Green innovation and financial performance in the agri-food industry: The moderating role of environmental norms and regulations

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Green innovation and financial performance in the agri-food industry: The moderating role of

environmental norms and regulations

Department: Economics and Business

Master: Business Administration – International Business Thesis Supervisor: Dr. Mashiho Mihalache

Second Reader: Dr. Ilir Haxhi

Author: Elise Noordberg

Student Number: 11324872

Date: January 28, 2022

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Statement of originality

This document is written by Elise Noordberg who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Table of contents

Abstract ... 5

1. Introduction ... 6

2. Literature review ... 9

2.1 Firm performance ... 9

2.2 Green innovation ... 10

2.3 Institutional context - Environmental regulations and environmental norms ... 12

2.3.1 Environmental regulations ... 14

2.3.2 Environmental norms ... 15

3. Theoretical framework ... 17

3.1 Hypothesis development ... 17

3.1.1 Relationship between green innovation and financial performance ... 17

3.1.2 Moderating effect of institutional context ... 19

3.1.3 Moderating effect of environmental regulations ... 20

3.1.4 Moderating effect of environmental norms ... 21

3.2 Conceptual model ... 23

4. Methodology ... 24

4.1 Sample ... 24

4.2 Data collection ... 25

4.3 Variables ... 26

4.3.1 Dependent variable ... 26

4.3.2 Independent variable ... 26

4.3.3 Moderators ... 27

4.3.4 Control variables ... 28

4.4 Descriptive statistics ... 31

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4.5 Statistical analysis ... 31

4.5.1 Assumption of normality ... 32

4.5.2 Assumption of the absence of multicollinearity ... 33

4.5.3 Assumption of homoskedasticity ... 34

5. Results ... 36

5.1 Regression analysis ... 36

6. Discussion ... 40

6.1 Findings ... 40

6.2 Implications ... 42

6.2.1 Academic implications ... 42

6.2.2 Managerial and policy implications ... 43

6.3 Limitations and future research ... 44

7. Conclusion ... 46

References ... 48

List of figures Figure 1: Conceptual model ... 23

List of tables Table 1: Overview of variables ... 30

Table 2: Descriptive statistics ... 31

Table 3: Correlation matrix ... 33

Table 4: Results of random-effects linear regression model on Financial Performance ... 38

Table 5: Robustness test, Financial Performance measured by Return on Sales ... 39

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Abstract

In recent years, many scholars studied how green innovation relates to firm performance.

However, few studies paid attention to businesses operating in the heavily polluting agri-food industry. This study aims to shed light on aforementioned relationship within this industry from the perspective of institutional theory. Therefore, it hypothesizes moderating effects of the regulatory and normative dimensions of the institutional context. This paper uses panel data on 51 listed multinational agri-food enterprises headquartered across 11 different countries in Asia during the period 2016-2020. The findings show no significant evidence of the levels of green innovation on improving financial performance and no moderating effects of the environmental norms and regulations. Based on these findings, we provide several recommendations for managers and policymakers to make effective decisions regarding green innovation. Also, we discuss the limitations of this study and implications for future research.

Keywords: green innovation, firm financial performance, institutional context, environmental norms, environmental regulations, green patents, agri-food industry

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1. Introduction

Recently, global warming has become an increasingly hot topic in the public debate. The ramifications of climate change will lead to numerous societal challenges, many of which can be irrevocably linked to agricultural sustainability and food production. The agri-food industry has a major negative environmental impact as a result of overexploitation of natural resources, soil and water pollution, and enormous CO2 emissions, among others (Vermeulen et al., 2012).

Currently this industry is responsible for generating 19-29% of total greenhouse emissions (World Bank, 2021). Greenhouse emissions are proven to be the primary cause of global warming (Depoers et al., 2016). Besides the fact that firms in the agri-food industry consume large percentages of the world’s natural resources, the industry has played a big role in scandals that have taken place over the last decades, like deforestation (Meyfroidt & Lambin, 2011).

After oceans, forests are the largest storehouses of carbon, which shows the important role deforestation plays in the climate change issue.

The urgency to deal with climate change has become more palpable for business managers ever since consumer attitudes towards consumption changed and sustainable needs emerged. The existence of stricter regulations and laws increase pressure on firms to adopt strategies to undertake environmental management (Díaz-García et al., 2015). One important corporate strategy is to use green innovation (GI) (Dixon-Fowler et al., 2013). This form of innovation incorporates technological improvements and reformation of production processes by adjusting phases and patterns of production and use, resulting in more efficient use of natural resources (Kemp & Pearson, 2008; García‐Sánchez et al., 2021). Managers understand that GI is important for the development of companies and that it can even offer competitive advantages (Song & Yu, 2018). Adding to this, the most important incentives for firms to act on green production innovation are found to be cost savings, competitive advantage, higher profits and

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(2013) the performance is affected through two distinct mechanisms here. First, because there is a more efficient use of resources, there will be a reduction of costs. Second, firms can reach consumers’ segments for which environmental issues are more important and can eventually benefit from market differentiation.

Although it is often assumed that engaging in GI practices positively impacts the performance of a firm, there are also studies that actually report adverse effects, or no effect at all (Fernando et al., 2009; Przychodzen & Przychodzen, 2015; Aguilera-Caracuel & Ortiz-de Mandojana, 2013). This shows the lack of consensus in literature concerning this relationship.

Most studies look into high-technology industries, while only a handful of scholars take into consideration low-technology sectors such as the agri-food industry (Rabadán et al., 2019). The few studies with a focus on this specific industry show conflicting results regarding the relationship between GI and firm performance (Briones Peñalver et al., 2018). Scholars show that firms experience different institutional pressures to engage in GI practices (Keshminder &

Pablo del Rio, 2019). This has to do with the fact that firms are required to conform to rules and non-formal constraints in order to be considered legitimate (Bansal, 2005). Therefore, this study will take into account the institutional context of multinational enterprises (MNEs), which will be grounded in institutional theory, as proposed by North (1991). Drawing from existing literature, the institutional context will include the formal and informal dimensions of the concept: national environmental regulations and environmental norms. Scholars agree about the importance of these constructs in the field of green innovation (Aguilera-Caracuel & Ortiz- de Mandojana, 2013). By addressing the research gap on the relationship between green innovation and financial performance for MNEs belonging to agri-food industry, and based on institutional theory, this thesis complements existing literature. I pose the following research question: How is the relationship between green innovation and financial performance of MNEs affected by the institutional context in the agri-food industry?

