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The Influence of Target Design in Green Supply Chain Management’s Targets for Environmental Performance

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University of Groningen, Faculty of Economics and Business

Master’s Thesis MSc Supply Chain Management 13th of July, 2020

The Influence of Target Design in Green Supply Chain Management’s

Targets for Environmental Performance

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ABSTRACT

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TABLE OF CONTENTS

1. INTRODUCTION ... 4

2. THEORETICAL FRAMEWORK ... 7

2.1. Environmentally Sustainable Business Practices ... 7

2.2. Green Supply Chain Management ... 7

2.3. Green SCM and its Effect on Environmental Performance ... 7

2.4. Targets ... 8

2.5. Natural Resource Based View ... 8

2.6. Hypotheses ... 9 2.6.1. Target Presence ... 9 2.6.2.Target ambitiousness ... 10 2.6.3. Target scope ... 11 2.6.4.Target timeframe ... 12 3. METHODOLOGY ... 13 3.1. Research Design ... 13 3.2. Sample ... 13 3.3. Variable Measurement ... 13

3.3.1. Dependent Variable: Environmental Performance ... 13

3.3.2. Targets Yes/No: Independent Variable ... 14

3.3.3. Target Design: Moderators ... 14

3.3.4. Control Variables... 15

3.4. Measurement Analysis ... 16

4. RESULTS ... 19

4.1. The Effect of Targets on Environmental Performance ... 19

4.2. The Effects of Target Design Choices on Environmental Performance... 20

5. DISCUSSION ... 22

5.1. Conclusion ... 24

5.2. Managerial Implications ... 25

5.3. Limitations & Future Research ... 25

6. REFERENCES ... 27

7. APPENDICES ... 32

7.1. Use of CDP Questions ... 32

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1. INTRODUCTION

Environmental problems, such as global warming, are a pressing issue in current society. 80% of consumers see businesses as to blame for these problems (CEO magazine, 2019). Firms are therefore increasingly stimulated to adopt environmentally sustainable business practices, which are business practices wherein environment protection is taken into account (Tseng, Islam, Karia, Fauzi & Afrin, 2019). As environmental sustainability and the competitive advantage that can be gained from it is rising in importance to firms (CEO magazine, 2019; Gallotta, Garza-Reyes, Anosike, Lim & Roberts, 2016; Mariadoss, Tansuhaj & Mouri, 2011), it becomes increasingly important for firms to gain knowledge on environmentally sustainable business practices. Much of the environmental impact of a firm appears through the supply chain of firms, rather than within a firm (Tseng et al., 2019). Consequently, firms should also take the influence of their external supply chain in account, when creating environmentally sustainable business practices.

In response to this, Green Supply Chain Management (Green SCM) is gaining attention in creating environmentally sustainable businesses (Tseng et al., 2019). Green SCM allows firms to managerially integrate material and information flows throughout the supply chain to satisfy the demand of customers for green products and services produced by green processes (Seuring, 2004; Green, Zelbst, Meacham & Bhadauria, 2012). Literature shows that the adoption of Green SCM can lead to improved environmental and financial performance (Gallotta et al., 2016). Although, while there is more attention for Green SCM, firms experience that creating positive outcomes from Green SCM is often difficult, given the complexity that the implementation of Green SCM involves. This complexity is caused by firms having to consider all influences and parties in their supply chain (Zhu, Sarkis, & Lai, 2012).

The Natural Resource Based View (NRBV) recognizes the importance and complexity of collaboration with the supply chain of firms (Hart, 1995). It focusses on how firms can derive competitive advantage from external relationships in which a firm is involved in their natural environment (Hart, 1995; Cucciella, Koh, Shi, V. Koh, Baldwin & Cucchiella, 2012). This is in line with Green SCM, which also acknowledges the importance of involving the whole of the supply chain, in- and external to firms (Cousins, Lawson, Petersen, & Fugate, 2019). Moreover, they both focus on the benefit that engaging in environmentally sustainable business practices can bring to firms (Hart, 1995; Cousins et al., 2019).

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goals, are more likely to influence environmental performance of a firm positively (Dahlmann, Branicki & Brammer, 2019).

As the NRBV believes that collaboration affects the possibility of competitive advantage, and environmental targets are supposed to be designed as such, that they facilitate this through environmental performance (Hart, 1995; Tseng et al., 2019), firms require to know how to design targets when faced with collaboration with supply chain partners. Rather than targets for internally focused environmentally sustainable business practices, targets in Green SCM quarry to diminish emissions not only within their firm, but beyond, within their (external) supply chain (Zhu, Sarkis, & Lai, 2012). It requires supply chain partners to collaborate in setting and trying to reach targets to minimize pollution aspects and mutually achieve environmental goals (Tseng et al., 2019). Though, involvement of the supply chain and its emissions can also make setting targets more complex, which is recognized as possibly harmful to performance as well (Vernet & Agné, 2017).

The way in which these targets are designed can influence the effect they have on environmental performance (Dahlmann, Branicki & Brammer, 2019), which is why it is important for firms to know how they should do so. Green SCM involves supply chain partners in the reaching of targets, which adds to the complexity of choosing a target design (Zhu et al., 2012). It is unclear how the design of targets in such a case, involving collaboration with supply chain partners, affects the collaboration in the supply chain and the environmental performance that can be thereby facilitated (Dahlmann, Branicki & Brammer, 2019; Cucciella, et al., 2012). Therefore, this research aims to answer the following question:

RQ: How does the design of targets involving Green SCM affect the environmental performance that can be reached?

