The effect of product portfolio greening strategies on legitimacy granted by Main Street and Wall Street in the automotive industry

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The effect of product portfolio greening strategies on legitimacy granted

by Main Street and Wall Street in the automotive industry

University of Groningen Faculty of Economics and Business MSc BA Strategic Innovation Management

Mark Schooneman – S2682923 Supervisor: Prof. Dr. J. Surroca Co-assessor: Dr. P. Steinberg January 20, 2020 Word count: 15213

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Abstract

This study examines the effect of green firm strategies in the automotive industry to secure the legitimacy on the stakeholder legitimacy granted by two stakeholder groups, the general public (“Main Street”) and the investors (“Wall Street”). Two product portfolio greening strategies have been identified: restructuring and extending. Financial data from renowned databases on 13 global car manufacturing companies from 2006 through 2019 are combined with publicly available data in a panel data set. The data set yields measures on the sentiment of Main Street, the sentiment of Wall Street and 9 control variables. A fixed effects regression model was fit to the data. The results show no effect of greening strategy on legitimacy granted by either Main Street or Wall Street. The control variables show an effect of age, performance and marketing intensity of the firm on the sentiment of Main Street. Furthermore, an effect is found of performance and R&D intensity on the firm on the sentiment of Wall Street. From these results it is concluded that the partial greening of the product portfolio of a firm, irrespective of the applied strategy, is not rewarded with legitimacy by Main Street and Wall Street. Furthermore, the data suggests that both Main Street and Wall Street have a static or polarized view on car manufacturers being either green or brown.

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ACKNOWLEDGEMENT

First and foremost, I want to express my gratitude towards my supervisor, J. Surroca, who provided me with valuable support, motivation and feedback along the road of completing this project. Also, I want to thank all the people involved in the SIM master. Their efforts and inspiration have made the realization of this thesis possible. Furthermore, I want to thank my fellow students with whom I have been able to share my thoughts and struggles with.

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1. INTRODUCTION

Throughout the years, awareness about the environmental sustainability increased. News regarding climate change and environmental pollution have reached many people. As a result of this, a growing concern about the impact of behavior on the environment (Krause, 1993) can be noted. This made actors at all levels of the market, consumers, governments and business organizations willing to undertake action. One of these actions is a shift in consumer preferences. Gradually, consumer preferences are changing from non-green or brown products to green products today (Martin & Simintiras, 1995; Saxena & Khandelwal, 2008). Businesses respond to this accordingly by inducing corporate ecological responsiveness through adjusting their product portfolio towards a more green portfolio in the hope to gain legitimacy and competitiveness (Bansal & Roth, 2000). However, do businesses truly gain from this?

A product can be labeled ‘green’ when it, consumes less resources when developed or used. This is reflected in the definition used in academic literature, where green products have been coined as products “that will not pollute the earth or deplore natural resources, and can be recycled or conserved” (Shamdasani, Chon-Lin & Richmond, 1993). There are two options in converting the brown product portfolio into a greener one. The first option is to restructure the current brown product portfolio by redesigning or substituting (successful) brown products for green products. The second is to extend the existing brown product portfolio through the introduction of new green products. In the business environment, innovation is a necessity for firm survival. According to Porter (1985), being one of the first companies in an industry to change, in this case towards green products, provides these businesses a sustainable competitive advantage. However, where green products for some entrepreneurs come as an opportunity, it can come as a threat to others. To dodge this threat and survive businesses are required to introduce green products to complement their product portfolio (Yenipazarli & Vakharia, 2015). However, a shift towards a green(er) product portfolio must be made carefully, as formerly brown companies are vulnerable to accusations of greenwashing (Delmas & Burbano, 2011).

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interests of stakeholders into consideration (Jugend, da Silva, Salgado, & Miguel, 2016) when implementing greening strategies to gain legitimacy from either one or both of them.

As environmental sustainability became a prominent topic in the public debate, it raised the attention of researchers related to business, resulting in an increase in studies regarding the topic. However, some themes within the fields of business have not been investigated yet. Considering the field of sustainability and product portfolio some interesting researches have been conducted Khalili-Damghani and Tavana (2014) tested an integrated project portfolio selection approach for strategic and sustainable projects. Jugend et al. (2017) explored how product portfolio and new product development (NPD) are influenced by green and traditional practices of NPD. While Yenipazarli and Vakharia (2017) provided insights considering a firm’s green strategy, taking pricing, environmental benefits and economic return into account. However, to the best of my knowledge, the degree to which the greening of a product portfolio contributes to the enhancement or diminution of a firm’s legitimacy, has not been studied. By the same token, the legitimacy granting audiences of Wall Street and Main Street have not been discussed in this vein of greening strategy research. Legitimacy is a highly important factor for a firm’s continuity in the automotive industry (Rao et al., 2008). Therefore, the unique contribution of my research will be based on this void of studies on legitimacy and investigated on data from the automotive industry.

One industry whose products and processes have always been a significant source of environmental impact is the automotive industry. Therefore, this paper focuses on the automotive industry setting. This will show that when a car manufacturer introduces a green product to its portfolio, it is pivotal to have a thorough understanding of how Main Street’s and Wall Street’s different perceptions of greening strategies will influence the legitimacy each strategy grants to the firm. The strategies investigated in this particular industry are the introduction of greener alternatives (hybrid, electric and fuel-cell powered engines) by virtue of product portfolio extending and restructuring. Due to time constraints and unavailability of data, other strategies car manufacturers have adopted to green their products are not incorporated in this study. The results of this study will enrich the automotive industry’s product portfolio management problem by providing guidance to business managers in choosing the best portfolio greening strategy considering the consequences it will have on the legitimacy granted by the stakeholders of Wall Street and Main Street.

In addressing this, the following thesis is leading:

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The analysis done in the current study finds no evidence for the effect of greening strategies on legitimacy granted by either Main Street or Wall Street. Hence, none of the assumptions about the automotive industry are proven wrong or right.

In the subsequent sections of this paper, the first chapter presents the empirical setting regarding the automotive industry. Second, theoretical framework is outlined which introduces institutional theory and legitimacy to start with. Succeeding, the two audiences under study are acquainted together with their varying demands and needs. Consecutive, the two green strategies of product portfolio greening are further defined. Finally, the hypotheses under study are presented. After this, the third chapter will present the sample along with the methodological decisions and measures considering the variables of data gathering are described. Fourth, an analysis of the gathered data is conducted and results from the analysis are provided. The fifth chapter named conclusion presents the theoretical and managerial implications of this study and an overall conclusion. Lastly, a discussion in which the limitations and recommendations for future research are discussed is presented.

