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Determinants of a Complementor’s R&D
Expenditures: The Influence of Fundamental Firms,
Organizational Homophily and Upper Echelons
Theory
Master Thesis
University of Groningen
Faculty of Business and Economics
MSc BA: Strategic Innovation Management
20
thof June 2017
Supervisor: Dr. F. Noseleit
Co-Assessor: Dr. C. Carroll
By
Luke Berkemeijer
S3020851
Lukemarthijn@hotmail.com
Coehoornsingel 34
9711 BT Groningen
Word count: 12.182
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Abstract
The phenomenon of complementarity has been studied in several literature streams such as Dominant Design, Network Externalities and Alliance Portfolio Theory. However, none of these literature streams study the interaction between the R&D expenditures of fundamental firms and their complementors.
In this study it is argued that a relationship between the R&D expenditures of fundamental firms and their complementors exists. It is predicted that a rise in the R&D expenditures of the fundamental firms, has a positive influence on the R&D expenditures of complementors. Besides the hypothesized relationship, Organization Homophily and certain CEO characteristics are expected to moderate this relationship.
By using data collected from the ORBIS database, evidence supports the existence of a positive relationship between the R&D expenditures of fundamental firms and their complementors. The results also show evidence that Organizational Homophily moderates the relationship between the R&D expenditures of the fundamental firms and the complementors. Bloomberg.com, 4-Traders.com and LinkedIn.com have been used to gather information concerning the current, and previous CEOs, of the complementors. Results provide insights into which CEO characteristics of the complementors moderate the main effect. The CEO’s Tenure and CEO’s Job Experience of the complementor indeed moderates the main effect. No support is found for the hypothesized moderating effect of the complementor’s CEO’s Age and CEO’s Formal Education. It is found that a complementor’s CEO’s Organizational Experience does moderate the main effect but only under certain circumstances. Unexpectedly, the results also show evidence for a direct effect of Organizational Homophily and the complementor’s CEO characteristics, except CEO Formal Education, on the complementor’s R&D expenditures.
This study provides managers of fundamental firms with insights explaining how a fundamental firm can influence the R&D expenditures of their complementors, if desired. Additionally, managers of fundamental firms are provided with insights into factors influencing the R&D expenditures of their complementors without the fundamental firms’ involvement. On the other hand, managers of complementors are provided with explanatory factors that increase or decrease their R&D expenditures.
Keywords: R&D expenditure, fundamental firms, complementors, organizational homophily, upper
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Table of Contents
Abstract ... II
1. Introduction ...1
2. Literature Review ...3
2.1 Dominant Design and Network Externalities ...3
2.2 Organizational Homophily ...4
2.3 Upper Echelons Theory ...5
2.3.1 CEO Age ...6
2.3.2 CEO Tenure ...6
2.3.3 CEO Formal Education ...7
2.3.4 CEO Prior Experience ...7
3. Methodology...9 3.1 Data Collection ...9 3.2 Measurements ... 10 3.2.1 Dependent Variables... 10 3.2.2 Independent Variables ... 11 3.2.3 Control Variables ... 13 3.3 Analyses ... 14 4. Results ... 16 4.1 Descriptive Statistics ... 16 4.2 Regression Analyses ... 19 4.2.1 Main Effect ... 19 4.2.2 Moderator Variables ... 20 5. Discussion ... 23 5.1 Main Findings ... 23 5.2 Theoretical Implications ... 26 5.3 Managerial Implications ... 27 6. Conclusion ... 29
7. Limitations and Future Research ... 30
References ... 32
Appendix ... 37
Appendix I: NACE Rev. 2 Industry Codes ... 37
Appendix II: List of Fundamental Firms ... 38
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1. Introduction
Complementarity has been a long researched topic in extant literature. It has received attention in the Dominant Design Theory (Srinivasan et al. 2006; Suarez, 2003; Teece, 1986), in Network Externalities Theory (Schilling, 2002; Soh, 2010) and even in Alliance Portfolio Theory (Lavie, 2007; Wassmer 2010). Both Dominant Design and Network Externalities Theory, define complementarity as a fundamental product’s increase in value as the consequence of present complementary products (Schilling, 2002; Teece, 1986). Alliance Portfolio Theory, however, mainly refers to complementarity as complementary firm characteristics that increase the value of an alliance (Wassmer, 2010).
This study focuses on complementarity as defined in Dominant Design and Network Externalities Theory. However, instead of focusing on the product, this study focusses on the firms producing the products. Therefore, a “fundamental firm” is defined as a firm that produces a fundamental product for which complementary products are produced and exist. The “complementor” is defined as the firm that produces these complementary products for the fundamental product.
Dominant Design and Network Externality both claim the necessity of complementary products for a fundamental product to be successful (Schilling, 2002; Teece, 1986). As suggested by Hill (1997), the fundamental product and its complementary products have a self-reinforcing character; meaning that both products enlarge their own and each other’s installed base (Hill, 1997). Subsequently, it can be argued that both fundamental firms and complementors thus have an incentive to innovate. Additionally, complementors are also incentivized to innovate in order to stay on par with the fundamental firms. This raises the question, if a fundamental firm raises its R&D expenditures, will a complementor react by increasing their R&D expenditures in order to stay on par with the fundamental product?
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Additionally, Upper Echelons Theory might also provide compelling insights. Scholars have long studied the relationship between CEO characteristics and firm performance (Wang et al., 2016; Nadkarni & Hermann, 2010; Zhang & Rajagopalan, 2010), R&D spending (Barker & Mueller, 2002; Wang et al., 2016) and a firm’s acquisition behaviour (Hitt & Tyler, 1991; Yim, 2013). This research has been performed using the Upper Echelons Theory by Hambrick and Mason (1984), which states “that executives' experiences, values, and personalities greatly influence their interpretations of the situations they face and, in turn, affect their choices”. As Hambrick and Mason (1984) state, Upper Echelons Theory consists of two sets of characteristics namely, “Psychological” and “Observable” characteristics. “Psychological” characteristics consist of cognitive biases, values and perceptions of CEOs and “Observable” characteristics are, for example, a CEO’s age, a CEO’s career experiences and a CEO’s education (Hambrick and Mason, 1984; Wang et al., 2016). Reviewing Upper Echelons Theory raises the third question, do the complementors’ CEO characteristics have a moderating effect on the main effect?