To answer the research question data from the Carbon Disclosure Project (CDP) were analysed, which are part of a reliable and large database. The CDP conducts a yearly survey among thousands of firms all over the world, which allows insight in the carbon emissions and environmentally sustainable business practices of firms (Dahlmann, Branicki & Brammer, 2019). Also, firm data from Compustat, a highly reliable database for general and financial firm data, is used to add to the dataset (Dollinger & Golden, 1992). The data that was found was analysed using SPSS.

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knowledge gap becomes more pressing as focus in business turns more towards environmental sustainability and the Green Supply Chain Management (Mariadoss, Tansuhaj & Mouri, 2011).

Added knowledge on this paper’s subject can also help practitioners, as an increasing amount of them face the challenges that come with environmental sustainability and the performance thereof (Cucciella, et al., 2012). They need to be able to identify what is the best approach when collaborating with supply chain partners in setting targets, as it will affect their performance (Mariadoss, Tansuhaj & Mouri, 2011). This research can expand to the knowledge on this matter.

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2. THEORETICAL FRAMEWORK

2.1. Environmentally Sustainable Business Practices

Globally, interest towards securing the environment is rising and an increasing focus of consumers on environmentally sustainable products and services appears. In response to this, firms choose to adapt their processes to the market’s wishes (Adomako, Amankwah‐Amoah, Danso, Konadu & Owusu‐Agyei, 2019; Walsh & Dodds, 2017). They thereby aim to create a competitive advantage (Walsh & Dodds, 2017). Hence, an increasing amount of firms have developed a focus on environmental sustainability in their practices, meaning that they aim to reduce and/or minimize the emissions that come forth from their business practices; creating environmentally sustainable business practices

(Dahlmann et al., 2019). For example, Coca Cola launched an initiative last year that aims to minimize

their strain on the environment (ClimateAction, 2019) and IKEA also puts major focus on environmental sustainability in their product development (Virgin, 2016).

2.2. Green Supply Chain Management

A way in which firms can initiate environmentally sustainable business practices is by engaging in Green Supply Chain Management (Green SCM). This particular approach towards environmentally sustainable business practices focusses on the Supply Chain of firms, which is defined as being ‘the linked operations to source and provide goods and services to the end users’ (Ali, Bentley, Cao, & Habib, 2017, P. 24; Slack, Chambers & Johnston, 2009). Green SCM follows those linked operations in a way that facilitates environmental sustainability in the providing of goods and services (Ali et al., 2017). Green SCM can therefore be referred to as a set of managerial practices that integrate environmental issues into supply chain management to ensure environmental sustainability of the supply chain of firms and their products and services (Lee, 2015).

Over the last years, literature has showed a lot of interest in Green SCM. Green SCM claims that environmental sustainability can be achieved in engaging the supply chain of firms, which includes their supply chain partners that they collaborate with in this supply chain (Cousins et al., 2019). So, in becoming environmentally sustainable, firms often choose to involve other firms in their supply chain, as often a significant part of the emission reduction can be achieved there (Tseng, Islam, Karia, Fauzi & Afrin, 2019). Firms thereby engage in supply chain collaboration (Chen, Zhao, Tang, Price, Zhang & Zhu, 2017). Literature also recognizes supply chain collaboration as key in realizing environmentally sustainable business practices (Irani, Kamal, Sharif & Love, 2017).

2.3. Green SCM and its Effect on Environmental Performance

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environmental performance to create a competitive advantage in their markets (Dubey, Gunasekaran & Papadopoulos, 2017). Although, it is not completely known how the positive effect on Environmental Performance that Green SCM has is most likely to be reached (Gallotta et al., 2016). Competition based on the competitive advantage of environmental performance has however moved from a firm-versus-firm to a supply-chain-versus-supply-chain form (Tuni, Rentizelas, & Duffy, 2018; Cabral, Grilo & Cruz-Machado, 2012). The environmental performance of other parties also matters, when firms want to influence the environmental sustainability the external parts of a supply chain (Lee, 2015; Tuni, Rentizelas, & Duffy, 2018). The effects of this shift make for a lack of research on the performance measurement in this situation and a need has arisen for the development of tools in measuring and enhancing environmental performance in a Green SCM situation (Tuni et al., 2018).

2.4. Targets

Targets are often set by firms to measure their performance (Gallotta et al., 2016). This can be for all types of goals, for instance financial goals, but also for environmental goals that are set in Green SCM. The targets focused on environmental performance are set in order to decrease intensity emissions (Blanco, Caro & Corbett, 2020). These targets can help not only in identifying goals, but can also really improve performance, compared to cases wherein targets were not set. This is a consequence of the focus on environmental performance that the incentive of reaching the target goal brings (Ioannou, Li & Serafeim, 2016). The targets that are set can differ in terms of target design. This can affect the outcome of environmental performance that the targets strive for (Tseng et al., 2019).

Dahlmann, Branicki & Brammer (2019) identified four different target design criteria that could have an influence on the target and its effect on environmental performance; namely ‘target type (absolute vs. relative emissions reductions targeted); target scope (broad vs. narrow scope of emissions reductions targeted); target ambitiousness (scale of emissions reductions targeted); and target time frame’ (Dahlmann, Branicki & Brammer, 2019). For the broad target scope, high target ambitiousness and a long target time frame, a positive influence was found. However, these design criteria have not been assessed focusing on the supply chain, involving supply chain collaboration, and on the performance and competitive advantage of the supply chain, while this becomes increasingly important due to the interest in Green SCM (Cucciella, et al., 2012; Tuni et al., 2018). This interest includes the setting of targets involving the SC in implementing and executing Green SCM (Gunasekaran, Subramanian & Rahman, 2015), which adds to the importance of knowledge on setting environmental targets when involving Green SCM.

2.5. Natural Resource Based View

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and capabilities (Corbett and Claridge 2002; Yu, Chavez & Feng, 2017). It focuses on the resources internal and external of the firm (Barney, 1991). RBV claims that firms thrive on creating resources that are valuable, rare, inimitable and non-substitutable (Barney, 1991).