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This paper contributes to existing literature in multiple ways. First, despite the growing literature on the relationship between GI and FP, the outcomes are mixed and conflicting (Albertini, 2013). One possible explanation is that the relationship is measured across different industries. Therefore, this study aims to investigate this relationship in a specific industry, thereby differentiating itself from the existing literature. Second, previous research that analysed the relationship between GI and financial performance of agri-food companies only looked at smaller firms in case studies (Rabadán et al., 2019; Dangelico et al., 2019). This study extends previous research by making use of MNEs firm-level secondary data. Third, this paper advances research on GI and FP by addressing the nuanced impact of the institutional context, by taking the environmental institutional dimensions of norms and regulations into consideration.

The present thesis is organized in seven sections. The first section reviews and discusses the existing literature on the main concepts. The next section consists of the theoretical framework, the proposed hypotheses and the conceptual model. In the third part the methodology of the data collection, the variables and the analyses are discussed. The fourth section consists of the results based on these analyses. The following section includes the discussion and implications of the results. The sixth section offers a discussion of the main findings, recommended implications for other scholars and managers and the limitations of this study. This is all brought together in the final chapter, in which the conclusion of the study is set out.

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2. Literature review

In the following section an overview of relevant existing literature is presented. The definitions of financial performance, green innovation, institutional context, environmental regulations and environmental norms are discussed.

2.1 Firm Performance

Most firms try to enhance their performance in any way possible, as this will lead to more chances of survival and success in a competitive business environment. Therefore, it is of great interest for researchers to assess the performance of an organization. Although the concept of performance is frequently used and common in business literature, there is still no broad consensus on how to define and measure it best. Venkatraman and Ramanujam (1986) studied ten measurements of firm performance and found that financial performance (FP) is most frequently used in business strategy literature to indicate the improvement of performance.

They define FP as a company’s financial viability or the extent to which a company achieves its economic goals (Venkatraman & Ramanujam, 1986).

Existing literature shows that three different approaches can be distinguished which are mainly used to assess FP: perceptual survey-based, accounting-based and market-based measurements (Wang et al., 2016; Boaventura et al., 2012). The first approach, the survey- based approach, attempts to measure the firm’s performance by asking respondents to rate how, according to them, the firm is doing. Since this method is rather subjective, questions can be raised about the accuracy of this method. Therefore, this approach will not be applied in this study.

Second, the market-based measure shows an indication of the view of the external market on the actions of a firm. The FP is calculated here from the shareholders’ perspective (Cochran & Wood, 1984). This method commonly draws upon changes in share price to

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indicate the satisfaction of shareholders regarding the firm, as they buy and sell shares based on their view of the stock returns and risks of a company. Tobin’s Q is often used here to calculate the FP of the firm by measuring the growth of a company estimated by the market (Cavaco & Crifo, 2014). Since there is not much data available regarding this indicator, this approach will not be applied here.

Lastly, the accounting approach aims to estimate the firm’s internal performance and efficiency in a quantifiable manner (Boaventura et al., 2012; Callan & Thomas, 2009). As this approach focuses on the earnings of a company, it uses measures such as return on equity, return on assets or return on sales. The method uses historical performances to calculate financial performances (Cochran & Wood, 1984). In this study, we will apply the third approach as data concerning the accounting income indicators are easily derived (Galant & Cadez, 2017).

2.2 Green innovation

The existing literature shows no consensus on the term of GI as multiple synonyms are mentioned, including environmental, ecological and sustainable innovation (Schiederig et al., 2012). This multiplicity of terminologies makes the conceptualization of GI more difficult (Ahmed et al., 2012). Chen et al. (2006, p. 332) define GI as “hardware or software innovation that is related to the use of green products or processes, including the technological innovations that are involved in energy conservation, pollution prevention, waste recycling, green product design, and corporate environmental management”. They also argue that GI focuses on enhancing the environmental management of firms to comply with environmental regulations.

Other scholars put the emphasis on the fluid aspect of GI and state that firms that participate in GI are involved in “a process of change and continuous development that commonly results in tangible green developments, i.e. products or technology” (Aguilera-Caracuel & Ortiz-de-

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innovation is pointed out by Rennings (1998). He calls this the ‘double externality effect,’

suggesting that GI also produces other positive externalities that reduce external environmental costs. Rabadán et al. (2019) categorize GI in technological and non-technological GI where the first one refers to eco-production processes and eco-products, and the second one refers to marketing or business methods that reduce negative environmental impacts of the company’s activities. This current study will focus on technological GI.

With the emerging importance of GI since the late 1990s, researchers have addressed GI from three different perspectives. First are those scholars that identify the drivers of GI and the performance outcomes that arise from it. Second are the studies that identify the dimensions of GI. Third is related to the measurement of GI (Cheng & Shiu, 2012). Since this paper looks at the relationship between GI and FP, we apply the first perspective.

The first group of scholars that studies GI from this perspective focuses on exploring and identifying the determining factors, which refer to the internal drivers and external drivers that are behind GI (Tariq et al., 2017). The internal drivers include organization capabilities, technological capabilities and corporate social responsibility. External drivers are customers’

green demands, competition pressures and environmental regulations (Cai & Zhou, 2014). In the existing literature on drivers, scholars take different points of view. One view is technology- push, which incorporates technological capability and development, where the opposing view is market-pull, which includes pressures from stakeholders (Rehfeld et al., 2007). In this paper the view of technology-push is taken. Research by Dangelico (2016) shows that the most important motives for GI are the opportunities to achieve competitive advantage, reduction in costs, market benefits and environmental regulations. Looking specifically at the numerous reasons for agri-food firms to act in a more sustainable and green manner, the most important reasons are risk management and brand protection (Rankin et al., 2011). Research by Cuerva et al. (2014) on eco-innovation of companies in low-tech sector argues that the implementation

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of GI certifications like Quality Management Systems (QMS) is the strongest determinant of implementing environmental innovation strategy. Firms that have quality concerns and decide to implement a standardized QMS, will be more eager to adopt GI.

The second group of scholars that studies GI from this perspective focuses on the possible organizational and environmental consequences of GI. Those studies focus on the relationship between green innovation and firm performance (Tariq et al., 2017). While many papers study the financial performance, there are still mixed results regarding the effect on it (Li, 2014; Albertini, 2013). Existing literature shows that GI can positively contribute to the environmental performance (e.g., Dangelico & Pujari, 2010; Chiou et al., 2011). Other scholars found that following strategies with GI practices doesn’t lead to a greater environmental performance (Bönte & Dienes, 2013). The majority of earlier studies measured the relationship between GI and firm performance across different industries, which could be a reason for these conflicting results (Horváthová, 2010). For that reason, we will investigate the relationship in a particular industry.

