Exploring the Impacts of Stakeholder Pressures and Green Manufacturing on Environmental, Operational and Financial Performance

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MASTER THESIS

Exploring the Impacts of Stakeholder Pressures and

Green Manufacturing on Environmental, Operational

and Financial Performance

Anastasia Zuchowski (s2554259/14048923) Double Degree MSc in Operations Management April, 2016

Supervisors: Dr. Stefano Fazi

University of Groningen

Faculty of Economics and Business Dr. Jingxin Dong

Newcastle University

Newcastle University Business School

Abstract

Due to growing environmental concerns, organisations are subjected to pressures from various stakeholders to reduce their negative impact on the environment. As a result, concepts such as Green Manufacturing (GMfg) are gaining increasing attention. Since GMfg implementation requires significant resource investments, there is a need to investigate impacts on performance outcomes and provide suggestions for implementation strategies. This research explored the effects of market and regulatory stakeholder pressures on the adoption of GMfg practices (Environmental Management, product stewardship and process stewardship) and examined the impacts of GMfg on environmental, operational and financial performance. A multi-industry survey study conducted in 155 manufacturing companies in Europe showed that market pressure had a significant positive impact on the adoption of Environmental Management (EM), which positively affected financial and operational performance through improved environmental performance. Product stewardship was found to have a direct positive impact on operational performance. The impact of EM on product stewardship was mediated by process stewardship, suggesting a staged implementation of these three elements. Regulatory pressures were found to have a direct negative impact on environmental performance. Finally, a moderation analysis showed no significant effects of GMfg on firm performance, when groups of companies were compared based on the time that had passed since they first started implementing GMfg practices.

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List of Figures

Figure 2.1: Elements of Green Manufacturing ... 4

Figure 2.2: Theoretical model ... 15

Figure 4.1: Significant direct paths ... 36

List of Tables

Table 3.1: Construct items and sources ... 18

Table 3.2: Demographic characteristics of respondents ... 22

Table 3.3: Construct reliability and factor loadings ... 24

Table 3.4: Discriminant validity (Fornell-Larcker criterion) ... 25

Table 3.5: Discriminant validity (HTMT criterion) ... 25

Table 4.1: Descriptive statistics of constructs and items ... 26

Table 4.2: Average stakeholder pressure by country ... 27

Table 4.3: Averages for firm performance by time ... 29

Table 4.4: Averages for stakeholder pressures and GMfg by firm size ... 29

Table 4.5: Averages for firm performance by firm size ... 30

Table 4.6: Inner model assessment ... 32

Table 4.7: Direct model paths and coefficients ... 33

Table 4.8: Results of mediation analysis ... 38

Table 4.9: Results of multi-group analysis ... 40

List of Abbreviations

AVE……….Average Variance Extracted CFA………..Confirmatory Factor Analysis EM………Environmental Management ENV……….…….Environmental Performance FIN………Financial Performance GMfg………Green Manufacturing NGO……….Non-Governmental Organisation OPR...Operational Performance PC……….Process Stewardship PD……….Product Stewardship PLS………Partial Least Squares

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Introduction

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Introduction

Due to increasing global concerns about climate change and the negative impact of business activities on the environment, organisations are pressured to act in a more environmentally friendly way (Ambec and Lanoie, 2008; Chan et al., 2012), since most pollutants are caused by the production process of goods and services (Rao, 2004). Already at the beginning of the current millennium, Sarkis (2001) stressed the important role of the manufacturing function in achieving environmental sustainability. Over a decade later the issue has not lost any of its importance and has spawned a constantly growing body of research in Green Supply Chain Management (Sarkis, 2003; Zhu, Sarkis and Lai, 2007, 2008; Chiou et al., 2011; Tachizawa, Gimenez and Sierra, 2015) and Green Operations (Sarkis, 1998; Angell and Klassen, 1999; Rao, 2004; Ferguson and Toktay, 2006; Deif, 2011; Wong et al., 2012).

The term Green Manufacturing (GMfg) has been created to incorporate the new paradigm of environmentally-friendly practices into operations strategy and can be understood as a “manufacturing approach that is aware of its production/product impact on the environment

and resources and includes such impact in its overall efficiency planning and control” (Deif,

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Introduction

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after some time has passed (Russo and Fouts, 1997; González-Benito and González-Benito, 2005; Ambec and Lanoie, 2008; Zhu, Sarkis and Lai, 2013). Yet, no research using primary data has approached this assumption so far.

This study seeks to contribute to the existing knowledge and fill the above-mentioned gaps by addressing the following main research question: What are the links between stakeholder

pressures, GMfg adoption and firm performance? An empirical examination of these

relationships, using primary data gathered from 155 manufacturing companies in two European countries, Germany and Poland, is set out to answer this research question. The first contribution of this study is the distinction of market and regulatory pressures in a European context. Secondly, the model analyses the three main elements of GMfg, which allows a deeper understanding of the relationships that each element has with the antecedent factors and performance outcomes. These relationships were further explored by a mediation analysis. Recent studies presented evidence suggesting that certain elements of GMfg should be implemented at different stages, specifically managerial aspects should precede process or product-related practices (Green et al., 2012; Yu and Ramanathan, 2015). This finding was also included and developed in this research. Finally, the possible moderating effect of time was tested by the means of a multi-group analysis.

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Theoretical Background and Hypotheses

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Theoretical Background and Hypotheses

This section first defines the key constructs of Green Manufacturing (GMfg), stakeholder pressure and firm performance by reviewing the relevant literature. Based on the review the hypotheses for this research are formulated. Finally, the theoretical model is illustrated with a summary of all hypotheses.

2.1. Green Manufacturing

GMfg involves the use of environmentally-friendly manufacturing practices which aim at a sustainable treatment of resources and the reduction of wastes and pollution caused by production processes (Baines et al., 2012). The use of the best available resources, together with the goal of zero waste, may enhance productivity, quality and cost efficiency resulting in higher competitive advantage (Dubey, Gunasekaran and Samar Ali, 2015). According to the natural-resource-based view of the firm developed by Hart (1995), innovative environmental strategies can lead to firm-specific competencies that form the basis of competitive advantage. Potential savings from GMfg stem from improved manufacturing processes, lower energy and materials consumption as well as lower spending on waste treatment and safety expenses (Porter and Van Der Linde, 1995). From a component perspective, operating issues related to the environment such as recycling and reuse or waste and pollution reduction should be regarded as an essential part of operations strategy and therefore managed on an operational level as well. Decisions made by operations managers have a large influence on a firm’s environmental performance, since the choice of product design and process technology has a direct impact on waste generations, pollution emission and resource consumption (Angell and Klassen, 1999).

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environmental management system (EMS) that gathers data used to generate indicators and target levels. Similarly, a product life-cycle analysis study cannot be performed without tools that measure process efficiency, material usage or waste generation.