This study provides insights into whether a relationship between the R&D expenditures of the fundamental firms and their complementors actually exists and whether Organizational Homophily and Upper Echelons Theory is able to provide insights on how this relationship is moderated. Previous literature has not addressed this important question yet, even though it provides important insights regarding the investments of firms into Research and Development. Additionally, this study will contribute to existing literature regarding not only interactions between fundamental firms and complementors explained by Dominant Design and Network Externalities Theory, but will also contribute to existing literature on Organizational Homophily and Upper Echelons Theory. In order to provide these insights, the following research question will be answered: Do complementors respond to fundamental firms increasing their R&D expenditures by increasing their own R&D expenditures? Additional questions will be answered in order to provide insights into the effect of Organizational Homophily and Upper Echelons Theory on this relationship: Does Organizational Homophily positively moderate the relationship between the R&D expenditures of fundamental firms and its complementors? And, do certain CEO characteristics of the complementor moderate the relationship between the R&D expenditures of fundamental firms and their complementors?
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2. Literature Review
This section will discuss the findings of extant literature and bring arguments forward in order to develop hypotheses. First, Dominant Design Theory and Network Externalities Theory will be reviewed. Secondly, Organizational Homophily will be discussed and lastly Upper Echelons Theory will be reviewed.
2.1 Dominant Design and Network Externalities
As discussed in existing literature, complementary products are needed for a certain technology or innovation to be successful (Schilling, 2002; Srinivasan et al. 2006; Suarez, 2003; Soh, 2010; Teece, 1986). In Dominant Design theory, complementary products partly determine which technological design arises as the dominant design in an industry (Srinivasan et al. 2006; Teece, 1986). Without complementary products, that use the existing knowledge of fundamental products, it will be challenging to successfully commercialize the innovation (Suarez, 2003; Soh, 2010; Teece, 1986). As Srinivasan et al. (2006) address, complementary goods are sometimes not just value enhancing but might also be necessary in order to meet the user’s needs. This means that fundamental products lose value, when complementary products are not available. Complementary products, besides being a necessity to meet the customer’s needs, can also enhance the value of fundamental products by “increasing the utility that customers derive from the fundamental product” (Srinivasan et al., 2006). Several examples of industries in which this occurred are; computer hardware that requires software (Teece, 1986) and the production of LAN technologies for Ethernet (Soh, 2010).
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As can be concluded from both streams of literature, the existence of complementary products in an industry is important and benefits not only the fundamental product (i.e. the dominant design) but also the complementors that create the complementary product, due to its self-reinforcing character (Hill, 1997). Resulting from this, complementors have to increase their R&D expenditures if the fundamental product is changed, in order for their products to stay compatible with the fundamental design. Thus, I hypothesize:
H1: As the R&D expenditures of the fundamental firms increase, the complementors’ R&D
expenditures increase as well.
2.2 Organizational Homophily
Research concerning network analysis has mainly focused on the individual-level and thus neglects how firms take collective action (Powell, White, Koput & Owen-Smith, 2005). However, several scholars have tried to fill this gap in the network analysis literature: Collet & Philippe (2014) tried to explain tie formation between organizations based on status homophily (i.e. organizations with a similar status), Bevc, Retrum & Varda, (2015) explored the how “silo effect” (i.e. preferential partnering) persists in interorganizational boundaries based on organizational homophily (i.e. organizations with similar characteristics), and Powell et al. (2005) developed explanations for how the commercial field of biotechnology has shaped its opportunity structure by exploring several factors, including organizational homophily constructs (e.g. difference in age or size).
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Homophily scholars, on the individual level, group homophilous individuals based on similar characteristics, e.g. background or social behaviour (Cheung & Lee, 2010; Shen et al., 2015). Based on that, Organizational Homophily scholars group organizations on similar characteristics; Collet & Philippe (2014) group organizations on similar status, Powell et al. (2005) group organizations on differences in age, size, governance structure and co-location, and Grein (2000) groups organizations based on market similarity (i.e. “firms operating in markets that are similar to one another”). Subsequently, this study follows the approach of Grein (2000) and organizations are homophilous when they operate within the same industry.
Following Homophily research, on the individual level, that established imitative behaviour between homophilous individuals (Morvinski et al., 2017; Cheung & Lee, 2010), it can be argued that homophilous organizations thus also show imitative behaviour between one another. As these homophilous organizations also interact more often with each other (Bevc et al., 2015), it is more likely that homophilous organizations will imitate each other’s behaviour. Therefore, if complementors are similar to fundamental firms (in terms of industry), it is likely that the complementors will imitate the fundamental firms’ R&D expenditures. Thus, I hypothesize that:
H2: When the fundamental firms and its complementors show strong signs of homophily, it is
more likely that complementors increase their R&D expenditures when the fundamental firms increase their R&D expenditures.
2.3 Upper Echelons Theory
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2.3.1 CEO Age
Upper Echelons Theory proposes that a CEO’s age is a predictor for a CEO’s risk-taking behaviour (Hambrick and Mason, 1984; Hitt and Tyler, 1991; Yim, 2013). The general finding in Upper Echelons Theory regarding CEO age is that older CEOs, as opposed to younger CEOs, are less prone to engage in risky behaviour (Hambrick and Mason, 1984; Hermann, 2002; Serfling, 2014). This finding is confirmed by various studies. Wang et al. (2016) found a relatively strong negative relationship between CEO age and a firm’s risk taking and product innovation. A study by Barker and Mueller (2002) shows that CEO age is the strongest predictor for R&D spending and also shows a negative relationship between CEO age and R&D spending. Moreover, Dechow and Sloan (1991) found that during a CEOs final years in office, R&D spending is reduced.
Examining these findings it can be concluded that as the age of the CEO increases, the CEO starts to act more risk-averse. Because R&D spending is risky behaviour (Barker and Mueller, 2002), R&D spending is expected to decrease as the age of the CEO increases (Barker and Mueller, 2002; Dechow and Sloan, 1991). Thus, I hypothesize that the age of the complementor’s CEO moderates the main effect as follows:
H3a: As the age of the complementor’s CEO increases, it is less likely that a complementor raises
its R&D expenditures when the fundamental firms increase their R&D expenditures.
