Master Thesis
Greenwashing: The Influence of Product Involvement on
Stakeholder Scrutiny
An Empirical Cross-Cultural Analysis
Daniel M.C. Do S3130460
MSc. BA – Strategic Innovation Management d.m.c.do@student.rug.nl
Abel Tasmanplein 94 B, 9726 EP Groningen
University of Groningen Faculty of Economics and Business Date of Submission: January 18, 2021
Supervisor: prof. dr. J. (Jordi) Surroca Co-accessor: P. (Pere) Arque-Castells, PhD
Table of Contents
Abstract……….3 1. Introduction……….…………..4 2. Literature Review………..….8 a. Institutional theory………….………..8 b. Product involvement…….……….………..9 c. Interference of Culture……….………..12 d. Conceptual Model……….……….14 3. Methodology………...14 a. Data collection………..……….……14 b. Measurements………15 c. Data analysis………..……..………..………19 4. Results……….20 5. Discussion………29 a. Implications………32b. Limitations and Future Research…………...……….32
6. Conclusion………...33
7. References………...35
Abstract
Recently, stakeholders show a higher interest in the environment. Hence, greenwashing which describes an unethical behavior where one pretends to be more environmentally friendly than one is, might be alarming for various stakeholders. This paper investigates scrutiny by proposing product involvement and masculinity to trigger scrutiny through greenwashing. In total three hypotheses have been postulated based on previous literature. The conceptual model has been analyzed with a Random Effects Approach while using a panel dataset consisting of 192 large EU and U.S. firms (2011-2019). A combination of datasets has been used (EIKON, Orbis, LexisNexis & Hofstede Insights) to compile the aforementioned sample. As previous papers have mentioned, this study confirms that greenwashing has a positive effect on stakeholder scrutiny. Even though only Hypothesis 1 was supported, the insignificance of the remaining Hypotheses progresses the under-researched product involvement and culture domain on the subject of Greenwashing. Consequently, this thesis paper is contributing to the immature understanding of scrutiny. According to the existing literature, triggers for stakeholder scrutiny seem to be fully understood. Yet this study introduces a new trigger, namely product involvement which in combination with high masculinity shows negative results on scrutiny.
1. Introduction
Environmentally friendly as well as social actions also referred to as “Corporate Social Responsibility” (CSR) have been a subject of awareness in recent decades (Loungee & Wallace, 2008). For instance, very recent movements such as the “Fridays for Future” initiated by Greta Thunberg (FridaysForFuture, 2020), are just underlining the fact that concerns regarding the environment and especially in terms of environmental responsibility of companies has risen drastically (Akturan, 2018). Therefore, firms nowadays tend to appear green because legal boundaries for such ethical behavior barely exist (Delmas & Burbano, 2011). Thus, CSR actions that claim to be environmentally friendly (Parguel et al., 2015) not only please the stakeholders of a company positively but also sculpt a company image or simply reputation (Du et al., 2010). Such false claims are perceived as Greenwashing (Vos, 2009). The price paid for this unethical behavior is external pressure or rather stakeholder scrutiny (Delmas & Burbano, 2011).
claims trigger research and information gathering of the respective company (Leonidou & Skarmeas, 2017). Consequently, public scrutiny is resulting from such false claims (Barnett & King, 2008) which is shortly describing the increased rate of “information-seeking behavior” (Akturan, 2018, p. 810). Subsequently, stakeholders are laying trust in e.g., advertisements that state positive-sounding keywords such as sustainable or earth-friendly. Nevertheless, disinformation as such, hence intentionally misleading individuals, is diminishing the trust among stakeholders and this is consequently leading up to “[..] negative market reactions” (Du, 2014, p. 584). In such cases, stakeholders become investigative or rather scrutinize a company and if found guilty of greenwashing this leads to negative market reactions like reputational issues, negative attitudes towards the company, purchase intention problems (Nyilasy et al., 2014, Lyon & Maxwell, 2011).
Accordingly, current research suggests that in essence that if firms are greenwashing this will lead to increased stakeholder scrutiny (Marquis et al., 2016). Other triggers to affect stakeholder scrutiny have also been the firm size (Udayasankar, 2007). Hence, rather big companies would be scrutinized more when doing wrong. Therefore, current research portrays stakeholder scrutiny as a rather fully understood field covering triggers of scrutiny like the visibility of a firm or level of maturity (Brower & Mahajan, 2013). Nevertheless, these studies have only shed a light on firm characteristics, whereas scholars such as Kong and Zhang (2013) have urged the need for the inclusion of product categories to green stakeholder response studies. Indeed, corporate social responsibility studies of product categories regarding to stakeholder responses have been undertaken by for example Montoro Rios et al. (2006) or Mainraiet al. (1997). However, studies as such have covered one product category only, such as e.g., shampoos, PCs, deodorants (Kong & Zhang, 2013). While conducting research, it has been found that Ahmed et al. (2002) determined that consumer evaluation criteria such as the product of a given company affect the cognitive evaluation process and hence affecting scrutiny. Additionally, the communication studies field found product types or rather the involvement level of products to be important in terms of a factor why someone considers buying a product (Kong & Zhang, 2013).
response to a product category” (Quester & Lim, 2003, p. 24). Additionally, and to be more specific this could translate to a e.g., Apple (brand) or e.g., mobile phone (category), (Quester & Lim, 2003). Low-involvement products refer to a rather simple purchase decision, where the consideration is usually linked to its affordable price, whereas high-involvement products are the opposite, therefore, a sophisticated mental examination has to take place prior to the purchase (Cambridge, 2020). A high-involvement product (e.g., smartphone, tablet,etc.) not only confirms the latter but the purchase motivation is also linked to “(the) symbolic meaning, image reinforcement ,or psychological satisfaction” (Radder & Huang, 2007, p. 233). Hence, there has been little to no research in terms of scrutiny by stakeholders being dependent on the involvement-level of a product. Particularly important is the fact that scholars (Lin & Chen, 2006; Bauer et al., 2006; Brisoux & Cheron, 1990) identified which factors are actually affected by the involvement level of a product. Such factors were determined as product adaption timing, the decision-making process of a stakeholder, and especially information-seeking behavior of a product, hence scrutiny. Likewise, the latter was drawn-out by Zaichkowsky (1985) who stated that a high-involvement product would lead to the effect of a search of background information on such a product. Additionally, previous studies have mainly focused on the relation between the involvement level and the perceived risk (e.g., own reputational damage) that one would obtain which was referred to as consumer risk (Dholakia, 2000). Therefore, the product level involvement would then essentially affect one’s behavior, therefore potentially scrutinize a company.
greenwashing perception scenario. Therefore, the following will explore the consecutive research question:
“How does product involvement affect general stakeholder scrutiny towards Greenwashing?”