2.3 Institutional context - Environmental regulations and environmental norms

The first and widely accepted definition of institutions came from North (1991, p. 97). It declares that institutions are “humanly devised constraints that structure political, economic and social interaction” and that they set out “the rules of the game” of a society (North, 1991, p. 361). Institutional theory states that the actions of a firm are not only affected by their corporate objectives and competitive pressures, but also by their institutional and social environments, which refers to formal rules and laws that are set by governments and other regulatory authorities (North, 1991). These pressures also refer to informal constraints, which are made and supported by the society in which the firms are located (DiMaggio & Powell,

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1983). Examples of informal constraints are taboos, traditions, behavioural patterns and shared values.

According to institutional theory, the actions of an organization are affected by social pressures. This is mainly because organizations strive for legitimacy and want to secure their position (Scott, 1995; Berrone et al., 2013). Legitimacy refers to the social approval of the actions of an organization and is seen as a valuable resource to attract capital and increase sales (Suchman, 1995). Achieving this legitimacy can be done by conforming to the rules and norms that are present in the institutional environment the firms are in (Bruton et al., 2010).

Over the years, institutional theory has been broadly used as a theoretical foundation in several domains and different kinds of sciences, ranging from economics and political science to business science (DiMaggio & Powell, 1991). In the field of business science, multiple studies have considered the importance of different institutions’ effects on the firms’ decision- making process (Jennings & Zandbergen, 1995; Henriques & Sadorsky, 1996). One of the most well-known typologies of these institutions is proposed by Scott (1995). He argues that three pillars compose institutions: regulatory, normative and cognitive dimensions. These dimensions find their origin in institutional theory. Although these elements mainly work altogether, their relevance is context-specific.

The first dimension, the regulatory component, refers to “the existing laws and rules in a particular national environment that promote certain types of behaviour and restrict others”

(Kostova, 1999, p. 314). Compared to other national institutional dimensions the regulatory component is easier to conduct research on. Because regulations can be found in the law and sanctions, they are more easily observable, understandable and interpretable (Kostova & Roth, 2002; Scott, 1995).

The second dimension, the normative component, is based on behaviour that stems from professional, social and organizational interaction (Scott, 2007). This dimension defines what

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is appropriate in social situations and can work as a guide for firms on how to behave, as it constitutes the norms and values in a society. Norms define how people think and behave, and control peoples’ behaviour. Values are related to what is perceived as morally bad or good (Frese, 2015). The third dimension, the cognitive component, reflects the cognitive structures and symbolic systems that people share. Examples of these structures and systems are shared knowledge and cultural elements (Trevino et al., 2008).

When looking specifically at environmentally sensitive industries, scholars agree that regulatory and normative dimensions are the most relevant and prominent in shaping the institutional field and at the same time are the most reflective of how different countries handle environmental issues (Buysse & Verbeke, 2003; Hoffman, 1999). Therefore, we will focus on these two dimensions specifically in this study.

2.3.1 Environmental regulations

Around 1970, from a societal perspective, people started having concerns regarding the negative impact business activities have on the environment. This led to a substantial growth in environmental regulations throughout the world (Rugman & Verbeke, 1998). These regulations can play an important role in controlling the harmful effects of economic activity on the natural environment (Shindell et al., 2017; Chen, 2008). The implications for firms were perceived to be unfavourable as organizations now had to make costly adjustments to be able to conform to the rules (Deephouse, 1996). Opposing this traditional view, Porter and van der Linde (1995) support the view that these environmental regulations can improve the performance of firms via their impact on innovation. Eiadat et al. (2008) further stated that environmental regulations could help firms to become more active, accept new ideas, stimulate the process of creative thinking and invest in technological improvements.

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As there are dissimilarities on cross-national level regarding institutions, regulations have a different influence on organizations depending on the country they are based in (Gooderham et al., 1999). Adding to this, institutional theory states that firms which are located in the same environment strive for the same sort of legitimacy. This could lead to organizations becoming isomorphic, which means that they implement the same actions (Kostova et al., 2008).

Examples of environmental regulations are environmental certifications (Galarraga Gallastegui, 2002). Regulatory pressures like these generally have environmental goals in terms of pollution emissions (Berrone et al., 2013). Policy makers use a variety of tools to stimulate firms to act in a more environmentally friendly manner. An example of environmental regulations that has been created to stimulate GI is the Eco-Management and Audit Schema which is used in the European Union (EMAS) (Strachan, 1999). Regulations like these are not world widely institutionalized, and are therefore not sufficient for research on MNEs as their activities go beyond borders. The independent and non-governmental International Organization for Standardization (ISO) has developed standards that are internationally accepted and are used in various industries, like mechanical engineering, transport, information technology, health, food and agriculture.

2.3.2 Environmental norms

Besides the formal dimension of the institutional pressures firms experience when doing business, the informal dimension in the form of environmental norms also puts pressure on firms to act in an environmentally favourable way, as this is related to the issue of legitimacy.

Kostova & Roth (2002) state that the normative dimension of institutions “reflects the cultural values, beliefs and goals of the society that determine the legitimacy of the displayed organizational behaviour” (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013, p. 368). Norms

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specify how things should be done and people should behave, as they are seen as the standards for values that exist among people of a group or category (Hofstede, 1991). Rules of thumb and the standard for operating procedures are included in these normative aspects (Kostova, 1997).

While the environmental regulations are characterized as governmental, environmental normative pressures generally arise from professional organizations and other focal social actors, which are tasked with the role of defining what is appropriate behaviour and which standards apply to members of a social group (Berrone et al., 2013). An important characteristic of these norms is that they are implicit and relate to legitimacy, which relates to the acceptance by society firms strive for when they conform to institutional pressures (Bansal & Clelland, 2004). Firms try to behave according to the norms that apply within their institutional field (Walls et al., 2012). Normative pressure mainly comes from consumers, suppliers, media, and the public sector, which leads to the measurement of national norms varying broadly across different studies (Zhu, 2016).

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3. Theoretical framework

This chapter discusses the theoretical framework resulting in hypotheses and a conceptual model.

