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Three essays on young entrepreneurial firms
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Content
THREE ESSAYS ON YOUNG ENTREPRENEURIAL FIRMS
by
Heejin Woo
_____________________________________________________________
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)
August 2015
Copyright 2015 Heejin Woo
ii
ACKNOWLEDGEMENTS
I would not have been able to complete my dissertation without the guidance of
my committee members, help from friends, and support from my family.
First and foremost, I would like to thank co-chairs of my dissertation committee,
Nandini Rajagopalan and Yongwook Paik. Throughout my doctoral education, they
trained, nurtured, encouraged and challenged me to move forward and achieve my goals.
I appreciate all their contributions of time and ideas to make my PhD experience
productive and stimulating. I would also like to thank Kyle Mayer and Janet Fulk for
unstinting support. I would like to thank the whole MOR department and the PhD
program at the Marshall School of Business for their assistance.
I would like to thank my family and friends for their unwavering support and
encouragement throughout my graduate school years. I thank my brother-like, life-long
friends, Iltak Seo and Byoungkyu Kim for being a part of my life wherever I am. I am
also grateful to many PhD fellows at the Marshall School. I thank my mother and sisters
for their constant support. I wish my father could celebrate this together. I thank my
adorable daughter Erin for making my life more meaningful. Most of all, I thank my wife
Sooyean Sophia Jin for her encouragement, unconditional support, and love.
iii
Table of Contents
Acknowledgements ................................................................................................................
ii
List of Tables .........................................................................................................................
vi
List of Figures ........................................................................................................................
vii
Abstract ..................................................................................................................................
viii
Chapter 1: Introduction ........................................................................................................... 1
1.1 Young entrepreneurial firms .................................................................................. 1
1.2 Overview of studies ............................................................................................... 4
1.3 Chapter 1 references .............................................................................................. 6
Chapter 2: The effects of corporate venture capital ownership and founder incumbency on
R&D investment of young entrepreneurial firms ................................................................ 8
2.1 Introduction ............................................................................................................ 8
2.2 Theory and hypotheses ......................................................................................... 12
2.2.1 CVC ownership effect as a shareholder ................................................... 12
2.2.2 Founder incumbency effect as a significant manager .............................. 16
2.3 Methods ................................................................................................................ 19
2.3.1 Sample ..................................................................................................... 19
2.3.2 Measures ................................................................................................... 21
Dependent variables ................................................................................ 21
Independent variables .............................................................................. 22
Control variables ..................................................................................... 22
2.3.3 Analytical approach ................................................................................. 24
2.4 Results .................................................................................................................... 29
2.4.1 Main results .............................................................................................. 29
2.4.2 Additional results ..................................................................................... 35
2.5 Discussion .............................................................................................................. 38
2.6 Contributions and practical implications ............................................................... 41
2.7 Limitations and future research ............................................................................. 44
2.8 Conclusion ............................................................................................................. 45
2.9 Chapter 2 references .............................................................................................. 46
Chapter 3: When do corporate investors benefit from a portfolio company’s strategic
alliance? : Interorganizational knowledge acquisition perspective ..................................... 53
3.1 Introduction ........................................................................................................... 53
3.2 Theory and hypotheses .......................................................................................... 58
3.2.1 Industry relatedness and strategic benefits............................................... 61
3.2.2 Moderating effect: Absorptive capacity................................................... 64
3.2.3 Moderating effect: Geographic proximity................................................. 66
3.2.4 Cases of unrelated CVC investments: Industry relatedness with alliance
partner....................................................................................................... 68
3.3 Methods ................................................................................................................ 70
iv
3.3.1 Sample and data sources ......................................................................... 72
3.3.2 Measures ................................................................................................. 73
Dependent variables ............................................................................... 73
Independent variables ............................................................................ 74
Control variables .................................................................................... 75
3.4 Results .................................................................................................................. 76
3.5 Discussion and conclusion ................................................................................... 80
3.6 Chapter 3 references ............................................................................................. 85
Chapter 4: The contingent effect of major customer concentration on the profitability of
young firms ........................................................................................................................ 91
4.1 Introduction ........................................................................................................... 91
4.2 Theory and hypotheses .......................................................................................... 93
4.2.1 The effect of major customer dependence on supplier profitability ....... 93
4.2.2 Manufacturing industry .......................................................................... 95
4.2.3 Service industry ...................................................................................... 100
4.3 Research design and methods .............................................................................. 103
4.3.1 Data and sample description ................................................................... 103
4.3.2 Measures ................................................................................................. 105
Dependent variables ............................................................................... 105
Independent variables ............................................................................ 105
Control variables .................................................................................... 106
Econometric approach ........................................................................... 108
4.4 Results .................................................................................................................. 108
4.4.1 Descriptive statistics ............................................................................... 108
4.4.2 Fixed-effect regression results ............................................................... 110
4.4.3 Robustness tests ..................................................................................... 112
4.4.4 Additional analysis .................................................................................. 113
4.5 Discussion ............................................................................................................ 114
4.5.1 Limitations and future research .............................................................. 117
4.6 Conclusion ............................................................................................................ 117
4.7 Chapter 4 references ............................................................................................. 118
v
List of Tables
Table 2.1 Summary statistics and correlation matrix for all variables.................................... 30
Table 2.2 CVC ownership, founder incumbency, and R&D intensity of IPO firms: main
results...................................................................................................................................... 31
Table 2.3 Heckman selection model: first-stage regression results ....................................... 33
Table 2.4 Founder-CVC, founder-financial slack interaction effects, and founder-CEO vs.
founder-CTO.......................................................................................................................... 36
Table 3.1 Descriptive statistics and correlations ................................................................... 77
Table 3.2 Cumulative abnormal returns contingent on industry relatedness ......................... 78
Table 3.3 Regression results: dependent variable = CAR [-2,2] ........................................... 79
Table 3.4 Regression results: dependent variable = CAR [-1,1] and CAR [-3,3] ................. 81
Table 4.1: Distribution of firms by the major group affiliation ............................................ 104
Table 4.2: Within-firm descriptive statistics .......................................................................... 109
Table 4.3: Descriptive statistics for variables in the regression analyses ............................. 109
Table 4.4: Effects of major customer concentration on profitability ................................... 111
Table 4.5: Effects of major customer concentration on net income per employee (NIPE) .... 112
Table 4.6: Pairwise correlations ............................................................................................ 113
vi
List of Figures
Figure 1.1 Summary of the dissertation ................................................................................ 5
Figure 2.1 Timeline for relevant variables used in the Heckman two-stage estimation
procedure ................................................................................................................................ 26
Figure 3.1 Knowledge overlap between CVC, portfolio company, and alliance partner ...... 69
Figure 4.1 The division of value ............................................................................................ 96
vii
ABSTRACT
In this dissertation, I explore the interorganizational relationships of young
entrepreneurial firms. In the first essay, I examine how the relationship of a young
entrepreneurial firm with a corporate venture capital (CVC) affects the firm’s R&D investment
strategy. In the second essay, I investigate how a strategic alliance formed by a young
entrepreneurial firm influences the strategic benefits of a CVC firm. In the third essay, I examine
how relationships of a young entrepreneurial firm with major customers affect the profitability of
the firm. By exploring the interorganizational relationships of young entrepreneurial firms, this
dissertation attempts to understand better how young entrepreneurial firms interact with
stakeholders surrounding them.
In the first essay, I examine the effects of CVC ownership and founder incumbency on
R&D investment strategy of a young entrepreneurial firm. R&D investment strategy is one of the
most important resource allocation decisions that investors and top-level managers make, and it
is particularly important for young entrepreneurial firms in technology-intensive industries.
Although prior studies have examined how different types of ownership affect R&D investment
strategy in large public corporations, we still know little about corporate governance–related
determinants of R&D investment strategy in young entrepreneurial firms. To fill this research
gap, I focus on the most significant stakeholders in young entrepreneurial firms (i.e., venture
capital firms, corporate venture capital firms, and founders) and argue that CVC ownership and
founder incumbency positively affect R&D investment strategy in entrepreneurial firms. I found
empirical evidence supporting my hypotheses.
viii
In the second essay, I explore when a strategic alliance formed by a portfolio company
creates strategic benefits for its CVC firm. Prior studies show that CVC investments create value
for investing firms by providing learning opportunities and a window on new technologies.
Focusing on the conditions which facilitate knowledge transfer from a portfolio company to a
CVC firm, I argue that strategic benefits that a CVC firm captures from a strategic alliance
formed by a portfolio company is positively associated with the industry relatedness between the
CVC and its portfolio company. Moreover, I argue this effect will be more salient 1) as a CVC’s
absorptive capacity increases and 2) when a CVC and its portfolio company are located
geographically close because the CVC’s absorptive capacity provides the foundation upon which
the CVC learns better from the portfolio company and proximity allows a CVC to capture more
effectively knowledge from its portfolio company. In a sample of alliances formed by CVC-
backed U.S.-based startups, I found evidence. This study expands the scope of sources for
strategic benefits in CVC investments.
In the third essay, I study the relationship between a young firm and its major customers.
In particular, I examine how the major customer dependence of a young firm affects the
profitability of the young firm. From a bargaining power perspective, a high degree of
dependence on major customers can be an impediment to supplier profitability. However, as a
nascent player who faces resource limitation, a young firm may take advantage of the beneficial
effects in the relationship with major customers. Taking these counter balancing effects into
account, this paper explores the effect of major customer concentration on the profitability of
young entrepreneurial firms and argues that this effect may differ depending on the type of
markets in which the firm competes. More specifically, I argue that major customer
concentration is positively associated with the profitability of a young entrepreneurial firm in the
ix
manufacturing industry while it is negatively associated in the service industry. In the
manufacturing industry, young firms take advantages of scale economies and learning
opportunities from the relationship with major customers. However, in the service industry,
young firms experience diseconomies of managing for extensive maintenance and follow-up
services for major customers. This study underscores the importance of the customer-side in
understanding the profitability of young firms.
1
CHAPTER 1
INTRODUCTION
1.1 Young entrepreneurial firms
Prior research provides evidence that young entrepreneurial firms play an important role
in a knowledge-based economy. Entrepreneurial firms create jobs (Birch, 1981), lead
technological innovation in both products and production processes, and drive economic
transformation (Spulber, 2008). Recent dynamic growth of innovative firms such as Amazon,
Google and Facebook revalidates the importance of young entrepreneurial firms. Besides, among
the top 100 ranked corporations in the Fortune 500, several innovative companies such as Apple,
Hewlett-Packard, Cisco, and Oracle started as entrepreneurial start-ups. By young
entrepreneurial firms, I mean corporations that pursue profits with novel ideas that do not exactly
duplicate existing businesses (Schumpeter, 1934; Rumelt, 2005).
1
The Schumpeterian
entrepreneur is primarily an agent of change searching for new opportunities (Hagedoorn, 1996)
rather than a risk-taking, strictly rational, economically maximizing agent.
2
These new organizations face a constellation of problems associated with their newly
founded status, namely the liability of newness (Stinchcombe, 1965). Audretsch and Keilbach
(2006:283) highlight that small and new firms “were burdened with a size-inherent handicap in
terms of innovative activity.” The conventional view shaped largely by Schumpeter (1942),
Galbraith (1962) and Chandler (1977) is that large corporations lead innovation and
technological change while small firms will fall victim to their own inefficiencies (Audretsch
and Keilbach, 2006). However, as we observe the examples of successful entrepreneurial firms
1
This definition is similar to the description of entrepreneurship by Schumperter (1942). He described
entrepreneurship as activities combining resources in new ways.
2
A risk-taking, strictly rational, economically maximizing agent is a description by the “classical” theories of
entrepreneurship by Frank Hyneman Knight (Hagedoorn and Roijakkers, 2000).
2
in the real business world, young entrepreneurial firms often overcome the liability of newness
and grow sustainably as incumbent corporations. One possible way to overcome their limitations
is through interorganizational relationships. Yli-Renko, Autio, and Sapienza (2001) suggest that
leveraging relational resources for knowledge acquisition and exploitation helps to explain how
and why some young entrepreneurial firms can survive, thrive, and grow despite the resource
limitations. Considerable empirical evidence suggests that many young entrepreneurial firms use
diverse types of interorganizational relationships such as strategic alliances, investment
relationships, and supplier-customer partnership to access necessary resources (Baum, Calabrese,
and Silverman, 2000; Rothaermel and Deeds, 2004; Park and Steensma, 2012; Diestre and
Rajagopalan, 2012).
Interorganizational relationships are the vehicles through which firms gain access to a
variety of resources held by other actors (Hoang and Actoncic, 2003). For young entrepreneurial
firms and entrepreneurs, interorganizational relationships provide beneficial information and
advice. In the entrepreneurial process, entrepreneurs use networks to get ideas and gather
information to recognize entrepreneurial opportunities (Birley, 1985). Entrepreneurs benefit
from the networks with venture capitalists and professional service firms to tap into key talent
and market information (Freeman, 1999). In addition, relationships with distributors, suppliers,
or customer organizations are important conduits of information and know-how (Brown and
Butler, 1995).
The relationships of young entrepreneurial firms with other prestigious organizations also
have signaling effects. While there is a wide range of uncertainty around entrepreneurial
activities, information on young entrepreneurial firms is largely limited to potential exchange
partners such as investors, customers, alliance partners, and employees. Accordingly, the
3
potential exchange partners need to depend on diverse implicit cues which help to gauge the
underlying potential of a young entrepreneurial firm (Hoang and Antoncic, 2003). By associating
with well-regarded organizations, young entrepreneurial firms can signal their reliability to the
potential exchange partners. Potential exchange partners use the endorsement to evaluate the
quality of young entrepreneurial firms. This signaling effect leads to subsequent resource
exchange. For example, Stuart, Hoang, and Hybels (1999) found that entrepreneurial
biotechnology firms that formed a strategic alliance with prominent partners could go public
faster and at higher market valuation. In sum, the interorganizational relationship of young
entrepreneurial firms plays a significant role in overcoming resource limitation, and, hence, it is
valuable to understand the strategy and performance of young entrepreneurial firms.
The primary objective of this dissertation is to explore the interorganizational
relationships of young entrepreneurial firms. I believe that we need to study the context of the
interorganizational relationships of young entrepreneurial firms differently from that of
established firms for the following reasons. First, because young firms are malleable, their
interactions with other organizations can more substantially affect their strategic decisions and
outcomes than those of established firms. As a young entity, the routines of young
entrepreneurial firms are not rigidly institutionalized and structural inertia is less likely to have
set in (Hannan and Freeman, 1984). Top managers and the board of directors in the young
entrepreneurial firms may have greater discretion than those of established firms. Therefore, the
degree of impact of interorganizational relationships on strategy and performance is greater in
the entrepreneurial firm than that in the established firm. Second, in the context of
entrepreneurial firms there are unique stakeholders who are not observed in the context of
established firms. For example, venture capitalists are significant stakeholders who substantially
4
impact firm strategy and outcome in a young entrepreneurial firm. Established market leaders
also invest in young entrepreneurial firms through corporate venture capital (CVC) programs. In
particular, they are strategic investors who pursue not only financial returns but also strategic
benefits from the investment (Dushnitsky and Lenox, 2005). Founders are incumbent executives
in many young firms with significant ownership stakes. Accordingly, young entrepreneurial
firms will experience agency problems in different ways from those of established firms. This
dissertation attempts to understand better how young entrepreneurial firms interact with
stakeholders surrounding them.
1.2 Overview of studies
This dissertation consists of three independent essays. The first essay examines how a corporate
venture capital (CVC) firm affects the R&D investment strategy of young entrepreneurial firms
by using the agency theory lens (Jensen and Meckling, 1976). R&D investment strategy is one of
the most important resource allocation decisions that investors and top-level managers make, and
it is particularly important for young entrepreneurial firms in technology-intensive industries.
This study shows that CVC ownership, along with founder incumbency, leads to higher levels of
R&D investments in entrepreneurial firms.
The second essay investigates how young entrepreneurial firms affect the firm value of
strategic investors (i.e., CVCs). More specifically, this study focuses on the strategic alliances
that young entrepreneurial firms form. Drawing on the interorganizational knowledge transfer
perspective, I argue that the alliances formed by the investee create knowledge that is beneficial
to strategic investors and, accordingly, leads to abnormal returns for the investing firms.
5
The third essay examines the relationships of young firms with major customers. From a
bargaining power perspective, a high degree of dependence on major customers can be an
impediment to supplier profitability. However, as relatively unknown players in the market,
young entrepreneurial firms may take advantage of the beneficial effects created by volume
purchases of major customers. Taking these counter balancing effects into account, this essay
explores the effects of major customer concentration on the profitability of young entrepreneurial
firms and argues that this effect may differ depending on the type of markets in which the firm
competes.
The scope of these three essays is summarized in Figure 1.
Figure 1.1 Summary of the dissertation
6
1.3 Chapter 1 references
Audretsch DB, Keilbach M. 2006. Entrepreneurship, growth and restructuring. The Oxford
Handbook of Entrepreneurship. Oxford University Press: Oxford, UK.
Baum JAC, Calabrese T, Silverman BS. 2000. Don't go it alone: Alliance network composition
and startups' performance in canadian biotechnology. Strategic Management Journal
21(3): 267-294.
Birch D. 1981. Who creates jobs? The Public Interest 64(1):3-14.
Birley S. 1985. The role of networks in the entrepreneurial process. Journal of Business
Venturing 1(1): 107–117.
Brown B, Butler JE. 1995. Competitors as allies: a study of entrepreneurial networks in the U.S.
wine industry. Journal of Small Business and Management 33(3): 57–66.
Chandler AD. 1977. The Visible Hand: The Managerial Revolution in American Business.
Belknap Press: Cambridge, MA.
Diestre L, Rajagopalan N. 2012. Are all ‘sharks’ dangerous? New biotechnology ventures and
partner selection in R&D alliances. Strategic Management Journal 33(10): 1115-1134.
Dushnitsky G, Lenox MJ. 2005b. When do firms undertake R&D by investing in new ventures?
Strategic Management Journal 26(10): 947-965.
Freeman J. 1999. Venture capital as an economy of time. In: Leenders RTAJ, Gabbay SM. (Eds.),
Corporate Social Capital and Liability. Kluwer Academic Publishihing: Boston, MA.
Galbraith JK. 1962. Economic development in perspective. Harvard University Press:
Cambridge, MA.
Hagedoorn J. 1996. Innovation and entrepreneurship: Schumpeter revisited, Industrial and
Corporate Change 5(3): 883-896.
Hagedoorn J, Roijakkers N. 2000. Small entrepreneurial firms and large companies in inter-firm
R&D networks: The international biotechnology industry. Conference paper.
Hannan MT, Freeman J. 1984. Structural Inertia and Organizational Change. American
Sociological Review 49(2): 149-164.
Hoang h, Actoncic B. 2003. Network-based research in entrepreneurship: a critical review.
Journal of Business Venturing 18(2): 165-187.
Jensen MC, Meckling WH. 1976. Theory of the firm: Managerial behavior, agency costs and
ownership structure. Journal of Financial Economics 3(4): 305-360.
7
Park HD, Steensma HK. 2012. When does corporate venture capital add value for new ventures?
Strategic Management Journal 33(1): 1-22.
Rothaermel FT, Deeds DL. 2004. Exploration and exploitation alliances in biotechnology: a
system of new product development. Strategic Management Journal 25 (3): 201-221.
Rumelt RP. 2005. Theory, strategy and entrepreneurship. In: Alvarez SA, Agarwal R, Sorenson
O. (Eds.), Handbook of Entrepreneurship Research: Disciplinary Perspectives. Springer.
Schumpeter JA. 1934. The theory of economic development, London, Oxford University Press.
Schumpeter JA. 1942. Capitalism, socialism and democracy, New York, Harper and Row.
Spulber DF. 2008. The economic role of the entrepreneur. Northwestern University Working
Paper.
Stinchcombe AL. 1965. Social structure and organizations In J. G. March (ed.), Handbook of
Organizations: 142–193. Chicago: Rand McNally.
Stuart TE, Hoang H, Hybels RC. 1999. Interorganizational Endorsements and the Performance
of Entrepreneurial Ventures. Administrative Science Quarterly 44(2): 315-349.
Yli-Renko H, Autio E, Sapienza HJ. 2001. Social capital, knowledge acquisition, and knowledge
exploitation in young technology-based firms. Strategic Management Journal 26(6-7):
587-613.
8
CHAPTER 2
THE EFFECTS OF CORPORATE VENTURE CAPITAL OWNERSHIP AND
FOUNDER INCUMBENCY ON R&D INVESTMENT OF ENTREPRENEURAIL FIRMS
2.1 Introduction
Research and development (R&D) investment seeds new technological capabilities and is
a critical determinant of innovation in firms, ultimately leading to heterogeneous productivity
and profitability across firms (Branch, 1974; Hill and Snell, 1989). From a long-term perspective,
R&D is an important means of accumulating innovation capability, which is a fundamental
factor in maintaining competitive advantage (Dierickx and Cool, 1989; Oliver, 1997) and
superior absorptive capacity (Cohen and Levinthal, 1990; Lieberman, 1989). Not surprisingly,
studies have shown that R&D investment is the primary source of product innovation and
economic gain (Acs and Audretsch, 1988; Aghion and Tirole, 1994), especially in technology-
intensive industries (e.g., Arend, Patel, and Park, 2013; Kor, 2006).
Sustained R&D investment is particularly important for entrepreneurial firms that intend
to compete with established incumbents in technology-intensive industries by commercializing
disruptive products and services (Adner and Zemsky, 2005; Gans, Hsu, and Stern, 2002;
Schumpeter, 1942). In particular, R&D investment is critical for young entrepreneurial firms
because such firms usually lack brand awareness of their products and services and stable
relationships with business partners (Baum, Calabrese, and Silverman, 2000) and, accordingly,
rely on their creative and innovative business ideas and technologies in order to survive against
competition from incumbents (Gans and Stern, 2003). However, because high investment in
R&D is generally a high-risk, high-return strategy (Baysinger, Kosnik, and Turk, 1991; Leiponen
and Helfat, 2010), there is no guarantee that the efforts to innovate will indeed actualize
innovation (Barker and Mueller, 2002; Oriani and Sobrero, 2008). Thus, a necessary prerequisite
9
to sustained R&D investment is support from shareholders and top managers (Aghion, Van
Reenen, and Zingales, 2013), which is particularly necessary for young entrepreneurial firms that
are still in the process of becoming recognized within the industry and that face serious resource
limitations.