Natural Resource Based View is introduced by Hart (1995) and adds an extra factor to the RBV; it involves the natural environment that a firm and its supply chain is in. While RBV focusses on creating a competitive advantage while being internally focused, NRBV states that competitive advantage that is internally based may prove inadequate due to the external relationships in which a firm is involved in their natural environment (Hart, 1995; Cucciella et al., 2012). Green SCM fits in this view, as it acknowledges the importance of involving the whole of the supply chain, in- and external of the firm (Cousins et al., 2019).

Moreover, NRBV recognizes the challenges that a firm’s natural environment imposes, and that the competitive advantage that can be created is dependent on a firm’s capability to facilitate environmental responsible activity (Hart, 1995; Cucciella, et al., 2012). Green SCM aims to diminish the environmental impact that arises from a firm’s product or service supply chain, as this can create competitive advantage (Tseng et al., 2019). Hence, NRBV and Green SCM have a similar view the creation of competitive advantage via a firm’s supply chain is combined with focusing on environmental sustainability.

In setting targets, firms also face the influence that their natural environment imposes on them. When firms want to involve their supply chain in setting targets, in line with Green SCM, the influence of the natural environment can alter the probability of reaching the targets and designing the targets optimally (Tuni et al., 2018; Dahlmann, Branicki & Brammer, 2019). NRBV recognizes the effects that this external influence can have on a firm (Cucciella, et al., 2012). Moreover, NRBV focusses on the effect that the relationships of external parties with a focal firm, initiating environmental performance targets for the supply chain, have. According to the NRBV, the relationships between these involved parties can influence the competitive advantage that firms can get out of these external relationships (Hart, 1995). When targets are set for environmental performance, this influence reflects on the performance as well, which means it can reflect on the goal of the target (Cucciella, et al., 2012; Gallotta et al., 2016).

2.6. Hypotheses

NRBV offers a certain perspective on Green SCM and setting environmental performance targets, which makes for a theoretical perspective on which the following hypotheses are based.

2.6.1. Target Presence

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improvement of environment, can be difficult for firms (Dahlmann, Branicki & Brammer, 2019; Tseng et al., 2019). Despite the complexity of setting environmental performance targets creating a positive effect to actual environmental performance, literature states that it still has a positive effect most of the time (Cucciella, et al., 2012). So, targets for environmental sustainability have a positive effect within firms.

When setting targets involving the supply chain in Green SCM, complexity is also an issue, as the involvement of supply chain partners in setting targets and working on improving environmental performance throughout the target’s scope is complex as well, according to the NRBV (Hart, 1995; Vachon & Klassen, 2008). But, NRBV still advocates the importance and potential of involving the supply chain of firms in creating a competitive advantage, in this case though possible environmental performance (Hart, 1995). Given the knowledge on targets within firms, and the benefits of involving supply chain partners as portrayed by the NRBV and in Green SCM, the following hypothesis is proposed:

H1: Presence of targets for environmental sustainability in the supply chain of a firm has a positive effect on environmental performance.

2.6.2.Target ambitiousness

Targets that are set for environmental performance can differ in ambitiousness (Magnusson, Lindström & Berggren, 2003). Research suggest that in general, target ambitiousness has a positive effect on the environmental performance of firms (Dahlmann, Branicki & Brammer, 2019), as ‘the core premise behind target setting in that ambitious targets, that is, those including a larger percentage of emissions to be reduced, are more likely to be effective’ (Dahlmann, Branicki & Brammer, 2019; Ioannou, Li & Serafeim, 2016).

Supply chain collaboration, evolving from Green SCM in environmentally sustainable business practices, makes for collaborating in setting and reaching for targets, as it involves supply chain partners (Yılmaz, Çemberci, & Uca, 2016). The NRBV recognizes that such collaboration is advisable, in trying to reach targets and improve environmental performance optimally (Gunasekaran, Subramanian & Rahman, 2015). The thought behind this is that the environmental performance, which can offer competitive advantage, relies on the external, natural environment that a firm is in (Hart, 1995). So if firms want to gain or improve competitive advantage, they need to take this into account, also when setting targets which aim to enhance environmental performance (Lee, 2015; Haffar & Searcy, 2017).

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Sridharan, 2005; Haffar & Searcy, 2017). Their motivation facilitating collaboration can influence the ability to reach targets (Dahlmann, Branicki & Brammer, 2019). This positive attitude can, according to the NRBV, result in a competitive advantage for the focal firm as well (Hart, 1995). Therefore, proposed is that:

H2: A more ambitious target has a positive effect on the positive relationship between targets and environmental performance in the supply chain.

2.6.3. Target scope

According to the NRBV, competitive advantage can be created by using the relationships in the natural environment of the firm (Hart, 1995). When creating targets for Green SCM and environmental performance, NRBV therefore advocates the involvement of the natural environment of firms, as environmental performance can offer a competitive advantage (Cucciella, et al., 2012).

When addressing environmental performance and their target emissions, firms can opt to look at the scope of these emissions. They can create targets for scope 1 emissions (direct emissions), scope 2 emissions (indirect emissions) or scope 3 emissions (emissions in the total supply chain) (Dahlmann, Branicki & Brammer, 2019). The broader the scope of the target, the more focus lies on external parties and collaboration with these parties in reaching the targets (van den Berg, van Soest, Hof, den Elzen,

van Vuuren, Chen, ... & Kõberle, 2019). In scope 3 targets, the Green SCM focus is most present, as

this approach and the targets attached to this focus on the emissions of a whole supply chain (Gallotta et al., 2016). According to NRBV, such a focus is preferable, as involvement of the natural environment that a firm is in, is considered positively affecting environmental performance (Hart, 1995).