This study is contributing to the extensive current literature stream in the greenwashing field. Having examined the existing literature in the greenwashing subject matter, the research on stakeholder scrutiny under the influence of product types has yet been under-researched. The respective literature has assumed that due to stakeholder scrutiny the discovery of greenwashing will lead to punishment. Consequences include for instance reputational damage. Consequently, this thesis proposes that product involvement is triggering scrutiny from stakeholders and therefore context-dependent. Additionally, the moderating effect of masculinity has not been brought up in relation to stakeholder scrutiny and greenwashing. To that end, this research question will enrich the recent Greenwashing theme in terms of scrutiny from stakeholders towards firms that engage in Greenwashing. Besides, the differentiation between a low and high involvement product will highlight differences regarding scrutiny and the impact of cultural differences will, therefore, add to the understanding of the mechanisms which link greenwashing and scrutiny.
2. Literature review
a. Greenwashing through an institutional lens
Without a doubt, greenwashing stood its ground in the last decades and it should be noted that such practice has been widely used as a counteraction towards pressure from stakeholders (Du, 2014). Social and cultural pressures (Scott, 1995) towards a company’s actions usually fall under the so-called institutional theory. More specifically, DiMaggio and Powell (1983) have introduced the so-called institutional isomorphism which set a starting point for institutional theory research. In particular, it encompasses pressure from stakeholders of the civil society which have to be anticipated or rather satisfied in order to maintain business (Oliver, 1991). Preceding work in the respective research field has had a focus on governmental stakeholders seeking to decrease greenwashing activities (Marquis et al., 2016). Nevertheless, less focus has been shed on the impact of the broader public such as the “civil society” (Marquis et al., 2016, p. 484) and its influence on a corporation’s symbolic conduct. In fact, this “civil society” tends to criticize companies of their own interest in order to address relevant subjects to them such as e.g., environmental issues, misconduct, or labor conditions (McDonnel & King, 2012). King and Pearce (2010) advanced the previous by highlighting the fact that such social movement is used as a platform to portray their own views. Essentially, social activism is used to reach specific targets such as letting a company produce less waste.
As previously mentioned, stakeholders being investigative towards symbolic practices and therefore scrutinizing companies guilty of greenwashing an eminent phenomenon nowadays (Marquis et al., 2016). Scholars such as Bansal & Roth (2000) voiced evidence that scrutiny resulting from stakeholders is the case when a company’s visibility is greater. Having that said it is of utmost importance to state DiMaggio and Powell’s (1983) research which concluded that business entities generally match their social behavior with the demands of stakeholders. This is due to the fact such a business has to cultivate its legitimacy in the market (Scott, 1995). Therefore, once concerned about one’s own legitimacy, a company will undertake relevant socially-expected measures (Deephouse, 1999).
disclosure method (Deegan & Rankin, 1996), simply because “[…] the truth (is told), but not the whole truth” (Lyon & Maxwell, 2011, p.9). On the grounds that companies commit manipulation regarding their image improving practices, Marquis and Toffel (2012, p.1) specified such procedure as “subjected to civil society(‘s) scrutiny”. The term scrutiny itself refers to a continuous as well as investigative behavior of observation aimed at the respective company (Sutton & Galunic, 1995). Accordingly, companies merely state what is necessary to satisfy the demands of stakeholders that one may compress the extent of stakeholder scrutiny (Marquis & Toffel, 2012). Following the research of Gershoff and Frels (2015), the so-called green attributes were crystalized as crucial factors when stakeholders evaluate products in terms of greenness. Consequently, in the scenario that organizations only disclose a fraction of such green attributes also referred to as selective disclosure, one is in all likelihood to scrutinize the respective company (Marquis & Toffel, 2012). Moreover, scrutiny is occurring not due to the fact that the firm has a certain reputation, but it is rather about the fact that the wide majority of the public has access to it (Bansal & Roth, 2000). On account of the previous, the following Hypothesis can be stated as:
H1: Greenwashing has a positive effect on stakeholder scrutiny so that Stakeholder scrutiny
increases.
b. Greenwashing and product types
As prior research presents, lately, there is a higher green awareness amongst individuals. In particular, an increasing amount of people have been paying close attention to the fact that various products might have a negative effect on the environment (Kong & Zhang, 2013). In fact, marketing psychologists have given product involvement, attention as an explanatory variable in the research area regarding consumption behavior to sort of explain the reasoning behind the purchase of a low or high involvement product (Dholakia, 2000). Thus, so far, the product involvement level has only been a variable relevant to consumption, purchase intention or brand loyalty studies such as in Traylor (1981).
