Does the degree of competition in an industry influence firms’ level of greenwashing?

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Does the degree of competition in an industry influence

firms’ level of greenwashing?

Nina ter Beest

S2965232

MSc BA Strategic Innovation Management

Faculty of Economics and Business

University of Groningen

Supervisor: prof. dr. J. Surroca

Co-assessor: dr. P.J. Steinberg

Groningen, July 2020

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ABSTRACT

These days, firms face increasing pressures to behave responsibly towards the environment. This has incentivized firms to engage in greenwashing. Greenwashing gives firms a way to access the benefits of Corporate Social Responsibility (CSR), while at the same time avoiding the additional costs since they don’t actually implement the policy. Drawing on insights from institutional theory and the literature on CSR and organizational decoupling, I contribute to theory on greenwashing by investigating the influence of the competitive degree in the industry on greenwashing behavior by firms. Additionally, to delineate boundary conditions, I investigate the moderating effect of membership in polluting industries. The hypotheses are tested on a sample of 550 European publicly stock-listed firms using linear regression analyses. Although the findings show no significant effect of the competitive degree on greenwashing, this study advances our understanding of the interaction between competition, CSR and greenwashing. The main contribution of this study is the finding that when there is a high degree of competition in polluting industries, the level of greenwashing decreases. This increases our understanding of the institutional conditions under which firms will, or will not, engage in greenwashing.

Keywords:

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1. Introduction

In recent years, the natural environment has become a crucial component of the business life. The consumer and capital markets for green products, services and firms have been growing enormously (Delmas & Burbano, 2011). Simultaneously, the pressure for companies to act in a responsible manner toward the environment has increased (Kim & Lyon, 2015). Those growing societal expectations for corporate responsibility have incentivized firms to exaggerate their environmental accomplishment through information disclosure strategies, in other words: to greenwash (Delmas & Burbano, 2011; Kim & Lyon, 2015). Greenwashing is “the conjunction of two firm behaviors: poor environmental performance combined with positive communication about the environmental performance” (Delmas & Burbano, 2011, p.65). More and more firms are engaging in greenwashing; barely a month goes by without another well-known firm being caught with misleading communications about their environmental activities (Delmas & Burbano, 2011; Bowen & Aragon-Correa, 2014).

A growing body of literature analyzes the motivations of firms for engaging in greenwashing. According to Lyon and Montgomery (2013) those motivations can be divided into internal and external drivers. Internal drivers include individual (e.g. narrow decision framing) and organizational (e.g. firm incentive structure), whereas the external drivers can be divided into pressures from both non-market actors (e.g. regulators and NGOs) and market actors (e.g. consumers, investors and competitors) (Delmas & Burbano, 2011). The competitive landscape is an especially critical part of the market environment in which a firm is confronted with the decision of whether or not to greenwash (Delmas & Burbano, 2011). Competition in an industry can be fierce (Lusch & Laczniak, 1989), because it consists of many competitors which leads to an alleged lack of opportunities for growth (Auh & Menguc, 2005; Candi, Melia & Colurcio, 2019). Faced with intense competition from rivals, companies look for ways to innovate and differentiate to stay competitive (Jansen, Van Den Bosch & Volberda, 2006; Saemundsson and Candi, 2014). Thus, the competitive degree plays an important role in firms’ decisions on their corporate strategies (Tsai & Hsu, 2014; Theeke, 2016). Firms will likely try to develop solutions that undermine competitor actions (Candi, et al., 2019). Greenwashing might be one of those solutions. Surprisingly, however, no study has investigated the effect of the degree of competition on the level of greenwashing. In order to address this gap in the literature, this study seeks to answer the following research question: Does the degree of competition in an industry influence firms’ level of greenwashing?

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competition on CSR. Firstly, according to the differentiation effect, an increase in competition has a positive effect on CSR. Following the line of reasoning that an increase in competition means that a small advantage achieved by one of the competitors could easily lead to an increased market share (Fernandez-Kranz & Santalo, 2010). In industries with intense competition it’s easy for consumers to switch to firms that offer the best deal, since there is strong rivalry. (Auh & Menguc, 2005). Thus, when firms face intense competition from competitors they have to find ways to stay competitive and do so by differentiating their offering (Jansen, et al., 2006; Candi, et al., 2019). Some companies have decided to use environmental performance as a means to differentiate their product and through this gain a competitive advantage (Roy & Vezina, 2001). Secondly, following the rent dissipation effect, increasing competition would lead to reduced CSR. Since increased product market competition would reduce the product profit margin, which then reduces the marginal return of a CSR strategy (Fernandez-Kranz & Santalo, 2010). Companies dealing with intense competition will cut costs that fall outside of direct profit maximization (Campbell, 2007). More competition leads to a weaker economic position for the firm, therefore firms are less likely to sacrifice money for altruistic reasons like CSR (Fernandez-Kranz & Santalo, 2010). Thus, the opposed signs of the two effects shows that a paradox exists. There are benefits from CSR through differentiation, but at the same time there are concerns that the additional costs of investing in CSR would sacrifice the already small margins of firms in highly competitive environments.

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hypothesize that the level of greenwashing is lower in “dirty” industries where environmental scrutiny is high.

In order to test the theory, I performed structured research on 550 European public firms. The results of this study show, however, no significant increase in greenwashing in response to a high degree of competition. Findings do confirm that when there is high competition in dirty industries, the level of greenwashing increases importantly. From an academic perspective, this study enriches the literature on CSR and greenwashing and provides useful inputs by examining the effects of the degree of competition and public scrutiny on greenwashing. Very little is currently known about the relationship between the competitive degree and greenwashing, although the effect of competition on CSR has been studied extensively (Bagnoli & Watts, 2003; Fisman, Heal & Nair, 2006; Fernandez-Kranz & Santalo, 2010; Lee, Cruz & Shankar, 2018). The current paper builds upon the literature on CSR to advance the understanding of greenwashing. From a practitioner perspective, this study contributes by giving managers insights into the type of industries in which firms are most likely to greenwash. If a firm then decides to apply benchmarking they know when they shouldn’t take their competitors external communications too literal. It also presents insights for activist and policy makers, as it is shown that public scrutiny is effective in deterring greenwashing.