When targets are set involving emissions created outside their internal scope, other parties are involved in setting and reaching the target (Magnusson, Lindström & Berggren, 2003). For such involvement in setting targets for environmental performance, firms need to collaborate with the involved parties (Cucciella, et al., 2012). For firms to successfully collaborate, all involved parties must benefit from the collaboration, for them to feel the need and incentive of the collaboration (Gunasekaran, Subramanian & Rahman, 2015). For example, in Green SCM, challenge can arise in the collaboration with upstream suppliers, as they often do not benefit from the Green SCM approach (Gunasekaran, Subramanian & Rahman, 2015).

Successful collaboration within a supply chain is dependent on the mutual benefit that parties expect to get out of the collaboration (Gunasekaran, Subramanian & Rahman, 2015; Cucciella, et al.,

2012; Simatupang & Sridharan, 2005). This can also reflect in collaboration regarding the setting and

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H3: A broader target scope has a positive effect on the positive relationship between targets and environmental performance in the supply chain.

H3A: A broader target scope increases the positive effect that a more ambitious target has on the on the positive relationship between targets and environmental performance in the supply chain.

2.6.4.Target timeframe

Targets can be set for different timeframes; this means that there could be a difference in the time to reach the target that has been set; when the targeted improvement of intended emissions has to be reached (Chen et al., 2017). A longer timeframe is proven to have a positive effect on the environmental performance that can be reached, when assessed per year (Dahlmann, Branicki & Brammer, 2019).

NRBV focusses on the importance that external influences, evolving from relationships with supply chain partners, has on the probability of creating competitive advantage (Hart, 1995). Such competitive advantage can, according to Green SCM, evolve from better environmental performance (Haffar & Searcy, 2017). When setting targets for reaching environmental performance, it is important for them to collaborate with supply chain partners (Haffar & Searcy, 2017). Though, a prerequisite is that this has to be combined with a benefit for the collaborating partners in the supply chain (Cucciella, et al., 2012).

For Green SCM that enhances performance optimally, strong relationships with the partners which are involved in the supply chain collaboration are required (Gunasekaran, Subramanian & Rahman, 2015). Longer collaboration time makes for trust between involved parties, and make for a competitive capability (Salam, 2017). Also in Green SCM, a longer target timeframe makes for longer supply chain collaboration and trust in a mutual benefit (Chen et al., 2017). The more focus lies on supply chain partners, in reaching environmental targets, the more important the relationship with these partners becomes (Simatupang & Sridharan, 2005). Therefore, the following hypothesis is proposed:

H4: A longer target time frame has a positive effect on the positive relationship between targets and environmental performance in the supply chain.

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3. METHODOLOGY 3.1. Research Design

To test the hypotheses, secondary data was used for data analysis. Most of these data were derived from the Carbon Disclosure Project (CDP). Ever since 2002, the CDP has taken surveys among a large and expanding group of firms all around the world, including all kinds of industries. In 2015, 5500 firms already joined the project and disclosed the asked information in the yearly CPD survey, and the amount of firms that join is ever extending (Blanco, Caro & Corbett, 2020). The surveys asked after their firm data on emissions and the measures that they might take to reduce these emissions (Dahlmann, Branicki & Brammer, 2019). The CDP data are used in many studies (Dahlmann, Branicki & Brammer, 2019) and are recognized as highly reliable.

Also, data from the Compustat database was used to find data on the control variables of industry and firm age. Compustat is a database which includes general and financial data of thousands of firms (Dollinger & Golden, 1992). It is accepted in literature as a highly reliable source of firm data (Dollinger & Golden, 1992). By using these both regarded as highly reliable databases the reliability of the data that was used for the analysis could be assured.

3.2. Sample

Data from the CDP was used to deduce and thereby test the presented expectations in the hypotheses (De Groot, 1961). A selection of 227 firms in the database was made to create a sample including firms with all kinds of industries and firm ages. The actual industry codes and the firm ages were derived from the Compustat data, in which the firms in the sample were all included. The emissions in metric tonnes CO2e of the firms and their supply chains were used as dependent variable. Whether targets for lowering these emissions and if so, the scope, timeframe and ambitiousness of the targets were used as independent variable. The exact questions as asked after in the CDP dataset can be found in appendix 7.1.

The sample of the 227 firms include data from these firms for five reporting years, 2013 up until 2017. This time leap allows for assessing the effects that the targets have. As in the year that targets were set, results might not show immediately (Hartmann & Schreck, 2018). Therefore, a need occurred to assess independent variables of previous years, when assessing environmental performance in this research.

3.3. Variable Measurement

3.3.1. Dependent Variable: Environmental Performance

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widely accepted environmental performance measure (Dahlmann, Branicki & Brammer, 2019; Blanco, Caro & Corbett, 2020). As this research aims to assess the effect that targets have within their part of the supply chain, within the firm, their direct and indirect emissions in scope 1 and 2 were assessed. Scope 1 includes activities within the target setting firm, resulting from their company equipment, whereas scope 2 includes emissions created from electricity purchases required for their activities (CDP, 2017; Blanco, Caro, & Corbett, 2020). These two scopes make for the assessed part of the supply chain, as the data on scope 3 was, for all variables, found to be unreliable.

3.3.2. Targets Yes/No: Independent Variable

Given hypothesis 1, first was assessed whether targets have a positive effect on environmental performance in scope 1 and 2 in general, no matter the design of the targets. In the CDP questionnaire, firms were asked about whether and how they set targets (CDP, 2017). So, using a dummy variable in which is assessed whether a target is set (1) or not (0), the effect of the presences of targets could be assessed.