3.1 Hypothesis development

3.1.1 Relationship between green innovation and financial performance

When firms possess a higher level of green innovation, they will be better able to improve their performance due to a couple of phenomena, which partly are rooted in the Natural Resource Based View framework and Legitimacy theory (Hart, 1995; Thomas, 2007).

First, innovations stemming from environmental standards have the ability to lower product costs, as these allow for firms to use their inputs in a more productive manner (Porter

& van der Linde, 1995). In the short-term, companies have to deal with initial investments that are costly. These costs come from the adjustments of the manufacturing process to decrease the usage of energy. However, when looking at the long-term it will result in higher financial returns to these firms, as operating costs will be reduced. This can be explained by resource efficiency, e.g., through the reuse of materials (de Azevedo Rezende et al., 2019). This important ecological strategy is also referred to as the use of preventive pollution. By implementing this strategy firms seek to prevent waste and emissions by making changes in the process, instead of making use of a so called “end-of-pipe solution” which has a pollution- control approach (Hart & Dowell, 2011). Pollution prevention is one of the three strategic capabilities from Hart’s Natural Resource Based View framework (NRBV) (Hart, 1995). The key element of the NRBV is that a firm’s competitive advantage fundamentally depends on the relationship with the natural environment. The theory identifies how to generate a competitive advantage based on abilities that are supportive of sustainable development.

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Secondly, firms are able to develop environmental legitimacy and social approval by engaging in green innovation. Environmental legitimacy makes it easier to get support from stakeholders, as it can help a firm to reach congruence between their performance and the expectation of the environment (Chang & Chen, 2013). Also, it’s found to be important to reach legitimacy to become successful on the long-term and therefore to improve durable profitability (Thomas, 2007).

Thirdly, higher levels of green innovation can lead to an improvement of the financial performance of a firm through market differentiation (Aguilera-Caracuel & Ortiz-de- Mandojana, 2013). When firms participate in GI, they distinguish themselves from their competitors who are not participating in GI. In this way they are able to access new markets where customers are more environmentally aware (Ambec & Lanoie, 2008). Customers are increasingly focused on purchasing from green suppliers and are willing to pay for products that are produced in a non-harmful manner for the environment (McDonagh & Prothero, 2014).

Besides, there is a strong linkage between the involvement of external stakeholders like customers and distributors, and the presence of ISO environmental certifications in a company (Delmas, 2001).

Fourthly, firms that participate in higher levels of GI will be more likely to get a positive environmental reputation. This social approval eventually leads to an increase of total revenues (Christmann, 2004). Adding to this, environmental reputation possibly leads to a stronger employee commitment. This brings the advantage that committed employees go above their regular responsibilities when doing their work. It also increases their job-satisfaction and reduces the tendency to leave the firm. Besides, for people who are job-seeking, a good environmental reputation provides positive signals about the working conditions of a firm (Alniacik et al., 2011).

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Lastly, firms that are pioneers in GI may get the first mover advantages, due to preemptive strategies (Nehrt, 1996). When a company is one of the first in the market that sells a product that is environmentally friendly and innovative in a green way, it may get competitive benefits (Przychodzen et al., 2020). These benefits include decreasing costs because of an early learning curve (Suarez & Lanzolla, 2007). These firms can also achieve higher profits, because consumers adopt an early, and therefore stronger, preference for pioneer companies.

Additionally, possible pressures coming from non-governmental organizations towards firms operating in the agri-food sector which are not environmentally friendly are countered in this way. This can also have a positive influence on the green reputation of the firm and gives a firm competitive advantage over other firms (Tseng et al., 2013). The conclusion of the studies on the relationship between GI and FP regarding firms operating in the agri-food industry is that it pays to be green (Rabadán et al., 2019; Dangelico et al., 2019). This is mainly because of the improvement of reputation, acquirement of new customers, achievement of competitive advantage and higher profits (Dangelico et al., 2019).

Altogether, engaging in a higher degree of green innovation leads to lower production costs, a better reputation, opportunities for market differentiation, positive environmental legitimacy and reputation, stronger employee commitment and possible first mover advantages.

Therefore, it is expected that the higher levels of GI will lead to a positive impact on the financial performance.

Hypothesis 1. The level of green innovation of a firm will positively impact its financial performance.

3.1.2 Moderating effect of institutional context

This study builds on institutional theory. It has been used to understand management practices, as it explains organizational behaviours (Scott, 1995). These behaviours can vary depending on

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the country in which a firm is based, as MNEs should consider the context of institutions in which they operate (Peng, 2002). The institutional perspective states that the way organizations behave is imposed by public opinion, by the law, by social status, by the views of important constituents and by knowledge legitimated through the systems of education (Meyer & Rowan, 1977). As previously discussed in section 2.3, firms are in search for legitimacy. In their strive for legitimacy, they follow other firms’ management practices that are in their environment (DiMaggio & Powell, 1983). According to scholars in this field and the assumptions of institutional theory, it is key to consider two national dimensions which show how countries act upon environmental issues: the regulatory and normative dimensions (Aguilera-Caracuel &

Ortiz-de-Mandojana, 2013; Hoffman, 1999). Therefore, this thesis takes into account the moderating effect of the institutional context and looks at the environmental norms and environmental regulations.

3.1.3 Moderating effect of environmental regulations

Environmental regulations play a key role in the relationship between green innovation and the performance of the firm (Zhang et al., 2019). These regulations possibly cause trouble for firms as they are difficult to implement and, in some countries, are more stringent and inflexible than in others (Darnall, 2009). It was found that the more stringent country environmental regulations are, the lower the positive relationship between GI and improvement of the FP is (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013). The first underlying reason for this is that stringent regulations make it more challenging for firms to distinguish themselves from their competitors (Lieberman & Montgomery, 1988). As these firms are less able to differentiate from other firms this could have a negative effect on the relationship.

The second reason is that in countries where regulations are more stringent, stakeholders

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firms to these regulations. In countries with less stringent environmental regulations firms have more room to be innovative and distinguish themselves from other firms. Therefore, they achieve competitive advantage by implementing green innovation (Aguilera-Caracuel & Ortiz- de-Mandojana, 2013).

Third, inflexible regulations force firms to be reactive and therefore these regulations adversely affect the FP of firms that practice GI. As firms have to conform to these environmentally standards that are pre-specified, they won’t be encouraged in creative thinking and innovation. On the other hand, more flexible regulations support firms’ innovative capabilities to meet regulations and also improve their performance (Ramanathan et al., 2017).

