Will being green come out in the wash? A Content Analysis on Greenwashing
within Fortune Global 500 Companies
Sonja-Maaria Nikula 11368233 Master’s Thesis
Graduate School of Communication Master’s Programme Corporate Communication
Pytrik Schafraad 26.06.2020
Abstract
Appearing as an environmentally friendly corporation with sustainable products is becoming expanding in the 21st century, perhaps resulting from the increasingly environmentally aware consumers. This might cause corporations to appear more environmentally friendly than they
actually are, resulting in a phenomenon referred to as “greenwashing”. Greenwashing in its simplest definition occurs when corporations utilize false or misleading claims whilst communicating about their sustainable endeavors, often lacking appropriate sources or proof of these activities. Thus, this content analysis aims to shed light on the presence of greenwashing within the Fortune Global 500 companies, and draw relevant conclusions for practitioners and researchers alike. The sample consisted of 402 environmental claims of 40 Fortune Global 500 corporations. The analyses found a significant effect of corporation size regarding the level of greenwashing, thus suggesting that the larger the corporation, the more greenwashing it engages in. This empirical research extends the existing research and theories on greenwashing and thus acts as a relevant starting point for future research within the field of corporate communication, for practitioners and researchers alike.
Introduction
Tropical scenery. Soothing music. A young girl describing pristine, untouched nature. And how it should be left alone. This makes for a very aesthetically pleasing and calming advertisement, seemingly celebrating the beauty of nature. Except for the fact that this is an ad for a bottled water company, FIJI Water. The connotations clearly imply that FIJI Water is an inherent part of nature, with a minimal, if not non-existent environmental footprint. It displays the beautiful, tropical FIJI Water bottle against a gloomy and polluted backdrop of a concrete jungle. The advertisement includes no sources for the statements that are being made, and how exactly FIJI Water actually is benefiting the environment. For instance, the fact that plastic can take up to 450 to decompose (Ritchie, 2018) is completely ignored. So is the fact that FIJI Water is shipped daily from Fiji, to over 60 countries around the world ("About FIJI Water | FIJI Water", 2020). This is not very soothing. This is not very “natural”.
This example perfectly encapsulates the topic of this research, greenwashing. Greenwashing in its essence occurs when a company implies that they or their products are more environmentally friendly than they actually are. Greenwashing might be present when an
organization makes environmental claims without references or factual claims to support the statements. This research will examine the extent to which greenwashing is present within the CSR and sustainability communication of Fortune Global 500 corporations.
The societal relevance of this research lies in the fact that we live in a world where each and every corporation is expected to adhere and adopt some type of environmental policy. It is not enough to behave responsibly, but sustainably as well (Crane & Glozer, 2016). The scientific relevance of this research further lies in the fact that a large number of past greenwashing
researches has examined only advertisements (Baum, 2012; Fernando, Sivakumaran & Suganthi, 2014; Leonidou, Leonidou, Palihawadana & Hultman, 2011; Sivakumaran & Suganthi, 2014). There is a clear absence in the examination of corporate webpages. We aim to begin to fill this gap in literature. Hetze and Winiströfer (2016) state that corporate webpages can be seen as official presentations of corporations, thus they can be seen as fairly credible sources. Furthermore, websites are easily accessible and provide a wide array of CSR information (Hetze & Winiströfer, 2016), which gives reason to believe a comprehensive overview of the presence of greenwashing within the analyzed corporations can be provided. Analyzing corporate websites further fits into the domain of corporate communication because corporate websites can be seen as the primary
platform for online corporate communications (García García, Carrillo-Durán, & Tato Jimenez, 2017). If used correctly, corporate websites can be a great facilitator of a corporations’ goals for strategic communication as well as stakeholder management (García García et al., 2017). Regarding
the topic of this research, corporate webpages can thus be seen as a fitting and relevant unit of analysis.
Before we begin, it is appropriate to describe the structure of this empirical research. This research paper will begin with describing the relevant theories in relation to the concept of greenwashing. After the description of theory and the subsequent hypothesis development, we will move on to the method section, describing the sample and variables included in this research.
Thereafter, the analysis and results of the research question and hypothesis testing will be presented. This will be followed by the discussion section, describing the implications of the results in relation to the aforementioned theory, along with the limitations and directions for future research. Finally, we will end the empirical research with a conclusion.
Theoretical framework
The theoretical framework of this research will begin with operationalizing the concepts of CSR, the CCO perspective and greenwashing. Secondly, three drivers of greenwashing will be used as a structural element within which other relevant theories will be introduced. Based on these drivers, we will arrive at the research questions and hypotheses for this research.
CSR
Taking into account the topic of this research, it is useful to begin the theoretical framework with defining the concept of CSR. CSR is the practice of corporations responding to the societal
expectations of their stakeholders. Today, it is not enough for corporations to merely obey the law and be profitable, but they need to be socially responsible as well. Having a CSR policy
demonstrates that a company is taking the concerns, expectations and wishes of their stakeholders into account and integrating them into their business practices (Carroll, 2015). Jones (2017) further conceptualizes CSR as a concept of capturing a selection of social and environmental
responsibilities in order to respond to the growing demand of stakeholders regarding these topics. Furthermore, CSR communication can be operationalized as communication in which organizations integrate societal, environmental, ethical and stakeholder concerns into their business strategy (Ellerup Nielsen & Thomsen, 2018).
Frameworks of CSR. Carroll (2015) further states that there are four frameworks of CSR that are all
inherently related. These are Business Ethics, Stakeholder Management, Sustainability and Corporate Citizenship. All four of these CSR frameworks are useful to define here.
moral duties and obligations a business holds. As the name suggests, the focus of BE is more on ethical business practices than legal or economic dimensions of a company. Taking into account the definition of CSR provided above, the terms of BE and CSR are sometimes used interchangeably since essentially, they refer to the same subjects (Carroll, 2015).
Secondly, the framework of Stakeholder Management in relation to CSR is a framework under which a company can effectively carry out their mission with respect to the groups of stakeholders a firm has responsibilities towards. Stakeholder Management also relates to BE, since it is essentially ethical communication regarding a firm’s stakeholder groups. This particular framework will be examined more thoroughly in a following section of this paper.
Thirdly, sustainability in the context of CSR can be defined as a general concern for the present, as well as the future (Carroll, 2015). Sustainability is not only concerned with the natural environment, but the continuation of economic, environmental and social business practices (Carroll, 2015) Sustainability has become a global buzzword, as well as an integral part of business practices around the world. The issue with the notion of sustainability is the fuzziness of the
concept, in that it is lacking a concrete definition. Perhaps this is why it is relatively easy for companies to adopt the concept of sustainability in their business practices.
