THE IMPACT OF FUNCTIONAL BOARD CHARACTERISTICS ON THE IMPLEMENTATION OF GREEN SUPPLY CHAIN MANAGEMENT PRACTICES

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THE IMPACT OF FUNCTIONAL BOARD CHARACTERISTICS ON

THE IMPLEMENTATION OF GREEN SUPPLY CHAIN

MANAGEMENT PRACTICES

Master thesis Msc. BA, specialization Supply Chain Management University of Groningen, Faculty of Economics and Business

January 26, 2020 SVEN GOOS Student number: 2485737 E-mail: S.J.Goos@student.rug.nl Supervisors: Dr. ir. S. Boscari Dr. ir. T. Bortolotti Co-Assessor Dr. X. Tong Acknowledgments:

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ABSTRACT:

The majority of the ecological footprint of end-products is not generated through focal firms their own activities, but by partners in the supply chain. Whereas the majority of existing environmental studies address board characteristics related to demographics, structure or independence, this study focuses on functional background of board members. In particular, the effect of functional board characteristics on Green Supply Chain Management (GSCM) practices is examined. This study aims to contribute to research that addresses determinants of GSCM by proposing an influential characteristic of focal firms. Data is analysed from Standard & Poor’s 1200 firms that disclosed their environmental data in the Carbon Disclosure Project questionnaire of 2017. The results suggest that firms having board members with SC related backgrounds, have a more comprehensive implementation of GSCM. The experience and knowledge these board members add to the powerful body within the firm, is a plausible explanation for these findings.

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1. INTRODUCTION

In September 2019, the largest worldwide climate protest in history took place. With over 4,600 climate protests simultaneously spread over 150 countries, the world proved once more it demands action on climate change (Weston, 2019). Accordingly, growing concerns about global warming also influence businesses' daily decision making. Being pressured by customers, governments, and other stakeholders, companies feel the need to lower their ecological footprint (Cordeiro and Tewari, 2015). However, instead of focusing on their entire production chain, most firms only try to optimize single-firm sustainability practices to create an environmentally friendly image (Pagell and Shevchenko, 2014; Wang and Dai, 2018). This is problematic, since a recent study by the Carbon Disclosure Project showed that, on average, supply chain greenhouse gas emissions are 5.5 times greater than companies’ direct impact (Carbon Trust, 2019). Ignoring environmental issues in the management of a supply chain not only affects the environment, but can also cause significant damage to a firm’s reputation, operations, and overall financial performance (Gualandris, et al., 2015). It therefore seems remarkable that large corporations fail to understand how they can lower their supply chains’ environmental impact, despite the extensive data accumulation they use to analyse and control their production network (Gualandris, et al., 2015; O’Rourke, 2014). To address the growing environmental pressures, firms need to expand their focus to partners within their supply chain.

In 1997, Handfield, et al. (1997) established the first academic foundation for environmental practices in supply chain, proposing the concept of Green Supply Chain Management (GSCM). This concept can be described as applying environmental management principles in the entire set of activities across the whole customer order cycle. Ever since, a vast amount of literature examined the implications of supply chains that combine elements of corporate environmental management and supply chain management (O’Rourke, 2014; Wang and Dai, 2018). Various studies suggest that GSCM practices improve environmental performance within supply chains (Geng et al., 2017). However, researchers fail to find consistent evidence to support that GSCM has a positive impact on cost savings and profits as well. This stands in contrast to findings that suggest such a relationship for single firm environmental practices and might be one of the reasons why GSCM has not been widely implemented along supply chains (Silva, et al., 2019; Walker et al., 2008).

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and Iraldo, 2010) due to increasing stakeholder pressures (Sarkis et al., 2010) and the operational and reputational risks of environmental mismanagement (Berrone and Gomez-Mejia, 2009; Wang and Dai, 2018). Because of this strategic relevance, environmental practice is a topic that should be of concern for the board of directors (de Villiers et al., 2011). Unsurprisingly, several studies have explored board characteristics and how these affect the implementation of environmental practices. But as far as concerned, no investigations can be found that specifically cover the board’s influence beyond a company's scope on an environmental level, with regard to GSCM collaborations with partners in the supply chain. However, past research supports reasoning to believe that certain board characteristics contribute to the implementation of environmental supply chain practices. To illustrate, Gnan et al. (2013) found support that board characteristics can have a major impact on how stakeholders such as customers and suppliers are involved in business decisions related to environmental issues.

While the majority of environmental studies in the field of corporate governance addresses demographic board characteristics (e.g. board size and gender diversity), little is known about the impact of board functional background diversity in this debate, despite its fundamental influence on strategic board decisions (Minichilli et al., 2009). Functional background refers to an individual's functional specialization, such as finance, marketing, or operations (Bunderson, 2003). Teams composed of diverse fictional backgrounds can potentially enhance information processing by bringing a variety of perspectives and skills, necessary for making strategic decisions (Michie et al., 2002). Consequently, policymakers will raise the awareness of topics that fit their personal functional experiences (Hambrick, 2007). To illustrate, it is plausible to think that someone in the board with a functional background in marketing increases the awareness of the board on customer management, because of its personal experience and knowledge on this topic (Frankel et al., 2008). With the same reasoning, someone with a supply chain (SC) background in the board of directors can positively affect the board’s attitude towards collaboration with supply chain members, and consequently positively influence GSCM implementation. To contribute to the sustainability discussion within supply chains, this study addresses the following research question:

What is the impact of board members' functional background on Green Supply Chain Management implementation?

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researches, it exclusively examines the board of the focal firm, while other researchers on GSCM focus rather on external drivers of GSCM, such as willingness of customers/suppliers to cooperate, stakeholder pressures, reputation, and efficiency requirements led by the industry (Dai et al., 2014; Testa and Iraldo, 2010; Walker et al., 2008). Therefore, this study is of interest for corporate policy makers by highlighting the importance of functional background in board composition. To generate insights in GSCM practices of firms, data is retrieved from the Carbon Disclosure Project questionnaire. The data on board members and their functional backgrounds was obtained from the BoardEx database. The sample of this paper is a section of Standard & Poor’s 1200 companies.