2. EMPIRICAL SETTING

Although the ‘modern car’ was born by the filing of the Benz Patent-Moterwagen in 1886, cars came into global use in the 20th century. Cars have become crucial to developed economies, which is reflected in the size of the industry. The automotive industry is one of the world’s largest economic sectors by revenue with an output of 96.9 million vehicles in 2017 (OICA) and a total value of as much as 2 trillion US dollars (Jenkins, 2018). Because of this nature and the scale, the automotive industry has been involved in scandals and has been the target of scrutiny. Well-known scandals are the unsafe 1960 Chevrolet Corvair and Ford Pinto from the 1970’s due to respective design errors and cost cuts. The bribing of officials around the world by Daimler which came to the light in 2010. And the most recent and arguably biggest environmental scandal of all time: the Volkswagen diesel scandal. Harming not only the entire automotive industry, but also the segment related to diesel powered engines, like diesel fueled powerplants.

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Around the turn of the millennium the automotive industry realized they had to adjust their businesses to the current demand of tailpipe pollution reduction. Examples of adjustments of the industry are the arrival of the first commercially available hybrid vehicles like the Toyota Prius and the appearance of electric car manufacturer Tesla Motors, Inc. to the market in 2003. These archetypes show the implementation of environmentally friendlier and greener products, as they are powered by clean and renewable electricity.

There is a large global demand for cars and an increasing awareness of the relevance of less polluting cars (Randall, 2016). As a result, this combination raised a competitive force to green product portfolios by investing billions of dollars in alternative fuels (Bos & Hsu, 2019). However, well-known manufacturers might want to take into account that scandals regarding emissions have affected people’s trust in the existing automotive Industry (Barney & Hansen, 1994). As a result of these two different forces it remains unclear what strategy is best to adopt. The hypotheses of this research are based on this setting of not knowing what strategy is best to follow when firms want to satisfy either or both of the stakeholder groups.

3. THEORETICAL FRAMEWORK

In this section it will be theorized how the audiences of Main Street and Wall Street respond to different strategies firms implement in shifting towards a greener product portfolio. Ultimately, the audiences determine the legitimacy they award to a firm based on, among others, their liking of the chosen portfolio greening strategies. The first section of the theoretical framework will address the topic of legitimacy. The definition of legitimacy is discussed together with the importance of legitimacy to firms. The second part presents the relevant stakeholder groups and how they expect firms to operate in order to be awarded with legitimacy. Third, different product portfolio greening strategies will be described. Lastly, it will be hypothesized how the stakeholder groups in the current research are likely to respond to the greening strategies according to related and previous research.

3.1 Institutional theory & legitimacy

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on the normative pillar of legitimacy. This is chosen since stakeholder groups each have their own norms and values to which a firm adjusts its actions. This is a social obligation compared to the regulative and shared understanding that are at the foundation of respectively the regulative and cultural-cognitive pillar (Scott, 2013).

Most scholars that write on legitimacy take stance with Suchman’s (1995) definition of the subject. Within this definition the focal point of legitimacy is the acceptation of firm activities and goals by different audiences. However, for the context of this study the widely accepted definition of corporate environmental legitimacy of Bansal and Clelland (2004) is more suitable, since it focusses on one stakeholder dimension, namely the natural environment. Bansal and Clelland (2004) have defined this as the generalized perception or assumption that if a firm’s corporate environmental performance is desirable, proper, or appropriate, is assessed by stakeholders. These stakeholders include managers, customers, investors, and community members and grant “the firm’s legitimacy according to their own distinct and diverse norms, “cognitive maps,” and pragmatic preferences” (Bansal & Clelland, 2004). The current work focuses on a subset of activities of environmental legitimacy, being green legitimacy. Achieving, maintaining or repairing legitimacy is fundamental for any firm in that it helps to attract customers (Rao et al., 2008) and thereby boost sales, it can secure governmental protection (Aldrich & Fiol, 1994) and creates access to capital and (therefore) resources and new markets. In all, this ensures a firm’s continuity, making legitimization an essential good to any firm. Therefore, any attack on firm legitimacy should be countered, considering the fact that a destabilization of it can threaten its very survival (Dowling & Pfeffer, 1975)

3.2 Audiences

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From this it can be explained why different stakeholder groups form dissimilar opinions on the same phenomena.

In the corporate system of publicly traded stocks and stake investments, Wall Street is a dominant and determining stakeholder in many business decisions. Wall Street is dominant since they are partially the owners of the firms and determining as they grant legitimacy to the firm. Leading in the allotment of legitimacy by investors is “the long-run value of the firm and its future performance as reflected in its stock price” (Lamin & Zaheer, 2012). Correlated to a firm’s profits is the stock price, thus investor’s return on investment. Therefore, shareholders are concerned with a firm’s legitimacy residing within customers too, since this is related to sales and firm profit. Consequently, shareholders monitor a firm’s actions very closely since these might contribute to an increase or decrease of future cash flows. (Brealey & Myers 1984; Benner & Ranganathan 2009). To illustrate, when a firm is planning to implement greening strategies that might result in uncertain or decreased profits, this could potentially lower the stock price, thus the legitimacy granted to the firm by shareholders will be affected negatively. Shareholders then want the firm to adhere to them inconsiderate of the positive effects of the new strategy for society. According to investor’s, privileging the stockholders above the stakeholders is the appropriate role of the firm in society (Friedman 1962). Evan and Freeman (1988) noted that stakeholders make different claims on an organization. This discrepancy of interests between shareholders and non-shareholders becomes rather clear by the fact that firm actions which Wall Street perceives as positive, may be viewed upon with disapproval by non-shareholders. When, as an illustration, a river is contaminated due to a firm’s actions, non-shareholders are concerned about the effects on human health and nature, where shareholders are more interested in the resulting legal and thereby financial liabilities (P. Bansal & Clelland, 2004). According to Frank (1988) non-shareholders value a firm’s actions from the perspective of the broader societal impact, instead of the impact of the financial returns (Beauchamp et al., 2008). Furthermore, the general public evaluates a firm’s actions based on Suchman’s (1995, p. 574) principles of legitimacy whether “the actions of the firm are desirable, proper, or appropriate”. This evaluation can be viewed as a social judgement (DiMaggio and Powell, 1991) determining if a firm qualifies as a good corporate citizen that should be rewarded with legitimacy. Coming back to the case of the contaminated river. When the accountable firm takes responsibility to deal with the aftermath by solving the harm done to people and nature combined with the installment of measures to prevent this from happening again, the legitimacy granted to the firm by non-shareholders will be affected positively. Hence, for a firm to increase its legitimization with the stakeholder groups of Main Street and Wall Street, it is imperative to define and adhere to their varying demands, as Main Street and Wall Street grant legitimacy accordingly to the fulfillment of their needs.

3.3 Green strategies: greening the product portfolio

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For a firm to hold on to the legitimacy it has been granted by their stakeholders it is important to move along with the stakeholders’ changing demands. A firm must undertake actions to maintain, boost or restore its legitimacy. These actions are driven by strategic choices.