involvement have to be explained. These are defined by two levels, namely low involvement and high involvement. The former refers a process of little to no thought when buying a product such as buying an interchangeable product such as mustard. The latter however implicates that the product is of importance but is not bought frequently, hence more sophistication in involved (Principles of Marketing, 2010). In the past, the involvement level has been a critical advertising strategy component but more importantly has been a factor for differences in green perception (Kong & Zhang, 2013). In order to clarify the prior, one has to mention the psychological background behind such product involvement type. To be more specific, one has to see how stakeholders’ responses towards Greenwashing can be explained psychologically. This process of information gathering, and its cognitive transformation has been subject of the so-called elaboration likelihood model which is also referred to as ELM and has been developed by R. Petty and J. Cacioppo in 1983. The ELM simply illustrates the cognitive process of transmitting information up until the persuasion stage, where one has a final judgement about a product. To be more specific stakeholders can pass two stages while considering products, namely the peripheral or central track. The former refers to the fact that the cognitive process of persuasion is merely based on very obvious or rather transparent information (e.g., visual advertisements in a supermarket), whereas the latter (central track) is established around a rather complex and sophisticated reasoning. The central track includes information gathering based on one’s own values, beliefs but also simply goes beyond the first-instance information seeking like in a supermarket. Thus, a fairly high level of involvement is associated with a central track persuasion, while a low involvement degree is considered to be a peripheral course of action (Petty et al., 1983). Therefore, the involvement level will decide which cognitive route one takes and consequently this will result in a change of attitude towards the respective greenwashing company (Nyilasy et al., 2014). Consequently, a high-involvement product is assumed to trigger more investigative behaviour.
product, information and declarations which were communicated about the product seem to be meaningful or rather influential to the stakeholder, whereas for low involvement-products the aforementioned information is a “peripheral cue” (Akturan, 2018, p. 812) not captivating a communication-stream between both parties. Therefore, “a particular product category may be more or less central to people’s lives, their sense of identity, and their relationship with the rest of the world” (Kong & Zhang, 2013, p. 431).
Referring to the aforementioned fact that stakeholders are told that certain products are advertised as being green and environmentally friendly, there is an extensive volume of stakeholders which hover over the assumption that untruthful communication from the business side is the case (Atkinson & Rosenthal, 2014). To continue, Moussa and Touzani (2008) added to the discussion that the problem here is mainly related to the fact that credibility as well as trust are severely affected.
As stated by in Rothenhoefer (2019) who evaluated stakeholder’s perception on corporate social responsibility (CSR) that CSR is a way to respond to the society’s social pressures and therefore show therefore demonstrating non-ignorance towards the public (Caroll & Shabana, 2010). Indeed, when following O’Mara-Shimek et al.’s (2015) line of reasoning, one’s perception of a company is complementary to an individual’s evaluation of the moral condition a company pursues. Hence, how a stakeholder perceives a company is rather subjective. To be more specific, the perception process could be explained through a psychiatric model which basically creates boundaries and triggers mechanisms in one’s mind (Bauman & Skitka, 2012). Scholars have highlighted a very specific theory/model which is appropriate to describe the previous process (Rothenhoefer, 2018; Skowronski & Carlston, 1987 & 1989). The so-called category diagnosticity approach describes how key signs of either negative or positive manners have an effect on one’s perception about a respective company (Walker, 2010). This implies that one’s perception about Greenwashing is subject to an individual’s cognitive/psychological boundary (Rothenhoefer, 2018). Hence, stakeholders tend to look for accessible information in order to make a judgement that is in accord with their values (Chaiken et al., 1989). Basically, individuals collect and diagnose obvious information first to form an opinion e.g., about a company, if this information is not enough due to their own values further information is needed (Suh & Yi, 2006). Hence, the more information is needed to have a satisfying evaluation, the higher the product involvement level (Zhou et al., 2012). Therefore, the diagnosticity-framework is also applicable to this study.
and cry greenwashing at every turn” (Furlow, 2010, p. 2). Taking this fact one step further, Zimmer et al. (1994) even specify that such misleading claims about the environmental behavior of the respective company just pervade the market further. Consecutively, this has the effect that the extent of how sustainable or rather green a product is, will be more in question. Thence, one of the very prominent reasons why one would scrutinize a company based on the involvement level of a product would be the risk which could be drawn to one’s life after the purchase. This so-called “perceived risk” (Dholakia, 2000) could e.g., translate to reputational damage through peers caused by buying a very unsustainable product. In a more general sense, perceived risk describes the unpredictable event emerging from a purchase decision of a product (Bauer, 1960). It follows that scholars such as Jacoby and Kaplan (1972) highlighted the risk of social risk as in the example above which could cause some type of regret or worry among stakeholders (Perugini & Bagozzi, 1999). Notwithstanding, Dholakia (2000) highlighted in his paper about risk perception that indeed social risks would trigger an extensive cognitive mechanism evaluating information related to the particular product. Consequently, this research expects a moderating effect caused by the type of product involved and therefore the following hypothesis is formulated:
H2: A High-involvement product is moderating the base-line relationship positively, so that
Greenwashing has a negative effect on Stakeholder scrutiny .
c. The influence of cultural values
rather “environmental concern” (Antonides and Van Raaij, 1998) is indeed an individual approach. As a result, it can be said that each individual stakeholder is differing in terms of environmental concerns. In fact, one’s national culture is influencing how a company is being perceived in terms of greenness, based the own ethical values (Leonidou et al., 2012).
