Corporate Strategy, Market Valuation, and the Effect of Digital Platform Firms:
A Resource-Based View Perspective
Ido Tomas Buist 1,* | Supervised by prof. dr. J.D.R. Oehmichen 1 | Co-assessed by dr. F. Noseleit 1 | 1 Faculty of Economics and Business | University of Groningen | The Netherlands |
| * Corresponding Author | MSc BA Strategic Innovation Management | | Student No.: s3528790 | Email: i.t.buist@student.rug.nl |
Keywords: corporate strategy – market valuation – digital platforms – diversification – strategy uniqueness
Abstract: In this study, I investigate how strategy choice affects market valuation. By drawing on resourced-based theory, I attempt to explain why platform firms are valued differently compared to traditional firms in their pursuit of unique strategies and corporate diversification. Cross-business synergies are the central concept explaining corporate strategy success. However, the downsides of certain strategic choices should not outweigh the benefits. Unique strategies entail an increased information burden and costs to evaluate such strategies, which may lead to discounted prices in capital markets. Similar logic applies in the context of increased complexity that accompanies diversification strategies. While diversified firms may also suffer from an increased coordination burden and related costs. I test my hypotheses on a longitudinal dataset consisting of a sample of 2,575 unique firms from 48 different countries between 2008 and 2017. I find that increasing levels of diversification and uniqueness indeed lead to a discount in the market. Accordingly, I find that this effect is partially different when platform firms are involved. Thereby, this study contributes to the literature of corporate strategy and digital platform firms. It contributes to resource-based theory by arguing that the distinct digital resources of platform enterprises allow for novel synergies and reduced costs, thereby enabling more effective corporate strategies and the potential for sustained competitive advantage.
Master Thesis – MSc Strategic Innovation Management – January 20th 2020 – Word Count: 13,685
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1. Introduction
The digital revolution has transformed businesses, industries and societies with unprecedented speed and scale. In the past decade, a significant amount of digital platform firms have emerged using data-driven business models, pressuring traditional firms and disrupting many industries (Kenney and Zysman, 2016; Verhoef et al., 2019). Many traditional firms suffer as they have been surpassed by innovative fast-growing digital entrants (Ferrieres, 2019; Verhoef et al., 2019). New online retailers such as Alibaba and Amazon follow different strategies compared to traditional retailers, and do not limit their reach to the traditional retail industry. In their pursuit of further growth opportunities, they diversify into markets that were previously thought to be completely unrelated to retail, creating different resource combinations than their traditional counterparts (Verhoef et al., 2019). As a result, competition is changing dramatically (Kenny et al., 2019). Due to new technologies, competition has become more global and intense. Big information-rich platform firms such as Google, Facebook and Amazon start to dominate numerous industries as a result of their expansive strategies. This shift is also reflected in firm valuation as the top five most valuable firms, according to the S&P 500 index, are all digital. This in contrast to a decade ago, where traditional firms such as GE, Exxon and Gazprom were at the top of the list (Verhoef et al., 2019).
The possible link between these strategic choices and high valuation is interesting, as previous research on corporate strategy found evidence that capital markets actually discount diversification and choices for unique asset combinations compared to the industry standard (e.g. Litov et al., 2012; Lang & Stulz, 1994; Raghuram et al., 2000). Therefore, it is interesting to explore if capital markets treat digital platforms differently in terms of strategy choice, and why this may be the case. Could there be a discount for traditional firms, but a bonus for platform firms? More specifically, this thesis aims to answer the following two research questions:
RQ1: How does the choice of corporate diversification and strategy uniqueness affect market valuation?
RQ2: How do digital platforms moderate the relationship between strategy choice and market valuation?
In an attempt to investigate such situation, I apply a resource-based view (RBV) perspective which emphasizes the importance of a firm’s resources (Wernerfelt, 1984; Barney, 1991). The relationship between corporate strategy and market performance has been a frequently investigated research topic within the field of strategic management (Chatterjee and Wernerfelt 1991; Wan et al. 2011). In line, the RBV has proven to be an important concept in explaining why firms diversify, and why they may succeed (Wan et al., 2011). Cross-business synergies are an essential part of corporate strategy (Faes et al., 2000), and the central concept in explaining diversification success according to the RBV (Hauschild and Knyphausen-Aufseß, 2012). Firms can achieve synergistic effects and above average returns by sharing resources across their different businesses, enabling the potential to achieve a competitive advantage should those synergies be hard to capture by competitors (Chatterjee 1990; Montgomery and Hariharan, 1991; Barney, 1991). However, simply combining resources or adopting a diversification strategy may not necessarily lead to increased financial benefits (Wan et al., 2011). The possible synergistic use of resources across business units may not lead to improved performance unless the benefits outweigh the costs (John and Harrison, 1999), where costs are mainly caused by administrative burdens in coordination across businesses (Ravichandran et al., 2009).
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researchers, finance scholars view corporate diversification as a value-destroying strategy in general (Matsusaka, 2001; Schoar, 2002; Steiner, 1996). Next to the abundant studies that suggest that markets discount diversification, more recent research found that unique strategies are also discounted by the market. Litov’s et al. (2012) identified the so called uniqueness paradox. They observed that market intermediaries, such as analysts, undervalue firm strategies that are unique compared to the classical industry strategy, just because the assessment of such strategies is more complex. This while uniqueness in strategy choice, at least at the time of asset and resource acquisition, combined with some impediment to imitation, are necessary conditions for deliberate value creation according to the RBV (Barney 1991, Lippman and Rumelt, 2003; Litov et al., 2012).
This thesis aims to translate these findings to the digital platform context. These platforms and accompanying new business models differ from traditional firms in various ways (Evans, 2003; Gawer, 2014). A distinctive characteristic of digital platforms is their impressive growth figures (Schwarz, 2017; Verhoef et al., 2019). Google, for example, grew from 1 billion searches per year in 1999 to more than 2 trillion in 2016, implying a growth rate of 50 percent per year over a 17-year period (Rooney, 2017). Building on the core premise of the RBV that the firm is a bundle of resources (e.g. Wernerfelt, 1984; Barney, 1991), I argue that platform firms fundamentally differ in terms of the resources they possess and therefore pursue different strategies compared to traditional firms. Leading platforms are characterized by an eager tendency to colonize and converge into ever‐new markets (Schwarz, 2017). They use their digital resources to enter industries that were previously thought to be completely unrelated (Verhoef et al., 2019). The all‐purpose applicability and interchangeability of digital resources, and data in particular, allow for novel synergies and cause for a highly expansive nature of platform firms (Schwarz, 2017).
