012). Since the quantitative method relies on verifying and testing theories, the method is perceived to be higher in generalization and objectivity (Ghauri & Grønhaug, 2010). Furthermore, the quantitative method is said to follow practices and norms of positivism (Bryman & Bell, 2007).
2.3.1 Research Method of the Thesis
The chosen method in this study is the quantitative method, due to the data collection through a questionnaire, which contains closed-ended questions. The questionnaire is directed to German EEE manufacturers and focuses on their environmental and social performance as well as SC integration depth. Furthermore, the collected set of data is numerical, which is then analysed statistically. The statistical analysis has the purpose to investigate the effect of SC integration depth on the environmental and social performance of manufacturers. Thereby, the predetermined hypothesis, whether a high SC integration depth results in a high environmental and social performances can be developed.
2.4 Data Collection
The research literature distinguishes between two approaches of data collection, which are primary data collection and secondary data collection (Bryman & Bell, 2011).
Depending on the character of the study and the included research objectives, a choice has to be made between the two approaches (Hair et al., 2003).
Primary data is the term used for data that is newly collected (Saunders, Lewis &
Thornhill, 2012). There are two common reasons that require primary data collection.
The first reason is that secondary data is not available (Ghauri & Gronhaug, 2010). The second reason is that the secondary data is not suitable to answer the research question (Ibid). Furthermore, Zikmund et al. (2012) point out that that up-to-date and new data is required for the research as information change during the course of time, which is another significant reason for collecting primary data. According to Ghauri &
Gronhaug (2010) the main method of collecting primary data is through surveys or interviews. The interview or survey questions are commonly distinguished as structured, semi-structured, and unstructured questions (Ibid).
Structured questions, are predetermined and are in a specific order (Ghauri &
Gronhaug, 2010; Bryman & Bell, 2011). Furthermore, in structured questions the answers to be given are defined in advance (Ghauri & Gronhaug, 2010; Bryman & Bell, 2011). Thus, the study subject does not have the option to answer freely, which leaves no room for interpretation by the researcher. Semi-structured questions are also predetermined, however, they are not arranged in a specific order (Ibid). Moreover, open-ended questions are included, which require individual answers of the respondents (Bryman & Bell, 2011). Thus, the results leave room for interpretations by the researcher (Bryman & Bell, 2011). Unstructured questions feature broad topics allowing the study subject to respond independently (Ibid). This requires the researcher to analyse and interpret the respondents’ answers in detail, to extract significant data.
These forms of primary data collection can be conducted either impersonalized through mail or email, or personalized through face-to-face interviews or telephone interview (Hair et al., 2003).
Secondary data is referred to data that has been gathered beforehand by someone else in the course of his/her own research purpose (Zikmund et al., 2012). Due to the fact that secondary data are edited versions of primary data, they represent the thoughts of someone else (Sachdeva, 2009). Secondary data can be obtained from written documents such as governmental publications, organizational reports or scientific articles (Zikmund et al., 2012). The internet has increased the accessibility for secondary data significantly, since it provides numerous databases (Ibid). Thus, the researchers have a quick access to crucial external data sets (Ibid).
However, despite being easily available and having (usually) a high quality, authors stress out that secondary data bears the possibility of being unsuitable or inadequate in the context of the research problem at hand, as it was originally gathered within the scope of another research (Saunders, Lewis & Thornhill, 2012; Zikmund et al., 2012).
As a consequence, the researcher has to understand and modify the secondary data
set, in order to benefit from the information (Ibid). Moreover, the researcher has to validate secondary data regarding its accuracy or potential biases (Ibid). As pointed out by Cooper & Schindler (2001, cited by Gharuri & Gronhaug, 2005) if a researcher uses secondary data its reliability becomes the researcher’s responsibility. Furthermore, secondary data may be out-dated as information changes quickly due to the advanced technology (Zikmund et al., 2012).
2.4.1 Data Collection of the Thesis
In this study both, primary and secondary data is collected. The secondary data is collected in context of the literature review. For this purpose the databases OneSearch and Google Scholar are accessed through the interface of the Linnaeus University.
Thus, for this study a collection of full text articles, e-books, and reference databases are examined. Furthermore, external websites are consulted to gain information in particular about the EEE industry.
The primary data is collected through a structured questionnaire, which consists of close-ended questions. Since, the questionnaire is sent through email, the database ORBIS is consulted to search for the contact information of German EEE manufacturers.