Many incumbent firms are facing a decline in the legitimacy of their conventional, often polluting, technologies (Patala, et al., 2019). So is the automotive industry whose products are mainly powered by the usage of fossil fuels such as gasoline or diesel. Industries like this cannot turn away from their conventional brown products to an entire green product portfolio overnight, but they can transform their product portfolio in such a manner that it becomes less brown, or put differently, greener. This can be achieved through incremental innovations, such as increasing efficiency with hybrid vehicles and lightening vehicles or with more radical actions that are new to the market, like electrically powered vehicles. In literature (Ryan, Hosken & Greene, 1992; Wever, Boks & Bakker, 2008; Jabbour et al., 2015; Yenipazarli & Vakharia, 2017), the general approach to introduce green products is twofold.

3.3.1 Restructuring the product portfolio

The first strategy is referred to with a variety of names such as, redesigned product, greened-up product and refreshed brown product (Yenipazarli & Vakharia, 2017). This strategy encompasses the replacement or redesign of an existing brown product for improved environmental performance (Ryan et al., 1992) by a greener version or alternative. Within this thesis this is referred to as ‘restructured’ products. These brown products are improved or discontinued from the current portfolio as a result of their inferior performance on sustainability (Wever, Boks, & Bakker, 2008). In order to alter the attributes of current products and become green(er), the environmental impact of the entire product life cycle needs to be improved. This entails a reduction or substitution of environmental hazardous substances (González-Benito & González-Benito, 2006), reduction in the use of energy, water and other resources, lower carbon-emissions and waste (Lindell & Karagozoglu, 2001) and the implementation of biodegradable packaging (Kammerer, 2009; Yenipazarli & Vakharia, 2017).

3.3.2 Extending the product portfolio

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changes to them. An implication of this strategy is that the brown products in the portfolio are still available to the market as before and thereby they keep polluting the environment like before as well.

3.4 Hypotheses

Main Street as a stakeholder is concerned with the society as a whole (Frank, 1988). Given the current increasing focus on environmentally friendly lifestyles (Neuvonen et al., 2014; Capstick et al., 2015), they, as a group, want the environment to improve, instead of deteriorating it by the production and use of brown products. Some customers refuse to buy products that harm the environment (Qi, Shen, Zeng, & Jorge, 2010; Zeng et al., 2011; Weng, Chen, & Chen, 2015). As a result, companies are encouraged by Main Street to create green products. Therefore, Main Street is pleased with firms who address the problem of the polluting (brown) products by restructuring their product portfolios to become greener by substituting brown products by green environmentally friendly versions of their products. Main Street rewards firms that green their product portfolio with legitimacy. For these reasons, the following hypothesis is stated:

H1a: If a firm restructures its product portfolio by substituting brown products for green products, then Main Street’s perception of legitimacy of the firm will increase.

Restructuring a firm’s portfolio through the discontinuation of good selling brown products as demanded by the market, brings anticipated, but hard to predict opportunities and consequences for firm profitability. In some instances, cost savings occur due to more efficient production processes (Hart & Ahuja, 1996). In others, the product in the restructured portfolio could end up being more expensive after the brown version has been substituted by a green alternative (Yenipazarli & Vakharia, 2017). Possible factors causing the price increase after substitution are the costs of the innovation process itself and more expensive materials. As a result, the market has to pay a price premium for the green product. This can turn the bright prospect of increasing sales into a rather grim sales prospect, since not all consumers are willing to pay this premium (Miremadi, Musso, & Weihe, 2012), leading to a drop in sales and thereby firm profitability. Hence, the green restructuring of the product portfolio brings a lot of uncertainty considering the sales prospects, while profitability of the prior brown product was relatively certain. Therefore, it is proposed that:

H1b: If a firm restructures its product portfolio by substituting brown products for green products, then then Wall Street’s perception of legitimacy of the firm will decrease.

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scenario, Unruh and Ettenson (2010) have posed that a broader brand portfolio will have a firm more exposed to activist and consumer backlash. Not to mention that most firms have a lack of green heritage when they enter the spotlight with their freshly and one (of few) green product. The main reason of this originates from the fact that many incumbent firms have developed the products in their portfolio before sustainability was a point of concern. However, a too big imbalance between green and brown products can undermine a firm’s legitimate sustainability claims (Unruh & Ettenson, 2010). When this occurs, firms can end up being accused of greenwashing (Delmas & Burbano, 2011). Greenwashing is the act of positive communication about environmental performance to increase profits (Yadav & Singh, 2014), whereas in reality having a rather poor environmental performance (Delmas & Burbano, 2011). Wright (1986) found that acts of corporate social responsibility are discounted when they appear to be motivated by profit. Alternatively, Ashforth and Gibbs (1990) lay out another theoretical reason why firms that try to defend their legitimacy become the victim of their own portfolio restructuring strategy and actually worsen their legitimacy. In the case that a firm is under attack because of its lack of green products and answers with the introduction of some green products, this can lead to even more harm. Main Street does notice the new green product, yet it does not see the firm deal with the brown products the firm got in trouble for. The actual product portfolio of brown products remains the same and these brown products are kept in the market. Meaning that the negative effects of environmentally unfriendly products in the portfolio are not addressed, causing a dent in Main Streets judgement of the firm. For these reasons, the following hypothesis is formulated:

H2a: If a firm extends its product portfolio with additional green products, then Main Street’s perception of legitimacy of the firm will decrease.

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H2b: If a firm extends its product portfolio with additional green products, then Wall Street’s perception of legitimacy of the firm will increase.

4. METHODOLOGY

In this section the methodological decisions in the current research are described. The first section elaborates on the dataset, sample and time span. Second, the measurement of the dependent, independent, and control variables of interest are be described. Third, the technique of analysis is described.

4.1 Sample and data sources

In selecting a sample, it was chosen to focus on firms in the worldwide automotive industry. A new dataset was constructed by combining these four databases: Fortune, ASSET4, Eikon and OECD.Stat. Fortune’s yearly ‘World's Most Admired Companies list’, which is also referred to as the Fortune corporate reputation index (FRI). This list is used as a measurement of the public opinion on a firm over time. The firms included in the World's Most Admired Companies list are based on the public opinion and comes from either the FORTUNE 1000 and global 500 lists, which are based on respectively revenue and revenues of 10 billion or more. The Fortune database comprises 57 separate industry lists. From the Fortune database the list labeled ‘motor vehicles’ was selected as source list. The scores (ranging from one to ten) are based on surveys held among industry’s senior executives, directors, and industry analysts on nine criteria (Appendix A). The ASSET4 database of Thomson Reuters accessible through their DataStream service which provides environmental, social and governmental (ESG) information. This database has grown from a worldwide coverage of 1500 firms in 2002 to 7000+ firms present day. Eikon is a package of software products from Thomson Reuters too. This study used the Microsoft excel part that provides data on firm specific financial data like ASSET4. The OECD.Stat database is run by the Organization for Economic Co-operation and Development (OECD). This is an intergovernmental economic organization consisting of 36 member states. Their goal is to stimulate economic progress and world trade, of which is kept track in their own database on 22 different themes. The last source of data consists of publicly available data on products of car manufacturers gathered online.