Finally, more stringent environmental regulations reduce the firms’ flexibility as this leads to the demand of investing in parts of their organization that have not so much to do with green technological innovation (Portney & Stavins, 2000). Instead, companies are forced to put money in nonproductive environmental activities such as litigation and environmental auditing (Palmer et al., 1995).

Overall, when the environmental regulations are more stringent in a country, it is expected that these firms are less able to distinguish themselves, stakeholders pay less attention to GI, and firms are forced to be reactive instead of proactive. Therefore, it is expected that the relationship between green innovations and FP is negatively moderated by environmental regulations.

Hypothesis 2. The relationship between green innovation and financial performance is negatively moderated by environmental regulations.

3.1.4 Moderating effect of environmental norms

The relationship between GI and FP can also be affected by the environmental norms of the country where the firm is based. These norms tend to be less formal compared to regulations

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and are mainly shown through social obligation and legitimacy (Bruton et al., 2010). In societies where the environmental concerns are high, governmental and non-governmental organizations are more stimulated to concentrate on these types of issues in their management practices (Zhu

& Sarkis, 2007). Research pointed out that GI has a positive effect on increasing firm value, when environmental agency pressures are high (Yao et al., 2019).

Firstly, when environmental concerns are rising in a society, the public will strengthen pressures on the government to undertake climate actions. The government may react to this by providing policies and mechanisms which support firms’ green innovation and let firms reduce their operating costs (Child et al., 2007).

Secondly, when in a society there are stronger environmental norms, participating in green innovation helps firms to earn legitimacy as this type of behavior is seen as more desirable Aravind & Christmann, 2011). Achieving this legitimacy results in a more positive evaluation of the firm (Ashfort & Gibbs, 1990).

Thirdly, in societies where environmental issues are relevant, products and services that are produced in a green manner will be acknowledged and valued. This means that consumers are willing to pay extra money for these products, which leads to product differentiation (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013).

Finally, when environmental norms are stronger, stakeholders will evaluate firms that participate in green innovative practices more positively (Sarkis & Zhu, 2010). Therefore, these firms will achieve a greater approval from their stakeholders and will therefore improve their relationship with these stakeholders (Christmann, 2004).

Based on this reasoning, it is expected that countries where environmental norms are stronger and more present, governmental incentives will help firms that participate in GI. Also, they will achieve greater legitimacy, which leads to a more positive firm evaluation. People

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relationship with stakeholders will improve. Therefore, stronger environmental norms help firms to achieve higher revenues and better financial performance when participating in GI.

Thus, we expect that environmental norms positively moderate the relationship between GI and FP.

Hypothesis 3. The relationship between green innovation and financial performance is positively moderated by environmental norms.

3.2 Conceptual model

Figure 1 provides a summary of all the concepts and relationships. GI is expected to determine FP. The extent of this determination is affected by pressures coming from the institutional context, namely national environmental regulations and norms.

Figure 1. Conceptual model

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4. Methodology

In this chapter, the methodology of the study is described. It starts off by identifying the sample and data collection. It then explains and discusses the dependent, independent, moderating and control variables and the measurements of these variables. It concludes by discussing the applied statistical analyses.

4.1 Sample

In order to test the proposed research question, secondary quantitative data have been acquired from multiple databases. This thesis analyses the empirical setting of MNEs headquartered in Asia, in the agri-food industry, which were tracked in the period between 2016 up until 2020.

The first underlying reason for this choice is the growing interest in sustainability in this industry, which requires an enormous change in the manners of food production, transport and consumption (Bossle et al., 2016). Compared to other economic sectors the industry has a major potential to mitigate climate change (Vermeulen et al., 2012; Aznar-Sánchez, 2020). The reason of focusing solely on Asia, is because previous literature mainly focused on agri-food enterprises located in countries in North-America and Europe (e.g., Rabadán et al., 2019;

Dangelico et al., 2019), and by doing so they leave out one of the largest consuming and environmentally impactful areas in the world. Besides, most developing economies in Asia are even more carbon intensive than their Western counterparts (Tolliver et al., 2021). The rationale behind tracking longitudinal data for the specific time period of 2016-2020 is that 2020 is the latest data point available.

This study includes agri-food firms. Therefore, the sample consists of publicly listed agri-food firms tracked by the Compustat Global Database. This database is known for its listing of financial information on publicly listed and global companies throughout the world

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Classification (SIC) code 0100 (agri-cultural production-crops) and 0200 (agricultural prod- livestock & animal specialties) could be tracked by the Compustat Global Database. For the feasibility of this study the 51 largest firms, based on annual turnover, in this list were included and longitudinal panel data got tracked for 5 years (2016-2020). This resulted in a total number of n = 255 observations consisting of 11 different countries, which indicates that there is a broad variation in institutional context.

4.2 Data collection

The list of firms together with the information on financial performance were both obtained from Compustat Global Database. We also acquired data regarding the firm’s operating profits and missing data from the Bureau van Dijk’s Orbis Database. This database provides detailed information on more than 375 million firms worldwide. Similar to other researchers this study uses green patents as a measure of GI (Brunnemeier & Cohen, 2003; Carrión-Flores & Innes, 2010). Data regarding patents were obtained from the Global Patent Index, which is developed by the European Patent Office. The database contains more than 90 million patent documents of firms from all over the world and it presents the date on which a patent was granted. Data on national environmental regulations were tracked from the Environmental Sustainability Index (ESI) 2005. The index is a measure of the ability of 146 nations to protect the environment (Siche et al., 2008). The information on national environmental norms is extracted from the CIA World Factbook, which provides data and statistics on countries worldwide (Corbett &

Kirsch, 2001). To control for firm-dynamics, this study includes firm age, firm size, firm level of innovation and capital investment intensity as control variables tracked by the Compustat Global Database and Orbis Database.

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4.3 Variables

4.3.1 Dependent variable

Financial performance. The dependent variable will be measured by calculating the Return on Assets (ROA) and Return on Sales (ROS) for a given firm in a given year. These measures are typically used in literature on environmental management (Russo & Fouts, 1997). ROS is used as a second measurement to serve as a robustness check in post-analysis, leading to an increased validity of the results. ROA is measured by dividing net income by total assets. Net income can be defined as the earnings after tax, interest, depreciation and amortization (Barnett & Salomon, 2012). Total assets refers to the sum of the values of all assets owned by the company. ROS is measured by dividing operating profit/loss by total sales. The operating profit/loss are the company’s income minus all operating expenses (Fujii et al., 2013). The data on net income, total assets, and total sales were tracked from Compustat Global Database for 5 consecutive years (2016 to 2020). The data on operating profit/loss were retrieved from Orbis Database, as Compustat Global Database does not provide these data for the firms in the sample.