If cross-business synergies are an essential part of the corporate strategy on which its success depends (Feas et al., 2000), and if those synergies are the most important reason for firms to diversify (e.g. Teece, 1982), then one could expect that the distinct digital resources of platform firms play a significant role in enabling those synergies and successful corporate strategies. Although the existent literature in the area of corporate diversification and market performance is quite rich both theoretically and empirically, the empirical findings have not been consistent. In contrast, there are very few studies that explore the relationship between corporate strategy uniqueness and market performance (Litov et al., 2012; Benner & Zenger, 2016). Moreover, previous findings in the information systems literature found that digital resources in the form of IT assets reduces internal coordination costs (Hitt, 1999). It also enables effective knowledge transfer across business units and generally helps create synergies across business units (Tanriverdi, 2005; Ravichandran et al., 2009). For this reason, it could be argued that examining the effect of corporate strategy choice on market performance is rather incomplete without considering the unexplored effect of digital platforms and their distinct resources. That is, the effects of strategy choice on market valuation could be different when digital platform firms are involved. This in turn, could have important implications for the competitive position of traditional firms in this new digital world. By answering the research questions above, this thesis aims to fill the gap in the literature.
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expected to be different for these two types of firms. While traditional firms are expected to be discounted by the market as levels of diversification – as well as uniqueness – increase, this negative effect is expected to be reduced when concerning digital platform firms. I hypothesize on the two types of corporate strategies and their effect on market valuation. In addition, I hypothesize on the moderating effect of digital platform firms, thereby expecting that the negative relationship will be reduced. By conducting random effects generalized least squared (GLS) regressions, I test my hypotheses on a longitudinal dataset of 2,575 firms between 2008 and 2017. Accordingly, I find support for three of my hypotheses, including a positive moderating effect of digital platforms on the negative relationship between corporate diversification and market valuation.
Thereby, the contributions of this thesis are fourfold. First, it adds the effect of platform firms to the literature about corporate strategy and the diversification-performance relationship. Second, it contributes to the literature of corporate strategy by providing empirical evidence regarding the relationship between strategic choice and market valuation. Third, it contributes to resource-based theory by arguing that the distinct digital resources of platform enterprises allow for novel synergies, that enable effective diversification strategies and the potential for sustained competitive advantage. Forth, this thesis offers practical insights and managerial guidance about how sustained competitive advantage is obtained in this new digital world, and how established firms may react to digital disruption.
The subsequent section of this thesis provides theoretical background of the RBV that acts as a foundation for understanding choices regarding corporate strategy and as a foundation for hypotheses development. Section 3 presents the research methodology that is used in this study. The section thereafter presents the main results, as well as additional analyses, ensuring the robustness of these results. In the concluding section, the main findings are discussed, together with theoretical and practical implications, followed by limitations and suggestions for future research.
2. Theory and Hypotheses
2.1 Background
The RBV has emerged as the key theoretical foundation that has led to a significant development of the corporate strategy literature in strategic management (Wan et al., 2011). The core premise of resource-based theory is that the firm is a bundle of resources (Wernerfelt, 1984). These resources represent a firm’s ownership and control of assets and capabilities (Barney, 1991). Assets represent the resource endowments of the firm in physical and intellectual assets. Capabilities glue assets together to enable successful deployment and usually reside in the firm’s human, information, or organizational capital (Verhoef et al., 2019).
Resource-based theory is built on two important assumptions. The first being that different firms possess different bundles of resources. That is, resources are heterogeneous across firms. As a result, some firms may perform certain activities better than others in the same industry based on these differences (Barney, 1991; Dierickx and Cool, 1989). The second assumption is that resource differences among firms can be immobile due to rarity or uniqueness and inimitability of those resources and capabilities (Barney, 1991; Reed and De Fillippi, 1990). Resources that are valuable, rare, inimitable and non-substitutable provide potential for achieving a sustained competitive advantage (Barney, 1991).
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Considering a firm’s strategy, managers seek to generate economic value as they discover and create valuable resource and activity combinations (Lippman and Rumelt 2003; Barney 1991). These combinations or strategies are then assembled by acquiring resources such as assets, activities or entire businesses in strategic factor markets (Litov et al., 2012). Uniqueness in strategy choice, at least at the time of acquisition, combined with some impediments to imitation, are necessary conditions for sustained value creation and competitive advantage. Hence, to create value, firms must foster strategic positions where there is little or no competition, see value in opportunities and assets that competitors cannot, or exploit unique resources to which the competition has no access (Porter, 1996; Barney, 1986). To sustain value, these strategies must be difficult to imitate otherwise they will become common and no longer create value for the firm (Benner & Zenger, 2016).
The RBV provides a foundation for understanding how and why firms diversify and pursue unique strategies. It helps to clarify which resources and capabilities will provide value and lead to strategic advantage in multi-business firms (Chatterjee and Wernerfelt, 1991; John and Harrison, 1999). Firms tend to expand into (related) industries in order to make use of under-utilized resources such as excess capacity, capital, systems and infrastructure (Penrose, 1959; Mahoney and Pandian, 1992). Robins and Wiersema (1995) argue that it is important to take relatedness in terms of the underlying resources and capabilities into account, that make businesses similar to each other. When appropriately managed, relatedness among different business units should result in tangible and intangible synergies that make the corporate strategy more than the sum of the individual business unit strategies (John and Harrison, 1999). These synergies can thus be created by sharing resources between business units if production based on these resources and activities are subject to declining average unit costs. That is, if efficiency gains can be realized through economies of scale or scope (Wernerfelt and Montgomery, 1988; Iversen, 2011). Moreover, based on private foresight about the value of alternative combinations of resources and assets, and hence the supposed creation of synergies, managers choose strategies that they perceive as most valuable (Barney 1986, Lippman and Rumelt, 2003). Thereafter, market participants such as analysts and investors, evaluate these strategies, based on an assessment of costs and benefits and as shaped by their incentives. In the end, investors review the available information about the firm and decide to invest or divest (Litov et al., 2012).
At the beginning of the century, IT started to emerge as a strategic differentiator (Sambamurthy et al., 2003). As a result, there is greater interest in understanding how IT assets and digital resources influence superior performance. Prior research has already examined several performance benefits of IT-related resources and capabilities (e.g. Bharadwaj 2000; Bharadwaj et al. 2001; Mata et al. 1995). In today’s world, companies heavily invest in the development and acquisition of digital assets and technologies (i.e. hardware and software). Verhoef and colleagues (2019), nicely described which distinct digital resources and capabilities digital platforms possess. These are: digital assets; digital agility; digital networking capability; and big data analytics capability. Big data, as an example of a digital asset, can be leveraged by using a firm’s big data analytical capabilities to personalize and improve services and offers (Verhoef, 2019). In essence, the capability to acquire and analyse big data allows for better decision making (Bharadwaj et al., 2013). Digital agility involves the ability to sense and seize opportunities in markets enabled by digital technologies (Lee et al., 2015, Lu and Ramamurthy, 2011). Digital networking capability concerns the firm’s ability to match and bring together distinct users to address their mutual needs via the platform (Libert et al., 2016; Verhoeft et al., 2019). Drawing from the RBV, these distinct digital resources are expected to influence the strategies and opportunities for synergy creation of platform firms.