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2. THEORETICAL BACKGROUND

Nowadays, the vast majority of large firms has moderately adopted environmental sustainability practices in their day-to-day operations and even implemented a policy for it (Ioannou and Serafeim, 2017). Examples are workforce training requirements for environmental adaptation in the work routine, recycling of waste products, and energy saving adaptations (Kassinis and Soteriou, 2003; Sarkis et al., 2010). These practices are mainly constrained to the individual firm level, and do not reach to other actors in the supply chain (Pagell and Shevchenko, 2014). The majority of greenhouse gas (GHG) emissions take place upstream the supply chain, which means that a greater potential of reduction is noticeable outside the boundaries of focal firms (Carbon Trust, 2019). Nonetheless, a recent study among 115 major purchasing organizations around the world, found that only 35% of suppliers reported that they are engaging with their own suppliers on climate issues and helping them to change (Carbon Trust, 2019). Firms solely addressing environmental concerns is not sufficient to solve the problem of climate change. As Pagell and Shevchenko (2014) argue, individual sustainability practices only lead to environmental harm reduction instead of harm elimination. In other words; individual firms that aim to become more environmentally efficient might be neglecting how their current cooperation with supply chain partners can negatively affect the ecological footprint of their products or services. To illustrate, last decades’ globalization of supply chains has a large ecological impact, because the distances covered are longer. Although companies adopt ecological efficient transportation tools, this does not outweigh the ecological effects that the expansion to an intercontinental supply chain network entail (Gualandris et al., 2015). Apart from the ecological effects, firms should realize that they can be both held responsible for and be affected by the negative effects that all supply chain members cause in a supply chain. Environmental mismanagement from partners along the chain can seriously harm a focal firms’ reputation, operations, performance and hence their competitiveness (Walker et al., 2014; Wang and Dai, 2018). Altogether, ignorance of environmental sustainability in the supply chain both harms firms’ competitiveness and the environment.

2.1 Green Supply Chain Management (GSCM)

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integrating environmental thinking into supply chain management, including product design, material sourcing and selection, manufacturing processes, delivery of the final product to the consumers as well as the end-of-life management of the product after its useful life" (Srivastava, 2007). GSCM literature has developed substantially ever since the introduction of this term, due to the fact that value chains have moved from a simplified ‘chain’ to a more complicated ‘network’ of buying and supplying firms (Gualandris et al., 2015). Subsequently, products bridge more distance and intervene with more chains, making it more challenging to engage with suppliers, customers, and other partners in to make products less environmentally damaging. With this increasing complexity, studies among practices of greening the supply chain advanced as well (Ahi and Searcy, 2013).

In GSCM literature, there are two factors to measure the success of GSCM implementation: performances and practices. Whereas performance revolves around measurement of waste or emissions reduction, such as diminishment of greenhouse gasses, GSCM practices relate to the environmental strategy and actions taken by a supply chain (Testa and Iraldo, 2010, Chin et al., 2015). GSCM practices are proven to be a driver of different positive effects on firms and environment. Geng et al. (2017) found support that GSCM practices lead to positive environmental performance within supply chains. In GSCM literature, there is a deviation between internal and external GSCM practices (Vachon and Klassen, 2006). External GSCM is a practice whereby one firm's environmental management activities are integrated in the activities of other actors within the supply chain. It is also referred to as environmental collaboration and requires firms to devote specific resources to co-develop activities to address environmental issues along the supply chain (Vachon, 2007; Zhu et al., 2013). Examples are cooperation with customers and suppliers to develop cleaner production or green packaging methods, design of products to avoid or reduce the use of hazardous products or labelling of products based on their environmental harm (Zhu et al., 2005). Internal GSCM is a practice in which a firm requires other supply chain partners to adopt environmental management mechanisms (Vachon, 2007; Zhu et al., 2013). These are practices whereby no significant commitment of own resources is necessary to improve environmental performance outside a firm’s operations. Examples are the examination of a supplier’s environmental practices through disclosed environmental information or requiring certain environmental product specifications. When firms require other supply chain actors to act in a certain way, as has been illustrated in the examples above, it is referred to as environmental monitoring (Vachon, 2007; Zhu et al., 2013).

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2011; Cousins et al., 2019; Rao and Holt., 2005; Schmidt et al, 2017). In addition, other internal organizational factors, such as ethical values of the organization and personal commitment from leaders to environmental harm reduction, are drivers for GSCM implementation (Walker et al., 2008). Nonetheless, the majority of research addresses external (forces outside of firms’ boundary) forms of GSCM determinants instead of internal (forces inside firms) drivers. These include regulatory and societal pressures, willingness of customers and suppliers to cooperate, environmental risk minimization, and gaining competitive advantage over other players in the market (Walker et al., 2008). Despite these advantages of GSCM, various barriers withhold firms from the actual implementation of such practices. Walker et al. (2008) dedicated an entire study to summarize these drivers and barriers to GSCM practices. They found that most research on factors driving GSCM were forces outside of the focal firms’ scope, while most barriers were addressed by papers focusing on internal organizational reasons. As major internal barriers they identified insufficient cost effectiveness and a lack of legitimacy of GSCM practices. This illegitimacy, also known as greenwashing, is based on the fact that many companies advertise their alleged greenness, often without eligibility for such a claim. Important external factors that were identified relate to the poor commitment of suppliers, the unwillingness to exchange information with one and another, and the increasing complexity of supply chains which makes it harder to build GSCM relationships (Lee, 2008; Vachon and Klassen, 2006; Walker et al., 2008). To this day, several papers have contributed to GSCM literature by investigating reasons for or against the implementation of GSCM practices. However, the number and range of studies is substantially smaller compared to research on determinants of single-firm related environmental practices. Different from studies addressing determinants of single-firm environmental practices, the role of board of directors received very limited attention in GSCM literature (Walker et al., 2008).

2.2 Role of board in single-firm implementation of environmental practices

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strategies (de Villiers et al., 2011). Since then, environmental awareness has only increased (Kouloukoui et al. 2019).

Several arguments exist why environmental strategy should be addressed by the board of directors. First of all, the execution of a well-established environmental strategy can bring several opportunities, potentially increasing shareholder wealth (Berrone and Gomez-Mejia, 2009). Firms face market opportunities for environmentally conscious products and services, improved access to resources, and savings in the use of water and energy resources. Secondly, the potential consequences of environmental mismanagement are too disastrous to not be considered for the board of directors. Environmental mismanagement can cause extensive reputational damage, operational disruptions, and increased insurance and legal costs to firms (Berrone and Gomez-Mejia, 2009; Wang and Dai, 2018). Thirdly, environmental strategies play an important role in stakeholder management who pressure organisations to adopt environmental practices (Sarkis et al., 2010). Because climate change is an ever-growing societal issue, the scope of stakeholders that is affected by the environmental strategy is wider than before (Delmas and Toffel 2008). A wider range of stakeholders expects environmental accountability, including investors, customers, policy makers, employees, NGOs and various other players. Besides their controlling function, the board of directors is responsible to perform the strategic roles that are essential for a company's success (Pearce and Zahra, 1992). Due to the importance of environmental strategy on the success of a firm, it is a topic that should be one of the top priorities of the board of directors (de Villiers et al., 2011).