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2. Theoretical background and hypotheses

The following section aims to review the relevant literature related to competition and CSR and greenwashing. It will give an understanding of the current discussions among scholars. First, CSR, the competitive degree and their relationship will be explained. Followed by a description of greenwashing behavior. Thereafter, the hypotheses will be developed. Here greenwashing is posed as a way for firms to access the benefits of CSR without assuming the costs that occur under a high degree of competition. Finally, the moderating effect of public scrutiny will be explained.

2.1 Competition and CSR

In the past twenty years, companies have made great efforts to become more socially responsible in their activities and marketing. CSR has become a crucial construct in the business environment (Marín, Rubio & Ruiz de Maya, 2012). CSR is a firm’s commitment to take responsibility for the social, environmental and economic effects of its activities (Piasecki & Gudowski, 2017). To some the idea of firms acting in a socially responsible way would seem incorrect.If companies exist to maximize profit and shareholder value, then it goes without saying that companies will do all they can to achieve this goal, even if that includes acting in a socially irresponsible manner (Campbell, 2007). So, given the incentives for maximizing profit and shareholder value, why would a corporation choose to act in socially responsible ways? Bansal and Roth (2000) studied why firms aim for corporate environmental responsiveness and they found three basic motivations: competitiveness, legitimation and ecological responsibility. Firms motivated by competitiveness expect that their commitment to CSR will lead to a lasting benefit and thus improve their long-term profitability (Bansal & Roth, 2000). Whereas, the legitimation motivation refers to a firm’s desire to comply with institutional pressures such as those of investors, legislators and other industry members (DiMaggio & Powell, 1983; Alrich & Fiol, 1994; Pache & Santos, 2010; Bansal & Roth, 2000). The motive for environmental responsibility stems from a company's concern for its social obligations and values (Bansal & Roth, 2000).

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rewarded by stock market investors (Brown, 1998) and helps with avoiding expensive stakeholder conflicts (Schnietz & Epstein, 2005). Altogether, providing substantial evidence for managers that pursuing CSR will pay off. However, the results of the literature that tried to find the effect of CSR on the firm’s competitive position is inconclusive (Griffin & Mahoney, 1997; Margolis & Walsh, 2003). Researchers have found positive, negative and neutral results of CSR on financial performance. This ambiguity can be attributed to methodological and data quality issues (McWilliams & Siegel, 2001; Boulouta & Piletis, 2013).

Turning now to the interplay between the degree of competition and the level of CSR. In the literature, the term “competitive intensity” tends to be used to refer to the degree of competition (Cui, et al., 2005; Tsai & Yang, 2013). Competitive intensity in an industry originates from resource constraints (Lusch & Laczniak, 1989), the existence of numerous competitors, and the lack of opportunities for future growth (Auh & Menguc, 2005). An industry with high competitive intensity is characterized by greater rivalry among incumbents (Li, et al., 2008), the existence of stronger competitors (Ang, 2008) and competitor activities (Cui, et al., 2005) such as price competition, promotion competition (Auh & Menguc, 2005) and more advertising and product offerings (Li, et al., 2008). There is a shortage of resources, little room to move and shrinking margins (Candi, et al., 2019). Competitive intensity is thus a fundamental characteristic at the core of industry and market structure, firm conduct, and firm performance (Ang, 2008).

Does an increase in competition lead to more or less CSR? The results regarding this relationship are conflicting. According to Fernandez-Kranz and Santalo (2010) the ambiguous results are due to the two main effects of competition on a strategy: the “rent dissipation” effect and the “escape competition”, hereafter called the differentiation effect.

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competitors and can use CSR as a means to differentiate and thereby gain a competitive advantage (Roy & Vezina, 2001; Dupire & M’Zali, 2018). If the choice of CSR strategies is primarily driven by their utility to companies seeking competitive advantages, a more competitive environment can lead to more intensive use of CSR as a way to better differentiate or access new markets (Fernandez-Kranz & Santalo, 2010). Thus, according to the differentiation effect more competition would affect firm levels of CSR positively (Fernandez-Kranz & Santalo, 2010; Lee, Cruz & Shankar, 2018).

Empirically, Fisman, Heal and Nair (2006) found that the profitability of a differentiation strategy achieved by a CSR strategy is larger in markets with more competition, since the profitability of a differentiation strategy achieved by CSR activities is larger in markets with strong price competition. Fernandez-Kranz and Santalo (2010) also found strong empirical evidence that companies in more competitive markets are making greater CSR efforts. Flammer (2015) gives evidence of higher levels of CSR in reaction to import tariff reductions, which is a sign of an exogenous increase in competition. A number of other studies also find that firms engage in CSR in more competitive markets than in less competitive markets (Vilanova, Lozano & Arenas, 2009; Declerck & M’Zali, 2012; Roulet & Touboul, 2015; Kopel & Lamantia, 2018; Sheikh, 2018; Candi, et al., 2019)

However, following the rent dissipation effect, an increase in competition in the product market would decrease the profit margin of the product, which in turn would reduce the marginal return of any generic business strategy (Fernandez-Kranz & Santalo, 2010). Therefore, more competition would reduce CSR. Campbell (2007) more specifically states that companies are less likely to implement substantive CSR if there is too much competition. Since, if competition is too high, companies will cut costs that apparently fall outside the direct profit maximization. Fernandez-Kranz and Santalo (2010) explain this effect by stating shareholders are only willing to sacrifice money for altruistic reasons like CSR if their firm profits are substantial and increasing. More competition in the marketplace leads to a decrease in the economic position of the firm, which results in the firm engaging in less CSR.According to Sheikh (2018), thin profit margins because of intense competition lead to a reduction in free cash flows. Therefore, when competition is high managers are forced to focus on short-run NPV positive projects since they have a direct effect on shareholder equity, rather than investing in long term CSR projects. Sheikh (2018) also finds that high competition reduces returns to any differentiation strategy, which motivates managers to reduce investment in CSR (Sheikh, 2018).