2.1.1. Product Stewardship

Product stewardship is concerned with minimising the negative environmental impacts of a product, as well as all product-related parts and components, throughout their whole life cycle (Buysse and Verbeke, 2003; Wong et al., 2012) by using less hazardous and non-renewable materials in product design (Snir, 2001). A process commonly used to assess the entire environmental impact of a product “from cradle to grave” is called life-cycle analysis (LCA), which has been standardised by the ISO 14040-44 (Jacquemin, Pontalier and Sablayrolles, 2012). A full LCA includes all stages of a product’s lifetime from raw materials extraction and processing, through production and assembly, to distribution, use and finally waste management and recycling (Berkhout and Howes, 1997). Special design characteristics have emerged, that reflect the concern for the end-of-life stage of a product, the most conversant being design for reusability, for recyclability, for remanufacturing, for disassembly and design for disposal (Sarkis, 1998). The Volkswagen Group has set a corporate goal of improving each vehicle model’s efficiency by 10 to 15% compared to its predecessor and has established a Life Cycle Engineering working group, which uses LCA to identify opportunities for improvements, which are then developed into innovations. Regarding the end-of-life planning, a minimum of 85% of a vehicle needs to be recyclable and at least 95% fit for reuse or recovery (Volkswagen

Green Manufacturing

Product Stewardship - Life Cycle Analysis (LCA) - Design for reusability/recyclability / disassembly etc. - Green (consumer) packaging Process Stewardship - Efficient use of production resources - End of life/ Disposal management - Environmentally-friendly transportation Environmental Management - (Electronic) EMS - Cross-functional cooperation - Green Training - Certifications/ Audits

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AG, 2015). Hart (1995) stressed the importance of integrating key external stakeholders into the product stewardship strategy. A good example of customer integration is given by Aleris, a global manufacturer of aluminium rolled products. The company is collaborating with their customers to identify opportunities for product improvements and potential cost savings by the means of LCA. Through the joint close-loop projects, amounts as large as 70% to 80% of scrap waste can be returned and reused for every ton of plate aluminium that has been delivered to customers in the aerospace sector. The company has also partnered with the German automobile manufacturer Audi to develop a LCA for lightweight aluminium panels used in automotive components such as door panels. Apart from energy efficiency benefits of more lightweight vehicles, this cooperation benefited Audi by establishing a more sustainable source of materials, which included recycled content, and were produced in a plant using mainly green sources of energy (Aleris Inc., 2014). Paired with the reduced use of energy by lightweight vehicles, Audi could present itself with a better image to its stakeholders. Despite the growing interest in product stewardship, there has been a rather limited amount of empirical research, which mainly concentrated on pollution prevention and the financial benefits associated with it (Hart and Dowell, 2011).

2.1.2. Process Stewardship

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and significant effect on operational performance (González-Benito and González-Benito, 2005). Another environmental measure taken by AlzChem was the construction of a new logistics centre close to the main site. The larger storage capacity and better location reduced the previously high amount of truck traffic between multiple external warehouses and the manufacturing plants, which also lowered CO2 emissions and fuel costs and increased flexibility of raw materials supply (AlzChem AG, 2015).

White, Wang and Li (2015) pointed out that the reduction of packaging waste is an important area of operations strategy and it may have a significant positive impact on environmental performance of a company (Hollos, Blome and Foerstl., 2012). The company Saxonia-Franke, a German metal and plastic component supplier, reduced packaging waste in form of foil, pvc and cardboard by switching to metal boxes as the main storage unit (Saxonia-Franke GmbH & Co. KG, 2013). Many companies use reusable, standardised pallets for transportation. These can be returned through local trading services for a monetary reward or turned into reusable wood waste if they were not contaminated (BASF SE, 2015). Verghese and Lewis (2007) noted that recycling of transport packaging is usually much more cost effective than recycling of consumer packaging, since it is generally much more homogenous. The use of cleaner processes with the goal of improving resource efficiency should have a positive impact on a company’s product-related strategy. Once a company has started on improving its immediate environmental impact, it is logical to turn to the end-of-life considerations involved in product stewardship. Therefore, regarding the relationship between process and product stewardship, it is hypothesized that:

H1a: Process stewardship is positively associated with product stewardship.

2.1.3. Environmental Management

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the standards of the certifications (Kollman and Prakash, 2002). Phan and Baird (2015) found that companies with a more comprehensive EMS showed higher environmental performance and that the presence of an EMS allows companies to respond to coercive and normative pressures such as regulatory bodies and customers.

Research has shown that the successful implementation of EMS is highly dependent on the availability of training (Kollman and Prakash, 2002; Jabbour, Santos and Nagano, 2008) and that training is also a key element of such systems, since it is necessary not only in the implementation stage, but also for maintaining and daily operation (Balzarova and Castka, 2008; Sarkis, Gonzalez-Torre and Adenso-Diaz, 2010). In addition, environmental training has a direct positive impact on environmental performance (Longoni, Golini and Cagliano, 2014) and is among the most adopted practice in Green Human Resource Management (Guerci, Longoni and Luzzini, 2015). Especially when it comes to recycling, small changes can have a large impact on the efficiency of the whole process. After receiving feedback from their catering partner LSG Sky Chefs regarding the correct separation of waste, the environmental working group of Lufthansa CityLine began training their on-board crews in the segregation of recyclable materials such as glass and plastic bottles. Regular meetings between the partners and raising environmental awareness among employees were key requirements for the success of waste reduction measures taken by both sides (Lufthansa CityLine GmbH, 2015).

Yu and Ramanathan (2015) found that internal management partially mediated the relationship between process/product design and environmental performance and advocated a staged implementation, which had been previously suggested by Green (2012). Continuous improvements in processes and products require a system for monitoring and communication, which can be provided by the adoption of EM (Green et al., 2012). Therefore, the next hypotheses are:

H1b: Environmental Management is positively associated with (i) process stewardship and (ii) product stewardship.

2.2. Stakeholder Pressure and Green Manufacturing

According to Freeman (1984, p. 46) a stakeholder can be understood as “any group or

individual who can affect or is affected by the achievement of the organisation’s objectives”. In

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the various groups of stakeholders can be categorised. Sarkis et al. (2010) point out the difference between internal stakeholders such as management and employees, who have direct control over organisational resources as opposed to external stakeholders such as regulatory bodies and governments. Another categorisation divides stakeholders, based on the relationship of each group with the organisation, into internal primary stakeholders (employees, shareholders and financers), external primary stakeholders (domestic and international customers and suppliers), secondary stakeholders (rivals, NGOs, media and international agreements) and regulatory stakeholders (governments and local public agencies) (Buysse and Verbeke, 2003).