2.3 Role of board characteristics on environmental strategy

In the past, scholars have identified various board characteristics that are deemed highly relevant for not only firm, but also environmental and social performance. Such that these characteristics determine the course of action and code of conduct apprehended by an organization. For example, Kouloukoui et al. (2019) found that two aspects, namely independent board directors and the number of people in the board have a significant positive effect on the climate management of firms. Another study, conducted by Glass et al. (2016), investigated the impact of female leaders on the corporate environmental strategy of corporations. They found a minor positive link suggesting that firms with more gender diversity within the board have a better environmental performance strategy compared to firms with less diversity.

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(Michie et al., 2002). As Minichilli et al. (2009) argue, background diversity is a fundamental determinant of the board’s focus. Different backgrounds result in a diversity of expertise, which turns out to significantly affect outcomes such as turnover and performance because of the impact on affective, cognitive, and communication processes of the board of directors. According to Van der Walt and Ingley (2003), board members are selected based on their particular backgrounds. Their appointment can be motivated by the additional knowledge and experience they can offer to the board and the professional discipline they operate in. One form of background diversity is the so-called functional background of board members which relates to the job title or function that they perform besides their board duties (e.g. CFO, Sales Manager or Operations Manager). Functional background diversity is relevant because it adds a diverse range of skills and knowledge to a group, which is specifically relevant in organizational group settings (Ferrero‐Ferrero et al., 2015; Milliken and Martins, 1996). Furthermore, the expertise that comes with it is more job- or task-related than observable demographic characteristics of members, such as gender and age (Minichilli et al., 2009). Because functional characteristics are more closely connected to the tasks performed by a board, they are more likely to influence these (Minichilli et al., 2009). Furthermore, Bantell (1993) found that groups with different functional backgrounds are better in overall strategic decision making. Strategic decisions require the highest levels of information processing capacity (Michie et al., 2002). Therefore, boards composed of several functional backgrounds can potentially enhance information processing by bringing a variety of perspectives, knowledge, and skills to the decision-making process (Michie et al., 2002). Combining the research streams on functional background and GSCM, functional backgrounds can be considered as relevant for GSCM, which is considered a strategic attribute to firms.

2.4 Boards’ functional background and GSCM practices

Nowadays, companies do not exist in isolation, but belong to a complicated network of buying and supplying firms (Gualandris et al., 2015). Maintaining formal and informal inter-firm relations will promote trust, reduce risk, and strengthen commitment, cooperation, and hence profitability (Lai 2009). Besides that, integrating behind a company’s boundary with SC members is important to create more value for the chain. Supply chain integration is even more important with regards to environmental management, as focal firms are held accountable for environmental disasters occurring in their suppliers’ operations and there is greater potential in reducing the environmental harm done by the value chain (Seuring and Müller, 2008).

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functional background can have a major impact on strategic decisions of a group. It influences the way managers perceive situations and thereby affects their strategic choices (Wagner and Kemmerling, 2014). Being composed of different functional backgrounds gives boards therefore the potential to influence the environmental strategies within the supply chain.

One theory that supports this reasoning is the Upper Echelons Theory. According to Hambrick (2007), this theory consists of two interconnected assumptions: (1) executives act on the basis of their personalized interpretations of the strategic situations they face and (2) these personalized constructs are a function of the respective executives' experiences, values, and personalities. Consequently, organizational outcomes are partially influenced by managerial background characteristics of the top-level management team. Dai et al., (2014) conducted a study that linked both the Upper Echelons Theory and GSCM in the perspective of stakeholder pressure and concluded that top management plays a key role in the implementation of GSCM practices. According to the theory, top management is the primary interface between stakeholders and the firm in their day to day job. Consequently, top management faces stakeholder pressures directly and is therefore more likely to implement GSCM practices with supply chain partners (Dai et al., 2014).

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In summary, having a SC related background in the board increases presumably both the awareness for the importance of GSCM and the chances of implementing these practices. Based this reasoning, the following hypothesis is developed:

Hypothesis 1: Having one or more board representative(s) with a background in the field of supply chain management will positively influence the implementation of Green Supply Chain Management of focal firms.

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3. METHODOLOGY

3.1 Data collection and sample

To assess the relationship on GSCM practices, secondary data from a survey of The Carbon Disclosure Project (CDP) of 2017 is used. Since 2003, the CDP annually request the largest companies from several countries worldwide to voluntarily disclose their climate related activities by filling in a questionnaire (Depoers et al., 2016). The CDP requests data on direct and indirect GHG emissions (i.e. emissions from sources controlled directly by the firm and emissions from sources controlled by other entities, respectively), climate change risks and opportunities perceived by organizations, climate policies, and supply chain collaboration for environmental management (Ben-Amar et al., 2017). The CDP is rated as one of the most reliable, valuable and comprehensive sources of environmental data (Kouloukoui et al., 2019). The data is highly structured, because of the use of standard formats.

For the identification of functional background profiles, the BoardEx database of 2017 is used. The BoardEx database, composed by Management Diagnostics Limited, is a leading independent source for global board governance data, including data on the current non-board role of board members. The database contains profiles of more than 1.3 million individuals (BoardEx, 2019). It tracks historical information on board directors on private and public firms worldwide by analysing data from annual reports and company websites (Walls and Hoffman, 2013). The database is used in several fields of research that address the board of directors (Custódio, et al., 2013; Fracassi and Tate, 2012; Walls and Hoffman, 2013).

We use number of employees, board size, country and industry as control variables later in the analysis. Data used for the control variables is retrieved from multiple datasets. In addition to the BoardEx and CDP, the Standard & Poors’ Compustat database is used. The Compustat provides financial and market information on companies around the world (de Villiers, 2011).

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reliable and less susceptible to outliers. In addition to the available information on sustainable business activities, these firms are also widely represented in other databases, such as the BoardEx and Compustat.