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impact on environmental performance and that this effect is enhanced for firms adopting a cost-leadership strategy.

The opposed sign of these two effects shows that a paradox exists. On the one hand, CSR helps firms differentiate when competition increases, which then enables them to achieve a competitive advantage. On the other hand, in highly competitive environments the additional costs of investing in CSR would sacrifice the already small margins. Firms are less likely to implement CSR if there is high competition, since they will cut costs that fall outside of direct profit maximization. It follows that there is a strong incentive for firms to find a way that allows to differentiate using CSR, but without assuming the costs. This is where greenwashing is introduced – it could be the exact solution firms were looking for. Surprisingly though, the effect of competitive intensity on greenwashing has not been studied. This paper attempts to address this knowledge gap. More specifically, it explores if an increase (decrease) in competitiveness leads to an increase (decrease) in greenwashing behavior?

2.2 Greenwashing

Greenwashing occurs when companies communicate about the positive environmental actions, but hide the negative ones. That way, they can create a misleadingly positive corporate image (Lyon & Maxwell, 2011; Lyon & Montgomery, 2015). This paper builds on the larger literature on organizational decoupling to explain greenwashing behavior. This literature begins from the idea that organizations have to comply by the rules and demands of external constituencies, because only then can they avoid disapproval from stakeholders and achieve legitimacy (DiMaggio & Powell, 1983; Meyer & Rowan, 1977). Firms often respond to those institutional requirements by displaying symbolic compliance, where they only seem to meet the requirements (Westphal & Zajac, 1994; Marquis, et al., 2016). Decoupling occurs, since there is a lack of alignment between organizational policies and practices (Kim & Lyon, 2015). The following definition of greenwashing makes the link with decoupling very clear: “the gap between a firm’s external and prior internal actions, where external actions outweigh the internal” (Hawn & Ioannou, 2016, p. 2571). The internal actions entail “real” actions, actually creating organizational capabilities to meet the expectations of social actors. The external actions reflect public initiatives communication patterns to gain legitimacy, mostly by seeking public endorsement of the organization by outside audience (Hawn & Ioannou, 2016).

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which reduces its ability to obtain legitimacy, resources or social support (Lyon & Maxwell, 2011; Kim & Lyon, 2015). In sum, greenwashing has benefits when it is able to influence the opinions of consumers and investors regarding the firm positively, however it is discouraged if outside stakeholders, such as activists, are able to detect and punish the firm for its greenwashing behavior (Lyon & Maxwell, 2011; Lyon & Montgomery, 2013) or if regulatory pressures stop the firm from decoupling (Delmas & Burbano, 2011).

2.3 Hypotheses

As explained earlier in Chapter 2.1, the relationship between the competitive degree and CSR behavior indicates a paradox. Greenwashing addresses this conflict. Firms are able to access the benefits of CSR by disclosing positive information about their environmental actions, while at the same time avoiding the costs of CSR since they are not actually implementing the policy (Kim & Lyon, 2015).

According to Pache and Santos (2010) organizations are increasingly subject to the conflicting demands of their institutional environment. There are conflicting demands between on one side the consumers’ demands (who are stakeholders too) due the growing interest in socially responsible practices, who can be reached through differentiating with CSR. This is especially relevant in highly competitive environments, since consumers can easily switch between firms. On the other side, there are the shareholders’ demands whose main interest is the economic position of the firm. More intense competition in the marketplace decreases the strength of the firm’s economic position (Fernandez-Kranz & Santalo, 2010) and there is, therefore, less room for CSR. Thus, the firms face a tension between the demands of their stakeholders and shareholders. Firms are dealing with additional institutional demands, such as those of legislators, industry members and investors (Kim & Lyon, 2015). This makes compliance impossible to achieve because to meet some requirements, others must be challenged (Pfeffer & Salancik, 1978). Conflicting institutional requirements can therefore lead to organizational decoupling (Pache & Santos, 2010). The decoupling literature argues that decoupling occurs when institutional environments are versatile and when institutional demands for conformity are in conflict with each other (Pache & Santos, 2010; Smith & Lewis, 2011; Kim & Lyon, 2015), which is the case when the degree of competition is high.

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Hypothesis 1: The higher the degree of competition in an industry, the higher the level of greenwashing of the firms competing in that industry.

There are several organizational attributes and institutional mechanisms that discourage firms from engaging in greenwashing (Marquis, et al., 2016). I will focus on the moderating role of public scrutiny. Businesses are less likely to use greenwashing in institutional settings where public scrutiny (e.g. organized social movements and public voice) is more prevalent and where there is more normative pressure on disclosure (Marquis, et al., 2016). Prior research suggests that more visible firms are subject to more scrutiny, which results in less illegitimate behavior (Bansal & Roth, 2000; King, 2008). This is caused by greater perceived reputational risk. In more visible firms, their illegitimate behavior might be exposed sooner by external stakeholders, which then damages the firms’ reputation (Marquis, et al., 2016). Reputation indicates the quality and reliability of a company's practices, is linked to the legitimacy of the firm and distinguishes a company from its competitors (King, 2008). Thus, greater visibility leads firms to limit their illegitimate behavior in order to save their reputation.