Fourthly, the final framework is Corporate Citizenship. Corporate Citizenship is a more general term, similar to the term CSR itself. It views corporations as citizens willing to “give back” to the community (Carroll, 2015). Corporate Citizenship views companies as citizens who are morally obliged to certain duties and responsibilities in order to be accepted by the society and the field they operate in (Carroll, 2015). They adhere to the same obligations as citizens do in that they are expected to pay their employees on time, abiding the law and creating jobs. Thus, like
sustainability, no company directly opposes this concept and it too, spans across industries. The aim here was to illustrate how these four concepts are all related to CSR. In fact, they overlap so much that they can even be used as interchangeable terms (Carroll, 2015). It is hence useful to understand the broadness of CSR and the framework that goes with it. It is further important to note that the act communicating of CSR claims is related to the Communicative Constitution of an organization, or the CCO. This will be introduced in the following section.
CCO. The CCO perspective is an emerging theoretical framework within the field of corporate
communication. It essentially considers the organization as a dynamic process which can be shaped via its communication (Siano, Vollero, Conte, & Amabile, 2017). According to this view, texts and speech have performative power over an organization, in other words, they can be very effective in for example transforming the organization’s culture (Siano et al., 2017). Dobusch and Schoeneborn
(2015) state that the main principle behind the CCO perspective is the fact that organizations come to being with not just job titles, but with the layers upon layers of communication that constantly happens within an organization. However, it is of extreme importance to note that constitutive communication does not mean that an organization is made up of only communication (Putnam & Nicotera, 2009). The linguistic turn marked the beginning of the CCO perspective, since the creation of social realities were beginning to be created within language and texts. Thus, from the CCO perspective, communication is not a set of organizational structures but a process within which knowledge and activities are manifested in (Ashcraft, Kuhn, & Cooren, 2009). Moreover, from the CCO perspective, CSR communication can be seen as aspirational talk, acting as a goal towards which an organization’s activities are aimed at, in the form of communication. This only becomes an issue if an organization fails to reach its CSR goals. This is because if these CSR texts are not within an organization’s reach, the organization might still believe that they are achieving what they say they are achieving, as mentioned in their CSR communication. Thus, CSR
communication may yield unethical behavior within the organization, in their endeavors to achieve the CSR promises (Siano et al., 2017). This naturally gives the chance for greenwashing to occur.
According to this view, a corporation does not engage greenwashing deliberately, but it can be a natural occurrence of a corporation that is engaging in superficial CSR communication (Siano et al., 2017). This is because that corporation still believes they are carrying out the CSR activities as promised, because that is what their CSR communication has constituted for the organization. Taking the CSR framework and the CCO perspective into account, the main topic of this research, greenwashing will be operationalized next.
Greenwashing
The topic of greenwashing is highly relevant during a time when every company is expected to have a strong CSR policy (Crane & Glozer, 2016), which in turn can leave room for greenwashing
(Baum,2012). CSR and greenwashing are inherently related since greenwashing occurs when the promises made in a company’s CSR communication or CSR policies are actually not carried out in the activities of the company in question (Laufer, 2003). Greenwashing can further be defined as the discrepancy between substantive and symbolic actions (Siano et al., 2017), or between what the organization is claiming to do and what they are actually doing regarding environmental practices. Greenwashing is the act of misleading others about an organization’s environmental endeavors and attributes (Ottman, 2011). Thus, the research question this research aims to answer is:
RQ: To what extent do the Fortune Global 500 companies engage in greenwashing in their CSR
communication?
Along with the sub-question:
Which type of greenwashing is most common within the corporations analyzed?
Seele and Gatti (2017) wish to add the notion of subjectivity to the definition of greenwashing. They state that greenwashing occurs in the eye of the beholder, meaning that some stakeholders might consider a certain green message as greenwashing while others may not. Seele and Gatti (2017) thus emphasize that accusation must be present in order for greenwashing to be present as well. In other words, if a stakeholder group does not accuse and organization of greenwashing, greenwashing simply does not exist. This adds to the notion of the ambiguous nature of
greenwashing, in that it is not a black and white concept. Keeping this notion to greenwashing in mind, three drivers of greenwashing will now be examined in detail.
Drivers of greenwashing
The literature review of Lyon and Montgomery (2015) revealed that the most common actors of greenwashing include corporations, governments, political organizations, universities and even NGOs. It is thus clear that greenwashing is an industry-spanning phenomenon which is increasingly commonly utilized. Hence it is relevant to examine which specific circumstances or situations are most likely to cause greenwashing. Three levels of drivers of greenwashing have been found; the external, organizational and individual (Delmas & Burbano, 2011; Lyon & Montgomery,
2015). For the sake of this research the most relevant drivers are the external and organizational drivers. The aforementioned individual-level drivers of greenwashing will not be focused upon in this research.
1. External Drivers
Firstly, external drivers of greenwashing include pressure as well as demand from varying stakeholder groups or organizations, such as NGOs, regulators, consumers and competitors (Chithambo, Tingbani, Agyapong, Gyapong & Damoah, 2020; Lyon & Montgomery, 2015). Greenwashing that results from external drivers can be seen as a reactive response, with which organizations aim to respond to external pressures. It has also been argued that greenwashing can be used as a strategic tool to affect a change within the field in which an organization operates in (Lyon & Montgomery, 2015). Delmas and Burbano (2011) further state that due to the lack of
external regulations, greenwashing has the opportunity to arise. As is clear from this definition, the external drivers of greenwash can be seen as having similarities with stakeholder theory, which will be operationalized in the following paragraph.
Stakeholder Theory. Organizational sustainability essentially refers to the continuation of business
practices (Garvare & Johansson, 2010; Siano et al., 2017). Further, organizational sustainability can be defined as seeking economic growth whilst supporting biodiversity and without exploiting natural capital at the expense of long-term development (Aras & Crowther, 2008). In order to achieve this continuation, the needs and expectations of different stakeholders must be met. This is referred to as stakeholder management (Garvare & Johansson, 2010). Broadly speaking,
stakeholders are those who can affect or are affected by organizational perspectives. Stakeholders can vary in importance, depending on their power and legitimacy (Garvare & Johansson, 2010). It is thus important for an organization to map out its different stakeholder groups and their varying needs (Cornelissen, 2014; Fassin, 2009), in order to succeed in corporate sustainability.
Stakeholder theory is fundamentally related to the concept of greenwashing, because it can be argued that greenwashing can occur when there is increasing pressure from varying
stakeholders (Chithambo et al., 2020; Delmas & Burbano, 2011; Siano et al., 2017; Testa, Boiral & Iraldo, 2018). A key form of stakeholder pressure regarding greenwashing is regulatory pressure. Regulatory pressure can be measured with multiple variables (Huang & Kung, 2010). For the sake of this research, corporation size will be examined as a determinant. Chithambo et al. (2020) argue that the larger the firm, the more regulatory pressure it faces. This is because the larger the firm is, the more visible it is and the more prone it is to public scrutiny (Chithambo et al., 2020) Firm size within this research will be measured in accordance to Dang and Li (2013). Size here will be measured examining the total assets, sales and number of employees regarding each corporation. This is the most frequently utilized method to measure corporation size (Dang & Li, 2013; Huang & Kung, 2010). This brings us to our first hypothesis:
H1: Companies that are larger in size face more regulatory pressure, and thus engage in more greenwashing
Stakeholder Salience Model. In relation to Stakeholder Theory, the Stakeholder Salience Model is
useful to define here. In this model, an organization identifies its stakeholders based on their visibility or salience (Cornelissen, 2014) within the field they operate in. Stakeholder salience is determined based on their power, legitimacy and urgency. A stakeholder group that is said to have
power over an organization when they literally have power over an organization in some way. An example of powerful stakeholders is a prospective customer. Secondly, stakeholders have
legitimacy over an organization if they have some sort of legitimate claim over the corporation. An example of legitimate stakeholders is corporate charities. (Cornelissen, 2014) A more specific type of legitimacy especially relating to stakeholder theory is pragmatic legitimacy. It results from the pressures stakeholders hold against an organization, regarding what kind of corporate behavior is desirable. A corporation is said to have pragmatic legitimacy if its stakeholders consider its activities will benefit them personally (Palazzo & Scherer, 2006). Thirdly, stakeholders have urgency over an organization if their wants and needs warrant immediate action. An example of urgent stakeholders is a demonstrator on corporation property.