Reflecting the importance of R&D investment strategy, a number of studies have
examined the determinants of firm R&D investment. These determinants include CEO
characteristics (e.g., Barker and Mueller, 2002), types of shareholders (e.g., Baysinger et al.,
1991; Kim, Kim, and Lee, 2008), and the moderating effects of the composition of top
management teams and boards (e.g., Kor, 2006). Prior studies, however, only examine large
publicly owned firms and remain silent about R&D investment strategy in privately owned
entrepreneurial firms, despite the importance of R&D investment strategy in young
entrepreneurial firms in the technology-intensive industries (e.g., Arend et al., 2014; Kor, 2006).
Thus, this study attempts to fill this important gap in the literature and understand the
mechanisms supporting sustained R&D investment in young entrepreneurial firms.
While much can be learned from prior studies, the young entrepreneurial firm context is
significantly different from the established firm context. First, the investor community of
established firms differs from that of young entrepreneurial firms. Shareholders of established
firms can range from family owners, financial institutions, mutual funds, and pension funds to
individuals. However, because investing in a startup is very risky and its shares are not publicly
tradable, venture capital firms, both independent venture capital (IVC) firms and corporate
venture capital (CVC) firms, predominantly invest in young entrepreneurial firms, particularly in
technology-intensive industries. Second, the board composition of established firms differs from
that of young entrepreneurial firms. Board members in established firms may include executives
10
from another firm, experts from professional firms, and academics. These board members
monitor managers on behalf of a broad set of general shareholders. However, in young
entrepreneurial firms, most board members consist of representatives of IVC firms and CVC
firms. Thus, the board members in young entrepreneurial firms often represent specific
shareholders, thus affecting the governance of the venture. Finally, established firms are usually
managed by professional CEOs, whereas many young entrepreneurial firms are managed by
founders. The separation of ownership and control in large publicly owned firms can induce
potential conflicts between the interests of shareholders and those of professional managers
(Berle and Means, 1932). However, the severity of classical agency problems (Jensen and
Meckling, 1976) that are prevalent in established firms is different from or almost nonexistent in
founder-led young entrepreneurial firms (Wasserman, 2003, 2006). In sum, the unique board and
manager characteristics of the young entrepreneurial firm context call for studies adopting a new
perspective to elucidate R&D investment strategy in young entrepreneurial firms.
In this study, I argue that CVC ownership and founder incumbency in venture capital–
financed entrepreneurial firms play significant roles in spurring sustained R&D investment. In
the venture capital market, IVC firms, CVC firms, and founders (or entrepreneurs) are generally
considered to be the most significant players (Dushnitsky and Shaver, 2009). Accordingly, they
are the most influential stakeholders in a startup venture. Thus, I focus on these actors in the
corporate governance of entrepreneurial firms, as these actors form the most significant part of
the investor shareholders (i.e., CVC vs. IVC) and top management (i.e., founder) of young
entrepreneurial firms. Many scholars have noted that it is important to understand the effects of
these actors because the effects persist even after the venture goes public (Arikan and Capron,
2010; Nelson, 2003). In particular, I argue that CVC-funded ventures induce more sustained
11
R&D investment compared with IVC only–funded firms because their primary investment
objectives differ. And unlike professional managers, founders that are top managers, such as
chief executive officers (CEOs) and chief technology officer (CTOs)
3
, can induce more sustained
R&D investment owing to their strong personal attachment to the young entrepreneurial venture.
In the context of entrepreneurial R&D investment strategy, it is important to consider founder-
CTOs, which is novel in the literature, in addition to founder-CEOs, which are the main focus of
prior studies (e.g., Jayaraman et al., 2000; Souder, Simsek, and Johnson, 2012; Wasserman,
2003). My empirical findings support the main argument and address potential endogeneity
concerns due to CVC selection, following the econometrics approach suggested by Park and
Steensma (2012).
This study is related to, and extends, the discussion in Kor (2006) that contributes to the
broader literature on corporate governance related to R&D in technology-based entrepreneurial
firms (e.g., Kor, Mahoney, and Watson, 2008; Kor and Mahoney, 2005). Kor (2006) notes that
the role of top-level managers and board composition in making strategic choices about R&D
investment can be particularly relevant in young entrepreneurial firms. While Kor (2006) focuses
on a young entrepreneurial firm's post-IPO R&D investment strategy in the first several years
after an IPO, I focus on a young entrepreneurial firm's pre-IPO R&D investment strategy where
IVC firms, CVC firms, and founders play a major role in the venture's corporate governance.
Thus, I use insights from the technology entrepreneurship literature regarding corporate venture
capital (e.g., Dushnitsky and Lenox, 2005a; Dushnitsky and Shapira, 2010; Dushnitsky and
Shaver, 2009; Park and Steensma, 2012) and founders (e.g., Boeker and Karichalil, 2002;
3
In this study, I use the term chief technology officer (CTO) broadly to refer to a position in a firm, regardless of the
actual title, that can control the R&D activity of the venture. Thus, this term may refer to not only the chief
technology officer but also the chief scientific officer and vice president of R&D, among others.
12
Wasserman, 2003, 2006, 2012) to develop testable hypotheses about why certain actors (i.e.,
CVC and founders) can induce sustainable R&D investment in young entrepreneurial firms.
2.2 Theory and hypotheses
2.2.1 CVC ownership effect as a shareholder
In a standard venture capital investment cycle, IVC firms raise capital from limited
partners (e.g., pension funds, endowments, and wealthy individuals), forming venture capital
funds normally with a fixed life of ten years to invest in high-risk, high-return, early stage
ventures in technology-intensive industries in order to generate substantial returns via an IPO or
a trade sale of the portfolio company (Gompers et al., 2008; Gompers and Lerner, 2004).
Successful IPOs are generally more desirable for IVC firms (Gompers et al., 2010) and
positively affect their subsequent venture capital fundraising activity (Gompers, 1996). By
contrast, CVC firms usually run as a part of an incumbent corporation that has its own lines of
business (Dushnitsky and Shaver, 2009) and take a minority equity stake in privately held
entrepreneurial ventures (Gompers and Lerner, 2000). As strategic investors, CVC firms mainly
pursue strategic benefits from investment in young entrepreneurial firms (Dushnitsky and Lenox,
2005a; Maula, Autio, and Murray, 2009; Wadhwa and Kotha, 2006). Above all, their priority lies
in leveraging investments to acquire new technologies emerging from new ventures, thereby
gaining a 'window' on new technologies (Benson and Ziedonis, 2009). Young entrepreneurial
ventures are potentially an important source of knowledge for established corporations, because
established corporations are able to facilitate firm learning through CVC investment (Dushnitsky
and Lenox, 2005b; Sahaym, Steensma, and Barden, 2010). However, if a new venture that the
CVC invests in does not create valuable knowledge, the CVC firm’s strategic benefits will be
13
limited. To achieve this crucial objective, it is important for CVC firms to encourage their
portfolio companies to continuously invest in R&D. Even when R&D investment is not directly
beneficial to a portfolio company, a CVC may drive its investee to keep investing in a
development project, with the hope that such a project will be complementary to its parent firm’s
technology (Park and Steensma, 2013; Sahaym et al., 2010).
CVC investors can drive entrepreneurial firms’ R&D investment in several ways. First,
CVC investors can maintain board seats or board observation rights (Dushnitsky and Lenox,
2005b) and offer their opinions regarding the venture’s strategic direction in the board meeting.
When CVC firms invest in a young entrepreneurial venture, they usually co-invest with IVC
firms, forming a VC syndication (Lerner, 1994), and the greater the ownership stakes are, the
more likely it is to maintain a board seat and have a direct influence on the venture (Baker and
Gompers, 2003). However, unlike standard agency theory where all principals are assumed to
have homogenous preferences in maximizing shareholder value, IVC firms and CVC firms may
have heterogeneous preferences owing to their different investment objectives (Park and
Steensma, 2013). For IVC firms, the most important objective is to maximize the financial return
on investment. Thus, IVC firms prefer managers to accelerate marketing activities to increase the
number of service users, to improve their brand awareness, and to increase their market share,
especially before an IPO. Therefore, just before an IPO, IVC firms may not willingly support the
R&D investment of their portfolio companies because R&D investment may diminish more
immediate capital gains, which are generally preferred by IVC firms (Park and Steensma, 2013).
By contrast, CVC firms are interested in new technologies and their synergetic potential with the
business of CVC parent companies, often regarding CVC investment as R&D outsourcing (Basu,
Phelps, and Kotha, 2011; Mortara and Minshall, 2011). Although R&D investment by an
14
entrepreneurial firm may not always lead to a positive financial outcome, CVC investors may
encourage the managers of the venture to drive the project as long as it may benefit the CVC
parent company’s core business. Because of these differences in their primary investment
objectives, principal-principal conflicts may arise in the VC syndication between IVC firms and
CVC firms (Park and Steensma, 2013). Therefore, as CVC ownership in the venture increases,
CVC firms can have boarder interventions that counterbalance IVC firms' preferences. Through
such intervention, a CVC firm can influence the way that its portfolio company reflects its
interests.
In addition to influencing a portfolio company’s corporate governance by being on the
board of the venture, CVC firms use other mechanisms to direct young entrepreneurial firms
toward continuous R&D investment. For example, the parent corporation of the CVC firm can
share information with portfolio companies and give them advice based on its expertise. At the
same time, the corporation can provide its portfolio companies with opportunities to use
corporate facilities and other infrastructures for product development, manufacturing,
distribution, and so on (Dushnitsky and Shaver, 2009; Park and Steensma, 2012). Such support
can help the entrepreneurial venture save time and resources and encourage it to focus more on
technological development while still providing access to complementary assets that facilitate
commercialization (Gans et al., 2002; Park and Steensma, 2012; Teece, 1986). For instance,
Microsoft provides its portfolio software ventures 'with tools and services that enable companies
working with them to develop their ideas and adapt their products to work with the latest
technologies’ (Brian, 2011, p.1). Such support, however, cannot be easily expected in an arm's
length transaction (Williamson, 1975, 1979, 1985) with established corporations for non-
portfolio companies because there can be conflicts of interest and corporations may appropriate
15
intellectual property created by new ventures (Dushnitsky and Shaver, 2009; Gans et al., 2002;
Hellmann, 2002). Thus, CVC firms’ equity stake in the young entrepreneurial venture can
mitigate such conflicts of interest via a collaborative alliance (Dushnitsky and Lavie, 2010) and
support the entrepreneurial venture's continued resource allocation commitment to R&D
investment.
Finally, CVC investors can resolve the risk and uncertainty that young entrepreneurial
firms at the nascent stage of industry face regarding technology standards (Shapiro and Varian,
1999). Relative to established firms, this so-called technological uncertainty (Oriani and Sobrero,
2008; Toh and Kim, 2012) is greater for new ventures developing novel technologies owing to
their lack of legitimacy (Shane and Cable, 2002; Zimmerman and Zeitz, 2002). Because there is
widespread uncertainty in the market regarding the acceptance of young entrepreneurial firms’
novel technology as standard in the industry, young entrepreneurial firms at the very nascent
stage of industry may hesitate to fully allocate resources to their unique technological
development. However, when established incumbents in the industry back this technology, the
widespread uncertainty can be significantly reduced as a result of an endorsement effect (Gulati
and Higgins, 2003; Stuart, 2000; Stuart, Hoang, and Hybels, 1999), and the young
entrepreneurial firm can invest more extensively in their technology. For example, GridNet is a
smart grid startup that has an array of smart-grid products, including two software platforms.
Because the smart-grid technical standards are still in the stage of infancy, GridNet faced high
risk. However, when Cisco invested in GridNet, its risk significantly decreased because Cisco
formed a 'Smart Grid Technology Advisory Board' to lobby for the adoption of an Internet
Protocol (IP) standard for smart-grid communications (Lombardi, 2010). This kind of support
16
from an established CVC firm helps investee entrepreneurial ventures develop their technology
with confidence, which they would not have been able to do otherwise.
Based on the considerations outlined above, all else being equal, a CVC firm’s
investment in a new venture can lead to increased R&D intensity (i.e., R&D investment
normalized by firm size) by the new venture compared with IVC only–funded ventures, and
CVC firm preferences, relative to IVC firm preferences, can be increasingly reflected in the
entrepreneurial venture's R&D strategy as their power based on ownership increases (Finkelstein,
1992). I therefore propose the following hypothesis:
Hypothesis 1. Greater CVC ownership of an entrepreneurial venture is positively
associated with the entrepreneurial venture's R&D intensity.
2.2.2 Founder incumbency effect as a significant manager
Understanding the behavior of founder mangers that created a firm is important because
founder mangers are fundamentally different from professional agent managers, who are almost
always brought in from outside a firm (Nelson, 2003; Souder et al., 2012; Wasserman, 2003).
The differences between founder managers and professional agent managers are particularly
stark in young entrepreneurial firms but diminish with firm growth and each round of financing
from outside investors (Wasserman, 2003, 2006). To illustrate such differences, scholars have
noted that founders have a strong sense of attachment to the firm (Wasserman, 2006) and that
‘psychic income’ is as important as financial return (Gimeno et al., 1997). Founders also differ
substantially from agent managers regarding the knowledge, values, and attitudes that they bring
to managing firms (Jayaraman et al., 2000; Souder et al., 2012). In addition, founders often view
17
firms as extensions of themselves (Wasserman, 2012). Accordingly, they have stronger
commitment to their firm compared with non-founder executives (Carroll, 1984). Arthurs and
Busenitz (2003) refer to this special commitment of a founder as the 'ownership plus' mentality.
Although their actual ownership percentage becomes diluted with each round of fundraising,
founders will likely view their ownership level to be greater and keep making non-financial
investments of time, energy, and sweat equity (Arthurs and Busenitz, 2003).
In technology-intensity industries, founders in young entrepreneurial ventures mostly
start their businesses on the basis of certain novel and creative technological ideas. From the
founders' perspective, the quality of the technologies that their firm has is critical because it is
taken as the reflection of their own capability and accomplishment in the market (Arthurs and
Busenitz, 2003). Sometimes, this psychological attachment to the venture and its technology may
drive a founder to pursue completeness of technology development even at the expense of
missing a deadline for a prototype or going over budget. This spirit of craftsmanship is often
observed in founder-managed small business firms (Daily and Dalton, 1992). Thus, based on
founders’ spirit of craftsmanship and willingness to make efforts in technological development,
it can be expected that founder managers would invest in R&D activities to a greater extent than
non-founder managers would.
Founders' preferences regarding R&D investment, however, can be reflected in a firm's
strategies only when they are able to influence its strategic decisions. That is, founders can exert
their influence on firm strategy as executives who have the legitimacy to voice their opinions
directly (Finkelstein, 1992). As a young entrepreneurial firm grows, its founder often steps down
from being a top manager and may remain in the firm (even though it is being run by their
successors) (Wasserman, 2003) or leave the firm (Boeker and Karichalil, 2002). If the founder is
18
not an incumbent executive, his influence may be restricted in the firm because he loses
structural power that is based on hierarchical authority in the firm (Finkelstein, 1992). While in
office, however, founders actively engage in strategic decision making based on their preferences
on a regular basis.
Because I am considering R&D investment strategies in young entrepreneurial firms, I
include CTOs and CEOs as significant strategic decision makers in this study. Prior studies on
the 'founder effect' have, almost exclusively, focused on founder’s incumbency only as CEOs
(e.g., Jayaraman et al., 2000; Nelson, 2003; Souder et al., 2012; Wasserman, 2003). I argue that
such an approach may be misleading in young entrepreneurial ventures in technology-intensive
industries. In many technology-based entrepreneurial firms, founders exert a significant
influence on their firm strategy as the head of a technology division even after they step down
from the CEO position. For instance, Google’s co-founders Sergey Brin and Larry Page actively
engaged in the firm’s strategic decisions even when they were not CEOs after bringing in a more
seasoned executive manager, Eric Schmidt. Therefore, when the founder effect is discussed
regarding strategies in technology and R&D investment, it may be more reasonable to consider
founder incumbency as a top manager more broadly as founder-CTOs as well as founder-CEOs
than to consider just founder-CEOs. Based on these arguments, I propose the following
hypothesis:
Hypothesis 2. Founder incumbency as a top manager of the venture is positively associated
with the entrepreneurial venture’s R&D intensity.
19
2.3 Methods
2.3.1 Sample
I construct my measures based on data collected primarily from VentureXpert,
Compustat (e.g., financial information), and Form S-1, which is a required filing document used
by all public companies to register their securities with the U.S. Securities and Exchange
Commission (SEC) (e.g., ownership data). I employ data from VentureXpert to set the sample,
which has been extensively used in the CVC literature (Benson and Ziedonis, 2010; Dushnitsky
and Shaver, 2009; Park and Steensma, 2012). The sample consists of VC-backed U.S.
entrepreneurial firms that went public during the 2002-2011 period. The sample for this period
includes a variety of entrepreneurial firms that were founded before and after the internet bubble
period (i.e., the 1998- 2001 period), which include ventures that were first funded as early as
1986 to as late as 2009. In this period, CVC activity became prevalent (Gaba and Meyer, 2008),
and my sample includes both IVC only–funded ventures and CVC-funded ventures, where, in
most cases, VC firms usually co-invest (93% of the time in my sample).
Because the outcome variable relies on firms’ R&D expenditures and such information is
publicly available only when the venture goes public, this study inevitably has to rely on VC-
backed ventures that eventually went public. It is also necessary to use such ventures as the
sample because both independent variables can be identified only when these ventures file Form
S-1 upon going public. In addition, by the time that a venture goes public, there are both founder-
led and non-founder-led ventures in the population, allowing us to test the founder incumbency
effect (H2). I mainly rely on information revealed during the IPO process that corresponds to
data in the last year that the venture was a private firm (i.e., the year prior to the IPO year). I do
not use data ex-post IPO because the corporate governance structure vastly changes when
20
investors such as IVC firms and CVC firms liquidate their shares after the post-IPO lockup
period (Arikan and Capron, 2010), which will then render the setting inappropriate for my study.
Thus, while the sample selection is based on ventures that eventually went public, the data are
from the year that these ventures were actually private.
Since this study focuses on R&D investment, I use ventures from technology-intensive
sectors, such as the information and communication technology (ICT) sector and
Medical/Health/Life Science sector (e.g., biotechnology, medical device, and pharmaceutical
firms) in the VentureXpert industry classification, and exclude non-technology-oriented firms
4
.
These industries are R&D-intensive industries, which are appropriate for this study’s purposes
and contain the majority of VC-backed ventures. A sample drawn from multiple industries can
enhance the generalizability of the findings as well (Park and Steensma, 2013). In addition, to
focus on young entrepreneurial ventures, this study limits the sample to firms that were twenty
years or younger at the IPO. As a result of this criterion, 7% of IPO firms, which are
predominantly pure private equity transactions that do not conform to the notion of
entrepreneurship,
5
are dropped from the initial sample. While noisier, the results are qualitatively
similar with the inclusion of such firms.
Finally, I arrive at a list of N=319 young entrepreneurial ventures, of which 99 (31.03%)
are CVC funded. This portion of CVC-funded ventures is much greater than that reported in
previous CVC studies, which document that about 4-8% of the population of U.S. startup-stage
ventures receive CVC funding (Dushnitsky and Shaver, 2009; Gompers and Lerner, 2000;
4
VentureXpert classifies, for example, energy-related ventures, consumer-related ventures, and industrial-product
ventures as non-high technology industries. Our sample includes all ventures with Venture Economics Industry
Classification codes (VEIC code) 1xxx, 2xxx, 3xxx, 4xxx, and 5xxx, which constitute about 85% of the
VentureXpert database.
5
For example, an old public company that was taken private and then, after a corporate restructuring, went public
again.
21
Gompers, 2002). The stark difference stems from the fact that, in this study, I use a sample of
ventures that eventually go public. Therefore, within this sub-population of 'successful' firms, it
is natural to have more CVC-funded ventures than would be expected within the entire
population. It should be noted that while this approach does not diminish the internal validity of
this study, it does affect the generalizability of the study, and thus, interpreting the findings will
require caution. I discuss this issue in more detail in the discussion section. Throughout the
analyses, the unit of analysis is the venture.
2.3.2 Measures
Dependent variable. My dependent variable is R&D intensity at the time of the IPO,
which come from the last year that the venture was a private firm before eventually going public,
measured as the ratio of R&D expenditures to total assets. Because my sample includes young
ventures, they often have very small and unstable sales. This may cause biased R&D intensity
normalized by sales, in that some companies may have high R&D intensity not because of their
extensive R&D investment but because of their small sales. Hence, unlike established firms,
R&D intensity normalized by sales would not be a good measure for young entrepreneurial
ventures. By contrast, their total assets are relatively stable and reasonably measure firm size.
Hence, I normalize R&D expenditures by firm assets to measure the relative intensity of firms’
R&D investment. Other studies also standardize R&D investment by total assets because firms
often do not have sales in the early years of product development (e.g., Kor, 2006). While I
report all of my main results using this measure, I also use an alternative measure, R&D
investment per employee, following previous studies (e.g., Baysinger et al., 1991), as a
robustness check, and the results are qualitatively similar.
22
Independent variables
CVC ownership. For CVC ownership, I measure the proportion of shares held by a CVC
firm, calculated as the percentage of the total number of shares of the entrepreneurial firm at the
time of the IPO. Information on principal stockholders in Form S-1 (Prospectus) reasonably
reflects the composition of investors who substantially influence firm strategy because it includes
investors who retain their shares at the time of the IPO. Consistent with prior CVC studies, I
exclude diversified banks and insurance companies as CVC investors because the resources that
they provide do not directly relate to the technology commercialization or R&D of new ventures
(Dushnitsky and Shaver, 2009; Park and Steensma, 2012).
