“Corporate Strategies in Digital Platform Firms vs. Traditional Firms”

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0 Kristina Schenk

S3816613

MSc BA Strategic Innovation Management

Supervisor:

Prof. Dr. Jana Oehmichen Co-Assessor:

Dr. Killian McCarthy

Date: 20.01.2020 Word count: 10,622

“Corporate Strategies in Digital Platform Firms vs. Traditional Firms”

Abstract:

In this study it is examined how the corporate choice of strategic uniqueness and diversification differs in impacting firm performance of traditional and platform firms. Recent literature on firm strategy indicates a uniqueness and diversification discount for traditional firms. Thus, the study aims to contribute to the understanding of how this discount might change when looking at platform firms instead. Based on agency theory it is depicted how the information problem, which is a major factor for the undervaluation of unique and diversified firm strategies, is limited for digital platform firms. Moreover, drawing on contingency theory, it is assumed, that pursuing a unique corporate strategy and being diversified, better fits with the organizational environment of a digital platform, than with firms having a more traditional business model.

The empirical analysis of 20,208 observations, accounting for 2,746 unique firms over 10-years gives evidence that strategic uniqueness and diversification have a convex relationship with firm performance and that this relationship becomes more extreme for platform firms.

Taking an agency-contingency perspective

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1 Introduction

Managers generate economic value as they discover and create valuable resource and asset combinations, which are then valued in strategic factor markets (Barney, 1986; Montgomery & Wernerfelt, 1988). By identifying valuable strategies and implementing them, firms possess exclusive information about this combination of resources and its synergies, allowing them to purchase them below their market price. Thereby firms are able to capture above normal returns (Barney, 1986). Even though, possessing a unique combination of assets and resources before their value is detected by the market and other competitors is crucial to capture economic rents, these strategies are only as effective, as the market’s ability in accurately assessing the value of these strategies (Litov, Moreton, & Zenger, 2018). A precise valuation is important for firms, to communicate their performance to actors outside the organization. Hence, high valuations encourage external financing, since investors use this information to rate the risks and rewards of potential investments into a specific firm (Doukas, Kim, & Pantzalis, 2008; Healy & Palepu, 2001). However, previous research identified a significant information problem, actors in capital markets come across with, while assessing these strategies (Akerlof, 1970; Hubbard, 1998; Myers & Majluf, 1984). The lack of sufficient information disclosure (Healy

& Palepu, 2001), can lead to the “lemons” problem, meaning that market intermediaries cannot distinguish between valuable and invaluable business ideas (Akerlof, 1970). If this “lemons” problem is not fully resolved, capital markets are likely to undervalue unique but “good” corporate strategies over more familiar but “bad” ones (Healy & Palepu, 2001). In line with literature, also Litov et al. (2018) found, that firms pursuing less familiar combinations of businesses are particular costly for analysts to evaluate and thus, tend to be undervalued. In order to estimate the value of a unique combination of assets, firms need to understand complementarities and synergies which are created through these combinations. However, the more unique this combination is, the less likely it is that any market actor will be familiar with these synergies and able to assess them (Litov et al., 2018). The absence of comparative benchmarks and the costly process to develop such benchmarks further add to this problem (Akerlof, 1970; E. Zuckerman, 1999). Thus, due to the occurrence of information problems and the costs of acquiring lacking information, market intermediaries are not able to assess the performance-based value of a firm pursuing a unique strategy over short- to medium-term time horizons (Litov et al., 2018). By adopting more familiar and common strategies, firms can increase available information and thereby ease the strategy evaluation (Barney, 1986; Litov et al., 2018). Mainly grounded on information asymmetries, literature predicts a uniqueness discount for traditional firms. Simultaneously, the importance to possess a unique combination of assets in order to capture economic rents and stay ahead of competition in the long run is emphasized. Litov et al. (2018) refer to this trade-off managers face, namely to choose between long- run value maximizing strategies and less valuable ones, which are more easily to assess, as the “paradox of uniqueness”.

Next to uniqueness, also diversification increases the strategic complexity of a firm, and thus makes it harder to be assessed by external market actors. According to Rumelt (1982) “diversification takes place when the firm expands to produce and sell products or a product line having no market interaction with each of the firm’s other products” (p. 6).

Literature finds, that multi-divisional firms are more efficient and profitable than the separate businesses on their own, because they create larger internal capital markets (Berger & Ofek, 1995; Lang & Stulz, 1994) and firms might be able

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to enjoy increasing economies of scope (Rumelt, 1982). However, besides these advantages it is also examined that the value of these firms is generally forecasted less accurately (Duru & Reeb, 2002). With increasing diversity, it becomes more difficult for market actors to place these firms into normatively assessed industry categories (E.

Zuckerman, 1999; E. W. Zuckerman, 2000). Moreover, diversification into unrelated fields of businesses makes it more difficult to understand the value of synergies occurring between the different businesses and assets, which in sum, contribute to a firms value (Litov et al., 2018). Thus, literature predicts a diversification discount for firms due to the appearance of information asymmetries, which are difficult to understand and costly to evaluate by external actors.

Digital Platform Firms

Platform ecosystems and their innovations become increasingly involved in our everyday lives, leading to plenty of research about this phenomenon. The definition of platform firms, can be split into internal and external platforms.

Internal platforms on the one hand, are mainly a foundation to incremental internal innovation in the field of reusable technologies. External or industry platforms on the other hand, provide services, products or technologies which provide a foundation for external innovators, to develop their own complementary innovations and to generate network effects (Gawer & Cusumano, 2014). For this research, the definition of external platforms will be used, since its base is also open to the outside the firm itself and allows for the creation of network effects. Platforms do exist in a variety of industries, but mainly act in high-tech businesses driven by information technology. They are distinct from traditional firms in such a way, that they have a large network of partners which participate in their ecosystem and are often associated with network effects. As more users and providers of complementary goods join a platform’s ecosystem, these network effects provide increasing incentives for users and firms to adopt the platform as well (Gawer

& Cusumano, 2014). Especially due to these network effects, Cennamo & Santalo (2013) emphasize, that platform businesses follow different rules of competition, namely a “winner-take-all” ideology. In line with this, also Gawer &

Cusumano (2014) highlight, that in order to remain successful, platforms not only need to build an open architecture supporting third-party innovation. They also have to carefully manage ecosystem relationships and need to continuously evolve the platform and its ecosystem as markets emerge and technologies change. Additionally, platform firms need to decide which elements of their current strategies to prioritize and how to manage resources consistent with these strategic choices in a way that creates complementary value (Cennamo & Santalo, 2013).