4.3.2 Independent variable

Green innovation. This thesis follows scholars by measuring green patents as counting the number of granted green patents of a company (Brunnermeier & Cohen, 2003). Patents are an important indicator for innovation due to the standardized information related to technological development (Haščič & Migottoi, 2015). Green patents are patents for technologies that concern waste, wind power, geothermal energy, solar energy, tidal energy and biomass. It concerns innovations that include any changes by which a firm directly contributes to a reduction of its environmental impact (Gerstlberger et al., 2014). When a patent is granted, it means that the application of the patent has been successful. The number of granted green

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variable of Green Innovation Intensity. In collaboration with the International Centre on Trade and Sustainability and United Nations Environmental Program classification of Y02 and Y04 was created which is in the category of technologies that adapt or reduce processes involved against climate change’ (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013). Therefore, patents that are assigned with the code of Y02 or Y04 are considered environmental patents. Research has shown a time delay of 1 year before green innovation intensity shows a visible effect on the performance of a firm (de Azevedo Rezende, 2019). For that reason, the analysed period on this variable is 2015-2019. The data on granted environmental patents and general patents were acquired from Global Patent Index.

4.3.3 Moderators

National environmental regulations. Following the previous work of scholars, data on national environmental regulations have been tracked from the Environmental Sustainability Index (ESI) (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013). The ESI score (2005) provides a comprehensive signal of a country’s performance in terms of protecting and maintaining favourable environmental conditions (Esty et al., 2005). It is among the most used indices to measure the overall progress towards environmental sustainability per country (Siche et al., 2008). The ESI, proposed by the World Economic Forum, collects information on multiple aspects of environmental governance and regulatory stringency for each country. It tracks 21 indicators of environmental sustainability on natural resource endowments, pollution levels, environmental management efforts and the capacity of a country to improve its environmental performance (Esty et al., 2005). The ESI scores were calculated over 146 countries by equally weighing the average of the 21 indicators. The scale ranges from zero to 100 with the country with the lowest score being 29.2 and the highest 75.1. For each firm the ESI score of the country of the headquarter was collected.

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National environmental norms. To indicate the commitment of the environmental sustainability of a country, we used the measurement as presented by Corbett and Kirsch (2001). The number of international environmental agreements signed by the government of a country reflects the care of citizens on environmental issues and therefore reflects the environmental norms within that country. The data on environmental agreements were acquired via CIA World Factbook, which provides information on different types of agreements of countries all over the world. There are multiple agreements ranging from topics such as air pollution, ozone layer protection to topics such as marine life conservation (Aguilera-Caracuel

& Ortiz-de-Mandojana, 2013). For each firm, the total amount of signed international environmental agreements of the country where the headquarter of the firm was located were taken.

4.3.4 Control variables

To isolate the effect of GI on FP it is important to control for confounders, i.e. other factors that might also affect FP. Previous studies already identified several variables that could act as confounders and therefore might influence the results. We therefore chose to control for these possible confounding effects. The confounders identified are; firm age, firm size, firm level of innovation and capital investment intensity. This study does not include R&D intensity as control variable, since data on this is lacking.

Firm age. This study follows prior research in this field by including the age of the firm as a control variable, since logically older firms have more resources and experience which they can invoke to potentially develop green innovations (Tang et al., 2018). Consequently, this can positively influence financial performance. The age of a firm was established according to the method as previously claimed by other researchers, that firm age can be determined most

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2008; Shumway, 2001). Since the public listing of a firm affects ownership and capital structure, this creates more opportunities to grow and also increases media exposure (Loderer

& Waelchli, 2010). Therefore, the age of a firm can be determined by the number of years that elapsed since the year of establishment of the firm. The information on the firm’s incorporation year was available in the Orbis Database.

Firm size. According to Orlitzky (2001) a positive relationship between the size of the firm and FP could be anticipated, since the bigger the firm size the better control of resources, economies of scale and the holding of good employees. The use of firm size as a control variable has already been widely used in the field of environmental management (Li et al., 2017). Firm size was measured by the total assets a given firm has in a given year, since this measurement has been a popular firm size proxy for decades (Dang et al., 2018). Data on total assets were available in Compustat Global Database.

Firm level of innovation. It has been shown, that innovations have a positive effect on the financial performance (Bigliardi, 2013; Zhang et al., 2019). Innovativeness ultimately leads to new products and therefore profits (Penner-Hahn & Shaver, 2005). Hence, this thesis controls for the firm level of innovation. It follows prior literature which focuses on the relationship between the intensity of GI and FP by measuring this as the total number of granted patents per company (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013). Data on this topic were available in the Global Patent Index.

Capital investment intensity. The capital investment intensity is included as a control variable, since it has been frequently found to influence the financial performance of a firm (Manrique & Martí-Ballester, 2017). Specifically, higher investments could negatively affect the FP of a firm, as the purchasing of capital from suppliers usually requires great amounts of money, which leads to higher costs (Russo & Fouts, 1997). This is measured by dividing the

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capital expenditure by total sales. Data on this topic were available in Compustat Global Database.

Table 1. Overview of variables

Variable name Description Data source

Return on assets Net income divided by total assets Compustat Global

(%) Database (2021)

Return on sales Operating profit/loss divided by Compustat Global

total sales (%) Database (2021), Orbis

(Bureau van Dijk, 2021)

Green innovation Number of granted environmental Global Patent Index patents divided by granted general (2021)

patents

National environmental ESI score (0/100) Environmental

regulations Sustainability Index

(2021)

National environmental Number of signed environmental CIA World Factbook

norms agreements (2021)

Firm age Difference between current year Orbis (Bureau van and year of incorporation Dijk, 2021)

Firm size Total assets Compustat Global

Database (2021)

Firm level of innovation Number of granted patents Global Patent Index (2021)

Capital investment Capital expenditure divided Compustat Global

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4.4 Descriptive statistics

51 firms were tracked over a period of five years which resulted in a total number of observations of n = 255. Before the statistical tests were performed, the data got checked for missing values and certain outliers, which were not encountered. The descriptive statistics of the dependent, independent, moderator and control variables ROA, ROS, GI, national environmental regulations, national environmental norms, firm age, firm size, firm level of innovation and capital investment intensity are presented in Table 2 and show the mean, standard deviation and minimum and maximum values. We follow existing literature on lagging the independent variable with 1 year. As previously mentioned, the effect on FP is expected to be lagged.