Usually, the visibility of the organization is associated with the size, reputation and public relations strategy of companies (King & McDonnell, 2015). However, this paper focuses on another important source of visibility: the level of pollution of the industry where the firm is present. In their paper Bansal and Roth (2000) showed that industries labeled as “dirty”, such as the oil, chemicals, mining and forestry industries are under a lot of scrutiny. Since the corporate environmental performance of a firm is associated with many externalities the regulatory pressures are often strong (Bansal & Clelland, 2004). Short and Toffel (2008) found that firms that are subject to heavy regulatory surveillance are more likely to voluntarily disclose environmental information. The bad environmental reputations of "dirty" firms also put them on the radar screens of environmentalists and make them targets of protests, boycotts, lawsuits and the like (Chatterji & Toffel. 2010). Vogel (2005) found that even the mere threat of protest campaigns by non-governmental organizations has driven many companies to strengthen their social and environmental management practices. Literature has also focused on the effect of activism on greenwashing. Kim and Lyon (2011) found that activism dissuaded firms from participating in environmental programs that activists may see as greenwashing. Lyon and Maxwell (2011) found that greater activist pressure deters greenwashing and that this is more likely for firms with socially or environmentally damaging impacts, and if the firm is relatively well informed about the environmental or social impacts of its practices

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makes such environmentally damaging firms even more likely to avoid greenwashing. This argument leads to the following hypothesis:

Hypothesis 2: Public scrutiny moderates the positive relationship between the degree of competition and greenwashing, such that the level of greenwashing is lower in “dirty” industries where environmental scrutiny is high.

The conceptual model below provides a visual representation, which will clarify the hypotheses.

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3. Methodology

3.1 Data collection and sample

To obtain the data, the dataset from Thomson Reuters ASSET4 was used. ASSET4 is a subset of Thomson Reuters Eikon. ASSET4 specializes in providing objective, relevant and systematic CSR information (Cheng, Ioannou & Serafeim, 2014; Hawn & Ioannou, 2016). Their information comes from a variation of sources, such as stock exchange fillings, annual reports, media and nongovernmental organizations’ websites. The data is analyzed and divided into four pillars: economic, corporate governance, social and environmental. For environmental factors the data would include information on CO2 emissions, water recycled, energy used and more (Hawn & Ioannou, 2016). Using this information, ASSET4 provides an overall z-score that benchmarks the performance of the focal firm against the performance of the rest of the firms in the dataset. Additionally, for data on the market competition the database Orbis was used, which provides accounting statement information of public companies.

I used data on the firm’s prior internal environmental actions from 2017 and data on the firm’s external environmental actions from 2018. Moreover, data from 2016 for competition related information and the controls was used. This 1-year gap with the data on internal CSR was used, because the firm’s financial and competitive effects have a delayed impact on environmental practices and communications (Hawn & Ioannou, 2016). My initial sample, which was sourced from Eikon consisted of 1100 publicly traded European firms. After combing data sources and correcting for outliers, incomplete and missing data, and a standard deviation times 5 or higher the sample consisted of 550 publicly traded EU firms. Those firms were in 195 industries and 23 countries.

3.2 Measures

In order to conduct the analyses, one main dependent variable was built, an independent variable and a moderating variable. In Appendix A.1 an overview of all definitions and sources is presented.

3.2.1 Dependent variable

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environmental KPI’s. However, two variables that Hawn and Ioannou (2016) found were not available anymore. This was due to Thomson Reuters who changed the asset to ASSET4 from Datastream to Eikon. Therefore, I used six internal KPI’s (Appendix A.3). Moreover, if a firm missed data for a particular KPI I assumed that the firm did not do this action, since the firm would probably have documented the action if it did and then it would have been available.

Thereafter, the average of the individual indices was calculated and then multiplied by 100, which represents the firms’ Green Practices Index (GPI) or Green Communications Index (GCI) score. To calculate the final standardized measure of the GPI score the percentage of internal actions in the previous year was divided by the logged total assets of the previous year, 2017. For the final standardized measure of the GCI score, the percentage of external actions of the current year was divided by the total logged assets of the previous year (Hawn & Ioannou, 2016). For the formulas see Formulae 3.1 and 3.2.

Formulae 3.1

Standardized Green Practices Index ="#$%#&'()# +, '+'(- .&'#$&(- /&01$+&2#&'(- 3%'1+&4('67) 9+))#: ;+'(- 344#'4('67)

Formulae 3.2

Standardized Green Communications Index = "#$%#&'()# +, '+'(- /<'#$&(- /&01$+&2#&'(- 3%'1+&4(') 9+))#: ;+'(- 344#'4('67)

The next step was to build the Discrepancy Index. This index captures the relative gap between the firm’s standardized GCI and GPI score. This thus allows for the identification of greenwashing. To construct a measure of this gap, the GPI score was subtracted from the GCI score, and then divided by the logged total assets of the previous year (Hawn & Ioannou, 2016). A value above zero means that a firm has a relatively high level of external communications compared to internal practices. Therefore, greenwashing firms can be detected by looking at the high values (above zero) in the Discrepancy Index. The formula is presented hereafter (Formulae 3.3).

Formulae 3.3

Discrepancy Index = ='(&:($:1>#: ?@. 4%+$#(')–='(&:($:1>#: ?". 4%+$#('67) _{9+))#: ;+'(- 344#'4('67)}

3.2.2 Independent variable

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Industrial Organization literature (Rhoades, 1993; Fernandez-Kranz & Santalo, 2010). It is calculated by squaring the market share of all players operating in an industry in a given year, and then summing the outcomes (Formulae 3.4). To find the market share, I computed total sales in industry i by considering all the companies active in industry i from my initial Eikon sample. This measure of concentration is also used by Fernandez-Kranz and Santalo (2010) when studying the effect of competition on CSR.