Since not all the 1200 S&P companies were present in both the CDP and BoardEx dataset, a subsample of companies remained. Out of all Standard and Poor’s Global 1200 companies, 224 were present in both datasets. These 224 firms covered functional background profiles of 3102 board members. The original data on GSCM is qualitative, but for analysis purposes, being transformed into quantitative data by inductive coding. In Section 3.2, the measurement approach is clarified for every variable.

3.2 Variable measurements

3.2.1 Dependent variable: GSCM practices

The CDP survey is composed of different sections. Therefore, only a sub-sample of questions referring to GSCM practices as the unit of analysis is used to extract data (Forza, 2002). These three questions are displayed in Appendix A. Within the appendix an explanation is provided why the questions are chosen, and which parts of the questions are relevant for this research. To identify the presence of certain GSCM practices in the CDP survey, a framework is used based upon the GSCM scales of Zhu et al. (2008). Multiple prominent studies in the field of GSCM have used the scales of Zhu et al. (2008) in their research (Green Jr. et al., 2012; Laari et al., 2016; Ninlawan et al., 2010; Testa and Iraldo, 2010). The index indicates different types of GSCM practices. Based on work in the field of environmental studies and supply chain management, Zhu et al. (2008) refined 21 measurement items for individual GSCM practices, categorized in 5 different constructs. These five scales are Green Purchasing, Internal Environmental Management, Cooperation with Customers, Eco Design, and Investment Recovery. Internal Environmental Management and Eco Design are internal GSCM practices, because they can be implemented by a single firm. Green Purchasing and Cooperation with Customers are referred to as external GSCM (Zhu et al., 2012)

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theoretical framing of practices was conducted, as well as an analysis after coding for all GSCM practices. By means of these analyses, a decision was made on which practices of the framework of Zhu et al. (2008) would remain in order to make grounded assumptions on overall GSCM implementation.

For Investment Recovery, engagement with supply chain partners is not particularly needed of (Zhu et al., 2008). It is not likely that single-firm GSCM practices will be mentioned by respondents, because the CDP questions are constructed in a way that the GSCM practices are asked per supply chain actor. After analysing the answers on the CDP survey, the construct Investment Recovery was excluded because not a single practice was mentioned by the respondents. Likewise, four individual practices related to management support in the Internal Environmental Management construct and one practice in the Green Purchasing construct were removed because of a misalignment with the CDP answers. In Table 3.1 the final framework to identify different engagement practices is displayed.

Because the CDP questions will mainly consist of qualitative data, the answers will be matched to the different categories. An inductive binary coding method is applied (Karlson, 2016). If a respondent indicates a practice is being executed, a 1 is selected. An example for answers on the questions provided in Appendix A is given in Appendix B.

Table 3.1

Framework of GSCM practices

_Constructs _{Measurement items}

Green Purchasing GP1 Cooperation with suppliers for environmental objectives

(GP) GP2 Environmental audit for suppliers’ internal management

GP3 Suppliers’ ISO14000 certification

GP4 Second-tier supplier environmentally friendly practice evaluation Internal Environmental

Management

IEM1 Cross-functional cooperation for environmental improvements

IEM2 Environmental compliance and auditing programs

(IEM) IEM3 ISO 14001 certification

IEM4 Environmental management systems exist Customer Cooperation CC1 Cooperation with customer for eco-design

(CC) CC2 Cooperation with customers for cleaner production

CC3 Cooperation with customers for green packaging

Eco Design ED1 Design of products for reduced consumption of material/energy

(ED) ED2 Design of products for reuse, recycle, recovery of material, component parts

ED3 Design of products to avoid or reduce the use of hazardous

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3.2.2 Independent variable: functional background

As discussed in Section 2.4, several papers on GSCM apply a broad scope of SCM, including business processes such as purchasing, manufacturing, sales, and logistics. Therefore, next to supply chain managers, several other functional areas within SCM will be used. Rossetti and Dooley (2010) conducted a study to define the main functional areas within SCM and developed a framework which indicated the following four functional backgrounds:

1. Supply chain management: referred to as supply chain management 2. Sourcing: referred to as sourcing, purchasing, and procurement

3. Operations: referred to as operations, conversion, assembly, production scheduling, order processing, inventory management, and managing supply and demand.

4. Logistics: referred to as logistics, transportation, warehousing, and customer service. Within this framework, sourcing and logistics can be seen as functions with a somewhat external focus. These functions require interactions with other partners within the supply chain. Operations, however, is more internally focused because it mainly considers internal processes (Rossetti and Dooley, 2010). Therefore, it is plausible to think operations might demonstrate different effects on GSCM than the other three functional backgrounds. Operations will thus both be tested individually and jointly with the other functionalities. Board members are categorized as ‘supply chain managers’, ‘sourcing managers’, ‘operation managers’, and ‘logistics managers’ based on the descriptions of functionalities by Rossetti and Dooley (2010).

Although there is no unanimity in the literature, a common approach to see certain effects is to include time lags between the moment a board is operating and the moment effects of this are visible. Therefore, a decision had to be made on the amount of time a member would have to serve the board to be included in the sample. Some studies use a short-term timeframe of 0-1 year to measure impact (Liao et al., 2015). However, this study only includes directors with a time lag of two years or more. GSCM practices often stem from a certain philosophy or strategy, therefore it is likely that the effect of those strategies, such as implementation of GSCM practices, is better visible over a longer period of time (Testa and Iraldo, 2010).

3.2.3 Control variables

In order to ensure internal validity, control variables were added to the model. With adding control variables to the model, one is able to make a more reasonable estimate of the effect of functional backgrounds on GSCM implementation. Because to some extent, possible other predictors of GSCM are considered (Karlsson, 2016).

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Firm Controls

Due to impact risks, larger firms tend to identify and manage environmental concerns better (Clarkson et al., 2008). Besides that, larger firms generally have more resources to implement different strategies such as GSCM practices (Golicic, and Smith, 2013). Therefore, firm size is controlled for by means of a logarithm of the number of employees (Agarwal, 1979). This data comes from the Standard & Poors’ Compustat database. A very small number of firms had missing values in this dataset. To make sure these firms did not influence the effect, average scores of the sample group were used to replace the missing values.

Board governance controls

As explained in the theory section, different studies confirm that certain governance factors in boards have an influence on firms’ environmental strategies. De Villiers et al. (2011) and Kouloukoui et al. (2019) found that the size of the board has a proven positive effect on climate management by firms. Larger firms can include more directors, and are able to derive greater value from the diverse and extensive resources these board members bring (de Villiers et al., 2011). Furthermore, environmental uncertainty within firms leads to larger board sizes in order to allow firms access to the expertise needed to overcome these problems (Booth and Deli,1996). The data on number of directors is retrieved from the BoardEx database.