As is clear from the examples presented above, if a stakeholder possesses only one of the three Stakeholder Salience Model attributes, they can be fairly easily overlooked. The situation changes if the stakeholder possesses two or more attributes. The more attributes a stakeholder group possesses, the more important they become. Customers are an example of salient stakeholders. Customers usually have power over an organization, since they can have great influence on the company related to their purchasing behavior. They usually further have legitimacy, since they may have valid interests that the company has no choice but to serve. If the customers’ claim happens to be urgent, the corporation by definition has to respond swiftly (Cornelissen, 2014). Customers are also referred to as dominant stakeholders.
In this model, an organization identifies and classifies its stakeholders in relation to these three attributes, and can then prioritize communication with each group. As a rule, the more power, legitimacy and urgency a stakeholder group has, the more salient they are and thus require immediate action and attention (Cornelissen, 2014). For instance, customers are generally a stakeholder group that require constant and ongoing communication since they hold power,
legitimacy as well as urgency over an organization. The stakeholder salience model is a useful tool for practitioners since the stakeholder climate is in constant flux, and stakeholders’ expectations can change rapidly. Thus, it is important to map different stakeholders in an ongoing fashion.
In relation to this, Delmas and Burbano (2011) hypothesize that consumer product corporations, or B2C, face greater pressure to appear ecological and environmentally friendly, as compared to non-consumer product corporations, or B2B. Thus, for consumer product firms the stakeholder group of customers is very salient since customers naturally are the most powerful, legitimate and urgent stakeholder group for consumer product firms (Cornelissen, 2014). With this model in mind, we arrive at our second hypothesis:
H2: Companies within the consumer product sector engage in more greenwashing than companies within the business to business sector.
2. Organizational drivers
After describing the first driver of greenwashing, we can move onto the second driver. The second driver of greenwashing can be found on the organizational level. Organizational drivers of
greenwash determine how an organization responds to external pressures.
Firstly, organizational drivers include the organizational characteristics such as the industry in which an organization operates in (Delmas & Burbano, 2011). Organizational
characteristics can predict greenwashing in that they determine which communication strategies are available and most appealing to an organization.
Secondly, the industry in which an organization operates in can also determine the extent of which an organization faces greenwashing pressure (Delmas & Burbano, 2011; Ramus & Montiel, 2005). Baum (2012) further states that some industries face more regulation than others, hence resulting in different levels of greenwashing solely based on the industry. Baum’s (2012) study concluded that corporations within the food sector face more regulations than corporations within the personal goods sector, resulting in less greenwashing within the food sector. This brings us to our third hypothesis:
H3: Companies in the food industry will engage in less greenwashing than companies in the
automotive industry (Baum, 2012)
Thirdly, a final level of organizational drivers of greenwashing is organizational inertia.
Organizational inertia is the underlying mechanism of an organization that hinders strategic change, and is likely to be more rigid in older and more complex firms than in newer and less complex organizations (Delmas & Burbano, 2011). Generally, older corporations are more rigid in their business practices and thus respond to change in a slower fashion as compared to newer, more flexible companies (Delmas & Burbano, 2011). Complexity relates to organizational inertia in that corporations that operate in numerous countries simultaneously, face different regulations and jurisdictions depending on the country of operations. This makes their business practices more complex, since they have to simultaneously adhere to different regulations from different countries and regions (Delmas & Burbano, 2011). It is stated that particularly multinational corporations are high in complexity, since they operate in a predominantly uncertain regulatory environment.
Thus, if organizational inertia is very prevalent, greenwashing might be prevalent as well, since it takes a long time to carry out the communicated environmental claims. With this, we can arrive to our fourth and fifth hypotheses:
H4: Companies that are older, engage in more greenwashing than newer companies H5: Companies that are more complex in nature, engage in more greenwashing than less complex
corporations.
Below a conceptual model of all the relevant variables is presented. Based on our hypotheses (H1-H4) we can expect corporation size, industry, sector and founding year to have a positive effect on the level of greenwashing.
Method
The method used is for this research was content analysis. Content analysis is the most fitting method to study this topic, since it is a highly useful method to study a large number of online messages in a quantifiable way. The study of Baum (2012) included the coding of the presence of misleading claims in environmental advertising. In Baum’s (2012) study as well as this one, it is possible for each environmental claim to include more than one misleading claim. The possible presence of these misleading claims was then formed into a type of scale, to examine the level of greenwashing within these claims. This also presented an opportunity to perform a more advanced statistical analysis on the sample, such as a regression analysis. The study of Baum (2012) was used as a base for this research and codebook. A version of Baum’s (2012) codebook, modified for the needs of this research, is presented in Appendix 1.
Corporation • Size (Large) • Industry (Motor)
• Sector (B2C) • Founding year (Old)
• Complexity (High)
Sample
The corporations analyzed in this research were a number of Fortune Global 500 corporations. The Fortune Global 500 corporations are a widely used sample as the list is a good indicator of
corporate financial performance and revenue (Jung & Pompper, 2014). It is relevant to study the possible presence of greenwashing within such large companies, since especially Fortune 500 companies in the past have been proven to have been engaging in greenwashing fairly frequently (Laufer, 2003). The Fortune Global 500 companies were used instead of the Fortune 500 companies due to the wider array of countries present, as the Fortune 500 are all U.S.-based companies. The full list of companies can be found in Appendix 1. The Fortune Global 500 also includes companies from several industries which provides and interesting opportunity to compare different industries regarding their environmental claims. The two industries being compared in this research are the Food, Beverages & Tobacco and Motor Vehicles & Parts industries ("Global 500", 2020).
The unit of analysis for this research was the websites of the corporations selected, and the coding unit was each individual green or environmental claim. Previous research on greenwashing has mainly focused on the study of environmental advertising (Baum, 2012; Fernando et al., 2014; Leonidou et al., 2011.). Due to the aforementioned, desired corporate communication perspective for this research, advertisements will not be used. Instead, websites were used, in accordance of the research by Nyquist (2017).