This research looks at intensity based targets when assessing the further design of the targets. Intensity targets reflect ambitions to improve emissions or energy efficiency on a relative level, whereas absolute targets, which are also asked after in the CDP questionnaire, are focused on reducing a firms total Greenhouse Gas Protocol (GHG) emissions (Dahlmann, Branicki & Brammer, 2019; Slawinski, Pinkse, Busch & Banerjee, 2017; Pinkse & Kolk, 2009). The CDP questionnaire also asks after ‘other emission targets’ in the questionnaires of 2017 and 2016. Since these were asked after only in two of the five reporting years that this research includes, these other targets were left out of the discussion. The presence of emission targets therefore only included the presence of intensity or absolute targets.

Given the amount and completeness of the data on intensity based targets versus the data on absolute targets, the choice for assessing intensity targets only was made. Research found that the setting of absolute versus intensity based targets does not influence the environmental performance of firms (Dahlmann, Branicki & Brammer, 2019), so assessing one out of the two does not jeopardize the generalizability of the results that this research obtains.

3.3.3. Target Design: Moderators

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of ambitiousness, to enable with one case per firm, as firms also have a single number of environmental performance in scope 1 and 2.

The time frame that a target is set for, can also be derived from the CDP survey answers. The CPD asked after both the base year and the target year of the intensity targets within their firm (CDP, 2017). The time in between the two makes for the target time frame measured in years. When there were several targets and therefore several time frames in place, the average time frame was used for analysis, the same as for the ambitiousness of the targets. The averages measured from one year up to 52 years on average.

Another variable that was added to the analysis was the scope of the target. A dummy variable which assessed the focus on the external environment of the firm in question was created. More specifically, this dummy makes a distinction between firms which have targets that are solely focused on scope 1, 2 or both of these, and firms that had a more external, broader focus which included scope 3. This was chosen as targets including a broader scope of the supply chain in targets can positively affect performance within the supply chain of a firm (Simatupang & Sridharan, 2005; Hart, 1995).

3.3.4. Control Variables

Emissions can vary per industry due to their varying activities (Xu & Lin, 2016), and therefore do not automatically access the performance of the firm. Also, smaller firms are likely to have less emissions, due to the different scale of their overall activities (Xu & Lin, 2016). Less emissions are seen as ‘better performance’, but the impact that the targets have can be assessed by looking at the difference that the targets make for lowering the emissions. Therefore, control for the emissions of the previous year was chosen. This offers the opportunity to look at the difference that a new year, with or without targets, has made in the lowering of emissions.

In this research, control for the age of the target setting firm was also chosen. As, in setting targets, firms often use to incorporate multiple scopes and thereby include the activities of supply chain partners, the relationships that they have with these partners can influence the process and progress of the targets (Gunasekaran, Subramanian & Rahman, 2015). Relationships can differ when the age of a firm differs (Cucciella et al., 2012). Therefore, the age of the firm on the moment the target design are assessed was added to the analysis as a control variable.

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to the Global Industry Classification Standard (GICS) coding. GICS coding of industries is a reliable, often chosen form of dividing firms in industry categories (Hrazdil, Trottier & Zhang, 2013). The industries were divided in two groups, being of high or low risk for high carbon emissions (EEA, 2019). This coding is categorized as described in table 3.1.

Low Risk High risk

GICS Code Industry GICS Code Industry

35 Health Care 10 Energy

40 Financials 15 Materials

45 Information Technology 20 Industrials

50 Communication Services 25 Consumer Discretionary

60 Real Estate 30 Consumer Staples

55 Utilities

Table 3.1:Low and High Environmental Risk Industries divided by GICS Codes (GICS, 2018) 3.4. Measurement Analysis

To analyse the data, they had to be cleaned’ first by removing odd values. Moreover, a matrix like variable set was created, given the five years of data which were included. An example of the data in the matrix is given in appendix 7.2. It allows to test independent variables, moderators and control variables of the previous years onto the year of the dependent variable.

Then, the data was analysed using SPSS. Results of the targets within a data time frame of a year, with dependent T0 and other variables T-1, were used, as these made for the strongest results. This means that the emissions of a year were tested with the target presence and design and the controls of the previous year. However, this was not required for the variable which assesses whether a firm is in an industry with a high risk for high emissions, as assumed can be that firms stay within the same industry.

Descriptive statistics were obtained first to get a clear view of the actual used data. Moreover, the time frame and the ambitiousness were logarithmically transformed into variables that were more approximately normal, for the purpose of the linear regression analysis that is conducted (Benoit, 2011). The found descriptive statistics can be found in table 3.2 and for the dummy variables, the frequencies can be found in table 3.3.

Variable N Min Max Mean Std. Dev.

Emissions T0 665 42,00 1325,00 701,8301 285,10222

Emissions T-1 579 40,00 1087,00 586,4784 236,53905

Firm age T-1 885 1 536 272,87 145,991

Industry High Risk 1136 ,00 1,00 ,5634 ,49619

Targets Yes/No T-1 1136 ,00 1,00 ,3548 ,47865

Ambitiousness Scope 1+2 T-1 350 ,00 5,11 4,1507 ,90642

Time Frame T-1 405 ,00 3,95 2,8066 1,23280

External Scope Focus T-1 1136 ,00 1,00 ,6690 ,47078

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Variable Frequency Percentage

0,00 1,00 Missing N 0,00 1,00 Missing Total

Industry High Risk 496 640 100 1236 40,1 51,8 8,1 100 Targets Yes/No T-1 733 403 100 1236 59,3 32,6 8,1 100 External Scope Focus T-1 376 760 100 1236 30,4 61,5 8,1 100

Table 3.3: Frequencies of Dummy Variables

The descriptive statistics show a high difference in emissions, which is to be expected due to the difference of the firms in industry and size (Tseng et al., 2019). The difference between the firms also becomes clear from the age of the firms in the dataset, ranging from a mere age of one year up to 536 years (table 3.2). Moreover, concluded can be that all the questions forming all variables have been filled out often enough to be able to continue a statistical analysis. This also is the case for the dummy variables, as can be concluded from table 3.3. A hundred cases were missing, which were the same cases for each of the analysed variables, as was concluded in the further assessment of the data, which was conducted because of the similar number of missing cases.