Table 2. Descriptive Statistics

Variable Mean SD Min Max

1. Return on Assets 2.35 10.72 -58.22 61.25

2. Return on Sales 0.02 0.05 -0.06 0.31

3. Green Innovation 0.09 0.07 0 0.5

4. Environmental Regulations 46.90 6.74 38.6 57.3

5. Environmental Norms 17.03 2.55 12 21

6. Firm Age 33.31 26.04 2 110

7. Firm Size 8.80 2.84 3.60 2.44

8. Firm Level Innovation 1216.63 1691.46 0 9435

9. Capital Investment Intensity 0.55 6.59 0 104.74 n = 255 observations for 51 firms.

4.5 Statistical analysis

To evaluate the relationship between GI, FP, environmental norms and environmental regulations, we make use of the random-effects regression model in the software program STATA for two reasons. First, this is a common technique for testing hypotheses on panel data

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(Torres-Reyna, 2007). Second, the random-effects regression model gives the possibility to include time invariant variables. Both the moderator variables in this study are time invariant.

The Hausman (1978) test was applied to help determine whether a random-effects or fixed effects linear regression model would be more appropriate to test the hypothesises. The null hypothesis could not be rejected (p = .110), which indicates that a random-effect regression model should be used instead of a fixed effects model.

Before running the statistical analysis to test the hypotheses, three assumptions need to be met in order for the analysis to be valid and reliable: normality, no multicollinearity and homoskedasticity (Tabachnick et al., 2007). These assumptions have all been tested before the start of the analysis.

4.5.1 Assumption of normality

The assumption of normality means that you should make sure that the data roughly fit a normal shaped bell-curve. The Central limit theorem states that when the sample size is sufficiently large, above n = 30, the distribution of the sample means will be approximately normally distributed. As the sample size in this thesis is n = 255, the assumption is met according to this theory. However, to perform more accurate statistical tests, it is also important to look at the normal distribution of the variables (Field, 2009). The skewness and kurtosis scores indicate the normal distribution of a variable, where the boundaries for normality are between -2 and +2 for skewness, and between -7 and +7 for kurtosis. The skewness and kurtosis scores for the variables of country regulations, country norms, firm age, and firm innovation are within the acceptable range. However, the variables ROA, ROS, GI, firm size and capital investment intensity were not within this range. For that reason, the variables capital investment intensity and firm size have been transformed, by making use of a lognormal transformation. After this,

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ROS could not be logged due to negative values. Consequently, we chose to winsorize ROS and ROA with 4% on both ends, which also led these variables conform to the assumption of a normal distribution.

4.5.2 Assumption of the absence of multicollinearity

To ensure that the assumption of the absence of multicollinearity is met, an analysis of variance inflation factor (VIF) together with a correlation matrix is conducted. Multicollinearity does not exist when the levels of tolerance are above 0.2 and when the VIF scores are between 1 and 10 (Daoud, 2017). The whole model has a mean of 1.56. Adding, the highest VIF score is 2.30, which indicates that there is no multicollinearity in the sample. Next, a two-tailed correlation analysis was completed, which will be elaborated on below.

Table 3. Correlation matrix

Variable 1. 2. 3. 4. 5. 6. 7. 8. 9.

1. Return on Assets 1 2. Return on Sales .303** 1

3. Green Innovation -.075 -.00 1

4. Environmental regulations .423** .258** .164* 1

5. Environmental norms .427** -.112 .153* .007 1

6. Firm Age .255** .183** -.138* .563** .024 1

7. Firm Size -.512** -.633** -.008 .313** .021 -.002 1

8. Firm Level Innovation -.133* -.202** -.118 .118 -.061 -.049 -.16* 1

9. Capital Investment Intensity -.059 .028 -.089 -.171** .138* .138* .187** -.275** 1

n = 255 observations for 51 firms; **P < .01, two-tailed, *P < .05, two-tailed

To strengthen the assumption of the absence of multicollinearity, there should not be a strong correlation among the independent variables. Therefore, a Pearson’s correlation test is employed to identify possible correlations among the variables of GI, national environmental

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regulations, national environmental norms, firm age, firm size, firm level innovation and capital investment intensity. The results are presented in Table 3.

The value of the correlation coefficient can vary between -1 and +1. When the correlation coefficient is between .00 and (-).19 this indicates that the relation is very weak, and when it is between -.20 and .39 and between -.20 and -.39 it is considered weak. A correlation coefficient between .40 and .59 and -.40 and -.59 is considered moderate, and a correlation coefficient between .60 and .79, and -.60 and -.79 is considered strong. A coefficient between .80 and 1, and between -.80 and -1 indicates a very strong relation. To meet the assumption of no-correlation none of the independent variables should be strongly correlated with each other as this could lead to problems of multicollinearity. Multicollinearity makes the estimated coefficients unstable and difficult to interpret, and weakens the statistical power of the regression model.

As can be seen in Table 3, most variables have a low to moderate correlation. The moderating variable environmental regulations is found to have a positive and very weak relation with GI, which is significant (r = .164, p = .014). Adding, the moderating variable environmental norms has a negative but very weak correlation with GI, which is significant (r = -.153, p = .022). This suggests that firms that are headquartered in a country with more stringent environmental regulations participate in a higher degree of GI and in a country where the norms are more stringent firms participate in a lower degree of GI. As for the control variables, the matrix shows a negative weak relationship between firm’s age and GI (r = -.138, p = .039), which is significant. This suggests that older firms participate less in GI, compared to younger firms. The results of the correlation matrix show that there is no suspect of problems regarding multicollinearity.

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4.5.3 Assumption of homoskedasticity

The last assumption that must be met is that of homoskedasticity, which signifies that the residuals of the independent variable do not differ across all the values of that variable. A scatterplot was created to check for heteroscedasticity, as this shows the spreading of the values.

Adding, the Cook-Weisberg test was run, which estimates the variance of Y from the average of squared values of the residuals. When the p-value is higher than 0.05, this indicates that there is no problem with homoskedasticity. The scatterplot doesn’t show a clear pattern in the residuals and the Cook-Weisberg test (χ2 = 248.28, p < .001) also shows there is no problem with heteroscedasticity, which means that the last assumption is also met. With all three assumptions met, the random-effects regressions can thus be performed.