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of thirteen multinational companies considered to have a comparable global impact and knowledgeability.

The data gathered comprises observations of multiple phenomena over multiple time periods for the same firms, therefore it will be ultimately presented and analyzed as a panel data. As a result of the merger of the Fortune, Eikon and ASSET4 dataset, the panel data sample of this study consists of thirteen firms over the time frame of fourteen years (2006-2019), providing a total of 182 observations. The population under study represents around 64% of the global sales of automobiles (OICA, 2017).

4.2 Measurement of the variables

In constructing the panel data, data is gathered on several different measures for every firm and every year on December 31st. Two measures related to the product portfolio greening strategies are implemented and two to reflect the opinion of Main Street and Wall Street on the green strategies. Also, a few control variables are incorporated in the dataset. Due to endogeneity concerns all the independent variables are lagged with one year. Resulting from the fact that the dependent variable is measured one year later in time than the independent variables, the dependent variable can not influence the independent variables, but the independent variables can influence the dependent variables.

Greening strategies

In greening their product portfolio car manufacturers primarily look for alternative technologies to power their vehicles (Nunes & Bennett, 2010). Alternative powering of vehicles can be considered a valid measure of product portfolio greening. Replacing gasoline and diesel for hybrid, fully electric and in some cases (hydrogen) fuel-cell systems. The adoption of alternative fuels by manufacturers is taken as a measure for an assessment as greening strategy, given that it is the most direct measure. Data was collected on models that were introduced and sold in the given time frame and consume alternative fuels. This measure of product portfolio greening is segregated into two greening strategies. As this measure of greening strategies is not used priorly, validity will be subject of discussion in this thesis. In order to segregate the green strategy after its occurrence in the marketplace, the following rationales are leading:

RESTRUCTURING When an alternative fuel vehicle model at the moment of introduction is already offered in the form of a traditional fuel (diesel and/or gasoline), then the green strategy is subjected to the category of restructuring, because the portfolio is restructured through modification of an existing product.

EXTENTION When the alternative vehicle model at the moment of introduction is not present in the form of a traditional fuel (diesel and/or gasoline), then the green strategy is subjected to the category of extending, since the portfolio is extended through the introduction of a totally new product.

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Likewise, the application of the extending strategy in a year is labeled as 1 when it is present and 0 when it is absent. The overview was created by use of publicly available online trade platforms such as Gaspedaal.nl, Ebay.com and Cars.com.

Reviewing the sentiment of Main Street

Main Street’s attitude towards the greening strategies in the automotive industry is measured by use of reputation index data from Fortune. The scores in this range from one (poor) to ten (excellent). Despite the wide use in academic literature, the list has received the suggestion from Frombrun & Shanely (1990) and McGuire, Schneeweis, & Branch (1990) of being “an amalgamation of financial metrics that reflects a firm‘s overall financial health” (Hall & Lee, 2014). However, after research Lee & Hall (2008) conclude that “the validity of the FRI as an acceptable proxy for firm reputation and social responsibility has been reestablished”. When a firm’s score has improved (gotten higher) compared the previous year, this indicates that Main Street approves the greening strategy. This results in Main Street granting legitimacy to the firm, since the firm has delivered on the interests of them. For the firms included in the sample, missing values in the dataset were left as a blanc, not meaning a score of zero, but meaning that the firm was not included in the list of the specific year(s). Not insignificant to mention, is that there is a lag in creating the list. The list titled Most Admired Companies 2019 is based on the findings of the book year of 2018. The sentiment of Main Street was checked and controlled for its distribution and outliers. This variable was complied with the assumptions and no additional corrections have been performed.

Reviewing the sentiment of Wall Street

The sentiment of Wall Street regarding the strategic choices, is measured by the financial measurement of the Tobin’s Q. This market based measure illustrates the ratio between a firm’s physical asset market value and its replacement value. Data that are used as inputs to generate the Tobin’s Q score are retrieved from ASSET4 of Thomson Reuters DataStream. The current study uses the method of Chung and Pruitt (1994) to calculate the Tobin’s Q. This method is less sophisticated compared to the more traditional method of Lindenberg & Ross (1981). Nevertheless, when the financial and accounting data are put together, the formula by Chung and Pruitt (1994) is highly correlated with Lindenberg and Ross’s (1981). Following the formula used in the research of Chung and Pruitt (1994), Tobin’s Q is calculated as1_:

Tobin's Q = (MVE + PS + DEBT)/TA

The resulting scores can range from 0 until infinity. However, in most industries the average score will center around one. A score of one indicates that the market value of a firm’s assets is equal to the book

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value of its assets. In certain circumstances the assets of a firm are valued higher in the market than their actual book value, this results in a score higher than one. In other circumstances, it can be that the score is lower than one, indicating that a firm’s assets are valued lower in the market than their book value. With this knowledge, the Tobin’s Q is used to check whether the market value of a car manufacturer has increased compared to the year before implementation of the restructuring and/or extending strategy. An increase in the Tobin’s Q, and especially above one, implies that there is a positive sentiment with Wall Street. Indicating that, among others, the selected greening strategy has made the value of the firm rise over the past year. This results in Wall Street granting legitimacy to the firm, since the firm has delivered on the interest of them. A decrease in the Tobin’s Q, and especially below one, implies that there is a negative sentiment with Wall Street. Indicating that, among others, the selected greening strategy has made the value of the firm lower over the past year. This results in Wall Street denying or decreasing the legitimacy granted to the firm, since the firm has not delivered on the interest of them. Not insignificant to mention, is that the Tobin’s Q is a forward looking measure. To illustrate, the ratio of 2018 reflects Wall Street’s expectations for 2019. The sentiment of Wall Street was checked and controlled for its distribution and outliers. This variable did not comply with the requirements of a regression and was therefore winsorized at 0% and 98%.

Control variables

Firm Size

Total assets is used as a measure of firm size. Research from Kemp et al. (2003) found that firm size is an influencing factor on a firm’s innovation activity and performance. The logic behind this, according to Tsai, (2001), is that larger firms have more resources at their disposal in order to ameliorate their level of innovation and performance. However, apart from this benefit, large firms are also more likely to be under pressure to maintain legitimacy (Meyer & Rowan, 1977). Reason for this is that large companies are the target of regulators, media, communities and consumers when it comes to environmental complaints (Guoyou et al., 2013). This makes large firms commercially vulnerable to the judgements from these stakeholders (Roberts, 1992). The variable Total Assets is checked for its distribution, outliers and linearity with the dependent variables. The distribution of this variable has a natural logarithm pattern. Therefore, this variable was log-transformed.

Firm Age

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with the dependent variables. Based on this test the distribution of the variable Firm Age did not need to be adjusted.