Formulae 3.4

HHI = s1^2 + s2^2 + s3^2 + ... + sn^2

Sn = the market share percentage of firm n, expressed as a whole number

The HHI number can range from close to zero to 10,000. If an industry has characteristics of a monopoly, the concentration will be higher, which implies a lower degree of competition in the market. A market with an HHI of less than 1,500 is considered a competitive marketplace, an HHI of 1,500 to 2,500 is a moderately competitive marketplace, and an HHI of 2,500 or greater is a highly concentrated marketplace (Hayes, 2020). Therefore, in this paper industries with an HHI of less than 2,500 are considered as having a high degree of competition. I created a dummy variable, where a 1 was assigned to firms with an HHI lower than 2500, and a 0 if HHI was higher than 2500. The HHI index was calculated for 253 industries. For firms with the industry code NACE K64-66 (i.e. financial and insurance activities) data on total sales was not available and, therefore, the HHI could not be calculated, which is I why chose to delete those firms from the sample.

In addition to the dummy variable I conducted a robustness check where I took the normal value of HHI. As HHI is a measure of concentration, I reverted the coding in order to arrive at a measure of competition. I did this by subtracting the HHI with its highest possible score of 10,000.

Subsequently, I conducted another robustness where I used the number of competitors in the same industry as a proxy for the intensity of competition in industries. This number was defined at the four-digit NACE code level.

3.2.3 Moderating variable

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3.2.4 Control variables

CSR, and therefore greenwashing, is affected by several factors that must be included to the analysis. All the data for these control variables is from the year 2016.

R&D and advertising expenditures – It might be the case that firms operating in more competitive industries increase their R&D or advertisement in addition to undertaking CSR initiatives. Therefore, controls of R&D and advertising expenditures are needed to prevent an overestimation of the impact of competition on CSR (McWilliams & Siegel, 2000; Fernandez-Kranz & Santalo, 2010; Dupire & M’Zali, 2018). I measured R&D intensity using the ratio of R&D expenditures to total sales. For advertising a ratio of advertising expenditures to sales is captured. However, reporting R&D and advertising expenditures is not mandatory, therefore a large number of firms have missing values for those variables in Orbis. Following Fernandez-Kranz and Santalo (2010), I didn’t drop those observations, but instead replaced them with the industry mean and included a dummy in the regression analyses. A dummy for each variable that has a value equal to 1 if the company reports the respective type of expenditures, and zero otherwise. These two control variables were taken from Eikon.

Operating performance – It’s likely that firms in highly competitive environments have less resources available for socially responsible behavior. Thus, I followed Dupire and M’Zali (2010) and used return on assets (ROA) as a proxy for operating performance. This variable was taken from Orbis.

Firm size – There could be a spurious correlation between CSR and competition caused by a size effect. This can happen because less competitive industries are likely to have fewer and larger firms (Fernandez-Kranz & Santalo, 2010). Also, large companies are more likely to have greater visibility and as a result more social impact (Dupire & M’Zali, 2018). For these reasons, the logarithm of total net sales was used as a proxy for firm size. The data for control was taken from Orbis.

Debt to Equity (DtE) – This variable is related to the level of risk that a firm dares to take (Barnea & Rubin, 2010). It’s likely that firms with a big amount of debt are less likely to engage in CSR as this could be seen as a risky strategy. This variable was taken from Eikon.

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3.3 Empirical strategy

Multiple regression equations were formulated to test the proposed hypotheses, respectively for the control variables (Model 1), competitive degree (Model 2) and the moderator “dirty industries” (Model 3). Model 4 – 7 are auxiliary regression analyses using different proxies for the competitive degree. Model 8 tests if there is a non-linear relationship between competition and greenwashing. Model 9 and Model 10 are auxiliary regression analyses testing for the effect of the competitive degree on the Green Practices Index and Green Communications Index separately.

Table 1: Regression Equations

[Model 1] Y1GW = ß 0 + ß controls

[Model 2] Y1GW = ß0 + ß1HHIdummy + ßcontrols

[Model 3] Y1GW = ß0 + ß1HHIdummy + ß2DIRT + ß1HHIdummy * ß2DIRT + ßcontrols [Model 4] Y1GW = ß0 + ß1HHI + ßcontrols

[Model 5] Y1GW = ß0 + ß1HHI + ß2DIRT + ß1HHI * ß2DIRT + ßcontrols

[Model 6] Y1GW = ß0 + ß1PLAYERS + ßcontrols

[Model 7] Y1GW = ß0 + ß1PLAYERS + ß2DIRT + ß1PLAYERS * ß2DIRT + ßcontrols

[Model 8] Y1GW = ß0 + ß1HHI + ß2HHI^2 + ßcontrols

[Model 9] Y2GPI = ß0 + ß1HHIdummy + ßcontrols

[Model 10] Y3GCI = ß0 + ß1HHIdummy + ßcontrols

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4. Results

The following section provides the results of the equations and analyses described in the methodology. First, the descriptive statistics and correlations will be presented. Thereafter, the main regression results are presented. Followed by the results of the auxiliary regression analyses.

4.1 Descriptive statistics and correlations

The descriptive statistics and the correlations matrix for all the variables are presented in Table 3. The sample consisted of 550 firms. On average, the firms have a higher score for GPI (mean = 67,515) than GCI (mean = 30,667). Thus, firms perform more internal environmental actions on average than they communicate. HHI has a mean of 5641,978, which means that most firms are in an industry with a low level of competition, since the coding of HHI has been reversed and, therefore, firms with an HHI higher than 7500 are considered as having a high degree of competitiveness. The other proxy for the competitive degree, industry players, has a mean of 9,332. This means that on average a firm operates in an industry with ten players.

As expected the discrepancy index is correlated with the GPI (r = -0,512) and GCI (r = 0,366) as the discrepancy index is constructed with these variables. Moreover, the GPI and GCI are correlated (r = 0,576), however Hawn and Ioannou (2016) have shown that they are independent constructs. The critical cut-off value for correlation values is 0.7, the correlation matrix shows that there are no values exceeding this number (Luger, Raisch & Schimmer, 2018). To assure that there are no issues with multicollinearity I produced the Variance Inflation Factor (VIF) for the independent variables. The value of VIF should not exceed a threshold of ten (O’Brien, 2007), which is the case for all the variables and, therefore, multicollinearity is not an issue in this sample.