Industry Controls

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highly-complex, multicomponent products are more likely to have long term collaborations between suppliers and buyers. Therefore, a single dummy variable is adopted to indicate industries that are likely to collaborate based upon the assumptions of Grant and Baden-Fuller (1995).

Country Control

Prior research found that the location of a company is an important determinant of environmental practices (Ioannou & Serafeim, 2012; Testa and Iraldo, 2010). Particularly, country specific laws and regulations contribute to environmental performance differences among firms (Kagan et al., 2003), and also determine GSCM implementation (Walker et al., 2008). Therefore, the headquarters’ geographical location is added to the model. Jordan and Lenschow (2010) argue that especially in the European Union environmental policy integration has a relatively prominent status in both politics and law. Therefore, a single dummy variable is adopted to distinguish between European and Non-European companies. This data is disclosed in the CDP database and accordingly used for coding.

3.3 Measurement analysis

In total, 224 firms’ GSCM practices could be identified. Table 3.2 demonstrates the count of GSCM practices. There are large differences between individual practices. However, on average, constructs do not differ tremendously, considering several of them consist of four individual practices, and others of three.

Table 3.2

GSCM practices sample distribution GSCM

Practice _{GP1 GP2 GP3 GP4 IEM1 IEM2 IEM3 IEM4 CC1 CC2 CC3 ED1 ED2 ED3}

Number ₁₁₃ 68 10 6 8 128 14 53 86 29 12 96 36 15

Construct Green Purchasing Internal Environmental Management

Customer

Cooperation Eco Design

Number 197 203 127 147

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the different independent variables. One may note that Customer Cooperation in particular has a smaller scale relative to the others. This indicates that no large differences were noted within this construct.

Table 3.3

Descriptive statistics GSCM practice constructs

Min. Max. Mean Std. Dev. Skewness Kurtosis

Green Purchasing -0,941 3,398 0 1 0,745 -0,291

Internal Environmental Management -1,004 3,485 0 1 0,739 -0,075

Customer Cooperation -0,744 1,915 0 1 0,927 -0,624

Eco Design -0,78 2,833 0 1 1,159 0,611

Total GSCM score -1,105 2,99 0 1 0,62 -0,592

In Table 3.4, the identified board functional backgrounds are displayed. Out of 3102 board profiles, 74 profiles of board members with a supply chain related background were identified. The majority of identified individuals has an operations background, while the board members in supply chain, sourcing, and logistics are less prominently present.

Table 3.4

Number of identified functional backgrounds

Functional background Number of boards with functional background Supply Chain 9 Sourcing 11 Logistics 5 Operations 49 Total 74

3.3.1 Measurement assessment

To be able to derive statistical analysis on the variables, some preliminary tests need to be executed on the data. Because a single independent variable will be used for every regression, tests will be executed on the dependent variables.

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(Garver and Mentzer, 1999). To assume normality, skewness and kurtosis are tested. According to Table 3.3, data is distributed to the left. Because for all constructs, the mean is relatively closer to the minimum as it is to the maximum value. This indicates that the majority of firms implemented not more than half of the GSCM practices. This is confirmed by the values in Table 3.3. Kurtosis remains within the +2.00 to -2.00 threshold, where the skewness did not. Due to the small GSCM scales, the issue relating to the skewness is not caused by outliers (George and Mallery, 1995). According to the research of Green Jr. et al. (2012), that also used the constructs of Zhu et al. (2008), a skewness coefficient within the -2.00 and +2.00 range is appropriate to assume that the data on GSCM practices is normally distributed.

Eventually the SC related functional backgrounds in the board are tested on their relationship with the four GSCM Practices together, by combining the different constructs in one model. For the situation whereby the constructs are put jointly in one model, correlation is relevant (Garver and Mentzer, 1999). As demonstrated in the correlation matrix in Table 3.5, the determinant has a score of .234, which is well above .001, indicating that the items are to some extend related, and can be measured together. However, a correlation value of above >.8 suggests that the items are explaining the same thing, indicating multicollinearity. According to the correlation matrix (Table 4.3), this is not violated (Garver and Mentzer, 1999).

Table 3.5

Correlation matrix and KMO test of GSCM practice constructs

GP IEM CC ED

Green Purchasing 1

Internal Environmental Management ,629* 1

Customer Cooperation ,407* ,570* 1

Eco Design ,361* ,541* ,480* 1

Determinant = ,276

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0,740

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meaning that the factors are not interrelated. Accordingly, individual constructs do not have to be deducted to make the analysis on the total GSCM practices more reliable. The items for the accumulated GSCM scores all scored high on a single construct with high reliability and a sufficient number of items. To conclude, the EFA allows for a multiple regression analysis to test whether SC related functional background influences all four GSCM practice constructs together (Anderson and Gerbing, 1988).

Table 3.6

Factor analysis GSCM practice constructs

Component 1

Green Purchasing 0,757

Internal Environmental Management 0,879

Customer Cooperation 0,776

Eco Design 0,746

3.4 Model and data analysis

To make a detailed analysis on the hypothesized relationship, multiple analytical tests are conducted. The first analysis tests the effects on individual practices, with board members’ functional backgrounds being the independent variable and GSCM practices the dependent variable. First, a Chi-Square is executed because both the independent and dependent variable are binominal and data is skewed (Karlsson, 2016; McHugh, 2013).

Secondly, the individual practices are grouped into the four different constructs of GSCM proposed by Zhu et al. (2008), to test both the relationship on the four constructs of GSCM. Every construct is tested separately in a multiple linear regression, including the control variables (Karlsson, 2016; Petrocelli, 2003). Next, the constructs are measured together by combining them in one multivariate multiple regression (Hair et al, 2010). Lastly, all the individual practices GSCM are grouped into one construct and tested using a multiple linear regression analysis. For clarification on the different regressions, their models are visualized below.

