TOWARDS A MORE KNOWLEDGEABLE UNDERSTANDING OF GREEN SUPPLY CHAIN MANAGEMENT: THE ROLE OF NATIONAL CULTURE

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TOWARDS A MORE KNOWLEDGEABLE

UNDERSTANDING OF GREEN SUPPLY CHAIN

MANAGEMENT: THE ROLE OF NATIONAL

CULTURE

Master thesis Supply Chain Management

University of Groningen

Faculty of Economics and Business

Joost Ruikes

S3847306

J.R.W.Ruikes@student.rug.nl

Supervisors:

Dr. ir. T. Bortolotti

Dr. ir. S. Boscari

Co-assessor: Dr. ir. N.J. Pulles

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ABSTRACT

The effect of green supply chain management (GSCM) on environmental performance is somewhat variable and can be explained by contextual variables. Drawing on contingency theory, this research examines the moderation effect of national culture on the direct relationship between GSCM and greenhouse gas (GHG) production. So far, research primarily focused on company-specific contextual variables, whereas more general contextual variables are underexposed to explain inconsistencies in the relationship between GSCM and environmental performance. Survey data collected by the non-profit organization Carbon Disclosure Project about environmental performances of organizations was used for this research. The statistical analysis reveal several interesting outcomes. First, GSCM does not have a significant negative effect on GHG production using a two-year time lag. Secondly, power distance, individualism and uncertainty avoidance significantly moderate the practice-performance relationship. This research helps managers to identify strengths and weaknesses when they aim to reduce GHG production by implementing GSCM practices.

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1. INTRODUCTION

It has become essential for organizations to engage in environmental sustainability practices and to actively manage their emissions to remain competitive (Dillard, Pullman, Loucks, Martens, & Cho, 2010; Gualandris, Klassen, Vachon, & Kalchschmidt, 2015). Organizations that try to reduce their emissions often experience that their direct emissions are overshadowed by the emissions from other members of the supply chain (Larsen, & Hertwich, 2009; Plambeck, 2012; Tidy, Wang, & Hall, 2016). According to Plambeck (2012), the direct emissions covers, on average, only 14% of the total emissions created by all members of the supply chain. Therefore, green supply chain management (GSCM) seems to be needed to reduce the total created emissions (Green, Zelbst, Meacham, & Bhadauria, 2012). GSCM aims at adopting strategic collaborative practices within the supply chain, e.g., joint product and process design, to significantly reduce the emissions that occur within and external the boundaries of a company (Downie, & Stubbs, 2013; Seuring, & Gold, 2013; Tidy et al., 2016). However, GSCM implementations are complex and vary in its success to reduce emissions in the supply chain (Fang, & Zhang, 2018), which increases the need to understand what factors need to be considered that influence this relationship.

Much research is performed about GSCM to reduce emissions in the supply chain (Chen, Zhao, Tang, Price, Zhang, & Zhu, 2017; Green et al., 2012). Critical in reducing the emissions is the board of directors (BOD). (Ortiz‐de‐Mandojana, Aragón‐Correa, Delgado‐Ceballos, & Ferrón‐Vílchez, 2012). The BOD influences strategic decisions made at headquarters since the directors possess critical information and experience that help to improve the strategic decision making of the top management team (Ortiz‐de‐Mandojana et al., 2012).

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external contexts of an organization determines the success of the actions taken by the organization (Chen et al., 2017). Fang, & Zhang (2018), used the contingency perspective and conducted research in which they test the effect of various moderators, such as industry type, ISO certification, and export orientation, on the relationship between GSCM and environmental performances. A big drawback of their research is that Fang, & Zhang (2018), used relatively ‘old’ data for their research that possibly constrain the validity of their results. Additionally, their attempt to test the moderation effect of national culture failed because the different data obtained could not be converged (Fang, & Zhang, 2018).

Research is limited that studies the moderation effect of national culture on the relationship between GSCM and environmental performances. National culture is about norms and values that we share within a society but differ from other societies and can be explained by five cultural dimensions (Hofstede, 1983). National culture is acknowledged as a critical parameter that influences the effectiveness of strategic organizational decisions as GSCM (Halkos, & Skouloudis, 2017; Jaskyte, 2015; Li, & Harrison, 2008). The reason for this is that organizational decisions might clash with national cultural values that determine the willingness of people to engage in sustainability practices (Eisend, Evanschitzky, & Gilliland, 2016; Li, & Harrison, 2008; Tata, & Prasad, 2015). GSCM practices are widely adopted because organizations encounter external pressures from stakeholders to reduce their environmental impact and internally to remain competitive (Gualandris et al., 2015). However, the effectiveness of these GSCM practices differs due to a potential misfit between managers at the headquarter who should support pursuing the GSCM practices and their willingness to engage in sustainability practices (Tata, & Prasad, 2015).

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environmental performances. GSCM practices are widely adopted, but the effectiveness is determined by the fit between national cultural values and GSCM practices. This thesis aims to answer the following research question:

“How does national culture influence the relationship between GSCM and environmental performances?”

To answer this research question, secondary data obtained from surveys were used. These surveys were held by a non-profit organization called carbon disclosure project (CDP) that runs a global disclosure system that helps organizations to control their environmental impact (CDP, 2020). Prior research by Hofstede (2015), was used to determine the values regarding national cultural dimensions and to determine its moderation effect. The results were conducted by utilizing a statistical analysis in the form of hierarchical linear regressions.

So far, it is unknown how the national culture of the focal firm’s headquarter moderates the relationship between GSCM and environmental performances. This research will contribute to the existing literature by taking the contingency theory perspective as proposed by Chen et al., (2017), and study whether national culture moderates the relationship between GSCM and environmental performances. In this way, this research will contribute to the need to establish a more knowledgeable understanding of how GSCM influence environmental performances (Goldsby, & Autry, 2011). Therefore, this research helps to develop substantive theory consequences that the theory is applicable in a more generalizable context (Holmström, Ketokivi, & Hameri, 2009). By answering this research question, practitioners will be provided with expanded knowledge under what circumstances GSCM is (more) beneficial to improve environmental performances and, thus, more imperative to implement. Because till now, prior attempts that test the impact of cultural dimensions failed because the literature dataset could not be converged due to inconsistencies (Fang, & Zhang, 2018).