Next, to create an idea of how the values of the emissions, the control variables and the target characteristics correlate with each other, a Pearson, Two-Tailed correlation test was conducted. The results are presented in table 3.4.

Emissions T0 Emissions T-1 Firm age T-1 Industry High Risk Targets Yes/No T-1 Ambitiousness Scope 1+2 T-1 Time Frame T-1 External Scope Focus T-1 Emissions T0 1 Emissions T-1 0,526** 1 Firm age T-1 -0,035 0,002 1

Industry High Risk -0,041 -0,046 -0,033 1

Targets Yes/No T-1 -00,039 0,012 -0,008 0,007 1 Ambitiousness Scope 1+2 T-1 -0,081 0,081 0,059 -0,076 0,119* 1 Time Frame T-1 -0,157** -0,098 0,115* -0,051 0,015 0,051 1 External Scope Focus T-1 0,024 -0,013 0,016 -0,016 -0,921** -0,106* -0,104* 1 *=P<0,05, **=P<0,01

Table 3.4: Data Correlation Matrix

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Moreover, a negative correlation between the target time frame and the emissions in T0 are found. So a shorter target time frame correlates with higher emissions in T0, and a longer target time frame with lower emissions. This means that for example, firms with low emissions often have chosen for long time frames for their environmental targets. This is in line with the expectations, as hypothesis 4A proposes a positive relationship between the two. Other than that, no clear correlation regarding choices for a target time frame, target ambitiousness and the target scope could be found in the correlation matrix.

Lastly, correlation could be found between the target design variables, the ambitiousness, time frame and scope of the targets, and the presence of a target. As a target that is not in place also has no design possibilities, this makes sense.

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4. RESULTS

In this section, the results of the data analysis will be presented. First, the relationship between the setting of targets and the impact thereof will be discussed (section 4.1). Second, the effects that were found regarding the proposed target design options will be assessed (section 4.2).

4.1. The Effect of Targets on Environmental Performance

First, the relationship between the setting of targets and the Environmental Performance was analysed by conducting a hierarchical regression analysis. Found in step 2, which included both the independent as the control variables, was that the target setting had no significant relationship with the carbon emissions of scope 1 and 2. Also, it shows that the addition of the Target Yes/No variable, which shows whether an environmental target is in place in the assessed firm’s situation does not make for a higher explained portion of the dependent’s value, given the slightly lower Adjusted R2. The summarized results can be found in table 4.1.

This is not in line with the expectation set in hypothesis 1, which can therefore be rejected. The presence of targets alone, not matter their design, does not have positive effects on the lowering of emissions in scope 1 and 2, and therefore not on the environmental performance in these scopes as was expected because of previous literature (Cucciella, et al., 2012). Although, as discussed in the theory section, setting targets which commend environmental performance, can be complicated (Zhu, Sarkis, & Lai, 2012). This might explain why there is no relationship found between the mere presence of targets and the environmental performance that can be reached in scope 1 and 2.

Dependent Variable

Emissions Scope 1+2 T0 (N = 407)

Variable Entered Step 1 Step 2

Emissions Scope 1+2 T-1 ,533*** ,533***

Firm Age -,048 -,048

Industry High Risk ,015 ,015

Target Yes/No T-1 ,004

F for the step 54,174*** 40,533***

Adjusted R2 0,282 0,280

*=P<0,1, **=P<0,05, ***=P<0,01

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4.2. The Effects of Target Design Choices on Environmental Performance

Second, the effect of the ambitiousness of targets towards the environmental performance of scope 1 with the moderating effects of the target time frame and the target scope were assessed using a hierarchical regression analysis. The results of this were summarized in table 4.2. For all tested steps, the models were significant on a below 0,01 level.

Step 2 of the hierarchical regression analysis showed the significance of the target ambitiousness in scope 1+2 has a significantly lowering effect on emissions in scope 1 and 2 on a below 0,1 level (Beta -0,096). This is partially in line with hypothesis 2, in which expected was that ‘A more ambitious target has a positive effect on the positive relationship between targets and environmental performance in the supply chain’. Subsequently, in step 3 of the analysis, which adds the target time frame and the inclusion of emissions external to the firm, the ambitiousness of the target remains a variable which significantly lowers the emissions in scope 1 and 2. Therefore, hypothesis 2 can be partially accepted, as the mere presence of targets did not have a significant effect on the lowering of emissions.

Moreover, the time frame of the target highlights a significant effect on the emissions in scope 1 and 2, and thereby on environmental performance. This significant effect on a below 0,1 level (Beta -0,10). Hence, the higher the target time frame, the lower the emissions in scope 1 and 2. As this hypothesis 4 claimed that ‘A longer target time frame has a positive effect on the positive relationship between targets and environmental performance in the supply chain.’, this hypothesis can be partially accepted, again because the mere presence of targets did not have a significant effect on the lowering of emissions.

