The Effect of Green Innovation on Firm Financial Performance and the Mediating Effect of Internationalization and Corruption
Marinde Rozemie Vos 12799351
MSc Business Administration International Business
University of Amsterdam Amsterdam Business School
Supervisor: Dr. Mashiho Mihalache Second Reader: Dr. Ilir Haxhi Final Version
January 28, 2021
Capitalizing on Green Innovation
Marinde Rozemie Vos 12799351
MSc Business Administration - International Business University of Amsterdam, Amsterdam Business School
Supervisor: Dr. Mashiho Mihalache Second Reader: Dr. Ilir Haxhi
Final Version January 28, 2021
The Effect of Green Innovation on Firm Financial Performance and the Mediating Effect of Internationalization and Corruption
Capitalizing on Green Innovation
Statement of Originality
This document is written by Student Marinde Vos 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.
Table of Contents
Statement of Originality ... 4
Table of Contents ... 5
List of Tables and Figures ... 6
Abstract ... 7
1. Introduction ... 8
2. Literature Review ... 12
2.1 Green innovation ... 12
2.2 Internationalization ... 15
2.3 Corruption ... 19
3. Theoretical Framework ... 22
3.1 Green innovation and firm financial performance ... 22
3.2 Moderation effect of internationalization ... 24
3.3 Moderation effect of corruption ... 25
3.4 Conceptual model ... 27
4. Methodology ... 29
4.1 Sample ... 29
4.2 Data collection ... 29
4.3 Variables and measures ... 30
4.3.1 Dependent variable ... 30
4.3.2 Independent variable ... 31
4.3.3 Moderator variables ... 31
4.3.3.1 Internationalization ... 31
4.3.3.4 Corruption ... 32
4.3.4 Control variables ... 32
5. Results ... 35
5.1 Preliminary statistics ... 35
5.2 Correlation ... 36
5.3 Regression ... 38
5.3.1 Assumptions of a multiple linear regression ... 38
5.3.2 Hierarchical regression ... 40
5.4 Additional results ... 43
6. Discussion ... 45
6.1 Findings ... 45
6.2 Theoretical contributions ... 48
6.3 Practical implications ... 50
6.4 Limitations and future research recommendations ... 51
7. Conclusion ... 53
References ... 55
Appendix ... 63
List of Tables and Figures
Figure 1: Conceptual model ... 28 Table 1: Descriptive statistics and correlation matrix ... 36 Table 2: Hierarchical regression with FFP as the outcome variable ... 41 Table 3: Regression of the industry Engineering and Construction with FFP as the outcome variable ... 44 Appendix A: Industry and year dummies and correlation matrix ... 63
Abstract
The concept of green innovation has gained substantial attention of scholars in the scientific realm. The term is presented as an innovation that is related to green products and processes which aim to mitigate a firm’s impact on the environment. Even though the effects of green innovation on financial performance are well-researched, scholars do not seem to find conclusive evidence about the direction of the relationship and the influencing factors.
Therefore, this research aims to shed new light on the relationship between green innovation and financial performance and the possible moderating effects of the variables internationalization and corruption. Using a vast sample of 180 firms located in 26 countries and 19 industries that are tracked over 5 years, we find that the relationship between green innovation and firm financial performance is negative. However, we declare that this is due to the existence of a time lag between green innovation investment and financial pay-out.
Moreover, the study does not find significant evidence for the moderating effect of a firm’s internationalization scale, which can be explained by the difficulty of leveraging green innovation across different markets and the capital intensity related to green innovation investment. Furthermore, this research does not find a significant moderating effect of the variable corruption on the relationship between green innovation and financial performance.
This is because firms are expected to be biased in their location choice for green innovation facilities and, therefore, the measurement of firms located in corrupt countries is impeded.
Above findings expanded the knowledge on the theory of environmental economics.
Moreover, we provide the managerial advice to take the time lag of green investment and pay-out into account, whilst realizing that internationalization and corruption are not of critical consideration when enhancing financial performance by means of green patenting.
Keywords: Green Innovation, Financial Performance, Internationalization, Corruption
1. Introduction
“Until man duplicates a blade of grass, nature can laugh at his so-called scientific knowledge”- Thomas Edison
The famous American inventor Thomas Edison spoke these words in the early 1900’s. Even though humankind has not yet exactly duplicated a blade of grass, there are technical solutions, derived from scientific knowledge, that have the potential to exert a highly positive influence on the environment. Globalisation and increasing economic growth have put significant pressure on the environment and caused severe problems such as an increase in pollution, water deterioration, and a rise in the water and sea levels (Dreher, Gaston, &
Martens, 2008; Borghesi & Vercelli, 2003). Subsequently, innovative solutions and creative scientific ideas, defined as green innovations, are increasingly being brought forward.
Green innovation encompasses creative ideas that are geared towards sustainable economic development and focus on areas such as energy saving, pollution prevention, and waste recycling (Chen, Lai, & Wen, 2006). These types of innovations have an effect on a firm’s environmental, social, as well as financial performance (Chen et al., 2006). Several scholars investigated how firms that incorporate green innovations into their strategy have the ability to enjoy significant positive outcomes regarding their competitive advantage (Chang, 2011; Chen et al., 2006; Chiou, Chang, Lettice, & Chung, 2011) and their performance on a social level and environmental level (Huang & Wu, 2010; Peng & Lin, 2008; Zailani, Govindan, Iranmanesh, Shaharudin & Chong, 2015; Zhu & Sarkis, 2004).
Among the performance outcomes on the different levels, the relationship between green innovation and financial performance has gained substantial attention by scholars in contemporary scientific literature. The research gap regarding this relationship becomes
evident when revising the inconclusive and conflicting results provided by researchers in the field. While some scholars mention that engaging in green innovation results in more profit (Ar, 2012; Chen et al., 2006; Zhu & Sarkis, 2004;), other researchers claim that green innovation will limit financial returns (Driessen, Hillebrand, Kok & Verhallen, 2013;
Fernando, Sharfman, & Uysal, 2010; Palmer, Oates, & Porteny, 1995). The financial outcome of firm engagement in green innovation is yet to be determined. Therefore, this study aims to shed new light on the relationship between green innovation and financial performance and the possible contingency effects that can either positively or negatively impact this main relationship.