Stakeholder Theory (Freeman, 1984) is a widely accepted approach used to examine the effect of stakeholder pressure on a firm’s decision-making and has been adopted in many studies concerned with sustainability in supply chains (Meixell and Luoma, 2015). Previous studies have provided ample empirical evidence for the role of stakeholder pressure as an antecedent for the adoption of environmental practices (González-Benito and González-Benito, 2005; Sarkis, Gonzalez-Torre and Adenso-Diaz, 2010; Zhu, Sarkis and Lai, 2013; Yu and Ramanathan, 2015). Some of the empirical researches considered stakeholder pressure in an aggregate form (Sarkis, Gonzalez-Torre and Adenso-Diaz, 2010; Yu and Ramanathan, 2015), while others decided to separate the pressures into various groups and categories such as internal, external, primary, secondary and regulatory stakeholders (Betts, Wiengarten and Tadisina, 2015). In order to examine the effect of specific stakeholder pressures on environmental practices and performance, certain studies also concentrated only on chosen groups such as customer pressure and regulatory pressure (Guerci, Longoni and Luzzini, 2015), rival pressure and other stakeholder pressure (Dai, Montabon and Cantor, 2015) as well as entrepreneurial orientation, government regulations and customer environmental orientation (Menguc and Ozanne, 2005).

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however, point out that rather than external pressures it is the internal and supplier management capabilities that have a major impact on the implementation of sustainable practices (Bowen et

al., 2001). Nevertheless, it is clear that stakeholder pressures are an important driver for the

implementation of environmentally friendly manufacturing. In this study, only external stakeholders are considered, since they seem to be the stronger influencers.

2.2.1. Market Pressure

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Nowadays a green supply chain and the adoption of GMfg practices is seen as an important competitive factor (Kumar, Teichman and Timpernagel, 2012). The Schumpeterian view of competition has already been used in the context of green innovation and technology (Phillimore, 2001) and more recently has been applied to environmental operations management literature by two studies by Hofer, Cantor and Dai (2012) and Dai, Montabon and Cantor (2015). The theory states that competitive action from one firm will cause a response from another firm (Schumpeter, 1934). Hofer, Cantor and Dai (2012) found that a firm’s engagement in green operations activities is strongly influenced by actions of their competitors, which indicates the strategic importance of environmental concerns in gaining competitive advantage. Moreover, they underline the fact that competitive behaviour has an even stronger impact on a firm’s commitment to environmental activities than legislative or regulatory pressures. Dai, Montabon and Cantor (2015) analysed the influence of competitive and stakeholder pressure on top management support in regards to the adoption of green supply chain management and conclude that competitive pressures have the stronger influence on the willingness of the top management to provide support for environmental activities.

Competitors’ actions and strategies determine the market environment in which a firm operates and these actions are closely observed by the firm (Narver and Slater, 1990). Since the adoption of environmental practices has shown to be a source of competitive advantage, other firms perceive pressure from competition to engage in green activities as well, in order to stay profitable (Kumar, Teichman and Timpernagel, 2012). Similarly, customer demands have a large influence on a company’s decision-making and determine a firm’s strategy and product specifications. Customers and end-customers are increasingly more aware of the impact firms have on the environment (Autry et al., 2013) and, especially business customers in developed countries, face governmental regulations themselves. Researchers also found that customer pressure had a stronger impact on environmental performance than regulatory pressure (Zhu, Sarkis and Lai, 2007; Guerci, Longoni and Luzzini, 2015), since customers will not be appeased by symbolic efforts (Berrone et al., 2013). Based on these arguments and the proposition of staged implementation of GMfg, where EM should be the first practice to be adopted (Green et

al., 2012; Yu and Ramanathan, 2015), the following hypothesis is proposed: H2a: Market pressure is positively related to Environmental Management.

2.2.2. Regulatory Pressure

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stakeholder pressure is non-market or regulatory pressure, the most evident source being the government and regulatory bodies (Delmas and Toffel, 2010). Governments worldwide and international organisations, like the UN, have been constantly introducing new measures, such as taxes, inspections or penalties, aimed at the promotion of environmentally friendly manufacturing practices. With the Kyoto Protocol from 1997 and then the Copenhagen Protocol from 2009 in place, governments started to introduce various regulations to meet international environmental goals. In consequence, companies are pressured to adopt green practices to reduce their environmental impact. This development leads to GMfg becoming a necessity and not just an option (Deif, 2011).

In the literature, regulatory pressure is found to be one of the major factors influencing a firm’s environmental conduct (Christmann, 2004; Chen, Lai and Wen, 2006; Delmas and Toffel, 2010). For example, Porter and Van Der Linde (1995) reported a positive relationship between regulatory pressure and environmental innovation and performance. However, there is concern whether regulatory requirements have a strong enough effect on organisations to adopt preventive tactics instead of measures that are merely reactions to new regulations or even just symbolic efforts (Nash and Ehrenfeld, 1997; Berrone et al., 2013). Nonetheless, regulatory pressures have an undeniable impact on a firm’s environmental strategies. Companies strive to comply with the regulations in order to avoid fines, larger penalties or even lawsuits. Failure to comply does not only result in monetary losses, but also in damages to the image and customer relations (Sarkis, Gonzalez-Torre and Adenso-Diaz, 2010). Hence, the following hypothesis:

H2b: Regulatory pressure is positively related to Environmental Management.

2.3. Firm Performance and Green Manufacturing

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indicating that there are cases where greening activities consume resources form other business areas and can therefore negatively impact the financial performance of a firm.

In a recent study, Ramanathan and Oluwatomi (2015) discovered that operational efficiency has a moderating effect on the relationship between environmental and financial performance. Therefore, it can be concluded, that in order to achieve the desirable financial benefits of environmental strategies, it is important to improve the operational performance as well. At the same time it seems that firms that improved their environmental performance also saw an improvement in operational performance (Pagell and Gobeli, 2009). The direction of causality between different areas of performance is not unambiguous and might depend on many other factors such as experience with environmental management and the availability of (financial) resources. It is apparent that there still is room for research in the area of firm performance, specifically on the relationship between GMfg practices and environmental, operational and financial performance.

2.3.1. Environmental Performance

High environmental performance is achieved by the reduction of air emissions, solid waste and waste water, the decrease of usage of toxic or hazardous materials as well as a reduction of environmental accidents (Zhu and Sarkis, 2004; Zhu, Sarkis and Lai, 2008). The main goal of GMfg is to minimise a company’s negative impact on the environment, thereby improving the company’s environmental performance, by the means of product and process stewardship as well as EM. All these elements taken together enable organisations to control pollution emission and waste generation as well as reduce environmental accidents (Phan and Baird, 2015). Performance indicators provided by EMSs allow setting targets on energy consumption, waste generation as well as water and air pollution. Following these targets should gradually improve the overall environmental balance of an organisation. Based on these arguments, the next hypothesis is as follows:

H3 (i): (a) Product stewardship, (b) process stewardship and (c) Environmental Management are positively related to environmental performance.