2.3.2 CEO Tenure
CEO Tenure can be defined as the amount of years a CEO has been in his or her current CEO position (Barker & Mueller, 2002; Hambrick & Fukutomi, 1991; McClelland, Liang & Barker, 2010). Prior research greatly studied the effect of CEO tenure on R&D investments, firm performance and strategic decision making and concluded that as the CEO’s Tenure increases, the more the likelihood of the CEO undertaking risky behaviour decreases (Hambrick & Fukutomi, 1991; Matta & Beamish, 2008; Sanders, 2001). This finding is supported by previous studies. Finkelstein and Hambrick (1990) prove that risk aversion increases when tenure increases and that a negative relationship between tenure and strategic experimentation exists. Wang et al. (2016) found that long-tenured CEOs are risk-averse and thus, as R&D expenditures equals risky behaviour, invest less in R&D. Due to the risky nature of R&D expenditures (Barker & Mueller, 2002), a higher tenure of the complementor’s CEO will most likely have a negative influence on the complementor’s R&D expenditures. Thus, I hypothesize the following moderator effect:
H3b: As the tenure of the complementor’s CEO increases, it is less likely that complementors
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2.3.3 CEO Formal Education
CEO Formal Education is defined as the amount of post-secondary degrees a CEO holds (Wang et al., 2016), such as a Bachelor’s degree, a Master’s degree or a Doctorate degree. Upper Echelons Theory proposes that a CEO’s Formal Education has a positive relationship with strategic decision making and R&D expenditures (Barker & Mueller, 2002; Hermann, 2002; Wang et al., 2016). The literature offers several explanations for this positive relationship. Thomas, Litschert and Ramaswamy (1991) argue that due to formal education, CEOs possess rich knowledge bases and skills sets, which increase their receptivity to new ideas. Kimberly and Evanisko (1981) found a positive relationship between formal education and receptivity to innovation and change. Following these findings, I hypothesize that if the CEO of the complementor has enjoyed higher formal education, he or she will be more likely to increase R&D expenditures when the fundamental firms increase their R&D expenditures.
H3c: As the amount of formal education of the complementor’s CEO increases, it is more likely
that complementors increase their R&D expenditures when the fundamental firms increase their R&D expenditures.
2.3.4 CEO Prior Experience
Upper Echelons scholars define CEO prior experience as earlier experience in various roles, functional areas or even international experience (Hambrick & Mason, 1984; Hermann, 2002; Shen & Cannella, 2002). As Wang et al. (2016) points out, CEO prior experience is often measured with different constructs. Functional Experience is defined as experience in different functional roles before becoming a CEO (Hambrick & Mason, 1984). Hermann (2002) defines international experience as a CEO’s experience in a different country than his or her home country and argues that the CEO gains a higher capability in information processing. As extant literature offers many definitions, this study focusses on two distinct types of prior experiences that have been defined by Wang et al. (2016), namely “organizational experience” and “prior job experience”.
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H3d: As the organizational experience of the complementor’s CEO increases, it is less likely that
complementors increase their R&D expenditures when the fundamental firms increase their R&D expenditures.
H3e: As the prior job experience of the complementor’s CEO increases, it is more likely that
complementors increase their R&D expenditures when the fundamental firms increase their R&D expenditures.
Figure 1 shows the conceptual model used in this study.
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3. Methodology
This study is of a theory testing nature. According to Van Aken, Berends, & Van der Bij, (2012), theory testing should be used when literature is mature, however, a gap in the literature remains. Firms continuously face strategic decisions necessary to stay successful. This study provides firms with insights into the response of complementors to fundamental firms raising their R&D expenditures. This study provides an answer in a quantitative fashion.
3.1 Data Collection
In this study, I collected secondary data from existing databases. In order to collect initial data on the fundamental firms and complementors over the last 10 years (from 2007 to 2016), I used the ORBIS database. The ORBIS database mainly contains data of private companies. More than 99% of the data available concerns private companies, but ORBIS also holds data on stock traded companies (ORBIS, 2017). From the ORBIS database I collected data on 104 fundamental firms and 282 complementors. The distinction between a fundamental firm and a complementor was made based on their NACE Rev. 2 Industrial Code (NACE Rev. 2, 2008). As stated in section 2.2, Organizational Homophily was determined based on firms operating within the same industry. The NACE Rev. 2 Industry Code clusters firms together that operate in the same industry and assigns each of these firms the corresponding code. The fundamental firms were chosen based on the industry code “2640”, which is defined as “Manufacturer of Consumer Electronics”. The market of Consumer Electronics is often used in studies and was thus chosen for this study as well (Huang & Holden, 2016). The NACE Rev. 2 Industrial Code assigns main groups of industries a 2-digit code and, to further specify an industry, more digits are added to the first two digits. In the case of the fundamental firms, the 2-digit code of “26..” refers to “Manufacturers of computer, electronic and optical products”, however, the code “2640” refers only to “Manufacturers of Consumer Electronics”. Complementors were chosen from different markets that produce products that could act as complementary products for the defined fundamental products (consumer electronics). The following markets, their definitions can be found in Appendix I, were chosen as complementary markets “10..”, “11..”, “2620”, “2630”, “2670”, “2750”, “2790”, “31..” and “3240”.
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years”. “Net Debt” and “Total Assets” were collected to calculate the Leverage of the complementors.
Leverage will be further explained in section 3.2.3 (control variables).
In order to collect data concerning the CEOs of the complementors, I used websites that provide company information from stock listed firms in order to provide investors with the necessary information to make profitable investments. I used the following databases: Bloomberg, “a network to connect decision makers to a dynamic network of information, people and ideas” (Bloomberg, 2017); 4-Traders, “an online media for active investors specializing in stock exchange, financial & economic trend analysis, financial news, dynamic quotes, charts, investments and trading tips” (4-Traders, 2017) and LinkedIn, a social network to connect and inform business people (LinkedIn, 2017). If available, data regarding all the complementors’ CEOs over the last 10 years was collected. Given a change of CEO within the past 10 years, data of any preceding CEO within the specified time frame was collected as well. The data collected regarding the complementors’ CEOs concerns “CEO Age”, “CEO Tenure”, “CEO Formal Education”, “CEO Organizational Experience” and “CEO Job Experience”.