While there is a lot of research about the platform industry, it is still unknown how different strategy choices as uniqueness and diversification differ in impacting the performance of digital platform firms and traditional firms. As explained before, the literature predicts a uniqueness and diversification discount on the performance of traditional firms, mainly based on the occurrence of information asymmetries. However, as platforms thrive to open up in order to generate an increasing ecosystem, they naturally increase the firm’s diversification. Furthermore, as firms build upon a platform, they become part of the platform’s ecosystem which is essential for the platform to grow. These complementors are not only important to determine business success or failure, but they are also relevant players in calculating a firm’s value. So for platform firms, market intermediaries are often not completely external anymore, as

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they join a platform’s ecosystem and thus, the information problem they face might be limited. In the end, this can lead to higher market valuations of platform firms.

Therefore, this study aims to contribute to strategy research by examining the relationship between a firm’s strategic uniqueness and level of diversification and its market performance. It uses the agency theories argument, that these kind of strategies lead to an information asymmetry in the market, making it harder to be understood and evaluated by market intermediaries also increase organizational complexity for the management (Litov et al., 2018; Lloyd & Jahera, 1994). Additionally, this study contributes to platform research by clarifying if there is a uniqueness and diversification advantage for platform firms, while there is a discount for traditional firms. Thus, it is empirically tested if a unique or diversified strategy has a different impact on platform firms than on traditional firms (Doukas et al., 2008; Eisenmann, Parker, & Van Alstyne, 2006; Litov et al., 2018). In line with the goals of this study, the following research question is identified: Does the success of a firm’s corporate strategy, in terms of uniqueness and diversification, differ between traditional and digital platform firms in such a way, that it leads to a systematic competitive advantage for digital platform firms?

Therefore, first of all the theoretical lens of the agency and contingency theory is introduced. Thereafter, the main conceptual model is presented, focusing on the general relationship between (1) strategy uniqueness and performance as well as (2) diversification and firm performance. The study proceeds with introducing platform economics and continues with the conclusion of a moderating effect, which platform firms might have on the previously assessed relationships.

2 Theory and Hypotheses 2.1 Underlying Theories

This study will be based on the theoretical lens of the agency and contingency theory.

Foremost, it draws on agency theory, which is based on an agency relationship, where the principal delegates work to an agent, who performs this work. There are two problems that can occur in agency theory. First of all, the agency problem can arise through goal incongruence or in a state where it is challenging and costly for the principal to evaluate what the agent is actually doing. This leads to the problem that the principal cannot verify an appropriate behavior of the agent and can further lead to adverse selection. Adverse selection refers to a principals misrepresentation of an agent’s abilities, leading to the issue that he cannot verify the information given by the agent (Eisenhardt, 1989). The second possible problem arises when the principal and agent have different attitudes toward risk and therefore, also prefer diverse actions (Eisenhardt, 1989). While shareholders mainly value a maximization of welfare, firm managers, seen as the agents here, can have different interests and objectives, like minimizing the effort and maximizing their short term value (Balsam, Fernando, & Tripathy, 2011). This might lead to a moral hazard problem, which refers to the absence of proper incentives to take correct actions, since due to information asymmetries these actions cannot be observed and contracted upon (Hölmstrom, 1979). In order to minimize agency problems, incentives are necessary in order to align the interests of the principal and the agents (Eisenhardt, 1989).

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This study draws predominantly on the agency theories assumption that market intermediaries have imperfect information about a firm’s strategy, leading to information asymmetries and thereby create moral hazard and adverse selection problems.

Second, contingency theories assumptions are used, which suggest that an organizational outcome is the consequence of a fit or match between two or more factors, for example the organizational environment, strategy, culture or structure (Van de Ven & Drazin, 1984). Structural contingency theory states that for a given environment or technology a particular structure is appropriate (Pennings, 1975). In line with these theoretical assumptions, it is assumed that for a firm, pursuing a particular structure, namely having a unique corporate strategy and being diversified, there is a better fit with the organizational environment of a platform business model than with firms having a traditional business model.

2.2 Conceptual Model

2.2.1 Uniqueness & Firm performance

In order to create superior value and capture economic rents, firms need to acquire a unique combination of assets and resources, before their value is detected by the market and other competitors (Barney, 1986). But following agency theory, this uniqueness can create an information problem, where the firm possesses superior knowledge compared to other market players. Although in the long term these market intermediaries may recognize the value of a specific and unique combination of assets, in the short term they may not. Information asymmetry imposes a cost on the transmission of information, leading to equity markets that lack full understanding of a firm’s strategy and might therefore, lead to an adverse selection problem and a discount of value for firms with unique and hard-to-assess strategies. Moreover, this undervaluation can hinder external financing by potential investors (Doukas et al., 2008;

Healy & Palepu, 2001). This leads to a strategic dilemma manager’s face, with the consequence that if rewarded on the basis of stock performance, managers are likely to reduce strategic uniqueness in order to limit information asymmetries and gather higher valuations. This strategic dilemma is referred to as the paradox of uniqueness (Litov et al., 2018). Besides, this dilemma might also lead to a moral hazard problem, if managers limit the uniqueness of strategy and thus, do not act in accordance with shareholders, who value sustainable long-term growth.