All claims including the words “environmental”, “ecological”, “sustainable”, “natural”, and “green” (Leonidou et al., 2011) were coded. At least one of these words must have been present in the claim in order for it to be included in the sample. The entire corporate webpage was taken into account and examined thoroughly. However, if there was an environmental claim linking to an external website, this unit was not included in the sample in an effort to reduce the complexity of the research. It was noted in the codebook whether the publication included a link to an external website or includes a form of CSR or sustainability report as an attachment. Thus, any external links or attachments will not be examined in this research. There are ten companies present in each industry, thus giving a total of 20 companies. Some initial work was done by examining the websites of some of the companies in the sample, as to ensure that a sufficient sample can be achieved. The initial investigation revealed that there are in fact a sufficient amount of CSR and sustainability communications on each of the websites. The sample of this research includes 401 environmental claims. This can be considered a sufficiently large sample, and thus we can proceed with the analysis
Variables
The dependent variable for this research is the type of misleading claim, which was operationalized in accordance to Baum (2012), and as presented in Table 1. In order to have been able to begin the hypothesis testing, all of the misleading claims were summed up and computed into a new variable. We refer to this variable as the level of greenwashing (M=.87, SD=1.27). The level of greenwashing thus ranges from 0-5, with 0 indicating no presence of greenwashing and 5 indicating a very high level of greenwashing. This variable was treated as an interval variable. The codebook involves five different types of environmental claims which are product orientation, process orientation, image orientation, environmental fact and other. Further, it involves five different types of misleading claims, which are the following: hidden tradeoff, no proof, vague, irrelevant and lesser of two evils. These environmental, as well as misleading claims are operationalized briefly in Table 1. The full conceptualization, along with full examples, can be found in the codebook, in the aforementioned Appendix 1.
Table 1
Operationalization of environmental claims and misleading claims (Baum, 2012)
Type of environmental claim Definition Example Product Orientation The claim focuses on the
environmentally friendly attributes that a product possesses.
Gasoline would be classified as a consumer product, whereas wind energy would not.
Process Orientation Claims will be labeled process when the process in which the product is made has an environmental benefit or feature.
Services advertised often fall under “process” orientation, rather than “product.”
Image Orientation The claim associates an organization with an environmental cause or activity.
“We are committed to preserving our forests.”
Environmental Fact The claim involves an independent statement that is allegedly factual in nature
“The world’s rainforests are being destroyed at a rate of two acres per second.”
from an organization about the environment
Other
The claim does not meet portray any of the above characteristics.
Type of Misleading Claim Definition Example
Hidden Tradeoff Claim that suggests a product is ‘green’ based on an unreasonably narrow set of attributes without
attention to other important environmental issues.
Paper, new sources of energy, household insulations, office technology.
No Proof Environmental claim that
cannot be substantiated by easily accessible supporting information or by a reliable third-party certification.
Products or statements that claim various percentages without providing any evidence.
Vague Vague claims are claims that
don’t explicitly explain what a company is doing to reduce their environmental impact.
“All natural”, “non-toxic,” “Green,” “Environmentally friendly,” and
“Eco-conscious”
Environmental Fact Environmental claim that may be truthful but is
unimportant or unhelpful for consumers seeking
environmentally preferable products
‘CFC-free’
Lesser of Two Evils Environmental claims that may be true within the product category, but that risk distracting the consumer from the greater
environmental impacts of the category.
Organic cigarettes, fuel-efficient sport-utility vehicles, “green”
insecticides/herbicides, and “cleaner” petroleum-based fuels.
Furthermore, the independent variables include size, which also translates to regulatory pressure in this context (Chithambo et al., 2020). The variable of corporation size includes total sales, total
assets and the total number of employees of each corporation. These are common, yet simple ways to measure corporation size (Chithambo et al., 2020; (Dang & Li, 2013; Huang & Kung, 2010). All of the required information was acquired from the “Global 500” (2020) webpage.
To further examine the size of each corporation, a correlation test for these two variables was run. In order to run a sufficient statistical analysis regarding the corporation size, we checked for correlations between the two variables of total revenue, which included the total sales and total assets, as well as the total number of employees. The Pearson correlation test was chosen since we are examining two continuous variables. The Pearson correlation test revealed a strong, positive correlation, r (400) = .77, p<.001. We can thus conclude that there is a strong, positive correlation between the variables of revenue and employee number. Thus, only the revenue variable was used as an indicator for corporation size, since it can be considered as a sufficient
measurement. Further, it is of relevance to note that in order to standardize the variable of size, a logarithm transformation was used. This is a useful mathematical technique when a standardization of variables is required. The logarithm transformation was computed into a new variable, which we will refer to as log*size (M= 4.92, SD= .39). This variable will be used for the remainder of the analysis and will be referred to as the size variable.
The second independent variable is the industry (M= 1.39, SD= .49) of the corporation. Two industries were included in this sample, which are the Food, Beverages & Tobacco Industry and the Motor Vehicles & Parts Industry. These two industries were chosen for the purpose of analyzing H2 (Baum, 2012). All of the required information was acquired from the “Global 500” (2020) webpage.
The third independent variable is the sector (M= 1.51, SD= .50) of the corporation. In this research, we will be comparing corporations from the business to consumer sector as well as the business to business sector. Each corporate webpage was analyzed in order to determine to which sector each corporation belongs to. An arbitrary division was made based on each corporation’s products and business practices. All relevant information was acquired from each corporate webpage.
The fourth independent variable is founding year of the corporation (M= 1918,38, SD= 54.93). This was examined simply by the founding year of each individual corporation, which was acquired from the corporate webpages. The sample includes corporations’ founding year ranging from 1818 to 2008.
The fifth and final independent variable is corporation complexity (M= 119,00, SD= 62.86). This was examined by analyzing the number of countries each organization has operations
in. The sample includes corporations having operations in ranging from having operations in 19 to 260 countries. All of the required information was acquired from the “Global 500” (2020) webpage.
Inter Coder Reliability Analysis. Before the analysis was able to begin, an intercoder reliability test
was performed. This was conducted by coding 10% of the full sample twice, which in this case meant 41 units. Due to the nature of this research, the author carried out both the full coding process as well as the inter coder reliability test. Inter coder reliability was determined by the common statistical test, Krippendorff’s alpha. The KALPHA value for the variable of misleading claim was .81, which can be considered a very high value, and thus we can conclude that the codebook has sufficient reliability. The KALPHA analysis was run only for the misleading claims since other variables include corporation characteristics, such as business sector or founding year, which were expected to be coded with 100% accuracy. Next, we will move on to analyze descriptive statistics, a chi-square test and a multiple regression analysis.
Results
Before we move onto the actual analysis of this research, it was appropriate to run some descriptive analyses. Firstly, 60.7% (N=244) of the units were from Food, Beverages & Tobacco corporations (M=6.05, SD=2.76), whereas 39.3% (N=158) of the claims were from Motor Vehicles & Parts corporations (M=4.86, SD=2.88). The most common environmental claim type within the entire sample was the Product claim (M=.42, SD=.49), whereas the most common misleading claim was Vague (M=.23, SD=.42).