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significantly influenced by its resources and capabilities (Wan et al., 2011). Given the heterogeneity of the resource bundles of firms, different firms are presented with different opportunities for combining resources and creating synergies (Iversen, 2011). Research on the link between corporate strategy and market valuation remains somewhat incomplete without taking into account the distinct resources of digital firms. It could be argued that the heterogeneity of resources across digital and traditional firms could have implications on the performance effect. This thesis aims to investigate if platform firms – and their distinct digital resources – allow for different opportunities in terms of synergy as opposed to traditional firms and, as a result, could differ in their performance regarding the pursuit of diversification and unique strategies. In line with recent work in the field of strategy, I conceptualize strategies as theories of value creation that guide choices about the composition and combinations of assets and activities that a firm accumulates, develops, and deploys (Ghemawat 2005; Benner and Zenger, 2016). The unit of analysis is senior management's choice of corporate strategy, which is defined according to Litov et al. (2012, p. 1799) “as the composition of businesses bundled within the firm”. I specifically focus on the level of uniqueness and complexity (i.e. diversification) in the resource combination that could lead to sustainable value creation but could also be subject to dis-synergies, such as increased coordination costs and information asymmetries between management and investors, which will be discussed in the sections hereafter.
2.2 Diversification and Market valuation
Corporate diversification represents one of the most prominent research topics in the field of business (Hoskisson and Hitt, 1990; Palich et al., 2000). The performance effects of diversification have been studied extensively in finance and strategy literature, which has been focused on the economic rationale behind the diversification–performance relationship (Ravichandran et al, 2009). According to the RBV, a firm has an incentive to diversify if it has control over the necessary excess resources to make diversification economically feasible (Teece, 1982; Wernerfelt, 1984; Wan et al., 2011). Transferring such resources to related business units within a diversified firm represents an optimal strategy. This because the marginal costs of utilizing these resources within the current industry are often minimal. However, the benefits of utilizing them in another business unit can be substantial (Barney, 1997; Wan et al., 2011). Hence diversification research, viewed through a RBV lens, posit that related diversification can lead to superior performance, compared to firms with a narrow or focused strategy. This because diversified firms can maximize the use of their resources across several business units to realize additional returns (Wan et al., 2011). Hence, firms can achieve synergistic effects and above normal returns by sharing resources between different business units (Chatterjee 1990; Montgomery and Hariharan 1991). The abundant studies that found a direct positive relationship between related diversification and performance (firm and market) underline this thought (e.g. Markides and Williamson, 2007; Miller, 2006).
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Zenger, 2016) highlighted the information problem that accompanies a complex portfolio of businesses and the resulting strategic complexity, that is, strategies with multiple interacting elements or decisions (Rivkin 2000). While it could be argued that this complexity heightens causal ambiguity and therefore hinders replication by rivals, it also increases the information asymmetry between senior management and the capital market. That is, diversification strategies that are difficult for competitors to understand and copy are, in a very similar way, difficult for market actors to understand and value (Benner & Zenger, 2016). As a result, markets may discount a diversification strategy that is too difficult to value.
Moreover, a plethora of researchers, mainly in finance literature, argue that diversification is a value-decreasing strategy and leads to a discount (e.g. Matsusaka, 2001; Schoar, 2002; Steiner, 1996). Evidence indicates that diversified firms tend to have lower market performance measures such as Tobin’s Q (Wernerfelt and Montgomery, 1988; Lang and Stulz, 1994). In a similar fashion, Berger and Ofek (1995) found that diversified firms trade at a discount of up to 15 percent, when compared to focused firms. It must be noted, however, that the vast majority of the finance scholars do not distinguish between related and unrelated diversification, or merely focus on the latter (Wan et al., 2011).
To sum up, diversification is believed to benefit firms only if excess resources can be optimally utilized in related industries. By doing so the firm creates synergies, maximizes the use of resources across businesses and can realize additional returns. However, the costs of diversification, manifested through coordination costs, should not outweigh the benefits. Clearly, it is important to take into account the relatedness of a firm’s resources and capabilities that provide the potential for cross-business synergies, when studying the diversification-performance relationship (Grant 1988; Robins and Wiersema 1995). The bulk of research suggests that, on average (i.e. not taking into account contingency factors), corporate diversification leads to a discount. Mechanisms that explain the discount include increased coordination costs, and the information asymmetry that accompanies complex diversification strategies. This thesis fails to measure the distinction between related and unrelated diversification in terms of the underlying firm resources and capabilities, which is a clear limitation. As such, in line with previous research, I hypothesize that an increased level of corporate diversification – on average – leads to a valuation discount in the market. More specifically:
Hypothesis 1: There is a negative relationship between corporate diversification and market valuation (as
measured by Tobin’s Q).
2.3 Corporate strategy uniqueness and market valuation
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for deliberate value creation, especially in the long run (Litov et al., 2012; Benner & Zenger, 2016). It is a firm’s senior management that possesses private information about the perceived value of specific asset combinations (Litov et al., 2012). This information allows the firm to purchase "underpriced" assets and thereby appropriate value and earning above average returns (Barney 1986). These above average returns result from the foresight and creativity of managers. They use their closer proximity to customer’s needs, the market environment in general and their own resources to identify untapped potential for generating additional value. In the context of the RBV of corporate strategy, the private information contains insights regarding the potential value of synergies among business units that may allow for economies of scale or scope, or for the introduction of valuable new products or services (Barney, 1986; Litov et al., 2012). However, management with incentives to pursue unique and valuable asset combinations, have to take into account the capital market's efficiency in making an accurate assessment of the value of the adopted strategy. Market participants may face a severe information problem in valuating unfamiliar strategies (Akerlof, 1970; Litov et al., 2012).
More familiar strategies allow market participants to evaluate more easily using existing knowledge and capabilities. It follows that less familiar strategies are more difficult and costly to evaluate and asses (Litov et al., 2012). Evaluating strategies can thus be challenging, as it involves more than simply summing up the present market value of assets. Instead, strategies are considered plans of action, or theories, that present paths of sustainable value creation. This guides choice about which resource and activity combinations to assemble (Porter, 1996; Benner & Zenger, 2016). Hence, evaluating strategies involves assessing the future returns of senior managers’ theories of value creation (Benner & Zenger, 2016). Forecasting these returns is very difficult and involves a lot of uncertainty. Moreover, assessing these theories not only requires an understanding of the stand-alone industries in which business units compete. Additionally, an understanding of any synergies or complementarities that are generated through the combination are required. As the uniqueness of the assembled combinations increases, the less likely it is that market participants will be familiar with the created synergies (Litov et al., 2012).