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2. THEORETICAL BACKGROUND

2.1. Environmental Sustainability

Environmental sustainability has become a central issue among policymakers, governments, researchers, and the public (Čuček, Klemeš, & Kravanja, 2012; Seuring, & Gold, 2013). A well-known and widely used definition in the literature and provided by the Brundtland commission is: “meeting the needs of the present generation without compromising the ability of future generations to meet their own needs” (Baumgartner, & Rauter, 2017; Morelli, 2011; Rajeev, Pati, Padhi, & Govindan, 2017; Seuring, & Müller, 2008).

To comply with the previous definition, organizations are encouraged to find ways to improve their environmental performance, and thus, reduce their production of greenhouse gases (GHG). The World Resource Institute and World Business Council for Sustainable Development created a protocol that offers organizations guidelines on how to measure their GHG production (Huang, Weber, & Matthews, 2009; WRI, n.d.). These protocols vary in their scope (i.e., scope 1, scope 2, and scope 3). Scope 1 emissions are created by processes that are under the direct control of a company, scope 2 emissions are indirect emissions that are related to the purchasing of energy, and scope 3 emissions are indirect emissions that occur somewhere downstream or upstream in the company’s supply chain (Huang et al., 2009). However, organizations do not report much information about emissions that occur in scope 3 since it is hard to trace all the emissions that occur outside the organization’s boundaries. Therefore, this study will be limited only to GHG production in scope 1 and 2. In order to counteract the GHG production in all scopes, organizations introduce green practices.

2.2. Green supply chain management

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innovation” (Zhao, Liu, Zhang, & Huang, 2017: 1). Building on this definition, several researchers differentiated several GSCM practices: Internal environment management, eco-design, green purchasing, client cooperation, and investment recovery (Cousins et al., 2019; Vanalle, Ganga, Godinho Filho, & Lucato, 2017).

More specifically, green purchasing focuses on the upstream supply chain by specifying product and process requirements that a supplier should accede to meet environmental targets (Fang, & Zhang, 2018). Specific product or process requirements are, for example, ISO 14001 certificates, design specifications, or eco-labels (Tachizawa, Gimenez, & Sierra, 2015; Zhu, Sarkis, & Lai, 2008).

Client cooperation involves collaboration with the downstream customers via collaborative activities like educating customers, customer support, and joint ventures that aim to improve the willingness of customers to participate in green responsible supply chain operations (Fang, & Zhang, 2018).

Investment recovery is another critical practice of GSCM and usually happens at the end of a supply chain cycle (Fang, & Zhang, 2018). Investment recovery aims at encouraging the recycling of a product when it reaches its end of life to reuse the materials to reduce the negative environmental impacts (Fang, & Zhang, 2018). The sale of excess inventory is also considered in investment recovery (Zhu et al., 2008).

Eco-design is another central practice of GSCM. Eco-design or design for environment mainly focuses on ensuring that the design of new products is developed in an environmentally friendly manner (Fang, & Zhang, 2018; Zhu et al., 2008). The scope of eco-design is on the entire life-cycle of a product: from procurement of raw materials to the production process, the utilization, and the final disposal stage (Fang, & Zhang, 2018). An organization can commit themselves to eco-design when they participate, for example, in joint product design, or joint waste reduction (Tachizawa et al., 2015).

The last key practice of GSCM is internal environment management. It implies that BOD and top-managers support and show commitment to the establishment of green supply chain management practices (Green et al., 2012). This means that GSCM is considered as a strategic imperative within the organization and is reflected by the support and commitment of the BOD and management, green compliance, and auditing programs (Fang, & Zhang, 2018; Zhu et al., 2008).

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audits and examinations of environmental records (Tachizawa et al., 2015). Client cooperation and eco-design belong to the collaboration practices since these practices focus on supplier and customer involvement in green collaborative activities like joint product design (Tachizawa et al., 2015).

2.3. Contingency theory

The empirical results of the effect of GSCM on GHG production are inconsistent, and a possible explanation for this phenomenon lies in the contingency theory (Fang, & Zhang, 2018). The contingency theory started to develop in the 1970s in an attempt to explain variations in management practices that occurred at that time (Otley, 2016; Sousa, & Voss, 2008). According to Alves, de Sousa Jabbour, Kannan, & Jabbour (2017: 225), contingency is defined as “an outside event that affects organizations, over which organizations cannot exert direct control.’’ The success of an organization depends on the fit between the structure of the organization and the environmental context in which it is operating (Drazin, & Van de Ven, 1985; Volberda, van der Weerdt, Verwaal, Stienstra, & Verdu, 2012). Following the contingency theory, managers will cautiously analyze the environment in which the organization is operating (Volberda et al., 2012). By considering the internal characteristics of the organization, the organization will shape their practices to the environmental context (Volberda et al., 2012).

In an attempt to explain the variations in the empirical results of the effect of GSCM on GHG production, this research will take the contingency perspective. A few researchers already took the contingency perspective to explain inconsistencies in these findings (Fang, & Zhang, 2018; Schneider, Wallenburg, & Fabel, 2014). However, despite multiple research, researchers only limited themselves to firm-specific contingency variables like ISO certificates or industry type. They did not consider a more general contingency approach like national culture.

2.4. National culture

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Geert Hofstede is one of the most famous researchers in cultural science and developed a cultural index to explain cultural differences across countries (Hofstede, 1991). This paper will make use of Hofstede’s cultural index to explain national cultural differences across different countries, and therefore, Hofstede’s definition of national culture is used. National culture is “that part of our conditioning that we share with other members of our nation, region, or group but not with members of other nations, regions, or groups” (Hofstede, 1983: 2). Hofstede developed five different cultural dimensions that allow predicting cross-cultural differences (Hofstede, 1991). These dimensions will be discussed below.

2.4.1. Power distance

The first dimension formulated by Hofstede is power distance (Hofstede, 1991). Power distances within a society refer to how well people accept inequalities among different people because they possess a different level of power (Hofstede, 1991). In high power distance cultures exists a great authoritarian difference between people in what they do have and what they do not (Nakata, & Sivakumar, 2001). People within this society are expected to accept these hierarchical differences (Hofstede, 1991).