Frequency of Misleading Claims
Assumptions. In order to answer the first research question, a chi-square analysis was executed. In
order to run a chi-square test, two assumptions have to be met. The first assumption is that less than 20% of the cells in the table can have an expected count of less than five. In this sample, 0% of the cases had an expected count of less than five. Secondly, all of the units used contribute to only one cell of the contingency table, which demonstrates independency of measurement. These are both visible from the footnotes of the chi-square output generated in SPSS. We can thus consider both of the assumptions for the chi-square test to be met, and we can move on with the analysis. The most relevant statistics from the chi-square analysis are presented in Table 2 and discussed in more detail below.
Analysis. Firstly, for the misleading claim Hidden Tradeoff, we found a significant difference of
industry χ2(1, N=402) =58.09, p<.001, Goodman & Kruskal’s tau= .14. Secondly, for the
misleading claim No Proof we found a significant difference of industry χ2(1, N=402) =10.32, p< .001, Goodman & Kruskal’s tau= .03. Thirdly, for the misleading claim Vague, we found a significant difference of industry χ2(1, N=402) =15.00, p< .001, Goodman & Kruskal’s tau= .04. Fourthly, for the misleading claim Irrelevant, we found a significant difference of industry χ2(1, N=402) =18.43, p< .001, Goodman & Kruskal’s tau= .05. Fifthly, and finally, for the misleading claim Lesser of Two Evils, we found a significant difference of industry χ2(1, N=402) =13.79, p< .001, Goodman & Kruskal’s tau= .03.
Table 2
Frequency of misleading claims by industry
Type of misleading claim
Food & Beverages Industry
Motor Vehicles & Parts Industry χ2 p Goodman & Kruskal’s tau Hidden Tradeoff 9.8% 42.4% 58.09 .000 .14 No Proof 14.8% 27.8% 10.32 .001 .03 Vague 16.8% 33.5% 15.00 .000 .04 Irrelevant 10.2% 26.6% 18.43 .000 .05
Lesser of two evils 1.2% 8.9% 13.79 .000 .03
Hypothesis Testing
Assumptions. Multiple regression was used to answer the hypotheses of this research. Before the
analysis could commence, the assumptions for regression analysis had to be met, or at least checked for. The assumptions for a regression analysis are related to the heteroscedasticity of the sample. Firstly, the sample must be normally distributed. This can be checked with generating a histogram. From the generated histogram it was clear that sample was fairly normally distributed. This makes analyzing the histogram more difficult, but still the curve of a normal distribution could be
identified. The second assumption of a regression analysis is that on all levels of the predicted outcome variable, the residuals are equally distributed. This can be checked by adding a horizontal line to the generated scatterplot. From the generated scatterplot and its horizontal line, we can see that the residuals are fairly equally distributed. Furthermore, it is of great importance to also check for outliers, since a regression analysis can be sensitive towards outlier effects. The generated scatterplot helped us identify one outlier. Due to the fact that there was only one outlier, it was still
included in the sample and the multiple regression analysis could begin. Further, it is important to note that the variables of industry as well as sector were recoded into dummy variables, in order to carry out the regression analysis. Thus, the analysis includes dummy variables for the Motor Vehicles & Parts industry, as well as from the B2C sector.
Analysis. A hierarchical entering method for multiple regression was utilized, since the aim is to
answer all the hypotheses whilst examining significant effects. Thus, all variables are needed in this regression analysis. However, the order of entry for multiple regression can alter the results. The order of the included variables was determined by a correlation analysis. This was chosen due to the lack of expected substantial effects grounded in theory. The correlation analysis can act as an
arbitrary indicator of the most significant predictors. A Pearson’s correlation was run regarding the correlation of all the independent variables regarding the level of greenwashing revealed that three of the variables correlated significantly with the level of greenwashing. The correlations were significant for the variables of size, r (400) = .36, p <.001; industry, r (400) = .33, p<.001; sector, r (400) = -.30, p<.001 and complexity, r (400) = .13, p<.001. Size and industry are thus moderately and positively correlated with the level of greenwashing, sector is moderately and negatively correlated with the level of greenwashing and complexity has a small, yet significant correlation with the level of greenwashing. Finally, the variable of founding year, r (400) = .06, p= .214, correlated in-significantly with the level of greenwashing.
Secondly, the correlation matrix was also used to check for multicollinearity.
Multicollinearity refers to the perfect correlation between two or more variables. In other words, if multicollinearity is present, we will be unable to draw estimates regarding the coefficients, since their regression coefficients become interchangeable. Multicollinearity is said to be present if any variables in the correlation matrix have a correlation of .80 or higher. In this analysis, this was not the case so we can conclude that multicollinearity was not present.
Now we can move onto describe the results for the actual regression analysis. The results indicate that we only have one regression model that yielded significant results, this is Model 1, which only includes the variable of size. The multiple regression analysis revealed that the only significant independent variable was the variable of size F (1, 400) = 58,38, p< .001. Thus, we can reject the null hypothesis that size does not predict the level of greenwashing. The regression model therefore predicts 12.7% of the variation in the level of greenwashing on the basis of corporation size. Thus, size, b*=.36, t=7.6, p< .001, 95% CI [.87, 1.47] has a significant, moderate association with the level of greenwashing. One additional unit of corporation size increases the level of greenwashing by .36. Thus, we can accept H1.
Table 2
Hierarchical Multiple Regression Analysis Predicting Level of Greenwashing
Variable b b* t p 95% CI R2 Model 1 .127 (Constant) -4.89 -6.47 < .001 [-6.38, -3.40] Log*size 1.71 .36 7.64 < .001 [.87, 1.47] Model 2 .133 (Constant) -3.33 -2.79 .006 [-5.70, -.98] Log*size .83 .25 3.23 <.001 [.32, 1.33] Industry .34 .13 1.67 .095 [-.06, .74] Model 3 .138 (Constant) -2.18 -1.53 .127 [-4.98, .62] Log*size .56 .17 1.82 .069 [-.04, 1.18] Industry .36 .14 1.78 .076 [-.04, .76] Sector .26 .10 1.50 .135 [-.08, .59] Model 4 .140 (Constant) -1.31 -.75 .454 [-4.75, 2.13] Log*size .40 .12 1.08 .280 [-.33, 1.12] Industry .44 .17 1.97 .050 [.00, .88] Sector .41 .16 1.66 .098 [-.08, .89] Complexity -.00 -.06 -.85 .396 [-.00, 00] Model 5 .141 (Constant) -.30 -.13 .895 [-4.82, 4.22] Log*size .53 .16 1.27 .204 [-.29, 1.35] Industry .39 .15 1.67 .095 [-.07, 85] Sector .35 .14 1.39 .164 [-.15, .86] Complexity -.00 -.06 -.78 .437 [-.00, .00] Founding_year -.00 -.04 -.68 .498 [-.00, 00]
Secondly, we can conclude that on average, the corporations within the Motor Vehicles & Parts industry, B=.34, t=1.67, p=.095, 95% CI [-.059, .74], engage in more greenwashing than corporations in the Food, Beverages & Tobacco industry . However, this association was non-significant, meaning H2 cannot be accepted.