4.2 Main regression results

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The overall fit of Model 1 with the data is significant (F(43, 505) = 1.323; p = 0,087). The proportion of variance that can be explained by the control variables is 2,5% (adj. R-squared = 0,025). However, ROA, net sales, debt to equity, R&D intensity and advertising intensity showed no significant effect and were consistently higher than alpha 0,1%. I also controlled for the industry and country of the firms and here I did find significant results.

Model 2 tests the main hypothesis (H1) and shows a significant fit with the data (F(44, 504) = 1,318; p = 0,088). The adjusted r-squared remains 0,025. Hypothesis 1 predicted that the higher the level of competition, the higher the level of greenwashing. Model 2 shows a positive coefficient for HHI (b = 0,053), however the effect for this variable was not significant as the alpha was higher than 0,1% (p = 0,295). Therefore, hypothesis 1 is not supported.

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Table 2. Descriptive statistics and correlations matrix

Variables ₁ ₂ ₃ ₄ ₅ ₆ ₇ ₈ ₉ ₁₀ ₁₁ ₁₂

(1) Green Practices Index 1

(2) Green Communications Index 0.576** 1

(3) Discrepancy Index –0.512** 0.366** 1

(4) Competitive degree (HHI dummy) 0.04 0.086* 0.040 1

(5) Dirty industry 0.123** 0.058 –0.097* –0.168* 1

(6) ROA 0.017 0.069 0.042 –0.036 0.013 1

(7) Net sales (log) 0.465** 0.477* –0.030 –0.058 0.069 –0.012 1

(8) Debt to Equity 0.030 0.012 –0.022 –0.025 –0.018 –0.092* 0.049 1

(9) R&D intensity –0.069 –0.043 0.035 0.00 0.116** –0.038 –0.104* –0.026 1

(10) Advertising intensity –0.023 –0.005 –0.001 –0.019 0.049 0.00 –0.107* –0.001 0.038 1

(11) Competitive degree (HHI) –0.008 0.043 0.06 0.705** –0.149** –0.004 –0.078 –0.017 –0.051 –0.012 1

(12) Competitive degree (players) 0.011 –0.013 –0.032 0.070 –0.032 0.092* –0.001 –0.047 0.003 0.047 0.068 1

VIF DI – – – 2.021 1.054 1.021 1.038 1.014 1.035 1.018 2.012 1.019

Mean 67.515 30.667 -0.392 0.35 0.46 4.262 6.493 111.158 0.055 0.004 5641.978 9.322

S.D. 22.365 20.017 0.219 0.479 0.499 6.069 0.661 262.058 0.264 0.026 3008.841 10.670

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Table 3: Multiple regression analyses results

Model 1 Model 2 Model 3

Variables DI DI DI

Coef. Sig. Coef. Sig. Coef. Sig.

ROA 0.041 0.371 0.040 0.377 0.038 0.403

Net sales (log) –0.055 0.290 –0.058 0.262 –0.053 0.301

Debt to Equity 0.003 0.951 0.002 0.959 0.006 0.884

R&D intensity 0.031 0.503 0.031 0.509 0.042 0.389

Advertising intensity 0.020 0.692 0.018 0.719 0.018 0.716

Competitive degree (HHI dummy) 0.053 0.295 0.043 0.397

Dirty industry –0.142 0.412

Moderator (HHI dummy x Dirty Industry) 0.090 0.068*

Industry fixed effects Yes Yes Yes

Country fixed effects Yes Yes Yes

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4.3 Auxiliary regression analyses

Before I discuss the results in more depth, I provide additional analyses on the effect of the competitive degree on greenwashing, this time with different proxies for competitiveness. Thereafter, I perform additional analyses to test for non-linearities. Finally, additional analyses on the Green Practices Index and Green Communications Index are presented.

4.3.1 Additional analysis on the proxy for competitive degree

Firstly, I took the normal value of HHI instead of using a dummy. However, I did not use the HHI directly, as HHI is a measure of the level of concentration in an industry and my goal was to measure competition. Therefore, I had to revert the coding by subtracting the HHI with the highest possible score of 10,000. I used this reversed number of HHI in Model 4 and Model 5, which can be found in Table 4. Model 4 tests hypothesis 1 and has a significant fit with the data (F(44,504) = 1,391; p = 0,053). The model increases its predictive power over Model 2 as the adjusted R-squared increases from 0,025 to 0,030. Remarkably, the variable with the reversed HHI score does show a significant result (p = 0,046) as opposed to the dummy variable of HHI in Model 2. The coefficient is positive, which means that hypothesis 1 is supported.

In Model 5 the second hypothesis is tested. The model has a significant fit with the data (F(46, 502) = 1,417; p = 0,041). Similar with Model 2 and Model 3, the predictive power has increased from 0,030 for Model 4 to 0,041 for Model 5. Moreover, the predictive power of Model 5 is bigger than that of Model 3 (adj. R-squared = 0,029). The model shows a significant result for the interaction variable (p = 0,066) and the coefficient of the variable “dirty industry” is negative, therefore hypothesis 2 is supported too. To conclude, using the normal (reversed) value of HHI instead of the dummy variable makes a big difference for the results.

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(p = 0,067), therefore here hypothesis 2 is supported too. It can be concluded that using players as a proxy instead of HHI did not make a difference for the results.

4.3.2 Additional analyses to test for non-linearity

I performed an additional test where I tested for non-linearity in the relationship between competition and greenwashing. It could be the case that more competition leads more greenwashing, but only until a certain level of competition is reached. After that point the greenwashing behavior might decrease again, as the effects of the extreme levels of competition are too pressing. I looked for non-linearity by using the reversed HHI score as a proxy for the competitive degree and then including the squared value of this score. The results of Model 8 can be found in Table 6. The coefficients for HHI and the quadratic HHI are not significant, therefore the results did not show a non-linear relationship between competition and greenwashing.