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5. Results

In this chapter we present the results of the statistical tests that were performed. We test the hypotheses with a random-effects regression model. The results will be elaborated on and consequently we will discuss the outcomes of the robustness test.

5.1 Regression analysis

The results of the random-effects regression models are reported in Table 4 with the dependent variable FP. Table 4 shows models 1-6 including ROA as dependent variable and Table 5 shows model 7 which includes ROS as dependent variable, for robustness check. In the first model, only the control variables are included. In model 2 the effect of the moderator variables on the dependent variable has been integrated. Model 3 shows the effect of the independent variable green innovation on the dependent variable. Model 4 and 5 show the interaction effects of environmental regulations and environmental norms on the dependent variable, respectively.

Model 6 includes all the variables, the direct effect of GI and both interactions on the dependent variable.

The first hypothesis proposed a positive direct relationship between the degree of GI and the FP. Model 3 shows that there is a statistically insignificant effect for GI on FP

(B = -0.27, p = .600). Therefore, hypothesis 1 is rejected.

Hypothesis 2 proposed that the relationship between GI and FP is negatively moderated by environmental regulations. To test for these effects, an interaction between GI and environmental regulations was created in model 4. The model shows that the interaction term for the degree of environmental regulations in a country is statistically insignificant (B = -0.00, p = .994), which leads to the rejection of the second hypothesis.

The third hypothesis proposed that the relationship between GI and FP would be

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and environmental norms, to test for the effects in model 5. This interaction term was found to be not significant (B = 0.02, p = .930). Thus, the last hypothesis is also rejected.

Additionally, the results in model 2 show that the moderating variables environmental regulations (B = 0.31, p = .014) and environmental norms (B = 1.23, p < .001) both have a significant positive direct effect on firm financial performance. The results imply that higher levels of environmental regulations and environmental norms are associated with a higher financial performance of a firm.

In the final step all the variables are included by running model 6. The results show that the effects stay the same.

Lastly, we conducted a robustness test to confirm the results, which is performed by model 7 and presented in Table 5. ROS is used here as an alternative measure of the financial performance. Overall, the results say the same, except that the model shows significant interaction effects for environmental norms and green innovation (B = -0.00, p < .001) on ROS.

Nevertheless, as the coefficient is extremely small, the effect can be ignored. In the next chapter the results of the random-effects GLS regression analysis will be further elaborated on.

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Table 4. Results of random-effects linear regression model on Financial Performance Model (1) (2) (3) (4) (5) (6) Firm Age 0.07** 0.02 0.07* 0.02 0.02 0.02 (0.03) (0.03) (0.03) (0.03) (0.03) (0.03) Firm Size -0.67*** -0.68*** -0.68*** -0.66*** -0.66*** -0.66**

(0.21) (0.19) (0.21) (0.20) (0.21) (0.21) Firm level of innovation -0.00 -0.00 -0.00 -0.00 -0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (1.40) (0.00) Capital Investment Intensity 0.00 -0.04 -0.05 -0.09 -0.00 -0.09

(0.15) (1.45) (0.15) (0.15) (0.00) (0.15) Green Innovation -0.27 -0.11 -0.00 0.09

(0.51) (3.37) (0.12) (4.08) Environmental regulations 0.31** 0.32 0.33** 0.33

(0.12) (0.22) (0.13) (0.23) Environmental norms 1.23*** 1.27*** 1.23** 1.23**

(0.25) (0.28) (0.51) (0.52)

Green Innovation * -0.00 -0.01

Environmental regulations (0.07) (0.00)

Green innovation * -0.02 0.00

Environmental norms (0.17) (0.01)

Constant -16.80** -16.91 16.38 -19.14 -18.54 -18.79 (5.44) (9.30) (5.82) (13.09) (12.78) (14.53) Year fixed effects included Yes Yes Yes Yes Yes Yes R2 0.31 0.53 0.37 0.53 0.53 0.53 Wald χ2 24.00 66.46 23.09 58.67 58.55 57.60

n = 255 observations for 51 firms; standard errors are in parentheses.

Note clustered robust standard errors in parentheses *p < .05. **p < .01. ***p < .001 (two- tailed)

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Table 5. Robustness test, Financial Performance measured by Return on Sales

Model (7)

Firm Age 0.00** (0.00)

Firm Size 0.00*** (0.00)

Firm level of innovation -1.68 (1.18) Capital Investment Intensity -0.00*** (0.00)

Green Innovation 0.03 (0.02)

Environmental regulations -0.00 (0.00) Environmental norms -0.01** (0.00) Green Innovation * 0.00 (0.00) Environmental Regulations

Green Innovation * -0.00*** (0.00) Environmental norms

Constant 0.37*** (0.07) Year fixed effects included Yes

R2 0.48

Wald χ2 144.43

n = 255 observations for 51 firms; standard errors are in parentheses.

Note clustered robust standard errors in parentheses *p < .05. **p < .01. ***p < .001 (two- tailed)

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6. Discussion

In this chapter, the outcomes of the performed analyses will be discussed and reflected on.

Consequently, these findings' academic relevance and managerial implications will be reviewed, putting the results in perspective. Finally, the limitations of the current study and the subsequent recommendations for future research will be put forward.

6.1 Findings

This study aimed to analyse the relationship between green innovation, firm financial performance, environmental regulations, and environmental norms. The research question was proposed from the perspective of institutional theory, evaluated by testing three hypotheses through conducting regression analyses, which have been described in the previous chapter.

Contrary to expectations, the results contradict all the proposed hypotheses. The first hypothesis stated that the level of green innovation, operationalized by the part of green patents compared to total patents owned by a firm, positively impacts its financial performance, operationalized by ROA. The empirical data show no influence of the level of green innovation on firm financial performance. A possible explanation might be found by taking a closer look at the sample.

Although the sample size was big enough (n = 255) to meet the assumptions for conducting the analyses, it is rather small compared to the average for published studies. This could undermine the power of the study leading to a smaller chance of finding a significant result. Another possible explanation could be that the time lag of one year incorporated in this study, following the previous work of scholars, is not sufficient for the impact of innovation on pay-out for firms in the agri-food industry. Indeed, there are scholars that argue that the existing time lag between the initial investment in green innovation and the financial reward is two years (e.g., de Azvezdo Rezende et al., 2019) or even three years (e.g., Artz et al., 2010). Adding, a meta-analysis

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References

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