Second, according to the literature on international business, multinationality could impact strategic decision-making by the exploitation of economies of scale, higher efficiency rates, lower production costs, and increased learning opportunities arising from host-country knowledge and assets (Hennart, 2007; Lu and Beamish, 2004). Between 1995 and 2003, European multinational investments regarding green research and development almost doubled from 25% to 44% (Reger, 2002). The role of knowledge spillover plays a big part in transferring green innovations from multinational enterprises (MNEs) to local firms and vice- versa. An example of the importance of using locally sourced advantages in the aim for green innovative practices can be seen in the case of General Motors (GM). The company opened a new GM China Advanced Technical Centre in Shanghai where they employed 300 scientists to work on green technological innovations. The motivation for the decision to open a factory in China was based on the abundance of Chinese scientists and green engineers, proximity to local resources of magnesium used in batteries, and the presence of many producers of automotive parts (Noailly & Ryfisch, 2015). This case illustrates the impulse that is given to engaging in green innovation and sustainable solutions by means of internationalization.
Extensive research has been carried out on the direct effect of internationalization on
sustainability practices (Attig, Boubakri, El Ghoul & Guedhami, 2016), and on the direct effect of internationalization on financial performance (Contractor, Kundu & Hsu 2003;
Gomes & Ramaswamy, 1999; Kotabe, Srinivasan & Aulahk, 2002; Lu, & Beamish, 2001).
However, there has been no empirical investigation on the moderating effect of a firm’s internationalization scale on the relationship between green innovation and financial performance. Therefore, this research seeks to examine this gap in the field of international business by investigating the moderating role of a firm’s internationalization scale by using a diverse set of empirical data.
Third, the institutional based view explains that firms should always take the institutional environment of their business field into account and tailor their strategy according to the specific context the firm is placed in (Peng, 2000; Peng, Sun, Pinkham &
Chen, 2009). Since corruption is highly linked to a country’s institutional environment, it can influence the strategic choices made by firms when conducting business (Lin, Zeng, Ma, Qi,
& Tam, 2014). The presence of corruption can be considered as a restraining factor for firms to invest in green innovation because of weak institutions, weak enforcement of the law, and a lack of transparency (Abdullah, Zailani, Iranmanesh & Jayaraman, 2016). Due to these reasons, corruption can cause firms to make different strategic choices (Doh, Rodriguez, Uhlenbruck, Collins, & Eden, 2003). Therefore, this variable is expected to have a moderating effect on the relationship between green innovation and financial performance.
Researchers have not investigated this possible moderating effect of home-country corruption levels in full detail in the past. Hence, this research will review what the impact of home- country corruption is on the relationship between green innovation and financial returns.
Green innovation has the ability to function as a great source for firm profits (Chen et al., 2006; Aguilera & Ortiz, 2013), and contingency effects such as multinationality and corruption have the ability to positively or negatively influence this relationship. Previous
research does not provide a clear answer with regards to the interconnectedness of the variables (Abdullah et al., 2016; Attig et al., 2016; Contractor et al., 2003). Therefore, the objective of this study is to extend scientific knowledge regarding the relationship between green innovation and financial performance by answering the following research question:
RQ: Does the firm’s level of green innovation affect its financial performance, and is this relationship moderated by the firm’s internationalization scale as well as home- country corruption levels?
The major objective of this research is to shed more light on the nature of the main relationship between green innovation and financial performance. Moreover, this study will systematically review the data of a firm’s internationalization scale and home-country corruption levels, aiming to provide an answer as to whether these variables have a moderating effect on the relationship between green innovation and financial return. Hereby, the research aims to contribute to, and broaden the scope of, the existing literature on green innovation. Moreover, the study’s empirical research using real-world data will provide valuable managerial implications on how this knowledge could be adopted in future business practices. Managers and practitioners could apply the findings in order to benchmark and improve their venture’s activities
2. Literature Review
This section provides an overview of the core concepts in this study, namely; green innovation, internationalization and corruption. First, the concept of green innovation will be explained, followed by the drivers for engagement in green innovation. Thereafter, the concept of internationalization will be discussed, as well as recent theories regarding the subject. Lastly, the concept of corruption will be touched upon.
2.1 Green innovation
In 1987, the Brundlandt Commission disseminated the concept of sustainable development, which evolves around aiming to meet the needs of the present without compromising the ability to meet future needs (Eden, 1994). In the years after, academics, politicians, businessmen, and the general public felt a strong urge to pursue the concept of sustainable development by making the globe, and the activities conducted on it, more environmentally friendly (Marcus & Fremeth, 2009; Morelli, 2011). Hereby, the concept of green innovation has become increasingly important and gained substantial attention from scholars in the field of sustainability (Chen et al., 2006). A green innovation is a “hardware or software innovation that is related to green products or processes, including the innovation in technologies that are involved in energy-saving, pollution-prevention, waste recycling, green product designs, or corporate environmental management” (Chen et al., 2006, p. 332). Hence, green innovations are geared towards sustainable economic development and focus on reducing the environmental footprint of the firm (Chen, 2008; Chen et al., 2006; Lai, Wen &
Chen, 2003).
Green innovation consists of two main categories, i.e. green product innovation and green process innovation. Green product innovation is related to green product design, the usage of environmentally friendly materials and packaging, and eco-labelling (Chen, 2008;
Chiou et al., 2011). Examples of green product innovations are soy-based cushions for Ford car seats and Lipton tea sourced from completely sustainable tea plantations (Dangelico &
Pujari, 2010). Another important stream in green product innovation is recycling (Chen et al., 2006). Waste materials are re-used to serve new purposes and consequently reduce the consumption of raw materials and resources (Hopewell, Dvorak & Kosior, 2009).
Green process innovation is related to improving existing processes in the supply chain or developing new processes that are aimed at reducing their environmental impact (Chen, 2008; Chiou et al., 2011). Researchers defined different forms of green process innovations such as pollution control and prevention technologies, eco-efficiency, cleaner production processes, and recirculation (Ma, Hou & Xin, 2017). The latter is closely related to the concept of the ‘circular economy’, which evolves around materials being used in a closed loop (Kirchherr, Reike & Hekkert, 2017).
Firms can decide to engage in green innovation for several reasons, which are identified as the ‘drivers for ecological responsiveness’ (Bansal & Roth, 2000). The concept of ecological responsiveness is defined as “a set of corporate initiatives aimed at mitigating a firm’s impact on the natural environment” (Bansal & Roth, 2000, p. 717). The authors disaggregate ecological responsiveness into three main motives: competitiveness, legitimation, and ecological responsibility (Bansal & Roth, 2000). The first driver, competitiveness, is focused on the Resource Based View (RBV) of the firm, and implies that a firm will develop green resources and capabilities over time that will results in a competitive advantage. Firms will engage in green innovation because they aim to strengthen their position in the competitive market place and wish to increase their long-term profitability (Bansal & Roth, 2000; Haigh & Jones, 2006). Some academics, such as Marcus and Fremeth (2009), disagree with this first driver of green innovation and argue that green management is important regardless of the financial returns. By using ethics to underlie their
reasoning, the authors explain that managers have the moral and normative responsibility to handle the world’s resources in a responsible way in order to protect the environment (Marcus, 2005; Marcus & Fremeth, 2009).