(1) MODEL: GSCM Construct = 𝛽0 + 𝛽1FUNCTIONAL BACKGROUND + 𝛽2Control + e

(2) MODEL: GSCM Constructs (GP+IEM+CC+ED) = 𝛽0 + 𝛽1FUNCTIONAL BACKGROUND

+ 𝛽2Control + e

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Because the number of observations on several functional backgrounds is insufficient to do a proper regression analysis, some functional backgrounds are combined into one group. As explained in Section 3.2, the supply chain, sourcing and logistics backgrounds are separated from the operations background, because they structurally interact more with parties outside the firm (Rossetti and Dooley ,2010). When this groups are tested separately, a deeper and more diverse analyses can be provided. For clarification, the different test groups are displayed in Table 3.7.

Table 3.7

Sample of independent variables

Functional background Number of boards with functional background Group 1 2 3 Supply Chain 9 x x Sourcing 11 x x Logistics 5 x x Operation 49 x x Total 98 23* 49 70*

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4. RESULTS

In the results section, outcomes of the quantitative analyses are presented. As explained in Section 3.4, the effect on three different levels of GSCM practices will be examined; individual GSCM practices (Section 4.1.1), GSCM practices per construct (Sections 4.1.2 & 4.1.3) and the overall GSCM practices (Section 4.1.4).

4.1 Relationship analyses

4.1.1 Functional background as a determinant of Individual GSCM practices

To test the relationships of functional background and individual GSCM practices, a Chi-squared difference test is conducted. A high, significant Chi-squared is a strong indicator for a vast difference between the situation with and without a certain functional background within the board (Anderson and Gerbing, 1988; Garver and Mentzer, 1999). The summarized results can be found in Table 4.1, and a more detailed version is displayed in Appendix C. The individual practices returned significant differences at the 0.05 level in all groups. The most significant and relative strong positive relationships are highlighted in the group with all the SC-related backgrounds combined (Group 3). In both Groups 1 and 3, most significant values indicate a moderate effect, whereas in Group 2, mostly small effects are demonstrated (McHugh, 2013). When comparing the group that interacts relatively more with parties outside the firm (Group 1) and operations functional backgrounds (Group 2), Group 1 has more significant and stronger positive relationships. However, Group 2 with the operations functional background demonstrates more individual positive relationships in customer cooperation than Group 1.

Table 4.1

Summarized Chi-square significances on individual GSCM practices (based on Appendix B) GSCM Practices

Significant* relationships Number of

practices Group 1 Group 2 Group 3

Green purchasing 4 4 1 4 Internal environmental management 4 3 2 3 Customer cooperation 3 1 2 2 Eco design 3 1 2 2 Total significant relationships 14 9 7 11

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4.1.2 Functional background as a determinant of GSCM practices per construct

After the individual analysis of GSCM practices, the GSCM practices are tested per construct, using a hierarchical multiple linear regression. The purpose of this hierarchical regression is to examine the effect of the independent variable more precisely and whether it influences the exploratory power of the model (Petrocelli, 2003). Therefore, this hierarchical multiple regression analysis is important to test the hypothesis. The results are shown in Table 4.2. The analysis is divided in two steps. First, the control variables were entered in the model (Group 0), as proposed in 3.2. In the second step, each functional background group is separately added to the model.

The results demonstrate that adding the functional backgrounds related to Groups 1,2 and 3 all give significant positive effects for all the four different GSCM constructs. According to the adjusted R2_{, all}

models seem to increase the explanatory power if the functional backgrounds are added. Group 3 has both the most exploratory power and the largest observed positive effect. The R2_{values are similar to other}

studies identifying GSCM determinants (Liu et al., 2012; Testa and Iraldo, 2010; Wu et al., 2012), which can be explained by considering that GSCM practices can be influenced by a large number of variables and practices. Also, the R2_{is not decisive for this research, since the aim was not to build a powerful model,}

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Multiple linear regression of GSCM practice constructs

Group 0 Group 1 Group 2 Group 3

Panel A: Green Purchasing

Constant -1,304** -1,073** -1,346** -1,269** Functional background 0,768** 0,36* 0,549**

Controls

Employees 0,224 0,151 0,225 0,203 Number of board directors 0,065** 0,054* 0,061** 0,054* Country 0,105 0,054 0,115 0,108 Industry pollution 0,149 0,096 0,204 0,18 Industry collaboration 0,233 0,169 0,203 0,17 Adjusted R squared 0,084 0,128 0,15 0,138 N observations 224 224 224 224

Panel B: Internal Environmental Management

Constant -1,129** -0,877** -1,18** -1,093** Functional background 0,834** 0,445** 0,558**

Controls

Employees 0,363** 0,283* 0,364** 0,342** Number of board directors 0,022 0,01 0,017 0,01 Country 0,35* 0,296 0,363** 0,354** Industry pollution 0,325* 0,268* 0,393* 0,357* Industry collaboration 0,195 0,125 0,158 0,131 Adjusted R squared 0,12 0,173 0,15 0,176 N observations 224 224 224 224 Panel C: Customer Cooperation Constant -0,722* -0,55 -0,797* -0,681* Functional background 0,571* 0,653** 0,636** Controls Employees 0,247 0,193 0,25 0,223 Number of board directors 0,019 0,01 0,011 0,005 Country 0,143 0,105 0,161 0,147 Industry pollution 0,098 0,059 0,197 0,134 Industry collaboration 0,037 -0,011 -0,017 -0,036 Adjusted R squared 0,014 0,036 0,082 0,087 N observations 224 224 224 224

Panel D: Eco Design

Constant -0,914** -0,82** -0,97** -0,887** Functional background 0,311* 0,482** 0,431**

Controls

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4.1.3 Functional background and GSCM practice constructs jointly

In this section, the 4 types of GSCM practices constructs are tested together in a multivariate multiple regression (Karlsson, 2016). In Table 4.3 the results are displayed. Wilks’ Lambda significance scores are below 0.05 for all independent variables. This indicates that there is a significant difference between boards with and boards without an SC related functional background when considered jointly on the four different GSCM variables. Wilks’s Lambda for groups 1,2 and 3 is between .882 and .923, and significant, meaning that a moderate effect is predictable on the overall GSCM practices when there is a presence of a board member with an SC functional background (Hair et al., 2010). For both the multiple regression analysis and the multivariate multiple regression analysis, the effects (beta coefficients) of SC functional backgrounds in the board on the GSCM constructs are similar to the ones in Table 4.2, and for that reason are not displayed in this section.