After collecting the data, some observations had to be removed due to non-compliance with the selection criteria. Five complementors were deleted from the existing dataset due to their lack of available data. For these five firms, the last year of available data was before 2007 and thus did not cover the specified timeframe (2007 to 2016). Removing these reduced the dataset, concerning the complementors, to 277 observations. Furthermore, there were also two firms in the dataset that did not meet the criteria of being a stock listed firm. These observations were also removed, reducing the total amount of observations, concerning the complementors, to 275 firms. No adjustment was necessary for the dataset concerning the fundamental firms. The final datasets consisted of 104 fundamental firms and 275 complementors.
3.2 Measurements
This chapter discusses the variables (dependent, independent and control variables) used in this study and their measurements. First, the dependent variables will be discussed followed by the independent variables. Lastly, the control variables will be explained.
3.2.1 Dependent Variables
R&D expenditures of the complementors: The R&D expenditures of the complementors are
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the complementors. Therefore, the R&D expenditures of the complementors is an essential variable to study, to answer if this self-reinforcing character influences the R&D expenditures of complementors. Different measures for R&D expenditures have been used in the past, namely; the natural logarithm of R&D expenditures (Czarnitzki & Toole, 2013; Einiö, 2014), R&D intensity constructed as “R&D expenditures deflated by book value of equity” (Alam, Liu & Peng, 2013), R&D intensity constructed as the “R&D expenditures to asset ratio” (Ciftci & Zhou, 2014), R&D intensity constructed as the R&D expenditures to net sales ratio (Lee, & Wu, 2015) or even R&D intensity constructed as “the change in the intangible assets between two periods scaled by the firm’s turnover” (Iturriaga & López-Millán, 2016). In this study, the natural logarithm of R&D expenditures is used as a measure of R&D expenditures of the complementors due to the good fit to the expected main effect (Czarnitzki & Toole, 2013; Einiö, 2014). In order to create this measure, the natural logarithm of the complementors’ R&D expenditures was taken.
3.2.2 Independent Variables
R&D expenditures of the fundamental firms: The R&D expenditures of the fundamental
firms are considered to be the fundamental firm’s total investment in one year for the purpose of Research and Development. Due to self-reinforcing character of the dominant design and complementary products (Hill, 1997), this variable is similar to the dependent variable but concerns the fundamental firms instead of the complementors. Due to similarity to the dependent variable, the R&D expenditures of the fundamental firms will is also measured with the natural logarithm of the fundamental firms’ R&D expenditures (Czarnitzki & Toole, 2013; Einiö, 2014). In order to create this measure, the natural logarithm of the fundamental firms’ R&D expenditures has been taken.
Organizational Homophily: Organizational Homophily is a moderator variable due to its
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CEO Age: R&D expenditures is risky behaviour (Barker & Mueller, 2002) and can be predicted
by CEO Age (Hambrick and Mason, 1984; Hitt and Tyler, 1991). As R&D expenditures of the complementors is the dependent variable, CEO Age is a fitting moderator to predict the effect of the fundamental firms’ R&D expenditures on the complementors’ R&D expenditures. The CEO Age of the complementors was measured in the last year that the CEO was in his or her function. If there was a CEO change, the age of the previous CEO was also be measured in the last year that the CEO was in his or her function. For the previous years, the age of the complementors’ CEO was calculated by subtracting one year for each previous year.
CEO Tenure: Much like CEO Age, CEO Tenure was predicted to have a moderating effect on
risky behaviour (Hambrick & Fukutomi, 1991; Matta & Beamish, 2008). Therefore, CEO Tenure is a fitting moderator for the effect that the fundamental firms’ R&D expenditures might have on the complementors’ R&D expenditures. CEO Tenure is defined as the amount of years a CEO has been in the current CEO position (Barker & Mueller, 2002). In this study, the tenure of the complementors’ CEO started with the employment of the CEO and ended with the resignation of the CEO. CEO Tenure was then coded as an integer. In order to avoid cross observations, if the new CEO of the complementors was appointed before July 1st (which marks the second half of the year) it was counted as one year. If the new CEO was appointed after July 1st, counting started from the next year. Consequently. the
complementor’s CEO that had the most influence, due to the longest appointment, in that year was assigned to that year.
CEO Formal Education: CEO Formal Education is defined as the amount of post-secondary
degrees that a CEO holds (Wang et al., 2016). CEO Formal Education is argued to increase a CEO’s receptivity to new ideas and innovation (Kimberly & Evanisko, 1981; Thomas, Litschert & Ramasway, 1991). An increased level of receptivity towards new ideas and innovation could influence the R&D expenditures of the complementors. Thus a complementor’s CEO Formal Education is an equally fitting moderator for the effect that the fundamental firms’ R&D expenditures have on the complementors’ R&D expenditures. The CEO’s Formal Education was measured by counting the amount of post-secondary degrees. This means that when a complementor’s CEO received a Bachelor’s Degree, it is counted as one post-secondary degree. A Master’s degree is counted as two (a Bachelor’s degree is in order to achieve a Master’s Degree), and so on (Wang et al., 2016).
CEO Prior Experience: Due to many different definitions of a CEO’s Prior Experience
(Hambrick & Mason, 1984; Shen & Cannella, 2002), I decided to split a CEO’s Prior Experience into a complementor’s CEO’s Organizational Experience and a complementor’s CEO’s Job Experience.
CEO Organizational Experience: A CEO’s Organizational Experience in defined as the amount
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Experience is a fitting moderator for the effect of the fundamental firms’ R&D expenditures on the complementors’ R&D expenditures. The complementor’s CEO’s Organizational Experience was measured from the first year he or she entered the firm until the last observed year of the complementor’s CEO. If a previous CEO was observed, however, is not in the position of CEO anymore, the remaining years that he or she remains in the firm are not counted. Because the CEO Organizational Experience was measured in the last observed year, the years before were calculated by deducting one year for every previous year.
CEO Job Experience: A CEO’s Job Experience is defined as the similar work roles to a CEO
function that the CEO has fulfilled previous to his or her current CEO position (Wang et al., 2016). This concerns work roles such as being a director in a board or previous CEO positions but does not include management positions (Simsek, 2007). As Simsek (2007) argues that CEO Job Experience allows the CEO to better deal with strategic risk and thus increases risky behaviour, CEO Job Experience is thus a fitting moderator for the effect of the fundamental firms’ R&D expenditures on the complementors’ R&D expenditures. A CEO’s Job Experience was measured from every year that he or she fulfilled a similar work role. Years in between similar work roles, not fulfilling a similar work role, were not counted.