The second driver for ecological responsiveness is legitimation, which indicates that a firm complies with the norms and values present in the given context in order to have a license to operate (Bansal & Roth, 2000). Darendelli and Hill (2016) distinguish various forms of legitimation, of which pragmatic legitimacy and moral legitimacy are highly related to green innovation. Pragmatic legitimacy is focused on the firm meeting the interest of its stakeholders (Darendelli & Hill, 2016). Since engagement in green innovation is linked to positive corporate performance on several levels (Chen et al., 2006; Chiou et al., 2011;
Huang & Li, 2017; Peng & Lin, 2008; Zhu & Sarkis, 2004), firms can feel significant pragmatic pressure to conform to certain stakeholder demands. Moreover, moral legitimacy focuses on the firm’s ability to benefit society and adhere to cultural, regulatory, and social norms (Darendelli & Hill, 2016). Engaging in activities that advance society at large and not just the firm’s competitive positioning has become increasingly more important over the last decades (Morelli, 2011). This leads to firms potentially feeling the moral obligation to act more sustainably and, subsequently, engage in green activities.
The third driver for green innovation is ecological responsibility. This driver stems from intrinsic firm motivation and concern to act and do good in society (Bansal & Roth, 2000). As opposed to the driver competitiveness, ecological responsibility is focused on ethical bases rather than the aim for economic benefit. Therefore, firms that act by reasons of ecological responsibility are prone to be more innovative and creative in comparison to firms that simply mimic other best practices (Bansal & Roth, 2000). Moreover, the authors emphasize the importance of strong ecological leadership that is geared towards implementing the greenest solutions (Bansal & Roth, 2000). Paul Polman, former CEO of
Unilever, touches upon the fact that sustainable aspirations within a firm mostly start with intrinsic motivation stemming from the CEO and a persistent outside-in perspective. He adds that sustainability is more than merely a change initiative. For successful implementation strong ecological leadership is paramount (Bhattacharya & Polman, 2017).
An important impulse for adopting green innovations is provided by global accords such as The Paris Climate Agreement (2015), as well as national legislation, and regulatory frameworks (Dresner, 2008). Besides these overarching regulatory institutions, scholars stress the importance of an individually-tailored approach compatible with local enterprises, adding that local firms are the building blocks for enhancing green innovations (Zhang, Rong
& Ji, 2019). Firms should use their existing dynamic capabilities, or develop new ones, to integrate their internal and external resources to respond to environmental challenges.
Furthermore, firms must socialize to build collaborative networks in order to mobilize change and environmental progress (Cuerva, Triguero-Cano & Córcoles, 2014). The advancement in green innovation relies on knowledge exchange and expertise between valuable players within the supply chain (Huang & Li, 2017).
2.2 Internationalization
The internationalization of firms is an omnipresent topic in the field of international business. Due to increasing globalisation in the last eras, the interconnectedness of regional markets, people, and information became more prevalent. Therefore, firms began to develop a global mind-set (Dreher, Gaston, & Martens, 2008). Efficiency and asset motives, as well as market growth opportunities, created the opening of the global barriers of doing business (Dunning, 2015). Nowadays, a large part of international trade is in the hands of large multinational corporations that have their value chains dispersed across the globe (Markusen, 2004).
The modern theory of the multinational firm can be traced back to as early as 1937, where economists such as Coase (1937) wrote about the concept of multinationality. A multinational firm can be defined as “a firm which owns outputs of goods and services originating in more than one country” (Rugman, 2013, p. 36). The foundation of the modern theory for multinationals was laid by the proposition that in order to compete with local and indigenous firms, MNEs must possess a compensating advantage. Grounded on the reason that indigenous firms possess knowledge of the local market, business practices, and environment, the entering foreign firm had to possess skills and resources that would compensate for the lack of local expertise (Hitt, Bierman, Uhlenbruck & Shimizu, 2006).
Internationally-transferrable advantages could initiate from different areas such as internal and external economies of scale, assets from good markets such as product differentiation, assets from factor markets such as access to knowledge, and government intervention (Buckley & Casson, 1985).
A firm can decide to expand its operations across national borders for four different reasons (Dunning, 2015). First, natural resource seeking focuses on obtaining certain natural resources that a foreign country possesses, such as easy access to raw materials. Secondly, market seeking motivations arise if a firm aims to enlarge its existing market share. Thirdly, efficiency seeking motives relate to the procurement of more efficient ways of conducting business, which could for example entail a better organized production process. Lastly, strategic asset seeking motives focus on obtaining local knowledge or other valuable, mostly intangible, assets (Dunning, 2015; Dunning & Lundan, 2008).
After a firm has decided to expand its operations overseas for one of the four above- mentioned reasons, it will establish foreign subsidiaries. A subsidiary is the establishment of the firm in the host-country which is owned or controlled by the parent company (Birkinshaw, 1998). Subsidiary evolution and strategic decisions can arise from the parent
firm’s headquarter, the subsidiary itself, or the local environment (Birkinshaw, 1998).
According to Vernon (1966) and Johanson & Vahlne (1990), the headquarter is the most important determinant for subsidiary strategy and output, emphasizing that the subsidiary is an instrument of the parent company and that it acts according to headquarter decisions.
Furthermore, Vernon (1966) adds that strategic choice and co-ordination of knowledge and technology is transferred from the parent to the subsidiary. According to Ghoshal et al.
(1995), the headquarter acts as the orchestrator and controls corporate resources. The headquarters’ role in guiding subsidiary strategy can be reinforced by control mechanisms such as reward procedures, career planning, and career measurement (Gates & Egelhoff, 1986). Other authors contradict this view and claim that the subsidiary’s role is more independent and that it acts according to the subsidiary’s own managers (Prahalad & Doz, 1981; Rugman & Verbeke, 1992). Another stream of research implies that the specific subsidiary environment defines the role of the subsidiary. This direction of study focuses on the adaptation to exogenous factors, such as local institutional frameworks, in which the subsidiary strategy is formed by the local environment (Birkinshaw, Hood, & Young, 2005;
Hannan & Freeman, 1977; Meyer & Estrin, 2014).