R&D Intensity

Research and development (R&D) intensity is used as a measure of the amount of money a firm spends on R&D. It is the percentage of a firm’s turnover spend on R&D. A higher percentage could result in a higher number of green innovations (McWilliams & Siegel, 2000). This could provide more incentive to Main Street and Wall Street to increase or decrease a firm’s legitimacy based on their desires. Performance

Return on Assets (ROA) is used as a measure of performance, the relationship between financial and social performance has been proven in other investigations by among others Dam & Scholtens (2012). Also, Zahra, Neubaum & Huse (2000) showed that the availability of resources can enhance the development of innovative and environmental activities. This variable is checked for its distribution, outliers and linearity with the dependent variables. Based on this test the ROA did not comply with the requirements of a regression analysis and was therefore winsorized at 6% and 98%.

GDP per Person Employed

GDP per person employed is a measure used as an indication of the size of economies in which the car manufacturers are head quartered based on the income of the people employed in these countries. Even though the automotive industry is an international industry ordinarily, a large proportion of its sales take place in the domestic market. Ordinarily the country with the most sales per head of the population is the home country, as can be seen with Renault (Renault, 2019) and Mercedes-Benz (Daimler, 2018). GDP per person employed indicates the disposable income of the population and thereby the opportunity for them to buy a (domestically produced) vehicle. This variable is checked for its distribution, outliers and linearity with the dependent variables. The distribution of this variable has a natural logarithm pattern. Therefore, this variable was log-transformed.

Gov. R&D Inv.

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Free cash flow is a measure used to monitor the slack resources available (Brown et al., 2009). It is calculated as operating cashflow minus capital expenditures (Bloch, 2005). Literature is not unambiguous with regard to the effect of free cash flow on innovation. Agency theory claims that slack resources obstruct innovation activity, while behavioral theory argues that slack resources foster innovation activity (Lee & Wu, 2016). This variable is checked for its distribution, outliers and linearity with the dependent variables. Based on this test, the performance variable of this variable did not comply with the requirements of a regression and was therefore winsorized at 23% and 83%.

Marketing Intensity

Marketing intensity is operationalized via the Selling and general expenses (S&GE) (Mizik et al., 2007; Luo, 2008; Kurt & Hulland, 2013). This measure is used as a proxy of the marketing efforts made to communicate and convert a business’s mission to the public. The S&GE measured via Datastream includes the R&D expenses. Therefore, to create a more accurate measurement of marketing intensity and following Kurt and Hulland (2013) this study subtracted the R&D expenses from the S&GE expenses. Marketing activities of a firm are the communication with the outside world. Therefore, both Main Street and Wall Street may be influenced by the marketing activities. Thereby, higher S&GE expenditures could indicate that more information is available for both Main Street and Wall Street to influence their perception.

4.3 Technique of the analysis

The current study used two dependent variables. The first dependent variable is a measure to determine the sentiment of Main Street. Sentiment was measured by the FRI score published by Fortune. The score ranges from one to ten with and is measured at a two decimal level. Since this variable has the nature of an interval/ratio type variable, a standard parametric test could be performed. The second dependent variable measures the sentiment of Wall Street, which was performed by the Tobin’s Q value. This value can range from zero to infinity. The Tobin’s Q measurement also has the nature of the interval/ratio type variable. Therefore, a standard parametric test could be performed here as well.

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Both dependent variables were inspected for their distribution and outliers, because a regression is sensitive for influential data points and outliers (Stevens, 1984). The measurement on sentiment of Main Street complied with the requirements of a regression and is used unaltered. The measurement on sentiment of Wall Street, however, did not comply with the requirements of a regression and was therefore winsorized at 0% and 98%. The independent variables in this study are lagging because the sentiment of Main Street is based on the test results of the previous year. Time alignment of the dependent and independent variables was performed by lagging the independent variables accordingly. Since the risk of endogeneity with the sentiment of Main Street variable is low, no additional measures were necessary. The independent variables and the sentiment of Wall Street could suffer from endogeneity problems. Causality between the market value of the company and the sentiment of Wall Street is conceivable. The management of the company could perform certain actions in the expectation of a higher stock price, leading to causality between the independent variables and the sentiment of Wall Street. For this reason, the independent variable related to the Wall Street sentiment data are lagged to reduce the possible reversed causality problems.

The analysis was characterized by the following regression formulas:

Sentiment of Main Streetit = B0 + B1 * Restructureit-1 + B2 * Extendit-1 + B3 * Firm Sizeit-1 + B4 * Firm Ageit-1 + B5 * R&D Intensityit-1 + B6 * Performanceit-1 + B7 * GDP Employedit-1 + B8 * GOV Env. R&Dit-1 + B9 * Free Cash Flowit-R&Dit-1 + BR&Dit-10 * Dummy R&D Intensityit-R&Dit-1 + BR&Dit-1R&Dit-1 * Marketing Intensityit-R&Dit-1 + εit Sentiment of Wall Streetit = B0 + B1 * Restructureit-1 + B2 * Extendit-1 + B3 * Firm Sizeit-1 + B4 * Firm Ageit-1 + B5 * R&D Intensityit-1 + B6 * Performanceit-1 + B7 * GDP Employedit-1 + B8 * GOV Env. R&Dit-1 + B9 * Free Cash Flowit-R&Dit-1 + BR&Dit-10 * Dummy R&D Intensityit-R&Dit-1 + BR&Dit-1R&Dit-1 * Marketing Intensityit-R&Dit-1 + εit

5. RESULTS

5.1 Descriptive statistics and correlations

In table 1 and table 2 the sample statistics are respectively presented for the available observations of the dependent variables of Sentiment Main Street and Sentiment Wall Street. The presented sample statistics include the mean, standard deviation, minimum and maximum of all variables incorporated in this research. Additionally, the dummy variable introduced for the R&D intensity variable is shown.

Descriptive statistics for Main Street analysis

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the firms on average have 1369895094 dollars’ worth of assets. Firm Age (M = 83.483, SD = 18.229) show that the firms are on average over three quarters of a century old. From this it could derived that they are well established. R&D Intensity (M = 2.266, SD = 2.113) shows that the average investment in R&D is 2.266% of the total revenue. Performance (M = 3.532, SD = 1.769) determined by the ROA, shows that the net income is 3.567 dollars per one dollar in assets under the control of the firms. GDP Employed (M = 11.344, SD = 0.153) is relatively high (Worldbank, 2019). Gov. R&D Inv. (M = 2.041, SD = 0.978) presents that 2.041% of the R&D investments the governments of the head quarter based countries is spend on environmental R&D. Free Cash Flow (M = -9.88e+08, SD = 2.44e+10) indicates that the average cash flow is negative with -9.88e+08.