On the other hand, there are cultures with lower power distances, and people are less willing to accept authoritarian differences (Hofstede, 1991). Inequalities in low power distance cultures are not well tolerated, and therefore, attempts are made to level the inequalities within cultures (Nakata, & Sivakumar, 2001). The starting point of these cultures is that everyone deserves the same chances despite personal background (Nakata, & Sivakumar, 2001).

2.4.2. Individualism

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The third dimension formulated by Hofstede is the level of masculinity (Hofstede, 1991). Masculinity refers to what extent a society is subject to competition, challenges, success, and achievements (Nakata, & Sivakumar, 2001). Nevertheless, masculinity is characterized by assertiveness, robust, and focused on achieving materialized success (Hofstede, 1991). Therefore, cultures that are characterized by a high degree of masculinity are focused on achieving good performances (Nakata, & Sivakumar, 2001). However, achieving good performances might be on the costs of others since these cultures are focused on the needs of the individuals (Nakata, & Sivakumar, 2001).

The opposite of masculinity is femininity, where the quality of life plays a more central role (Fang, & Zhang, 2018). Femininity refers to cultures where the role of genders overlap, so both men and women are modest, tender, and concerned with life quality (Hofstede, 1991). Femininity cultures are more focused on helping each other, improve the quality of life, and do not experience a high need for self-recognition (Hofstede, 1991).

2.4.4. Uncertainty avoidance

The fourth dimension formulated by Hofstede is uncertainty avoidance (Hofstede, 1991). Uncertainty avoidance refers to the extent to which people in cultures feel threatened and uncomfortable by unfamiliar situations (Hofstede, 1991). People in cultures that are characterized by high uncertainty avoidance look for predictable and stable situations, try to avoid uncertain situations, and try to reduce their discomfort (Nakata, & Sivakumar, 2001). Discomfort can be reduced by developing technological laws, social norms, or developing a religion (Nakata, & Sivakumar, 2001).

On the other hand, people in cultures that are characterized by low uncertainty avoidance feel more comfortable in unexplored situations (Nakata, & Sivakumar, 2001). People in low uncertainty avoidance cultures are more optimistic about the future and are more tolerant of aberrant people and ideas and are more willing to take risks (Nakata, & Sivakumar, 2001).

2.4.5. Confucian dynamic

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in their obligations (Hofstede, 1991; Nakata, & Sivakumar, 2001). The long-term orientation emphasizes a long-term view where people are motivated to overcome obstacles by making the required sacrifices (Nakata, & Sivakumar, 2001).

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3. HYPOTHESES DEVELOPMENT

3.1. The direct effect of GSCM on GHG production

Currently, organizations increasingly rely on each other, and supply chain collaboration is needed to achieve a serious improvement in sustainability performance and not to lose competitive advantage (Gualandris et al., 2015; Plambeck, 2012; Seuring, & Gold, 2013). Prior studies found strong relationships between GSCM practices and GHG production (Cousins et al., 2019; Fang, &, Zhang, 2018; Geffen, & Rothenberg, 2000; Green et al., 2012). The reason for this is that GSCM practices are originally designed to reduce GHG of organizations by possessing elements that enhance supply chain collaboration and monitoring (Fang, & Zhang, 2018; Green et al., 2012). Collaboration and monitoring practices reduce the production of GHG since these practices focussing on elements that reduce waste, make use of recycled materials, reduce energy consumption, and make use of fewer components (Cousins et al., 2019). Besides, collaboration and monitoring practices enhance operational efficiency and allow insights into the GHG production (Cousins et al., 2019; Geng et al., 2017).

More specifically, internal environment management creates internal commitment and support for sustainability initiatives to reduce waste and improve efficiency (Green et al., 2012). Green purchasing sets stringent environmental standards for suppliers that result in more environmentally friendly products and processes (Green et al., 2012). Investment recovery reduces waste and improves efficiency by monitoring the reversed logistics of products to ensure the reuse or recycling of products when products meet their disposal stage (Green et al., 2012). Eco-design lowers GHG production by a joint redesign of more environmentally friendly products with suppliers (Green et al., 2012). Client cooperation lessens GHG production by creating customer support for sustainability initiatives and by training them to act more environmentally friendly (Green et al., 2012). To conclude, it is expected that GSCM collaboration and monitoring practices have a negative and direct relationship with GHG production, and thus, proposed is that:

Hypothesis 1a: There is a negative direct relationship between GSCM collaboration practices and GHG production.

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15 3..2. Power distance as a moderator

Beliefs and perceptions of sustainability are influenced by values about power and hierarchies (Tata, & Prasad, 2015). People in high power distance cultures are more likely to accept social inequalities, and authority goes along with inequalities (Tata, & Prasad, 2015). High power distance cultures are “prone to the manipulative use of power, a lack of equal opportunities for minorities and women, and a lack of personal or professional development within the organization” (Waldman et al., 2006: 826).

GSCM collaboration practices require equal treatment, mutual dependency, and cost-sharing with suppliers and customers (Cousins et al., 2019). A feeling of great authoritarian differences conflicts with the GSCM collaboration practices. BOD and top-managers in high power distance cultures are expected to be less willing and facing difficulties to accept equal treatments, mutual dependency, and cost-sharing (Hofstede, 1991). This will constrain a close collaboration with customers and suppliers. A close collaboration would increase relationship performances and the ability to meet common environmental goals (Tidy et al., 2016).

However, power distance fits with monitoring practices. Organizations in high power distance cultures are hierarchical and exert their hierarchical control over other organizations (Tata, & Prasad, 2015; Wincel, & Kull, 2013). Monitoring suppliers and customers could be regarded as a method to exert hierarchical control. BOD and top-managers in high power distance cultures are expected to increase their monitoring efforts with the aim to get more hierarchical control over other organizations. This will positively influence the relationship between GSCM monitoring practices and GHG production because suppliers and customers are more motivated to reduce their GHG production (Plambeck, & Taylor, 2016). Hence, it is proposed that:

Hypothesis 2a: National cultures characterized by a high power distance will negatively influence the relationship between GSCM collaboration practices and GHG production.

Hypothesis 2b: National cultures characterized by a high power distance will positively influence the relationship between GSCM monitoring practices and GHG production.