At low levels of strategic uniqueness, the information problem in capital markets is limited. Markets hold relevant knowledge and can easily and cheaply estimate firm strategies, without facing the problem of adverse selection (Litov et al., 2018). Moreover, lower risks of failure are associated with common and less unique strategies, since they are less innovative (Balsam et al., 2011). Thus, for low levels of uniqueness, it is presumed, that a firm’s performance measured by its Tobin’s q will be high. At moderate levels of uniqueness, information asymmetries start to rise, making a firm’s strategy and the synergies among the assets harder to understand for market intermediaries. If intermediaries cannot assess the value of a firm’s combination of assets properly anymore, uncertainty increases and the danger of adverse selection occurs. At the same time, the combination of a firm’s assets is not unique and rare enough to capture above normal economic rents though the valuable and innovative strategy. Thus, for this intermediate level of uniqueness, it is assumed that firm performance will be lower. As mentioned before, high levels of uniqueness are

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essential for a firm to capture economic rents by positively influencing the ratio of value versus costs (Barney, 1986;

Litov et al., 2018). Additionally, unique strategies contribute to creating long term value and help firms to get ahead of their competitors in the market. Moreover, although market actors may face information problems in the short term, in the long run they might be able to overcome this information asymmetry and hence, might be able to recognize the superior performance of a unique asset combination (Litov et al., 2018). This helps limiting the danger of the adverse selection problem, which occurs at moderate levels of uniqueness. Summed up, it is proposed, that firms emphasizing long term value and taking effort in resolving the information problem will be able to gather a high performance trough pursuing strategies with a high level of uniqueness. Overall, it is hypothesized:

H1: There is a U-shaped relationship between a firm’s strategic uniqueness and its performance measured by its Tobin’s q.

2.2.2 Diversification and Firm Performance

A firm is diversified, if it is active in many different areas of business. Firms can have different reasons to diversify, for example as a reaction to technological change (Berger & Ofek, 1995) or a means if firms do no longer see growth opportunities within their industry (Lang & Stulz, 1994). Theory suggests that diversification has both value-enhancing and value-reducing effects, compared to the value of a firm’s segments if they were operated as separate firms (Berger

& Ofek, 1995).

At low levels of diversification, companies focus on their core skills. They are specialized and have high levels of expertise in their area of business, helping them to maximize their short-term value (Lloyd & Jahera, 1994). Firms avoid information problems by keeping away from unfamiliar segments, in which they cannot estimate risks and which might possibly provide only limited opportunities (Berger & Ofek, 1995). This helps them to achieve a high performance. At intermediate levels of diversification firms move away from the exploitation of their core skills into exploring new and unrelated fields. By doing so, the strategic complexity of a company rises and the probability that the management lacks expertise to efficiently direct the firm increases (Lloyd & Jahera, 1994). This lack of expertise can create information asymmetries between the actual market situation and the situation assessed by the management, increasing the risk of adverse selection in investment options and overinvestments in segments with limited opportunities. This can subsequently lead to cross-subsidization of poorly performing divisions by better performing ones, if firms drain resources and misalign incentives (Berger & Ofek, 1995). Therefore, at intermediate levels of diversification, a firm’s performance is supposed to be significantly lower than at low levels of diversification. On the contrary, if diversification is high, firms pursue large internal capital markets, helping them to resolve information problems and thus, also reduce the danger of adverse selection. Since these internal capital markets do not suffer from informational asymmetries, firms are better able to allocate resources more efficiently, and thus, can make even more positive net present value investments than their segments would make as separate firms (Berger & Ofek, 1995; Lang

& Stulz, 1994). Furthermore, according to Teece (1982) diversification can be a main engine for corporate growth and lead to a greater operating efficiency, since it provides less incentives to forego positive net present value projects.

Additionally, since successfully managed diversification leads to the combination of businesses with imperfectly

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correlated earnings streams, a firm’s debt capacity increases. This increased debt capacity then can create value by increasing interest tax shields (Berger & Ofek, 1995). To conclude, due to the creation of internal capital markets and increased debt capacity, this study proposes that high levels of diversification lead to a higher Tobin’s q. Altogether, it is hypothesized:

H2: There is a U-shaped relationship between a firm’s level of diversification and its performance measured by its Tobin’s q.

2.2.3 The Moderating Role of Platform Firms

Platforms are emerging and companies as Amazon, Google or Uber radically change the way of socializing, working and value creation in the economy (Kenney & Zysman, 2016). They facilitate the conversion of consumption goods into monetized goods, as for example AirBnB does, by providing apartments to travelers. They remain an intermediate player, not owning any property. Thus, it can be said, that platforms reshape the way participants interact with each other within a multi-sided digital framework (Gawer & Cusumano, 2014; Kenney & Zysman, 2016). They bring together groups of users in two-sided networks and therefore differ from traditional firms in a fundamental way. Instead of the value moving from the left to the right, costs and revenues in digital platforms are both, to the left and the right, because platform firms have distinct groups of users on each side. This enables them to profit from occurring network effects, which are “feedback loops that grow at exponentially increasing rates as adoption of the platform and the number of complements rise” (Gawer & Cusumano, 2014, p. 422) and thus, lead to increasing returns to scale (Eisenmann et al., 2006). There are four type of network effects, namely (1) direct, (2) indirect, (3) cross-side and (4) same-side network effects. If network effects are direct, an increase in the installed base of users results in a higher valuation of users and thus, also attracts more potential users (Schilling, 2010). This happens for example, if Facebook users attract friends and these friends also attract new users. Similar to these effects are same-side effects, meaning that an increase of the user-base on one side can either be highly or not valued by the users of this same side, therefore leading to either higher or lower user adoption (Eisenmann et al., 2006). On the contrary, indirect network effects can be described in such a way, that an increased availability of complementary goods leads to higher user adoption. Cross- side effect can be depicted in a way, that one side is highly valued by users of the other side and therefore increases the utility of a platform and additionally the user adoption of the other side (Binken & Stremersch, 2009; Eisenmann et al., 2006). An example for generating cross-side effects is AirBnB, where an increase in offered property, which is a complementary good, induces a higher user benefit and increased user adoption.

Due to these network dynamics, platforms firms need to follow a different strategic approach than traditional firms, namely a Get-big-fast strategy in order to rapidly acquire and grow an installed base of users and lock these users in.

Literature predicts a “winner-take-all” outcome for digital platform firms, in which the platform with the biggest user base will win the market (Cennamo & Santalo, 2013; Eisenmann et al., 2006). In order to cope with this “winner-take- all” competition, platform firms need to offer a use that is essential to a broader technological system and solve a business problem for many firms and users in the industry (Gawer & Cusumano, 2014). Companies can achieve this

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for example, by developing a unique and diversified corporate strategy, since due to high multi-homing costs or homogeneous demand, platforms often cannot coexist (Eisenmann et al., 2006).

Uniqueness, firm performance & platform firms

As explained before, platforms follow different mechanisms of competition than traditional firms, mainly due to the existence of network externalities and the importance of complementary goods (Wareham, Fox, & Cano Giner, 2013).