Thus, managers may face severe impediments in credibly signalling the value of strategies to investors (He and Wang, 2009). Wu and Knott (2006) add that senior management have incentives to praise the value of their strategies, even to a great extent, or they could simply be overconfident. This can increase uncertainty among investors regarding the accuracy of management’s projection of value, increasing the information problem (Benner & Zenger, 2016). Furthermore, Espeland and Stevens (1998) argue that part of the information challenge in assessing unique resource combinations, is due to the absence of comparative benchmarks and the necessity to engage in a costly process to develop them.
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that information asymmetry increases the costs of strategy evaluation, leading market participants to lack a full understanding of the resource combination and, subsequently, to discount the equity value of firms with unique and unfamiliar strategies.
Hypothesis 2: There is a negative relationship between corporate strategy uniqueness and market valuation (as measured by Tobin’s Q).
2.4 The moderating effect of digital platform firms
When applying the concepts of cross-business synergies and information asymmetry to the digital platform context, one may expect certain differences. The RBV suggests that a firm’s corporate strategy and its performance are contingent on its resources and capabilities, and corresponding opportunities for exploiting cross-business synergies (Wan et al., 2011). The creation of cross-business synergies, however, may be subject to increased coordination costs and information asymmetry, leaving investors uncertain about the potential for future returns. Given the heterogeneity of the resource bundles of digital firms in relation to traditional firms, and the information available to management, digital platform firms are presented with different opportunities for combining resources and creating different synergies. Their theories of value creation that guide choices about the composition of assets and activities may differ as a result of this. Hence, in light of corporate strategy, this thesis posits that the degree of cross-business synergies, coordination costs and information asymmetry may differ in the context of digital platform firms.
I adopt Gawer’s (2014) conceptualization of a technological platform, which can be seen as “evolving organizations or meta-organizations that: (1) federate and coordinate constitutive agents who can innovate and compete; (2) create value by generating and harnessing economies of scope in supply or/and demand; and (3) entail a technological architecture that is modular and composed of a core and a periphery” (Gawer, 2014; p. 1239). More specifically, this thesis is focused on (digital) industry platforms, defined as (online) “products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or services” (Gawer and Cusumano, 2013; p. 417). As such, platform firms are described as multi-sided, in that platform owners provide access to, and support interaction among, multiple groups of users, including customers, suppliers and complementary service providers (Zhu & Liu, 2018). According to Verhoef and colleagues (2019), a distinctive characteristic of digital platform firms is their impressive growth rates. Leading platforms have an eager tendency to colonize and converge into ever‐new markets (Schwarz, 2017). There are two key drivers behind the remarkable growth of digital platforms: high scalability and reinforcing network effects (Verhoef et al, 2019). Platforms and their digital infrastructure can expand quickly and can easily cope with a growing number of users because the costs of serving additional users are low, sometimes even negligible. To refer back to Penrose (1959), and Mahoney and Pandian (1992), they can make use of excess capacity, systems and infrastructure. This allows for significant efficiency gains through economies of scale and scope, fostering synergies (Wernerfelt and Montgomery, 1988; Iversen, 2011). In addition, a growing number of users on one side of the platform (e.g. suppliers and customers) attracts users on the other side. This because they receive increased utility from using the platform. This, in turn, attracts more suppliers and customers, creating virtuous loops and increasing network effects (Eisenmann, 2006; Verhoef et al., 2019).
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multiple user groups of the platform. The accumulated information is more valuable than separate datasets that users can observe (Martens, 2016). Smaller datasets are way less efficient than big datasets, as accumulated data across many transactions and industries gives a more comprehensive overview of the market and, therefore, provides more value than individual datasets on single transactions. As such, there are increasing returns to scale or scope in data acquisition, as platforms can learn more from these aggregated datasets (Martens, 2018). Moreover, with the accumulation of consumer data, there are additional network effects. The growing number of users means increased data collection. With more consumer data a firm has more scope to improve its product offering and innovate, which in turn attracts more users, generating even more data, again creating virtuous loops (Parker et al, 2016; Hagiu and Wright, 2020). Hence, the all‐purpose applicability and interchangeability of consumer data, and the intelligence that can be extracted from it, enable digital platforms to create novel forms of synergies (Schwarz, 2017). These novel synergies allow for economies of scale or scope, and for the introduction of valuable new products and services (Barney, 1986).
Moreover, big data receives an increasing amount of attention for strategy purposes as business opportunities and other sources of macro (e.g., economic trends) and micro (e.g., customer preferences) environmental change can be more easily identified (Constantiou and Kallinikos, 2014; George et al., 2014). Data can be used as fuel for predictive models capable of probabilistically determining preferences and purchase behaviours (Nuccio & Guerzoni, 2019). Digital resources enable platforms to gain predictive insights and make current and evidence-based decisions that competitors without digital resources and insight will be hard-pressed to match. Ultimately, value is created as a result of improved decision making enabled by digital resources (Bharadwaj et al., 2013). The ability to use digital resources for improved decision making is essentially connected with the firm’s strategy or theory for value creation that guides choices about the composition and combinations of assets and activities (LaValle et al., 2011). In addition, digital resources may help senior management in credibly signalling the value of strategies to investors. As such, theories of value creation that are supported by predictive data and improved decisions, may reduce the uncertainty amongst market participants about the accuracy of management projections of value, reducing information asymmetry.
In sum, capturing cross-business synergies is an essential part of corporate strategy on which its success depends. However, the pursuit of unique corporate strategies are subject to an information problem, hampering market performance. As digital resources allow platforms to create novel synergies (i.e. increased benefits) and may help to reduce some of the information asymmetry, I expect that digital platforms are more effective and successful in pursing unique corporate strategies than traditional firms. As such, I hypothesize that the digital platform nature of firms positively moderates the relationship between corporate strategy uniqueness and market valuation.
Hypothesis 3: Digital platform firms positively moderate the relationship between corporate strategy uniqueness and market valuation.
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processes. It is not surprising that prior research found that IT reduces internal coordination costs (Dewan et al., 1998; Hitt, 1999; Hitt et al., 2002), allows for the effective knowledge transfer across businesses and generally helps to create cross-business synergies (Tanriverdi, 2006). Indeed, Ravichandran and colleagues (2009) found support that information technology positively moderates the relationship between diversification and Tobin’s Q.
In conclusion, resource-based theory suggests that cross-business synergies are the principal motive for firms to diversify (Barney, 1997; Teece, 1982; Wan et al., 2011). Digital resources allow platform firms to create economies of scope and scale and foster novel synergies while expanding. Previously unrelated industries become related on the basis of consumer data (Schwarz, 2017). In addition, part of the information problem that accompanies complex diversification strategies may be reduced as digital resources may help to credibly signal the value of a strategy and may promote the accuracy of management projections of value. The digital assets and infrastructure allows firms to efficiently coordinate processes, reducing internal coordination costs, and ensures an effective knowledge transfer across business units. Thereby enhancing synergies or reducing the costs of enabling such synergies to occur. As such, there are increased benefits (i.e. synergies) and reduced costs due to a lower coordination burden. Hence, I expect that digital platform firms are more successful in terms of corporate diversification, compared to traditional firms, and that this will be reflected in market valuation. As such, I hypothesize that digital platforms and their bundle of digital resources, positively moderate the relationship between corporate diversification and market valuation.