Founder incumbency. Founder incumbency takes the value of 1 if the founder is a CEO
or a technology-related executive, such as CTO or chief scientist, and 0 otherwise. If I cannot
identify the founder in Form S-1, we collect the information from Factiva, Lexis-Nexis, or the
internet, including the company’s website. If there is more than one founder, I consider the
founder to be incumbent if any of the co-founders is a CEO or a technology-related executive.
Control variables
I control for additional factors that may affect R&D intensity, such as factors pertaining
to other shareholders’ shares, venture characteristics, and pre-IPO financing processes. As
discussed earlier, different types of investors may have heterogeneous preferences concerning
R&D investment (Hoskisson et al., 2002; Kim et al., 2008). Depending on the relative
proportion of shares owned by other types of shareholders, the influence of CVC on R&D
investment can vary. Therefore, I control for other shareholders’ effects by including founder
23
shares and VC shares, which are calculated as the percentage of the total number of shares of the
entrepreneurial firm that founders and VC firms, respectively, own at the time of the IPO.
6
Prior studies also find that financial slack affects R&D investment (e.g., Kim et al., 2008;
Nohria and Gulati, 1996). Following prior studies, to control for the effect of financial slack on
R&D investment, I include financial slack as a control variable, which is calculated as the
natural log of cash and cash equivalents. In addition, I control for venture age at the IPO (Kim et
al., 2008), as entrepreneurial ventures may use their financial resources for purposes (e.g., more
marketing expenses) other than R&D as they become more mature. Corporate governance status
may also affect firms’ R&D investment. Prior studies (e.g. Kor, 2006) find that CEO-chairman
duality and board member composition (insider vs. outsider) affect firm R&D investment. To
control for these effects, I include number of inside board members, number of outside board
members, and CEO-chairman duality, which takes a value of 1 if the CEO is also a chairman and
0 otherwise.
Pre-IPO financing processes that a startup has gone through may reflect venture
characteristics that also affect R&D investment. For example, entrepreneurial ventures that
particularly require extensive R&D investment for their businesses may pursue more external
financing rounds (e.g., higher burn rate), pursue more investors (i.e., tap into a variety of investor
sources), and, accordingly, raise more funds. I control for the number of fund raising rounds, the
natural log of total invested amount, and the number of total investors. In the regression analyses,
I also include industry fixed effects and state fixed effects.
6
Some IPO firms have shareholders other than VC firms, CVC firms, and founders. Such shareholders include individual
investors, universities, and bank-affiliated organizations. Shares owned by these other shareholders are relatively minor,
especially at the time of the IPO, so their shares are omitted in the analysis for the purpose of constructing control variables.
24
2.3.3 Analytical approach
To test the hypotheses, I examine how CVC ownership and founder incumbency affect
the R&D intensity of an entrepreneurial venture using ordinary least squares (OLS) regression.
However, recent CVC literature (e.g., Park and Steensma, 2012, 2013) suggests that CVC-
funded ventures may be systematically different from IVC only–funded ventures, which could
lead to selection bias. That is, rather than CVC firms leading ventures to become more R&D
intensive via corporate governance, as I hypothesize in H1, CVC-funded ventures may be a mere
selection of more R&D-intensive firms (or may be more likely to be successful firms) relative to
IVC only–funded ventures to begin with because CVC firms expect those selected ventures to be
more advantageous to the CVC parent firms’ core business (Benson and Ziedonis, 2009;
Dushnitsky and Lavie, 2010; Dushnitsky and Lenox, 2005b, 2006). Unlike Park and Steensma
(2012), however, I conjecture that my sample selection scheme significantly reduces such
concern because my sample consists of ventures that eventually go public, which is a relatively
homogenous sample of successful and perhaps more R&D-intensive firms, compared with the
entire population of entrepreneurial ventures. Hence, even the IVC only–funded ventures in my
sample are successful firms. Nonetheless, I employ a Heckman selection model (Greene, 1981;
Heckman, 1979) along with my main outcome model (OLS) in a two-stage estimation procedure
to address such endogeneity due to selection bias (Hamilton and Nickerson, 2003; Shaver, 1998)
and test the validity of my conjecture about my sample selection scheme.
Figure 1 shows the timeline for the relevant variables that are used in my Heckman two-
stage estimation procedure. All ventures enter the database once they receive first round (FR)
venture capital funding from an external investor, and some are funded by a CVC at t
1
, whereas
others do not. As mentioned above, 31.03% of my sample gets CVC funding. I then capture my
25
outcome variable, R&D intensity, at t
2
, which is the last year that the venture was private before
eventually going public at t
3
. My first-stage Heckman selection model estimates the propensity
of new ventures receiving CVC funding given the initial venture characteristics at t
0
. In addition,
a Heckman selection model needs to exploit at least one exogenous variation that is not part of
the second-stage outcome model. In my case, I follow the approach suggested by Park and
Steensma (2012) and use the availability of CVC funds at the industry level. That is, I use the
total dollar amount of CVC investment in a given year, which aggregates all CVC investments
across all CVC firms that are active in the focal year (industry level). This measure reflects how
much money is available at the CVC industry level for investment purposes, which I assume is
beyond the control of any entrepreneurial venture. I expect it to be more likely that a new venture
is funded by a CVC firm when the availability of CVC funds is high and less likely that a new
venture is funded by a CVC firm when the availability of CVC funds is low (Gompers et al.,
2008; Paik and Woo, 2013). I capture this measure at t
1
, i.e., at the time of actual CVC funding,
for CVC-funded firms. Because I cannot observe t
1
for IVC only–funded firms, I take the
average lag between the first round funding (FR) and actual CVC funding, i.e., τ = (𝑡 1
− 𝑡 0
),
from the subset of CVC-funded ventures and use t
1
=t
FR
+ τ for IVC only–funded ventures to
determine the availability of CVC funds. In my sample, τ is 2.3 years. The aggregate CVC fund
availability data are from the National Venture Capital Association (NVCA) website and are
measured annually. Therefore, I use a 2-year lag in my regressions. As a robustness check, I also
use a 3-year lag, and the results are fully robust. The NVCA data only start in 1995, so I lose 7
observations (2.2%) due to missing data when using the Heckman two-stage estimation
procedure.
26
Figure 2.1 Timeline for relevant variables used in the Heckman two-stage estimation
procedure.
From the first-stage selection equation, I compute the inverse Mills ratio (λ: Lambda) that
is used in the second-stage outcome equation to correct for any selection bias (Wooldridge,
2002). Hence, my two-stage procedure has the following functional form:
Prob(𝐶𝑉𝐶 = 1) = Φ(ℤ𝛾 1
) [Selection equation]
𝔼 (𝑦 |𝐶𝑉𝐶 = 1) = 𝕏 ′
𝛽 + 𝜌𝜎 𝜆 ̂
(ℤ𝛾 1
) [Outcome equation]
𝜆 ̂
(ℤ𝛾 1
) =
𝜙 (ℤ𝛾 1
̂ )
Φ(ℤ𝛾 1
̂ )
[Inverse Mills ratio]
where, in the first-stage selection equation, CVC=1 indicates whether a new venture receives
CVC funding and ℤ is a set of explanatory variables, including availability of CVC and initial
venture characteristics at t
0
, such as the number of investors, total amount invested in the venture,
venture age, and a series of dummies for a company’s stage of development (i.e.,
Seed/Startup/Early Stage/ Expansion/Later Stage). In the second-stage outcome equation, 𝑦
𝑡 0
=First Round
(FR)
𝑡 1
=CVC Funding
τ = ( 𝑡 1
− 𝑡 0
)
IPO
𝑡 2
=R&D Intensity
(outcome variable)
27
denotes the dependent variable, R&D intensity, and 𝕏 is a set of explanatory variables, including
my potential endogenous variable due to selection bias, CVC ownership. 𝜆 ̂
is the inverse Mills
ratio computed from the first stage to correct for any selection bias. I report the results of the
Heckman two-stage estimation procedure along with the base OLS regressions in the next
section.
In addition to the selection bias noted above, there is also a concern of reverse causality
(e.g., Singh and Mitchell, 2005) at the time of the IPO. That is, a CVC firm’s retained ownership
of a new venture at the time of the IPO may be affected by the venture's greater R&D intensity
rather than the other way around, as I hypothesized in H1. For example, a CVC may determine
the amount of investment depending on the venture’s effort toward investing in R&D. If the
venture invests in development projects in a sustained manner, the CVC will invest more and
keep its shares as the venture grows. On the other hand, if the venture does not invest enough in
R&D, the CVC may reduce its investment and even withdraw its investment altogether. I address
this problem by using a two-stage least squares (2SLS) regression analysis with an instrumental
variable (Bascle, 2008; Hamilton and Nickerson, 2003; Wooldridge, 2002). In the first stage, I
predict retained CVC ownership at the time of the IPO of a venture with an instrumental variable,
CVC fund size, which is a CVC firm-specific measure captured at time 𝑡 2
in Figure 1 and varies
substantially across CVC firms. This variable measures the total dollar amount of financial
resources available to the CVC firm for investment purposes and is positively and significantly
correlated with CVC ownership (0.35), my potential endogenous variable, but not significantly
correlated with my outcome variable, R&D intensity (0.01). In the second stage, the predicted
value of the endogenous variable from the first stage is used in the outcome equation. Thus, my
two-stage least squares estimation (2SLS) procedure has the following functional form:
28
𝑅 &𝐷 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 = 𝛽 1
∙ 𝐶𝑉𝐶 𝑂𝑤𝑛𝑒𝑟𝑠 ℎ𝑖𝑝
̂
+ 𝛽 2
∙ 𝐹𝑜𝑢𝑛𝑑𝑒𝑟 𝑖𝑛𝑐𝑢𝑚𝑏𝑒𝑛𝑐𝑦 + 𝕏 ′
𝛽 3
+ 𝜀 1
(2
nd
stage)
𝐶𝑉𝐶 𝑂𝑤𝑛𝑒𝑟𝑠 ℎ𝑖𝑝 = 𝛾 1
∙ 𝐶𝑉𝐶 𝑓𝑢𝑛𝑑 𝑠𝑖𝑧𝑒 + 𝕏 ′
𝛾 2
+ 𝜀 2
(1
st
stage)
Just as IVC firms raise a certain amount of funding from limited partners and invest in
portfolio companies within the limited available resources, CVC investment managers invest in
portfolio companies within the resources available to them. If more financial resources are
available, CVC investment managers have greater ability to invest, increase their investment in a
focal venture, and retain their shares longer in the venture that they have already invested in. By
contrast, if less financial resources are available, CVC investment managers tightly manage their
investment in portfolio companies, liquidate their investment relatively early, and then invest in
other seed-stage or early stage new ventures. Such CVC investment behavior was confirmed
during my interviews with CVC managers. Thus, the size of the fund that a focal CVC firm
manages should be positively associated with the shares that the CVC owns at the time of the
IPO of a given venture. On the other hand, the size of the fund may be able to affect the R&D
intensity of the focal venture only through the CVC ownership (i.e., exclusion restriction) of the
venture (Bascle, 2008; Wooldridge, 2002), which is consistent with the correlation pattern
mentioned above. Thus, I believe that my instrument provides a relatively 'clean' measure that
facilitates a causal interpretation of my results. Methodologically, in the weak instrument test,
the F-statistics of the instrumental variable is 16.704, which is significantly larger than 10, the
required F-statistic threshold in the weak instrument test suggested in the econometrics literature
(Staiger and Stock, 1997; Stock, Wright, and Yogo, 2002; Stock and Yogo, 2002). This result
29
provides additional support that my instrumental variable is valid and strong. I present my results
in the following section
7
.
2.4 Results
2.4.1 Main results
Table 1 presents the descriptive statistics and pairwise correlations for all the variables.
Although not reported separately, I computed variance inflation factors (VIFs) to determine
whether there are any multicollinearity concerns in my regression analyses. All of the VIF values
are below 3, which is significantly below the suggested cut-off of 10, indicating that
multicollinearity is not a problem in my analysis (Kennedy, 2003; Kutner et al., 2005).
Table 2 provides a series of regression results predicting the R&D intensity of ventures.
Model 1 shows the inclusion of only the control variables, and Models 2-4 show the inclusion of
main variables of interest, CVC ownership and founder incumbency, separately and jointly in the
base OLS regressions. Control variables suggest that ventures that raise external funding more
frequently (i.e., number of fund raising rounds) have higher R&D intensity and that ventures that
are more mature at the time of the IPO (i.e., venture age) have lower R&D intensity, perhaps
because more mature ventures need to use their funds for a variety of activities beyond R&D,
such as expansion, marketing, and advertising. Financial slack is also negatively associated with
R&D intensity, perhaps because, as cash starts to accumulate in an entrepreneurial venture, the
venture needs to allocate its cash to alternate uses beyond R&D (Nohria and Gulati, 1996).
7
In this study, I am less concerned about reverse causality between R&D intensity and founder incumbency because
founding an entrepreneurial venture and strategically allocating resources by the founder always precede any
venture activity, such as R&D. In addition, by definition, a founder cannot 'join' a venture with greater R&D
intensity. Methodologically, to the best of our knowledge, currently, the econometrics literature (e.g., Angrist,
Imbens, and Rubin, 1996; Angrist and Pischke, 2008) is still silent about how to cope with multiple endogenous
variables with multiple instruments in a single regression framework. Therefore, I treat founder incumbency as
given but do not make any strong causal statement regarding H2.
30
Table 2.1 Summary statistics and correlation matrix for all variables
Variables Mean S.D. min max (1)
(2)
(3)
(1) R&D intensity 0.240 0.264 0.00 2.29 1
(2) CVC ownership 0.045 0.087 0.00 0.57 0.19 * 1
(3) Founder incumbency 0.605 0.490 0.00 1.00 0.19 * 0.01
1
(4) Founder shares 0.107 0.161 0.00 1.00 -0.04
-0.05
0.31 *
(5) VC shares 0.495 0.258 0.00 1.00 -0.03
-0.24 * -0.14 *
(6) Financial slack 4.054 1.563 0.51 11.74 -0.23 * 0.08
0.00
(7) Venture age 8.088 3.588 2.00 20.00 -0.20 * 0.03
-0.26 *
(8) Number of inside board members 1.367 0.640 0.00 5.00 -0.08
-0.06
0.26 *
(9) Number of outside board members 5.727 1.561 1.00 12.00 0.09
0.10
-0.06
(10) CEO-chairman duality 0.411 0.493 0.00 1.00 -0.15 * -0.06
0.14 *
(11) Number of fund raising rounds 5.972 2.939 1.00 15.00 0.22 * 0.08
0.00
(12) Total invested amount 11.257 1.103 4.94 15.35 0.08
0.16 * -0.05
(13) Number of total investors 9.790 5.833 1.00 31.00 0.23 * 0.16 * 0.00
(14) Availability of CVC ($M) 4476.220 4236.578 470.33 15196.72 -0.10
-0.01
-0.12 *
(15) CVC fund size ($M) 1.156 3.819 0.00 35.83 0.01 0.35 * 0.08
Variables (4)
(5)
(6)
(7)
(8)
(9)
(4) Founder shares 1
(5) VC shares -0.40 * 1
(6) Financial slack 0.13 * -0.02 1
(7) Venture age 0.08 -0.14 * 0.00 1
(8) Number of inside board members 0.27 * -0.12 * -0.02 -0.06 1
(9) Number of outside board members -0.18 * 0.07 0.05 -0.03 -0.28 * 1
(10) CEO-chairman duality 0.18 * -0.17 * -0.02
0.06
0.18 * -0.13 *
(11) Number of fund raising round -0.20 * 0.12 * 0.00
0.03
-0.11 * 0.31 *
(12) Total invested amount -0.29 * 0.11 * 0.17 * -0.09
-0.15 * 0.41 *
(13) Number of total investors -0.32 * 0.00
0.04
0.02
-0.18 * 0.34 *
(14) Availability of CVC ($M) -0.07
0.00
-0.03
0.20 * -0.03
-0.02
(15) CVC fund size ($M) 0.00 -0.13 * 0.25 * -0.03 -0.09 0.02
Variables (10)
(11)
(12)
(13)
(14)
(10) CEO-chairman duality 1
(11) Number of fund raising round -0.16 * 1
(12) Total invested amount -0.18 * 0.48 * 1
(13) Number of total investors -0.15 * 0.62 * 0.44 * 1
(14) Availability of CVC ($M) 0.20 * 0.02
-0.05
0.17 * 1
(15) CVC fund size ($M) 0.03 0.14 * 0.20 * 0.26 * -0.02
N = 319, * significant at the 5% level or higher
31
Table 2.2 CVC ownership, founder incumbency, and R&D intensity of IPO firms: main
results
DV= R&D intensity OLS Heckman IV – 2SLS
Variables (1) (2) (3) (4) (5) 2nd stage (6) 1st Stage (7) 2nd Stage
Founder shares 0.1220 0.1570 0.0488 0.0847 0.2243 -0.0819*** 0.1102
(0.0984) (0.0980) (0.1014) (0.1009) (0.1446) (0.0311) (0.0917)
VC shares -0.0186 0.0260 -0.0154 0.0284 -0.0461 -0.0850*** 0.0594
(0.0573) (0.0586) (0.0568) (0.0581) (0.0704) (0.0205) (0.0686)
Financial slack -0.0331*** -0.0355*** -0.0320*** -0.0343*** -0.0471*** 0.0015 -0.0360***
(0.0086) (0.0085) (0.0085) (0.0085) (0.0106) (0.0029) (0.0120)
Venture age -0.0110*** -0.0111*** -0.0082** -0.0083** -0.0111** 0.0009 -0.0085***
(0.0037) (0.0037) (0.0038) (0.0038) (0.0048) (0.0018) (0.0030)
Number of inside board -0.0261 -0.0257 -0.0355 -0.0349 -0.0422 0.0028 -0.0345*
(0.0217) (0.0214) (0.0218) (0.0215) (0.0284) (0.0062) (0.0193)
Number of outside board -0.0034 -0.0031 -0.0035 -0.0032 -0.0056 0.0014 -0.0030
(0.0094) (0.0093) (0.0093) (0.0092) (0.0101) (0.0038) (0.0084)
CEO-chairman duality -0.0218 -0.0193 -0.0305 -0.0279 0.0059 -0.0074 -0.0260
(0.0279) (0.0276) (0.0278) (0.0275) (0.0330) (0.0104) (0.0219)
No. of fund raising rounds 0.0124** 0.0132** 0.0121** 0.0128** 0.0168** -0.0015 0.0133*
(0.0060) (0.0059) (0.0059) (0.0059) (0.0073) (0.0023) (0.0077)
Total invested amount -0.0136 -0.0176 -0.0136 -0.0175 -0.0171 0.0059 -0.0203*
(0.0148) (0.0147) (0.0147) (0.0146) (0.0247) (0.0045) (0.0121)
Number of total investors 0.0044 0.0039 0.0036 0.0031 0.0025 -0.0001 0.0027
(0.0031) (0.0031) (0.0031) (0.0031) (0.0036) (0.0011) (0.0031)
CVC ownership 0.4554*** 0.4475*** 0.4666*** 0.7644*
(0.1563) (0.1549) (0.1573) (0.4324)
Founder incumbency 0.0769*** 0.0753*** 0.0650* -0.0004 0.0742***
(0.0293) (0.0289) (0.0335) (0.0095) (0.0269)
LAMBDA (𝜆 ̂
) -0.0493
(0.0632)
CVC fund size ($M) 0.0071***
(0.0021)
Constant 0.5442*** 0.5446*** 0.5127*** 0.5138*** 0.6522*** 0.0175 0.5147***
(0.1718) (0.1697) (0.1706) (0.1685) (0.2510) (0.0548) (0.1595)
State dummies Yes Yes Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes Yes Yes
Observations 319 319 319 319 312 319 319
F-stat/Wald Chi2 6.02*** 6.22*** 6.12*** 6.29*** 147.06*** 9.23*** 217.60***
R-squared 0.3279 0.3465 0.3431 0.3610 0.2530 0.3520
Standard errors are presented in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
32
Hypothesis 1 states that CVC ownership is positively associated with the R&D intensity
of an entrepreneurial venture. As shown in Models 2 and 4, CVC ownership is indeed positively
associated with R&D intensity at the 1% significance level. Hypothesis 2 states that founder
incumbency is positively associated with the R&D intensity of an entrepreneurial venture. The
results for Models 3 and 4 suggest that founder incumbency is indeed positively associated with
R&D intensity at the 1% significance level. Therefore, these OLS regressions are consistent with
both hypothesis 1 and hypothesis 2. However, as discussed, CVC ownership may be endogenous,
and the OLS regression results may be biased due to selection bias and reverse causality. Hence,
I use these OLS results only as a benchmark to compare with my Heckman two-stage estimation
results and two-stage least squares (2SLS) estimation results.
First, I consider whether selection bias is present. That is, I investigate whether CVC-
funded ventures are more R&D intensive to begin with compared to IVC only–funded ventures.
Table 3 reports the first-stage estimation results (i.e., selection equation) and Model 5 of Table 2
reports the outcome equation, correcting for selection bias with the inclusion of the inverse Mills
ratio (𝜆 ̂
: Lambda) calculated from the first stage. In Model 5, CVC ownership remains positively
associated with R&D intensity at the 1% significance level even after correcting for selection
bias. As we can see from Model 5, though, 𝜆 ̂
is not statistically significant (p=0.241), and a
comparison of coefficients between Model 4 and Model 5 for CVC ownership using a Wald test
shows that the two coefficients (0.4475 vs. 0.4666) are not statistically different (Clogg, Petkova,
and Haritou, 1995; Shaver, 1998). Therefore, selection bias is not a concern in my sample
8
.
8
I do not deny that selection bias documented in previous studies could be a concern (e.g., Park and Steensma, 2012). I only
note that my sample selection scheme reduces concerns regarding selection bias in my setting, allowing me to identify the
CVC effect free of any selection bias concerns.