3.3. Individualism/collectivism as a moderator

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individualistic cultures might be less willing to share as many resources and create mutual dependency compared to collectivistic cultures (Tata, & Prasad, 2015). However, limiting resource sharing and mutual dependency will reduce the ability to create more environmentally friendly processes and products since there is less coordination (Hofstede, 1991). BOD and top-managers in individualistic cultures might decide not to share crucial information with suppliers and customers. This will ultimately constrain the reduction of waste and the creation of more efficient processes, and therefore, negatively influence the relationship between GSCM collaboration practices and GHG production.

Additionally, individualistic cultures do not fit with monitoring practices. Individualistic cultures favor their interests over the group and perceive sustainability as less important (Nakata, & Sivakumar, 2001; Tata, & Prasad, 2015). This statement is supported by McCarty & Shrum (2001), who found that people in individualistic societies have a bad attitude towards recycling. Therefore, BOD and top-managers in individualistic cultures might limit their monitoring efforts. As discussed earlier, limiting monitoring efforts will negatively influence the relationship between GSCM monitoring practices and GHG production because suppliers and customers are less motivated to improve their environmental impact (Plambeck, & Taylor, 2016). Hence, individualistic societies will negatively impact the relationship between GSCM and GHG production and proposed is:

Hypothesis 3a: National cultures characterized by a high individualistic focus will negatively influence the relationship between GSCM collaboration practices and GHG production.

Hypothesis 3b: National cultures characterized by a high individualistic focus will negatively influence the relationship between GSCM monitoring practices and GHG production.

3.4. Femininity/masculinity as a moderator

National cultures that possess a high level of femininity are willing to help each other and to improve the quality of life (Hofstede, 1983; Tata, & Prasad, 2015). These cultures are more likely that they believe in the importance of improving the environmental conditions in order to improve life quality (Tata, & Prasad, 2015). In contrast, masculine cultures are more materialized and focused on achieving success (Tata, & Prasad, 2015).

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of new products and processes (Tachizawa et al., 2015). High involvement in collaboration practices can be seen as an obstacle to economic growth (Tata, & Prasad, 2015). BOD and top-managers might be somewhat reserved investing in collaboration practices due to a perceived lack of short-term return on investment (Krause, Handfield, & Tyler, 2007). They are expected to invest fewer resources in collaboration practices in contrast to BOD and top-managers in feminine cultures who consider life quality. Therefore, masculinity will negatively influence the relationship between GSCM collaboration practices and GHG production.

Moreover, masculine cultures also conflict with GSCM monitoring practices. Performing audits at the suppliers’ and customers' site require managerial resources (Duan, 2019). Increasing these audits or time interval between audits requires more managerial resources and eventually increases costs (Plambeck, & Taylor, 2016). Therefore, BOD and top-managers in masculine cultures might limit their monitoring efforts to reduces costs, and because short-term returns are perceived as low (Krause et al., 2007). This will eventually negatively influence the relationship between GSCM monitoring practices and GHG production. This is because suppliers and customers are less motivated to reduce their GHG production (Plambeck, & Taylor, 2016). Thus, it is proposed that:

Hypothesis 4a: National cultures characterized by a high masculine focus will negatively influence the relationship between GSCM collaboration practices and GHG production.

Hypothesis 4b: National cultures characterized by a high masculine focus will negatively influence the relationship between GSCM monitoring practices and GHG production.

3.5. Uncertainty avoidance as a moderator

Organizations in uncertainty avoidance cultures are looking for predictable and stable situations to reduce their discomfort (Nakata, & Sivakumar, 2001). In contrast to organizations in low uncertainty avoidance cultures who are better able to cope with unexplored situations (Hofstede, 1991).

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& Prasad, 2015). Innovation and information sharing require openness to the unknown, and BOD and top-managers in high uncertainty avoidance cultures are facing difficulties to be completely open to the unknown (Shane, 1992; Tata, & Prasad, 2015). They will be less willing to redesign products and processes that are entirely new and are more focused on minor product and process changes. This will negatively influence the relationship between GSCM collaboration practices and GHG production. The reason for this is that uncertainty avoidance will slowdown the innovation speed and the ability to think outside the box (Hoonsopon, & Ruenrom, 2012).

However, organizations in uncertainty avoidance cultures match with GSCM monitoring practices. Organizations in uncertainty avoidance culture are looking for predictable situations (Hofstede, 1991). Monitoring will increase the predictability of future situations and will reduce organizations’ discomfort. Research showed that monitoring is part of organizations that deploy uncertainty management strategies (Simangunsong, Hendry, & Stevenson, 2012). Therefore, BOD and top-managers in uncertainty avoidance cultures might intensify their monitoring efforts in order to reduce uncooperative behavior. (Yan, & Dooley, 2013). This will eventually positively influence the relationship between GSCM monitoring practices and GHG production. This is because suppliers and customers are more motivated to reduce their GHG production (Plambeck, & Taylor, 2016). Therefore, it is proposed that:

Hypothesis 5a: National cultures characterized by high uncertainty avoidance will negatively influence the relationship between GSCM collaboration practices and GHG production.

Hypothesis 5b: National cultures characterized by high uncertainty avoidance will positively influence the relationship between GSCM monitoring practices and GHG production.

3.6. Confucian dynamic as a moderator

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supplier and customer development that includes efforts that improve their (environmental) performance (Krause et al., 2007). BOD and top-managers in short-term oriented cultures have more difficulties to overcome obstacles that come along with collaboration practices, and therefore, limit the number of long-term relationships (Tata, & Prasad, 2015). This will ultimately constrain the reduction of GHG production because short-term benefits are lower than long-term benefits.

Furthermore, monitoring practices clash with short-term oriented cultures. Supplier monitoring requires time and resources, and the benefits of monitoring will occur over time (Foerstl, Reuter, Hartmann, & Blome, 2010). BOD and top-managers at headquarters in short-term oriented cultures are expected to reduce their efforts of long-short-term monitoring. This will ultimately reduce supplier and customer motivation to decrease their GHG production, and therefore, negatively influence the relationship between GSCM monitoring practices and GHG production (Plambeck, & Taylor, 2016). Therefore, it is proposed that:

Hypothesis 6a: National cultures characterized by a short-term orientation will negatively influence the relationship between GSCM collaboration practices and the production of GHG production.