In order to capture these network effects, firms need to grow fast and hence, are often more willing to take higher risks.

To align this with their strategy, platform firms often put less emphasis on financial measures, as sales or the return on assets, than traditional firms (Balsam et al., 2011). Moreover, according to Gawer & Cusumano (2014) for platform firms it is crucial to perform a function essential to a broader technological system in order to fulfill an industry-wide role and convince other firms to adopt the platform as their own. On top of that, by creating distinctiveness platforms make themselves more attractive for a higher number of complementors to join their ecosystem. Thus, being unique and therefore also more essential, becomes even more important for platform firms in order to capture economic rents (Gawer & Cusumano, 2014). Traditional firms face the risk, that due to information asymmetries, external market actors might not be able to assess the value of innovative strategies properly and thus under-evaluate more unique corporate strategies, leading to a lower perception of performance outside the firm (Doukas et al., 2008). For platform firms this appears to be different, since these market intermediaries, that highly determine customer satisfaction and in the end can influence business success or failure, are often part of a platform’s whole ecosystem (e.g. as complementors). Besides this, platform firms are also more dependent on their ability to overcome this information problem in order to attract producers of complementary products to build upon their firm and thus, need to engage in disclosing their value to specific stakeholders (Akerlof, 1970; Cennamo & Santalo, 2013). If these actors are not completely external anymore, since they are part of the platform ecosystem, they get deeper insights into the platform’s corporate structure and the information problem is likely to decrease. Therefore, it is hypothesized, that the business model of a platform firm has a better fit with the strategic choice of uniqueness and moderates the relationship between uniqueness and firm performance in such a way that the turning point moves to the left. Thus, by increasing the strategic uniqueness, higher levels of Tobin’s q can be reached earlier and the U-Shaped curve is steepened. Furthermore, this leads to the assumption, that for platform firms it is crucial to decide to be either not unique at all and maximize the short term value, or to try to reach high levels of uniqueness in order to be successful in the long term, grow fast and capture network effects. It is assumed that:

H3: The relationship between a firm’s strategic uniqueness and its Tobin’s q is moderated by pursuing the business model of a platform firm, such that the turning point from which additional uniqueness has positive effects on firm performance is reached at lower values of uniqueness and that the U-Shaped curve is steepened.

Diversification, firm performance & platform firms

According to Gawer & Cusumano (2014) platforms need to solve a business problem for many firms and users in an industry, indicating an advantage of being diversified for them. By expanding the scope of the platform and integrating into complementary markets firms are able to push forward the platform interface by creating incentives for

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complementors to innovate on top of the newly extended platform (Gawer & Cusumano, 2014). This also contributes to reducing information asymmetries, since complementary firms that largely impact company success become part of the internal capital markets within a platform’s ecosystem. Yet, not only should it be easy to connect or build upon the platform for complementors to expand the system, but also to allow new and even unintended end-users to become part of the platform ecosystem. The more diversified a platform is, the more opportunities appear for complementors and users to adopt the platform and expand its usage, supporting growth and the creation of network effects (Gawer &

Cusumano, 2008). These complementors can also bring in information about new fields of business or customers and important skills, helping the platform firm to acquire these skills in order to successfully operate in the newly entered segments. This further contributes to decreasing information problems that might occur if firms expand in unrelated areas and helps prevent adverse selection in the choice of future business opportunities. Moreover, diversification helps to increase organizational flexibility. As platforms and markets evolve even quicker than industries traditional firms operate in, it can happen that the leader of one generation can lose control over the next one. To prevent this, organizational flexibility is an equally crucial capability for digital platform firms as right management and customer knowledge (Gawer & Cusumano, 2014). With a higher level of organizational flexibility and the knowledge and prevalence of the complementors, it is easier for platform firms to handle the increased strategic complexity and shift away from its core competencies, than for traditional firms as they start to diversify. Therefore, in line with contingency theory, it is proposed that the environment of a platform firm better fits the structure of increased organizational complexity and thus positively influences the relationship between diversification and firm performance. It is assumed, that the turning point is moved to the left so it is already reached at lower levels of diversification, and subsequently also higher levels of Tobin’s q can be reached at lower levels of diversification. Additionally, it is proposed that the U-Shaped curve is steepened. The shifts in the U-Shaped curve leads to the presumption, that for platform firms it is even more important than for traditional firms to make the decision, to either completely focus on a familiar core business without diversifying or to engage in diversification in order to be flexible and relevant to a broader scope of users and complementors.

H4: The relationship between a firm’s diversification and its Tobin’s q is moderated by pursuing the business model of a platform firm, such that the turning point from which additional diversification has positive effects on firm performance is reached at lower values of diversification and that the U-Shaped curve is steepened.

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Fig. 1. Overall conceptual Model

3 Methodology 3.1 Sample and Data

The sample included panel data from MSCI ACWI with important and large firms throughout 2008-2017, provided by the chair. Thomson Reuters’ Datastream/ Eikon was used to find out the main 10 SIC Codes with the corresponding sales and asset values for all companies per year from the panel data. The DS Codes from the MSCI ACWI panel were used as a unique identifier for the companies.

After this, the files for every year were appended in Stata and the datasheet was preprocessed. First of all, all observations with a corresponding SIC-Code value of “9999” were removed. In case one SIC code occurred more than once for a specific firm in a given year t, the corresponding sales and asset values were added up to ensure the primary industry of a firm could be calculated exactly, as the industry with the highest value of sales in this year. Thereafter, observations reporting negative sales were dropped, and also all observations in sales and assets, in case there was no corresponding SIC code listed for a firm in this specific year. Following Barth, Kasznik, & McNichols (2001) and Litov et al., (2018) firms in the financial industry (as defined by the Compustat-provided one digit SIC code header 6) were excluded from the sample, justified with the explanation that financial companies are prohibited by the Glass- Steagall Act (Pub. L. No. 73-66, 48 Stat. 162, 1933) of owning non-financial businesses for investment purposes.

Subsequently, the dataset was merged with financial data, gathered from Datastream/ Eikon and observations for companies and years, which were not part of the original sample were dropped. In order to ensure sufficient observations for the regressions, missing R&D values were turned into 0. Due to other missing control variables, the sample used ends up with 20,208 observations covering 2,746 unique firms over 10 years.