Table 4.3

Wilks’ Lambda tests on the overall effect on GSCM constructs

Group 1 Group 2 Group 3

Value 0,882 0,923 0,878

F 7,272 4,569 7,6

Significance 0 0,001 0

4.1.4 Functional background as a determinant of overall GSCM practices

Because the GSCM practice constructs demonstrate similar results in the regression, all the scores of the individual GSCM practices are accumulated to a total GSCM score for every single firm. This implies that the scores on the scales of Green Purchasing, Internal Environmental Management, Cooperation with Customers and Eco Design are combined to one value. Again, a hierarchical multiple regression analysis is conducted, which results are displayed in Table 4.4. Compared to the regressions in 4.1.2, the R2_has

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constructs. When both these groups are combined in one sample group (Group 3), effects seem not necessarily to become stronger, but more significant, accordingly resulting in a higher R2_.

Table 4.4

Regression overall effect of SC related backgrounds in the board on GSCM practices

Group 0 Group 1 Group 2 Group 3

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5. DISCUSSION

The purpose of this research is to understand the role of functional backgrounds in boards on the implementation of environmental practices in the supply chain. This hypothesis was formulated using the Upper Echelons Theory (Hambrick, 2007). In the context of this research, this theory suggests that having a SC-oriented representative on the board might increase the awareness towards the importance of GSCM within a firm's strategic choices (Dai et al., 2014; Hambrick, 2007). A board represented with a member (or members) with a SC background will better understand the relevance of extending environmental management to the supply chain, because of the expertise, knowledge, and skills that this functional background adds to the board. Therefore, these boards are more likely to make choices or give advice that generally support the implementation of GSCM. In line with the hypothesis, the model which examined a subsample of companies listed in the S&P 1200, displayed that firms which had board members with SC related backgrounds, had a more comprehensive implementation of GSCM practices. On all three different GSCM levels, namely the 14 individual practices, the four constructs (Green Purchasing, Internal Environmental Management, Customer Cooperation, and Eco Design) and the overall GSCM practices score, significant positive relationships are revealed. The overall results of this study suggest that supply chain functional backgrounds in the board of directors positively influence the implementation of GSCM. This supports Hypothesis 1 and thereby answers research question.

A distinction of SC-related backgrounds can be made based their interaction with other supply chain actors. While backgrounds such as supply chain, logistics, and sourcing have relatively more interaction, operations is more internally focussed (Rossetti and Dooley, 2010). When comparing all their relationship scores, the strongest positive relationships are present in the group that interacts relatively more with the supply chain partners. A plausible reasoning is that operation functions are predominantly focused on the processes of the focal firm (Koyuncu et al., 2010). To a lesser extent, they possess the influential knowledge, skills, and experience required to address the importance of GSCM practices to the board (Michie et al., 2002; Testa and Iraldo, 2010).

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score better on the external GSCM practices than the operations background. However, contradictory to this reasoning, the results of this study do not suggest the existence of a major difference. They suggest that the presence of operation backgrounds is not necessarily only beneficial for the implementation of internal GSCM practices, but for external too. Within the regression analysis displayed in Table 4.2, the operations group (group 2) even has a relatively stronger effect on Customer Cooperation. This construct is, next to Green Purchasing, one of the two external GSCM constructs (Zhu et al., 2013). Similar results are visible when testing for the individual GSCM practices, displayed in Table 4.1. A reason for the misalignment between the results and theory could be that operations managers are very dependent on the supply chain to execute their own tasks. The operations function is a linking factor that manages supply and demand (Koyuncu et al., 2010). Denying the possible effects that environmental mismanagement within the supply chain causes can have tremendous consequences for the disruptions of supply chains (Wang and Dai, 2018). In this regard, it has a direct consequence for the responsibilities of the operations managers.

This study provides further evidence on the effects of functional backgrounds within boards. It contributes a functional perspective to the current body of literature that has so far mainly focused on board effects related to members’ demographic characteristics. As proposed by Minichilli et al. (2009), more research needs to be done on the functioning of boards and possibilities to increase efficiency. Research using non-demographic related determinants of board performance, such as functional background, adds to the overall understanding of the board’s focus and actions, and broadens the discussion (Minichilli et al., 2009). Additionally, this research is distinctive because it focusses on the presence of one specific functional background instead of the overall functional diversity of the entire board of directors. It implements a new setting that combines the Upper Echelons Theory and the effects of functional backgrounds within the boards.

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6. CONCLUSION

This research hopes to contribute to the knowledge on GSCM, by providing new insights into the factors that influence GSCM implementations. With mankind scaling up activities to preserve the environment, there is significant potential for inter-firm collaboration on environmental activities. It seems that a majority of firms are now trying to reduce their single-firm environmental behaviour, but this is only a treatment of the symptoms of the problem of climate change, and will not solve it.

This research highlights functional background to be a determinant of GSCM practices, due to the strategic relevance of environmental practices for companies. As suggested, addressing a person with supply chain experience in the board of directors may increase the awareness of the importance of supply chains in environmental strategies and will consequently increase the chances of incorporating supply chain actors within GSCM activities. Firms willing to lower the ecological footprint of their activities and products should reconsider the authority given to supply chain related functional backgrounds within their firm. Furthermore, the boards of focal firms can play an important role in the implementation of GSCM practices. This argument is in contrast with other research in the field of GSCM, that believes GSCM is mostly driven by forces that apply beyond a firms’ boundaries. Hopefully, the possible impact of functional background as suggested in this study can create a tendency to explore more functional background related rationales in the field of corporate governance.

6.1 Managerial implications

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focus only. Consequently, they have a greater chance of being exposed to risks such as extensive reputational damage, operational disruptions, and increased insurance and legal costs (Berrone and Gomez-Mejia, 2009; Wang and Dai, 2018).

6.2 Limitations and future research

There are several limitations of this study that provide possibilities for future research. First, one could argue that firms that are greatly dependent on their supply chain have a greater likeliness of having board members with supply chain experience, and simultaneously will make resources available for GSCM implementation (Hillman and Dalziel, 2003). In this scenario, the real determinant of GSCM will be the dependency of the supply chain for the focal firm. It is hard to assess the consequences this scenario has on this research, since with the data available, this study was unable to control for SC dependency. Though, other supply chain related characteristics that were tested on the sample group (e.g. supply chain position, sector or B2B/B2C client base) display no significant differences in effect on GSCM implementation. However, future research could add supply chain dependency as a control variable to overcome this problem and make the analysis more robust.