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20 3.7. Conceptual model Internal environment management Eco-design Green purchasing Investment recovery Client cooperation Monitoring practices Collaboration practices Scope 1 Scope 2 Total GHG -Power distance Uncerainty avoidance Masculinity Short-term orientation Individualism

-National cultural dimensions

Power distance

Uncerainty

avoidance Masculinity Individualism

Short-term orientation

National cultural dimensions

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4. METHODOLOGY

4.1. Research design

The primary purpose of this study was to test whether national culture moderates the relationships between GSCM collaboration and monitoring practices and GHG production. To answer this research question, a confirmatory survey research was designed. The confirmatory survey research design best suited to meet the research goal because of multiple reasons. First, knowledge about the different constructs within the research (GSCM, environmental performance, and national culture) is extensive, and therefore, well-defined hypotheses could be developed (Karlsson, 2016). Since constructs are very well formalized in literature, the hypotheses were formulated deductively (Karlsson, 2016). Secondly, this research design allowed testing the relationships between the different constructs (Karlsson, 2016). Finally, this research used secondary data, and this research design allowed to cope with secondary data (Karlsson, 2016).

Additionally, this research was a longitudinal study, and observations of the GSCM practices were done over a longer time horizon, and more specifically, from 2014 up to and including 2017 (Karlsson, 2016). The motivation for a longitudinal study is that it was expected that the GHG production reduced after GSCM practices were adopted.

4.2. Sample and data collection

In order to execute this research, secondary data from the Carbon Disclosure Project (CDP) was used. CDP is a non-profit organization that aims to reduce greenhouse gas emissions and mitigate risks concerning climate change (CDP, 2020). Since 2003 CDP annually requests large organizations around the world to voluntarily filling in their questionnaire (Depoers, Jeanjean, & Jérôme, 2016). Currently, more than 8400 organizations all around the world reported through CDP on climate change (CDP, 2020). The organizations that participated in the CDP questionnaire were selected based on economic and environmental criteria (CDP, 2020). Data from the questionnaire was used to measure the GSCM practices executed by firms and the GHG in scope 1 and scope 2. The survey questions that were used from the CDP questionnaire can be found in appendix 1.

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2006). To identify the country of the headquarter location, the Standard & Poor’s Compustat database was used. The standard & Poor’s databases is a leading provider of global financial and industry data (WRDS, 2020). After identification of the country of the headquarter location, Hofstede’s cultural dimension data matrix of 2015 was used to determine the organization’s national culture. Hofstede developed a cultural data matrix in which he assed countries according to the cultural dimensions (GeertHofstede, 2020). The cultural scores on each dimension range from 0 to 100. The latest data (2015) was used to measure the national culture. Even though it is likely that national culture possibly changes over time, it was not expected that it would change drastically within four years, and therefore, this data was considered valid (Griffin, Guedhami, Kwok, Li, & Shao 2017).

A couple of control variables were used in this research: firm size (number of employees), financial performance (ROA), and industry classification (GIC-code). Again the Standard & Poor’s Compustat database was used to retrieve the data concerning the firm size, financial performance, and industry classification.

4.3. Data measurement

4.3.1. Independent variable: GSCM practices

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The CDP data regarding the GSCM practices consisted only of qualitative data. Since the questions do not cover the implementation of GSCM practices separately, some measurement items might not be included in the answer of the respondent. Therefore, a deductive binary coding method was used to code the data (Karlson, 2016). If at least one measurement item of a GSCM practice was mentioned in the answer of the respondent, it was coded as 1 and 0 otherwise. Appendix 2 provides examples of how the data was coded. The individual practices were summed to calculate the existence of collaboration and monitoring practices. The standardized versions of the collaboration and monitoring practices were used in the statistical analysis.

Factors Measurement items

Internal environment management

IEM1 Commitment of GSCM from senior managers IEM2 Support for GSCM from mid-level managers

IEM3 Cross-functional cooperation for environmental improvements IEM4 Total quality environment management

IEM5 Environmental compliance and auditing programs IEM6 ISO 14001 certification

IEM7 Environmental Management System exist Green purchasing GP1 Eco-labeling of products

GP3 Environmental audit for suppliers’ internal management GP4 Suppliers’ ISO 14000 certification

GP5 Second-tier supplier environmentally friendly practice evaluation Client

Cooperation

CC1 Cooperation with customers for eco-design CC2 Cooperation with customers for cleaner production CC3 Cooperation with customers for green packaging

Eco-design ECO1 Design of products for reduced consumption of material/energy

ECO2 Design of products for reuse, recycle, recovery of material, component parts

ECO3 Design of products to avoid or reduce use of hazardous products and/or their manufacturing process Investment

recovery

IR1 Investment recovery (sale) of excess inventories/materials IR2 Sale of scrap and used materials

IR3 Sale of excess capital equipment Supplier

collaboration

SC1 Collaboration with supplier for environmental objectives SC2 Collaboration with supplier for cleaner production SC3 Collaboration with supplier for green packaging

TABLE 1. ‘List of measurement items for GSCM practices implementation’ Zhu et al., 2008b.

4.3.2. Dependent variable

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of GHG produced in scope 1 and scope 2 (Huang et al., 2009). Scope 3 was excluded since the available information about emissions in scope 3 was too limited to allow reliable statistical testing. The CDP questionnaire was used to track the GHG of the organizations in both scopes, and the organizations themselves measured this data. To calculate the total GHG, this study used a similar approach as in previous research and summed the GHG in scope 1 and scope 2 (Doda, Gennaioli, Gouldson, Grover, & Sullivan, 2016). Eventually, the natural logarithm of the summed GHG was used in the statistical analysis.

4.3.3. Moderator

The moderator in this research was the national culture of the country where the headquarter is located. The dataset contained multinationals with headquarters in different countries. Compustat offers a variable which was used to identify the country of the company’s headquarter. This variable comes from the International Standards Organization (ISO), which is an independent organization that develops international standards (ISO, 2020). However, some company codes were not recognized by Compustat, and thus, missing values occurred. Since the country of the headquarter was a main variable in this research, annual reports were used in addition to find the country of the headquarter of the missing organizations.