Strategic Uniqueness of the Firm

Diversification of the Firm

Firm Performance/ Tobin’s q

Type of Firm

Traditional Firm/ Platform Firm U

U

Moves tipping point to the left and steepens the curve

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12 Platform firms

Platform firms exist in a variety of industries, especially in high-tech businesses driven by information technology.

They differ between internal and external platforms. Internal platforms can be defined as a set of assets organized in a common structure from which a company can efficiently develop and produce a stream of derivative products. Whereas external platforms are products, services or technologies that act as a foundation upon which external innovators can develop their own complementary goods. This study is based on the definition of external industry platforms, mainly since they allow for network effects (Gawer & Cusumano, 2014). Moreover, Kile & Phillips (2009) provide an overview of 3-digit SIC codes from High-Technology Industries as well as Internet & IT Services (Appendix A). The only 3-digit SIC code appearing in both categories is the code 737. Subsequently, a bottom-up approach was used by looking at the definition of the various segments of SIC codes. According to SICCODE.com (2019) the code 737 stands for “Computer Programming, Data Processing, and other Computer Related Services”. A list of the S&P Capital IQ (2019) was used, indicating the largest internet companies worldwide in 2019 to cross-check the provided examples of firms within this 3-digit SIC code in the panel data. The 3-digit SIC code 737, was found to be a reasonable definition for platform firms for this research. In the final sample, 1,235 observations were identified with this primary industry, covering 181 unique firms. For robustness, the 4-digit SIC Code 7375, defined as “Information Retrieval Services”

was used as a more specific sample of platform firms and additionally tested. The sample included 329 observations belonging to 60 firms.

3.2 Measures

3.2.1 Dependent Variable

Measure of firm performance: Tobin’s q

Tobin's q is a financial-market measure and defines the ratio of market value to the replacement cost of the firm. It incorporates a market measure of firm value which is forward-looking, risk-adjusted and less susceptible to changes in accounting practices (Bharadwaj, Bharadwaj, & Konsynski, 1999; Montgomery & Wernerfelt, 1988). Furthermore, in research, Tobin’s q has been widely used as a measure for a firm’s intangible value. It is based on the presumption that in the long run, the equilibrium market value of a firm needs to be equal to the replacement value of its assets. The extent to which Tobin’s q differs from “1” thus, can be seen as a measure of the extent to which a company’s capitalized rents differ from the market price of its physical assets. The deviations of a significantly higher Tobin’s q value than

“1” represent an unmeasured source of value, in general attributed to the intangible value of a firm (Bharadwaj et al., 1999; Montgomery & Wernerfelt, 1988).

In this study the use of Tobin’s q is especially useful, since firms with different levels of diversification and uniqueness are found to concentrate in different industries (Montgomery & Wernerfelt, 1988). As defined, the Tobin’s q represents the replacement cost of a firm’s assets. Therefore, it is calculated as the sum of market capitalization, preferred stock and long term debt, divided by the difference of the total assets and the short term debt of the firm:

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Tobinq2 = (mark_cap + pref_stock + lt_debt) / (tot_ass - st_debt)

Moreover, for robustness there will be also a second calculation of Tobin’s q, as the sum of market capitalization, the total assets and the negative total shareholder’s equity of the firm. This value is then divided by the value of the total assets of the firm:

Tobinq1 = (mark_cap + tot_ass - tot_sh_equ) / tot_ass

3.2.2 Independent Variables

In line with (Litov et al., 2018) the strategic uniqueness and the level diversification of a firm are calculated, since both are meant to raise the costs of market intermediaries to assess and understand a firm’s strategy and therefore also its value.

Uniqueness of the firm

Firms that pursue common strategies are expected be more familiar and subsequently more easily to be assessed by market intermediaries. Therefore, and following Litov et al. (2018), the uniqueness of a firm is measured as the similarity of a firm’s strategy relative to other firms’ strategies in its primary SIC.

For each firm i the vector of its sales across all segments, N in a given year t is defined as si,t = [sales 1,i,t … sales N,i,t]’.

N=707 is the number of all listed four-digit SIC codes in 2008-2017 within the dataset. The vector is then normalized to unit length, by dividing all vector elements by a firm’s total sales, Σj salesj,it’, whereas i indexes the firm and j the set of N segment industries for a given year t, representing the firm vector. Next, the primary industry for each firm per year is defined as the industry with the highest fraction of total corporate sales. Thereafter, the number of firms active in each primary industry is calculated and subsequently also the total sales for each of these primary industry segments.

This centroid of sales is defined as sj*,t= [Σi sales1,j,t … Σi salesN,i,t], indicating the total sales per primary industry segment j* per year t. i indexes the firms in each of the N=707 segment industries that have j* as their primary industry in year t. Then for each firm the fraction of total sales in j* the firm accounts for, is calculated. After this, for all firms with the same primary industry, the total sales for their other (non-primary) segments are calculated. Additionally, also the total value of sales, of all firms that are active in one primary industry segment, is build. Thereafter, the industry vector is created, representing the relative fraction of each industry, under the condition the firms contributing to its sales are all active in the same primary industry, compared to the total sales of all firms within this primary industry.

Ultimately, using the firm vector and a firms’ primary industry vector, the measure of uniqueness is defined as the distance of the sales “distribution” from the centroid of its counterparts within the same primary industry. It is calculated as UNIQUEi,t= (si,t –sj*,t)². Subsequently, for each firm the values of uniqueness per segment are added up, in order to calculate the uniqueness of the firm (Litov et al., 2018).

Strategic Complexity: Diversification

The cost of strategy assessment also rises with the complexity that accompanies diversification. As firms enter related or unrelated industries, analysis of the firm becomes more complex and market intermediaries need to develop

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expertise across multiple industries. Therefore, analyzing the firm is more costly than analyzing a single-segment firm.

Following Litov et al. (2018), the total number of reported segments is measured, using a set of dummy variables (Segment1, Segment2, Segement3, etc.), that are coded as 1 if the firm is active in that segment and 0 otherwise. These dummies are then added up to calculate the number of segments a firm is active in.