33
Table 3. Heckman selection model: first-stage regression results
DV: CVC=1 (Probit) Heckman
Variables 1
st
stage
Availability of CVC ($M) 0.0001***
(0.0000)
Number of total investors at First Round 0.0761
(0.0519)
Total invested amount at First Round -0.0000
(0.0000)
Venture age at First Round -0.0366
(0.0355)
Seed stage at First Round -0.4449
(=1 if startup was Seed stage at 1st Round) (0.7304)
Early stage at First Round -0.6644
(=1 if startup stage was Early at 1st Round) (0.7167)
Expansion stage at First Round -0.6147
(=1 if startup stage was Expansion at 1st Round) (0.7468)
Later stage at First Round -0.2151
(=1 if startup stage was Later at 1st Round) (0.9018)
Acquisition stage at First Round -1.6758**
(=1 if startup stage was Acquisition at 1st Round) (0.8419)
Constant 5.3421***
(1.0254)
First round year fixed effect Yes
State dummies Yes
Industry dummies Yes
Observations 312
34
Next, I report the two-stage least squares (2SLS) estimation results that address reverse
causality at the time of the IPO. That is, I attempt to tease out whether CVC ownership in fact
leads entrepreneurial ventures to invest more in R&D, as I hypothesize (H1), or whether it is
more R&D-intensive ventures that attract more funding from CVC firms. The relationship
perhaps operates in both directions, but my instrument, CVC fund size, varies extensively across
CVC firms, independent of the entrepreneurial ventures' R&D spending decisions, facilitating a
determination of causality between CVC ownership and R&D intensity (H1) in my 2SLS
framework. The size of the CVC investment budget is primarily determined by an administrative
process at the corporate parent level. Models 6 and 7 show the results from the first stage and
second stage of 2SLS, respectively. The instrument, CVC fund size, is positive and statistically
significant at the 1% level in the first stage, suggesting it is a valid instrument. In the second
stage, Model 7, both CVC ownership and founder incumbency remain positive and statistically
significant even after adjusting for reverse causality, but the magnitude of the coefficient in CVC
ownership is changed to a certain degree.
Because Model 7 facilitates a causal interpretation, I compute the marginal effects using
coefficients from Model 7 (2SLS) at the mean value of the explanatory variables. According to
Model 7, for every 1% point increase in the CVC ownership of the focal venture, the venture
increases its R&D intensity by approximately 0.76% point, and having the founder as a CEO or a
CTO that can make R&D decisions for an entrepreneurial venture increases the R&D intensity of
the focal venture by 7.42% relative to a venture that does not have the founder as a top manager.
Thus, the results are not only statistically significant but also economically significant—and
realistic. Therefore, while addressing both the selection bias and the reverse causality of my data,
I find strong support for both hypothesis 1 and hypothesis 2.
35
2.4.2 Additional analyses
Founder-CVC interaction effect
As an extension to the main analyses (Table 4), I test the interaction effect of CVC
ownership and founder incumbency. I may expect that there will be a positive interaction effect
above and beyond the additive effects of CVC ownership and founder incumbency. This
interaction effect may result from the synergy between a particular type of board member (i.e.,
CVC) and the founder manager's decision-making process as a result of goal congruence
regarding more R&D investment. In Model (1) of Table 4, contrary to my expectation, however,
I do not find any significant effect of the interaction term between CVC ownership and founder
incumbency (p=0.852). The lack of an interaction may imply that while there may be goal
congruence between CVC firms and founders regarding more R&D investment, there may also
be goal incongruence regarding what type of R&D projects to pursue. For example, the corporate
parent may be enthusiastic about R&D projects that complement the sustainability of their own
core business or that enhance new knowledge acquisition (Dushnitsky and Lenox, 2005a, 2005b;
Wadhwa and Kotha, 2006), while the founder may be interested in radical innovations that can
challenge the status quo (Hellmann, 2002). Thus, the lack of an interaction effect between CVC
ownership and founder incumbency is consistent with the notion of the 'paradox of corporate
venture capital' documented in Dushnitsky and Shaver (2009).
Founder CEO vs. founder CTO
I also assess the significance of the founder incumbency construct. I verify whether the construct
of founder incumbency is meaningfully different from the construct of founder-CEO, which is
36
Table 2.4 Founder-CVC, founder-financial slack interaction effects, and founder-CEO vs.
founder-CTO.
DV= R&D intensity Founder × CVC founder-CTO vs. -CEO Founder × financial slack
VARIABLES (1) OLS (2) OLS (3) OLS
Founder shares 0.0649 0.1002 0.0927
(0.0878) (0.1031) (0.0998)
VC shares 0.0175 0.0151 0.0220
(0.0544) (0.0583) (0.0575)
Financial slack -0.0271** -0.0278*** -0.0098
(0.0108) (0.0086) (0.0128)
Venture age -0.0100*** -0.0120*** -0.0104***
(0.0031) (0.0038) (0.0038)
Number of inside board -0.0276 -0.0180 -0.0310
(0.0205) (0.0213) (0.0215)
Number of outside board -0.0099 -0.0093 -0.0108
(0.0090) (0.0095) (0.0093)
CEO-chairman duality -0.0156 -0.0112 -0.0142
(0.0225) (0.0277) (0.0272)
No. of fund raising rounds 0.0130* 0.0127** 0.0130**
(0.0075) (0.0059) (0.0059)
Total invested amount -0.0176 -0.0174 -0.0144
(0.0122) (0.0148) (0.0147)
Number of total investors 0.0030 0.0036 0.0035
(0.0031) (0.0031) (0.0031)
CVC ownership 0.3915* 0.4331*** 0.4270***
(0.2083) (0.1585) (0.1562)
Founder incumbency 0.0652** 0.1899**
(0.0286) (0.0735)
CVC ownership × Founder incumbency 0.0529
(0.2840)
Founder-CEO 0.0183
(0.0291)
Financial slack × Founder incumbency -0.0304*
(0.0168)
Constant 0.3983** 0.4435* 0.2793
(0.1586) (0.2670) (0.2721)
Observations 319 319 319
R-squared 0.4132 0.4026 0.4198
37
the dominant paradigm in the literature (e.g., Nelson, 2003; Souder et al., 2012; Wasserman,
2003, 2012). Instead of coding both founder CEOs and founder CTOs as the founder
incumbency construct, I follow the convention in the literature (e.g., Nelson, 2003; Souder et al.,
2012; Wasserman, 2003, 2012) and code a dummy variable, founder-CEO, that equals 1 if the
founder is only the CEO and 0 otherwise. In my sample of 319 entrepreneurial ventures, the
founders are CEOs in 140 ventures and CTOs in 52 ventures, and the founders are not present in
127 ventures at the time of the IPO. In Model 2 of Table 4, I repeat the regression analysis and
find that founder-CEO is positive but not statistically significant at any conventional level
(p=0.529). This result does not necessarily invalidate my hypothesized founder effect on
entrepreneurial R&D investment strategy. That is, the non-significance of founder-CEO is
observed not because founder-CEOs are investing in R&D no differently from non-founder
CEOs but because a significant portion of non-founder CEOs are founder-CTOs who behave
much like founder-CEOs regarding R&D investment. In fact, when I drop ventures with founder-
CTOs and compare founder CEOs and non-founder CEOs, founder-CEO becomes positive and
significant again (p=0.022), supporting hypothesis 2. This result implies that the conventional
approach of considering only founder-CEOs in some contexts can be misleading. Therefore, it is
necessary to consider not only the CEO but also significant executives, such as the CTO, when I
investigate the founder effect on firm strategy in entrepreneurial ventures.
Founder-financial slack interaction effect
Finally, I test a boundary condition to the founder incumbency effect hypothesized in H2. I add a
financial slack × founder incumbency interaction term and find that it is negative and statistically
significant in Model 3 of Table 4. This result implies that the founder incumbency effect in H2 is
38
dampened when more financial resources are available, which is consistent with the notion that
the distinction between founder managers and professional agent managers become diminished
as firms grow (Wasserman, 2006). Conversely, when financial resources are tight, the positive
effect of founder incumbency on R&D intensity is stronger. This is consistent with the notion
that founders have psychological attachment to the venture and that they may pursue the
completion of technology development even at the expense of going over budget.
2.5 Discussion
R&D investment strategy is one of the most important resource allocation decisions that
investors and top-level managers make (Oriani and Sobrero, 2008), and it is particularly
important for young entrepreneurial firms in technology-intensive industries (Kor, 2006).
However, because high investment in R&D is generally a high-risk, high-return strategy
(Baysinger et al., 1991), a necessary prerequisite to sustained R&D investment is willingness and
support from shareholders and top managers (Aghion et al., 2013). Therefore, factors related to
corporate governance are important determinants of firms’ R&D investment strategy. Although
prior studies have examined how different types of ownership affect R&D investment strategy in
large public corporations (e.g., Baysinger et al., 1991; David, Hitt, and Gimeno, 2001; Kim et al.,
2008), we still know little about the corporate governance–related determinants of R&D strategy
in young entrepreneurial firms. To fill this research gap, this study focuses on the most
significant stakeholders in young entrepreneurial firms (i.e., IVC firms, CVC firms, and founders)
and argues that CVC ownership and founder incumbency positively affect R&D investment
strategy in young entrepreneurial firms. My empirical analysis correcting for selection bias and
reverse causality supports the hypotheses.
39
CVC ownership is positively associated with greater R&D intensity in a young
entrepreneurial venture because of what I call i) the direct corporate governance effect, ii) the
CVC-venture alliance effect, and iii) the technology endorsement effect. These mechanisms are
relatively less prevalent in the established firm context but are important for young
entrepreneurial firms. The direct corporate governance effect relates to the fact that CVC firms
can serve on the board of a new venture as ownership increases and keep IVC firms' preferences
in check as they influence the venture's R&D investment strategy because IVC firms and CVC
firms have different primary investment objectives (i.e., financial return vs. strategic return),
which may lead to a potential principal-principal conflict. Such a potential conflict was
confirmed in my interview with CVC investment managers. For example, an interview with a
CVC investment manager working for a Silicon Valley–based solar panel manufacturer that has
invested in a solar leasing startup revealed that when the solar leasing startup considered
developing and launching a system that manages nationwide contractors (installers), the CVC
firm supported this strategy because it believed that the system could help its core business in the
solar panel market. However, an IVC investor strongly opposed this strategy because it might
negatively influence 'numbers' at the IPO. This example clearly illustrates how differences in the
primary investment objectives of investors in the VC syndication can result in principal-principal
conflicts. I highlight this issue by showing that CVC investors encourage their portfolio
companies to engage in more R&D investment, which may not always be supported by IVC
investors.
The CVC-venture alliance effect refers to the fact that CVC firms add value to the
portfolio companies by treating the venture as an alliance partner and thus by providing
technological support, manufacturing capacities, and access to marketing and distribution
40
channels, consistent with what other scholars have noted (e.g., Dushnitsky and Shaver, 2009;
Ivanov and Xie, 2010; Park and Steensma, 2012). The technology endorsement effect refers to
the fact that CVC investors can resolve the risk and uncertainty that young entrepreneurial firms
face regarding technology standards and help investee entrepreneurial ventures focus on
technological development with confidence. CVC firm’s technology endorsement effect is
something that has not received much attention in the literature so far but is certainly an
important resource for young entrepreneurial ventures in technology-intensive industries. Overall,
this study sheds light on the fact that CVC investors not only are financial resource providers but
also significantly affect venture strategy through ownership. This finding is aligned with the
argument that owners and investors play important roles in shaping the strategic activities of
firms in the established firm context (e.g., Fiss and Zajac, 2004).
Another critical factor that supports sustained R&D investment is the willingness of
managers to invest in R&D (Aghion et al., 2013). Because of psychological attachment to the
firm, a venture founder who pursues creative and novel technology has a strong willingness to
drive intensive R&D activities. Such willingness is particularly important when a founder is able
to direct strategy as a significant figure in a firm, such as a CEO and a CTO. As ventures grow,
they often seek to acquire professional managers as CEOs, and founders may then assume a
more specific role in a firm (Wasserman, 2003). Although he may step down from the CEO
position, the founder can still significantly influence firm strategy when he holds a critical role,
such as a CTO. I find that a venture invests more in R&D when its founder is incumbent as a
CEO or CTO than otherwise. I also find that such a founder effect is pronounced when financial
slack is tight for the venture. As we often observe that founders actively engage in strategic
decisions as non-CEO executives in the real world, I suggest that founder incumbency as a non-
41
CEO executive should be considered a meaningful unit of analysis in the literature concerning
the continuing role of founders in the development of ventures. This study also suggests that
while there may be goal congruence between CVC firms and founders regarding more R&D
investment, there may still be goal incongruence regarding what type of R&D projects to pursue,
thus allowing for the 'paradox of corporate venture capital' (Dushnitsky and Shaver, 2009).
2.6 Contributions and practical implications
This study makes several contributions. First, this study has implications for agency theory and
the corporate governance literature. While research on principal-agent conflicts (Berle and
Means, 1932; Jensen and Meckling, 1976) dominates the corporate governance literature, there is
a growing body of literature that investigates principal-principal conflicts and that departs from
the core assumptions of classical agency theory (e.g., Connelly et al., 2010; Kim et al., 2008;
Young et al., 2008). This study suggests that the entrepreneurial financing context provides a
novel context in which to understand a new source of principal-principal conflicts and a
mechanism regarding R&D investment. Prior studies examining the effect of shareholders on
R&D investment mainly explain heterogeneous preferences regarding R&D investment based on
investment time horizons (e.g., Graves, 1988; Hoskisson et al., 2002; Kim et al., 2008). For
example, Hoskisson et al. (2002) notes that among institutional investors, pension fund managers
inherently have a long-term investment horizon and professional investment managers inherently
have a short-term investment horizon because of the way that they are compensated and that
such a difference in investment horizons results in a 'conflicting voice' regarding a corporation’s
high-risk, high-return R&D strategy. Baysinger et al. (1991) provide the underlying mechanism
of the shareholder effect on firm R&D strategy. In large public corporations, investors who
42
pursue short-term returns “derive their power over top managers from the mere size of their
equity holdings: heavy institutional selling can cause drastic declines in a firm’s market value”
(Baysinger et al., 1991, p. 206). However, in the entrepreneurial financing context, the
investment time horizon may not be the critical factor that explains heterogeneous preferences
regarding R&D investment. This is because, by nature, the entrepreneurial financing market does
not allow for convenient share transactions, and thus, a transient investor would unlikely enter
the VC financing market to make profits through short-term trading or influencing firm strategy.
This study suggests that differences in primary investment objectives (i.e., financial return vs.
strategic return) can be a source of principal-principal conflicts related to heterogeneous
preferences regarding entrepreneurial firms’ R&D investment.
Second, this study contributes to the technology entrepreneurship literature (e.g.,
Dushnitsky and Lenox, 2005a; Dushnitsky and Shaver, 2009; Park and Steensma, 2012) by
taking the investee perspective and suggesting that CVC firms are not only resource providers
but also stakeholders that significantly affect firm strategy. Thus far, most studies on CVC firms
focus on CVC from the perspective of the corporate parent (e.g., Benson and Ziedonis, 2009,
2010; Dushnitsky and Lenox, 2005a, 2005b; Wadhwa and Kotha, 2006) and pay less attention to
the consequences for the investee firms. This study sheds new light on the significant impact of
CVC on investee firm strategy and joins the nascent literature on CVC firms from the
perspective of the investee (e.g., Katila, Rosenberger, and Eisenhardt, 2008; Park and Steensma,
2012). While prior research extensively examines the effect of shareholders on firm strategy in
the context of large public corporations (e.g., Connelly et al., 2010; David et al., 2001), we know
very little about how different types of investors in the entrepreneurial financing market affect
the strategies of private entrepreneurial firms. As such, this study complements these existing
43
studies by filling this research gap and identifying the underlying mechanisms through which
CVC ownership significantly influences startups’ R&D investment strategy.
Finally, this study has implications for the founder-CEO literature (e.g., Boeker and
Karichalil, 2002; Wasserman, 2003, 2006) by suggesting that founder incumbency can be a
meaningful construct. Previous studies on founders solely focus on founder-CEOs (e.g., Nelson,
2003; Souder et al., 2012) to identify founder effects on firm outcomes. However, a founder’s
effect on a firm might not be limited to periods when the founder is the CEO of the young
entrepreneurial firm. This study shows that considering only founder-CEO as the unit of analysis
can be misleading in the entrepreneurial venture context, as many founders stay with their firm
(unlike CEO succession events in large public corporations) in significant managerial positions
(especially as the CTO) that affect firm R&D investment strategy even after a professional CEO
is brought into the venture.
This study may have practical implications regarding when to retain founders in the event
of a founder-CEO succession (e.g., Boeker and Karichalil, 2002; Wasserman, 2003). In
technology-intensive industries, it may be beneficial to retain the founder as a CTO instead of
forcing the founder to leave if R&D strategy is important because of the technological expertise,
motivation, and craftsmanship stemming from the 'ownership plus' mentality of the founder. In
addition, new ventures developing novel technology standards might want to consider the
technology endorsement effect provided by CVC investors, rather than the standard financial
resources and other ancillary resources provided by IVC firms, while carefully weighing the pros
and cons of CVC investment, especially in light of potential principal-principal conflicts of
interest.
44
2.7 Limitations and future research
This study uses young entrepreneurial firms financed by venture capital that eventually
went public as the sample because of data availability. However, I acknowledge that this sample
is not a representative sample of young entrepreneurial firms. My sample is more of a
homogenous group of 'successful' ventures. The benefit of this sample is that selection bias
concerns that are prevalent in other CVC studies (e.g., Park and Steensma, 2012) are mitigated.
Nonetheless, the generalizability of my results may be diminished, as CVC-funded ventures are
oversampled in my data and we still know little about corporate governance in young
entrepreneurial firms that are not financed by venture capital. Extensive data on private
entrepreneurial ventures can enrich our understanding in future studies.
While I have made appropriate steps to address any endogeneity concerns, this study only
relies on cross-sectional data and focuses on the dynamics of significant stakeholders at the time
of the IPO, which diminishes my ability to make causal interpretations. Future studies can gather
longitudinal data on young entrepreneurial firms and explore the dynamics of corporate
governance in private entrepreneurial firms. In addition, it may be informative to investigate the
effect of corporate governance of entrepreneurial firms on other outcomes, such as firm growth
and innovative outputs, which may reflect the 'productivity' of R&D investment (Aghion et al.,
2013), and the long-term implications of such an effect.
Another avenue for future research is teasing out the mechanisms that I have theorized in
the effect of CVC in driving R&D investment strategy in young entrepreneurial ventures and
examining the relative importance of them. In other words, future scholars can investigate the
relative importance of the direct corporate governance effect, the CVC alliance effect, and the
technology endorsement effect, which I was not able to do because of data limitations.
45
2.8 Conclusion
In conclusion, despite some limitations, I believe that this study significantly improves
our understanding of corporate governance in young entrepreneurial ventures and the effects of
corporate venture capital and founder incumbency on entrepreneurial R&D investment strategy. I
hope this study can serve as a stepping stone for understanding and reconciling the differences in
corporate governance in large public corporations and young entrepreneurial ventures.
46
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53
CHAPTER 3
WHEN DO CORPORATE INVESTORS BENEFIT FROM A PORTFOLIO
COMPANY ’S STRATEGIC ALLIANCE? :
INTERORGANIZATIONAL KNOWLEDGE ACQUISITION PERSPECTIVE
3.1. Introduction
A growing body of literature on corporate venture capital (CVC) investments has
suggested that CVC investments create value for investing firms (e.g., Dushnitsky and Lenox,
2006; Wadhwa and Kotha, 2006). Established firms not only gain financial benefits from CVC
investments, but also take advantage of strategic benefits. Such strategic benefits include an
opportunity to learn novel technologies from entrepreneurial ventures (Dushnitsky and Lenox,
2005a; Wadhwa and Kotha, 2006) and to recognize technological discontinuities (Maula, Keil
and Zahra, 2013). Because established firms often face difficulties in generating ground breaking,
radical innovations (Tushman and Anderson, 1986; Henderson, 1993), many are intentionally
exposed to an unfamiliar business environment in order to prevent rigidity (Matusik and Hill,
1998). Entrepreneurial ventures are a source of new knowledge for investing firms (Dushnitsky
and Lenox, 2005a). Innovative ideas are more likely to be created in young entrepreneurial
ventures. Almeida and Kogut (1997) highlight that startups play an important role in the
exploration of new technological areas. Entrepreneurial innovators who have a promising
business idea and technological capabilities will opt away from fixed salary as an employee in a
corporate research and development (R&D) lab and toward profit sharing as a founding member.
In fact, entrepreneurial, human capital-intensive ventures generate higher level of patenting
output than established firms (Kortum and Lerner, 2000). Accordingly, established firms that
seek novel technologies and/or business ideas use CVC investments as an exploratory process
through which to acquire innovative capabilities from entrepreneurial ventures (Wadhwa and
54
Kotha, 2006). By recombining newly acquired knowledge with existing knowledge (Nelson and
Winter, 1982), established firms that actively invest in entrepreneurial ventures can initiate new
lines of products and services (Keil, Zahra and Maula, 2004) and develop novel technologies that
enable them to sustain competitive advantages in the market.
From the inter-organizational learning perspective, in the CVC investment, the key
interests of a CVC firm
9
that seeks strategic benefits are 1) knowledge acquisition of a portfolio
company and 2) knowledge transfer from the portfolio company to the CVC firm. The knowledge
of a firm is not static but dynamic. For example, a firm can increase its knowledge base through
both internal and external learning. Internal learning can be achieved through in-house R&D and
employee training. External learning can be attained by acquisitions, joint ventures, and hiring
new people (Kogut and Zander, 1992).
Entrepreneurial ventures that are normally resource-constrained extensively engage in the
external learning opportunities to expand their knowledge base. Young firms count on other
firms for much of their technological knowledge (Almeida and Kogut, 1997). Strategic alliances
are one of the most common external learning opportunities. Colombo and his colleagues (2009)
emphasize the consensus among entrepreneurship scholars that strategic alliances are critical to
the success of startups. Studies have shown that strategic alliances at the early stage of a firm
positively affect its performance and survival (e.g., Baum, Calabrase and Silverman, 2000).