Thirdly, we can conclude that corporations within the B2C-sector,
B=.41, t=1.66, p=.098, 95% CI [-.08, .89] engage in more greenwashing than corporations within
the B2B-sector. However, this association was again non-significant, meaning H3 cannot be accepted.
.396, 95% CI [-.004, 002] has a negative association with the level of greenwashing, meaning that the more complex an organization is, the less it engages in greenwashing. However, this association was non-significant, meaning H4 cannot be accepted.
Fifthly, and finally, we can conclude that the association of founding year b*=-.04, t= -.68, p= .498, 95% CI [-.003, .002] has a negative association with the level of greenwashing, meaning that the younger the corporation is, the less it engages in greenwashing. However, the association was non-significant, meaning H5 cannot be accepted.
Discussion
Applying the content analysis of Baum (2012), this empirical research yielded significant as well as insignificant results. Firstly, to answer the research question “To what extent do the Fortune Global
500 companies engage in greenwashing in their CSR communication” we can conclude that
Fortune Global 500 companies engage in greenwashing to a small, but significant extent. As illustrated, this is perhaps less than was to be expected, which makes this a very relevant conclusion. Further, to answer our sub question “Which type of greenwashing is most common
within the corporations analyzed?” we can conclude that the most common type of greenwashing
was the Vague claim. This misleading claim by default is a fairly fuzzy concept, as is clear from the definition in Table 1. This should be kept in mind when drawing conclusions, since this claim might be up for interpretation by the researcher. Another important factor to note is that the presence of misleading claims was not equal. Some topics were more prevalent than others, the least frequent misleading claim being the Lesser of two Evils claim. This provides a relevant finding in that it indicates that greenwashing might occur more within a certain environmental claim category than in others. This could also indicate that more attention should be paid to specific types of
environmental claims and their green marketing (Nyquist, 2017)
Based on the results of the chi-square test we can conclude that the proportion of corporations within the Motor Vehicles & Parts industry are utilizing the misleading claims
significantly more than corporations within the Food, Beverages & Tobacco industry. This does not mean however, that they are engaging in significantly more greenwashing, as illustrated by the rejection of H3. This merely indicates that the difference between these two industries is significant. This is in line with the results of the study by Baum (2012), which is most likely explained by the similar measurement instruments. We will now examine the results of the hypothesis testing, and what these results mean regarding this empirical research.
As is presented in the results section above, only H1 can be accepted. Thus, we can conclude that larger corporations engage in in significantly more greenwashing. This finding can be considered a relevant one, since it adds to the notion that corporations that are larger in size, face more regulatory pressure and thus engage in more greenwashing (Chithambo et al., 2020); Delmas & Burbano, 2011).
Hypothesis Implications
Four of the five hypotheses (H2-H5) were insignificant and thus have to be rejected. Firstly, regarding H2 we must conclude that corporations within the B2B sector do not engage in
significantly more greenwashing than corporations within the B2B sector. This could relate to the findings of the study of Ramus and Montiel (2005). They found that corporations across sectors engage in similar CSR communications, due to the ease of copying each other as well as pressure to adhere to social obligations. This could explain the insignificant results since according to this study, a difference in the level of greenwashing across sectors does not exist.
Secondly, regarding H3 we must conclude that corporations within the Motor Vehicles & Parts industry do not engage in significantly more greenwashing than corporations within the Food, Beverages & Tobacco industry. This could result from the fact that we only compared corporations from two industries, thus failing to provide accurate comparisons between multiple industries. This is a threat to the external validity of this research. This finding can be nevertheless considered relevant, since the difference in the frequency of claims was significant, but the overall difference in the level of greenwashing was not.
Thirdly, regarding H4 we must conclude that older corporations do not engage in significantly more greenwashing than newer corporations. This could be due to the fact that the sample did not include a wide array of corporations that are new, perhaps resulting in the inability to draw accurate comparisons. Again, this poses a threat to the validity of this research.
Fourthly, and finally, regarding H5 we must conclude that more complex corporations do not engage in significantly more greenwashing than less complex corporations. This might be due to the fact that this research did not examine the specific greenwashing regulations within in each country, which could add relevant contributions to this conclusion. Based on the research of (Delmas & Burbano, 2011) the country of operations can be hypothesized to correlate with this variable. Thus, if the regulatory climate of each country would have been examined alongside the variable of complexity, it would perhaps have improved the significance.
Moreover, the insignificant results nevertheless are results themselves. The insignificance may be caused by numerous factors. The first general contributor might be the
inaccurate measurement within the codebook. This poses a threat to the reliability of this research. For instance, the variable of complexity within this research was reduced to extremely limited terms, which, ironically, can be seen as lacking complexity itself. Secondly, this research provides an arbitrary starting point for further greenwashing research, especially within the corporate communication domain. Due to the infancy of this research avenue, it could mean that some confounding variables were completely overlooked. These could for example the dominant organizational culture (Ashcraft et al., 2009; Delmas & Burbano, 2011; Dobusch & Schoeneborn (2015; Siano et al., 2017) or other organizational characteristics (Delmas & Burbano, 2011), which were beyond the scope of this research. With the inclusion of a wider array of variables, the
research would become more complex and perhaps would yield more significant results.
Theoretical Implications
Regarding the theoretical framework, some conclusions can be drawn. All of the CSR frameworks by Carroll (2015) can be related to the environmental claims within the sample of this research. It is thus useful to use CSR frameworks to examine greenwashing in the future, since these two concepts are inherently related. Greenwashing further most often usually occurs only within the CSR
communications of a corporation, meaning these two concepts should be analyzed in tandem. Moreover, according to the CCO perspective, CSR communication acts as goals that a corporation should strive towards. If these goals cannot be met, it leads to discrepancies between the internal organizational processes and the external CSR communication. This naturally leaves room for greenwashing. Thus, from this perspective greenwashing can be seen as the discrepancy between CSR aspirations and actual activities. This implies that greenwashing should be examined in tandem with the CCO perspective, because it provides opportunity for greenwashing to arise. The CCO perspective provides useful insights to especially the internal manifestation of CSR
communications within an organization.
The acceptance of H1 is in line with the results of the research conducted by Chithambo et al. (2020) as well as Delmas and Burbano (2011). We can thus make a tentative conclusion that stakeholder pressure does indeed lead to increased greenwashing. Thus, corporations should thoroughly and frequently map its different stakeholders, and track their expectations as well. This is in accordance with the Stakeholder Salience Model presented above. This finding further highlights the importance of dynamic and frequent stakeholder mapping, since according to this research, regulatory pressure is the largest determinant regarding the presence of greenwashing. It is important to note that despite the insignificant results, all of the examined variables correlated with each other, to a moderate and significant extent. The chosen variables
were thus logical to be examined together, especially since they all are drivers of greenwashing. This should be kept in mind for future research as well, since these variables do actually fit together not only in a logical, but a statistical sense as well. We will now describe the limitations of this research.