When a firm decides to expand its operations across national borders and, consequently, establishes foreign subsidiaries, it can do so at a certain pace, scope, and rhythm. Vermeulen and Barkema (2002) describe these variables as, respectively, the velocity of the internationalization, the span relative to the firm’s products and geography, and the regularity of the internationalization. Since scholars pointed out that the concept of pace is the most important one for truly understanding the development of firm internationalization (Casillas & Acedo, 2013; Prashantham & Young, 2011), this research will use pace as the measurement for firm expansion. The idea of pace is linked to the so- called ‘Uppsala Model’, which describes that the process of internationalization is a step-by-
step undertaking where firms gradually expand their international presence (Vahlne &
Johanson, 2017). This model indicates that firms, when expanding their operations abroad, will first choose countries that are culturally and institutionally closer to the country of their headquarters. After moving into countries that have more proximity to the home-country on multiple levels, the firm will expand into regions and countries that have a larger cultural and institutional distance (Vahlne & Johanson, 2017).
Besides the influence of the Uppsala Model, the pace of internationalization is also determined by environmental influences and industry conditions that have the possibility to accelerate the process of internationalization (Knight & Cavusgil, 1996; Oviatt &
McDougall, 2005). Examples of accelerative influences and conditions are cost-effective transportation and technological improvements. These factors for example enable faster circulation of raw materials and products and facilitate faster communication (Oviatt &
McDougall, 2005). Furthermore, the entrepreneur is an important driver behind the speed of internationalization, because he or she is central to the discovery, investigation, and exploitation of foreign opportunities. Through the entrepreneur’s lens, which is formed by personal characteristics and personality traits, decisions about capitalizing on international market opportunities are made (Oviatt & McDougall, 2005; Schweizer, Vahlne, & Johanson, 2010). Adding to this note, the decisiveness of entrepreneurs is subject to bounded rationality. This implies that cognitive limitations and time availability restrict the entrepreneur’s ability to process information when making decisions (Eisenhardt & Zbaracki, 1992). This could result in the fact that certain aspects in the decision-making process are not taken into account by entrepreneurs.
Another term that is closely related to the pace of internationalization is the concept of ‘born globals’. Although this term is relatively new in the field of internationalization, it has gained substantial attention from scholars (Bell, McNaughton & Young, 2001; Knight &
Cavusgil, 2004; Weerawardena, Mort, Liesch & Knight, 2007). Firms characterized as born globals engage in international activities from the very early stages. Born global firms are defined as “business organizations that from inception, seek to derive significant competitive advantages from the use of resources and the sale of outputs in multiple countries”
(Weerawardena et al., p. 294). Born globals do not adhere to the traditional beliefs of the aforementioned Uppsala Model in which the process of internationalization is incremental.
Instead, born global firms expand their operations across borders at a highly accelerated pace (Bell, McNaughton, Young & Crick, 2003). Once again, scholars define entrepreneurial orientation as one of the most important factors for successful development of born global firms (Kuivalainen, Sundqvist & Servais 2007; Oviatt & McDougall, 2005; Schweizer et al., 2010).
2.3 Corruption
Corruption is a historical problem that has been present in the political, social, and economic realm for centuries (Van Klaveren, 2002). Bardhan (1997) describes that there are reports of corruptive actions dating from at least four centuries before Christ. The degree of destruction and the effects of corruption vary over time and between actions. Corruption is defined as “the misuse of public power for private benefit” (Lambsdorff, 2007, p. 241).
Private benefits can occur in the form of money, valuable assets, an increase in power or status, and receiving future favours or benefits (Lambsdorff, 2007). Corruption constrains decision making by means of non-transparency and untrustworthiness of the actors and their intents (Gaviria, 2002).
Corruption is embedded in a country’s institutional context and can therefore shape the environment that we live in and form the manner in which firms conduct business (De Vaal & Ebben, 2011). Institutions are defined as the “rules of the game” or “the formally devised constraints that shape human interaction” (North, 1991, p. 97). Williamson (2000)
discusses the concept of institutions in his research on New Institutional Economics (NIE).
He divides the institutional context into four levels and elaborates that these levels interact with each other through feedback loops. The first level consists of informal rules, which are related to culture and embeddedness. The second level contains the formal ‘rules of the game’ and relate to the constitution and judicial systems. The third level is defined by governance and has to do with the ‘play of the game’. Comprised within this level are contract laws and other mechanisms which ensure that the judicial system is being carried out according to the law. The fourth level is related to resource allocation and the supply and demand curve of the ‘classic economic’ theory (Williamson, 2000). North’s research on institutions relates to Williamson’s research on NIE, since the first and second level of Williamson are related the informal and formal rules of the game described by North.
Corruption is deeply ingrained within society and can, therefore, be seen as a cultural aspect belonging to the first level of Williamson’s NIE. Since corruption is deeply rooted into a society primarily due to historical reasons, the pace of change is very slow (Williamson, 2000). Hence, the concept of ‘path dependency’ becomes visible. This term indicates that a current situation is a result of what happened in the past (North, 1991). The formal and informal institutional framework in which a firm is placed has a result on the future.
Consequently, a different set of opportunities arising from the institutional framework will lead to a different future. From this it can be deduced that a country’s institutional context, including the presence of corruption, has an influence on the firm’s strategic decisions (Wang
& You, 2012).
As became clear from the concept of path dependency, whether a country has a major or minor presence of corruption is a result of the events that have occurred in the past.
Murphy, Shleifer, and Vishny (1993) remark that country-specific governmental structures and political processes, which are an indirect result of past happenings, notably influence the
level of corruption prevalent in a country. The scholars add that if governments and their adherent institutions are considered as weak, the amount of control over owned agencies is low, therefore resulting in higher levels of corruption (Gaviria, 2002; Olken & Pande, 2012;
Murphy et al., 1993). Moreover, Tanzi (1998) suggests that the presence of excessive non- transparent regulations in developing countries increases the interaction of players in the private sphere, resulting in a more frequent occurrence of corruptive behaviour. On the other hand, countries that are characterized as a developed country have a smaller presence of corruption due to more stable governments and stronger institutions. Besides, public pressures such as the press, as well as democratic elections, ensure that corruption rates in developed countries are low (Murphy et al., 1993).