2.3.2. Operational Performance

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resources (Porter and Van Der Linde, 1995) resulting in lower expenditures on raw materials and energy (Ambec and Lanoie, 2008). This would imply a decrease in unit costs, if all other variables remain constant. Porter and Van Der Linde (1995) further argued that the benefits of process and product innovation brought by stricter environmetnal regulations can offset the additional costs posed by complying with the regulations. From this reasoning, companies which adopted product and process stewardship should notice positive impacts on productivity and unit costs, if not in the short then at least in the long run. Another important meaure of operational performance is customer satisfaction. Anderson et al. (1995) used customer satisfaction as the performance outcome measure for quality management. Different industries and different organisations will have customers with varying goals and values. For example, a manufacturer in Corbett’s study from 1992 (Cited in Beamon, 1999) prefered on-time delivery more than shorter lead times. Therefore, a speedy delivery would not be an accurate measure of performance for all companies. Similarly, the quality of the product is appropriate when it meets the requirements of the customers. For these reasons this study will use customer satisfaction as a measure for operational performance. As shown in previous examples, the greening of logistics processes can have a positive impact not only on the environment, but also on the operational performance (González-Benito and González-Benito, 2005). Based on the arguments above, the following hypothesis is proposed:

H3 (ii): (a) Product stewardship and (b) process stewardship are positively related to operational performance.

2.3.3. Financial Performance

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had a negative financial impact, it had a positive influence on process stewardship, which in turn was positively associated with financial performance and pollution reduction. Improvement in processes and product design might lead to a decrease in fees for waste treatment and discharge (Zhu, Sarkis and Lai, 2013). In addition, product and process stewardship supports complying with local and international environmental regulations, which has the advantage of avoiding or reducing costs for penalties or legal fees (Hunt and Auster, 1990). Hence, the next hypothesis:

H3 (iii): (a) Product stewardship and (b) process stewardship are positively related to financial performance.

Since the positive changes in operational indicators are associated with the reduction of costs and improvements in environmental performance of the firm, it is also hypothesized that:

H4a: Environmental performance is positively related to (i) operational and (ii) financial performance

H4b: Financial performance is positively associated with operational performance

2.4. Moderating and Control Variables

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H5: There is (i) a negative moderating effect of time on the relationship between stakeholder pressures and GMfg and (ii) a positive moderating effect on the relationship between GMfg and firm performance.

In addition, it is important to control for the effects of firm size, since larger companies might experience different outcomes than smaller companies. For one, bigger firms are more “visible” to various stakeholder groups including customers, regulators or the media and therefore experience higher levels of pressure (Rivera-Camino, 2007). Secondly, larger organisations are expected to have higher adoption rates of GMfg practices, since they usually have more available resources (Zhu and Sarkis, 2004; Guerci, Longoni and Luzzini, 2015). Thirdly, manufacturers with higher volumes of production benefit from economies of scale and therefore might experience better performance outcomes (Newman and Hanna, 1996). Hence, the last hypotheses:

H6: Larger firm experience i) higher stakeholder pressures ii) higher adoption rates of GMfg and iii) better performance outcomes.

2.5. Theoretical Framework

The above-hypothesised relationships are illustrated in the following theoretical model:

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Summary of hypotheses:

H1a: Process stewardship is positively associated with product stewardship.

H1b: Environmental Management is positively associated with (i) product stewardship (ii) process stewardship

H2a: Market pressure is positively related to Environmental Management H2b: Regulatory pressure is positively related to Environmental Management

H3a: Product stewardship is positively associated with (i) environmental (ii) operational and (iii) financial performance.

H3b: Process stewardship is positively associated with (i) environmental (ii) operational and (iii) financial performance.

H3c: Environmental Management is positively associated with environmental performance. H4a: Environmental performance is positively related to (i) operational and (ii) financial performance

H4b: Financial performance is positively associated with operational performance

H5: There is (i) a negative moderating effect of time on the relationship between stakeholder pressures and GMfg and (ii) a positive moderating effect on the relationship between GMfg and firm performance

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Methodology

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Methodology

This section begins by discussing the choice of research method. Next, operationalisation of the measures is described along with the sources from which the construct items were adopted. Finally, the data collection procedure is presented and measurement validation and reliability is assessed.

3.1. Research Method

Since the proposed theoretical framework required an analysis of the direction and strength of the relationship between variables, it was chosen to conduct a quantitative survey research. A major advantage of this approach is that it allows testing hypothesised relationships in various real life contexts, which ensures a high generalisability of the findings. The use of questionnaires is a very efficient method for data collection, since many variables are collected at the same time from various organisations with relatively limited effort (Karlsson, 2010). Variables and context of the study need to be defined at the very beginning of the research process and therefore it is important that a body of knowledge is already available in the desired research area. There is substantial knowledge and experience in the area of Green Operations and Green Supply Chain Management (Sarkis, Zhu and Lai, 2011; Gimenez and Tachizawa, 2012; Bhattacharya, Dey and Ho, 2015; Meixell and Luoma, 2015) that provides the necessary prerequisites for conducting a survey research.

The data collected by the survey questionnaire is used to build so called latent or unobservable constructs (Rao, 2004). Many variables cannot be measured directly and instead perceptive measurements are used, which are obtained by the means of survey items. Two or more items together are used to measure the unobservable construct. These constructs form the independent and dependent variables used to test the relationships or paths that were specified in the research framework. The application of structural equation modelling (SEM) allows the inclusion of such unobservable constructs into multiple interdependent regression equations that are solved simultaneously. The decision about which variables are modelled as predictors or criterion variables is made based on earlier research and available theory (Rao, 2004), hence the importance of the existence of previous studies that provide a theoretical base needed for the construction of a survey.

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that for each organisation only a single respondent is interviewed. In addition, the quality of the obtained answers is heavily reliant on the goodwill, accuracy and level of understanding of the respondents. Although unpractical and costly, personal contact with respondents would reduce the risks of misunderstandings or a lack of proper engagement with the questionnaire and would provide the opportunity to gain a deeper understanding of certain mechanisms and challenges organisations are facing in their daily operations. Nonetheless, survey research carries some major advantages compared to other research methods and provides the researcher with large-scale real world data.

3.2. Measures

All items used in the constructs of this study were formed based on literature in related research fields. Where necessary, the wording of survey items was adjusted to ensure consistency throughout the whole questionnaire. The constructs and sources are summarised in Table 3.1.