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3. Methodology
3.1 Data
In order to analyse these empirical predictions and test the hypotheses, I have constructed a ten year panel data set consisting of firms from 48 different countries between 2008 and 2017. In constructing the dataset, I used an existing panel data set from the MSCI All Country World Index, combined with financial data provided by the University of Groningen. In addition, I collected segment data from the Thomson Reuters Datastream database. A unique company identifier (DS code) was used to extract corresponding Standard Industry Classification (SIC) codes, up to ten different segments (i.e. SIC codes) per company. Data on the amount of sales and asset investments in each segment is also included. Consistent with previous research (e.g. Barth et al., 2001; Litov et al., 2012) I exclude firms from the financial industry, by excluding 4 digit SIC codes starting with a 6. In addition, I exclude the observations of which the variables used for analyses report missing values. The final sample includes 20,016 observations and 2,575 unique companies, primarily active in 577 different industry segments (i.e. segments with largest percentage of sales).
3.2 Measures
3.2.1 Independent variable: Uniqueness in Strategy Choice
In order to compute the measure of uniqueness, I adopt the method used by Litov and colleagues (2012) by measuring the similarity of a firm's strategy relative to other firms in the same primary industry (SIC). For each firm in a given year (i.e. observation), I define the vector of its sales across all the segments in which the firm operates, in that particular year (i.e: sales SIC1; sales SIC2 etc.). Accordingly, I normalize this vector to unit length by dividing each vector element by the total amount of sales for the firm in question. By doing so, I define the primary industry for each firm in a given year as the industry with the largest percentage of total corporate sales. For example, Anglo American, a British multinational mining company, had SIC 1222 (“Bituminous Coal Underground Mining” ) as its primary industry in 2012, as the firm had its highest amount of corporate sales in that industry in that year: roughly 22 percent (see Table 1 below). Next, for each primary industry, I define the industry vector (centroid) of sales by listing the total sales of each firm in that particular industry (vector elements) in a given year. Again, this vector is normalized to unit length by dividing each vector element by the total sales of all firms with the same primary industry in that given year.
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3.2.2 Independent variable: Diversification
Increased diversification is subject to coordination challenges and related costs. Also, the complexity that accompanies diversification could lead to increased information asymmetry between managers and capital markets, making the strategy more difficult and costly to evaluate. The computation of the diversification measure in this thesis is rather straight forward and is consistent with previous studies (e.g. Litov et al., 2012). I measure the total number of reported segments that a firm is active in, during a given year, using a series of dummy variables (Segment1, Segment2, etc.). The measure is coded as 1 (2,3 etc.), if the firm is active in 1 (2,3 etc.) segment(s) respectively.
3.2.3 Dependent Variable: Tobin’s Q as a Measure for Market Valuation
I use Tobin's Q as a measure for market valuation of the firm, consistent with previous studies (e.g. Morck et al, 1988; Bai et al., 2004). Tobin’s Q is defined as the ratio of the total market value of the firm to the replacement cost of the firm (Montgomery and Wernerfelt, 1988). The advantage of using Tobin’s Q as a measure is that it incorporates intangible assets more effectively, as opposed to accounting-based measures. In addition it has the advantage of being future-oriented, since it represent the shareholders’ expectations of the value and future development of the firm (Richard et al. 2009).
In essence, Tobin’s Q represents the relationship between market value and the intrinsic value of the firm. Thus, it is a useful measure for determining if a firm is over or undervalued by the market. If Tobin’s Q is low (between 0 and 1) it implies that the costs to replace a firm’s assets is greater than the value of stock, meaning that its stock is undervalued. A ratio of greater than 1 would imply that its stock is more expensive than the replacement costs of the firm’s assets. Hence, it suggests that the firm is overvalued. If diversification or uniqueness in strategy choice contributes to value, it could imply that the combination of (intangible) assets and synergies increases market value relative to its replacement costs (Lang & Stulz, 1994). On the other hand, if diversification or uniqueness in strategy is indeed to destroy value, diversified or unique firms will have a lower Q, compared to less unique or focused firms.
Tobin’s Q is calculated as follows: (market capitalization + preferred stock + long term debt) / (total assets – short term debt & current portion of long term debt).
3.2.4 Moderating Variable: Digital Platform Firms
In order to identify digital platform firms in the sample, I use a mixed methods approach. First, I adopt findings of a study by Kile and Phillips (2009) who developed methods, based on prior literature, for selecting and partitioning samples of high-technology firms using SIC codes and other industry classification codes. Then, I proceed with a ‘bottom up’ approach in which I zoom in on the sample to identify the industry segment with leading digital platform firms.
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Alibaba, Facebook and other social networks), which al appeared to be in SIC 737. More specifically, the above mentioned digital platforms reside in SIC 7375; “Information Retrieval Services”. Table 3 presents an overview of the specified 4-digit SIC codes along with a description and industry examples.
The sub-segment descriptions within SIC 737 call for a more specified approximator for digital platform firms, as the type of firms included are still rather broad (e.g. firms active in computer rental services, computer facility management services, consulting and programming). While, for example, SIC 7372 includes platforms such as Apple and Microsoft, the majority of the firms in this segment (and 7373) produce software and applications for such platforms (e.g. Citrix, Adobe systems, Symantec, and Fortinet). Other well-known non-platform firms that have 737 as their primary industry in a given year include HP, Atos, Capgemini, Xerox and Samsung.
For this reason, I use SIC 7375 as a proxy for digital platform firms. In order to do so, I create a dummy variable “platform” and subsequently assign values of “1” for firms active in SIC 7375 and values of “0” for non-7375 firms. Two reasons motivate my choice. First, by looking at the sample, I find that the majority of the well-known digital platform firms are included in this segment. Examples include: Google, Facebook, Twitter, LinkedIn, Alibaba, Yahoo, Rakuten, and Baidu. A list of firms that have 7375 as their primary industry in a given year can be found in Table E1 in the appendix. Second, this segment is specifically focused on consumer data, and information retrieval, which characterizes digital platform firms and which is at the centre of the arguments leading up to hypotheses 3, and 4. In addition, segment 7374 is also focused around data (i.e. data preparation and processing services). While this segment is not included in the primary platform dummy, the results are robust when included in a second platform dummy: “platform2”.
3.2.5 Control Variables
I include several firm (and one industry specific) control variables in my analyses, consistent with previous theoretical and empirical work, that can explain a firm’s market valuation as measured by Tobin’s Q. For variables subject to skewness, logarithmic transformation is applied (Jong & Freel, 2010).