Since corruption is deeply embedded into a society, the effects on society can be profound. Fisman and Svensson (2007) note in their paper that much research has been carried out on the effect of corruption on economic growth. Negative effects of corruption are related to higher transaction costs due to uncertainty, risk, secrecy, and a non-transparent environment (Fisman & Svensson, 2007). Furthermore, corruption leads to eroding trust, a weakened democracy, and a decline in economic development (Chang & Chu, 2006). In addition, it contributes to higher poverty, greater inequality, and a bigger division between rich and poor in society (Dimant & Tosato, 2018). In summary, the costs associated with corruption appear in a multi-tiered terrain, i.e. economic, social, environmental, and political (Transparency International, 2020)
3. Theoretical Framework
This chapter will propose a theoretical framework regarding the relationship between the concepts of green innovation and firm financial performance. Furthermore, the potential moderating role of a firm’s internationalization scale, as well as the home-country corruption levels, will be presented. Lastly, the conceptual framework of hypothesis will be portrayed.
3.1 Green innovation and firm financial performance
Environmental economics, of which the foundations are laid by Pigou (1920), is a sub-branch of the neoclassical economic theory. Environmental economics theory focuses on the environmental externalities that arise in modern business. The theory suggests that there are market failures present, among which environmental externalities, which can harm economic activity (Fisher, 1981). Green innovations can be used as a tool to reduce externalities and enhance a firm’s environmental as well as financial performance (Weng, Chen & Chen, 2015). The focus on the relationship between green innovation and competitive advantage has only recently gained the attention of scholars and researchers around the globe (Chang, 2011; Chen et al., 2006; Gürlek & Tuna, 2018; Lin, Tan & Geng, 2013). According to Chen et al. (2006), this is due to a shift in international environmental regulation and environmental consciousness by consumers. Investing in green innovation is a costly undertaking, resulting in the question of whether true green innovation can occur at the same time as financial growth and profits (Hsu & Ziedonis, 2008). At first, many corporations believed that engaging in corporate environmentalism and implementing environmentally-friendly aspects into their strategy would hold back their growth and stagger their development. In 1995, Palmer, Oates, and Portney argued that corporate environmentalism does not necessarily result in higher revenues due to inefficiencies and the loss of productivity. High resource consumption during the process of engaging in green
innovation thus resulted in the expectation that the costs were likely to not exceed the benefits (Jaffe, Peterson, Portney & Stavins, 1995; Palmer, Oates & Portney, 1995).
Additionally, practicing green innovation is associated with high risk and uncertainty, which generally deteriorate financial results (Tseng, Wang, Chiu, Geng & Lin, 2013).
However, more recent research shows that firms have realized that the capitalization on green innovative solutions could result in positive financial outcomes (Ar, 2012; Zhu, Sarkis, & Lai, 2012). Porter and van der Linde (1995) point out that pollution is the concrete evidence that the supply chain is inefficient and that precious resources are wasted. There are several reasons that present how green innovation could lead to improved financial results.
First, by applying green innovative technologies, firms could ameliorate their inefficient usage of resources and become more productive (Porter & van der Linde, 1995).
Other scholars add to this point stating that certain pollution control solutions can also reduce operation costs (Carrion-Flores & Innes, 2010; Hart, 1995). Moreover, green innovation by means of recycling, could result in lower costs related to the purchase of raw materials because products are circulating in a loop (Hart, 1995; Kirchherr et al., 2017).
Second, green innovation incorporates green product design and green packaging options. On the one hand, this could result in the decline of production costs due to increased efficiency in material usage and energy consumption. Green innovation would allow companies to use their resources more productively and to create less waste during the production process (Azevedo Rezende, Bansi, Alves & Galina, 2019; Huang & Li, 2017; Zhu
& Sarkis, 2004). On the other hand a greener product design and packaging could improve the product’s quality. Consequently, this could reflect positively on the product’s image and its differentiation advantages. An increase in quality and a differentiating environmental aspect to the product could boost sales (Chen, 2008; Chen et al., 2006).
Last, through the engagement in green innovation firms develop green resources and capabilities. By gaining experience and expertise in the field of green innovation, these green resources and capabilities can boost long-term profitability (Bansal & Roth, 2000). These developed green resources and capabilities could improve the firm’s social approval and legitimacy (Aguilera & Ortiz, 2013). One the one hand this approval results in the benefit of positive deals and the opportunity to enjoy premium pricing (Aguilera & Ortiz, 2013; Bansal, 2005), while on the other hand it can result in the possibility for firms to differentiate their products and services (Ar, 2012). This differentiation could lead to the exploration of new markets and the reaching of new customer segments which are more sensitive with regards to the environment. This customer segment is more likely to engage with environmentally friendly corporations and buy green products (Ar, 2012; Azevedo Rezende et al., 2019;
Rivera, 2002). Based on the above-mentioned arguments, we develop the following hypothesis:
H1: Green innovation is positively related to firm financial performance.
3.2 Moderation effect of internationalization
A firm that expands to foreign countries and internationalizes its operations can enjoy several benefits related to green innovation and financial performance. The exploitation of scale and scope economies could create easier access to raw materials that can be used in green products and processes (Noailly & Reifish, 2015). Adding to this, local proximity to firms and suppliers in the same line of products could decrease waste and by-product streams (Roberts, 2004). Moreover, proximity to local demand increases the awareness of how to adapt existing products to local preferences (Noailly & Reifish, 2015). This could result in the attraction of new customers and the discovery of new segments and fields to operate in (Lu & Beamish, 2001; Ruigrok & Wagner, 2003). Furthermore, by internationalizing, firms can increase their market power over existing suppliers, distributors, and customers (Buckley
& Casson, 1985). In addition, internationalization can result in positive outcomes on the financial front (Lu & Beamish, 2001). A higher degree of multinationality can serve as a risk- reducing factor by means of diversification of the financial portfolio (Hennart, 2007).
Furthermore, higher multinationality leads to higher performance due to learning opportunities provided by the host-country environment (Hitt et al., 2006).
Another important benefit regarding multinationality is related to the exploitation of host-country specific advantages and, consequently, the creation of firm-specific advantages.
Advantages that rise from the host-country can serve as knowledge and capability pools from which tangible and intangible assets can be extracted and created (Hall, 2011). Local proximity to competitors is highly desirable because of the possibility for face-to-face interaction between scientists (Noailly & Reifish, 2015). In this interaction, the role of knowledge spillover is paramount since host-country expertise can be used for developing green innovations (Gao et al., 2018; Noailly & Ryfisch, 2015). Moreover, research has found that the development of green innovations mostly depends on the technology that is derived from host-countries (Noailly & Shestalova, 2013). MNEs that are dispersed around the globe can thus benefit from technology which originates from subsidiary locations and is not accessible at home (Hall, 2011). Hence, a firm that has a high degree of internationalization will have access to more foreign insights and knowledge. Due to the aforementioned reasons, firms can use internationalization as a volume button to improve financial performance by means of green innovation. Therefore, we develop the following hypothesis:
H2: A high scale of internationalization has a positive moderating effect on the relationship between green innovation and firm financial performance.