Table 3.1: Construct items and sources

Construct/Items Sources (adapted from)

Stakeholder Pressure Market Pressure

Our customers require environmental certifications such as ISO/EMAS

(Guerci, Longoni and Luzzini, 2015), (Dai, Montabon and Cantor, 2015)

Our main competitors that have implemented green manufacturing practices became more competitive

Regulatory Pressure

EU legislations put pressure on my company in adopting green manufacturing practices

The government puts pressure on my company in adopting green manufacturing practices

Green Manufacturing Product Stewardship

We use LCA (Life-Cycle-Assessment) for product design (Wong et al., 2012), (Montabon, Sroufe and Narasimhan, 2007), (Yu and Ramanathan, 2015) We consider opportunities for recycling/reuse/easy disassembly and

recovery of material or component parts in product design

Process Stewardship

Our production processes are designed for more efficient use of resources

We use recyclable or reusable packaging and pallets for transportation

Environmental Management

We have a clear environmental management (information) system to collect data on environmental impacts

We provide environmental training to employees and managers

Financial Performance

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We notice a decrease of fee for waste treatment and/or waste discharge

Environmental Performance

My company has regularly achieved targets imposed on energy conservation, recycling or waste reductions

(Yu and Ramanathan, 2015), (Zhu and Sarkis, 2007)

On an average, overall environmental performance of my company has improved in the past five years

Overall, the frequency for environmental accidents has decreased

Operational Performance

We see a decrease in our unit cost of products (Rahman,

Laosirihongthong and Sohal, 2010)

We see an overall increase in productivity

We see an overall increase in customer satisfactions

Stakeholder pressures were operationalised as market and regulatory pressures. The first were measured as perceived customer and competitive pressures and the second as pressure exerted by the national government and the European Union. It was decided to divide stakeholder pressures into market and regulatory pressures to be able to differentiate the effects of each group on GMfg adoption. The customer and regulatory construct items were adopted from Guerci, Longoni and Luzzini (2015) who concluded that these two types of stakeholders are critical influencers with regards to environmental practices. The competitive pressure item was adopted from Dai, Montabon and Cantor (2015) who were one of the first to include Schumpeterian theory of competition into their analysis of the link between rival, stakeholder pressure and green supply chain management.

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environmental (information) management systems and the provision of environmental training for employees and managers.

Financial performance was defined as the savings a company can achieve from its environmental activities, specifically the reduction of costs of materials purchasing, energy consumption and fees for waste/discharge treatment. The scale was adopted from Zhu and Sarkis (2007) who examined the effects of institutional pressures, green supply chain management and firm performance. The environmental performance measures were adopted from Yu and Ramanathan (2015) and Zhu and Sarkis (2007) and operationalised as the extent to which an organisation has achieved its targets of energy conservation, recycling and waste reduction, the decline in frequency of environmental accidents and the improvement in the overall environmental performance over the past five years. Operational performance was defined as the extent of perceived improvements in three operational performance indicators: unit cost of products, overall productivity and customer satisfaction. These measures were based on a scale used by Rahman, Laosirihongthong and Sohal (2010) who analysed the impact of lean management practices on operational performance.

All constructs were measured on a five-point Likert scale, where 1=”strongly disagree” and 5=”strongly agree”. This type of scale is viewed more positively by respondents as it permits faster responses than open questions, but still allows to capture their position on certain issues (Karlsson, 2010). A higher value on the scale indicated a higher level of pressure, higher adaption of GMfg practices or a stronger improvement in performance. The control variable for firm size was coded as an ordinal variable from 1 to 7, according to the category the plant belonged to where 1 was assigned to small sites with less than 50 employees and 7 for the very large plants with more than 10.000 employees. The control variable for time was measured in years. The complete questionnaire can be found in Appendix I.

3.3. Sample and Data Collection

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establish an environmental management system. The EMAS-certification covers all requirements of the ISO 14001 and automatically grants the ISO-certification to participating organisations. However, EMAS is more comprehensive and includes features not required by ISO such as public dialogue, employee participation and regular environmental statements and audits (Zippel, 2015). Which of the certifications is more popular in one country seems to be connected to the organisation of business interests. More centrally organised and coordinated countries such as Germany, saw a greater popularity of the EMAS standard, while countries with more fragmented organisation leaned more towards the ISO (Kollman and Prakash, 2002). At the end of the year 2015, there were 2031 EMAS-registered sites in Germany, including service and manufacturing sites in a total of 1216 organisations (Flechtner and Bolay, 2016), while the Polish EMAS-registry currently contains 66 entries including service and waste management companies (Generalna Dyrekcja Ochrony Środowiska, 2014). This shows that the ISO 14001 is still by far the more popular certification in Poland.

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Out of the 512 companies that were contacted, 137 questionnaires were received in the first round of responses. After an additional round of contacting non-respondents, another 20 responses were received. Two of the questionnaires that were returned by mail were incomplete and excluded from the analysis, which resulted in 155 usable responses. An effective response rate of 30% was achieved, which is very good considering that the survey was unsolicited. This result is sufficient for empirical studies in production and operations management (Malhotra and Grover, 1998) and comparable with other studies in this field ( for example Wong et al. (2012) achieved 18%, Phan and Baird (2015) 26.5% and Delmas (2009) 36%). The demographics of the respondents are summarised in

Table 3.2. The majority of respondents were from middle-level management or higher. Company sizes ranged from small companies, with less than 50 employees, to very large plants with more than 10.000 workers, with the majority of respondents falling into the medium to large sized categories. The majority of companies belonged to the chemical and pharmaceutical sector (26%), followed by the automotive (15%) and metalworking sector (13%). The category “other” amounted to 15% and included various industries ranging from machinery, products for the rail sector, cooling systems, to medical equipment and magnetic systems.

Table 3.2: Demographic characteristics of respondents

Demographic characteristics Number Percentage (%)

Respondent position Lower Management

Middle Management Senior Management CEO/Owner 21 78 37 19 14 50 24 12

Firm size (number of employees at plant site)

<50 50-250 251-500 501-1000 1.000-5.000 5.000-10.000 >10.000 30 42 28 23 19 6 7 19 27 18 15 12 4 5

Industry Chemical and pharmaceutical

Automotive Metalworking Printing

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Methodology

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Due to the use of Likert-scales, there were no outliers in the survey items. The online survey tool had in-built controls that did not allow skipping answers, which ensured that there were no missing values in the online questionnaires. Boxplots showed three outliers in the measurement for time, for example, “1999” was entered instead of the number of years since GMfg adoption. Other outliers such as “75 years”, which were conspicuously high compared to other answers, were corrected to the overall mean of the variable. While it is possible that a firm engaged in environmentally friendly practices since its formation, leaving such high outliers in might cause an unnecessary skew of the data. Checking the standard deviation of responses showed enough variance to conclude that respondents were sufficiently engaged with the survey.

Non-response bias was assessed by conducting an independent t-test to check for differences between early and late respondents (Armstrong and Overton, 1977). The 19 survey items showed no significant differences (p<0.005) between the two respondent groups, which suggests that non-response bias was not an issue. Since only a single respondent from each company was involved, common method bias (CMB) might occur (Podsakoff, MacKenzie and Podsakoff, 2012). First, social desirability bias was minimised by allowing anonymity of the respondents. Next, Harman's single-factor test was applied to test CMB statistically (Harman, 1976). Unrotated principal component analysis showed multiple factors with Eigenvalues greater than 1, which is the same result as an analysis with Varimax rotation provided. When only a single factor was fixed in the extraction, it did not account for the majority of the variance (<30%). These findings suggest that CMB was not present.