Descriptive statistics for Wall Street analysis

From table 2 the following descriptive statistics on the mean and standard deviation are derived on the Sentiment of Wall Street (M = 0.573, SD = 0.165). This indicates that automotive industry in this sample has a Tobin’s Q score of 0.573. This is a normal score for this industry (Khoo, 2019). Based on research of Khoo (2019) the Tobin’s Q for a car manufacturer such as Honda varies between 0.2784 and 0.4462. Firm Size (M = 20.894, SD = 2.401) which indicates the firms on average have 1186175378 dollars’ worth of assets. Firm Age (M = 87.497, SD = 18.998) show that the firms are on average over three quarters of a century old. From this it could be derived that they are well established. R&D Intensity (M = 2.259, SD = 2.055) shows that the average investment in R&D is 2.259% of the total revenue. Performance (M = 3.477, SD = 1.816) determined by the ROA, shows that every dollar the firm has put in assets has a return of 3.477 dollars. GDP Employed (M = 11.347, SD = 0.147) is relatively high according to the Worldbank (2019). Gov. R&D Inv. (M = 2.072, SD = 0.933) presents that 2.072% of the R&D investments the governments of the head quarter based countries is spend on environmental R&D. Free Cash Flow (M = 3.46e+09, SD = 2.55e+10) indicates that the average cash flow is positive with 3.46e+09.

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TABLE 1: Descriptive statistics - Sentiment of Main Street

Variables Mean Std. Dev Min Max

Sentiment Main Street 5.675 1.174 3.200 7.990

Restructuring 0.271 0.446 0 1 Extending 0.093 0.292 0 1 Firm Size (ln) 21.038 2.519 17.849 25.827 Firm Age 83.483 18.229 38 117 R&D Intensity 2.266 2.113 0 6.540 Performance (wins) 3.532 1.769 0.070 7.710 GDP Employed (ln) 11.344 0.153 10.974 11.725 Gov. R&D Inv. 2.041 0.978 0.385 4.124 Free Cash Flow (wins) _{-9.88e+08 2.44e+10 -3.07e+10 4.51e+10} Dummy R&D Intensity 0.390 0.490 0 1

Marketing Intensity 0.108 0.035 0.032 0.187

Number of observations: 118; Std. Dev: standard deviation; Min: minimum; Max: maximum

TABLE 2: Descriptive statistics - Sentiment of Wall Street

Variables Mean Std. Dev Min Max

Sentiment Wall Street 0.573 0.165 0.225 1.136

Restructuring 0.239 0.428 0 1 Extending 0.107 0.310 0 1 Firm Size (ln) 20.894 2.401 17.849 25.903 Firm Age 87.497 18.998 38 118 R&D Intensity 2.259 2.055 0 6.540 Performance (wins) 3.477 1.816 0.070 7.710 GDP Employed (ln) 11.347 0.147 10.974 11.711 Gov. R&D Inv. 2.072 0.933 0.397 4.124

Free Cash Flow (wins) 3.46e+09 2.55e+10

-3.07e+10 4.51e+10 Dummy R&D Intensity 0.365 0.483 0 1

Marketing Intensity 0.111 0.035 0.032 0.187

Number of observations: 159; Std. Dev: standard deviation; Min: minimum; Max: maximum

The correlation matrix in table 3 and table 4 show respectively the correlations of the dependent variables Sentiment of Main Street and Sentiment of Wall Street.

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Extending and Sentiment of Main Street (Ahmad & Usop, 2011). Admitting it is only a weak correlation, this is not in line with the theoretical expectations discussed in the theoretical framework.

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25 TABLE 3: Correlation Table - Sentiment Main Street

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1. Sentiment Main Street _1.000

2. Restructuring 0.090 1.000 (0.334) 3. Extending 0.183 0.067 1.000 (0.047) (0.473) 4. Firm Size 0.050 0.019 0.184 1.000 (0.587) (0.837) (0.046) 5. Firm Age -0.072 -0.050 -0.087 -0.744 1.000 (0.437) (0.592) (0.348) (0.000) 6. R&D Intensity 0.099 -0.200 0.138 0.112 -0.071 1.000 (0.289) (0.030) (0.136) (0.226) (0.447) 7. Performance 0.361 0.084 -0.013 0.065 -0.057 0.002 1.000 (0.000) (0.368) (0.885) (0.487) (0.538) (0.983) 8. GDP Employed -0.003 0.010 -0.140 -0.799 0.799 -0.031 0.050 _1.000 (0.972) (0.913) (0.130) (0.000) (0.000) (0.737) (0.591)

9. Gov. R&D Inv. 0.268 0.087 0.025 -0.212 -0.113 -0.076 0.013 -0.084 1.000 (0.003) (0.350) (0.792) (0.021) (0.223) (0.412) (0.888) (0.366)

10. Free Cash Flow -0.039 -0.054 -0.174 -0.252 0.111 -0.102 0.287 0.163 0.056 _1.000 (0.672) (0.562) (0.059) (0.006) (0.232) (0.270) (0.002) (0.078) (0.548)

11. Dummy R&D Intensity -0.010 0.177 -0.077 -0.213 0.225 -0.861 -0.065 0.194 -0.041 0.038 _1.000 (0.919) (0.055) (0.407) (0.020) (0.014) (0.000) (0.487) (0.035) (0.662) (0.683)

12. Marketing Intensity -0.336 -0.137 -0.108 0.043 -0.261 0.212 0.030 -0.298 -0.117 0.283 -0.248 1.000 (0.000) (0.138) (0.242) (0.643) (0.004) (0.021) (0.748) (0.001) (0.206) (0.002) (0.007)

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26 TABLE 4: Correlation Table - Sentiment Wall Street

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1. Sentiment Wall Street _1.000

2. Restructuring -0.030 _1.000 (0.705) 3. Extending 0.082 0.045 1.000 (0.304) (0.576) 4. Firm Size (ln) 0.013 0.054 0.198 _1.000 (0.869) (0.502) (0.012) 5. Firm Age -0.183 -0.082 -0.137 -0.724 1.000 (0.021) (0.306) (0.085) (0.000) 6. R&D Intensity 0.380 -0.142 0.083 0.053 -0.108 _1.000 (0.000) (0.074) (0.299) (0.505) (0.177) 7. Performance 0.142 0.095 0.013 0.086 -0.052 -0.004 1.000 (0.074) (0.232) (0.866) (0.278) (0.519) (0.961) 8. GDP Employed -0.032 -0.034 -0.099 -0.801 0.673 0.025 0.045 1.000 (0.688) (0.671) (0.213) (0.000) (0.000) (0.751) (0.575)

9. Gov. R&D Inv. -0.082 0.016 0.010 -0.182 -0.039 -0.080 0.022 -0.087 _1.000 (0.302) (0.842) (0.903) (0.021) (0.628) (0.316) (0.779) (0.273)

10. Free Cash Flow 0.000 -0.014 -0.050 -0.106 0.145 -0.139 0.298 0.018 0.072 _1.000 (0.998) (0.862) (0.528) (0.183) (0.068) (0.080) (0.000) (0.821) (0.369)

11. Dummy R&D Intensity -0.239 0.157 -0.051 -0.100 0.156 -0.835 -0.019 0.090 -0.081 0.077 _1.000 (0.002) (0.048) (0.525) (0.211) (0.050) (0.000) (0.817) (0.261) (0.312) (0.337)