3.2.3 Control Variables
Firm Size: Firm Size, measured in terms of employees, is an often used control variable in
studies regarding R&D expenditures (Baum, Caglayan & Talavera, 2016; Barker & Mueller, 2002; Einiö, 2014). It is expected that a large firm size is positively related to a firm’s R&D expenditures because of their potentially higher resource base (Barker & Mueller, 2002). In order to deal with skewed distribution, the natural logarithm of the complementor’s firm size was used (Baum, Caglayan & Talavera, 2016; Barker & Mueller, 2002).
Leverage: Leverage can be defined as the debt to assets ratio and has been used in many studies
where R&D expenditures is studied (Barker & Mueller, 2002; Iturriage & López-Millán, 2016; Zhang & Rajagopalan, 2010). If a firm possesses less debt and more internal resources, it is more likely to invest in R&D (Barker & Mueller, 2002). A high debt to low internal resources ratio is represented by a high value for Leverage; a low debt to high internal resources ratio is represented by a low value for Leverage. Thus, the lower the value of Leverage, the higher the expected R&D expenditures of the complementor. In order to deal with a skewed distribution, the natural logarithm of the complementor’s Leverage is used.
Independence: Firm independence can be defined as a low concentration of shareholders. This
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R&D expenditures of the complementor. In order to measure the level of independence, the BvD Independence indicator of the ORBIS Database was used. The ORBIS Database defined the companies as A+, A and A-, where no shareholder possesses more than 25% of the shares. For analysis, firms with “A+” independence were coded as “1”; firms with “A” independence were coded as “2” and firms with “A-“ independence were coded as “3”.
Continent: As often argued in literature discussing spillover effects and complementarity, the
geographical location of a company has a positive effect on their own innovative performance (Sun, Lee & Hong, 2017; Yuah-Chieh, 2017). Drawing from this perspective, I want to examine the possibly positive effect that geographical location could have on the complementors’ R&D expenditures. The Continent variable indicates in which continent the company is located. The continents were coded as follows: Asia “1”; the US “2”; Europe “3”; the Middle East “4” and Oceania “5”.
Dummy Variables: In order to separate data and allow analyses to be performed, several
dummy variables have been created.
Fundamental Firm Dummy Variable: The fundamental firm dummy is a dummy
variable that indicates if the firm is a fundamental firm or a complementor. Fundamental firms were coded as “1” and complementors were coded as “0”.
CEO Switch Dummy: The CEO Switch Dummy indicates that in a specific year, a new
CEO was appointed. If a new CEO was appointed in that specific year, the CEO Switch Dummy was coded as “1”. Otherwise, the CEO Switch Dummy remained “0”.
Same Continent Dummy: This dummy variable indicates whether the companies are
located on the same continent. It uses the coding of the Continent control variable. When companies are located on the same continent it was coded as “1” and in case of dissimilar continents it was coded as “0”.
3.3 Analyses
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4. Results
This section of the paper provides the descriptive statistics, the correlations and the results of the random effects regression models that have been performed. Firstly, the descriptive statistics show the means and standard deviations of the variables used. Additionally, the correlations between the variables are presented in order to check for multicollinearity. Secondly, the results of the random effects regression models are presented. First, a simple random effects regression model and its results are presented. Followed by a random effect regression model with interaction for every moderator variable, to see the interaction effects between the R&D expenditures of the fundamental firms and the specific variable.
4.1 Descriptive Statistics
The descriptive statistics of the used variables are provided in table 1, on the next page. The descriptive statistics show an average R&D expenditures of the complementors (ln) of 3.92 (respectively 50.04 M USD) and an average R&D expenditures of the fundamental firms (ln) of 3.71 (respectively 40.85 M USD). The average of the dummy variable CEO switch is 0.10. This means that on average, every year 10% of the complementor observations experience a CEO change. The average tenure of a complementors’ CEO (CEO Tenure) is nine years, which implies that on average there is at least one CEO switch every 10 years. The average age of the complementors’ CEOs (CEO Age) is almost 56 years (respectively 55.82). The statistics also show the averages of CEO Organizational Experience and
CEO Job Experience. On average, the complementors’ CEOs have rather high Organizational
Experience (respectively 18.20 years) and high Job Experience (respectively 13.12). Furthermore, the complementors’ CEOs are generally highly educated (CEO Formal Education) with an average of 1.83 post-secondary degrees. This means that, on average, all the CEOs of the complementors have at least a Bachelor’s Degree and many CEOs have at least a Master’s Degree. The average Firm Size (ln) of the complementors is 8.36 (respectively 4272.69 employees) and the average Leverages (ln) of the complementors is -1.77 (respectively a Leverage of 0.17), meaning that the average complementor has a small debt. In addition, the average complementor is very independent due to the average of 1.07
Independence. This means that most complementors are coded as 1, which indicates the highest possible
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Table 2, on the next page, presents the correlations. Although there are some large correlations, there is no sign of multicollinearity between the variables used in this research.
Table 1. Descriptive Statistics Mean Std. Dev.
Complementor’s R&D Exp. (ln) 3.92 1.55
Fundamental Firm’s R&D Exp. (ln) 3.71 1.64
Organizational Homophily 0.63 0.46
CEO Switch (Dummy) 0.10 0.30
CEO Age 55.82 8.32
CEO Tenure 9.03 7.90
CEO Org. Exp. 18.20 13.03
CEO Job Exp. 13.12 8.87
CEO Formal Educ. 1.83 0.71
Firm Size (ln) 8.36 1.77
Leverages (ln) -1.77 1.32
Independence 1.07 0.35
Continent 1.61 0.76
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4.2 Regression Analyses
This section presents the results of the performed regression analyses. First, a simple random effects regression model has been performed. Subsequently, random effects regression models with interaction have been performed for every moderator, except for CEO Formal Education, and lastly, a random effects regression model with interaction of all the valid moderator variables has been performed.
4.2.1 Main Effect
Table 3, on page 22, represents all the regression analyses that have been performed. Model 1 is the simple random effects regression model. In this model all the variables have been tested in correspondence to the R&D expenditures of the complementors (ln). Further regression models represent the random effects regression models with an interaction to moderator variables, including CEO Switch, to test the relationship of those moderator variables with the R&D expenditures of the fundamental firms
(ln). CEO Switch is included in the random effects regression models with interaction because CEO Switch shows a pattern that might be of importance regarding the moderation effects of CEO
characteristics.