By dividing the difference between the value of the independent variable and its mean through its standard deviation, both independent variables were standardized to make results comparable and to reduce multicollinearity,

3.2.3 Control variables

It is controlled for firm size, using the logarithm of the firm’s employees. Furthermore, it is controlled for a firm’s 2- year annual growth rate in sales and for the years to identify time effects. It is further controlled for the intangible assets of the firm and research and development expenditures, as a share of total expenses. Moreover, the study controls for the firms return on average equity (ROE) and finally for leverage, as a measure of the disciplinary effect of debt, which is calculated as total debt divided by total assets.

3.3 Analysis

An ordinary least squares (OLS) regression is performed to test the hypothesis. Both independent variables uniqueness and diversification were standardized as described above. Moreover, for robustness the same regression is executed again, once by excluding firms that are the only ones in their primary industry in a given year t and once with an alternative calculation of Tobin’s q. Moreover, the interaction effect is tested using a more specified sample of platform firms. Then, in order to remain more conservative an additional regression is executed, taking into account random effects, since this allows independent variables that are constant over time (Certo & Semadeni, 2006). To ensure the results are not driven by outliers, furthermore all variables generated from accounting data are winsorized at the 2.5 percentile in each tail, for this regression.

4 Results 4.1 Strategy Choice: Uniqueness and Diversification

The primary interest of this research is to examine the relationship between different choices of corporate strategy and firm performance. Table 1 shows the descriptive statistics and correlations of the variables used in the regressions.

Model 1 in Table 2 presents the main results of an OLS regression.

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The two main hypothesis predicted a U-shaped relationship between uniqueness and Tobin’s q as well as between diversification and Tobin’s q. To test these relationships, the linear and the quadratic terms of both measures, uniqueness and diversification, were included.

The coefficient for uniqueness was negative and significant (p<0.01), meaning that an increase in uniqueness by “1”

would lead to a decrease in firm performance by “0.083”. However, the quadratic term (uniqueness squared) was positive and significant (p<0.05). This positive and significant quadratic term of the independent variable supports the hypothesis of a U-shaped relationship (Haans, Pieters, & He, 2016). Using the utest command in Stata 15, it is found that the shape of the curve was significantly different from a linear shape, which further supported the assumption of the prevalence of a U-shaped relationship between uniqueness and Tobin’s q. Furthermore, the utest command calculated a turning point at a uniqueness value of 2.117, which is located within the data range (Haans et al., 2016) providing further support of a U-Shaped relationship. On top of this, the results were plotted in Fig. 1, to depict the shape of the curve graphically. The plot provided additional support for the existence of a U-shaped relationship between uniqueness and firm performance. Therefore, the results support Hypothesis 1. Equally, the coefficient for diversification was negative, whereas its quadratic term (diversification squared) was positive and significant (p<0.01).

As explained before, the utest command in Stata 15 further supported the U-shaped relationship between diversification and Tobin’s q and identified a turning point at a diversification level of 2.694. Since this turning point is located within the data range, this yields further prevalence of the predicted relation (Haans et al., 2016). The plot of the curve is depicted in Fig. 2. and provides additional support of the U-shaped relationship between diversification an firm performance. Therefore, the results also support Hypothesis 2.

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4.2 Moderator Platform Firms

Model 2 shown in Table 2 includes the interaction terms between uniqueness and platform firms, diversification and platform firms, as well as the interaction terms between their squared terms and platform firms. As an outcome of the regression, the interaction term between uniqueness and platform firms is negative, while the interaction term between the quadratic term (uniqueness squared) and platform dummy is positive and significant (p<0.01). It was proposed, that the turning point would be shifted to the left, while the curve itself gets steeper. Accordingly to Haans et al. (2016), the turning point moves to the left if:

β1 β4 – β2 β3 < 0 and the curve is steepened if: β4 >0.

Here, β1 represents the value for uniqueness and β2 the value of the squared term, while β3 measures the interaction term and β4 the interaction term with the quadratic value of uniqueness.Since (-0.111*0.129) – (0.028*(-0.277)) = -0.007 and 0.129 > 0, both assumptions can be confirmed for the received regression results. Further support is provided again by the utest command, which calculates a new turning point at a uniqueness level of 1.069 and indicates a shift to the left. Next to this, support is provided by the plot of the curve, which is depicted in Fig. 3.

Equal results occur for the interaction term between diversification and platform firms. The interaction term between diversification and platform firms is negative, whereas the interaction between the quadratic term (diversification squared) and platform firms is positive and significant (p<0.01). It was hypothesized that the turning point would be shifted to the left, while the curve steepens. Following Haans et al. (2016), since (-0.284*0.128)*(0.050*(-0.202) = -0.0004 and 0.128 > 0 the presumptions can be confirmed. The utest command calculates the new turning point at a diversification level of 0.786, providing further support for the predicted moderation. The plot of the curve is shown in Fig. 4 and gives additional support for the hypothesized effect, that the business model of a platform firms moderates the relationship between diversification and Tobin’s q in such a way that the turning point is reached at lower levels of diversification already and that the U-Shaped curve becomes steeper.

Thus, the regression results support Hypothesis 3 and 4.

Fig. 1. U-Shaped relationship between uniqueness and Tobin’s q.

1.51.61.71.8Linear Prediction

-2 0 2 4 6

Uniqueness Standardized Predictive Margins

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Fig. 2. U-Shaped relationship between diversification and Tobin’s q.

Fig. 3. The effect of pursuing the business model of a platform firm on the relationship between strategic uniqueness and Tobin’s q.

Fig. 4. The effect of pursuing the business model of a platform firm on the relationship between diversification and Tobin’s q.

1.21.41.61.82Linear Prediction

-.9 -.4 .1 .6 1.1 1.6 2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 Diversification Standardized

Predictive Margins

246810Linear Prediction

-.777 .223 1.223 2.223 3.223 4.223 5.223 6.223

Uniqueness Standardized

platform_dummy=0 platform_dummy=1 Predictive Margins of platform_dummy with 95% CIs

02468Linear Prediction

-.9 .1 1.1 2.1 3.1 4.1 5.1

Diversification Standardized

platform_dummy=0 platform_dummy=1 Predictive Margins of platform_dummy with 95% CIs

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4.3 Robustness

Various checks were performed to validate both, the quality and the robustness of the results. The variance inflation factors (VIF)were calculated to ensure that multicollinearity among the control variables was not biasing the results.

All control variables VIF were significantly lower than 10, which is the established threshold that would signal multicollinearity problems (Grewal, Cote, & Baumgartner, 2004).