4.3.3 Additional analysis on the GPI and GCI

Finally, I performed analyses on the effect of the competitive degree on the Green Practices Index and Green Communications Index separately. I conducted these to provide a more complete insight into the interplay between internal environmental practices and external environmental communication. It also offers more insight into whether the results are in line with the reasoning in the literature section. Table 7 displays the output of Model 9 and Model 10.

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Table 4: Auxiliary regression analyses for the competitive degree proxy (HHI)

Model 4 Model 5

Variables DI DI

Coef. Sig. Coef. Sig.

ROA 0.038 0.399 0.036 0.425

Net sales (log) –0.058 0.255 –0.054 0.289

Debt to Equity 0.002 0.966 0.006 0.893

R&D intensity 0.039 0.405 0.048 0.320

Advertising intensity 0.020 0.688 0.020 0.690

Competitive degree (HHI) 0.097 0.046** 0.089 0.067*

Moderator (HHI x Dirty industry) 0.088 0.072*

Industry fixed effects Yes Yes

Country fixed effects Yes Yes

Constant –0.003 0.940 0.013 0.775 Prob > F 0.053* 0.041** R-squared 0.108 0.115 Adj. R-squared 0.030 0.034 N 548 548 Note: * p < 0.1, ** p < 0.05, *** p < 0.01

Table 5: Auxiliary regression analyses for the competitive degree proxy (industry players)

Variables DI DI

ROA 0.046 0.320 0.043 0.349

Net sales (log) –0.051 0.328 –0.047 0.364

Debt to Equity 0.002 0.967 0.006 0.891

R&D intensity 0.034 0.467 0.045 0.347

Competitive degree (players) –0.046 0.296 –0.042 0.339

Moderator (players x Dirty Industry) 0.090 0.067*

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Table 6: Auxiliary regression analyses testing non-linearity

Model 8

Variables DI

Coef. Sig.

ROA 0.009 0.392

Net sales (log) –0.013 0.249

Debt to Equity 0.000 0.985

R&D intensity 0.009 0.378

Advertising intensity 0.004 0.697

Competitive degree (HHI) 0.044 0.218

Competitive degree (HHI^2) –0.025 0.503

Industry fixed effects Yes

Country fixed effects Yes

Constant –0.393 0.000*** Prob > F 0.061* R-squared 0.109 Adj. R-squared 0.029 N 548 Note: * p < 0.1, ** p < 0.05, *** p < 0.01

Table 7: Auxiliary regression analyses for GPI and GCI

Variables GPI GCI

ROA 0.049 0.219 0.096 0.017

Net sales (log) 0.481 0.000*** 0.433 0.000***

Debt to Equity 0.016 0.670 0.024 0.528

R&D intensity 0.007 0.862 0.025 0.539

Competitive degree (HHI dummy) 0.016 0.709 0.082 0.065*

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5. Discussion

Building upon the work of Fernandez-Kranz and Santalo (2010) and using insights from institutional theory and the organizational decoupling literature, this thesis aims to advance knowledge about the relationship between the competitive degree and the level of greenwashing in an industry. Subsequently, the research question to be answered is: “What is the relationship between the degree of competition in an industry and the level of greenwashing behavior in the industry?”. To answer this question, I performed multiple regression analyses on a sample of 550 European publicly traded firms. I also test for the moderating effect of public scrutiny on the relationship between competition and greenwashing. The results show no significant difference in greenwashing in response to a higher competitive degree (H1). The findings did show a significant moderating effect for dirty industries (H2). Demonstrating that even when the competition in polluting industries is high, the level of greenwashing decreases. Possible explanations for these (insignificant) results are presented below.

The results of this paper show that hypothesis 1, which suggests that more competition leads to more greenwashing behavior, is not supported.

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(Eisenhardt, 1989) explains that managers, when facing opposing interests with their investors and owners, may start using organizational decoupling tactics.

Another possible explanation for the insignificant result is that other industry characteristics, such as the type of industry, play a bigger role than the competitive degree in causing or dissuading greenwashing behavior. This thesis found that the level of greenwashing in highly competitive environments is lower in dirty industries. This can be attributed to the high level of public scrutiny in those types of industries. This finding is consistent with that of Marquis and Toffel (2016), Bansal and Roth (2000) and King (2008), who found that more visible firms are subject to more scrutiny which led them to temper their illegitimate behaviors. Further research is needed to discern what other industry characteristics are determinant. As Scott (2001) notes, there are many studies that reveal the existence of organizational decoupling, but few have investigated the conditions under which organizations engage in such activities.

Results showed that the gap between GPI and GCI (i.e. greenwashing) was not significantly influenced by the competitive degree. However, the auxiliary analyses showed that the GCI was significantly influenced and thus that the competitive degree had a positive effect on external communications. In contrast, the analyses showed an insignificant result for the effect of the competitive degree on GPI, which caused the insignificance for the gap. So why did the GCI show a significant effect while the GPI did not? Perhaps because the data for competition was taken from 2016 and the data for the internal practices from 2017. It could be that the one-year gap was not enough to see the effect of competition. For external communications a two-year gap was used as the data for GCI came from 2018. Future research could look into this.

A final explanation for the insignificant result of the first hypothesis has been demonstrated in the additional analyses. When I used the normal value of HHI instead of the dummy variable the effect of competition on greenwashing turned significant. By using a dummy the variation of the variable reduced, which could explain the insignificant effect. Moreover, it could be that the dividing line between a low and highly competitive industry was too stringent. I used an HHI lower than 2500 to describe a high degree of competitiveness, but it could be that an HHI of 15000 would be better as this is still seen as a moderately competitive industry (Hayes, 2020).