Strategic alliances allow young firms to fill the resource and competence gaps in the early stage
(Pisano, 1991; Eisenhardt and Schoonhoven, 1996; Gans and Stern, 2003).
9
Governance structure of corporate venture capital investment varies depending on firms. Some firms have an
independent organization (also called a corporate venture capital program) that is responsible for venture investment
(e.g., Intel capital, Google ventures, Adobe Ventures, TI Ventures, etc.). Other firms allow a business unit to invest
directly in a startup (e.g., Apple strategic group, Hearst Interactive Media, UPS Strategic Group, etc.). In this study,
I do not distinguish CVC firms based on governance structure because both firms explicitly pursue strategic benefits
and even independent CVC subsidiaries closely communicate and cooperate with the parent firms to maximize the
strategic benefits. When CVC strategic benefits are discussed, this means the strategic benefits that are ultimately
helpful to the parent firm.
55
One critical mechanism of this positive effect is knowledge acquisition and creation
through interorganizational collaboration (Shan, Walker and Kogut, 1994; Baum, Calabrase and
Silverman, 2000; Ahuja, 2000; Roathaermel and Deeds, 2004; Park and Steensma, 2012; Diestre
and Rajagopalan, 2012). Through strategic alliances, young firms can access social, technical,
and commercial resources that normally require years of operating experience to acquire (Baum
et al., 2000). Interactions with alliance partners allow entrepreneurial ventures to increase
substantially their knowledge base through inter-organizational learning (Mowery, Oxley and
Silverman, 1996; Almeida, Dokko and Rosenkopf, 2003). In many cases, much of the young
firms’ knowledge is created through strategic alliances with other firms and knowledge
acquisition and exploitation from relational resources helps to explain why some young firms
can survive, thrive and grow despite resource limitation (Yli-Renko, Autio and Sapienza, 2001).
A corporate investor who focuses on strategic benefits wants its portfolio company to
develop capabilities and increase knowledge base in a direction that is beneficial to the business
of the investor. If a strategic initiative that a portfolio company takes distracts the business/
technological direction which its corporate investor wants to explore in the CVC investment, the
initiative can be detrimental to the strategic benefits of the investor. For example, Google
Ventures invested in Urban Engines to explore the big data analytics on transportation. With
expertise in analyzing traffic patterns and flow, the startup provides traffic information for users
so that they can make better decisions about transportation. Its technological capabilities in that
area are the interest of Google. However, if the startup forms alliances with a mobile game
company to diversify its business in the mobile game area that is not what Google is interested in;
this alliance may be beneficial to Urban Engines but undesirable to Google. From the perspective
56
of a strategic investor, there is potential conflict of interest in the strategic alliance of a portfolio
company.
Despite the common phenomenon that established firms invest in entrepreneurial
ventures for the benefit of access to new knowledge
10
and that the ventures extensively engage in
strategic alliances to compensate for resource limitation, we still have incomplete understanding
of how a portfolio company’s strategic alliance (which I refer to as an investee alliance)
11
affects
the strategic benefits of a CVC. While prior studies consistently show strategic alliances lead to
inter-firm learning (e.g., Hamel and Doz, 1989; Gulati, 1995; Inkpen and Crossan, 1995; Doz,
1996; Eisenhardt and Schoonhoven, 1996; Mowery et al., 1996; Stuart and Podolny, 1996;
Almeida et al., 2003), my interest in this study is to understand when knowledge created by
investee alliances adds value to the strategic investors who pay sharp attention to the knowledge
of the investee startup. More specifically, the research question of this study is under what
conditions a corporate investor can benefit from investee alliances which increase the knowledge
base of a portfolio company through interfirm learning.
Drawing upon the inter-organizational knowledge acquisition perspective and the
combinative capabilities perspective, I examine how the commonly shared knowledge base
between an investing firm (CVC) and its portfolio company (entrepreneurial venture) influences
the strategic benefit that is created by an investee alliance and captured by the CVC firm. Kogut
and Zander (1992:391) define combinative capabilities as “the intersection of the capability of
the firm to exploit its knowledge and the unexplored potential of the technology.” CVC firms
attempt to utilize “the unexplored potential” by learning from entrepreneurial ventures in which
10
CVC investments have continuously increased and, according to Global Corporate Venturing, more than 1,100
established firms were running CVC programs in 2014 (Rahal, 2014).
11
I refer to an investee alliance as a strategic alliance formed by its portfolio company from the investor’s
perspective.
57
they invest. When the knowledge base of a venture expands by forming strategic alliances with
other firms, CVC firms can take advantage of the expansion of the knowledge base. However,
firms learn in areas related to their existing business and the learning effect is limited when the
firm moves away from its knowledge base (Kogut and Zander, 1992). Accordingly, the
commonly shared knowledge base between a CVC and its portfolio company will play an
important role in determining the effect of an investee alliance on strategic benefits to the CVC.
I argue that the strategic benefits that a CVC captures from investee alliances are
positively associated with the industry relatedness between the CVC and its portfolio company.
When the industry of a CVC firm and its portfolio company is closely related, common
background knowledge facilitates interorganizational knowledge transfer between a CVC and its
portfolio company and the created knowledge is beneficial to the CVC. Moreover, this effect will
be more salient 1) as a CVC’s absorptive capacity increases and 2) when a CVC and its portfolio
company are located geographically close because the CVC’s absorptive capacity provides the
foundation upon which the CVC learns better from the portfolio company (Dushnitsky and
Lenox 2005a) and proximity allows a CVC to capture more effectively knowledge from its
portfolio company (Funk, 2014). Meanwhile, in the unrelated CVC investment,
12
the strategic
benefits created by an investee alliance are positively associated with the industry relatedness
between the corporate investor and the alliance partner of the portfolio company. I test my
hypotheses on a longitudinal sample of alliances formed by CVC-backed U.S.-based startups
during 1996–2012.
This study makes three important contributions to the literature on CVC. First, by
demonstrating that an investee alliance can increase the market value of an investing firm under
12
Guided by the typology (related acquisition vs. unrelated acquisition) in acquisition literature (e.g., Singh and
Montgomery, 1987; Graham, Lemmon and Wolf, 2002), I refer to unrelated CVC investment as the cases that a
CVC invests in a venture in a different industry from the CVC’s industry.
58
certain conditions, I expand the scope of sources for strategic benefits in CVC investments.
Corporate investors can benefit from not only the existing knowledge of a portfolio company but
also knowledge which is newly created by the strategic actions of the portfolio company. Second,
I identify conditions under which a CVC firm can facilitate knowledge acquisition from its
portfolio company. Consistent with other interorganizational learning, this study shows evidence
that the knowledge transfer between a CVC and a startup is influenced by common knowledge,
the absorptive capacity of a knowledge receiver, and geographic proximity. Finally, this study
identifies conditions under which a CVC can benefit from an investee alliance in the context of
unrelated CVC investment where a CVC and a startup do not share common knowledge. In this
case, the common knowledge between a CVC and an alliance partner of a portfolio company
makes the newly formed strategic alliance beneficial to the CVC.
3.2 Theory and hypotheses
Strategic alliances provide firms with a unique opportunity to leverage their strengths
with the help of partners (Inkpen, 1996). Firms can achieve competitive advantages through
strategic alliances which enable them to gain market access (Kogut, 1991), achieve economies of
scale (Doz and Hamel, 1998) enhance legitimacy (Baum and Oliver, 1991) and develop
competence through collaboration (Larsson, Bengtsson, Henriksson and Sparks, 1998). Among
these benefits, knowledge created through having a window on partners’ capabilities and other
relevant competences is one of the most valuable benefits because this knowledge can be useful
not only for that alliance-specific project/task but also for other relevant work in the future.
Through the “grafting” process (Huber, 1991), a firm internalizes knowledge not previously
available within the firm (Inkpen, 1996). New knowledge created in this process can be a source
59
of competitive advantages for the firm (Dyer and Singh, 1998). Simonin (1999) suggested that
strategic alliances are the most adequate vehicle for internalizing the other firm’s competency.
The benefits of strategic alliance can be applied to young entrepreneurial ventures as well.
In particular, young entrepreneurial ventures can mitigate the liability of newness (Freeman,
Carroll and Hannan, 1983) through relationships with key business partners because the
knowledge that partners provide will tend to compensate for the disadvantages of organizational
inexperience (Baum, Calabrese and Silverman, 2000). For example, Shan, Walker and Kogut
(1994) found that there was a positive association between interfirm cooperation and the
innovative outputs of startup firms in the biotechnology industry. Cooperation between small
startups and large established firms allows the participants to exploit technological spillovers and
to transfer resources for product commercialization. Strategic alliances create knowledge which
would not have been created without cooperation. Further, Stuart (2000) found that young and
small firms benefit more from strategic alliance partners than old and large firms. An alliance is
a way to build relationships with firms in the network which allows startups to be exposed to
information on markets and technologies.
From a CVC’s perspective, knowledge creation in a portfolio company can be a source of
strategic benefits. A CVC makes investments in young entrepreneurial ventures to track
technologies, and creates “real options” on new technology or business directions for the CVC
parent corporation (MacMillan, Roberts, Livada and Wang, 2008). A CVC’s strategic goals
include exposure to new markets and technologies and market extension possibilities (Siegel,
Siegel and MacMillan, 1988). From the vicarious learning perspective, by observing the
innovative activities of portfolio companies and their outcomes, a CVC can use the portfolio
companies’ experience as a guide for future solution search (Yang, Phelps and Steensma, 2010).
60
This interfirm learning is possible because often a portfolio company and operating units of a
CVC parent corporation collaborate. A survey conducted by the National Venture Capital
Association (NVCA) in 2007 documented that more than two-fifths of CVC firms reported they
were engaged in four or more collaborations with portfolio companies and that about two-fifth of
CVCs report 1 to 3 collaborations (MacMillan, Roberts, Livada and Wang, 2008). Through R&D
collaborations with portfolio companies and commercial collaborations for licensing or sales, a
CVC parent corporation can learn from the new knowledge created by investee alliances.
In addition to this, another important goal of CVC investments is to explore new business
direction. The strategic alliances of entrepreneurial ventures are closely related to the future
directions of the ventures because the contents of alliances will reflect a firm’s strategic
directions such as to which geographical market (including international markets) the firm enters,
which technology the firm drives as a core technology, where the firm procures raw materials,
and so on. Because young entrepreneurial ventures are mostly private firms until they go public,
detailed information on strategic directions is not publicly available. By interacting with people –
founders, key executives, engineers, and researchers – in a portfolio company, a CVC can learn
about the entrepreneurial initiatives of a portfolio company. Interactions can occur during board
meetings, a CVC manager’s on-site visits, and even conversations over the phone or by email.
With the indirect experiences, a CVC can identify potentially valuable knowledge components
and combinations, avoid detrimental elements and combinations, and gain insight into the
organizational routines that led to the creation of the innovation (Sorenson, Rivkin and Fleming,
2006; Yang et al., 2010).
By closely observing the alliance experience of its portfolio company, a corporate
investor may come up with a synergetic business idea that can extend its current core business.
61
For instance, Amazon.com, a dominant online bookseller, invested in a startup, Drugstore.com,
an online retailer in health and beauty products in 1999. Amazon.com was looking for the
opportunity to move beyond its print, music, and video market into a far larger prescription drug
market. Later that year, Drugstore.com formed a strategic alliance with Healtheon, which tried to
streamline communication and paperwork in the U.S. health care system. The alliance of
Drugstore.com helped Amazon.com better understand the prescription market and stimulated its
new business opportunities. Based on its competitive advantage in the regular deliveries of
necessities at specified internals, Amazon.com seriously considers entering the prescription drug
market soon and industry experts expect that it can disrupt the prescription-drug market and take
on the FDA to drive change-related regulations (Diamond, 2014). Knowledge created by an
investee alliance may flow to a corporate investor through CVC investment ties, and it may help
the corporate investor by reducing search cost in the relevant areas.
3.2.1 Industry relatedness and strategic benefits
Strategic alliances lead to interfirm learning (Hamel and Doz, 1989; Gulati, 1995; Inkpen
and Crossan, 1995; Doz, 1996; Eisenhardt and Schoonhoven, 1996; Mowery et al., 1996; Stuart
and Podolny, 1996; Almeida et al., 2003) and knowledge is created through the learning process
(Lane and Lubatkin, 1998). Then, the question is when the newly created knowledge is
beneficial to a CVC firm in terms of strategic value. I focus on the industry relatedness between
a corporate investor and its portfolio company.
13
Industry relatedness between a corporate
investor and its portfolio company concerns the value and transferability of the knowledge
13
In this study, as discussed earlier, I consider a CVC firm a part of the corporation that runs a CVC investment
program. Therefore, CVC industry means the industry that the parent corporation is affiliated with. For instance, the
industry of Intel Capital is the semiconductors and related devices (SIC 3674), not venture capital financing industry.
Likewise, by the industry relatedness between a CVC and its portfolio company, I mean the industry relatedness
between a CVC parent corporation and a portfolio company.
62
created by an investee alliance in several aspects. First, some level of common background
knowledge is necessary in interorganizational knowledge transfer (Simonin, 1999). A firm's
knowledge includes both easily communicated articulable knowledge and tacit knowledge which
is difficult to convey due to interconnections with other aspect of the firm such as its processes
and social context. Although articulable knowledge is relatively easily transferable, the effective
acquisition of tacit knowledge from an external source requires a firm to have relevant schema in
the field. Cohen and Levinthal (1990) argue that a firm that exploits external knowledge must
possess some prior related knowledge, which includes basic skills, a shared language, and
awareness of recent technological developments in a given field. Firms operating in the related
industry share a common competence base because they use similar technologies, serve
comparable customers, and provide related products and services (Dussauge, Garrette and
Mitchell, 2000). Having the relevant basic knowledge
14
increases the transferability of new
knowledge created by investee alliances and allows a CVC to absorb better the new knowledge.
Industry relatedness between a CVC and its portfolio company guarantees that two firms have
enough relevant basic knowledge, leading to a higher level of absorptive capacity (Cohen and
Levinthal, 1990). On the other hand, significant differentials in the knowledge base and skills
between firms prevent interfirm learning (Simonin, 1999). Hamel (1991:97) highlights “If the
skills gap between partners is too great, learning becomes almost impossible.” If the primary
business of a CVC is not related to that of a portfolio company, the lack of understanding in the
business of a portfolio company requires a CVC to make additional effort to absorb knowledge
created by the investee alliance.
14
Lane and Lubatkin (1998:464) refer to basic knowledge as “a general understanding of the traditions and
techniques upon which a discipline is based.”
63
Second, as the CVC industry and the portfolio company’s industry are more closely
related, a CVC and its portfolio company may interact with each other more frequently.
Entrepreneurial ventures face a strong challenge in securing complementary assets that are
necessary for their technology commercialization (Park and Steensma, 2012). To help a portfolio
company overcome this challenge, CVC parent firms often “provide startups with technological
and R&D support, product development assistance, manufacturing capacities and access to
marketing and distribution channels (Ivanov and Xie, 2010:133).” However, complementary
assets vary in their specificity (Teece, 1986; Park and Steensma, 2012). Depending on the degree
of relatedness of complementary assets, ventures can take advantage of the complementary
assets to a varying degree. As CVC industry and a portfolio company’s industry are more related,
complementary assets that the CVC possesses are more likely to be beneficial to the business of
the portfolio company. That is, industry relatedness increases the interdependence of knowledge
between a CVC and a portfolio company. In turn, it becomes more likely the investing firm and
the portfolio company will have frequent interactions. The frequent interactions can facilitate
knowledge transfer in both directions. On the other hand, if a CVC and its portfolio company are
in unrelated industries, it becomes more difficult for them to share their resources and
capabilities than when they are in a related industry. Thus, it becomes less likely a CVC and a
portfolio company will have frequent interactions.
Finally, the applicability of knowledge created by an investee alliance increases in the
CVC firm as the CVC industry and the portfolio company’s industry are more related. Resource
relatedness increases the applicability of resources across businesses because redeployability
15
of
related resources allows the resource to be useful in the related context (Sakhartov and Folta,
15
Sakhartov and Folta (2014: 1783) suggest that “redeployability identifies the extent to which resources are useful
across a particular pair of businesses.”
64
2014). Likewise, newly created knowledge is more useful in terms of the broad applicability of
knowledge when it is related to the CVC’s core business. Although an investee alliance creates
significant knowledge, the value of the knowledge is limited for the CVC unless the knowledge
can be applied to its business and capabilities. In sum, industry relatedness between a CVC and
its portfolio company increases the transferability, interdependence and applicability of
knowledge created by investee alliances. Therefore, I propose:
Hypothesis 1: Industry relatedness between a CVC and its portfolio company is
positively associated with the CVC’s strategic benefits from the investee alliance.
3.2.2 Moderating effect: Absorptive capacity
CVC firms and the operating units of CVC parent corporations may differ in their
absorptive capacity which is a firm’s ability to recognize the value of new, external knowledge,
assimilate it, and apply it commercially (Cohen and Levinthal, 1990). Absorptive capacity is one
of the most central concepts in the literature on interfirm knowledge transfer and learning
(Schildt, Keil and Maula, 2012). As Cohen and Levinthal (1990) elaborate, absorptive capacity
develops cumulatively and builds on prior related knowledge. Firms that possess relevant prior
knowledge in a given research domain are more likely to exhibit higher levels of knowledge
absorption from external sources (Pisano, 1991). To generate valuable outputs from newly
acquired knowledge from external sources, firms need the capacity to identify the value of
knowledge, absorb/internalize the knowledge, and use the knowledge in a way that is beneficial
to their own business.
65
Similar to the general interfirm knowledge transfer, a CVC firm’s absorptive capacity
will facilitate interfirm learning from a portfolio company to the CVC firm. Dushnitsky and
Lenox (2005b) found that the degree to which a CVC firm learns from its CVC investments
increases as the firm’s absorptive capacity increases. They highlight “the ability of an investing
firm to transfer or create knowledge through its interaction with a venture likely requires a firm
to have sufficient technical understanding to both grasp and capitalize on that knowledge”
(Dushnitsky and Lenox, 2005b: 620). In the context of investee alliances, a CVC firm’s
absorptive capacity facilitates knowledge transfer from a portfolio company to the CVC, too. A
CVC may be unaccustomed to the newly created knowledge by investee alliances when a
portfolio company attempts to expand the boundaries of existing capabilities through a strategic
alliance. The CVC investment per se is an explorative effort for a CVC since CVCs mainly use
CVC investments as an explorative instrument to search for entrepreneurial activities in the
market (Dushnitsky and Lenox, 2005a). Then, a portfolio company’s effort to expand its
competence by engaging in the creation of new knowledge is something unfamiliar to a CVC.
When knowledge that is newly created by investee alliances is explorative from the CVC
perspective, the CVC’s absorptive capacity becomes more important. Cohen and Levinthal (1990)
argue that a firm’s absorptive capacity permits the firm to recognize the value of the outside
knowledge, assimilate, and exploit it when the outside knowledge is less targeted to the firm’s
particular needs and concerns. Greater absorptive capacity allows a CVC to recognize better the
value of knowledge in the unfamiliar, explorative contexts such as investee alliances. Therefore:
66
Hypothesis 2: The effect of industry relatedness between a CVC and its portfolio
company on the CVC’s strategic benefits of the investee alliance is positively
moderated by the CVC’s absorptive capacity.
3.2.3 Moderating effect: Geographic proximity
Geographic proximity plays a significant role in knowledge flow (Audretsch and
Feldman, 1996; Morgan, 2004). Several factors support this view. First of all, proximity enables
firms to capture large volumes of knowledge through spillovers from nearby firms (Funk, 2014).
Second, valuable knowledge is often embedded in people, organizational routines, and social
relations (Argote and Ingram, 2000; Granovetter, 1985). Due to the embeddedness, inter-
organizational knowledge transfer is often viewed as a social process and thus the inter-firm or
inter-personal relations and communications are important parts in interorganizational
knowledge transfer (Kogut and Zander, 1992) and knowledge tends to be localized. However,
such interactions between organizations or persons demand a close proximity (Haveman and
Rider, 2013; Saxenian, 1990; Zucker et al, 1998). Physically distant parties are difficult to be
engaged in social processes relative to physically close parties. In addition, proximity influences
the recipient firm’s ability to assess the knowledge characteristics with greater accuracy, to
evaluate the condition of the knowledge source in a timely manner, and to build and manage
linkages to the source.
Furthermore, effective external learning can be limited geographically (Jaffe, Trajtenberg
and Henderson, 1993) and firms also tend to identify and absorb external knowledge within their
geographic proximity (Wagner, Hoisl and Thoma, 2013). Empirical studies have shown that
knowledge moves more slowly across boundaries, whether national (Kogut, 1991) or regional
67
(Almeida and Kogut, 1999), and knowledge spillover tends to be localized within boundaries
(Jaffe, 1989). For example, Jaffe and his colleagues (1993) found that inventors are more likely
to cite other inventors who are geographically proximate.
There are two major reasons why geographic proximity between a CVC and its portfolio
company may affect the extent to which the CVC absorbs knowledge created by investee
alliances. First, knowledge created by investee alliances is likely regional-specific. For example,
in the renewable energy-related industry, useful knowledge is often embedded in the institutional
environment such as the state policy and regulations. Strategic alliances formed by an
entrepreneurial venture likely aim to utilize advantages (e.g., government subsidies) or overcome
disadvantages (e.g., low level of demand) which are related to the embeddedness. Strategic
alliances enable a venture to access resources which are not available in the specific area.
Alternatively, the venture uses its resources related to the location to attract an alliance partner
who can create synergetic value from the resources. When a CVC is closely located near its
portfolio company, it can better understand the nuanced, tacit knowledge embedded in the
region-specific institutional environment.