Limitations
The first limitation is related to the nature of the research. The coding process was conducted by the author, which can bias especially the score of the intercoder reliability test. Secondly, a number of the variables in the codebook can be seen as lacking complexity. The variables for size and complexity can be argued to be too arbitrary, which necessarily does not measure the actual corporation size or complexity. If these variables would be more complex, it would improve the internal validity of this research. Thirdly, the coding of misleading claims can be seen as fairly subjective. As Baum (2012) mentions, greenwashing can be sometimes viewed as a latent concept. What might appear to be greenwashing to someone, might not be that according to someone else. Hence, this research is deeply rooted within the perceptions of the coder, since they ultimately made the decision on whether greenwashing was present or not. This must also be kept in mind when analyzing the results and drawing conclusions results.
Future research
Regarding future research, there is naturally room for improvement. As mentioned above, other indicators should be added to the variables used in this research. Secondly, regarding H1, H3, H4 and H5, this research failed to yield accurate comparisons. This is due to the fact that the sample consisted solely of Fortune 500 companies, yielding a very homogenous sample. If this research were to be conducted again, a wider array of corporations should be included. Perhaps it would be interesting to examine very large corporations, as with this research, and compare them to medium as well as small corporations, such as start-ups. By doing this, more accurate conclusions could be drawn. This would improve the external validity of the results. By including start-ups, we would not only include smaller companies, but newer and less-complex ones as well. A further relevant
research avenue would be to examine corporations’ social media pages in parallel to the corporate webpages. This could yield extremely applicable results regarding the different CSR
communication strategies, depending on the medium of publication. ‘
Conclusion
This research found that the level of greenwashing within the examined companies is small, but significant. This nevertheless illustrates a presence of greenwashing, which in itself is a finding of great relevance. As illustrated, greenwashing does not only occur with lax regulation, but numerous drivers contribute to its presence. This is especially true within the complex environment
corporations today have to operate in, catering to the varying needs of increasingly demanding stakeholders. These factors illustrate the societal relevance of analyzing greenwashing.
As mentioned, the results of this empirical research can be used as a baseline for future research, which will continue to fill the gap in literature identified in this research. This relates to the scientific relevance of this research. However arbitrary in nature, this research nevertheless extended the existing theory and examined the presence of greenwashing in a novel way, mainly by analyzing corporate webpages. The developed measuring instrument can be further developed for the needs of subsequent research. It hence provides a theoretically valid starting point for researchers within the field of corporate communication. Despite the relevant conclusions drawn from this research, the question thus still remains; are corporations actually contributing towards a more sustainable tomorrow, or will being green come out in the wash?
APPENDIX 1: Codebook
This measuring instrument is based on the codebook by Baum (2012), and was modified for the purposes of this research. All publications from corporate websites including the words (at least one of the following) “environmental”, “ecological”, “sustainable”, “natural”, and “green” will be coded.
1. General Data
A. Industry: Indicate the industry associated with the publication using the following numeric system
1- Food, Beverages & Tobacco (=1) 2- Motor Vehicles & Parts (=0)
B. Corporation: the corporation responsible for producing the publication containing the environmental claim/image. List the full name of the corporation as it appears in the publication.
Food, Beverages & Tobacco sector 1. Nestle (=1)
2. PepsiCo (=2)
3. Archer Daniels Midland (=3) 4. Anheuser-Busch InBev (=4) 5. JBS (=5) 6. Bunge (=6) 7. Wilmar International (=7) 8. Louis Dreyfus (=8) 9. Tyson Foods (=9) 10. CHS (10)
Motor Vehicles & Parts sector
11. Volkswagen (=11) 12. Toyota Motors (=12) 13. Daimler (=13) 14. Ford Motor (=14) 15. General Motors (=15) 16. Honda Motor (=16)
17. SAIC Motor (=17) 18. BMW Group (=18) 19. Nissan Motors (=19) 20. Bosch Group (=20)
C. Country: Indicate which country (in words) the corporation originates from
D. Does the publication include a link to a related article on an external website? (Yes/No) E. Does the publication include a link to a related article on an external website? (Yes/No)
2. Publication Data
F. Title of publication (Code 0 if title is missing)
G. Publication claim type: Indicate the publication claim orientation by denoting “1” for
present and “0” for absent. More than one claim type may be present in a publication.
1- PRODUCT orientation: The claim focuses on the environmentally friendly attributes that a product possesses. Publications that fall into the “Product” category will be tangible consumer goods that are available to the public in the form presented in the publication. The environmental claim should be about the product. Example: Gasoline would be classified as a consumer product, whereas wind energy would not; Claims such as “This product is biodegradable” would classify the orientation of the publication as “Product.”
2- PROCESS orientation: Claims will be labeled process when the process in which the product is made has an environmental benefit or feature. Process claims will be claims that deal with an organization’s internal technology, production technique and/or disposal method that yields environmental benefits. The environmental claim should be about the process/service. More often than not, the claim deals with the process in which a product is made. Example: “Twenty percent of the raw materials used in producing this good are recycled.” Services advertised often fall under “process” orientation, rather than “product.”
3- IMAGE orientation: The claim associates an organization with an
environmental cause or activity for which there is broad-based public support. Example: “We are committed to preserving our forests.” Must be working to fix the environmental issue at hand. Publications claiming to implement/find ways to combat major environmental problems (energy crisis, water shortage, population growth, global warming/climate change, deforestation,
desertification, biodiversity loss, etc.) for the greater good of
society/environment are usually classified as having an “Image” orientation. 4- ENVIRONMENTAL FACT: The claim involves an independent statement that
is allegedly factual in nature from an organization about the environment at large, or its condition. Typically, “environmental fact” orientation and “image” orientation will go hand-in-hand, but not always. Look for clues to distinguish the two: Does the environmental claim(s) have anything to do with the
corporation? If the environmental claim is an environmental fact about pertinent environmental issues (energy crisis, water shortage, population growth, global warming/climate change, deforestation, desertification, biodiversity loss, etc.) that does not explicitly mention what the corporation is doing to combat the issue, then it would just be classified as “environmental fact” orientation.
Example: “The world’s rainforests are being destroyed at a rate of two acres per second.”
5- OTHER: The claim does not meet portray any of the above characteristics, but rather focuses on communicating an environmental claim in another way.