3.4. Measure Validation and Reliability

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matrix with the cross-loadings and the maximum likelihood matrix can be found in Appendix II.

Table 3.3: Construct reliability and factor loadings

Construct Loading Reliability/ Validity

Stakeholder Pressure

Market Pressure (mean=3.34, SD=1.13)

Our customers require environmental certifications such as ISO/EMAS

0.930 α =0.717, AVE=0.772, CR=0.871 Our main competitors that have implemented green manufacturing

practices became more competitive

0.825

Regulatory Pressure (mean=3.42, SD=1.14)

0.938 α =0.821, AVE=0.846, CR=0.917 The government puts pressure on my company in adopting green

manufacturing practices

0.901

Green Manufacturing

Product Stewardship (mean=3.40, SD=1.16)

We use LCA (Life-Cycle-Assessment) for product design 0.883 α =0.777, AVE=0.816, CR=0.899 We consider opportunities for recycling/reuse/easy disassembly

and recovery of material or component parts in product design

0.924

Process Stewardship (mean=4.23, SD=0.76)

Our production processes are designed for more efficient use of resources

0.907 _{α =0.738,} AVE=0.791, CR=0.883 We use recyclable or reusable packaging and pallets for

transportation

0.871

Environmental Management (mean=4.19, SD=0.99)

We have a clear environmental management (information) system to collect data on environmental impacts

0.914 α =0.816, AVE=0.845, CE=0.916 We provide environmental training to employees and managers 0.924

Financial Performance (mean=3.43, SD=1.09)

We notice a decrease of cost for materials purchasing 0.844 α =0.826, AVE=0.741, CR=0.896 We notice a decrease of cost for energy consumption 0.868

We notice a decrease of fee for waste treatment and/or waste discharge

0.870

Environmental Performance (mean=4.22, SD=0.79)

0.851 α =0.807, AVE=0.722, CR=0.886 On an average, overall environmental performance of my

company has improved in the past five years

0.866 Overall, the frequency for environmental accidents has decreased 0.831

Operational Performance (mean=3.40, SD=0.93)

We see a decrease in our unit cost of products 0.885 α =0.792, AVE=0.705, CR=0.877 We see an overall increase in productivity 0.844

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The average variance extracted (AVE) for each construct was above the minimum of 0.5, suggesting sufficient convergent validity. Construct reliability was assessed using Cronbach’s alpha (α) and composite reliability (CR). Both indicators were above 0.7 for all constructs, showing high reliability of scale (Nunnally, 1987).

Discriminant validity was tested using the Fornell-Larcker Criterion (Fornell and Larcker, 1981), where correlations of latent variables are compared to the square root of AVE. None of the off-diagonal correlations was higher than the diagonal square roots indicating good discriminant validity for all constructs. A summary of means, standard deviations and construct correlations is shown in Table 3.4. Since a partial least squares (PLS) method was used for the analysis, the heterotrait-monotrait ratio of correlations (HTMT) criterion was checked as well, since it is said to provide superior performance in assessing discriminant validity in variance-based structural equation modelling such as PLS (Henseler, Ringle and Sarstedt, 2015). As can be seen in Table 3.5 all HTMT-values were below the threshold of 0.9, which supports the result of sufficient discriminant validity between all constructs. The PLS-SEM method is further discussed in Section 4.2.1.

Table 3.4: Discriminant validity (Fornell-Larcker criterion)

Variables Mean S.D. ENV FIN EM PC PD OPR STM STR

ENV 4.22 0.79 0.850 FIN 3.43 1.09 0.487 0.861 EM 4.19 0.99 0.377 0.202 0.919 PC 4.23 0.76 0.307 0.180 0.498 0.890 PD 3.40 1.16 0.225 0.193 0.382 0.549 0.904 OPR 3.40 0.93 0.389 0.421 0.161 0.169 0.290 0.840 STM 3.34 1.13 0.159 0.129 0.270 0.133 0.228 0.222 0.879 STR 3.42 1.14 -0.054 -0.009 0.059 -0.016 0.061 0.178 0.491 0.920 Note: In bold on the diagonal is the square root of AVE, the off-diagonal values are construct correlations; ENV: Environmental performance; FIN: Financial performance; EM: Environmental management; PC: Process stewardship; PD: Product stewardship; OPR: Operational performance; STM: Market pressures; STR: Regulatory pressures

Table 3.5: Discriminant validity (HTMT criterion)

Variables ENV FIN EM PC PD OPR STM STR

ENV FIN 0.590 EM 0.466 0.241 PC 0.389 0.223 0.642 PD 0.275 0.231 0.482 0.718 OPR 0.475 0.504 0.193 0.222 0.379 STM 0.194 0.167 0.317 0.169 0.316 0.324 STR 0.073 0.085 0.070 0.028 0.082 0.230 0.650

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Analysis and Results

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Analysis and Results

This section first analyses the descriptive statistics by comparing the average answers given for the items and constructs. Next, the structural model is tested using partial least squares structural equation modelling (PLS-SEM) to determine the significance of the hypothesised relationships that were summarised in Chapter 2.5.

4.1. Descriptive Statistics

Table 4.1 shows the descriptive statistics for the constructs and items.

Table 4.1: Descriptive statistics of constructs and items

Item Mean S.D.

Market Pressure 3.339 1.127

Our customers require environmental certifications such as ISO/EMAS 3.594 1.177 Our main competitors that have implemented green manufacturing practices

became more competitive

3.084 1.019

Regulatory Pressure 3.423 1.135

3.594 1.138 The government puts pressure on my company in adopting green

manufacturing practices

3.252 1.114

Product Stewardship 3.400 1.165

We use LCA (Life-Cycle-Assessment) for product design 3.000 1.190 We consider opportunities for recycling/reuse/easy disassembly and recovery

of material or component parts in product design

3.800 0.996

Process Stewardship 4.229 0.759

Our production processes are designed for more efficient use of resources 4.303 0.759 We use recyclable or reusable packaging and pallets for transportation 4.155 0,757

Environmental Management 4.187 0.989

We have a clear environmental management (information) system to collect data on environmental impacts

4.245 1.009 We provide environmental training to employees and managers 4.129 0.972

Financial Performance 3.434 1.094

We notice a decrease of cost for materials purchasing 3.129 0.985 We notice a decrease of cost for energy consumption 3.613 1.136 We notice a decrease of fee for waste treatment and/or waste discharge 3.561 1.099

Environmental Performance 4.217 0.788

3.987 0.806 On an average, overall environmental performance of my company has

improved in the past five years

4.387 0.697 Overall, the frequency for environmental accidents has decreased 4,277 0.810

Operational Performance 3.398 0.929

We see a decrease in our unit cost of products 3.026 0.980

We see an overall increase in productivity 3.561 0.933

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Given the measurement on a 1-5 Likert-Scale, all constructs scored relatively high averages, with the lowest being market pressure (M=3.339) and the highest process stewardship (M=4.229). Among the three performance measures, environmental performance scored the highest (M=4.217), followed by financial performance (M=3.434), while the lowest average score belonged to operational performance (M=3.398).