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polluting industries (Bansal & Roth, 2000). Firms are afraid of damaging their reputations, which is why they limit their illegitimate greenwashing behavior (Marquis, et al., 2016).

The following section presents an in-depth discussion of the empirical results. First, the theoretical implications are discussed. Thereafter, the practical implications will be discussed. This shows what the research outcome can mean for firms and managers and what can be drawn from it at the practical level. Followed by the limitations of this study and suggestions for future research.

5.1 Theoretical implications

The findings of this study provide a number of important implications for existing research.

Firstly, the importance of competition in relation to CSR is highlighted. Competitive intensity is a fundamental characteristic at the core of industry and market structure, firm conduct and firm performance (Ang, 2008). Thus, the competitive degree plays a pivotal role for a firm when deciding whether they want to engage in CSR or not. This paper highlights a paradox between the advantages and the disadvantages of CSR in a highly competitive environment, which was manifested in literature. On one side, according to the differentiation effect, an increase in competition has a positive effect on CSR as a small advantage acquired by any of the competitors could easily lead to a bigger market share (Auh & Menguc, 2005; Jansen, et al., 2006; Fernandez-Kranz & Santalo, 2010; Candi, et al., 2019). On the other side, the rent dissipation effect shows that an increase in competition has a negative effect on CSR, since the additional costs of investing in CSR would sacrifice the already small margins of firms in highly competitive environments (Campbell, 2007; Fernandez-Kranz & Santalo, 2010). As mentioned above, future research should further look into this paradox.

Secondly, this thesis addresses the knowledge gap regarding the effect of competition on greenwashing. Although literature on the effect of competition on CSR exists, no previous study has investigated the relationship between the competitive degree and greenwashing. The current paper builds upon the literature on CSR and hypothesized that greenwashing is the solution to the paradox mentioned previously. Surprisingly, this hypothesis could not be supported, but more research regarding this relationship is still needed. For instance, with a different measure of competitiveness as the auxiliary regression demonstrated that this makes a difference for the results.

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These studies show that greater visibility leads to reduced illegitimate behavior, such as greenwashing, since they are likely to receive more attention and pressure from the public (Bansal & Roth, 2000; King, 2008; Marquis, et al., 2016). The underlying mechanism for this effect is greater perceived reputational risk. Firms are particularly susceptible to attacks against their image as reputation signals the quality and reliability of a firm’s practices, indicates the overall level of prestige in the market and is what distinguishes a firm from its competitors (King, 2008). I focused on environmental damage as a kind of visibility, as it exposes firms to attention from both regulators and the public (Bansal & Roth, 2000; Bansal & Clelland, 2004; Short & Toffel, 2008; Marquis, et al., 2016). Other causes of visibility, such as firms’ size, reputation and public relations strategy, and their effect on greenwashing need further investigation (Marquis, et al., 2016).

5.2 Practical implications

This thesis provides some valuable insights for practice.

First of all, society and investors are made aware of the fact that firms deliberately pursue misleading communication strategies regarding their environmental practices. This is something they should consider when they make decisions about where to buy or invest. A firm might seem the most responsible green choice but this is not necessarily the case.

Secondly, this thesis sheds lights on the type of industry in which greenwashing is most likely to happen. Dirty industries are less likely to greenwash, because of the high level of public scrutiny. It can therefore be concluded that greenwashing is more likely to occur in industries in which there is less monitoring and visibility of firms. If a firm decides to apply benchmarking they know that they shouldn’t take the external communications of these competitors too literal.

Thirdly, this also provides insights for activist groups and policy makers who want to deter greenwashing. It turned out that public scrutiny really dissuades companies from greenwashing. The reason being that getting caught at it can significantly damage their reputations (Chatterji & Toffel, 2010; Lyon & Maxwell, 2011).

5.3 Limitations and future research directions

These findings may be somewhat limited due to several data limitations.

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Secondly, the sample existed of European firms only, which limits the generalizability of the results. The results are thus to be interpreted with caution in other parts of the world.

Thirdly, the data on competition was taken from 2016 only and data on greenwashing came from just 2017 and 2018, limiting the options for a panel regression. More complete and longitudinal data on competition and greenwashing could help further validate the proposed effects.

Fourthly, although Orbis and Eikon provided detailed information, there was still a lot of data missing. Reporting on R&D and advertising expenses is not obligatory, therefore Eikon had many firms with missing data on these control variables. Orbis missed a lot of financial data of firms and therefore it was not possible to investigate all firms in all industries due to data availability. For example, firms in the “financial and insurance activities” industry missed data on total sales, therefore calculating the HHI was not possible and those firms were removed from the sample.

Finally, future work could also include more industries in the sample as other industries could have shown different outcomes.

Another source of uncertainty is the method of measurement for greenwashing. In their article, Hawn and Ioannou (2016) carefully selected CSR data points, which resulted in a validated method of measurement. However, in this article only the environmental indices were used, which may have had interfered with the reliability of the variable greenwashing. Future research should investigate whether the results would be different if all the ESG indices used by Hawn and Ioannou (2016) are picked.

Another concern is the measure of competition. The competitive degree was derived from the Herfindahl Hirschmann Index, which focuses on firms and their market share. Other complementary measures could have been used for a more complete picture of the competition. The HHI does not take much of the definition of intense competition into account, such as the resource constraints, lack of growth opportunities or strong competitor activities such as price competition and advertising. A suggestion for future studies is to include multiple factors such as uncertainty and dynamism as that would provide more solid insights into the competitive degree and its influence on greenwashing. Furthermore, the HHI was calculated with data on total sales from just the firms that were in the initial Eikon sample. Future research should calculate the HHI using all the firms from the industry as this would give more accurate information on the degree of competition.

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Does the degree of competition in an industry influence firms’ level of greenwashing?