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R&D expenditure (Heyden et al., 2015). In the absence of data concerning R&D expenditure, the missing values are replaced by a “0”. Wagner (2007) argues that excluding the firms without reported R&D expenditure may lead to a sample bias.
Moreover, I include return on assets (ROA) as a proxy for firm performance consistent with prior research (e.g. Greve, 2003; Heyden et al., 2015). Differences in financial performance may influence the strategic direction of the firm (Heyden et al., 2015). Following Litov et al. (2012), I include a variable for each firm’s sales growth rate over the past three years, as a proxy for growth. A firm’s past or current growth rate may be perceived to predict a firm’s future growth opportunities (Fu et al., 2016). In addition, I control for financial leverage that could affect market performance. In this thesis, financial leverage is defined as the ratio of the total amount of short term debts to total assets of the firm. High financial leverage may entail a higher probability of financial distress and a risky investment. Viewed from a corporate governance perspective, managers in firms with more leverage may have a superior incentive to perform. Conversely, managers that have free cash flow at their disposal could make suboptimal investment decisions, dampening firm value (Jensen, 1986; Welch, 2011). Additionally, I include an industry specific variable in my analyses to control for competition, measured as the number of firms in the primary industry of the firm. Finally, I include year dummies to control for fixed year effects since the observations range from 2008 to 2017.
3.3 Analytical Method
The constructed longitudinal dataset consists of unbalanced panel data, which includes 20,016 observations and 2,575 unique firms in total. The average number of yearly observations per firm is 7.8. In order to test the hypotheses, various models are constructed. Ordinary least squares (OLS) models based on panel data could be biased and inconsistent due to heteroscedasticity and autocorrelation in these regressions (Certo and Semadeni 2006; Oehmichen et al., 2019). This could be avoided by selecting multilevel models. The Breusch-Pagan Lagrange multiplier (LM) test (stata: xttest0) is performed in order to confirm which type of regression analysis is preferred (OLS regression or random effects generalized least squares regression (GLS). That is, to determine whether to take into account unobserved heterogeneity of firms in the sample (Breusch and Pagan, 1980). According to the LM test results, the null hypothesis that variances across entities is zero, is rejected at 5 percent level of significance. Hence, the GLS random effects regression is preferred over the OLS regression. The outcome of this test can be found in appendix A. The choice for a random effects model, as opposed to a fixed effects model, rests on the assumption that the models include a rather time-invariant variable (platform). That is, firm type is likely to be consistent over time. The GLS fixed effects model does not allow the estimation of time-invariant and rarely changing variables (Baltagi, 2001; Plümper and Troeger, 2007). As such, GLS random effects regressions are performed in order to ensure the reliability of my results. I first run a restricted model in which I regress Tobin’s Q on the control variables. In the second and third model the independent variables are added in order to test the first two hypotheses. Model 4 and Model 5 include the interaction effects in order to test hypotheses 3 and 4 respectively, followed by a full model including all direct and interaction effects.
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4. Results
4.1 Descriptive Statistics and Correlations
Table 4 presents a summary of the descriptive statistics for all the firms in the sample. In addition, a distinction is made between firms that are active in SIC 7375, labelled as “Digital Platform Firms” (n = 44, obs. = 210) and all other firms, referred to as “Traditional Firms” (n = 2,558, obs. = 19,806). These statistics indicate that, on average, digital platform firms have a higher Tobin’s Q than traditional firms. One could therefore argue that markets, on average, have increased expectations of the value and of the future development of platform firms (Richard et al. 2009), in relation to traditional organizations, as firms with a higher measure of Tobin’s Q represent better growth opportunities (Lang et al., 1996). In terms of strategy, the table indicates that traditional firms are slightly more unique and diversified, compared to digital platform firms in the sample.
Furthermore, there are some overall differences observable in the control variable measures. Traditional firms seem to be slightly larger in terms of employees and have a marginally higher financial leverage ratio. In turn, digital platform firms, spend more on R&D, on average. This difference was anticipated, as R&D expenditure is likely to be higher in high-tech industries (Shefer and Frenkel, 2005; Falk, 2007) Moreover, there is a noticeable difference in terms of the return on assets ratio. Again, this does not come as a surprise as the ratio may differ significantly across industries. Firms in technology industries have different asset compositions compared to more traditional industries for example. Similarly, return on assets will be higher for service providing companies than for capital intensive firms in the steel and oil industry (Selling and Stickney, 1989).
The relevant correlations of the variables included in the analyses are presented in table 5. In general, there are no high correlations observable (r < 0.4). There are, however, some correlations that deserve further elaboration. Return on assets and Tobin’s Q represent the highest significant correlation (r = 0.315). It is not surprising that a firm’s financial performance will grab the attention of investors and thereby affect market performance. This is consistent with prior findings in the literature (e.g. Wernerfelt and Montgomery 1988; Lang and Stulz, 1994; Lloyd and Jahera, 1994). Moreover, the positive and significant correlation (r = 0.240) between diversification and uniqueness was also expected. By diversifying into uncommon domains compared to industry counterparts, firms become more unique in terms of their corporate strategy. This finding is consistent with the study of Litov et al. (2012). As firms diversify, they become larger as a result of the strategy focused on growth. Hence, in line with prior work (e.g. Lang and Stulz, 1994; Berger and Ofek, 1995), the positive and significant correlation (r = 0.222) between diversification and firm size was anticipated. Furthermore, large firms are more likely to secure the necessary funding for large scale R&D (Shefer and Frenkel, 2005). Therefore, the positive and significant correlation (r= 0.185) between firm size and R&D investments is not surprising and is consistent with previous studies (e.g. Acs and Audretsch, 1988; Shefer and Frenkel, 2005).
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4.2 Regression Results and Hypotheses Testing
In order to test my hypotheses, random effects GLS regressions on Tobin’s Q are performed, of which the results are presented in Table 6. All models are ran with fixed year dummies to control for year effects. Model 1 presents the restricted, or baseline, model including the dependent variable and control variables. The results provide an overall R-squared of 0.142 (within = 0.046; between = 0.197) with a significant Chi-squared statistic of 1265.43 (p < 0.01) . The control variables firm size and number of firms in the industry exhibit a negative and significant relationship with Tobin’s Q, while return on assets and R&D investments display a significant positive coefficient (p < 0.01). This implies an increase in Tobin’s Q, or market valuation, for every additional degree of return on assets and R&D investment. The opposite implication holds for the former two variables (i.e. decreased market valuation). These effects remain stable in all subsequent models.
In Model 2 the direct effect of diversification on market valuation is added. Again the model is statistically significant (Chi2 = 1293.32, p < 0.01) with an overall R-squared of 0.153. The significant negative coefficient as demonstrated by diversification (β = 0.048, p < 0.01) implies that, on average, a decrease in Tobin’s Q is observed with increased levels of diversification. This is in line with my expectations. Therefore, I find empirical support for hypothesis 1 and reject the null hypothesis.