2.5 Questionnaire Design
Developing a viable questionnaire is the nucleus of every empirical study as Saunders, Lewis & Thornhill (2009) stress out. In this context Saunders, Lewis & Thornhill (2009) introduce four key survey components, which are:
A cover letter to introduce the research purpose
An introduction of the questionnaire to put the survey in context
The actual questionnaire
An ending of the questionnaire, which includes a thank-you-note and the researchers’ contact information
Moreover, the authors stress the importance of the questionnaires’ structure and wording, in order to receive crucial information, wherefrom valid conclusions can be drawn (Ibid). Saunders, Lewis & Thornhill (2009) describe 17 important factors regarding the structure of questions. The following points provide the essence from the original list:
Questions should be formulated as simply as possible (short, specific, simple
Questions should be formulated respectfully (avoid embarrassment or offence
for the participant)
Questions should be formulated one at the time
Avoid double-negative items in questions
Avoid double meanings in questions
Avoid biased questions
Develop a logical order of question
Provide clear instructions how to answer the questions
By considering the survey components and the advices regarding the structure of the questions, the relevance and accuracy of the survey itself increases significantly (Saunders, Lewis & Thornhill, 2009).
An additional factor, according to Bryman & Bell (2011) is the integration of open- ended and/ or closed-ended question. Closed-ended questions are usually applied in quantitative methods, since answers are predetermined (Ibid), for example:
Binary response formats (Agree __ Disagree __)
Numerical response formats (5 4 3 2 1 à 5 = strongly agree; 1 = strongly disagree)
Frequency response formats (Often __ Sometime __ Never __)
Open-ended questions on the other hand require the respondent to develop own answers (Ibid). This leaves the researcher with the often difficult task to correctly interpret the answers (Ibid). Therefore, according to Bryman & Bell (2011) closed- ended questions are usually, however not exclusively, used in qualitative researches.
Figure 4: Types of questionnaires, adapted from Saunders, Lewis & Thornhill (2009, p. 363)
Figure 4 provides an overview of possible questionnaire administrations (Saunders, Lewis & Thornhill, 2009). Even though Bryman & Bell (2011) do not distinguish between the terms ‘self-administered’ and ‘interviewer-administered’ as Saunders, Lewis & Thornhill (2009) do. However, they both agree that self-administered questionnaires are more appropriate to reach large sample sizes (Saunders, Lewis &
Thornhill, 2009; Bryman & Bell, 2011). Since, electronic or even postal deliveries are much more time-efficient than personal interviews or telephone questionnaires (Saunders, Lewis & Thornhill, 2009; Bryman & Bell, 2011). However, it is mentioned that interviewer-administered questionnaires are associated with significantly higher response rates and higher validity (Saunders, Lewis & Thornhill, 2009; Bryman & Bell, 2011). This derives out of the personal connection between the interviewer and the interviewee (Bryman & Bell, 2011; Saunders, Lewis & Thornhill, 2009). Therefore, researchers have to weigh pros and cons of the two delivery types and decide which is the most appropriate for their research project (Ibid).
2.5.1 Questionnaire Design of the Thesis
The research choice of this paper is the quantitative method, due to the structured questionnaire that collects numerical data through close-ended questions. As Kannegiesser & Günther (2014) stress out, it is important to ensure comparable measurement metrics, so that the empirical findings can be related to each other and statistical relations can be discovered. In order to do so, measurement scales, such as liker scales are developed for the questionnaire, which are assessable for the statistical program SPSS. Moreover, as Saunders, Lewis & Thornhill (2009) explain, in order to develop generalizable research conclusions, a high response rate of the survey is required, which in turn requires a large survey distribution. Thus, the database ORBIS provides relevant contact information of EEE manufacturers located in Germany.
Based on these considerations, a self-administered survey approach via email has been chosen, which takes the four survey components of Saunders, Lewis & Thornhill (2009) into account. After the initial survey distribution, three weekly follow-up emails are directed to the non-respondents are complemented.
2.6 Population and Sample
Ghauri & Grønhaug (2005) present six steps to develop a research population and sample (Figure 5).
(1) A key part of the research process is to define a specific population, which delivers relevant information for the research purpose (Ibid).
(2) A followed sampling frame is proposed to define the most important elements of a study population, which the developed sample has to reflect (Ibid).
(3) Selecting the most suitable sampling procedure ensures the best reflection of the population (Ibid). To develop a representative sample, literature proposes probability or non-probability sampling (Ghauri & Grønhaug, 2005; Saunders, Lewis & Thornhill, 2009). Probability sampling techniques such as simple random sampling or systematic