In particular, national values such as the aforementioned materialism have been subject in the national culture framework of Hofstede (1983). In total it encompassed five main dimensions, namely (1) Power distance, (2) Individualism (+ collectivism), (3) Femininity & Masculinity, (4) Uncertainty avoidance, (5) Long-term orientation (Hofstede, 2001). Not only do these dimensions grant invaluable insights of a country’s culture, yet they also have been a reliable predictor in cross-cultural studies in the environmental field such as Christie. et al. (2003). Especially, one dimension, namely masculinity, is in particular interesting due to the fact that it is intertwined with materialism (Burnasheva et al., 2019). More specifically, masculinity describes the desire for materialism, success, (materialistic) achievements as well as competition (Crotts & Erdmann, 2000). In that respect, it was added by Odgen and Cheng (2011) that the aforementioned dimension is related to a “high degree of materialism”. Therefore, national cultures which score high in the masculinity dimension praise status symbols to a higher extent than low masculinity scores or rather referred to as femininity (Burnasheva et al., 2019). For example, one that scores higher on the masculinity dimension perceives luxury items as important to express their wealth. Such a luxury product could be for example electronic consumer goods which fall under the categorization of a high-involvement product due to the fact that it represents status, “[…] symbolic meaning, image reinforcement or psychological satisfaction” (Radder & Huang, 2007, p. 235). On account of the previous, it could be said that a high-involvement product with a bad green perception (greenwashing) could affect one’s own image negatively. A negative image might affect people from a high scoring masculine country more due to the fact that their status and/or reputational image could be damaged (Swaidan, 2012) by buying a product with a bad green perception of others. Additionally, countries belonging to the high masculinity spectrum are affected by unethical behavior and therefore discipline such companies through more scrutiny (Ramasamy &Yeung, 2009). Thereupon, the following hypothesis is postulated:
H3: The degree of Masculinity of a country positively moderates the relationship between
d. Conceptual model
A conceptual model is given below to visualize the above-mentioned and goes as follows:
Figure 1. Conceptual model
3. Methodology
a.
Data collection
In order to analyze such, this quantitative study has made use of an initial sample of a total of 5966 international companies. Moreover, this sample consisted of manufacturing industries in order to assess the product level measure in an appropriate manner. Hence, this study had to select deeper roots that are based on the aforementioned product involvement definitions. Therefore, the food, tobacco and regular textile manufacturing industry were selected for low-involvement products due to the fact that these products are given little thought about when bought (Richins & Bloch, 1986). As for high-involvement products industries that produce vehicles and high-tech consumer devices have been appointed because such products are given considerate thought before the purchase (Huper & Gardner, 1971; Richins & Bloch, 1986). Therefore, the ISIN codes have been extracted from Orbis, a large databank for company- specific data provided by our institution. Plus, the data for environmental measures were derived from EIKON which is an extensive databank provided by Thomson Reuters.
Furthermore, the data bank offers a substantial amount of data on the so-called ESG data (Environmental, Social and Governance). Its data is conscientiously obtained from various sources of information such as CSR reports, annual reports or generally available viewpoints (Thomson Reuters, 2020). This category covers data from ten themes such as human rights, emissions and policies. (Refinitiv, 2020). Hence, EIKON’s provided data will be used to establish the greenwashing variable, therefore its data is appropriate to demonstrate environmental controversies. Moreover, stakeholder scrutiny is extracted from Lexis Nexis, a well-known databank for journalistic as well as legal matters. And lastly, the moderator variables are provided by Orbis (Bureau van Dijk) for product involvement through the selection of the primary industry and products. And Hofstede’s cultural dimensions are freely available on Hofstede Insights (2015). Lastly, all respective data has been collected for a timespan between the 1st of January 2011 up until the 31st December 2019. Following the
previous, after cleaning the data sample, this study resulted in 192 companies on which data was available for the stated period.1
b. Measurements
Dependent variable: Stakeholder Scrutiny
This study’s dependent variable is stakeholder scrutiny which in simple terms describes the process of information seeking in a very cautions and analytic way (Cambridge Dictionary, 2020), even resulting in activism (Marquis & Toffel, 2012). For this study, it has been decided to use LexisNexis as a base to measure stakeholder scrutiny. This is due to the fact that the media itself has been mirroring firms guilty behaviour of greenwashing (Delmas & Burbano, 2011). More importantly, the authors added that the amount of greenwashing of companies is bringing more media scrutiny. Especially, industries used in this sample which produce consumer goods have a high tendency to be scrutinized publicly, so that the consumer side gains more information behind the product (Meznar & Nigh, 1995). Hence, each company has been manually searched up, while selecting popular news outlets such as the DPA, Federal US News and Newstex. Therefore, it has been counted how often a company was mentioned for each year. This is a much respected and validated approach to measure social
1 Appendix 2 offers an overview of the included countries (incl. masculinity values) as well as further
movements/concerns following scholars in such field (McDonnell & King, 2013; McAdam & Su 2002; van Dyke et al., 2004).
Independent variable: Greenwashing
In addition to the above stated, this study determined greenwashing as an independent variable which is measures the degree of potentially untrue published information of a company (Kayser et al., 2014). Indeed, the process of greenwashing itself could refer to a form of selective disclosure (Delmas & Burbano, 2011) which articulates that companies “[…] disclose relatively benign impacts, creating an impression of transparency while masking their true performance” (Marquis et al., 2016, p. 483). Hence, this behavior is resulting in a disparity between the disclosed information and the actual behavior. Therefore, in order to measure greenwashing, this study decided to partially adopts Hawn and Ioannou’s (2016) approach of measuring greenwashing. Their approach highlighted the internal as well as external CSR habits in order to establish the difference among them which is referred to as greenwashing. Consequently, the Refinitiv database (EIKON) has been used and accessed through our university’s license to Thomson Reuters Eikon database. In fact, Refinitiv is a very reliable and hence a validated database for the Corporate Social Responsibility field of interest by various scholars such as Cheng et al. (2013).
variables, whereas the internal CSR scores have been divided by the internal CSR variables. This has been done for each year. Finally, the external CSR results have been subtracted from the internal CSR results. This procedure has given the final Greenwashing variable.2
As for the moderation variables, this study has established for one the product type, namely the involvement level of a product (high vs. low involvement) and for two a cultural framework such as Geert Hofstede’s cultural dimensions.
The product involvement level was measured by categorizing the sample to specific industries which are following one major product stream. For instance, as for Mercedes-Benz it could be stated that they not only produce high-involvement products such as their cars but also apparel such as t-shirts and hoodies which refer to low-involvement products. Yet, this study has decided that each company is assigned to one major product line, hence Mercedes-Benz is only producing high-involvement products such as cars, therefore belonging to the automotive industry. The major product line has obtained from Orbis, carefully read and manually assigned to the respective product level. The industries selected in this study include merely very large (with recent financial data) manufacturing companies which deal either with textile/food/tobacco production (low-involvement) and vehicle/consumer electronics (high-involvement) all retrieved from Orbis. A binary variable has been created to differentiate both involvement levels, hence 1 translates to high-involvement product, whereas 0 stands for low-involvement product.