4.3.4. Control variables

In order to increase the validity, control variables were considered in this research. These control variables were unrelated to the main theoretical model and hypotheses. The reasoning behind adding control variables was that they delete variation in results that were caused by predictable variables that gave different explanations (Helmuth, Craighead, Connelly, Collier, & Hanna, 2015).

Firm size

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This research controls for the business environment of organizations by including industry type of each company (Jacobs, Swink, & Linderman, 2015). Implementing GSCM practices and reducing the environmental impact of organizations requires innovation. The extent of innovativeness of organizations is industry dependent (Mackelprang, Habermann, & Swink, 2015) because some industries have more availability of resources that are required to implement GSCM practices (Golicic, & Smith, 2013). Three commonly used industry classification systems are known in the literature: North America Industry Classification (NAIC), Standardized Industry Classification (SIC), and Global Industry Classification (GIC) (Kile, & Phillips, 2009). This research will use the 2-digit GIC classification because this system is more complete and offers improvements over the other two industry classification systems (Kile, & Phillips, 2009).

Financial performance

The third control variable that was added in this research is related to the firm’s financial performances. Financial performances influence the decision making of organizations (Roh, Krause, & Swink, 2016). Specifically, return on assets (ROA) was considered in this research because firms with a higher ROA typically are more profitable, and thus, possess more financial resources (Wiengarten, Fan, Lo, & Pagell, 2017). The ROA is the ratio of income before extraordinary items and the total assets expressed in percent (Kim, & Zhu, 2018).

4.4. Measurement analysis

In total, 117 organizations their GSCM practices were coded over four years (2014 – 2017). Table 2 shows the numbers of organizations that implemented different GSCM practices. There are significant differences between individual GSCM practices. However, the number of organizations that implemented a GSCM practice increased for almost all individual practices. Furthermore, monitoring practices are widely adopted in contrast to collaboration practices. Additionally, table 3 shows the statistics of the 117 organizations that were included in the sample size. GSCM practices Internal environment management Green purchasing Investment recovery Total monitoring practices Eco-design Client cooperation Supplier collaboration Total collaboration practices 2014 100 52 6 100 57 25 49 57 2015 102 56 8 102 57 34 46 57 2016 109 64 8 109 69 34 51 69 2017 112 65 8 112 80 34 44 80

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Employees Number ROA Frequency

<5000 11 <0% 11

5000-20000 14 0-5% 57

20000-50000 27 5%-15% 48

50000-100000 30 >15% 1

>100000 35

Headquarter location Number Headquarter location Number

Belgium 1 Ireland 3

Brazil 1 Japan 19

Germany 5 Netherlands 1

Denmark 1 Norway 2

Spain 5 Sweden 1

Finland 1 Switzerland France 1

France 12 Switzerland German 5

Great Britain 7 United States 52

Total different headquarter locations: 16

Industry (2-digit GIC code) Number Industry (2-digit GIC code) Number

Materials 7 Financials 6

Industrials 28 Information Technology 18 Consumer Discretionary 7 Telecommunication Services 9

Consumer Staples 16 Utilities 7

Health care 12 Real Estate 1

Total different industries: 10

TABLE 3. Sample size statistics.

4.5. Data analysis

Different statistical tests were performed to test the relationships between the dependent, independent, and moderator variables. In total, five linear regression analyses were executed to test for moderation effects of national culture on the GSCM collaboration and monitoring practices. Each test included the control variables firm size, industry, and ROA. Commonly, the effects of GSCM collaboration and monitoring practices on GHG production occur over time. Therefore, a decision regarding a time lag between the adoption of GSCM collaboration and monitoring practices and GHG production had to be taken. The literature is not very explicit about the commonly used time lag. However, the adoption of GSCM practices is seen as part of the internal business strategy (Testa, & Iraldo, 2010). Besides, prior research showed that the occurrence of positive effects of GSCM practices could have a time lag of at least two years (Huatuco, Montoya-Torres, Shaw, Calinescu, Wang, & Sarkis, 2013). Hence, a time lag of two years was used in this research.

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except for the ROA, monitoring moderator power distance, and collaboration moderator masculinity meaning that these data have heavier tails compared to their normal distribution.

Variable Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Total emissions 6,86 15,16 10,89546067 1,547294959 0,125 -0,356 Firm size 0,081347308 2,807732513 1,626965577 0,512955729 -0,142 -0,242 ROA -0,551146423 0,484287469 0,043274292 0,052745136 0,024 29,167

Industry 10 60 32,84 12,89 0,249 -1,212

Collaboration practices -1,3179 1,78015 -2,38095E-06 1,00000119 0,251 -0,91 Monitoring practices -2,52368 2,38347 2,04762E-06 1,000001856 -0,355 -0,218 Collaboration moderator power

distance -2,901027375 3,761314538 -0,158333469 0,999939781 0,069 2,903 Monitoring moderator power

distance -5,332333946 4,030190776 0,041202747 1,042539769 -1,053 6,288 Collaboration moderator invididualism -2,757381144 2,6015346 0,123672072 0,877493223 0,101 0,669 Monitoring moderator individualism -3,691899691 2,006619558 -0,063943806 0,894874441 -0,249 0,519 Collaboration moderator masculinity -3,187572193 3,719456454 -0,005289715 0,915350899 0,394 5,273 Monitoring moderator masculinity -2,110373138 3,811383042 0,151212598 0,825000314 0,616 2,49 Collaboration moderator uncertainty avoidance -2,058981528 2,781163948 -0,139211309 0,914773696 0,16 0,609 Monitoring moderator uncertainty

avoidance -3,21643016 3,72374285 0,095505187 0,963778884 -0,294 0,864 Collaboration moderator

short-term orientation -2,826202 2,092324 -0,12839014 0,972519268 0,399 0,706 Monitoring moderator short-term

orientation -3,784045 2,083248 0,13433531 0,934857015 0,140 0,270 TABLE 4. Descriptive statistics.