12. Marketing Intensity 0.204 -0.060 -0.070 0.125 -0.168 0.171 0.075 -0.360 -0.100 0.385 -0.121 1.000 (0.010) (0.455) (0.383) (0.116) (0.034) (0.031) (0.347) (0.000) (0.210) (0.000) (0.128)

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27 Correlations Main Street

When the correlations between the independent variables have a correlation of .80 or higher, multicollinearity could be present (Bryman and Cramer, 1997). As shown in table 3 and 4 there were no high correlations. However, there are some other high correlations. For the variable of Sentiment of Main Street, there are strong correlations between Firm Size and Restructuring (r = 0.019, p = .837), Performance and Extending (r= -.013, p= .885), Gov. R&D Inv. and Extending (r = .025, p = .792), Gov. R&D Inv. and R&D Intensity (r = .013, p = .888) and between Marketing Intensity and Performance (r = .030, p = .748). Furthermore, there were very strong correlations between Performance and R&D Intensity (r = 002., p = .983), GDP Employed and Sentiment Main Street (r = -.003, p = .972), GDP Employed and Restructuring (r = .010, p = .913) and between the Dummy R&D Intensity and Sentiment Main Street (r = -.010, p = .919). Therefore, a variance inflation factor inspection is performed. The highest VIF value found between Firm Size and GDP Employed in the regression analysis is Firm Size with a value of 6.40. This high VIF value may be caused by the strong correlation between Firm Size and GDP Employed. This suggests that the larger car manufacturers are located in more wealthy countries. However, this study has not formulated a hypothesis with regard to these variables. Therefore, the high VIF value is of no consequence for the hypotheses tested. Craney & Surles (2002) advice a cut-off point of 10, so in this sample there is no indication for multicollinearity.

Correlations Wall Street

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28 5.2 Regression results

Table 5 presents the results of the fixed effects regression analysis performed with Sentiment of Main Street and Sentiment of Wall Street as dependent variables.

Sentiment Main Street analysis

In model 1 there was a negative not significant relation between restructuring and the sentiment of Main Street (B = -0.050, p = .733). This indicates that the green strategy of Restructuring of the product portfolio has no effect on the Sentiment of Main Street. This leads to the rejection of hypothesis 1a. In model 1 there was a positive insignificant relation between Extending and the Sentiment of Main Street (B = 0.019, p = .920). This demonstrates that the green strategy of product portfolio Extending has no effect on the Sentiment of Main Street. This leads to the rejection of hypothesis 2a.

In model 1 there was a positive significant relation between Firm Size and the Sentiment of Main Street (B = 1.289, p < .001). This indicates that larger firms receive more legitimacy from Main Street. Firm Age had a negative significant effect on the Sentiment of Main Street (B = -0.117, p < .001). This indicates that the general population receives older firms in general as less innovative in the area of product portfolio greening. Performance has a positive significant effect on Sentiment of Main Street (B = 0.212, p < .001). Meaning that profitable firms have their processes under control, efficiency, happy people. The following variables had no significant effect on the variable of Main Street, R&D Intensity (B = 0.057, p =.755), GDP Employed (B = 7.553, p = .107), Gov. R&D Inv. (B = 0.221, p = .131), Free Cash Flow (B = -0.000, p = .172), Dummy R&D Intensity (B = -0.177, p = .854) and Marketing Intensity (B = -6.312, p = .041).

Sentiment Wall Street analysis

In model 2 there is a positive insignificant relation between Restructuring and the Sentiment of Wall Street (B = 0.006, p = .674). This demonstrates that the green strategy of product portfolio Extending has no effect on the Sentiment of Wall Street. Therefore, hypothesis 1b is not confirmed. In model 2 there is a positive insignificant relation between Extending and the Sentiment of Wall Street (B = 0.008, p = .734). This indicates that the green strategy of product portfolio Extending has no effect on the Sentiment of Wall Street. Therefore, no supporting evidence is found for hypothesis 2b.

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consideration of Wall Street. The following variables had no significant effect on the variable of Sentiment of Wall Street, Firm Size (B = 0.019, p = .845), Firm Age (B = 0.000, p = 0.998), GDP Employed (B = -1.246, p = .027), Gov. R&D Inv. (B = -0.019, p = .355), Free Cash Flow (B = 0.000, p = .710) and Marketing Intensity (B = 1.011, p = .193)

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30 TABEL 5: Fixed Effects Regression

Dependent variable: Sentiment Main Street Sentiment Wall Street

Model 1 Model 2 Restructuring -0.050 0.006 (0.144) (0.014) Extending 0.019 0.008 (0.188) (0.022) Firm Size 1.289*** 0.019 (0.171) (0.093) Firm Age -0.117*** 0.000 (0.022) (0.006) R&D Intensity 0.057 0.063*** (0.178) (0.015) Performance 0.212*** 0.018** (0.039) (0.005) GDP Employed 7.553 -1.246* (4.340) (0.495)

Gov. R&D Inv. 0.221 -0.019

(0.136) (0.020)

Free Cash Flow -0.000 0.000

(0.000) (0.000)

Dummy R&D Intensity -0.177 0.260**

(0.942) (0.069) Marketing Intensity -6.312* 1.011 (2.753) (0.734) Constant -97.973† 13.939* (50.300) (5.157) Observations 118 159 R-squared (within) 0.445 0.210 R-squared (between) 0.064 0.018 R-squared (overall) 0.021 0.027 Highest VIF 6.400 5.730 Number of firms 13 13

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6. DISCUSSION

In the past two decades the automotive industry has experienced the emergence of alternative fuel vehicles. The underlying motivation for most companies is to gain in legitimacy and competitiveness and to become ecological responsible (Pratima Bansal & Roth, 2000). While car manufacturing companies invest billions (Bos & Hsu, 2019) in the development of green strategies, this research is the first to investigate and answer the question whether product portfolio greening strategies of restructuring and extending strategies lead to an improvement of the legitimacy granted by the stakeholders of Main Street and Wall Street. In the current section, the theoretical implications, managerial implications and an overall conclusion of the results are given. Furthermore, a discussion of the limitations and advice for future research is presented.

6.1 Theoretical implications

This research started off with the objective to fill the gap in literature on whether legitimacy could be derived from stakeholders by implementing green product portfolio strategies. The information on this topic is, if available, minimal with regard to the existing green strategies for auto manufacturers to enhance their legitimacy. This research contributes to the existing literature on greening strategies in the automotive industry.

First, it was examined whether the restructuring of the product portfolio enhances the legitimacy received by car manufacturers from Main Street (H1a). Non-shareholders value a firm’s actions from the perspective of the broader societal impact (Frank, 1988). Accordingly, it was hypothesized that firms could achieve legitimacy from Main Street by the restructuring of the product portfolio. However, the current research did not provide evidence that restructuring does lead to the rewarding with legitimacy from Mainstreet. A possible explanation of the absence of an effect of restructuring on Main Street legitimacy may be found in the unchanged production of brown products. As Ashforth and Gibbs (1990) posed, Main Street is able to notice when car manufacturers introduce a new green product, but does not deal with the existing brown products in the meantime. The imbalance between green and brown products undermines a firm’s legitimate sustainability claims (Unruh & Ettenson, 2010). Which implies that the greening efforts of car manufacturers do not change the perception of Main Street with regard to sustainability/greening and therefore do not grant any additional legitimacy to the restructuring car manufacturing. However, as long as there is no accusation of greenwashing against the firm, the legitimacy does not worsen either.