Dependent Variable Emissions Scope 1+2 T0 (N = 238)

Variable Entered Step 1 Step 2 Step 3

Emissions Scope 1+2 T-1 ,533*** ,501*** ,484***

Firm Age -,048 -,048 -,026

High risk Industry ,015 -,024 -,036

Ambitousness Scope 1+2 T-1 -,096* -,097*

Target Time Frame T-1 -,100*

External Scope Focus T-1 -,036

F for the Step 54,174*** 20,590*** 14,277***

Adjusted R2 0,282 0,247 0,252

*=P<0,1, **=P<0,05, ***=P<0,01

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In step 3 of the hierarchical regression analysis, the possible influence of both the scope for which the target is set is also added to the analysis. This effect did not appear in the hierarchical regression analysis, so hypothesis 3 could be rejected.

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5. DISCUSSION

In this section of this paper, the results of the research will be discussed. This will show how the target design of the environmental targets, when engaging in Green SCM, has on environmental performance of the supply chain. The results will be linked to literature in order to do so.

This research aimed to contribute to the discussion of about effects that target design can have and the influence of Green SCM. The hypotheses that were proposed to aid in this, derived ideas from the Natural Resource Based View (Hart, 1995). The NRBV recognizes the importance of firms collaborating and interacting with their environment for enhancing environmental performance (Hart, 1995; Vachon & Klassen, 2008). With that, it emphasizes the importance of including the supply chain in facilitating environmental performance (Vachon & Klassen, 2008). The way in which the design target affects the environmental performance in supply chains, was proposed with the NRBV in mind. A selection of firm data from the CDP database was used to test the hypotheses regarding the target design, including data from five reporting years of 227 firms.

Firstly, the relationship between the presence of targets pursuing improvement environmental performance in the supply chain of firms and the actual environmental performance was assessed. There was no significant relationship found between the presence of targets and the height of emissions and the in the assessed part of the supply chain (scope 1 and 2). This is not in line with literature, as it validated the setting of targets for environmental performance facilitate the improvement of it (Ioannou, Li & Serafeim, 2016). Although, the designing of targets is assumed to be difficult, as is the adjustment of the environmental performance in a supply chain (Vernet & Agné, 2017). As the emissions in both scope 1 and 2 were assessed, part of the (external) supply chain is included in the environmental performance measurement. The NRBV validates the complexity of including a firm’s environment and supply chain in their activities (Hart, 1995). Therefore, despite literature illustrating the positive and motivating effect of targets (Ioannou, Li & Serafeim, 2016), NRBV argues for the benefit, but also for the complexity of interaction with a firm’s environment (Hart, 1995).

Adding to that, the alignment of supply chain members in achieving the overall system objectives, such as the targets for environmental sustainability that are set, is key to success in managing performance emulated in the supply chain of firms (Manuj & Sahin, 2011; Sahin & Robinson, 2005, 2002). That being set, the complexity of both designing targets and the complexity of collaborating with supply chain partners (Manuj & Sahin, 2011; Vachon & Klassen, 2008) might intervene too much with the advantages of doing so, to show significant environmental performance in scope 1 and 2 of a firm’s emissions.

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more ambitious an environmental performance target, the more firms can benefit from the target. This is in line with expectations, since literature stated that more ambitious targets can, in general, improve performance (Simatupang & Sridharan, 2005). Moreover, literature discussing Green SCM points out the importance of giving supply chain partners an incentive for participating in reaching the environmental performance targets (Zhu, Sarkis, & Lai, 2012; Dahlmann, Branicki & Brammer, 2019). It has to pay to be green in order for firms in the supply chain to participate in reaching the environmental performance targets as initiated by another firm (Dahlmann, Branicki & Brammer, 2019).

NRBV connects the benefit of environmentally sustainability and improving this to the financial benefit that can be gained from it (Hart & Dowell, 2011). The improvement in environmental performance is assumed to coexist with the change that can be made financially (Berchicci & King, 2007). This offers an incentive for not only the target setting firm, but also for other involved parties (Hart & Dowell, 2011), which can explain the positive effect of ambitiousness of the environmental targets in the supply chain on the environmental performance in this scope.

Moreover, literature discussing the implementation of Environmental Management Systems (EMS) addresses that firms feel increasing pressure from society to improve their environmental performance and lower their emissions (Anton, Deltas & Khanna, 2004). They do not want to fall behind in their EMS compared to their competition. With that in mind, firms could feel pressure and motivation to keep up to speed on their environmental performance improvement (Anton, Deltas & Khanna, 2004). A greater initiated change, through a more ambitious target, could thereby aid environmental performance improvement. This could, following the required benefit that supply chain partners need to experience in partaking in reaching environmental performance targets (Zhu, Sarkis, & Lai, 2012), aid in their motivation to reach targets and improve environmental sustainability of the supply chain.

Also, a longer time frame for the targets showed to have a positive effect on the change in emissions in scope 1 and 2 that can be made. This is interesting, as the change that this research looks at is only over one year. Therefore, assumed can be that the change is not only greater because there is more time to achieve change, but the change per year, and therefore the improvement in environmental performance, is really greater when a firm’s target focuses on a longer time frame. This is to be expected, as a long term target makes for a goal in which commitment is important. A temporary enthusiasm is not enough to ensure commitment to long term goals and targets (Haffar & Searcy, 2017). The long term commitment that firms with targets which have longer time frames aim for, can positively influence the effect that they are able to reach (Haffar & Searcy, 2017).

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(Hart, 1995). A long collaboration can make for a relationship between parties in which trust between parties that is built over time can have positive effects on performance (Chen et al., 2017).

The importance of such a relationship is also in line with the relational view of firms. The relational view in literature proposes that the ‘relation-specific assets, knowledge-sharing routines, complementary resources/capabilities, and effective governance constitute critical sources of interorganizational advantage’ (Dyer & Singh, 1998; Gölgeci, Gligor, Tatoglu, & Arda, 2019). By setting collaborative targets, and creating a mutual goal, advantage for all parties involved can be acquired. As this mutual goal, and the relationships that are required to aid this, benefit from longer collaboration according to the relational view (Touboulic & Walker, 2015), the positive effect of a longer target time frame confirms this view.