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28 Number Variables 1 2 3 4 5 6 7 8 9 10 11 1 Total emissions 1 2 Firm size ,223** 1 3 ROA -,099* -,049 1 4 Industry ,026 -,154 -,095** 1 5 Collaboration practices -,012 -,039 ,079 -,136 1 6 Monitoring practices ,053 ,134 -,069 ,025 ,287** 1 7 Power distance ,094* ,055 -,121** ,108** -,152* ,040 1 8 Individualism -,051 -,009 ,084** -,012 ,132 -,068 -,535** 1 9 Masculinity ,098* -,025 -,105** -,072* -,006 ,160* ,116** -,315** 1 10 Uncertainty avoidance ,081 ,041 -,144** -,002 -,138* ,095 ,744** -,781** ,379** 1 11 Short-term orientation ,047 -,116** ,150** ,070* ,130 -,136 -,520** ,835** -,352** -,788** 1 **. Correlation is significant at the 0.01 level (2-tailed).

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5. Results

The result section describes the outcome of the tested hypotheses. This research used a hierarchical moderated regression analysis, which is a similar approach as Zhu, & Sarkis (2007). In total, five hierarchical moderated regressions analyses were executed to help understand the moderation effect of national culture on the direct relationships between GSCM collaboration and monitoring practices and GHG production. Each cultural dimension was tested separately on its direct and moderation effect.

This research follows the same methodology as Zhu, & Sarkis (2007), who also tested various moderating effects. The methodology consists of four different hierarchical steps. The control variables: ROA, firm size, and industry, entered the regression in the first step. Second, GSCM collaboration and monitoring practices entered the model. Then, the standardized value of one cultural dimension was added. Lastly, the interaction effect between the GSCM collaboration and monitoring practices with the moderator entered the regression. Each regression includes the adjusted R2, which describes the explanatory power of the model.

Table 6 illustrates the results of the direct effect of the GSCM collaboration and monitoring practices and control variables. The findings show that the standardized beta (β) for collaboration and monitoring practices are both not significant. Therefore hypotheses H1a and H1b are both rejected. Moreover, only the control variable ROA has a significant (at the 0.05 level) negative β on GHG production.

Table 6 also shows the results of the moderation effect of power distance on the direct relationships between GSCM collaboration and monitoring practices and GHG production. Power distance has a significant (at the 0.05 level) negative β on the GSCM collaboration practices. However, the result shows significance in the opposite direction compared to the hypothesized relationship. The moderating effect of power distance is negative, meaning that the interaction between GSCM collaboration practices and power distance decreases the GHG production. Power distance has not a significant β on the GSCM monitoring practices. Hence, hypotheses H2a and H2b are rejected.

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GSCM monitoring practices and total GHG production is not significant, causing rejection of hypothesis H3b.

As shown in table 8, masculinity has not a significant β on the direct relationships between GSCM collaboration and monitoring practices and GHG production. Therefore, both hypotheses H4a and H4b are rejected.

Table 9 shows that uncertainty avoidance has a significant (at the 0.05 level) negative β on the direct relationship between GSCM collaboration practices and GHG production. This result is in the opposite direction as proposed, and therefore, H5a is rejected. The moderating effect of uncertainty avoidance is negative, meaning that the interaction between GSCM collaboration practices and power distance decreases the GHG production. Uncertainty avoidance has not a significant β on the direct relationship between GSCM monitoring practices and total GHG production. Hence, hypotheses H5b is rejected.

Finally, table 10 illustrates the interaction between short-term orientation and GSCM collaboration and monitoring practices. Short-term orientation has not a significant β on the direct relationships between GSCM collaboration and monitoring practices and GHG production. As a result, both hypotheses H6a and H6b are rejected.

Dependent variable: Total GHG

Variable entered Step 1 Step 2 Step 3 Step 4

Firm size ,211** ,049 ,055 ,087 ROA -,077 -,265* -,255* -,259* Industry ,034 ,098 ,095 ,063 Collaboration practices -,003 ,001 -,062 Monitoring practices ,010 ,010 ,019 Power distance ,046 -,028

Collaboration moderator power distance -,259* Monitoring moderator power distance -,071 F for the step 7,573 2,556 2,156 3,039

Adjusted R2 _,047 _,061 _,055 _,121

X_P<0,10

*P<,05 **P<,001

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Firm size ,211** ,049 ,047 ,047 ROA -,077 -,265* -,280* -,289* Industry ,034 ,098 ,090 ,090 Collaboration practices -,003 -,007 -,033 Monitoring practices ,010 ,012 -,002 Individualism ,073 ,080

Collaboration moderator individualism ,160X

Monitoring moderator individualism -,022 F for the step 7,573 2,556 2,230 2,056

Adjusted R2 _,047 _,061 _,058 _,066

X_P<0,10

*P<,05 **P<,001

TABLE 7. Hierarchical moderation regression with individualism.

Firm size ,211** ,049 ,059 ,065 ROA -,077 -,265* -,266* -,258* Industry ,034 ,098 ,099 ,104 Collaboration practices -,003 ,013 ,035 Monitoring practices ,010 -,011 -,011 Masculinity ,131 ,147

Collaboration moderator masculinity -,030 Monitoring moderator masculinity ,119 F for the step 7,573 2,556 2,506 2,074

Adjusted R2 _,047 _,061 _,071 _,067

X_P<0,10

*P<,05 **P<,001

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Firm size ,211** ,049 ,050 ,086 ROA -,077 -,265* -,263* -,279* Industry ,034 ,098 ,098 ,072 Collaboration practices -,003 -,002 -,037 Monitoring practices ,010 ,010 -,013 Uncertainty avoidance ,008 -,047

Collaboration moderator uncertainty avoidance -,261* Monitoring moderator uncertainty avoidance -,046 F for the step 7,573 2,556 2,113 2,853

Adjusted R2 _,047 _,061 _,053 _,111

X_P<0,10

*P<,05 **P<,001

TABLE 9. Hierarchical moderation regression with uncertainty avoidance.

Firm size ,211** ,049 ,020 ,025 ROA -,077 -,265* -,282* -,296* Industry ,034 ,098 ,102 ,097 Collaboration practices -,003 -,022 -,028 Monitoring practices ,010 ,028 ,017 short-term orientation ,015 ,013

Collaboration moderator short-term orientation ,122 Monitoring moderator short-term orientation -,032 F for the step 7,573 2,556 2,172 1,827

Adjusted R2 _,047 _,061 _0,58 _,055

X_P<0,10

*P<,05 **P<,001

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6. Discussion

The purpose of this study is to understand the influence of the national culture of the country of the headquarter on the direct relationship between GSCM and environmental performance. This study took a contingency approach to explain contradicting results on the effect of GSCM. In this way, this paper helps to build a more substantive theory on how GSCM influences environmental performance (Chen et al., 2016; Fang, & Zhang, 2018). Additionally, this study differs from prior work by taking a more general contingency approach. Prior attempts to test the impact of cultural dimensions failed because the data that was used could not be converged (Fang, & Zhang, 2018). This study theorizes and assesses a comprehensive model with GSCM practices and national cultural dimensions. Multiple interesting outcomes are presented of the influence of different national cultural dimensions on the relationship between GSCM practices and GHG production.