3.3 Moderation effect of corruption
As outlined in the literature review, corruption is part of a country’s institutional
framework and can have severe effects on society, firm strategy, and firm financial performance (Chang & Chu, 2006; Fisman & Svensson, 2007; De Vaal & Ebben, 2011).
Corruption causes transactions to become non-transparent which can, consequently, result in the erosion of trust between different parties (Fisman & Svensson, 2007). In order to attain and maintain entrepreneurial development and innovation, institutional trust is imperative (Lin et al., 2013). Corruption undermines the foundations of institutional trust to the extent that it can stagger innovative green development (Dimant & Tosato, 2018). When a country is overshadowed by the presence of corruption, the risk of engaging in green innovation increases due to the probability of corruptive behaviour by actors in the value chain. This could result in a decrease in profit for the entrepreneur due to profit appropriation by other parties (Anokhin & Schulze, 2009).
Furthermore, the effects of green innovation are difficult and costly to measure because they lie in the future and are uncertain (Azevedo Rezende et al, 2019). Therefore, a corrupt environment will discourage, as well as limit, innovation and green solutions because of higher transaction costs (Fisman & Svensson, 2007). Additionally, risk and uncertainty, which are highly related to a corrupt environment, disincentivize firms to invest in growth opportunities and to improve productivity through innovation (Murphy et al., 1993). The better a country is at controlling corruption, the higher the rate of innovation and entrepreneurial activity in that specific country (Anokhin & Schulze, 2009). In accordance with the article of Anokhin and Schulze (2009), a broad range of peer review papers find the higher the control rate of corruption, the better the economic welfare, capital investment, foreign direct investment, and total factor productivity (Carmignani, 2005; Kaufmann &
Kraay, 2003; Lambsdorff, 2003; Li, Xu and Zou, 2000; Mauro, 2003).
Besides the effects of corruption on green innovation, the linkage between corruption and firm financial performance is crucial and, therefore, well represented in scientific
research (Fisman & Svensson, 2007; Shleifer & Vishny, 1993; Van Vu, Tran, Nguyen, &
Lim, 2018). Some researchers claim that corruption does not have a significant negative impact on financial returns. The scholars reason that informal relations with other parties, which arise often in corrupt environments, can be considered a long-term investment for growth (De Jong, Tu & van Ees, 2012). Moreover, corruption would speed up bureaucratic processes which otherwise would have taken very long, resulting in higher growth rates and improved financial results (Banerjee, 2005; Bardhan, 1997; Svensson, 2005). However, the majority of scholars agree on the fact that corruption does more harm to a firm’s financial performance than good (Donadelli, Fasan & Magnanelli, 2014; Gaviria, 2002; Van Vu et al., 2018). Long-term costs of corruption can include depreciation of firm-specific advantages such as reputation, loss of profit, a decrease in competitiveness, and a decline in innovation activity (Donadelli et al., 2014; Van Vu et al., 2018). Moreover, the presence of corruption can have severe impact on sales and employment growth rates (Gaviria, 2002). Employee efficiency and labour output go down for firms that engage in bribes and operate in corrupt countries (Donadelli et al., 2014). Additionally, Donadelli et al. (2014) discuss that efficiency rates for firms in corrupt countries are 70% lower than the rates for firms in non-corrupt countries. Hence, all things considered, if a firms aims to increase its financial performance through the engagement in green innovation, the presence of corruption will negatively influence this interaction. Therefore, the following hypothesis is developed:
H3: A high level of corruption has a negative moderating effect on the relationship between green innovation and firm financial performance.
3.4 Conceptual model
The conceptual model of this study is shown in Figure 1. It will be investigated whether a firm’s level of green innovation affects its financial performance. Furthermore, the moderating effect of the firm’s internationalization scale, as well as the home-country
corruption levels, will be examined. Based on the findings from the theoretical framework, Figure 1 portrays the expected relationships and the directions of the relationships between the variables; green innovation, firm financial performance, internationalization, and corruption.
Figure 1: Conceptual model
4. Methodology
This chapter will outline the methodology that has been used to research the relationship between green innovation and financial performance and the moderating effects of internationalization and corruption. First, the sample and data collection will be discussed.
Second, the variables and measures will be outlined.
4.1 Sample
This study made use of the Fortune Global 500 list, which consists of the 500 largest firms in the world based on yearly revenue. This list was chosen for its relevance in previous academic articles (Kolk, 2003; Ma, Tong & Fitza, 2013; Murray, Kotabe & Wildt, 1995;
Rugman & Verbeke, 2004). This study incorporates the list of 2019 because it has complete adherent firm financial figures as opposed to the list of the year 2020. Companies on the list are obliged to publish financial data to government agencies. All of the firms in the list are MNEs and are represented by 26 countries. Since one of the moderator variables for this study is internationalization, the Fortune Global 500 list, which contains solely MNEs, is highly appropriate. Furthermore, the dispersion of home-country headquarters around 26 countries makes the use of the Fortune list very adequate in the research regarding the moderating role of the variable corruption. In short, the Fortune Global 500 list is highly suitable for this research because it contains rich data for the scope of this study. For the feasibility of the research, the first 320 firms on the list have been selected. However, due to missing data points of several variables, the final list consists of 180 firms. These firms were tracked over 5 years, from 2014 until 2019 in order to obtain longitudinal panel data.
4.2 Data collection
The list of companies was obtained from the website of Fortune Global 500.
Furthermore, the information on firm financial performance and internationalization was
retrieved from the ORBIS database. This database is designed by Bureau van Dijk and contains company information on more than 375 million firms worldwide, as well as detailed financial information on more than 40 million firms. It is the most extensive database on company and financial information and has been broadly used by scholars in extant researches (Cavaco & Crifo, 2014; Fankhauser et al., 2013; Paniagua, Rivelles, & Sapena, 2018). Moreover, following the much-cited article by Aguilera and Ortiz (2013) on green innovation, the data regarding green patents was obtained from the Global Patent Index (GPI), which is developed by the European Patent Office. This database contains more than 90 million patent documents of the worlds largest firms and is updated monthly by adding around 500 thousand new documents. Lastly, the data on home-country corruption levels was obtained from the Corruption Perceptions Index (CPI), which is developed by the organisation Transparency International. The CPI ranks countries based on how corrupt the public sector is perceived by experts and business executives. The data is retrieved through assessments and surveys that are collected by several reputable institutions (Transparency International, 2020). The use of CPI in measuring corruption is validated by multiple scholars (Lambsdorff, 2004; Rohwer, 2009; Wilhelm, 2002).