Although all hypotheses were confirmed in the above shown regression model, some further analysis was taken to ensure the robustness of the results.

First of all, in line with (Litov et al., 2018) the study controlled for firms that are alone in their primary four-digit SIC code in a given year. In the dataset, there are 1.653 such observations, accounting for 435 unique firms. A dummy variable Monopoly was created, and it was controlled for it in all regressions to ensure the results were not driven by these firms. The dummy variable has the value of “1” if the firm has a Monopoly in its primary SIC for the given year t, and “0” otherwise. The results are shown in Table 3 and the original results obtain in this analysis. Thus, all 4 Hypothesis are supported.

Second, a different calculation of Tobin’s q was used to see if the results would hold for this calculation as well.

Tobin’s q is now calculated, as the sum of the market capitalization, total assets and the negative total shareholder’s equity of a firm. This value is then divided by the value of the total assets of the firm:

Tobinq1 = (mark_cap + tot_ass - tot_sh_equ) / tot_ass

The results for the OLS regression are shown in Table 4. They are in line with the results obtained for the original calculation of Tobin’s q, such the coefficient for uniqueness is negative and significant (p<0.1) while the quadratic term (uniqueness squared) is positive and significant (p<0.05). This further promotes the prevalence of a U-Shaped relationship between uniqueness and Tobin’s q with a calculated turning point at level of uniqueness of 1.184. Thus, Hypothesis 1 receives statistical support. Subsequently, also the coefficient for diversification remains negative and significant while the quadratic term (diversification squared) remains positive and significant (p<0.01). The utest command computes a turning point calculated at a diversification level of 3.010. Therefore, also Hypothesis 2 is supported. With regard to the interaction effect, all coefficients remain significant. The coefficient for the interaction term between uniqueness and platform firms remains negative while the interaction between uniqueness squared and platform firms stays positive. The same occurs for the interaction terms between diversification and platform firms, as well as diversification squared and platform firms. Using again the utest command to generate the turning points, Stata calculates the turning point at a uniqueness level of 1.708 for the relationship between uniqueness and Tobin’s q if firms are identified as platform firms. Contrary to the previous results, the turning point is now shifted to the right, indicating that for platform firms the turning point is reached at higher levels of uniqueness. This is also confirmed by the calculation according to (Haans et al., 2016), since the term β1 β4 – β2 β3 > 0. As before the curve is steepened.

Hypothesis 3 thus, receives only partial support. Looking at the relationship between diversification and Tobin’s q for platform firms, this relationship is moderated as predicted, namely that the turning point moves to the left and the curve

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becomes steeper, which is also in line with the calculation according to Haans et al. (2016). With the utest command the turning point is calculated at a diversification level of 0.696 and thus, further supports Hypothesis 4.

Third, a more specified sample of platform firms is tested, consisting only of firms with a primary SIC code of 7375.

Results are shown in Table 5. For the identification of the SIC code, a bottom up approach was used, looking again at firms from the S&P Capital IQ (2019) list, as well as at generally known platform firms within the sample, and then checking their primary SIC within the panel data. This approach identified the SIC 7375 as a more specific proxy for platform firms. In the dataset, 329 observations were identified to this primary SIC, accounting for 60 unique firms.

The regression results are significant and support the hypothesized interaction effect for Hypothesis 3. Making again use of the utest command, a turning point is calculated at a uniqueness level of 2.027. Confirmed by the results, using the previous equations by Haans et al. (2016), it is shown that while the curve is steepened, the turning point moves to the left (turning point without interaction: 2.117). Thus, Hypothesis 3 is confirmed. Looking at the moderation effect the specified platform sample has on the relationship between diversification and firm performance, it appears that the interaction term for diversification and platform firms now is positive and significant. These results are contradictory to the results of the previously used sample of platform firms. Since these results contradict the presence of a squared U-Shaped relationship, a linear relationship is tested. Using the utest command, the null hypothesis of a monotone or inverse U-Shape can be rejected. The command further calculates a new turning point at a diversification level of -0.234 indicating that in order to capture a positive Tobin’s q at all, it is crucial for platform firms to be diversified. To get a better view of the relationship between diversification and Tobin’s q for this specified sample, the graph is plotted and indicates a non-linear convex relationship between diversification and firm performance.

Finally, a more conservative approach was taken, considering random effects. Next to this, to ensure the results were not driven by outliers all variables generated from accounting data were winsorized at the 2.5 percentile.

According to Certo & Semadeni (2006), panel data can often create statistical problems for ordinary least squares (OLS) regressions and thus, can lead to biased, inconsistent and incorrect results caused by heteroscedasticity, autocorrelation or contemporaneous correlation. Therefore, a random effect model was calculated and the analyses were conducted accordingly with the xtreg command in Stata 15.

Table 6 presents the results of the conducted regression. The moderation effect is included in Model 2.

Hypothesis 1 predicted a U-Shaped relationship between a firm’s strategic uniqueness and its performance measured by Tobin’s q. Although the coefficient for uniqueness was negative and significant (p<0.1) as before, the quadratic term (uniqueness squared) is now positive but not significant. Examining the plotted function, the relationship between uniqueness and Tobin’s q appears to be convex with a turning point at a uniqueness level of 5.196. Thus, Hypothesis 1 is not supported anymore, due to the occurrence of unknown random effects. Hypothesis 2 predicted a U-Shaped relationship between a firm’s level of diversification and its Tobin’s q. However, since the quadratic term (diversification squared) is not significant anymore, Hypothesis 2 also receives no statistical support anymore. The plot of the relationship between diversification and Tobin’s q indicates a convex relation with a turning point at a level of diversification of 5.555.

Similar results occur for the moderation effects. Neither the interaction term between uniqueness and platform firms, nor the term between uniqueness squared and platform firms is statistically significant, leading to a rejection of

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Hypothesis 3. Moreover, although the interaction effect between diversification and platform firms is significant (p<0.1), the interaction term between the quadratic term (diversification squared) and platform firms is not. Thus, also Hypothesis 4 is rejected.