The results show no significant effects of the direct relationships between GSCM collaboration and monitoring practices and GHG production. This result is in strong contrast with prior studies that showed a negative relationship between GSCM and GHG production (Cousins et al., 2019; Geng et al., 2017). One possible explanation for this could be that the implementation of GSCM practices is not sufficiently impact-oriented (Doda et al., 2016). Therefore, this finding contributes to the notion that the implementation of GSCM practices does not decrease GHG production by definition (Doda et al., 2016). This result adds to the discussion that organizations are recommended not merely to focus on the implementation of GSCM practices, but also pay attention to the efficacy of these practices to reduce GHG production.

Furthermore, this study contributes to the literature by providing empirical support for the hypothesized moderation effect of individualism on the practices-performance relationship. This result is in line with the stream of studies that show that high levels of individualism minimize the influence of supply chain collaboration on firm performance (Chang, Ellinger, Kim, & Franke, 2016; Zhang, & Cao, 2018). It seems that a high level of individualism has indeed a dampening effect on GSCM collaboration practices to reduce GHG production. This seems not astonishing because a high level of individualism limits BOD and top-managers’ willingness to share vital resources and to create a mutual dependency to reduce GHG production (Green et al., 2012; Tata, & Prasad, 2015).

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2019; Tidy et al., 2016). A possible explanation that requires future research stems from the operations research and is called the ‘replacement effect’ (Clement, 2008). The interpretation of the replacement effect implies that BOD and top-managers of organizations with headquarters in high power distance and uncertainty avoidance cultures will be more aware of the obstacles of collaboration practices. If these organizations aim to reduce GHG production, they have to force themselves to collaborate with supply chain partners and focus on how to improve these collaborations. In contrast, BOD and top-managers of organizations with headquarters in low power distance and uncertainty avoidance cultures act more naturally in collaboration practices and do not put in place formal practices (e.g. legal obligations) to improve the collaboration. Instead, they will have more informal ‘gentleman agreements’ in place. Touboulic, & Walker (2015) argue that to reduce GHG production, supply chain relationships should be accompanied by formal governance mechanisms. For example, the replacement effect concerning power distance. In organizations that did not implement GSCM collaboration practices, supply chain collaborations are hampered by high levels of power distance. Acting authoritarian and showing no willingness to create mutual dependence are obstacles for smooth supply chain collaborations (Cousins et al., 2019; Nakata, & Sivakumar, 2001). The obstacles that high power distance will bring in supply chain collaborations could be reduced by implementing formal GSCM collaboration practices. The reason for this is that it allows to manage the relationship and its obstacles, so it becomes more efficacy in its aim to reduce GHG production. A similar effect happens concerning uncertainty avoidance. The obstacles that uncertainty avoidance brings can be mitigated by introducing formal practices. Formal practices could help to reduce uncertainties and improve the effectiveness of GSCM collaboration practices.

The data does not show support for a significant effect of power distance and uncertainty avoidance on the direct relationship between GSCM monitoring practices and GHG production. However, the data seems to suggest that the interaction of power distance and uncertainty avoidance with GSCM monitoring practices decreases GHG production. This would be in line with the studies that argue that monitoring is a method for organizations to achieve more hierarchical control and to reduce discomfort (Simangunsong et al., 2012; Tata, & Prasad, 2015). This result can be a good starting point for future research to help better understand the influence of power distance on the GSCM monitoring practices.

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7. Conclusion

This study confirms that many organizations indeed increase the adoption of GSCM practices to counterattack their GHG production. According to earlier research, these GSCM practices should inevitably lead to better environmental performance, and thus, a reduction in GHG production. By using the survey data of a non-profit organization that focuses on reporting the environmental impact of organizations, this study found limited support for the effectiveness of the GSCM practices.

Furthermore, this research looked at the potential influence of the national culture of the headquarter location on the effectiveness of GSCM practices. The results show that national cultures that score high on power distance, individualism, and uncertainty avoidance interact with GSCM collaboration practices and improves environmental performance. Most of these findings were counterintuitive and resulted in support of the hypotheses in the opposite direction. The results did not show enough support for the remaining hypotheses. However, for most of these rejected hypotheses, the results seem to suggest to follow the logic of the hypothesized relationships.

7.1. Managerial implications

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taking this weakness into account, managers could take appropriate actions to push the relationship to improve its performances.

7.2. Limitations and future research

This study has several limitations but provided opportunities for future research. The first limitation of this study was that it did not include scope 3 in the computation of the total GHG production. However, also emissions that occur outside the company’s boundaries significantly contribute to the total GHG production (Green et al., 2012). Unfortunately, the data that was used in this research was too limited to include scope 3. Despite it would be interesting to see whether GSCM practices and national culture would have more impact on GHG production when scope 3 is included in the research. Future research could add scope 3 in the calculation of total GHG production.

Another limitation of this study is that it did not consider intra-country cultural diversity. There are differences between tight and loose cultures. Countries with tight cultures have strong social norms, and inhabitants are expected not to show deviant behavior (Beugelsdijk, Kostova, & Roth, 2017). In loose cultures, inhabitants are less restricted in their behavior, and deviation is more accepted (Beugelsdijk et al., 2017). Intra-country cultural diversity might change the influence of national culture on the effectiveness of GSCM practices. However, a reliable tightness-looseness index is missing in the current literature (Treviño et al., 2019), and therefore, it was not considered in this research. Future research can include the influence of intra-cultural diversity to determine the influence of national culture on practices-performance relationships.

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TOWARDS A MORE KNOWLEDGEABLE UNDERSTANDING OF GREEN SUPPLY CHAIN MANAGEMENT: THE ROLE OF NATIONAL CULTURE