Geert Hofstede’s cultural dimensions are based on an extensive pool of employees of the mother firm (IBM) as well as the respective subsidiaries (Hofstede & Bond, 1984). In total the cultural dimensions model is consisting of currently six dimensions, namely: Power Distance, Individualism vs Collectivism, Masculinity vs Femininity, Uncertainty Avoidance Index, Long Term vs Short Term Orientation and lastly, Indulgence vs Restraint (Hofstede Insights, 2020). Nevertheless, this study is aiming to explain differences in stakeholder scrutiny based on the fact how stakeholders perceive greenwashing in their eyes. Hence, the cultural dimension “Masculinity versus Femininity (MAS)” is most applicable to this study. Masculinity itself describes the eagerness in within a culture to attain success in distinct appearances such as materialistic success or heroism. In fact, a very masculine defined culture is indeed excessively
ambitious when it comes to competitiveness. On the contrary, femininity is defined by a society which is rather focused on cooperative instincts and most importantly finding a common ground (Hofstede Insights, 2020). As a consequence of the previous, the masculinity versus femininity dimension (MAS) is obtained for each respective country from Hofstede Insights (2020). Scores are scaled among a 0 to 100 scale.
Control variables
In order to control for other effects which might be affecting the conceptual model above, it has to be controlled for the following control variables.
This study therefore controls for the logged firm size which has been measured by taking into account the total number of employees which has been logged. Such a method has been used by Berrone et al. (2017). Accordingly, firm size has been used by various scholars to control for side effects in greenwashing studies with social concerns due to the fact that there is a potential that a rather small firm might have fewer social ties compared to a larger one (Pfeffer & Salancik, 1978; Berrone et al., 2017).
It is also controlled for the Research and Development intensity. Hawn and Ioannou (2016) have used this logged control due to the fact that it might highlight effects of intangible outcomes. The variable itself was derived by Research & Development expenditures over the overall sales of that respective year. Relevant data has been obtained from Orbis.
Furthermore, in order to control for the growth opportunities of a firm, this study has also used the so-called market-book-ratio such as Penman (1996). This has been calculated by dividing the market capitalization by the total book value of a firm, per year.
c. Data Analysis
This study’s model is anticipating the effect of greenwashing on stakeholder scrutiny while being moderated by two variables, namely: product involvement level, hence the product type and culture (masculinity).
The data analysis chapter will take into account the aforementioned hypotheses by analyzing each liaison individually by making use of a statistical analysis software, namely STATA SE. Moreover, in order to test the hypotheses depicted above, this research will make use of a panel regression analyses to model the relationship between the independent and dependent variable and its binary variables (Nimon & Ostwald, 2013). Therefore, the so-called “Random Effects Model” or also known as Partial pooling method will be used in order to analyze the balanced panel dataset due to the fact that this study also expects variation from industries on the dependent variable, hence a random effects model is applicable (Torres-Reyna, 2007). Regardless, merely the fact that there is a certain expectation is not enough, therefore a Hausman-test has been performed. Doing so will aid the decision to choose between the fixed and random effects model (Greene, 2008; Chapter 9). Indeed, the Hausman-test has confirmed the previous assumptions. Consequently, this paper states the following regression formulas: Stakeholder scrutinyit+1 = β0 + β1t Control Variables + α + uit + εit
Stakeholder scrutinyit+1 = β0 + β1t Greenwashing + β2t Control Variables + α + uit + εit Stakeholder scrutinyit+1 = β0 + β1t Greenwashing + β2t Involvement level + β3t Greenwashing x Involvement level + β4t Control Variables α + uit + εit
4. Results
Descriptive summary and correlation matrix
The tables below portray each variable measured per each individual year (t) for the respective time-period of 2011 up until 2019. In total the final sample consists of 192 firms belonging to the following eight sectors:
Table 1. Specified industries and sectors
The descriptive statistics (table 2) includes the standard deviation, mean as well as maximum and minimum of each variable used in the study. In total the panel data consists of 1728 observations for the timeframe of 2011 until 2019, hence 192 firms. On average a company is mentioned 2197 times per year in regard to ethical misconduct. Obviously, this
3 The three-digit SIC-code in paratheses describes the subsectors included in the sample e.g., poultry meat
production, canned food etc. are subsectors and belong to “Food and kindred Food”.
Sector SIC3 Subsectors N in sector
1. Food and kindred Food 200-209 (201-209) 8 46
2. Tobacco products 210-214 (211-213) 2 7
3. Textile products
4. Apparel and other textile products
5. Industrial Machinery and Equipment 222-229 (223-229) 230-239 (232-233) 350-359 (357) 2 2 1 13 3 8
6. Electronic and other electric equipment
360-369 (362-369) 7 38
7. Transportation equipment 8. Instruments and related
differs per firm, resulting in a maximum of 67659, following a standard deviation of 6011. Therefore, the natural logarithm has been taken to measure Stakeholder scrutiny. Firm size is on average 8.8, with a minimum of 2.9 and a maximum of 12.8. The return on assets has an average of 4.5 and -87.1 as a minimum, whereas 66.1 is the maximum. Research and Development intensity has a mean of -3.2, a minimum of -10.4 and a maximum of 1.1. Lastly, the market to book ratio shows a -5.7 and a minimum of -8.6, maximum equals 0.