The random effects regression models with interaction work as follows: Model 2, for example, represents the random effects regression model with interaction concerning the interaction of R&D
expenditures of the fundamental firms (ln) with Organizational Homophily. The random effects
regression analyses with interaction have been repeated for all the control variables, except for CEO
Formal Education, and CEO Switch. CEO Formal Education is not significant in the main model. As CEO Formal Education is not significant in the main model, it will also not show any moderation effects
with the main effect and has thus not been tested for an interaction with the R&D expenditures of the
fundamental firms (ln). Table 3 also shows that R² is high (around 0.7 for every model). According to
Hamilton (2012), this represents a proper fit of the models and that a large portion of the variance of the dependent variable is thus explained by the models.
Model 1 tested the direct relationship with the dependent variable, R&D expenditure of the
complementors (ln). Model 1 shows that the independent variable R&D expenditure of the fundamental firms (ln) has a positive significant relationship to, the dependent variable, the R&D expenditure of the complementors (ln). The relationship is positive with β = 0.028 and a α = 0.000, thus showing support
for H1. This relationship shows that if the R&D expenditures of the fundamental firm (ln) increases with
one unit, the R&D expenditures of the complementors (ln) increase with 2.8%.
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variable. Due to the insignificance of CEO Formal Education, more random effects regression models with interaction have only been performed on Organizational Homophily, CEO Switch, CEO Age, CEO
Tenure, CEO Organizational Experience and CEO Job Experience.
Model 1 also shows that all control variables have a significant effect. Firm Size has a positive significant relationship to the dependent variable with β = 0.537 and α = 0.000. Leverage has a negative significant relationship with the dependent variable with β = - 0.010 and α = 0.000. Independence also has a positive significant relationship to the dependent variable of β = 0.325 and α = 0.000 and the dummy variable Same Continent, representing Continent, has a negative significant relationship to the dependent variable of β = - 0.198 and α = 0.000.
4.2.2 Moderator Variables
First, CEO Formal Education has not been regressed with a random effects regression model with interaction to R&D expenditures of the fundamental firms (ln) due to an insignificant effect on the dependent variable in Model 1. Furthermore, other models do not show a significant moderator effect for CEO Formal Education with the main effect. Thus, H3c is not supported.
Model 2 shows the interaction of the moderator variable Organizational Homophily with the independent variable of R&D expenditures of the fundamental firm (ln). Organizational Homophily shows a negative but significant interaction (β = - 0.075 and α = 0.000) with R&D expenditures of the
fundamental firm (ln). Thus, Organizational Homophily is indeed a moderator of the main effect with a
negative effect on the relationship between R&D expenditures of the fundamental firm (ln) and the R&D
expenditures of the complementors (ln). This finding partially supports H2 but instead of a positive
moderating effect, it has a negative moderating effect. Besides the moderating effect on the main effect,
Organizational Homophily shows an even stronger significant direct effect (β = 0.920 and α = 0.000)
on the dependent variable in Model 2.
Model 3 shows the interaction of the dummy variable CEO Switch with the independent variable
R&D expenditures of the fundamental firm (ln). CEO Switch shows a small negative and significant
moderating effect ( β = - 0.005 and α = 0.033). Thus, CEO Switch shows signs of a moderator effect on the main effect.
Model 4 shows the interaction of the moderator variable CEO Age with the independent variable
R&D expenditures of the fundamental firms (ln). CEO Age shows an insignificant interaction effect with R&D expenditures of the fundamental firms (ln). Therefore, CEO Age is not a moderator of the main
effect. Thus H3a is not supported. Model 4 also shows that CEO Age has an insignificant effect on the
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Model 5 shows the interaction of the moderator variable CEO Tenure with the independent variable R&D expenditures of the fundamental firms (ln). CEO Tenure shows a small positive and significant interaction effect (β = 0.001 and α = 0.000) with R&D expenditures of the fundamental firms
(ln), thus partially supporting H3b. CEO Tenure is indeed a moderator variable of the main effect, but
instead of a negative moderator effect, it has a positive moderator effect. Besides the moderator effect, Model 5 shows that CEO Tenure has a small negative and significant effect (β = - 0.006 and α = 0.000) on the dependent variable.
Model 6 demonstrates the interaction effect of the moderator CEO Organizational Experience with the independent variable R&D expenditures of the fundamental firm (ln). CEO Organizational
Experience shows an insignificant interaction effect with R&D expenditures of the fundamental firms (ln). Therefore, no support is found for H3d. However, Model 8 shows that under certain circumstances CEO Organization Experience is indeed a moderator of the main effect with a negative and significant
effect (β = - 0.001 and α = 0.000). Furthermore, CEO Organizational Experience does show a positive and significant effect (β = 0.008 and α = 0.000) on the dependent variable.
Model 7 shows the interaction effect of CEO Job Experience with the independent variable
R&D expenditures of the fundamental firms (ln). CEO Job Experience reveals a positive and significant
interaction effect (β = 0.001 and α = 0.000) with R&D expenditures of the fundamental firms (ln), thus
H3e is supported. Model 6 also shows that CEO Job Experience has a negative but significant effect (β
= -0.006 and α = 0.000) on the dependent variable.
Model 8 shows the interaction effects of all moderator variables, except for CEO Formal
Education, and CEO Switch with R&D expenditures of the fundamental firms (ln). Model 8, mostly,
confirms the results found in the previous models. However, there are two new findings. In Model 8,
CEO Switch shows no signs of a moderating effect with the main effect. This means that, CEO Switch
only shows signs of a moderating effect when other moderator effects are not present. CEO Switch does still have a positive and significant effect on the main effect (β = 0.057 and α = 0.000). The second finding shows that, when all other moderators, except for CEO Formal Education, are present, CEO
Organizational Experience does show a negative and significant moderating effect (β = - 0.001 and α =
0.000) on the main effect. Thus, instead of not supported, H3d is partially supported on the condition
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5. Discussion
Complementarity has often been the subject of previous studies, however, focussing on how it influences a Dominant Design (i.e. fundamental product) and how it fits within Network Externalities Theory. On the other hand, a firm’s R&D expenditures have also often been researched concerning the influence of alliances, spillover effects, internal factors and the influence of (private) equity. This study attempts to explore the influence a fundamental firm has on its complementors R&D expenditures by increasing its own R&D expenditures. The results of this study are intriguing and will be discussed in this section. This section will additionally provide theoretical and managerial implications.