Hypothesis 1; “There is a negative relationship between corporate diversification and market valuation (as measured by Tobin’s Q)”, is supported.
Model 3 is constructed in a similar fashion but includes uniqueness in strategy choice instead of
diversification. The model is statistically significant (Chi2 = 1283.04, p < 0.01) with an overall R-squared
of 0.145. The negative and significant coefficient (β = -0.430, p < 0.01) indicates that, overall, increasing levels of corporate strategy uniqueness lead to reduced expected values of Tobin’s Q, holding all other variables constant. That is, uniqueness in strategy choice relative to industry peers, on average, dampens market valuation. Hence, in line with my expectations, I find empirical support for hypothesis 2.
Hypothesis 2; “There is a negative relationship between corporate strategy uniqueness and market valuation (as measured by Tobin’s Q)”, is supported.
Model 4 adds the interaction effect between uniqueness and digital platform firms as measured by the dummy variable platform. While the model itself is statistically significant (Chi2 = 1289.78, p < 0.01) with an overall R-squared of 0.148, the interaction effect (β = 0.893, p > 0.1) proves to be insignificant. The direct effect of the platform dummy on Tobin’s Q is also insignificant (β = 0.150, p > 0.1). In sum, I do not find support for hypothesis 3.
Hypothesis 3; “Digital platform firms positively moderate the relationship between corporate strategy uniqueness and market valuation”, is not supported.
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and significant (β = -0.473, p < 0.1). However, the interaction effect between diversification and digital platform firms indicate a significant positive relationship (β = 0.384, p < 0.01) with market valuation. This implies that, if the diversifying firm is also a digital platform firm, the negative effect of increased levels of diversification on market valuation is reduced. More specifically, figure 2 shows that platform firms actually benefit from diversity, while traditional firms tend to perform worse with a high degree of diversification. Hence, I find support for my last hypothesis.
Hypothesis 4; “Digital platform firms positively moderate the relationship between corporate diversification and market valuation”, is supported.
Model 6 includes both direct effects and both interaction effects of the prior models. Both the Chi-squared statistic (1321.73, p < 0.01) and overall R-squared (0.156) are slightly improved. In addition, all prior findings remain similar with minor changes in the coefficients and standard errors. The direct negative effect of the platform dummy on Tobin’s Q increased in terms of significance (β = -0.547, p < 0.05) .
Figure 2: Interaction Effect of Digital Platform Firms and Diversification on Tobin’s Q
4.3 Robustness of Main Results
In an attempt to further increase the reliability of my results, I performed various robustness checks. First, I conducted additional random effects GLS regressions with adjusted financial variables. The descriptive statistics in Table 4 display several variables with severe outliers, mainly in the financial data. For example, the minimum (-44.145) and maximum (57.068) values of Tobin’s Q appear to be extreme outliers when plotting the data. Under normal circumstances this ratio would not be negative. Also the maximum value of sales growth percentage is abnormal. A scatter graph of the two variables including their outliers can be found in appendix C. Hence, all the variables obtained from the financial dataset are winsorized by 1 percent. That is, values below the first percentile and above the ninety-ninth percentile are replaced by the values of those percentiles respectively (Ghosh and Vogt, 2012). Accordingly, the six models were replicated including several winsorized variables (Tobin’s Q, firm size, sales growth, return on assets, R&D investment, and financial leverage). The results are presented in Table D1 in appendix D.
M a rk et V a lu a ti o n ( T ob in 's Q )
Increased Levels of Diversification
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Model 7 presents the baseline model. The coefficients of return on assets and R&D investment vary
slightly and remain positive and significant (p < 0.01). Similarly, the coefficients of firm size and number of firms in the industry remain negative and significant, now both (p < 0.01). Interestingly, the previously insignificant coefficient of sales growth (β = 0.001, p > 0.1) has become positive and significant (β = 0.118, p < 0.01). Likewise, where the coefficient of financial leverage was insignificant β = -0.001, p > 0.1), it is now negative and significant (β = -0.223, p < 0.01). Hence, all control variables exhibit significant results and remain stable in the subsequent models. As a result, the Chi-squared statistic (4636.2, p < 0.01) and the values of R-squared have significantly improved (within = 0.160, between = 0.419, overall = 0.348). In the following models in table D1, the direct effects and interaction effects are added. While the value of the coefficients and significance levels differ here and there, the overall results remain similar: the direct effect of diversification is negative and significant in Model 8 (β = -0.035, p < 0.01); the direct effect of uniqueness is negative and significant in Model 9 (β = -0.159, p < 0.05); the interaction effect between uniqueness and platform remains insignificant in Model 10 (β = -0.244, p > 0.1); the interaction effect between diversification and platform remains positive and significant β = 0.128, p < 0.1). The direct effect of the platform dummy on Tobin’s Q portrays insignificant results in these models.
Moreover, a potential issue in my analyses is the inclusion of firms that are alone in their primary (and only) industry in a given year. The sample includes 369 of such “monopoly” firms, consisting of 1,348 observations in total (6.7 percent of all observations). These firms have a uniqueness measure of 0 as they define the industry. Consistent with Litov et al. (2012), these observations are omitted from the sample, in order to make sure that they do not drive my results. Subsequently the models are reproduced, both with and without winsorized variables. The overall results relating to hypothesis 2 remain and are presented in
Model 13 and Model 14 in Table D2 in Appendix D.
Another concern is the construction of the platform dummy. My initial analyses are run with a platform dummy consisting of SIC 7375 firms, as the highest concentration of well-known digital platforms appears to be in that segment, and this segment is data driven according to the descriptions in Table 3. As SIC 7374 firms are also focused on data related activities (i.e. data preparation and processing services), a second platform dummy is created including SIC 7375 and 7374 (n = 59, obs. = 269) . The overall results are consistent and are presented in Model 15 in Table D2. In addition, a third platform dummy is created consisting of all 737 firms (n = 151, obs. = 951) and the results are presented in Model 16 in Appendix D. The results obtained from the prior analyses are not robust to including all SIC 737 firms as a moderator, as both interaction effects are insignificant. Noticeably, the direct effect coefficient of SIC 737 firms on Tobin’s Q is positive and significant (β = 0.545, < 0.01), implying that, on average, markets seem to appreciate firms primarily active in this industry.
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5. Discussion & Conclusion
This thesis is motivated by a desire to understand how strategy choice affects market valuation, and if this effect is contingent on firm type. More specifically, this thesis aims to provide an understanding if, and why, digital platform firms are valued differently compared to traditional firms in terms of their corporate strategies. Prior studies on the corporate strategy-performance relationship have yielded different answers so far. In an attempt to explain why platform firms are valued differently, I highlight the importance of a firm’s resources in enabling cross-business synergies that are an essential part of corporate strategy, and allow for deliberate value creation and above normal returns (Hauschild & Knyphausen-Aufseß, 2012). By drawing on resourced-based theory, I argue that the distinct digital resources influence the strategies and opportunities for synergy creation of platform enterprises. Hence, the extant literature on corporate strategy, that studies the link with performance or market valuation, is rather incomplete without considering these new digital firms and their distinct resources. Accordingly, this study seeks to answer the questions of (RQ1); how does the choice of corporate diversification and strategy uniqueness affects market valuation? And (RQ 2); how do digital platform firms moderate this relationship?