4.3 Variables and measures 4.3.1 Dependent variable
The dependent variable of the study was Firm Financial Performance (FFP). This variable was measured by a company’s Return on Assets (ROA). ROA is measured by dividing net income by total assets. Relevant academic articles like the papers by Aguilera and Ortiz (2013), Kotabe, Srinivasan and Aulakh (2002), and Vermeulen and Barkema (2002), validated the use of ROA to measure FFP and highlighted the appropriateness of this measurement approach because it measures how efficiently a firm extracts profit from its assets regardless of firm size.
4.3.2 Independent variable
The independent variable of this study is green innovation. As seen in other relevant academic articles, green innovation is measured by the number of green patents that a firm acquires (Aguilera & Ortiz, 2013; Azevedo Rezende et al., 2019; Brunnermeier & Cohen, 2003). Using patents as an indicator for green innovation is highly suitable because it is one of the most important indicators for innovation due to the standardized information related to technological development (Frietsch & Grupp, 2003). By dividing the number of green patents by the total number of patents, the variable Green Innovation Intensity (GII) was created. Hence, this variable computed the ratio of green patents in the total patent portfolio of a firm. Patents are classified into specific technology groupings which are based on common subject matter. In collaboration with the United Nations Environmental Program and the International Centre on Trade and Sustainable Development, the classification Y02 was created, referring to patents that evolve around sustainable innovation (Aguilera & Ortiz, 2013). This entailed that for this study, all patents, which were extracted from the GPI, with the code Y02 were considered as green patents. The data on GII was derived from the database as follows; every firm was inserted in the query for each of the investigated years separately, i.e. 2014 up to and including 2019. The first search was carried out with no specific patent classification, the second search was performed including the specific green patent classification Y02. In this manner, the ratio of green patents to total patens, hence, the intensity of firm green innovation became visible.
4.3.3 Moderator variables 4.3.3.1 Internationalization
The first moderating variable in this study was internationalization, which was measured by its scale. The scale of internationalization could be described as the size of international operations relative to overall operations. As suggested by relevant articles on
international business, the variable internationalization was calculated by the percentage of sales from foreign operations to the total sales of the firm (George, Wiklund, & Zahra, 2005;
Rugman & Verbeke, 2004; Tallman & Li, 1996). Since scholars pointed out that pace is the most important aspect for truly understanding firm internationalization (Casillas & Acedo, 2013; Prashantham & Young, 2011), this study ensured to incorporate this notion. The pace of a firm’s international expansion was taken into account by measuring the internationalization scale for the 5 consecutive years, 2014 up to and including 2019. In this manner, the acceleration or deceleration of the firm’s internationalization process became visible.
4.3.3.4 Corruption
The second moderator variable was corruption. This variable was measured through home-country corruption scores, which were derived from the CPI from the years 2014 up to and including 2019. The score assigned to a country, which is updated each year, can vary between 0 and 100. A score close to 0 indicates that a country is highly corrupt, whereas a score close to 100 indicates that a country is free of corruption. Home-country corruption scores were used as opposed to host-country corruption scores because, as became clear from the literature review, the headquarter importance on subsidiary strategy is paramount (Ghoshal et al.,1998; Johanson & Vahlne, 1990; Vernon, 1966).
4.3.4 Control variables
Following relevant past research on firm financial performance (Lin, Cheah, Azali, Ho, & Yip, 2019; Majumdar, 1997; Vermeulen & Barkema, 2002), five control variables were determined. First, the variable firm age was used as a control for the model. Firm age was measured by subtracting the firm’s year of founding from the year of reference. This measure was in accordance with relevant research on the impact of a firm’s age on
operations, such as the paper from Majumdar (1997). This control variable was included because it considers that firms that exist longer might have a higher scale of internationalization. This could have given these firms the advantage of being able to develop and utilise their resources and capabilities to increase their involvement in green innovation.
Second, the variable firm size was used as a control for the data on financial performance. It could be assumed that larger firms have more and easier access to resources and capabilities that facilitate the adoption of green innovations (Kleinknecht, 1989).
Moreover, larger firms could face more external legitimation pressures from governments, NGOs, and consumers to engage in green innovation (Seroka‐Stolka & Fijorek, 2020).
However, research by Lin et al. (2019) demonstrates that smaller firms have higher green investment return rates because these firms enjoy higher efficiency scores. Hence, it becomes clear that a firm’s size can have an impact on the researched direct and moderating relationships. In this study, firm size has been measured through total assets. This method was in consonance with relevant academic papers on the subject of international business (Vermeulen & Barkema, 2002; Lin et al., 2019)
Third, the variable industry type was included as a control for the researched relationship between GII and FFP. Certain industries are more prone to active engagement in green innovation through, for example, governmental regulation and other incentives (Aguilera and Ortiz, 2013). However, there are also industries that historically do not have a strong focus on environmental or social concerns. Kaeufur (2010) argues that, for example, the financial industry often ignored negative externalities and that the main focus was directed towards profit maximization. These historic predispositions could have a continuing influence on the societal role certain industries take in the present. On the other hand, firms that operate in environmentally unfriendly markets such as oil, mining, and construction face more rigid control and supervision of governmental institutions and could, therefore, be more
impelled to engage in green patenting (Li, Zhao, Zhang, Chen, & Cao, 2018). In total, the sample consisted of 180 firms, which were, according to the specification of the Fortune Global 500 list, situated in 19 different industries. The industry types are the following;
Transportation, Motor Vehicles & Parts, Technology, Engineering & Construction, Food &
Drug Stores, Financials, Aerospace & Defense, Retailing, Materials, Telecommunications, Food, Beverages & Tobacco, Chemicals, Health Care, Energy, Industrials, Apparel, Wholesalers, Household Products, and Media.
Fourth, the control variable country was incorporated into the research. From the literature review it became apparent that each country has its own institutional framework with differences across economic, political and environmental lines (North, 1991). According to Poole and Van de Ven (2004), these differences can result in an elevated or lowered propensity for engagement in innovation. Furthermore, some countries have a tradition of extensive patent filing, while other countries are less inclined to this activity (Guellec & Van Pottelsberghe de la Potterie, 2002). The data consisted of 180 firms of which the headquarters are located in 26 different countries. The countries are the following: Denmark, Germany, Ireland, Spain, Japan, China, The Netherlands, The USA, Mexico, Belgium, Luxembourg, Italy, France, Australia, Britain, Norway, Switzerland, Taiwan, South Korea, India, Brazil, Canada, Malaysia, Russia, Saudi Arabia, and Singapore.