5.1 Main Findings
The main goal of this study was to find an answer to the hypothesized effect where the R&D expenditures of fundamental firms would have a positive influence on the R&D expenditures of complementors. This study found support for this hypothesis. The results thus support the notion of complementors responding to fundamental firms increasing their R&D expenditures by increasing their own R&D expenditures. The connection between a fundamental product and its complementary products was established decades ago when Teece (1986) theorized the importance of complementary products for a Dominant Design. According to Teece (1986), the amount of complementary products determined which design would become the Dominant Design in the market. This theory was later build upon by other scholars like Schilling (2002) and Suarez (2003). In the 1990s, Hill (1997) combined the theory of the Dominant Design and Network Externalities to theorize that the installed base, of a Dominant Design, attracts complementors in order to fill a need that is complementary to the Dominant Design and, complementors, in their turn, lead to a larger installed base of the Dominant Design. The results of this study indeed confirm this self-reinforcing character between a fundamental product and its complementary products (Hill, 1997). If the fundamental product has been innovated, the complementary products are required to still function with the improved version of the fundamental product. If the complementor decides not to innovate in order to stay compatible with the fundamental product, consumers will stop purchasing the complementary product due to compatibility problems and thus might lead to the firm exiting the market. In previous studies, scholars have often linked a failure to invest in R&D as a cause for a firm’s market exit (Børing, P. 2015; Ugur, Trushin, & Solomon, 2016). This notion can also be linked to the results from this study. When complementors see a fundamental firm investing in R&D, due to a fear for market exit, the complementor will start to invest in R&D as well.
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fundamental firms and complementors show signs of Organizational Homophily, complementors are more likely to also increase their R&D expenditures when the fundamental firms increase their R&D expenditures. Partial support was found for this hypothesis. Organizational Homophily indeed shows significant signs of a moderating effect, however, this moderating effect is negative instead positive, as was hypothesized. The result that Organization Homophily actually moderates the main effect negatively is surprising, as extant literature predicts similar behaviour between homophilous entities (Morvinski et al., 2017). A reason for this phenomenon could be that the products, of the fundamental firm and complementor, show signs of substitutability. Substitutability occurs when products are different but inherently fulfil the same function (Porter, 1979). Another remarkable result of this study is Organizational Homophily as a strong direct predictor for the R&D expenditures of complementors. A reason for this strong predictor effect could be that fundamental firms and complementors, that show signs of Organizational Homophily, often form alliances (Lavie, 2007; Wassmer, 2010). Wassmer (2010) argues that firms engage in alliances with firms where the possibility of creating synergies is high. Synergies occur when high levels of knowledge transfer and or economies of scale or scope are present. Synergies can occur due to firms supplying complementary characteristics within the alliance (Wassmer, 2010). This phenomenon increases the chance that firms with signs of Organization Homophily form alliances and thus might explain the strong direct effect of Organization Homophily on the R&D expenditures of complementors.
This study also researched which CEO characteristics, based upon the Upper Echelons Theory, have a moderating effect on the main effect. Five CEO characteristics of the complementors were researched in this study; CEO Age, CEO Tenure, CEO Formal Education, CEO Organizational Experience and CEO Job Experience. CEO Age was predicted to have a negative moderating effect on the main effect. This hypothesis was not supported due to insignificance of the moderating effect. Additionally, CEO Age has no significant direct effect on the dependent variable. Therefore, CEO Age has no influence on the R&D expenditures of the complementors. This result contrasts the findings of previous studies where strong negative effects on R&D expenditures was found as the CEO’s age increases (Barker and Mueller, 2002; Dechow & Sloan, 1991; Wang et al., 2016). This outcome could indicate that in independent firms, the top management team has a higher influence in decision-making than the CEO and consequently confirming the result found in this study (Carpenter, Geletkanycz & Sanders, 2004). Other studies also support this notion by moving the Upper Echelons Theory from CEOs to the top management team (Hambrick, 2007).
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when holding the CEO position for a longer period of time (Finkelstein & Hambrick, 1996). When the CEOs of the complementors perceives an increase in the R&D expenditures of the fundamental firms, the CEOs might be afraid that the status quo is endangered and thus be more inclined to also increase their R&D expenditures. Another reason could be that CEO Tenure shows equal traits to CEO Job Experience. CEO Job Experience refers to the fact that when a CEO has more experience in similar jobs to the CEO position, the ability to deal with strategic choices increases (Wang et al., 2016). Therefore, if CEO Tenure shows traits of this phenomenon, a higher CEO Tenure thus allows the complementors’ CEOs to deal with strategic choices more easily and consequently also with R&D expenditures.
CEO Formal Education was hypothesized to have a positive moderating effect on the main effect. The results show no support for this hypothesis as CEO Formal Education shows no significant effect. The result found in this study is contrary to previous studies, where it was found that the formal education of a CEO improves their receptivity to new ideas and innovation (Kimberly & Evanisko, 1981; Thomas, Litschert & Ramaswamy, 1991). A reason for this result could, again, be the reduced influence of the complementors’ CEOs and the increased influence of the top management team on decision-making in independent firms (Carpenter, Geletkanycz & Sanders, 2004).
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researched in this study (Hambrick & Mason, 1984). Psychological CEO characteristics are more complex than observable CEO characteristics and could thus incorporate several observable CEO characteristics explaining the second finding concerning CEO Organizational Experience.
Lastly, CEO Job Experience was hypothesized to have a positive moderating effect on the main effect. This hypothesis is supported. This finding is also consistent with prior studies where arguments are made that CEO prior job experience allows the CEO to deal with strategic risks more easily and lower the uncertainty regarding risk taking (Simsek, 2007). A surprising result is the direct negative effect on the dependent variable. A possible reasoning for this result might be that various ways of problem solving in the previous similar jobs functioned but do not function in the CEO position.
5.2 Theoretical Implications
Several implications for research can be drawn from the results. This studies’ intention was to partially fill the theoretical gap in the interaction between R&D expenditures of the fundamental firms and their complementors. This study found evidence that there indeed is an interaction effect between fundamental firms and complementors. The results show support that the R&D expenditures of fundamental firms positively influence the R&D expenditures of their complementors, meaning that the increase of R&D expenditures of fundamental firms, is imitated by the complementors.