Second, geographical proximity facilitates the most effective communication, in-person
meetings. As mentioned earlier, both a CVC investment manager and people in the operating
units of the CVC parent corporation often interact with people in its portfolio company
(MacMillan et al., 2008). In addition to the formal channels – such as board meetings and regular
meetings – through which a portfolio company reports the progress of its business to its major
investor, frequent informal conversations occur. Both voice and video conference calls and
emails are often used to communicate. However, CVC investment managers often visit their
portfolio company or bring a top manager of the portfolio company to their office to
68
communicate in person. Despite the advanced communication technology, in-person meetings
are still more important in knowledge transfer between a CVC and its portfolio company than
other remote communication methods. When a CVC investment manager and a top manager of a
portfolio company remotely communicate, tacit and nuanced knowledge and information may
not be effectively transferred because remote communications often focus only on planned
agenda. However, through in-person meetings, a CVC investment manager can better understand
the business progress of its portfolio company with additional cues which are not available in the
remote communications. Therefore, I propose:
Hypothesis 3: The effect of industry relatedness between a CVC and its portfolio
company on the CVC’s strategic benefits from the investee alliance is positively
moderated by the geographic proximity between them.
3.2.4 Cases of unrelated CVC investments: Industry relatedness with alliance partner
So far, I have discussed how industry relatedness between a CVC and its portfolio
company can affect the CVC’s strategic benefit derived from an investee alliance. In sum, I
suggested that shared common background knowledge plays an important role in the knowledge
transfer between a CVC and its portfolio company. Therefore, the question is how investee
alliances affect the CVC’s strategic benefits when the CVC and the portfolio company are not
related. CVCs that invest in ventures in the unrelated industry mostly expect to explore new
areas and to gain resources for complementing their businesses. For example, mobile device
manufacturers invest in startups developing application software and solar panel manufacturers
invest in startups developing a storage battery to utilize knowledge from the investment. A CVC
69
wants its portfolio company to focus on technology which is relevant to the CVC core business.
If the strategic direction of the portfolio company diverges from the CVC’s core business, the
strategic benefits will diminish.
From the CVC perspective, the benefit of investee alliances depends on the degree of
relevance of newly generated knowledge in investee alliances to the core business of the CVC. I
argue that the degree is a function of the relatedness between a CVC and the alliance partner.
This argument is illustrated in a brief, hypothetical model in Figure 1.
Figure 3.1 Knowledge overlap between CVC, portfolio company, and alliance partner
Each box represents knowledge base areas that a firm possesses. In both (a) and (b) cases, a CVC
has invested in an unrelated (i.e. different) industry. Therefore, there is no knowledge base
overlap between a CVC and its portfolio company in both cases. In case (a), the CVC industry
and an alliance partner’s industry are related. Therefore, two firms have common knowledge
base X. However, in case (b), the CVC industry and an alliance partner’s industry are unrelated
70
and their knowledge base does not overlap. When a portfolio company and an alliance partner
form a strategic alliance, the area of newly generated knowledge will include a part of the
portfolio company’s knowledge base area and a part of the alliance partner’s knowledge base
area. Thus, a part of the newly generated knowledge (Y) overlaps a part of the CVC’s knowledge
base area. I argue that this overlapping knowledge base is more beneficial to the CVC compared
with case (b) where newly generated knowledge by the alliance does not overlap the CVC’s
knowledge base.
The industry relatedness between a CVC and the alliance partner is advantageous in
terms of the easiness of the CVC’s knowledge acquisition. Because the CVC has a minimum
level of foundational background knowledge on the business of the alliance partner, through
observing the alliance, the CVC is able to learn more quickly how its industry competitor
explores entrepreneurial opportunities via a strategic alliance with an entrepreneurial venture. As
a window on new business and technology (Benson and Ziedonis, 2009), a portfolio company’s
strategic alliance with an industry competitor can provide a CVC with valuable information.
Therefore, I propose:
Hypothesis 4: When a CVC invests in a startup in an unrelated industry, industry
relatedness between a CVC and a portfolio company’s alliance partner is positively
associated with the CVC’s strategic benefits from the investee alliance.
3.3 Methods
Measuring strategic benefits created by an investee alliance is challenging because any
measure of strategic benefits may be influenced by other confounding factors. To minimize
71
confounding effects and focus on incremental value creation of a strategic action (i.e., an event),
prior research used event studies in contexts such as alliance formations and acquisitions (e.g.,
Anand and Khanna, 2000; Kale, Dyer and Singh, 2002; Gaur, Malhotra and Zhu, 2013).
Similarly, I use event studies of alliance announcements by assuming that the stock market
reaction to a CVC’s portfolio company announcement of an alliance formation evaluates and
reflects the CVC’s strategic benefits from the investee alliance. Since the ultimate effect of
strategic benefits to a CVC firm is to increase firm value (Dushnitsky and Lenox, 2006),
abnormal returns can be seen as a possible strategic benefit measure.
Using cumulative abnormal returns (CARs) as a proxy measure for strategic benefits has
advantages relative to alternative measures in several ways. First, strategic benefits generated by
investee alliances are a comprehensive concept so that a specific outcome variable, such as the
number of patents, does not accurately capture the concept. Second, when there is time lag
between the alliance formation and other outcomes, the alternative measure may be biased by
other confounding effects. Therefore, market reactions to the announcement of a portfolio
company’s alliance formation may reasonably measure the expected value creation generated by
the alliance. Prior studies have extensively used event study methodology to measure alliance
performance and economic value created by alliances (e.g. Das, Sen and Sengupta, 1998; Anand
and Khanna, 2000; Kale et al., 2002). Moreover, this method has recently been used by
researchers to examine the effects of extended network ties on the existing constituents or their
rivals (Wassmer and Dussauge, 2011; Oxely et al., 2009).
72
3.3.1 Sample and data sources
Testing the hypotheses requires three kinds of data: CVC investment records, portfolio
companies’ alliance announcement records, and the stock prices of CVC parents’ firms around a
portfolio company’s alliance announcement. I collected the data from VentureXpert, Securities
Data Company (SDC) Database, and Eventus, respectively. My empirical setting encompasses
all the alliances formed by CVC-backed U.S startups
16
during the period 1996–2012 across
industries. Because I use event studies, I include CVCs whose parent firms are publicly traded in
the US stock market.
From the VentureXpert database, I first collected information about CVC investments in
startups during the period 1996–2005. This consists of 791 CVCs invested in 4,797 startups.
Among them, I included CVC investments by public firms only because I measure the strategic
benefits by stock market reactions and stock prices are available only for public firms. I then
obtained data on strategic alliances formed by these startups during the period 1996–2012. This
process yielded 1,266 alliances formed by 354 startups with 860 partners, meaning that 120
CVCs had to be included in my sample. In the case of alliances with multiple participants, I
regarded each pair of participants as an individual alliance. Following the approach of previous
studies (Oxely et al., 2009; Wassmer and Dussauge, 2011), I excluded the alliance cases in
which a startup forms a strategic alliance with multiple partners from different industries, in
order to avoid the possibility of confounding effects that may lead to an abnormal return for
CVCs, but that are unrelated to the event of my interest. For instance, in 1997, Cisco Systems
(SIC 3576) invested in TIBCO, a provider of infrastructure software for the purpose of
development of an open standard for delivering subscriber-sensitive network capabilities. Later,
in 1999, TIBCO software formed a strategic alliance in which multiple partners participated
16
The startups in my sample were founded between 1996 and 2005.
73
simultaneously. The partners included Infosys (SIC 7376), Reuters Group (SIC 7383), American
Express (SIC 6141), and Sun Microsystems (SIC 3577). In this case, the effects of an alliance
between TIBCO and one of the participants may contaminate the effects of alliances between
TIBCO and others because participants are involved in different industries. Therefore, I excluded
cases in which a portfolio company forms an alliance with multiple partners whose SIC codes
are not all the same. This process reduced the size of my alliance sample to 981.
My empirical unit of analysis is at the strategic alliance announcement event. I use
standard event study methodology to estimate the stock market’s assessment of the value of a
CVC’s strategic benefits created by alliance formation between a portfolio company and its
alliance partner. I investigate the cumulative abnormal returns (CARs) of CVCs as a function of
the characteristics of alliances formed by CVCs’ portfolio companies. Specifically, I examine the
impact of 1) the industry relatedness between a CVC and its portfolio company and 2) the
industry relatedness between a CVC and the alliance partner on the CVC’s strategic benefit. I
then estimate the moderating effects of industry relatedness between a CVC and its portfolio
company with absorptive capacity and geographical closeness, respectively.
3.3.2 Measures
Dependent variable
I used the cumulative abnormal return (CAR) of a CVC at the time of alliance
announcement as my dependent variable in order to capture the CVC’s strategic benefits created
by investee alliance. Consistent with previous studies (e.g., Oxely et al., 2009), I used a five-day
window [-2, +2], where event day 0 is the alliance announcement date. I used the CRSP equal-
weighted return as the market responses and estimated the market model parameters over the
200-day period from event day -210 to event day -11 (Masulis, Wang and Xie, 2007).
74
Independent variables
Industry overlap. To capture industry relatedness, I draw on the industry affiliation of the
CVC and its portfolio company by using Standard Industry Codes (SIC) codes. Industry
relatedness based on SIC codes has been widely used in strategic management literature to
examine a variety of firm strategies such as alliances, acquisitions, and diversification (Robins
and Wiersema, 1995; Finkelstein and Haleblian, 2002; Villalonga and McGahan, 2005; Shildt et
al, 2005). Following prior studies (e.g., Yang, Narayanan and De Carolis, 2014), I constructed
the variable industry relatedness based on the primary SIC of a CVC and its portfolio company.
For the CVCs’ SIC codes, I collected data from COMPUSTAT. For the portfolio companies’
SIC codes, I used the information provided from the SDC alliance database. A value of 4 was
assigned when the corporate investor and the portfolio company had the same four-digit SIC
codes, a value of 3 when the first three digits were the same, a value of 2 when the first two
digits were the same, and a value of 1 when the first digit was the same. If there was no overlap,
a value of 0 was assigned. This measure is used to test the industry relatedness between a CVC
and the alliance partner of its portfolio company (H4) in the same way.
CVC absorptive capacity. A firm's absorptive capacity for learning from its partner
depends on its endowment of relevant technology-based capabilities upon entering the
relationship. R&D investment is a necessary condition for the creation of absorptive capacity
even though not necessarily sufficient. Cohen and Levinthal (1990)'s original test and the
subsequent examination of absorptive capacity by Gambardella (1992) use R&D intensity as a
proxy for absorptive capacity. Following prior research, to measure absorptive capacity, I use
R&D intensity, calculated by a CVC’s annual research and development expenditure divided by
its total annual revenues in the year of alliance announcements. R&D expenditure has widely
75
been used as a proxy of a firm’s absorptive capacity (e.g. Cohen and Levinthal, 1990;
Dushnitsky and Lenox, 2005a).
CVC-startup distance. To measure geographic proximity between a CVC and its portfolio
company, I use the distance between a CVC and its portfolio company. I calculate the physical
distance by miles based on the cities where firm headquarters are located. Natural logged value is
used for normalization.
Control variables
I control for additional factors that may affect the stock prices of CVCs around alliance
announcements. First, at the CVC firm-level, I include a firm-size control variable measured by
the natural log of total assets. It is important to control for firm-size in the event studies because
firm size is related to the degree of information asymmetry between insiders and the capital
markets (Strebulaev and Kurshev, 2006).
17
As a control for CVCs’ financial performance, I
include return on assets (ROA) variable calculated by net income divided by total assets. I
control for the growth rate of CVC firms calculated by increased revenue at t divided by revenue
at t-1. Also, I control for the financial characteristics of the CVC firms: book-to-market ratio,
debt-to-assets ratio, cash-to-assets ratio, and the total market value. These measures represent
valuations of the CVC’s assets, leverage in their capital structure, financial resources, and
operational efficiency and are controls used in prior studies that also used cumulative abnormal
returns as the dependent variable (Gaur, Malhotra and Zhu, 2013).
Alliance characteristics may affect the stock prices of CVCs around alliance
announcements. I control for the number of participants. The number of participants can have
mixed effects on an alliance: alliances with many different participants may have more
17
Strebulaev and Kurshev (2006) argue that the degree of information asymmetry between insiders and the capital
markets is lower for larger firms, for example, because they face more scrutiny by ever-suspicious investors.
76
difficulties in managing alliance projects, whereas the resources from which participating firms
can benefit are extended. I include technology transfer dummy variable which has a value of 1
when the alliance involves technology transfer between participants. I control for the investment
duration of the CVC which is calculated by subtracting the first CVC investment year from the
alliance announcement year. Lastly, I control year and CVC industry fixed effects.
3.4 Results
Table 1 presents the means, standard deviations, and pairwise correlations for variables
used in this study. Because the magnitude of correlations among the independent variables is
relatively small, the table suggests that correlation between two variables is unlikely to bias the
regression coefficients. Moreover, variance inflation factors were considerably below the
suggested cut-off of 10.
18
Therefore, multicollinearity was not an issue in the analysis.
Table 2 provides univariate analysis that compares the CAR by CVC-startup industry
relatedness. The mean of CAR is -0.0018 which is very close to zero. I compare two cases: when
CVC and startup industries are related vs. unrelated. When the CVC and startup industries are
related, the mean of CAR is 0.0073.However, when they are not related, the mean of CAR is -
0.0046. The difference between means is 0.0119 and the t-test shows that the two means are
statistically different (p<0.05).
Table 3 reports the results of the ordinary least squares (OLS) regression analysis for
CAR [-2, 2]. Model 1 is the baseline controls only model. Model 2 reports the test of hypothesis
1 for CAR to a CVC in the case that the CVC and its portfolio company have industry
18
The largest value of VIF is 3.12.
77
78
Table 3.2 Cumulative abnormal returns contingent on industry relatedness
CVC-Startup
Industry relatedness
ij
>0
CVC-Startup
Industry relatedness
ij
=0
Total Difference
Mean of
CAR[-2,2]
.0073 -.0046 -.0018 .0119*
n 235 746 981
relatedness. The coefficient of industry relatedness between the CVC and its portfolio company
is positive and statistically significant (β = 0.005, p < 0.05). Thus, Hypothesis 1 is supported.
Models 3 and 4 present the positive moderating effects of absorptive capacity and
geographical proximity between a CVC and its portfolio company on the CVC’s CAR,
respectively.
Hypothesis 2 predicted that a CVC’s absorptive capacity would positively moderate the
effects of the industry relatedness between a CVC and its portfolio company on the stock market
return of the CVC. As Model 3 shows, the coefficient of the interaction term between the two
variables is positive and statistically significant (β = 0.010, p < 0.05). Hypothesis 2 is supported.
In Model 4, interaction between CVC-Startup industry relatedness and CVC-Startup geographic
distance is statistically significant (β = -0.002, p < 0.05) in explaining CAR and is negatively
associated. Because the decrease of distance between two firms means they are located closely,
this result supports my Hypothesis 3. Model 5 includes both of the interaction terms in model 3
and 4, and the coefficients of the variables remains statistically significant. Finally, Model 6
shows that the coefficient for industry relatedness between a CVC and its portfolio company’s
alliance partner is positive and statistically significant (β = 0.004, p < 0.05), which is consistent
with my expectation in Hypothesis 4. For this model, the number of observations is 665 because
this model uses the sub-sample which includes only the cases of unrelated CVC investments.
79
Table 3.3 Regression results: dependent variable = CAR [-2,2]
(1) (2) (3) (4) (5) (6)
Variables Full-sample Sub-sample
CVC-Startup Relatedness 0.005* 0.004* 0.004* 0.004*
(0.002) (0.002) (0.002) (0.002)
CVC-Startup Relatedness 0.010** 0.008*
× CVC Absorptive capacity (0.004) (0.004)
CVC-Startup Relatedness -0.002* -0.002*
× CVC-Startup Distance (0.001) (0.001)
CVC-Partner Relatedness 0.002 0.001 0.001 0.001 0.001 0.004*
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
CVC-Startup Distance -0.001 -0.001 -0.001 0.000 0.000 0.001
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
CVC absorptive capacity 0.011 0.009 -0.018 0.008 -0.013 -0.005
(0.015) (0.014) (0.015) (0.013) (0.015) (0.015)
CVC firm size 0.005 0.005 0.005 0.007 0.006 0.009
(0.005) (0.005) (0.005) (0.005) (0.005) (0.009)
CVC performance 0.010 0.009 0.008 0.007 0.007 0.007
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
CVC growth rate 0.001 0.001 0.001 0.001 0.001 -0.007
(0.008) (0.008) (0.008) (0.008) (0.008) (0.009)
CVC’s book to market ratio -0.001 -0.002 -0.001 -0.002 -0.002 -0.002
(0.002) (0.002) (0.002) (0.002) (0.002) (0.003)
CVC’s debt to assets ratio 0.035* 0.034* 0.031* 0.029* 0.027† 0.016
(0.014) (0.014) (0.014) (0.014) (0.014) (0.018)
CVC’s cash to assets ratio 0.005 0.002 0.004 -0.012 -0.010 -0.016
(0.040) (0.039) (0.039) (0.039) (0.040) (0.056)
CVC’s market value -0.002 -0.003 -0.002 -0.004 -0.003 -0.001
(0.005) (0.005) (0.005) (0.005) (0.005) (0.006)
Number of alliance participants 0.032 0.036 0.037 0.041 0.041 0.045
(0.034) (0.034) (0.034) (0.034) (0.034) (0.034)
Technology transfer 0.008† 0.008 0.008 0.008 0.008 0.006
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)
Investment duration 0.001 0.000 0.000 0.000 0.000 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.002)
Industry fixed effect Incl. Incl. Incl. Incl. Incl. Incl.
Year fixed effect Incl. Incl. Incl. Incl. Incl. Incl.
Constant -0.075 -0.080 -0.081 -0.100 -0.099 -0.148
(0.080) (0.081) (0.080) (0.082) (0.081) (0.092)
N 981 981 981 981 981 665
R-square .042 .046 .049 .052 .053 .076
Standard errors in parentheses
† p<.10, * p<.05, ** p<.01, *** p<.001
80
Table 3.4 reports robustness checks by using different windows, [-1,1] and [-3,3]. As
presented, overall results are consistent across alternative windows.
3.5 Discussion and conclusion
In an interview with a journalist, Bill Gates confessed that what he worried about was not
any incumbent competitors such as Apple and IBM, but some unknown guys in a garage,
inventing a new technology Microsoft has never thought about (Bloomberg, 2011). In
technology-intensive industries, often innovative startups introduce a new business idea and
completely change the rules of the game in the existing market. Due to the limitation of the
ability to produce knowledge solely through internal development (Hagedoorn, 1993), many
firms seek out new technology from external sources. CVC investments constitute one of the
viable and significant sources. Previous studies have identified the value created by CVC
investments technologically as well as financially (e.g., Wadhwa and Kotha, 2006; Dushinitsky
and Lenox, 2005a). CVC investments provide an effective means of scanning the market for
novel technologies that can threaten and complement the existing businesses of incumbent firms
(Dushinitsky and Lenox, 2006).
Interestingly, as organizational theory literature describes, firms are not entirely
independent entities, but open systems that vigorously interact with their surrounding
environment including other firms (Scott and Davis, 2007). Interactions with other firms are an
important source of learning. Building on the interorganizational knowledge transfer perspective,
I extend CVC literature on strategic benefits of CVC investments by exploring conditions under
which investee alliances can create value for the CVC. Knowledge that a startup possesses is not
static within the firm, but evolves as the startup takes strategic competitive actions such as
81
Table 3.4 Regression results: dependent variable = CAR [-1,1] and CAR [-3,3]
(1) (2) (3) (4)
Variables CAR [-1,1] CAR [-3,3]
CVC-Startup Relatedness 0.004* 0.003* 0.004† 0.004†
(0.002) (0.002) (0.002) (0.002)
CVC-Startup Relatedness × CVC Absorptive capacity -0.001† -0.001*
(0.001) (0.001)
CVC-Startup Relatedness × CVC-Startup Distance 0.003 0.009*
(0.004) (0.004)
CVC-Partner Relatedness 0.000 0.000 0.001 0.001
(0.001) (0.001) (0.002) (0.002)
CVC-Startup Distance -0.001 0.000 -0.000 0.001
(0.001) (0.001) (0.001) (0.001)
CVC absorptive capacity 0.004 -0.005 0.020 -0.005
(0.010) (0.017) (0.016) (0.019)
CVC firm size 0.007 0.008 0.006 0.007
(0.006) (0.006) (0.007) (0.007)
CVC performance 0.004 0.003 0.005 0.003
(0.013) (0.013) (0.013) (0.013)
CVC growth rate 0.001 0.001 0.003 0.003
(0.006) (0.006) (0.006) (0.006)
CVC’s book to market ratio -0.000 -0.000 -0.002 -0.002
(0.002) (0.002) (0.004) (0.004)
CVC’s debt to assets ratio 0.021 0.017 0.027 0.021
(0.016) (0.017) (0.017) (0.017)
CVC’s cash to assets ratio 0.001 -0.007 -0.003 -0.014
(0.041) (0.041) (0.048) (0.047)
CVC’s market value -0.002 -0.003 -0.003 -0.003
(0.005) (0.006) (0.006) (0.006)
Number of alliance participants 0.005 0.008 0.054 0.059
(0.019) (0.019) (0.036) (0.035)
Technology transfer 0.005 0.005 0.009† 0.008†
(0.004) (0.004) (0.005) (0.005)
Investment duration 0.001 0.001 0.003† 0.003†
(0.001) (0.001) (0.002) (0.002)
Industry fixed effect Incl. Incl. Incl. Incl.
Year fixed effect Incl. Incl. Incl. Incl.