By performing random effects GLS regression analyses, I empirically test how measures of diversification and uniqueness in strategy choice affect Tobin’s Q. Subsequently, I investigate the interaction effects of the platform dummy and diversification – as well as uniqueness – on Tobin’s Q. I test my hypotheses on a longitudinal dataset consisting of a sample of 2,575 unique firms from 48 different countries, primarily active in 577 different industry segments, between 2008 and 2017. As a result, I find support for three of my hypotheses. To answer the first research question, I provide empirical evidence that increased uniqueness in strategy choice and increased levels of diversification, dampens market valuation. Indeed, the results suggests that markets, on average, seem to discount uniqueness in strategy choice and corporate diversification. These findings are largely consistent with prior research (e.g. Wernerfelt and Montgomery, 1988; Lang and Stulz, 1994; Litov et al., 2012). Regarding the second research question, I find support that this effect is partially different for digital platform firms. When moderated by the platform dummy, the relationship between diversification and Tobin’s Q is not always negative, and therefore, a more nuanced view is needed. More specifically, the empirical findings reveal that platforms actually benefit from diversification. In contrast to traditional firms, markets seem to accept diversity in digital platforms. Hence, these results support my theoretical arguments that platform firms could benefit from diversifying as its infrastructure and resources allow for effective coordination and significant efficiency gains through economies of scale and scope, fostering synergies, and creating above normal returns.
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and Cusumano, 2013). This, indeed, suggests that platforms benefit from growth and expansion as synergies can be exploited. While it is true that uniqueness in strategy can be created by expanding into unique domains compared to industry peers, the measure of uniqueness in this thesis is designed to be independent of the level of diversification (Litov et al., 2012). Therefore, uniqueness in strategy does not necessarily imply expansion. Recall the example of TPK Holding, a very focused firm that is unique compared to its industry counterparts in terms of strategy choice. As uniqueness could also imply a very narrow focus and platform firms seem to benefit from expansive strategies, it could be argued that markets do not necessarily appreciate uniqueness in strategy choice of platform firms. That is, if unique strategies are not focused on growth, they do not lead to a higher market valuation.
Another line of reasoning that could explain why I did not find support for the third hypothesis, is that there are issues in terms of industry classification. The adopted measurement of uniqueness, measures the similarity of a firm's strategy relative to other firms in the same primary industry (SIC). This raises several concerns. First, the SIC code system was established in 1930s when the economy was focused on manufacturing. Although SIC codes have been updated over time, they often fail to provide accurate classification for services and the emerging industries of the twenty-first century. In addition, the limited scope of this classification system often contains overlapping and ambiguous descriptions, further reducing accuracy (Kile and Phillips, 2009). Porter (1998) adds that the SIC system fails to capture many important actors and interactions in competition as well as linkages across industries. Therefore, the SIC system is less suitable for accurately classifying firms in this new digital world and fails to capture their competitive interactions accordingly.
Hence, defining the correct industry and its actors is difficult. For example, Google (which organized all its businesses under the name ‘Alphabet’ since 2015) owns more than 200 companies, all competing in a wide range of sectors (Johnston, 2019). Yet the SIC system does not capture these sectors and accompanying industry peers. Accordingly, the data suggests the firm was overall active in SIC 7375 “Information Retrieval Services”, with a uniqueness score of 0.092 in 2017. Thus, the uniqueness measure adopted in this study may be less suitable in the context of digital enterprises. The underlying SIC system is outdated, fails to shed light on the emerging industries and does not capture the complete picture of a firm’s activities. Another factor that could have an impact on this result, is the current proxy for platform firms based on SIC codes. While SIC 7375 indeed seems to have the highest concentration of digital platforms, several other well-known platforms are excluded (e.g. Amazon, Apple, Microsoft). In addition, the relatively small platform sample size could have limited the ability to obtain significant results. Besides these arguments, which seek to provide an explanation for the insignificance of hypotheses 3, this thesis also yielded significant results, which have important theoretical implications.
5.1 Theoretical Implications
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If, as my findings suggest, investors favour more common strategies over unique resource combinations, as a result of an acute information problem, this could have important implications with respect to corporate governance. Senior management could be pressured to settle for more common and easy to evaluate strategies in order to attract investors and increase firm value in the short and medium term. Benner and Zenger (2016) argue that this results in an adverse selection problem. They conceptually explored the theory of a ‘lemons market’ for strategies. Akerlof (1970) first illustrated the adverse selection or lemons problem with an example of the used car market. He argued how the quality of goods traded in the market can drop as a result of information asymmetry between sellers and buys, leaving only the inferior products (lemons) behind. Similarly, the results of this study could imply that only more common strategies (lemon strategies) stay behind in equity markets as a result of investors failing to understand more unique strategies. This thesis thereby also contributes to the literature on corporate governance by providing empirical evidence to support the theory of a lemons problem in the market for strategies, as proposed by Benner and Zenger (2016).
Next, this study contributes to the literature on corporate diversification. My empirical findings largely resonate with previous research studying the diversification-performance relationship (e.g. Wernerfelt and Montgomery, 1988; Lang and Stulz, 1994; Berger and Ofek, 1995). The first supported hypothesis suggests that corporate diversification, not taking into account the relatedness of underlying resources and capabilities, destroys value and therefore leads to a diversification discount. In this thesis, I argue that synergies are the central concept of diversification success, following prior research (Chatterjee 1990; Montgomery and Hariharan 1991, Barney, 1991), and that the benefits of those cross-business synergies should not outweigh the costs caused by coordination burdens. This could imply that mainly narrow diversification strategies (that limit coordination challenges and are motivated by underlying related resources and capabilities) enhance performance.
Indeed, a widely shared view in the strategy literature is that the diversification-performance relationship is inverted U-shaped (e.g. Hitt et al., 2017; Pierce and Aguinis, 2013), where small steps in related domains are beneficial but larger steps into unrelated domains are harmful. Moreover, finance scholars seem to embrace agency theory and explain that managerial agency costs and resource misallocation lead to a diversification discount (e.g. Jensen, 1986; Martin and Sayrak, 2003). Diversification allows firms to set up internal capital markets, pool and reallocate cash to divisions in order to meet financial criteria. Managerial access to an internal capital market and free cash flow, may provide the opportunity to over-invest, or allocate resources to the most inefficient business units, lowering value and causing a diversification discount (Shin and Stulz, 1998).