This study also tried to identify if Organizational Homophily and Upper Echelons Theory, focused on the CEO, would moderate the influence of this interaction between fundamental firms and complementors. There is indeed evidence that Organizational Homophily and Upper Echelons Theory, only partially proven, show signs of moderating this relationship. This study found evidence that Organizational Homophily has a negative moderating effect on the main effect. The results also show evidence that Organizational Homophily has a strong, and positive, predicting effect on the R&D expenditures of complementors.
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5.3 Managerial Implications
In this study, it is proven that an increase in the R&D expenditures of a fundamental firms causes an increase in the R&D expenditures of complementors. Organizations that show signs of Organizational Homophily have a strong influence on the R&D expenditures of complementors, thus meaning that R&D expenditures complementors will increase when more signs of Organizational Homophily are present. This study also shows insights on which CEO characteristics of the complementor’s CEO not only influence R&D expenditures directly but also influence the main effect.
As a fundamental firm, this paper shows insights on how complementors react to its own R&D expenditures. Since complementors increase their R&D expenditures when the fundamental firm increases its R&D expenditures, this opens up new doors to stimulate complementors to invest in R&D. As mentioned by Hill (1997), fundamental products and their complementary products have a self-reinforcing character, thus increasing the installed base of both products. Therefore, fundamental firms could desire R&D intensive complementors, which increases the installed base of the fundamental product. In order to stimulate complementors to invest in R&D, this study shows that increasing R&D expenditures, as a fundamental firm, stimulates complementors to increase their R&D expenditures and thus ultimately increasing the installed base of both products. This paper also shows how likely it is for a complementor to react to the level of R&D expenditures of the fundamental firm. When complementors shows signs of similarity, i.e. is organizationally homophilous, the complementors are less inclined to increase their R&D expenditures as a reaction to the increase in R&D expenditures by the fundamental firm. On the other hand, due to the high predictive effect of Organizational Homophily, it might not be necessary to stimulate the complementors. The complementors are already inclined to increase their R&D expenditures due to highly similar characteristics. Other predictors for the likelihood that complementors increase their R&D expenditures as a reaction to the fundamental firm’s increase in R&D expenditures can be found by studying the complementors’ CEOs. If the complementors’ CEOs have a long tenure and high job experience, it is more likely that the complementors will react to the fundamental firm increasing their R&D expenditures. In this case, stimulating the complementors by increasing R&D expenditures is likely to show better results. However, when R&D expenditures, of the fundamental firm, are not increased, the long tenure and high job experience of the complementors’ CEOs will lower the R&D expenditures of the complementors. When the complementors’ CEOs have high organizational experience, increasing R&D expenditures, as a fundamental firm, will not increase the likelihood of the complementors increasing their R&D expenditures. Again, similarly to organizational homophilous organizations, complementors’ CEOs with high organizational experience is already inclined to spend more on R&D.
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6. Conclusion
The objective of this study was to explore the relationship between the R&D expenditures of the fundamental firms and the R&D expenditures of their complementors. It was also the objective to find evidence for possible moderation effects regarding Organizational Homophily and the Upper Echelons Theory focused on observable CEO characteristics. This study proved that there indeed is a relationship between the R&D expenditures of the fundamental firms and their complementors. When fundamental firms increase their R&D expenditures, complementors will follow by also increasing their R&D expenditures. This relationship is moderated by Organizational Homophily and certain CEO characteristics of the complementors.
Organizational Homophily, between the fundamental firms and complementors, was determined based on the NACE Rev. 2 Industry Code. If companies belonged to the same main category of this code, defined by the first two numbers of the 4-digit code, Organizational Homophily was confirmed. Organizational Homophily does indeed moderate the main effect. However, this negative moderation effect is rather small. An intriguing finding was the strong and positive effect on the, dependent variable, R&D expenditures of the complementors. Consequently, if organizations show signs of similarity, R&D expenditures efforts of the fundamental firms will be replicated.
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7. Limitations and Future Research
A number of limitations can be pointed out concerning the findings of this research. The first limitation of this study might be the independent and dependent variable, i.e. the natural logarithm of R&D expenditures of fundamental firms and complementors. This measure has been used in previous literature (Czarnitzki& Toole, 2013; Einiö, 2014), but R&D intensity seems like a more accepted measure in extant literature (Alam, Liu & Peng, 2013; Ciftci & Zhou, 2014). It is, however, still not completely clear which construct of R&D intensity works best. R&D intensity is, for example, constructed as the R&D expenditures to net sales ratio (Lee & Wu, 2016) or even “the change in the intangible assets between two periods scaled by the firm’s turnover” (Iturriaga & López-Millán, 2016). Future research could thus use a different measure for R&D expenditures, such as R&D intensity constructed as the R&D expenditures to sales ratio (Lee & Wu, 2016), to study the main effect.
The second limitation of this study is regarding the measurement of Organizational Homophily in this study. This study used the concept of market similarity (Grein, 2000) to indicate whether companies showed signs of similarity. Market similarity is not the only construct that can be used to indicate signs of Organizational Homophily; previous studies have used the firm’s status (Collet & Philippe, 2014), age difference and firm size (Powell et al., 2005). Future research could focus on other elements of the firm, such as the characteristics mentioned above, to indicate signs of Organizational Homophily.
The third limitation concerns the Upper Echelons Theory. As literature points out, the top management team could have a bigger influence on the decision making process and thus might be better object to study than the CEO (Carpenter, Geletkanycz & Sanders, 2004; Hambrick, 2007). Due to time constraints, the complementor’s CEO was chosen as the object of study. Future research could focus on the top management team as the object of study, to further research the moderator effects.
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The results of this study indicate an unique effect concerning CEO Organizational Experience. When the moderating effect of the complementor’s CEO’s Organizational Experience is tested as the sole moderator on the R&D expenditures of the fundamental firms, it shows no signs of a significant moderator effect. However, when other moderator variables are present in the model, CEO Organizational Experience does show a significant moderator effect. As argued before, this could be due to an underlying mechanism within the moderator variables that is not measured in this study. Besides that, psychological CEO characteristics could shed more light on this phenomenon. Future research could be directed at studying which underlying mechanism explains this effect.
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