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premium green for vehicles (Miremadi et al., 2012). This can turn the bright prospect of increasing cash flows into a rather grim sales prospect. Consequently, it is expected that Wall Street perceives the greening of the product portfolio of car manufacturers as an underdeveloped area that has only limited profit potential at this moment in time. Notwithstanding, this research does not provide evidence for this rationale and the negative effect, nor does it prove restructuring to be of a positive effect. Therefore, if it is taken in consideration that shareholders evaluate a firm based on the future cash flows (approx. five years), the results may be explained by Wall Street expecting only limited profit potential. Wall Street may assume that either green cars are not profitable in the coming years or the number of green vehicles sold will be too low resulting in no response to the restructuring of the product portfolio.

Third, it was examined whether the implementation of the extending strategy had a negative effect on the legitimacy rewarded by Main Street to car manufacturers (H2a). It was argued that car manufacturers add green products next to the existing brown products in their product portfolio to prevent being targeted because of a lack of green vehicles in their offering. Based on research of Unruh and Ettenson (2010) it was expected that a broader brand portfolio would have firms more exposed to activists and consumer backlash and undermining of a firm’s legitimate sustainability claims. Ultimately leading to the accusation of greenwashing (Delmas & Burbano, 2011), owing to the fact that the firm is not taking action concerning the brown products in the product portfolio (Ashforth & Gibbs, 1990). However, no evidence was found on the stance that the extending strategy decreases or increases the legitimacy granted. This shows that superficial greening efforts do not have a relevant effect on the sentiment of Main Street. Which implies that the greening efforts of car manufacturers do not change the perception of Main Street with regard to sustainability/greening and therefore do not grant any additional legitimacy to the restructuring car manufacturing.

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33 6.2 Managerial implications

Besides the interest of extending the literature, an important motivation in the writing of this paper was to provide insights to the product portfolio management problem. Especially considering the enhancement of corporate legitimacy by the implementation of green product portfolio strategies. In building this research, managerial implications have progressively arisen. The knowledge aggregated throughout this research could be advantageous for managers in the automotive industry, but more specifically industry managers who operate in the countries from which the firms were studied in the sample.

Given the increased attention on environmental sustainability in the last two decades it can be considered sensible for car manufacturers to focus on greening the product portfolio. However, a positive or negative effect on granted legitimacy is not proven. The results do show a positive effect on granted legitimacy from Main Street for younger and profitable firms with intensive marketing. This would imply that a holding can best start a new brand in order to get rid of the, generally static and/or polarized view of the existing brand.

With regard to the sentiment of Wall Street, the results show a positive effect on granted legitimacy for performance and R&D intensity. This finding, combined with the absence of an effect of greening strategy on legitimacy granted by Wall Street, suggests that Wall Street does not expect a significant improvement of product portfolio greening on the cash flow.

In summary, the advice to product portfolio managers in the automotive industry is to focus the R&D research on a new, green brand such that the firm is ready to introduce the new brand as soon as the automotive industry is shifting towards green vehicles.

6.3 Conclusion

The aim of this research was to explore the effect of different product portfolio greening strategies on the legitimacy granted by stakeholders in the automotive industry. At the foundation of this is the trend of customers demanding green products (Randall, 2016) and the answer of car manufacturers in a varying array of strategies. The legitimacy granted to a car manufacturer is dependent on the sentiments of the stakeholders of Main Street and Wall Street. The research into the effect of greening the portfolio on legitimacy granted by stakeholders is an understudied area in the literature.

The results of this study show that both Main Street and Wall Street do not attach great importance to

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the dummy variable) are valued by Wall Street. Unexpectedly, the restructuring and extending strategies have no effect on the sentiment of both Main Street and Wall Street, which indicates that the average consumer and shareholder do not appreciate the greening of the portfolio of car manufacturers. In summary, Main Street is mainly interested in large profitable firms that let their products speak for themselves and Wall Street is mainly interested in countries with profitable firms. These results suggest that the partial greening of the product portfolio, irrespective of the applied strategy, is not rewarded with legitimacy by both Main Street and Wall Street. This data suggests that Main Street and Wall Street have a static or polarized view that car manufacturers are either green or brown.

6.4 Limitations and future research

In order to make future research aware of limitations, they will be provided here.

First, this research makes use of a small dataset with a limited statistical power. Therefore, only large effects could be detected by this study. That this study did not find any effect of the restructuring and extending on Main Street and Wall Street implies that these effects may be small and therefore beyond the detection possibilities of this dataset. There are two possible approaches that could be adopted to address the problem of a small sample size. The first and, in terms of success chances, most attractive approach is to use another proxy for the measurement of Main Street sentiment. This study used data from the FRI to measure the sentiment of Main Street. A more appropriate data source would be one that provides more data than only provided data on the top 15 car manufacturing firms per year. Furthermore, only FRI data of firms represented by the holding brand names with one specific main firm is used. To illustrate, General Motors as a holding does not have one leading brand. Whereas Toyota as a holding is represented by Toyota. Reason for this is that data is measured by interviewing people. To ensure the scores are based on complete knowledge, holdings that were not represented by a main firm were removed from the sample. Thus, the way data is gathered and provided by Fortune led to a select group of firms to represent the automotive industry in this sample. A second approach is to work with an international team, such that the currently encountered language barriers disappear and data from a wider range of countries could be incorporated. This originates from the reality that the automotive industry is global, and some brands are active in a restricted area (shanghai motors, dong feng motors), leading to a lack of data in the English language.

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Within this study a restricted amount of methods used by car manufacturers in greening their portfolio was used. It was chosen to focus only on alternative (green) fuels. However, there are more methods available to car manufacturers that may be related to greening their portfolio. These are vehicle down-sizing (weight and/or size reduction), phasing out of carbon intensive fuel (diesel) vehicles, emission reductions (adoption of cleaner fossil fuel engines) and the adoption of cleaner fossil fuels (compressed natural gas (CNG) or liquefied petroleum gas (LPG)). The current measurement was chosen because of the researchers gut-feeling that alternative fuels are best acknowledged by the public as a green approach of the company. Integrating these alternative methods into research creates important avenues for future research as it may lead to a more precise measurement of the greening strategy of the company. In addition, the proxy used for greening and restructuring via a binary variable limits the detection possibilities of this study. Therefore, other researchers may be able to detect the effect of restructuring and extending by using a larger dataset and developing a more accurate proxy of restructuring and extending.

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The effect of product portfolio greening strategies on legitimacy granted by Main Street and Wall Street in the automotive industry