5.1. Conclusion

This research hopes to add to the theory regarding the possible success of setting targets to improve environmental sustainability of firms and their supply chains by answering the following research question:

How does the design of targets involving Green SCM affect the environmental performance that can be reached?

The findings in this research add to theory in different ways. The presence of environmental targets did not have a significant effect on environmental performance in the supply chain, and thereby contradicts literature who point out that they ought to do so (Ioannou, Li & Serafeim, 2016). The design variables did however show significant results, which adds to the view that the design of a target can make or break its possible effect (Dahlmann, Branicki & Brammer, 2019).

Firstly, the importance of the ambitiousness of targets in both scope 1 and 2 for enhancing environmental performance in the supply chain is confirmed. This positive effect is in line with expectations, as literature states that high ambitiousness, and therefore expectations of a great advantage, makes for motivation to participating in reaching the target (Singh, Jain & Sharma, 2014). When targets include both scope 1 and 2 of a firms supply chain, such motivation is required for improving environmental performance in the supply chain (Zhu, Sarkis, & Lai, 2012).

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Chen et al., 2017), which is in line with the relational view of firms and their environments (Touboulic & Walker, 2015).

Lastly, the importance for focussing on the external parts of the supply chain in targets by having a broader target scope, in line with the NRBV (Hart, 1995), did not prove to be of significant importance for the success of targets. Although, NRBV also stresses the complexity of involving a firm’s supply chain in their activities (Hart, 1995). This complexity might be the source of the inconclusiveness of the results where the scope of the target is concerned, and might diminish the possible positive effect. Either way, this research gives an interesting addition to the complexity factors that can influence environmental performance within supply chains.

5.2. Managerial Implications

For practitioners, it is important to have a clear idea of what they can do to optimize the positive effect on environmental performance that their target might have (Haffar & Searcy, 2017). This research found that targets do not definitely have a significant positive effect on environmental performance in scope 1 and 2 of a firms supply chain. However, this does not mean that firms should be discouraged in setting targets for environmental performance. There are target design options that can improve the chance of a reduction in the emissions of a firm’s supply chain.

Setting ambitious targets, aiming for a high percentage of emissions change, makes for more change of emissions. It offers a motivation which is not only symbolical, but actually improves the change that can be made (Singh, Jain & Sharma, 2014). Therefore, managers can be advised to be bold in setting their goals when it comes to lowering emissions in their supply chain.

Additionally, found was that a longer time frame in targets has a positive effect on the change per year as well. Such a long time frame in trying to reach a target in the supply chain, makes for a long time frame in collaborating with involved supply chain partners (Dyer & Singh, 1998). This long term collaboration can make for trust and a more aligned goal in the supply chain, when it comes to improving environmental performance (Haffar & Searcy, 2017). Firms can benefit from this, according to the relational view of firms as described in literature (Touboulic & Walker, 2015). The choice for a short term target can therefore be discommended for managers designing their environmental performance targets.

5.3. Limitations & Future Research

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the outcome of the data analysis, as there is no possibility to know what these firms might have filled out.

Second, the data that was available was related to the years 2013-2017. Although, many firms answered that their target time frame was a lot longer than five years. There was a positive relationship found between the reported effect of a longer target time frame and the environmental performance in the next year, as expected (Dahlmann, Branicki & Brammer, 2019). There was no test conducted as to what stage of the target time frame these targets were in. These targets could have been in place for multiple years already, even before the first reporting year that was included in this research, or be ‘new’ for the firms and their supply chains. Therefore, it can be interesting for future research to investigate when this positive relationship with performance comes to light. For instance, it is often stated that environmentally sustainable business practices have a slow start in generating results throughout the supply chain (Eltayeb, Zailani & Ramayah, 2011). Given that firms do mention longer target time frames, and as a longer time frame showed to have a positive influence on environmental performance in scope 1 and 2, future research could shed a light on long term effects of targets and their effect across the time frame the target is set for. This could guide both researchers and practitioners in further exploring what results they can expect from set targets.

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7. APPENDICES

7.1. Use of CDP Questions

The following questions from the Carbon Disclosure Project questionnaire were used for this research. The used questions were asked for all reporting years. The questionnaire differed a bit in numbering of questions throughout the years, but the questions that this research used were asked after in a similar way for every year. These were the questions used, following the order of the 2017 questionnaire (CDP, 2017);

CC3.1 Did you have an emissions reduction or renewable energy consumption or production target that was active (ongoing or reached completion) in the reporting year?)

This question was used to determine whether a target was (not) in place in the reporting year.

CC3.1b Please provide details of your intensity target - Base year covered by target

- Target year - Scope

This question asked after details of the targets, among others after scope of the target and the base- and target year, which determined the time frame of the target.

CC3.1.c Please also indicate what change in absolute emissions this intensity target reflects - % change anticipated in absolute Scope 1+2 emissions

This question was used to determine the ambitiousness of the target. As can be concluded, all targeted change in emissions was brought back to a similar scale; absolute scope 1 and 2 emissions. This allowed for a comparison of ambitiousness between firms and their targets.

CC8.2 Please provide your gross global Scope 1 emissions figures in metric tonnes CO2e CC8.3a Please provide your gross global Scope 2 emissions figures in metric tonnes CO2e

These questions were used to find out the amount of emissions in both scope 1 and scope 2 together, by adding them up.

Moreover, the industry a firm is in and the age of firms were directly derived from the Compustat database. A combination of ISIN codes and firm names were used to make sure the data were correctly subscribed to firms.

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7.2. Example of Data in Matrix

This example relates to the time frame of targets of a few of the dataset’s firms.

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