Table 2. Descriptive Statistics*
Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
Scrutiny Media 1728 2196.945 6011.051 0 67659
Scrutiny log (lead) 1727 6.022 2.005 0 11.122
Greenwashing 1728 28.516 20.21 -3.235 73.235
Involvement level 1728 .484 .5 0 1
Masculinity 1728 54.839 18.686 5 79
Firm Size (log) 1728 8.842 1.797 2.944 12.765
RoA (log) 1728 4.508 11.801 -87.14 66.113
R&D intensity (log) 1728 -3.236 1.924 -10.436 1.074
MBR (log) 1728 -5.733 1.297 -8.601 0
Correlation Matrix
In terms of multicollinearity, the correlation matrix, which is shown in table 3, has been checked for any value above the threshold of 0.7 since above that value multicollinearity is present which is described by two strongly correlated independent variables (Dormann et al., 2013). Prior to a regression analysis this has to be checked due to difficulty in interpretation when ignoring multicollinearity (Graham, 2003). Later in the analysis part it has been picked up on this concern again and therefore it has been checked for the so-called Variance Inflation factor or VIF. This has been executed for the independent variables of this study, while keeping a close eye on the limit of the value of 10 (see appendix). If any VIF value goes beyond the value of 10, it can have the potential of posing a multicollinearity issue (O’Brien, 2007). Nevertheless, this has not been an issue in this study.
Regression results
Prior to the analyses of the dataset, a so-called lead variable was created in order to “lag” Stakeholder scrutiny for one year respectively, hence T+1. In addition, the sample has been split into two sub-samples, based on the median of the culture variable Mas (masculinity). The respective median was 62. Therefore, the low-masculinity sub-sample consists of everything below 62, including 62, whereas the high-masculinity sub-sample encompasses data above 62, excluding 62. Moreover, six regression models have been created for all samples which were stated before. To recall, three hypotheses were proposed previously. For one, model 1-4 covers Hypothesis 1 which stated that Greenwashing has a positive effect on Stakeholder Scrutiny, for two model 4-6 covers Hypothesis 2 which stated that high-involvement products are moderating the relationship from H1 positively (increasing Stakeholder Scrutiny). Due to the fact that a triple-interaction term is rather difficult to interpret for Hypothesis 3, both sub-samples will be compared based on model 4-6 again. The respective Hypotheses (H3) stated that Masculinity positively moderates the base-line relationship between Greenwashing and Stakeholder Scrutiny, in a sense that scrutiny increases.
Accordingly, Model 1 and Model 2 (table 4) is replicating the simplest model which includes all control variables, namely Firm size (log), RoA, R&D intensity (log) as well as MBR (log) and its effect on the T+1 lagged dependent variable Scrutiny (log) lead. The only difference between Model 1 and Model 2 is that for the latter, industry binary variables were not included due to the fact that it might be the case that the inclusion of industry dummies could absorb the variability explained by the level of product involvement. In fact, this procedure has been undertaken for every second model (Model 2, Model 4, Model 6). Additionally, Model 1 and 2 show that only Firm size (log) significantly influenced Stakeholder scrutiny with a !-value of 0.111 (Model 1) or 0.110 (Model 2) at a 0.001significance level. The F-statistic was manually computed by dividing the Wald-Chi2 by its degree of freedoms. Thence, the respective value for Model 1 (F=18.95, p<0.001, R2=0.1866) and for Model 2 (F=29.47, p<0.001, R2=0.1846).
terms of model significance, it can be stated that the model is significant (F=20.30, p<0.001, R2=0.2044). Excluding the industry dummy variables led to an overall statistically significant model as well (Model 4: F=30.68, p<0.001), R2= 0.2026). Taking both models into account, it can be stated that Hypotheses 1 is supported. Therefore, for each increase in greenwashing of company, scrutiny will increase by 0.0130. The more employees a company has will have a positive effect on this as well, hence increasing scrutiny.
Furthermore, the last two models, namely Model 5 and 6 are the most complete models which include the control variables, Greenwashing and additionally the interaction effect between Greenwashing and the Involvement level. This interaction effect is shown as Greenwashing*Involvement level. Consequently, the Greenwashing (0.0137, p<0.01) and the control variable Firm size (log) (0.101, p<0.01) are again significant for Model 5. Both variables are also significant for Model 6, where Greenwashing corresponds to (0.0138, p<0.01) and Firm size (log) to (0.0984, p<0.01). Overall, both models are significant given F being equal to 18.52 (p<0.001, R2=0.2045) for Model 5 and Model 6 (F=27, p<0.001, R2=0.2028). However, Model 5 and 6 show a negative non-significance for the interaction-term (!=-0.00156, p>0.01), suggesting that the level of involvement has no significant effect on the base relationship, namely that the degree of Greenwashing has a positive effect on Stakeholder Scrutiny and therefore increases it. As a result, the statistical analysis has shown no support for Hypothesis 2 and hence will be rejected accordingly.
significant, regardless of which subsample was in use (table 5: Model 5, F=13.08, p<0.001, R2=0.2125/ Model 6, F=19.40, p<0.001, R2=0.2122; table 6: Model 5, F= 6.86, p<0.001, R2=0.2237, F=9.26, p<0.001, R2=0.2191). Accordingly, Hypothesis 3 is not supported either, yet the statistical outcome shows a contrary influence on the dependent variable. Here, Stakeholder scrutiny decreases as soon as the involvement level as well as high masculinity interfere with the baseline relationship. Hence, a high involvement product as well as a high masculinity will essentially decrease scrutiny from Stakeholders.