Constant -0.036 -0.049 -0.158† -0.180*
(0.054) (0.054) (0.090) (0.090)
N 981 981 981 981
R-square .037 .040 .060 .066
Standard errors in parentheses
† p<.10, * p<.05, ** p<.01, *** p<.001
82
forming strategic alliances. Focusing on conditions that facilitate interorganizational knowledge
transfer and learning, I argued and found evidence that investee alliances create strategic benefits
for an investing CVC as the CVC’s and the portfolio company’s industries are more related. In
addition, I found that the relationship between the CVC-startup industry relatedness and strategic
benefits is positively moderated by the CVC’s absorptive capacity and the CVC-startup’s
geographical proximity. My findings point to the inherent advantages of commonly shared
knowledge background, underlying capabilities, and accessibility in interorganizational
knowledge transfer.
This study makes a number of contributions to the fields of strategy and entrepreneurship.
First, I extend CVC literature by shedding new light on potential strategic benefits from a
portfolio company’s strategic alliances. So far, CVC literature has focused on how an investing
firm takes advantage of the resources already possessed by a portfolio company. This study
makes a unique contribution by showing that, under certain conditions, a portfolio company’s
newly formed strategic alliances can create value for the investing firm. Resources and
capabilities of a startup are not static but evolving as the startup matures. A startup’s alliance
formation can be a significant source of learning and knowledge creation. Accordingly, for a
CVC that pursues strategic insights from investments in young entrepreneurial startups,
knowledge acquisition and learning of its portfolio company will be beneficial to the CVC firm
when the CVC can recognize, acquire and utilize the knowledge.
Although this study examines the context of CVC investments, the underlying
mechanisms by which a focal firm’s newly formed ties can create value for other linked firms
with a focal firm can apply to other contexts. For example, a new alliance formation of a firm
may create additional value for the firm’s existing business partners such as major customers
83
when the output of the newly formed alliance is relevant to the existing partners’ businesses. In
sum, this study underscores the potential strategic benefits from a partner’s new partner. Also,
findings highlight the significance of CVC investments as a window on novel technology and
business ideas because the scope of exploration by an incumbent firm is not limited to a focal
portfolio company but extended to its alliance partners.
Second, I identify conditions under which CVCs accelerate learning and knowledge
acquisition from strategic alliances formed by their portfolio companies. Potentially, a startup
can develop novel technologies or hone existing capabilities through strategic alliances with
other firms. Created knowledge through the alliances can be a source of strategic benefits for
CVCs. However, CVCs cannot take advantage of all newly created knowledge because the
degree of usefulness varies depending on the content of the knowledge. Also, conditions
regarding interorganizational knowledge transfer between a CVC and its portfolio company may
affect the ease of knowledge flow between firms. I found that industry relatedness, absorptive
capacity, and geographic proximity play a significant role in knowledge transfer. These findings
suggest that knowledge transfer hinges on shared knowledge between firms in an inter-
organizational relationship and familiarity with a partner’s business and technology and the path-
dependent nature of innovative performance (Cohen and Levinthal, 1990). Also, they reconfirm
that geographic proximity between a CVC and its portfolio company is critical in gaining
strategic benefits.
Finally, my finding highlights the conditions under which a CVC can obtain strategic
benefits from an unrelated CVC investment. Through CVC investments, incumbent firms often
explore new technologies and business ideas in other industries where they are not mainly
affiliated. In the case of this unrelated CVC investment, investee alliances may make its business
84
less relevant to the CVC’s core business and, accordingly, the potential strategic benefits created
by the investee alliances to the investing firm will be limited. However, if the portfolio company
forms a strategic alliance with a partner whose business is related to the CVC’s business, this
will likely create knowledge that is relevant and useful to the CVC. In such a case, the portfolio
company plays an intermediary role that enables the CVC to observe the competitor’s future plan.
My findings suggest that a CVC can utilize even unrelated CVC investment as a window on
novel technologies and strategies by acquiring knowledge and information generated by the
investee alliances.
This study has direct practical implications for CVC investment managers. I suggest that
the strategic competitive actions of a portfolio company can create value for investing firms.
Therefore, when CVC investment managers find and evaluate investment targets, they should
consider the potential strategic competitive actions of the targets because such actions can create
additional value depending on heterogeneous conditions.
85
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CHAPTER 4
THE CONTINGENT EFFECT OF MAJOR CUSTOMER CONCENTRATION ON THE
PROFITABILITY OF YOUNG FIRMS
4.1. Introduction
Young firms often grow by supplying products and services to incumbent corporate
customers. While corporations in the U.S. spend more than two trillion dollars annually buying
services and products from other companies including small businesses, powerhouses such as
Wal-Mart, Chevron, and IBM have launched programs to buy more from small companies
(Tabaka, 2013). For young firms that do not have enough market share to exert market power
(Yli-Renko and Janakiraman, 2008), serving corporate customers can be an opportunity to
generate stable revenues at their early stage. On the other hand, young firms may become
vulnerable to their major customers
19
because they could be excessively dependent on such
revenue sources. Since young firms are less likely to have bargaining power over the incumbent
corporate customers, they may face the risk of exploitation by established incumbents in power-
unbalanced relationships (Wilke, 2004; Bunkley, 2006). For example, GT Advanced
Technologies (GTAT), a sapphire furnaces maker that supplied sapphire material to Apple for
smartphone screens, recently ended with filing for bankruptcy as Apple offered a lower price for
the sapphire than GTAT expected (Arthur, 2014). These examples illustrate how major
customers can limit a young firm’s ability to scale production and handle pressure from
customers who demand lower costs.
19
‘Major customers (buyers)’ are defined as customers whose purchase comprises a significant proportion of the
revenues of a supplier. A firm may possess multiple major customers. The terminology is used in the Accounting
(e.g., Patatoukas, 2012) , Marketing (e.g., Phillips, 1981) and Industrial Organization Economics literature (e.g.,
Caves and Porter, 1977) as well as the Financial Accounting Standards Board (FASB).
92
By nature, a nascent firm has a small number of customers. As a firm grows, it
strategically chooses either to focus on its major customers or to diversify its customer base.
Each strategy has both advantages and disadvantages for a young firm. For example, a firm that
focuses on its major customers can sustainably scale based on secured volume purchase contracts
with the customers. Nevertheless, a high degree of dependence on a small number of contracted
customers puts a young supplier at a disadvantage because the termination of the purchase
contract by a major customer can result in substantial revenue loss for the supplier. On the other
hand, a firm that has the diversified customer base can acquire diverse information on
technology and market trends (Yli-Renko and Janakiraman, 2008). However, there may be
substantial cost to building a diversified portfolio of customers. Since customer concentration
potentially has both positive and negative effects on value creation and capture of young firms,
the net impact is ambiguous. The purpose of this study is to understand how customer
concentration affects the profitability of young firms contingent on the affiliated industry.
To explore this issue, I compare the manufacturing industry and the service industry. I
argue that distinguishing between manufacturing and service industries
20
is critical to
understanding the effects of customer concentration on the profitability of young firms because
the differing characteristics of each industry forms substantially contrasting conditions in the
supplier-customer relationship. The most fundamental difference between these two industries is
their scope of deliverables and business requirements. In the manufacturing industry, the
outcome deliverables in the transaction are tangible, clearly measurable and reasonably
predictable. Moreover, business requirements are defined as features that describe the physical
characteristics of the deliverables. In contrast, service firms that typically deal with production
process of customers face a high degree of uncertainty and complexity in the outcome of the
20
The boundary of the service industry in this study is explained in section 4.2.3.
93
transaction. Instead of tangible end products, service firms are required to manage a process that
typically involves frequent monitoring and updates. I argue that these different conditions in each
industry play a significant role on determining the effects of customer concentration on the
profitability of young firms.
In the next section, I develop the theory and hypotheses. Then I describe the data,
measures, analytic methods, and results. I then discuss implications based on my findings.
Finally, I conclude with the contributions and limitations of this study and highlight directions
for future research.
4.2 Theory and hypotheses
4.2.1 The effect of major customer dependence on supplier profitability
Numerous perspectives concerning customer concentration exist in the literature. The
bargaining power perspective considers a high degree of dependence on a small number of
customers an impediment to supplier profitability (Porter, 1985). For example, if a major
customer terminates or cancels a purchasing contract with its supplier, the termination or
cancellation may cause a substantial decline in revenues for the supplier. When a firm highly
depends on a small number of customers, these customers tend to have bargaining power over
the firm. Because the profitability of a firm depends on how value is divided up among parties in
the value chain (Brandenburger and Stuart, 1996), a customer’s strong bargaining power created
by a supplier’s high degree of dependence is expected to reduce the profitability of a supplier.
Consistent with the theoretical prediction, Galbraith and Stiles (1983) found that diffused
customer bases and customer power are strongly associated with higher profitability of firms in a
sample of U.S. manufacturing firms.
94
In the same vein, the accounting literature indicates that a high degree of concentration of
major buyers is a significant risk to suppliers because buyer power backed by high dependence
enables buyers to saddle suppliers with price cuts, carry lower inventories and extend the
supplier bill payment cycle (Gosman and Kohlbeck, 2009). For this reason, to provide risk-
relevant information for investors, the Financial Accounting Standards Board (FASB) and the
U.S. Securities and Exchange Commission (SEC) require a public firm to provide information
about the extent of its reliance on its major customers (FASB, 1997).
Industrial organization economists also consider the volume provided by a supplier to a
given buyer as one of the determinants of bargaining power between these parties in the
structural analysis of industries (Porter, 1985) and they expect that increased customer
concentration leads to lower supplier profitability. Empirical results support this argument. For
instance, Lustgarten (1975) and Schumacher (1991) found that buyer concentration is negatively
correlated with the price-cost margin. Gosman and Kohlbeck (2009) found that supplier
profitability is inversely associated with sales to major customers.
While the negative relationship between the degree of customer dependence and the
profitability of a firm is expected, the relational view (Dyer and Singh, 1998) may suggest
potential sources of interorganizational competitive advantage in the customer-supplier
relationship, in particular, when a supplier is a young firm that has limited resources and
experience. Dyer and Singh (1998) point out that the competitive advantages in the interfirm
relationship can be created through 1) investments in relation-specific assets, 2) substantial
knowledge exchange, 3) the combining of complementary resources or capabilities, and 4) low
transaction costs owing to more effective governance mechanisms. In the relationship of a young
firm with major customers, a high degree of concentration may create conditions that generate
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relational rent
21
which cannot be generated in normal arm’s-length market relationships. In sum,
the bargaining power mechanism serves as a liability to young firms, while the relational view
mechanism serves as an asset, and consequently, the net effect is ambiguous.
In the following section, I examine how the bargaining power mechanism and the
relational view mechanism apply differently in the manufacturing industry and the service
industry.
4.2.2 Manufacturing industry
Young firms in the manufacturing industry often produce parts for major customers. For
example, Cavium, Inc., a firm in my dataset from the semiconductor industry (SIC 3674),
produces integrated semiconductor processors and primarily sells these products to providers of
networking, wireless, storage and consumer electronic equipment. This firm collaborates with its
corporate customers to effectively meet the demand for its products. The major customers are
Cisco Systems, Nokia Solutions and Networks, and Amazon.com. The firm actively engages in
the process of product design and provides application-specific product information to the
customer’s employees, including system designers, engineers and procurement managers. Once
its product is incorporated into a customer’s design after careful consideration, the design is
likely to be used for the life cycle of the customer’s product because a redesign is generally a
time-consuming and expensive process. The manufacturing process is conducted based on
customer purchase orders. In such circumstance, the organizational boundary between a supplier
and a corporate customer is clearly divided. A supplier independently manufactures specified
products, and no additional follow-up effort is required unless defective products are delivered.
21
Dyer and Signh (1998: 662) define relational rent as “a supernormal profit jointly generated in an exchange
relationship that cannot be generated by either firm in isolation and can only be created through the joint
idiosyncratic contributions” of the specific exchange partners.
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Regarding the price determination, Cavium, Inc. describes “the price is considered fixed or
determinable at the execution of an agreement, based on specific products and quantities to be
delivered at specified prices...” (Cavium, Inc., 2015). In the manufacturing industry, by and large,
the value appropriation in the chain is traceable.
According to Brandenburger and Stuart (1996), a firm’s share of the value created is
determined by the difference between price and cost (see Figure 4.1). To increase a firm’s share
(i.e. to become more profitable), the firm should either raise the “willingness-to-pay and price”
or lower the “cost and opportunity cost”. I particularly focus on how customer concentration
affects the division of value.
Figure 4.1. The division of value (Source: Brandenburger and Stuart, 1996)
I argue that a high degree of customer concentration can increase the profitability of a
young supplier in the manufacturing industry. There are several mechanisms that may drive this
effect. First, secured volume purchases provide a young firm with more leeway to make
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customer-specific investments by virtue of economies of scale at the customer level. Young
firms can increase the efficiency associated with customization as they expand volume and scope
of transactions between the exchange partners (Dyer and Singh, 1998). More customized
investment creates a higher margin by raising the “willingness-to-pay and price” upward.
Customization that requires high degrees of asset-specific investments in the customizing
process potentially presents a hold-up problem (Williamson, 1999). However, secured volume
purchases can mitigate the level of risk in asset specificity by reducing the average cost of the
investment in customization. Market participants who engage in frequent, recurring transactions
can afford to adopt more specialized and complex governance structures (Williamson, 1985). At
the same time, more customized investments imply a higher level of differentiation relative to
competitors. Without secured volume purchase by major customers, young firms cannot invest
considerably in customization which is critical to increase profitability. However, a high degree
of major customer concentration can allow a young firm to have a high profit margin by making
customer-specific investment more efficient.
This advantage is related to the interfirm relation-specific assets (Dyer and Singh, 1998).
For example, relation-specific assets include site specificity and physical asset specificity
(Williamson, 1985). From the perspective of a young supplier in manufacturing, location
strategy is critical because production facilities are not easily mobile in nature. A young supplier
can actively invest in the site-specific assets only when a young supplier substantially depends
on a specific customer. For instance, when Hyundai Motors opened a car-manufacturing plant in
Montgomery, Alabama, more than 50 suppliers followed Hyundai to that area from Korea
(Bunkley, 2011). Some of them were young suppliers that decisively invested in the site-specific
assets with the expectation of competitive advantages over competing firms in distant places.
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Physical asset specificity refers to transaction-specific investments that tailor production
processes to particular exchange partners (Dyer and Singh, 1998). Young firms are incapable of
making physical asset specific investments without support from reliable customers. When
Apple and GT Advanced Technologies (GTAT) made a supply contract for sapphire iPhone
screens, Apple offered to lend GTAT $578 million to build 2,036 furnaces and operate a factory
for producing sapphire. In 2014, GTAT was developing a furnace that could produce a 578
pound boule, which is more than twice as large as what were then the biggest boules. The larger
boule would yield more iPhone screens, reducing costs. This is an example how a major
customer enables its young supplier to invest physical specific assets and ultimately create
relational rents which would not be captured without such investment.
Second, the interactions with a competent customer provide a young firm with a learning
opportunity (Alcacer and Oxley, 2014) which can increase the profitability of the young firm. In
particular, major customers provide their suppliers with several programs that improve the
product quality and operation efficiency. For instance, Intel Corporation, a semiconductor chip
maker, provides its suppliers with training programs such as Materials Quality and Reliability
(MQ&R) Supplier Training (Intel, 2009). Medtronic, a medical device company, also has
supplier development programs such as Supplier Lean Sigma Training (Medtronic, 2010). The
customer brings employees of suppliers to a formal classroom setting and offers lean sigma
training for select suppliers. Furthermore, the company allocates trained supplier development
engineers at the supplier’s site to help strategic suppliers drive process improvements throughout
the value stream that they share with Medtronic. The example of Medtronic illustrates how a
major customer contributes to value creation for its suppliers by providing direct training support
that improves the quality of products and the efficiency of manufacturing processes. However,
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interorganizational learning is not limited in the formal training programs. A young firm can
develop partner-specific absorptive capacity which refers to ‘the idea that a firm has developed
the ability to recognize and assimilate valuable knowledge’ from a particular exchange partner
(Dyer and Singh, 1998). By developing the interactions routine with a specific customer, a young
firm can maximize the intensity of sociotechnical interactions. Improved capabilities through
learning experience can apply to manufacturing better quality products for both the specific
major customer and other customers. In sum, learning experience from the interactions with
major customers can raise the “willingness-to-pay and price” upward by improving the product
quality.
Third, focusing on a few major customers enables supplier firms to decrease production
and distribution costs, marketing expenses, and administrative expenses (Patatoukas, 2012).
When a young firm has a major customer whose purchases constitute a significant proportion of
the total sales of the firm, the firm can maneuver the customer-specific enhanced product
distribution system at a low average cost by scale economies. The introduced system can reduce
administrative expenses and logistic costs. Furthermore, as the quantity of customer-specific
transaction increases, a young firm can learn how to perform logistical and administrative tasks
more efficiently. Through repetitive tasks, a young entrepreneurial firm can create routines that
can help it grow sustainably, and it can develop effective and efficient capabilities to manage
distribution and logistics. In addition to heuristic learning, it can learn how the established firm
effectively manages distribution and logistics when the young firm supplies products in large
quantities. Because an established firm can benefit from the improved effectiveness of its
suppliers, it is willing to support the young firm to build its capabilities by bringing in required
resources or complementary technologies (Yli-Renko and Janakiraman, 2008).
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Based on the previously discussed logic, I hypothesize:
Hypothesis 1. Greater major customer concentration in revenues is positively associated with
the profitability of young firms in the manufacturing industry.
4.2.3 Service industry
As service outcomes are substantially different from manufactured goods, the interactions
between a service supplier and a customer are different from those between a manufacturing
supplier and a customer (Song, Di Benedetto and Lisa, 1999). It is noteworthy to point out the
boundary of the service industry relevant to this study. So far studies in strategy have considered
the service industry as a whole (e.g., Song et al., 1999; Skaggs and Youndt, 2004). However, the
service industry is a broad term that includes a wide range of “soft” parts in the economy such as
transport, distribution, food/restaurant business, entertainment, healthcare/ hospitals and financial
sector. In this study, I focus on the relationship of a young supplier with major corporate
customers. Accordingly, the discussion on the service industry in this study is limited to
businesses that people offer their knowledge and time to improve productivity, performance, and
sustainability of customer businesses. These businesses are the area that many young
entrepreneurial ventures enter in (Delevett, 2012). Examples include customer relationship
management (CRM) service, security and compliance management solutions service, and web
application management and interactive business technology solutions. In general, these
businesses help their customers optimize business processes.
A key difference between the service industry and the manufacturing industry is the
scope of deliverables and business requirements. Unlike manufacturing firms which provide
customers with tangible and separable products, service firms typically interact with customers
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for the production process whose output is intangible and inseparable into units (Mills and
Moberg, 1982; Normann, 1984). That is, while the production process of a manufacturing
supplier is generally independent from a customer, the operational process of a service supplier
is embedded in the production process of a corporate customer. Therefore, a customer’s
production process and the service provided by a supplier are highly interdependent. Because of
this nature in the service industry, the production process of customers encompasses complex
tasks that require continuous maintenance and fine-tuning adjustments. For example, as
described in the previous section, a supplier in the manufacturing industry makes customer-
specific investment at the beginning of the transactional relationship with a customer. Later, the
supplier can simply manufacture designed products as specified in the contract. As the volume of
products for a specific customer increases, the average production cost decreases due to the
economies of scale and the learning effect (Adler and Clark, 1991) in the manufacturing industry.
In the service industry, a supplier applies its process solutions to a corporate customer.
During this process, the service supplier makes customer-specific investments. However, the
customization process is not restricted at the beginning of the transactional relationship with a
customer. Rather, as the volume of service for a specific customer increases, customized
maintenance is continuously required to operate the production process effectively. For example,
Chordiant Software, Inc., an entrepreneurial firm in my dataset from the prepackaged software
industry (SIC 7372), which supplies management solutions for the evaluation of customer
experience to a wide range of business customers such as telecommunications operators, credit
card companies and banks, provides its customers with support and maintenance services
including telephone support, web-based support and updates to their products and documentation.
Chordiant Software, Inc. provides product information and technical support information
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worldwide 24 hours a day, 7 days a week. In addition, this service firm often offers training
programs to its corporate customers to accelerate the implementation and adoption of its
solutions by the users. In the service industry, a service supplier has expertise in a specific field
(e.g., a data management firm has competent skills in data analysis and an Information
Technology (IT) security service provider has expertise in the defense and security sectors).
Therefore, if any training is necessary in the transaction between a supplier and a customer, it is
normally provided by a supplier to a customer, which is the opposite direction to the cases in the
manufacturing industry.
One might ask if the extensive maintenance service can be reflected in the contract and
the price. While it is relatively straightforward to evaluate the contract accomplishment and to
claim a reward for defect in the manufacturing industry, it is difficult to contain every possible
issue in contracts in the service industry. In particular, young firms that lack experience are not
able to expect contingencies accurately until unexpected problems surface. Normally, in the
contract, the fees for the operational service are determined based on the number of users,
customers or accounts, not based on the inputs of suppliers (i.e., the number of employees,
working hours, etc.). Once the contract is signed, a supplier is responsible for accomplishing
assigned processes no matter how much commitment is required. If a young service supplier has
bad reputation in the relationship with its major customer, it cannot survive in the competitive
market. Such a situation drives a young service supplier to take care of the business of a major
customer thoroughly at the expense of high maintenance costs. Therefore, at the customer level,
a young service supplier may face ‘diseconomies of managing’ (Shaver and Mezias, 2009).
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In sum, a young service firm that highly depends on major customers needs to make large
investments in high-maintenance services, which negatively affects the profitability of the
supplier. Therefore, I propose the following:
Hypothesis 2. Greater major customer concentration in revenues is negatively associated with
the profitability of young firms in the service industry.
4.3 Research design and methods
4.3.1 Data and sample description
The sample for the study included U.S.-based firms in the manufacturing and service industries
22
.