For stakeholder pressures, the average perceived market pressure (M=3.339) was slightly lower than the regulatory pressure (M=3.423). At the item level, the average perceived customer and EU pressures scored the highest (both M=3.594), while government pressure scored lower (M=3.252) and competitive pressure scored the lowest (M=3.084). Nearly 62% of respondents said that they feel pressure from the EU, 56% agreed that customers demand environmental certifications and only 48% said they feel pressure form the government. Merely 32% felt that rivals had become more competitive by adopting GMfg practices. For the full table on answer frequencies for stakeholder pressures see Appendix III. These results suggest that companies feel the strongest pressures from customers and from EU legislation. This remains true for the average result when only German companies are considered. In Poland, however, while EU pressure scored the highest, customer pressures seemed to have the least impact with an average of only 2.900 (see Table 4.2). These findings suggest that the effort made by the EU regarding environmental protection is noticeable in both countries, but the demand for environmental certifications expressed by customers differs quite substantially. This may have been caused by the distribution of industries in each sample, since the environmental sensitivity of customers in one industry might be very different from another. These results paint a different picture than the study of Schoenherr (2012) who found that emerging economies (including Poland) pursued an environmental certification with significantly higher emphasis than industrialised nations (including Germany).

Table 4.2: Average stakeholder pressure by country

Country (N) Cust Comp EU Gov

DE (125) Mean 3.760 3.072 3.560 3.216

PL (30) Mean 2.900 3.133 3.733 3.400

Total Mean 3.594 3.084 3.594 3.252

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they were considering opportunities for reuse, recycling, ease of disassembly etc. in product design. Berkhout and Howes (1997) noted, that LCA studies can be time-consuming and costly, which poses a major adoption barrier, especially for small companies. In the case of EM, almost 80% claimed to use EMS and provide environmental trainings. Nearly 90% of respondents’ companies were adjusting their processes to be more resource-efficient and about 85% included cleaner packaging into their transportation process. The complete table of answer frequencies for GMfg can be found in Appendix III.

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which companies did not perceive achieving savings anymore, seemed to occur in the 11-15 years category for materials and energy savings and 16-20 years for waste treatment fees. These initial results suggests that companies that adopted GMfg practices for a very long time did not seem to experience noticeably higher savings than companies who had just begun with the implementation. Also, the perceived savings seemed to decline after a certain point.

Table 4.3: Averages for firm performance by time

Financial Environmental Operational Time (years) material energy waste goals perf accid unitc prod satisf ≤ 5 N (36) 3.000 3.556 3.444 3.722 4.139 4.056 2.972 3.250 3.611 6-10 N (30) 3.100 3.567 3.467 3.867 4.233 4.200 2.833 3.633 3.700 11-15 N (36) 3.389 3.778 3.528 3.917 4.500 4.306 3.194 3.556 3.444 16-20 N (38) 3.105 3.658 3.789 4.211 4.500 4.421 3.053 3.737 3.711 ≥ 21 N (15) 2.933 3.333 3.533 4.467 4.733 4.533 3.067 3.733 3.533 Total N (155) 3.129 3.613 3.561 3.987 4.387 4.277 3.026 3.561 3.606

Note: Highest values are highlighted

Finally, it was examined whether the descriptive statistics also show any influence of firm size on stakeholder pressure and GMfg adoption (Table 4.4).

Table 4.4: Averages for stakeholder pressures and GMfg by firm size

Highlighted: highest values

Indeed, larger firms indicated higher perceived pressures from all four stakeholder groups (customers, competition, government and EU) and they also exhibitied higher adoption rates

Stakeholder Pressures Green Manufacturing

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for GMfg. The highest difference among the GMfg items when sorted by company size, occurred for the use of LCA, while the score for process improvements and greener transportation packaging remained almost constant across all size categories. The influence of size on firm performance seemed less apparent (Table 4.5). Larger firms exhibited somewhat higher values than small firms. The differences between averages were more noticeable in the case of operational performance, but not very distinct for financial and environmental performance.

Table 4.5: Averages for firm performance by firm size

Financial Performance Environmental Performance Operational Performance Firm size FN_ materials FN_ energy FN_ waste EN_ goals EN_ perf EN_ accid OP_ unitc OP_ prod OP_ satisf ≤50 N ₃₀ ₃₀ ₃₀ ₃₀ ₃₀ ₃₀ ₃₀ ₃₀ ₃₀ Mean 3.167 3.467 3.533 4.000 4.200 3.867 2.700 3.133 3.533 51-250 N 42 42 42 42 42 42 42 42 42 Mean 3.071 3.524 3.429 3.786 4.357 4.286 3.000 3.548 3.595 251-500 N 28 28 28 28 28 28 28 28 28 Mean 2.929 3.750 3.571 4.036 4.321 4.357 2.857 3.429 3.536 501-1000 N 23 23 23 23 23 23 23 23 23 Mean 2.957 3.435 3.609 4.043 4.522 4.609 2.957 3.565 3.522 1001-5000 N 19 19 19 19 19 19 19 19 19 Mean 3.263 3.789 3.684 4.211 4.579 4.263 3.632 4.053 3.632 5001-10000 N 6 6 6 6 6 6 6 6 6 Mean 3.667 4.500 4.167 4.000 4.500 4.333 3.500 3.833 3.667 >10000 N 7 7 7 7 7 7 7 7 7 Mean 3.857 3.571 3.429 4.143 4.571 4.571 3.429 4.429 4.429 Total N 155 155 155 155 155 155 155 155 155 Mean _3.129 _3.613 _3.561 _3.987 _4.387 _4.277 _3.026 _3.561 _3.606

Highlighted: highest values

4.2. Structural Model Test Results

4.2.1. Choice of Method

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technique (Hair, Ringle and Sarstedt, 2012) that allows to simultaneously assess the outer measurement model (i.e. the latent factors and their indicators) and the inner structural model, which defines the relationships between the latent factors or latent variables (Bollen, 1989). The advantages of SEM over first generation techniques, such as multiple regression, are the flexibility it provides regarding modelling relationships between multiple independent and dependent variables, the inclusion of unobservable latent variables and empirical testing of theoretical assumptions (Chin, 1998). The partial least squares method (PLS) is better at handling more complex models with mediation relationships and is less sensitive to sample size (Peng and Lai, 2012). Attaining large sample sizes is often difficult in Operations Management (OM) research, since its focus is directed at the firm or the supply chain level (Peng and Lai, 2012). However, the sample size should not be too small and still provide sufficient statistical power (Hair et al., 2012). As a rule of thumb, the minimum sample size should be not less than 100 and at least five times the amount if items used in the model (Malhotra and Grover, 1998). This study achieved 155 usable responses for 19 items, which fulfils these requirements. Another issue in OM research is that there are no standardised measurement scales (Roth et al. 2007 cited in Peng and Lai, 2012). Instead, researchers form new constructs based on scales used in previous literature. In addition, PLS has the advantage that it does not require the data to conform to a normal distribution, which is often the case in social science research (Peng and Lai, 2012).