H. Misleading/Deceptive claim type: Indicate if there is a misleading/deceptive environmental claim asserted by the corporation. Denote “1” for present and “0” for absent.
a. HIDDEN TRADEOFF: Claim that suggests a product is ‘green’ based on an unreasonably narrow set of attributes without attention to other important
environmental issues. Hidden tradeoffs often have more to do with the process of making the product than the product itself. In order to determine if an environmental claim has a hidden tradeoff, look for clues in the product packaging or overall design of the “environmentally-friendly” product or service.
i. Example: Many energy, utilities and gasoline corporations often advertise the economic and environmental benefits of finding new sources of energy. Some of them explain that they are tapping into unexplored areas to source oil and thus, in doing so, carry the hidden tradeoff of habitat destruction and biodiversity loss.
ii. Paper is not necessarily environmentally-preferable just because it comes from a sustainably-harvested forest. Other important environmental issues in the paper-making process, including energy, greenhouse gas emissions, and water and air pollution, may be equally or more significant.
iii. Example: Household insulation products (such as bat insulation products for home renovation projects) that claim indoor air quality benefits without attention to other environmental aspects such as recycled content and manufacturing impacts.
iv. Example: Office technology (printers, copiers, fax machines) that promote energy efficiency without attention to hazardous material content, indoor air quality, or compatibility with recycled paper or remanufactured toner cartridges.
v. Other products that often commit this “sin” include laundry detergents, dish detergents, air fresheners, bathroom cleaners, markers, flooring laminate, bags, multi-purpose cleaners, wood panels and pesticides.
b. NO PROOF: Environmental claim that cannot be substantiated by easily accessible supporting information or by a reliable third-party certification. If a corporation makes a claim that includes percentages or statistics that are not supported by fine-print text or a URL to find out more information, the claim should be classified as “No Proof.” Also, if the claim includes something that could be proved (i.e. a
rating/ranking) and wasn’t, it should be classified as “No proof.” Other examples can include things that are deemed blatantly false by the coder. Common examples are facial or toilet tissue products that claim various percentages of post-consumer recycled content without providing any evidence.
i. Other examples include household lamps and lights that promote energy efficiency without any supporting evidence or certification and personal care products (shampoos, conditioners, etc.) that claim not to have been tested on animals but provide no evidence or certification of the claim.
c. VAGUE:
i. Vague claims are claims that don’t explicitly explain what a company is doing to reduce their environmental impact. A vague ad will leave the reader feeling like the corporation is dedicated to environmental issues and has some sense of corporate responsibility, but couldn’t identify specific strategies the corporation is implementing.
ii. Also, vague claims are also those that talk about an environmentally sound process in which a product is made but does not address the question of how. iii. Furthermore, environmental claims that are poorly defined or too broad so
“All-natural” is an example. Arsenic, uranium, mercury, and formaldehyde are all
naturally occurring, and poisonous. “All natural” isn’t necessarily “green.” iv. Other examples include: “non-toxic,” because everything is toxic in sufficient
dosages; “Green,” “Environmentally friendly,” and “Eco-conscious” are also vague because they are utterly meaningless without elaboration.
d. IRRELEVANT: Environmental claim that may be truthful but is unimportant or unhelpful for consumers seeking environmentally preferable products. ‘CFC-free’ is a common example, since it is a frequent claim despite the fact that CFCs are banned by law.
i. Other cases may be harder to detect. The coder should use his/her own judgment to decide whether or not the claim is relevant to the product. (If a light bulb claimed water efficiency benefits you should be suspicious). If the claim seems illogical and disconnected from the product, it may very well be irrelevant.
e. LESSER OF TWO EVILS: Environmental claims that may be true within the product category, but that risk distracting the consumer from the greater
environmental impacts of the category as a whole. Claims falling under this category have to do mainly with products, rather than processes (for process related claims, see “Hidden Tradeoff”). Organic cigarettes are an example of this category, as are fuel-efficient sport-utility vehicles, “green” insecticides/herbicides, and “cleaner” petroleum-based fuels.
3. Organizational data
a.) Indicate the number of sales the corporation had in the past year (in U.S. Dollars) b.) Indicate the number of assets the corporation had in the past year (in U.S. Dollars) c.) Indicate the number of employees the corporation had in the past year
d.) Indicate the founding year of the corporation
APPENDIX 2: SYNTAX Inter Coder Reliability:
KALPHA judges = Sonja Sonjacoder/level = 1/detail = 0/boot = 10000.
Main Analysis:
FREQUENCIES VARIABLES=Q19 Q20 Q21 Q22 Q23
/STATISTICS=STDDEV VARIANCE MEAN MEDIAN MODE SUM /ORDER=ANALYSIS.
COMPUTE level_green=Q19 + Q20 + Q21 + Q22 + Q23. EXECUTE.
DESCRIPTIVES VARIABLES=Food_companies level_green /STATISTICS=MEAN SUM STDDEV VARIANCE MIN MAX.
DESCRIPTIVES VARIABLES=level_green Motor_companies /STATISTICS=MEAN SUM STDDEV VARIANCE MIN MAX.
CROSSTABS
/TABLES=Food_companies BY level_green /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI ETA CORR /CELLS=COUNT
CROSSTABS
/TABLES=Motor_companies BY level_green /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI ETA CORR /CELLS=COUNT
/COUNT ROUND CELL.
CROSSTABS
/TABLES=Motor_companies BY Q19 Q20 Q21 Q22 Q23 /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI ETA CORR /CELLS=COUNT
/COUNT ROUND CELL.
CROSSTABS
/TABLES=Food_companies BY Q19 Q20 Q21 Q22 Q23 /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI ETA CORR /CELLS=COUNT
/COUNT ROUND CELL.
FREQUENCIES VARIABLES=founding_year
/STATISTICS=RANGE MINIMUM MAXIMUM MEAN MEDIAN MODE /ORDER=ANALYSIS.
CROSSTABS
/TABLES=founding_year BY level_green /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI CORR /CELLS=COUNT
/COUNT ROUND CELL.
CROSSTABS
/TABLES=founding_year BY level_green /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI CORR /CELLS=COUNT
/COUNT ROUND CELL.
CROSSTABS
/TABLES=Q2 BY level_green /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CC PHI CORR /CELLS=COUNT
/COUNT ROUND CELL.
COMPUTE mean_size=MEAN(sales,assets). EXECUTE.
EXAMINE VARIABLES=level_green /PLOT BOXPLOT HISTOGRAM /COMPARE GROUPS
/STATISTICS DESCRIPTIVES EXTREME /CINTERVAL 95
/MISSING LISTWISE /NOTOTAL.
GRAPH
/SCATTERPLOT(BIVAR)=Mean_size WITH level_green /MISSING=LISTWISE.
COMPUTE log_size=LG10(Mean_size). EXECUTE.
RECODE Q2 (2=1) (ELSE=0) INTO dummy_industry. VARIABLE LABELS dummy_industry 'Motor_industry'. EXECUTE.
RECODE Sector (1=1) (ELSE=0) INTO Sector_dummy. VARIABLE LABELS Sector_dummy 'B2C_dummy'. EXECUTE.
CROSSTABS
/TABLES=Q19 Q20 Q21 Q22 Q23 BY Q2 /FORMAT=AVALUE TABLES
/STATISTICS=CHISQ PHI /CELLS=COUNT
/COUNT ROUND CELL. CORRELATIONS
/VARIABLES=level_green Sector founding_year operations log_size Q2 /PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES /MISSING=PAIRWISE.
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT level_green /METHOD=ENTER log_size /METHOD=ENTER dummy_industry /METHOD=ENTER Sector_dummy /METHOD=ENTER operations
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