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5 Discussion and Conclusions 5.1 Implications

This study is motivated to find out whether the structural context of platform firms better fits strategic choices of uniqueness and diversification, than the structure of traditional firms. Since platforms firms follow different rules of competition in general (Cennamo & Santalo, 2013), it is assumed that this assumption also holds for a firm’s strategy choice. Firms pursuing unique and diversified strategies create information asymmetries with market intermediaries,

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which can lead to a discount in the firm’s assessed value (Akerlof, 1970; Berger & Ofek, 1995; Healy & Palepu, 2001;

Litov et al., 2018). In this study it is proposed, that this discount will be lower for platform firms than for traditional firms, leading to a uniqueness and diversification bonus for these firms.

Although the tested hypotheses cannot be confirmed in the robustness checks due to the occurrence of random effects, it is still assumed that there is something like a uniqueness and diversification bonus for platform firms compared to traditional firms. This is indicated by the fact that in the original model, the turning point, from which uniqueness and diversification leads to an increase in Tobin’s q, is reached earlier for platform firms than for traditional firms.

Moreover, the regressions indicate a convex relationship for both, uniqueness and diversification with a firm’s performance. From this, the assumption is derived, that platform firms are highly depended on the decision whether to focus on short term value, by pursuing a familiar strategy and focus on the core competencies, or try enhancing the long term value, by creating more innovative and diverse strategies. Especially due to the “winner-take-all”

competition, platforms need to grow fast in order to capture network effects relatively early (Cennamo & Santalo, 2013). For doing so and in order to capture long term economic rents (Teece, 1982) firms need to invest in a unique set of assets, subsequently leading to a more unique firm strategy. Moreover, by increasing the diversity of the industries a platform is active in, its organizational flexibility is rising as well. Additionally, due to the increased internal capital markets, as well as the fact, that complementors as market intermediaries become part of the platform ecosystem, information asymmetries can be reduced (Berger & Ofek, 1995; Gawer & Cusumano, 2014; Lang & Stulz, 1994). This reduction of the agency problem in form of information asymmetries can furthermore prevent the threat of adverse selection in investment choices, and in return enhance the actual and the market assessed value of a firm (Eisenhardt, 1989).

Thus, for managers the following implications are derived.

First of all, although the regression results are influenced by random effects and thus, the conceptual model is not supported, the regressions show somewhat a tendency that a firm’s level of uniqueness and its diversification both boast a convex relationship towards a firm’s performance. Moreover, it seems that platform firms attain the turning point earlier, from which higher performance is achieved with increasing levels of uniqueness and diversification.

Thus, it is concluded that for platform firms it is crucial to decide to either pursue a highly unique or diversified strategy or stay with common, well-known and specialized strategies.

Second, also governance can have an important influence on strategy choice. With incentives focused on maximizing the current equity value of the firm, it encourages the adoption of familiar and more common strategies, leading to a short-term value maximization. On the contrary, governance emphasizing long-term value and creating incentives, which are less linked to the present market value, can encourage more unique and ultimately more valuable strategy choices. If companies lack these incentives, this can lead to moral hazard problems, if managers craft self-serving strategies that undermine shareholders’ interests (Hölmstrom, 1979; Litov et al., 2018).

Third, agency problems can arise when it comes to firm strategy. If firms are highly unique in their combination of assets or highly diversified into different industries, it is harder for market intermediaries to assess the value of its

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strategy due to the occurrence of information asymmetries. This can then lead to adverse selection problems in capital markets if managers intend to craft strategies that maximize the created value but are constrained in their choices because the effort to overcome this information gulf requires high effort. By selecting their strategy, managers can choose the magnitude of the information problem they impose on the market and thus, they might have incentives to choose easy to assess strategies. This problem is assumed be lower for platform firms, because complementors which can influence business success or failure, are part of the business ecosystem and internal capital markets. Thus, the information asymmetries are likely reduced for platform firms (Eisenhardt, 1989; Gawer & Cusumano, 2014; Litov et al., 2018).

To summarize, this study contributes to strategy research by examining the relationship between the choices of strategic uniqueness and diversification and the performance of a firm. It is found, that the relationship is not quadratic as predicted, but the plotted graphics indicate a convex relationship between uniqueness and firm performance as well as diversification and firm performance. Additionally, this study contributes to platform research by clarifying if the impact on performance is different for platform firms than for traditional firms. It was predicted, that the turning point, from which both strategic choices would positively impact Tobin’s q would be reached at lower levels of uniqueness and diversification for platform firms. The OLS regressions show support for these assumptions, however, since random effects lead to statistical insignificance of the model, this cannot be ultimately confirmed.

5.2 Limitations and Future Research

Despite its contributions, this research is not without limitations. First, the definition of uniqueness used in this research applies to corporate strategy, as opposed to for example product strategy. It is not measured here, if firms for example create innovative products or services, but have more common corporate strategies. Secondly, the calculation of uniqueness has two limitations. First of all, it is limited by the product lines defined in the SIC codes within the Compustat Segments file. Hence, if some of these SIC codes aggregate heterogeneous products, the full extent of strategic uniqueness would not be captured. Additionally, the choice to calculate the industry centroid based on the primary SIC code might result in different industry benchmarks for firms with somewhat similar corporate scope (e.g.

firms A with 60% of sales in primary industry X, and 40% of sales in primary industry Y, will have a different industry centroid than firm B with 40% of its sales in industry X and 60% in its primary industry Y) (Litov et al., 2018). Third, also the measure for firm diversification is limited to the amount of the reported 4-digit segment codes in the panel data. It does not take into account the degree of diversification if firms are diversified into completely different segments on a 2-digit level or if they are diversified within a broader industry. Thus, for future research it would be interesting to repeat the study with a diversification measure based on a 2-digit level. Additionally, future research should try to identify the random effects, which occurred in the robustness checks. Moreover, it would also be interesting to see, if and how the results will differ if firms engage in strategy uniqueness and diversification simultaneously compared to implementing only one of these choices.

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“Corporate Strategies in Digital Platform Firms vs. Traditional Firms”

“Corporate Strategies in Digital Platform Firms vs. Traditional Firms”

Table of Contents

1 Introduction

2 Theory and Hypotheses 2.1 Underlying Theories

2.2 Conceptual Model

3 Methodology 3.1 Sample and Data

3.2 Measures

3.3 Analysis

4 Results 4.1 Strategy Choice: Uniqueness and Diversification

4.2 Moderator Platform Firms

4.3 Robustness

5 Discussion and Conclusions 5.1 Implications

5.2 Limitations and Future Research

6 References