4.2.2. Model Evaluation

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operational performance (0.028) and between environmental and operational performance (0.040). Third, the predictive relevance of the inner model was tested using Stone-Geisser‘s Q2 (Geisser, 1974; Stone, 1974), which was calculated by the blindfolding procedure available in the SmartPLS software. Values of Q2_{>0 indicate predictive relevance of the path model for the} given construct (Hair et al., 2014).

Table 4.6: Inner model assessment

Construct/ Hypothesized Path f2 R2 Q2

Environmental Management

Market Pressure → Environmental Management

Regulatory Pressure → Environmental Management (ns)

Product Stewardship

Environmental Management → Product Stewardship (ns) Process Stewardship → Product Stewardship

Process Stewardship

Environmental Management → Process Stewardship

Environmental Performance

Environmental Management → Environmental Performance Product Stewardship → Environmental Performance (ns) Process Stewardship → Environmental Performance (ns)

Financial Performance

Environmental Performance → Financial Performance Product Stewardship → Financial Performance (ns) Process Stewardship → Financial Performance (ns)

Operational Performance

Product Stewardship → Operational Performance Process Stewardship → Operational Performance (ns) Environmental Performance → Operational Performance Financial Performance → Operational Performance

0.060 0.018 0.001 0.276 0.253 0.042 0.000 0.016 0.290 0.011 0.000 0.028 0.002 0.040 0.076 0.236 - - 0.368 - - 0.266 - 0.183 - - - 0.267 - - - 0.287 - - - - 0.182 - - 0.271 - - 0.190 - 0.126 - - - 0.178 - - - 0.174 - - - -

Note: ns= not significant path

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4.2.3. Hypotheses Testing

The parameters returned by the PLS algorithm were tested for significance using a bootstrapping procedure. The recommended number of resampling rounds is 500 (Chin, 1998), although given the computational power of computers nowadays, more rounds can easily be used. Peng and Lai (2012) recommend using different amounts of rounds to ensure the robustness of the significance of coefficients. This study initially tested the model with 500 rounds, as suggested, and then applied 1000 and 5000 rounds. All provided similar results indicating high robustness of findings. Results from the largest resampling run were used in the final report.

Table 4.7: Direct model paths and coefficients

Path Significant Coefficient T-Value P-Value

C o n tr o l V ar iab les

Size → Market Pressure Size → Regulatory Pressure

Size → Environmental Management Size → Product Stewardship

Size → Process Stewardship Size → Environmental Performance Size → Financial Performance Size → Operational Performance Time → Environmental Management Time → Product Stewardship

Time → Process Stewardship

Yes Yes Yes Yes Yes No No Yes Yes No No 0.274*** 0.263*** 0.195** 0.165* -0.126+ 0.039 0.056 0.188* 0.296*** 0.072 0.100 3.872 3.765 2.875 2.266 1.672 0.498 0.743 2.303 4.356 0.921 1.291 0.000 0.000 0.004 0.023 0.095 0.619 0.457 0.021 0.000 0.357 0.197 Time → Environmental Performance

Time → Financial Performance Time → Operational Performance

No Yes No 0.143 -0.168* -0.045 1.593 2.371 0.498 0,111 0.018 0.618 H1

H1a Process Stewardship → Product Stewardship H1b (i) EM → Product Stewardship

H1b (ii) EM→ Process Stewardship

Yes No Yes 0.488*** 0.025 0.493*** 7.606 0.328 6.796 0,000 0.743 0.000

H2 H2a Market Pressure → EM

H2b Regulatory Pressure → EM Yes No 0.248** -0.136 2.798 1.522 0.005 0.128 H3

H3a (i) Product Stewardship → Environmental P. H3a (ii) Product Stewardship → Operational Perf. H3a (iii)Product Stewardship → Financial Perf.

No Yes No 0.003 0.177* 0.109 0.025 1.979 1.238 0.980 0.048 0.216 H3b (i) Process Stewardship → Environmental P.

H3b (ii) Process Stewardship → Operational Perf. H3b (iii)Process Stewardship → Financial Perf.

No No No 0,152 -0.046 0.003 1.403 0.445 0.030 0.161 0.657 0.976 H3c EM → Environmental Perf. Yes 0.233* 2.162 0.031 H4

H4a (i) Environmental → Operational Performance H4a (ii) Environmental → Financial Performance H4b Financial → Operational Performance

Yes Yes Yes 0.209* 0.499*** 0.273** 2.515 7.492 3.346 0.012 0.000 0.001

Note: Significant coefficients are in bold. ***p<0.001; **p<0.01; *p<0.05; +_p<0.1

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impact on process stewardship was negative (p<0.1). This result corresponds with the impression gained by the descriptive statistic, but differs from the findings of Newman and Hanna (1996) who concluded that firm size had no effect on the degree of eco-manufacturing strategy integration in their study. As discussed earlier, several researchers suggested that firm size might also influence firm performance. The relationships between environmental and financial performance and firm size was not significant. This is in line with Zhu and Sarkis’s (2004) study on the impact of green supply chain management practices on environmental and financial performance, who also reported no significant effects of firm size. However, this model also considered operational performance and, for this performance measure, the impact of firm size was positive and significant (p<0.05). This might have been primarily caused by the presence of very large manufacturing plants in the sample, of which six were from the automotive industry and one from the chemical industry. This finding is contrary to González-Benito and González-González-Benito (2005) who reported a negative influence of size on operational measures, however, they had expected the opposite result, which has indeed occurred in this study. The hypothesis regarding the influence of the control variable for firm size could be confirmed in the case of stakeholder pressures and GMfg, with the exception of process stewardship. Firm size was also significantly correlated with operational performance, but not with the other performance measures. Hypothesis 6 could therefore be partially confirmed. Previous studies also pointed at the influence of time on the performance outcome of GMfg. While no significant relationship between time and operational or environmental performance was found, there was a significant negative association with financial performance (p<0.05). This result contradicts the assumption that financial benefits of GMfg might mainly occur in the long-run, as suggested by Zhu, Sarkis and Lai (2013). Since adoption rates of GMfg should also differ when time is considered, the relationships were tested as well and a positive correlation between time and EM was found (p<0.001). Product and process stewardship showed no significant association with time, which indicates that, the degree of adoption for these two GMfg elements was not dependent on how long a firm had been engaging in green practices.

Exploring the Impacts of Stakeholder Pressures and Green Manufacturing on Environmental, Operational and Financial Performance