Lastly, in terms of yearly effects it can also be seen that from 2014 onwards the !-values are increasing steadily, while being significant. This suggests that Stakeholder scrutiny increases respectively each year. Nevertheless, none of the industry binary variables were significant, regardless of which model this study posted.
t statistics in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001 * Df after Wald Chi2 value in parentheses
(1) (2) (3) (4) (5) (6)
Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Firm Size (log) 0.111*** 0.110*** 0.102*** 0.101*** 0.101*** 0.0984***
(5.61) (5.58) (5.22) (5.18) (5.16) (5.03) RoA (log) -0.00490 -0.00510 -0.00453 -0.00471 -0.00443 -0.00449 (-1.86) (-1.94) (-1.74) (-1.81) (-1.70) (-1.72) R&D (log) 0.0223 0.0215 0.0232 0.0225 0.0233 0.0228 (1.36) (1.32) (1.44) (1.40) (1.45) (1.41) MBR (log) 0.0429 0.0431 0.0396 0.0397 0.0391 0.0391 (1.75) (1.76) (1.63) (1.64) (1.61) (1.61) Year (dummies) Included Included Included Included Included Included Industry (dummies) Included Not Included Included Not Included Included Not Included
Greenwashing 0.0130*** 0.0131*** 0.0137*** 0.0138*** (6.22) (6.26) (5.21) (5.28) Involvement level 0.196 0.448 (0.60) (1.79) Greenwashing * Involvement level -0.00156 -0.00164 (-0.43) (-0.45) Constant 4.912*** 4.878*** 4.380*** 4.443*** 4.315*** 4.248*** (7.83) (18.08) (6.99) (16.10) (6.79) (14.30) N 1727 1727 1727 1727 1727 1727 R2 0.1866 0.1846 0.2044 0.2026 0.2045 0.2028 Wald Chi2 379.06 (20) 353.59 (12) 426.31 (21) 401.22 (13) 426.39 (23) 405.02 (15) F-statistic 18.95 29.47 20.30 30.86 18.52 27.00
(1) (2) (3) (4) (5) (6) Scrutiny log
(lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Firm size (log) 0.0958*** 0.0943*** 0.0901*** 0.0889*** 0.0897*** 0.0877***
(0.0230) (0.0229) (0.0226) (0.0226) (0.0227) (0.0226) RoA (log) -0.00391 -0.00399 -0.00364 -0.00372 -0.00365 -0.00367 (0.00297) (0.00297) (0.00292) (0.00292) (0.00293) (0.00292) R&D intensity (log) 0.0289 0.0261 0.0300 0.0275 0.0302 0.0278
(0.0235) (0.0234) (0.0231) (0.0230) (0.0231) (0.0230) MBR (log) 0.0336 0.0334 0.0337 0.0330 0.0345 0.0340
(0.0287) (0.0285) (0.0282) (0.0281) (0.0282) (0.0281) Year (dummies) Included Included Included Included Included Included Industry (dummies) Included Not Included Included Not Included Included Not Included
Greenwashing 0.0158*** 0.0162*** 0.0144*** 0.0147*** (0.00262) (0.00261) (0.00332) (0.00331) Involvement level 0.186 0.231 (0.426) (0.311) Greenwashing * Involvement level 0.00299 0.00313 (0.00443) (0.00442) Constant 4.912*** 5.088*** 4.043** 4.530*** 4.128** 4.423*** (1.477) (0.321) (1.466) (0.329) (1.474) (0.360) N 1178 1178 1178 1178 1178 1178 R2 0.1872 0.1870 0.2121 0.2117 0.2125 0.2122 Wald Chi2 255.80 (20) 242.38 (12) 300.15 (21) 288.68 (13) 300.89 (23) 290.66 (15) F-statistics 12.79 20.20 14.29 22.21 13.08 19.40 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 * Df after Wald Chi2 value in parentheses
(1) (2) (3) (4) (5) (6)
Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Scrutiny log (lead) Firm size (log) 0.180*** 0.183*** 0.168*** 0.171*** 0.168*** 0.166***
(0.0432) (0.0431) (0.0439) (0.0438) (0.0439) (0.0438) RoA (log) -0.00783 -0.00772 -0.00783 -0.00772 -0.00670 -0.00665 (0.00726) (0.00724) (0.00725) (0.00722) (0.00722) (0.00719) R&D intensity (log) 0.0151 0.0157 0.0159 0.0165 0.0160 0.0170
(0.0213) (0.0213) (0.0213) (0.0213) (0.0211) (0.0211) MBR (log) 0.0663 0.0668 0.0599 0.0605 0.0442 0.0459
(0.0512) (0.0510) (0.0513) (0.0511) (0.0513) (0.0510) Year (dummies) Included Included Included Included Included Included Industry (dummies) Included Not Included Included Not Included Included Not Included
Greenwashing 0.00533 0.00500 0.0116** 0.0114** (0.00356) (0.00356) (0.00427) (0.00426) Involvement level 0.355 0.848* (0.526) (0.431) Greenwashing * Involvement level -0.0172** -0.0171** (0.00644) (0.00641) Constant 4.004*** 4.111*** 3.838*** 3.989*** 3.460*** 3.607*** (0.806) (0.542) (0.814) (0.549) (0.834) (0.569) N 549 549 549 549 549 549 R2 0.2077 0.2024 0.2105 0.2055 0.2237 0.2191 Wald Chi2 139.79 (19) 126.79 (12) 142.36 (20) 129.14 (13) 150.88 (22) 138.92 (15) F-Statistics 7.36 10.57 7.12 9.93 6.86 9.26 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 * Df after Wald Chi2 value in parentheses
5. Discussion
Overall, this study has using institutional theory in order to describe the relationship between stakeholders, demands and Greenwashing clearer. Having that said, to recall, this paper’s main concern, namely: “How does a low vs high involvement product affect general stakeholder scrutiny towards Greenwashing?”, has yet to be answered. Consequently, a perfectly balanced panel-dataset has been composed, consisting of eight industries (see above),192 firms (North American and EU countries) and consisting of data from eight years (2011-2019), respectively. Data has been collected from multiple data portals such as EIKON, Orbis, LexisNexis and Hofstede Insights. Six regression models for the full sample and 6 each for the subsamples have been the result of this study, implicating theoretical results which will be elaborated on in the following discussion.
legislations do not account for greenwashing yet (Delmas & Burbano, 2011). As such changes, scrutiny from stakeholders, especially in combination with the media, could go beyond reputational blemishes.