Fifth, the variable year was included to control for FFP. This variable was included to control for, and capture the influence of, aggregate trends. Hair et al. (2010) advise to always control for year in a regression with longitudinal panel data. Since the firms in the sample have been measured over 5 years, we followed the advise by Hair et al. (2010) and decided to incorporate year as a control variable.
5. Results
In the following section the results of the performed statistical test will be presented. First, the preliminary descriptive statistics of the data will be discussed. Subsequently, the developed hypotheses are tested by means of a hierarchical regression. Moreover, additional noteworthy results of the regression will be considered.
5.1 Preliminary statistics
The descriptive statistics of the dependent, independent, moderator, and control variables are presented in Table 1, along with the outcomes of the performed correlation test.
180 Firms were tracked over five years resulting in a total number of observations of N = 900. However, after excluding missing data points and certain outliers, of which the motivation will be elaborated on below, the final number of observations is N = 886. Before performing the statistical tests, the data was first properly presented and checked for normality.
First, for the variables industry, country, and year dummies were created. For industry, 19 dummy variables were generated in alphabetical order. The same has been done for the variable country, resulting in 26 dummy variables in alphabetical order. Furthermore, for the variable year, 5 dummies were created in order of the 5 consecutive years, i.e. 2014 up to and including 2019. In appendix A, an overview of the dummy variables industry and year is presented including the frequency of both dummies and the percentage of a specific dummy to the total variable. The motivation for why the variable country is not included in Appendix A will become apparent below.
Furthermore, one of the assumptions of performing statistical tests is that the data is normally distributed. According to the Central Limit Theorem, if N is at least 30 the sample will have a normal distribution (Rosenblatt, 1956; Tabachnick & Fidell, 2007). This indicates
that for this research, where the sample size is N = 886, the statistical test can be performed regardless of a skewed distribution. However, in order to perform more accurate statistical tests, it is advised to transform certain variables so that they will follow a more normal distribution (Tabachnick & Fidell, 2007). The normality of a variable is evaluated based on the scores for Skewness and Kurtosis. According to Hair et al. (2010), the data is considered to follow a normal distribution if the scores for Skewness are between -2 and +2, and the scores for Kurtosis between -7 and +7. After considering the descriptive statistics of the variables FFP, GII, internationalization, corruption, firm size, and firm age, the following choices were made. The Skewness and Kurtosis scores for the variables FFP, GII, internationalization, and corruption were within the acceptable range. However, the values for firm size and firm age deviated from a normal distribution. Consequently, the decision has been made to transform these variables using a lognormal transformation. After this, the values for Skewness and Kurtosis for the variables firm size and firm age were within the acceptable range, therefore not deviating from a normal distribution.
5.2 Correlation
The correlation coefficients of the variables Firm Financial Performance (FFP), Green Innovation Intensity (GII), internationalization (Internat.), corruption, firm size, and firm age are presented in Table 1, alongside the descriptive statistics of these variables.
Table 1: Descriptive statistics and correlation matrix
Variables Mean SD 1. 2. 3. 4. 5. 6.
1. FFP 4,117 4,747 1
2. GII 0,114 0,16 -0,137** 1
3. Internat. 0,47 0,286 ,007 ,219** 1
4. Corruption 66,65 14,993 ,115** ,092** ,323** 1 5. Firm size 18,571 1,318 -,216** -.077* -,093** -,028 1 6. Firm age 3,84 0,915 ,078* ,045 ,138** ,166** ,145** 1
** Correlation is significant at the 0.01 level (2-tailed)
In order to evaluate the relationship between a pair of variables a Pearson correlation test was performed. The outcome of the correlation test and the related correlation coefficients portray the following relationships. First of all, GII has a significant negative correlation with FFP (r=-0.137 & p=0.000). This indicates that a higher ratio of green patents implies lower financial returns. This is in contradiction with hypothesis 1, since this hypothesis predicted that GII would have a positive relation with FFP. Even though there is a significant negative correlation, the correlation coefficient is fairly low and thus quite weak, indicating that there is not a very strong negative relationship between GII and FFP.
Additionally, it has been investigated whether lagging the variable GII with 1 year would result in a different correlation coefficient between GII and FFP. However, after executing a lag function in SPSS, the correlation coefficient was still negative (r=-.0152 & p=0.000), indicating that there still exists a negative relationship between green patenting and financial performance. Explanations for why this relationship is negative as opposed to the positive expectation are put forward in the discussion section of this research.
Another interesting finding is the significant positive correlation between internationalization and GII (r=0.219 & p=0.000). This coefficient would imply that the more international a firm becomes, the more the firm engages in green innovation. As discussed in the literature review, firms that operate abroad can enjoy several benefits with regards to green innovation such as the exploitation of host country knowledge spillovers and green resources. Moreover, there is a significant positive relationship between corruption and GII (r=0.092 & p=0.006) and between corruption and FFP (r=0.115 & p=0.001). These scores indicate that if corruption scores go up, meaning that a country becomes less corrupt, firms tend to engage more in green innovation and have higher financial returns. This is consonance with findings from the literature review, since firms in less corrupt countries are
more prone to engage in green innovation due to a more suitable innovation environment including more trust and transparency between parties.
In order to evaluate whether there are issues with multicollinearity, the values from the Pearson correlation test should assessed. According to Field (2013), all values should be below 0.9, which is the case for the all the variables included in the correlation matrix.
Therefore, there are no serious multicollinearity issues regarding the variables FFP, GII, internationalization, corruption, firm size, and firm age. Issues with respect to multicollinearity for the dummy variables industry, country, and year will be further investigated in the regression model.
5.3 Regression
After considering the correlation coefficients, a hierarchical regression was performed with all variables, including the dummy variables, in order to support or reject the developed hypotheses. In order to check the hypothesized relationships, a total number of 4 regressions were run. The industry dummy Energy was taken as the reference group because this dummy contains the largest group of firms, i.e. 16%. For country, the dummy USA was taken as the reference group since this dummy contains 31.5% of all firm observations. Lastly, the year dummy 2019 was taken as the reference group since this is the most recent year of analysis.
5.3.1 Assumptions of a multiple linear regression
Whilst running a multiple linear regression test, four main assumptions need to be met in order for the analysis to be valid and reliable (Tabachnick & Fidell, 2007). First, according to the linearity assumption, there must be a straight-line linear relationship between the outcome variable and the independent variable. The scatterplot from the performed regression portrays that the first assumption of linearity is met. Second, the assumption of normality indicates that the residuals of the dependent variable FFP should be more or less