Next, this research is dependent on the completeness of information of the BoardEx database. In the situation in which the data on individual board member profiles is incomplete, one can still indicate that SC-board members are present at certain companies, but is unable to rule out the possibility of ignoring board members that are not in the database. To a certain extend this is controlled for by excluding all the companies that have a small number of identified board members in the BoardEx database. Additionally, one could argue that since the BoardEx data is composed of publicly available data, board members that are not in the list are not as important compared to the ones that are in the list. Future research could overcome this limitation by identifying functional backgrounds of board members in a more structured way. For instance, by identifying the individuals that are serving the board, using yearly reports. A possible next step is to obtain information on their personal profile using company website information or LinkedIn profiles.

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be brought by considering the likeliness of greenwashing. Firms disclosing their data voluntarily are more likely to display an overly positive image of themselves (Delmas and Burbano, 2011). Considering this, one could argue that more than all the applicable GSCM practices are disclosed. To some extent these problems are controlled for, by leaving out certain practices, as explained in Section 3.2. However, the most obvious solution to overcome these problems, is to obtain data by using a customized survey.

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7. APPENDICES

Appendix A: CDP questions used for extraction of GSCM data

The 2017 CDP survey consists of three questions on GSCM practices. Since the output of these questions is both qualitative and quantitative data, they request a different approach on how to derive this output data. All three questions and their coding methods are listed below separately.

1. Do you engage with any of the elements of your value chain on GHG emissions and climate change strategies?

For question one, respondents could pick four possible elements, multiple answers were possible. The elements of the supply chain were divided into suppliers, customers, other partners in the value chain. This question clearly asks whether there is any form of GSCM present within the relationships with several supply chain actors of the firm. In question two, the respondent elaborates on these collaborations.

2. Please give details of methods of engagement, your strategy for prioritizing engagements and measures of success

The second question that is used from the CDP survey is open, resulting in a qualitative answer on the presence of GSCM practices. Firms elaborate on their engagement indicated in question one, by giving a structured answer on the methods of engagement, the prioritization of these engagements and how success is measured. To give an illustration on what type of answers were given, an anonymous example of an answer is given in Appendix B. Because question three is especially dedicated to suppliers, most firms only mention GSCM practices with their customers and other partners in the supply chain.

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3. Suppliers: Please give the number of suppliers with whom you are engaging, the type of engagement and the proportion of your total spend that they represent

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Appendix B: Example of answer on CDP questions

Question 2: Please give details of methods of engagement, your strategy for prioritizing engagements and measures of success

i) Engagement method

Suppliers: Besides economic standards, ESG standards represent criteria taken into account in Company X’s supply chain activities. These standards are defined in Company X’s Supplier Code of Conduct (SCoC), which sets out our sustainability requirements − incl. aspects related to resource conservation and climate protection, waste and emissions − and forms the basis for our collaboration with suppliers. We verify our suppliers’ adherence to the SCoC by way of supplier online assessments and on-site audits. In the event of unsatisfactory results, specific corrective measures are defined together with the suppliers. Moreover, as a member of the “Pharmaceutical Supply Chain Initiative” (PSCI) and as one of the

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ii) Prioritization strategy

Suppliers: Company X’s purchasing volume of ca. EUR 14.8 billion (representing ca. 50% of Company X’s revenue) from 97,270 suppliers in 151 countries in 2016 makes suppliers a strategic priority of our engagement activities. As we cannot thoroughly evaluate the sustainability performance of all of our suppliers through Sustainability assessments and audits, we strategically prioritize the selection of

suppliers based on a combination of country and business category risks as well as strategic importance in accordance with our targets. Customers: A strong focus has been given to countries with a higher

commitment from local organizations and food chain partners. iii) Measures of success

Suppliers: To measure the success of our engagement with suppliers, we set ambitious targets. By 2017, Company X plans to evaluate all strategically important suppliers according to sustainability-relevant criteria (target attainment as of 2016: 98%). By 2020, we aim to evaluate all those suppliers with a significant procurement spend (> €1 million p.a.) that are regarded as potentially high-risk suppliers (target attainment as of 2016: 83%).These measures are publicly reported in our Annual Report 2016, operationalized and reported internally in a monthly cross-functional Steering Committee of the

Sustainable Development in Supplier Management Program and reported to the Procurement Leadership Team every third month. Customers: Until now, around 200 Company X employees were trained as Bay G.A.P. experts under the train-the-trainer concept and 4 additional Facilitators Trainings are being prepared to target 100 more. In 2016, 15 pilot projects with food chain partners were carried out.

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Question 3: Please give the number of suppliers with whom you are engaging, the type of engagement and the proportion of your total spend that they represent

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Appendix C: Chi-square test on individual GSCM practices

Group 1 Group 2 Group 3

Practices (table 3.2) Observations out of 227 Relationship strength Chi-Square Sig. Likelihood ratio Relationship strength Chi-Square Sig. Likelihood ratio Relationship strength Chi-Square Sig. Likelihood ratio Green purchasing GP1 113 0,22* 0,001 0,163* 0,014 0,242* 0 GP2 68 0,227* 0,001 0,124 0,061 0,197* 0,003 GP3 10 0,212* 0,001** 0,01 0,044 0,508** 0,524 0,163* 0,014** 0,022 GP4 6 0,218* 0,001** 0,012 -0,020 0,767** 0,76 0,15* 0,024** 0,037 Internal environmental management IEM1 8 0,253* 0** 0,003 0,074 0,265** 0,297 0,156* 0,019** 0,029 IEM2 128 0,207* 0,002 0,159* 0,017 0,225* 0,001 IEM3 14 0,096 0,148** 0,196 -0,045 0,493** 0,473 0,012 0,851** 0,852 IEM4 53 0,332* 0 0,166* 0,012 0,26* 0 Customer cooperation CC1 86 0,279* 0 0,23* 0,001 0,294* 0 CC2 29 0,046 0,484** 0,502 0,216* 0,001 0,19* 0,004 CC3 12 0,051 0,441** 0,474 0,067 0,309** 0,334 0,082 0,219** 0,24 Eco design ED1 96 0,185* 0,005 0,158* 0,017 0,235* 0 ED2 36 0,134 0,044** 0,063 0,183* 0,006 0,15* 0,024 ED3 15 0,087 0,19** 0,236 0,076 0,252** 0,276 0,082 0,218** 0,236

Total significant relationships 9 7 11

* Indicates significance at the 0.05 level

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THE IMPACT OF FUNCTIONAL BOARD CHARACTERISTICS ON THE IMPLEMENTATION OF GREEN SUPPLY CHAIN MANAGEMENT PRACTICES