As for the second Hypothesis the statistical output shows no support for the assumption that high-involvement products are influencing the base-line relation in a sense that Stakeholder scrutiny will increase. However, as previously stated the moderation of the cultural dimension masculinity is expected to help explaining the mechanism of linking greenwashing to stakeholder scrutiny. Yet, as soon as the regression analysis has been re-estimated with the subsamples, interesting outcomes should be highlighted in the following. In fact, the moderation effect of culture and in particular high masculinity, have shown that the involvement level does play a significant role, though negatively affecting the base relationship. This contrary result to Hypotheses 2 and 3 could be explained by the fact that Vitell et al. (1993) highlighted, namely that countries scoring high on the masculinity scale tend to behave in an “selfish/opportunistic” way. This for example includes seeking for success and especially materialistic success. Interestingly enough, this seems to affect mostly males. Additionally, it is known that such personal values are linked to the heritage or simply culture (Phinney, 1992). Regardless, in order to reach such goals, one has no hesitation to immerse into unethical conduct. Following Vitell et al.’s (1993) line of reasoning, it can be stated that unethical behavior is somewhat ignored by stakeholders. As greenwashing is perceived as an unethical behavior (Delmas & Burbano, 2011), ignoring such would make oneself unethical. Hence, this moral ignorance from stakeholders is somewhat expected from companies. These companies are more or less betting on the fact that stakeholders “misinterpret” their information (Mitchell & Ramey, 2011). In essence, to maintain the (materialistic) success which is given through high-involvement products, achievements and competitiveness, rather masculine collectives tend to ignore or rather oversee ethical misconduct (Williams & Zinkin, 2008). In fact, when materialism is ranked higher within one’s personal values, hence one is very materialistic and therefore scoring high on masculinity, unethical behavior is “accepted” (Barrett, 1992). Belk (1988) has supported this argument by extending the previous with the fact that this low sensitivity towards ethics could be explained by “[…] an inevitable loss of sense of community which might in turn make people less sensitive to those behaviors (ethical misconduct) which might negatively affect others” (p. 138). Subsequently, this is one factor to explain the decrease in scrutiny from stakeholders.
The ELM model is stating that in case of a high-involvement product, the so-called central route is taken (Petty et al., 1983) which describes a mental process incorporating all fundamental information known to the individual in order to make a decision. On the contrary, the peripheral route encompasses a rather simple approach which merely uses easily accessible information and is used for low-involvement products (Sharma, 2011). Having that said, various ethical norms and values might be perceived as ethical, whereas some do not perceive such as ethical (Vitell et al, 1993). Therefore, this cultural difference, in this case in terms of masculinity could affect the “central route” which incorporates a cognitive process including all known information. Hence, if certain information is missing because of a (e.g.) different upbringing, the central route will be altered, and the outcome will differ. Petty et al. (1983) explain these beliefs as “truism” which are basically facts which one does not put in question. Such cultural truism is usually given by various respected individuals such as academic staff or parents. Subsequently, this will affect one’s life and will lead to a constant behavior. In case of this cultural difference in the masculinity range, one might scrutinize a company conducting ethical misconduct less, simply because one has been “taught” differently.
Lastly, further research has established that the product level involvement is affected by brand loyalty and consequently stakeholder decision making is affected (Quester et al., 2003). In fact, the relationship between both concepts is also referred to as “ego involvement” (Traylor, 1981). Particularly, Traylor (1981) stated that an increase in product involvement leads to a higher commitment to the brand or simply brand loyalty. This has been supported by various scholars such as Beatty et al., 1988 or Park et al., 1987, pointing out that a high involvement is a prerequisite in terms of brand loyalty. In addition, the more one feels more connected to a certain product category based on cultural values, personal beliefs etc., the higher the “psychological attachment to a particular brand within that product class” (Quester et al, 2003, p. 2). Accordingly, when following Dick and Basu (1994) who stated that as soon one is loyal to a specific in a product category, one commits to a repetitive and supportive behavior towards that brand, ignoring other factors. Such factors could include ethical misconduct and as a consequence companies guilty of greenwashing could receive less scrutiny from stakeholders.
a. Practical Implications
As previously mentioned, the yearly effects have shown that the awareness of Greenwashing is increasing in the last years, specifically from 2014 onwards. Additionally, this awareness could be leading to more scrutiny from stakeholders. However, if one considers to fully chronically backtrack environmental evidence and documentation, scrutiny is less of a matter for stakeholders. Hence, if companies enhance their current lacking ability to keep track of environmental records in order to contend external tension (Delmas & Toffel, 2004), external pressure will be less of an issue.
Lastly, stakeholders show a higher interest in environmental issues, while demanding more than just legal obedience and this study also showed that there is an increase in Greenwashing while companies face more scrutiny. Therefore, practitioners should act upon that by doing more than the bare minimum. “Societal needs” (McWilliams, 2014) should be covered since they are above and beyond the legislation.
b. Limitations and future research
Despite the previous contributions and implications, this study still suggests future improvements. These improvements will be based on the limits of this study and will aid future researchers on the direction they should take on.
effects. This might have to do with the fact that older firms have a higher potential to generate relationships with influential external actors (Singh, 1986).
Moreover, the two subsamples have consisted of countries of below 62 masculinity and above. Yet, the low-masculinity sample consisted to a big stake of the United States which might have affected the results. Hence, for further research, a more balanced sample in terms of countries should be generated.
Lastly, this study has established a pathway for involvement level studies in regard to greenwashing. For instance, following Lastovicka and Gardner (1979), the product involvement level is a so-called “two-dimensional construct” which encompasses the “commitment to (a) brand (and) normative importance” (Traylor, 1981). Henceforward, prospective research could investigate the “commitment to (a) brand” towards Greenwashing and external pressures. Additionally, in terms of generalizability, it is suggested to go beyond merely manufacturing industries and perchance examine e.g., financial institutions (banks, insurance companies etc.) which also fall under the high-involvement categorization (CEOpedia, 2020).
6. Conclusion
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