An ideal sample should include both private and public firms. However, because sales
information by customers and financial information of private firms are not publicly available,
inevitably only public firms are analyzed in this study. Despite this limitation, my sample serves
the main interest of this study, which is to examine the relationship of young firms with major
corporate customers. Nascent private startups may not have meaningful variation of their
dependence on major customers for a certain period – either they do not have major customers or
provide all their products for a single customer. Inclusion of these firms may make the result
biased. Rather, the sample of firms that went public at the early age makes it a reasonably
homogeneous group so that I can focus on the primary interest. Accordingly, I collected data on
firms that went public in the period from 1995 to 2010. I limited the analysis to firms that went
public within 10 years of being established, because this study focuses on the supplier-customer
relationship of young firms. Lists of firms that went public during this period were collected
22
In the SIC system, Division D is manufacturing (SIC 2000 through 3999) and Division I is services (SIC 7000
through 8999).
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from the SDC Global New Issues Databases and financial information was collected from the
Compustat database. Data on sales by customers were collected from both the Compustat
database and were matched with hand-collected information from the EDGAR
23
database to
ensure accuracy. The period of analysis is from the year of a firm’s IPO to 2013. The final
analysis includes a panel data set of 444 firms – 255 manufacturing and 189 service firms – with
2,797 firm-year observations. Table 4.1 describes the distribution of firms in each category of
SIC codes. Although the division category of the SIC system includes a wide range of industry
classifications, majority of the firms that generate revenues from major customers are affiliated
in few major groups. In particular, because the focus of this study is the relationship of young
suppliers with major corporate customers, for the service industry, both all sample analyses and
focused group sample (73: Business services only) analyses are conducted.
Table 4.1 Distribution of firms by the major group affiliation (First two digit of SIC code)
Major group Number of firms
Manufacturing
23: Apparel and other finished products 1
24: Lumber and wood products 1
28: Chemicals and allied products 126
30: Rubber and miscellaneous plastics products 1
35: Industrial and commercial machinery and computer equipment 20
36: Electronic and other electrical equipment and components 45
38: Measuring, Analyzing, And Controlling Instruments 61
Total 255
Service
70: Hotels, rooming houses, camps and other lodging places 1
73: Business services 149
75: Automotive repair, services and parking 1
78: Motion pictures 2
80: Health services 13
82: Educational services 2
83: Social services 3
87: Engineering, accounting, research, management, and related services 18
Total 189
23
EDGAR stands for Electronic Data-Gathering, Analysis, and Retrieval system.
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4.3.2 Measures
Dependent variable
The dependent variable, supplier profitability, was measured by the returns on asset (ROA) for a
given firm in a given year. ROA is defined as income before extraordinary items divided by the
total assets of a firm. ROA is frequently used as a profitability and performance measure in the
strategy and entrepreneurship literature (e.g., Arend, Patel, and Park, 2014).
One might argue that profitability calculated by accounting measures is not ideal to
evaluate the performance of rapidly-growing young firms because there is a tension between
profits and growth. However, a firm’s ability to be profitable in its early stages is certainly
critical sustaining profitability, particularly during times of financial difficulty. Since investors
value the fact that a firm can run a business profitably in the investment decision, failure to
maintaining sound financial standing can damage the subsequent fundraising activities of the
firm. In addition, the focus of this study is not the comparison of profitability across firms but the
examination of within-firm variation in profitability over time. Therefore, the accounting-based
profitability measure, ROA, has valid implications to understand the early-stage performance of
young firms.
Alternatively, to test the robustness of the results, I also use net income normalized by the
number of employees. Namely, ‘net income per employee’ measures how much each employee
creates net income in a firm.
Independent variables
Major customer concentration. I used the Herfindahl-Hirschman index to measure major
customer concentration in revenue. A major customer is defined as a customer that accounted for
106
more than ten percent of a supplier firm’s revenue in a given year. This definition is consistent
with the FASB’s and SEC’s major customer disclosure requirements. Following prior studies
(e.g., Patatoukas, 2012), the major customer concentration is calculated as:
Major Customer Concentration (MCC)
𝑖𝑡
= ∑(
𝑅 𝑒𝑣𝑒𝑛𝑢𝑒 𝑖𝑗𝑡 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑖𝑡
)
2
𝐽 𝑗 =1
,
where Revenue
ijt
represents the supplier firm i's revenues to major customer j in year t and
Revenue
it
represents supplier firm i's total revenues in year t. MCC ranges between 0 and 1,
where lower values correspond to a low concentration and higher values correspond to a high
concentration.
Industry. I segmented the data into two subsamples to investigate the empirical
relationship between major customer concentration and the profitability of young firms: (1) firms
in the manufacturing industry and (2) firms in the service industry. The industries are identified
by the Standard Industrial Classification (SIC) code. Firms in the manufacturing division (SIC
2000 through 3999) constitute the manufacturing industry sample and firms in the services
division (SIC 7000 through 8999) form the service industry sample. Again, because the focus of
this study is the relationship of young suppliers with major corporate customers, additional
analyses with sub-samples (73: Business services only) are conducted and reported in addition to
the analyses for the whole service industry.
Control variables
The primary explanatory variable in this study is constructed using the Herfindahl-
Hirschman index. This index captures two elements of concentration: the number of major
customers to which a supplier firm provides its products and/or services and the relative
107
importance of each major customer to the supplier firm’s annual revenue. Because the main
argument in this study focuses on the latter, I control for the number of major customers of a
supplier firm in a given year. The reason for controlling for the number of major customers is to
tease out the ‘number’ effect from the ‘dependence’ effect. One might be concerned that this
control variable may produce noise in the actual effects. However, analysis results without this
variable are consistent with reported results.
I control for additional factors that may affect the profitability of young firms. A body of
literature has articulated the relationship between firm size and financial performance,
particularly, in the context of entrepreneurial firms (Storey, 1989). Firm size is measured by the
logged sales of a firm in a given year.
24
The finance literature suggests that financial policies
such as the use of debt financing are associated with a firm’s financial performance (Campello,
2006). I control for the debt ratio, which is calculated by dividing total liabilities by total equity.
The level of investment in research and development (R&D) may affect the financial
performance of a young firm because R&D expenses are an element of total expenses, which
affect the net income. To control for the effect of R&D investment on financial performance, I
include R&D intensity, which is calculated by dividing R&D expenses by revenue. I control for
firm age to rule out an alternative explanation that major customer concentration and financial
performance may increase or decrease together as a firm matures. Finally, to control for the year-
specific effect on the profitability of young firms, year-fixed effects are included.
24
There are alternative ways to measure firm size, including the firm’s total assets and number of employees. I
report the results with the logged sales of a firm because the total assets are used in the left-hand side variable as a
denominator and may create bias. However, the results with the logged total assets or the number of employees are
robust.
108
Econometric approach
It is noteworthy to emphasize the focus of this study: the ways in which the variation of customer
dependence affects the profitability of a young firm. That is, to focus on the primary interest, this
study does not examine heterogeneity across firms. There may be several factors that affect the
profitability heterogeneity of young firms, including technological capabilities (Lee, Lee, and
Pennings, 2001). However, the recent strategy literature has emphasized the importance of the
analysis of value capture as well as value creation (Obloj and Zemsky, 2014). This study focuses
on the issue of value capture in particular in the relationship with major customers. To
understand the effect of customer concentration on profitability, this study holds other time-
invariant factors constant.
I use firm-fixed effects panel regressions to control for time-invariant unobservable firm
heterogeneity. I estimate a fixed-effects regression model of the following form:
𝑅𝑂𝐴 𝑖𝑡
= 𝛽 0
+ 𝛽 1
∙ 𝑀𝐶𝐶 𝑖𝑡
+ 𝛽 2
∙ 𝑁𝑀𝐶
𝑖𝑡
+ 𝛽 3
∙ ln(𝑅𝐸𝑉 )
𝑖𝑡
+ 𝛽 4
∙ ln(𝐷𝑅 )
𝑖𝑡
+ 𝛽 5
∙ 𝑅𝐷𝐼
𝑖𝑡
+ 𝛽 6
∙ 𝐴𝐺𝐸 𝑖𝑡
+ 𝜀 𝑖𝑡
where ROA
it
is the return on assets of firm i in year t, MCC
it
is the major customer concentration
of firm i in year t, NMC
it
is the number of major customers of firm i in year t, ln(REV)
it
is the
logged sales of firm i in year t, ln(DR)
it
is the logged debt ratio of firm i in year t, RDI
it
is R&D
intensity of firm i in year t, Age
it
is the age of firm i in year t, and ε
it
is the error term that is
clustered by the firm.
4.4 Results
4.4.1 Descriptive statistics
Table 4.2 presents the descriptive statistics for two subgroups. Because panel data was used in
the analysis, the table presents descriptive statistics of the mean value of the variables within
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firms (Shaver, 2011). First, the mean value of the variables for a firm across the years in each
subgroup was calculated. Then, these values were averaged across firms. The standard deviation
of the variable for each firm across the years in each subgroup was calculated first, and these
values were averaged across firms to yield the variable-level standard deviation by industry.
Table 4.2 Within-firm descriptive statistics
Manufacturing industry Services industry
Mean
a
s.d.
b
Mean
a
s.d.
b
Profitability (ROA) -0.50 [0.53] -0.36 [0.51]
Major Customer Concentration 0.25 [0.13] 0.09 [0.08]
Number of Major Customers 2.13 [0.62] 1.62 [0.77]
Revenue
*
3.13 [0.76] 3.66 [0.50]
Debt Ratio -1.19 [0.38] -1.04 [0.36]
R&D Intensity 0.33 [0.70] 0.15 [0.13]
Firm Age 8.74 [1.50] 6.75 [1.15]
N 1,665 1,121
a
First, the mean values of the variables for each firm over time were calculated, and these values were averaged
across firms.
b
First, the standard deviation of the variables for each firm over time were calculated, and these values were
averaged across firms.
*
Variables are in logarithmic form.
Table 4.3 presents the means, standard deviations, and correlations for all variables that
were used in the regression analyses. The panel data are unbalanced because of missing values in
the explanatory variables and differences in the founding years of the firms studied.
Table 4.3 Descriptive statistics for variables in the regression analyses
Descriptive statistics are for all observations without being broken down by subgroups.
Mean s.d.
Correlations
1 2 3 4 5 6 7
1. Profitability (ROA) -0.33 0.92 1.00
2. Major Customer Concentration 0.17 0.25 -0.09 1.00
3. Number of Major Customers 2.02 1.54 0.01 0.07 1.00
4. Revenue
*
3.65 2.04 0.36 -0.36 0.01 1.00
5. Debt Ratio -1.17 0.90 -0.34 -0.06 0.04 0.10 1.00
6. R&D Intensity 0.23 0.31 -0.59 0.30 0.05 -0.40 0.28 1.00
7. Firm Age 8.78 4.49 0.07 0.02 0.12 0.18 0.11 -0.01 1.00
N=2,786
*
Variables are in logarithmic form.
110
4.4.2 Fixed-effect regression results
Fixed-effect regression results are presented in Table 4.4. The effect of major customer
concentration on firm profitability was tested in each industry by sub-sample analyses. Model 1
presents the results for the manufacturing industry and Model 2 shows the results for the service
industry. Hypothesis 1 predicts that major customer concentration and the return on assets are
positively related in the manufacturing industry. As Model 1 indicates, the coefficients for major
customer concentration are positive and statistically significant (p<0.01) in the manufacturing
industry, which supports the hypothesis. In Hypothesis 2, I argue that the effects of major
customer concentration on the profitability of young firms are negative in the service industry. In
line with this expectation, Model 2 indicates that the coefficients for major customer
concentration are negative and statistically significant (p<0.05). Therefore, Hypotheses 1 and 2
are supported. Model 3 shows that the analysis results of service industry are consistent when the
sample includes only business services firms.
In addition to statistical significance, it is worthwhile to assess the economic significance
of this effect (Shaver, 2011). However, the interpretation of the coefficient is not straight-
forward because the independent variable, major customer concentration, is an index that
measures the concentration of the composition of sales. Alternatively, I interpret it based on the
size of the effect when the value of the independent variable changes by one standard deviation.
In the manufacturing industry, an increase of one standard deviation (0.13) in the independent
variable, major customer concentration, translates into a 0.02 increase in the dependent variable,
ROA. In the manufacturing industry subgroup of the sample, the mean of the total assets of firms
is 341.7 million US dollars. Thus, in an average-sized firm, a 0.02 increase in the ROA translates
into a net income increase of 6.8 million US dollars. On the other hand, in the service industry,
111
an increase of one standard deviation (0.08) in the independent variable, major customer
concentration, translates into a 0.04 decrease in the dependent variable, ROA. In the service
industry subgroup of the sample, the mean of the total assets of firms is 579.0 million US dollars.
Therefore, in an average-sized firm, a 0.04 decrease in the ROA translates into a net income
decrease of 23.2 million US dollars.
Table 4.4 Effects of major customer concentration on profitability
All specifications include firm-fixed effects, and error terms are clustered by firm.
(1) (2) (3)
DV=ROA Manufacturing Services
(All sample)
Services
(Business services only)
Major Customer Concentration 0.163** -0.499* -0.544*
(0.059) (0.247) (0.301)
Number of Major Customers 0.005 -0.015 -0.017
(0.008) (0.016) (0.033)
Revenue
a
0.042† -0.036 -0.045
(0.023) (0.041) (0.045)
Debt Ratio -0.168*** -0.189† -0.189
(0.013) (0.097) (0.116)
R&D Intensity -1.268*** -1.608** -1.649**
(0.132) (0.522) (0.638)
Firm Age -0.011† -0.003 -0.003
(0.006) (0.011) (0.013)
Year-fixed Effects Incl. Incl. Incl.
Constant 0.055 0.426* 0.485*
(0.075) (0.195) (0.322)
N of obs.(firm-year) 1,675 1,122 922
N of groups (firm) 255 189 149
Within R-square 0.5941 0.2959 0.3089
Standard errors in parentheses
† p<.10, * p<.05, ** p<.01, *** p<.001
a
Variables are in logarithmic form
112
4.4.3 Robustness tests
For young firms, ROA may not accurately capture the profitability, in particular when
total assets disproportionally fluctuate. In order to provide robust results, I use an alternative
dependent variable, net income per employee (NIPE), which is the firm’s normalized net income
divided by the number of employees in the firm. Because I use fixed-effect estimator that focuses
on within-firm variation, NIPE is an appropriate measure to compare profitability of similar
contexts. Consistent with the main results, Table 4.5 shows that major customer concentration is
Table 4.5 Effects of Major Customer Concentration on Net Income Per Employee (NIPE)
All specifications include firm-fixed effects, and error terms are clustered by firm.
(1) (2)
DV=NIPE Manufacturing Services
(Business services only)
Major Customer Concentration 0.223† -0.348†
(0.129) (0.207)
Number of Major Customers -0.019† 0.013
(0.011) (0.017)
Revenue
a
0.057* -0.066*
(0.027) (0.033)
Debt Ratio -0.023** -0.042
(0.008) (0.054)
R&D Intensity -0.295*** -0.029
(0.079) (0.129)
Firm Age -0.020** -0.008
(0.008) (0.005)
Year-fixed Effects Incl. Incl.
Constant -0.061 0.438†
(0.060) (0.226)
N of obs.(firm-year) 1,537 852
N of groups (firm) 251 148
Within R-square .0927 .1162
Standard errors in parentheses
† p<.10, * p<.05, ** p<.01, *** p<.001
a
Variables are in logarithmic form
113
positively associated with NIPE in the manufacturing industry at the ten percent significance
level and that it is negatively associated with NIPE in the service industry at the ten percent
significance level. Although the degree of the statistical significance somewhat decreases, the
signs of coefficients are consistent with the main effect.
4.4.4 Additional analysis
In the theory development section, the underlying mechanism to explain the opposite
effects of customer concentration on supplier profitability in the manufacturing versus service
industries is economies of scale. The results of this study indicates that for the manufacturing
industry, secured volume purchases by major customers reduce average production costs of
customized parts. In contrast, in the service industry, serving a major customer requires even
more extensive maintenance and commitments. To examine the relationship between customer
concentration and scale economies, I calculate the pairwise correlations between major customer
concentration and the number of employees, as presented in Table 4.6. In the manufacturing
industry, there is a statistically significant negative association between the number of employees
and major customer concentration. This relationship suggests potential economies of scale as
major customer concentration increases in the manufacturing industry. However, in the service
industry, the number of employees and major customer concentration are not significantly
correlated.
Table 4.6 Pairwise correlations
Manufacturing Service
Variables (1) (2) (3) (1) (2) (3)
(1) Employees 1 (1) 1
(2) MCC -.1467 * 1 (2) -.0429 1
(3) ROA
*
.0025 -.0105 1 (3) .0132 -.0519 † 1
*
ROA is normalized by the number of employees.
114
4.5 Discussion
The purpose of this study was to explore the relationship between customer dependence
and profitability in the context of young firms. Specifically, I analyzed how major customer
concentration affects the profitability of young firms, holding other factors – in particular, the
number of major customers and revenues of the firm – constant. While there are both risks and
benefits to young firms that depend on major customers, I argued that the net effect is contingent
on the affiliated industry (manufacturing vs. service). My findings suggest that the positive
effects of high customer dependence on the profitability are pronounced in the manufacturing
industry and that the negative effects are pronounced in the service industry.
For young firms that have relatively weak bargaining power in the relationship with
exchange partners, fewer major customers may increase appropriation risks because safeguards
in the market are imperfect (Williamson, 1985). In fact, extant studies in the strategy literature
have looked at the risks associated with young firms transacting with more established firms in
the contexts of corporate investment and alliance formation (e.g., Diestre and Rajagopalan, 2012;
Dushnitsky and Shaver, 2009; Katila et al., 2008). However, scholars have not yet studied the
supplier-customer relationship. Research that examines this relationship addresses the
conflicting theories of the bargaining power perspective (e.g., industrial organization economics
and power-dependence theory) and the relational view of competitive advantage (Dyer and Singh,
1998).
The relational view suggests that a relational rent can be generated when exchange
partners invest in idiosyncratic assets, knowledge, and resources and or they employ effective
governance mechanisms that lower transaction costs (Dyer and Singh, 1998). This perspective
proposes the potential supernormal profits jointly generated in an exchange relationship at a dyad
115
level (a pair of firms). However, the generated rents at the dyad level are not necessarily equally
divided between participants. The situation also implies another question of who is responsible
for the ‘relation-specific’ investments. Depending on how exchange partners bear expenses, a
firm may have to suffer from ‘relational costs’ instead of relational rents.
In the manufacturing industry, a young supplier provides tangible products for its major
customers. Expenses of relation-specific investments are relatively more predictable ex ante
compared to those in the service industries where the costs of process management are normally
determined at the end. Greater volume of interfirm transactions can enable firms to achieve
economies of scale in the relation-specific assets (Dyer and Singh, 1998). Under this condition, a
high degree of dependence on major customers is a source of relational rents for young suppliers.
On the other hand, in the service industry, there is dilemma in employing governance
mechanisms. By definition, young firms have only short-term partnerships with customers and
accordingly, the transactions are not able to depend on the informal self-enforcement governance
mechanisms. Then, contracts tend to include extensive detail. Unlike in the manufacturing
industry where expenses are relatively straight-forward, calculable and predictable, it is hard to
contain all possible issues in the formal contractual safeguards. Even if they are codified in the
contract, the extensive specifications inhibit flexibility (Weber and Mayer, 2011). In the cost-
sharing between a young supplier and an established corporate customer, the supplier is at a
disadvantage.
This study has two major implications. First, this research contributes to the
entrepreneurship literature by underscoring the importance of the customer-side in understanding
the profitability of young firms. A young firm that normally faces tight resource limitations relies
on other firms through interorganizational relationships. Given this condition, prior studies (e.g.,
116
Park and Steensma, 2012) have focused on young firms’ relationships with investors and alliance
partners to explain venture performance. However, their relationship with customers has not
been studied extensively, although the customer relationship is one of the most crucial factors
that affect the performance of young firms. This study attempts to fill the research gap and shows
that major customer concentration can affect the profitability of young firms.
Second, this study explores how market conditions, such as the affiliated industry, affect
the relationship between major customer concentration and supplier profitability. In particular,
the study compares the manufacturing industry with the service industry and suggests that under
the conditions which relational rents can be generated (e.g., the manufacturing industry), a high
degree of customer dependence is beneficial to the profitability of young firms but under the
conditions where diseconomies of managing for a specific customer can occur (e.g., the service
industry), a high degree of customer dependence is detrimental to the profitability of young firms.
Although the findings of this study suggest both positive and negative effects of major
customer concentration on profitability depending on the affiliated industry, they mean neither
that young manufacturers always rigorously need to focus on serving major customers nor that
young service firms need to avoid serving major customers. I focus on the profitability of young
firms in this study because the stable generation of profits is critical for young firms to grow
sustainably. However, in some cases, firms need to grow market shares at the expense of
profitability to establish themselves as incumbents in the market from a long-term perspective.
Therefore, managers have to think about the trade-offs inherent to a high degree of dependence
on major customers. For example, young service firms bear high costs in providing a high-
quality service for major customers. As the firms keep the transactions continuously with major
customers, they accumulate knowledge and knowhow of saving costs. In addition, a supplier and
117
a customer together incrementally learn effective governance mechanisms such as how to
contract with each other (Mayer and Argyres, 2004). Accumulated knowledge and informal
governance based on trust can bring out relational rents in the specific relationship with major
customers in the long run.
4.5.1 Limitations and future research
Because the sample used in this study consists of young firms that went public, I acknowledge
that the sample constitutes a group of successful ventures. However, private firms do not have
obligations to report on their major customers and their revenue composition. Thus, it is
technically challenging to include private young entrepreneurial firms in the sample. However, I
included not only venture capital (VC)-funded ventures but also non-VC-funded ventures in the
sample to avoid the selection bias prevalent in other entrepreneurship studies that include only
VC-funded ventures. Furthermore, firm-fixed effect panel regression analyses focus on the effect
of variation in an independent variable on the dependent variable. It is not apparent that my
findings would be systematically inconsistent in the private venture context. Future work with
extensive data on private ventures may expand on this study.
4.6 CONCLUSION
This study shows how major customer concentration can affect the profitability of
suppliers in the context of young firms. Integrating insights from a bargaining power perspective
and the relational view, I predict and find that the effect of customer concentration on
profitability is positive in the manufacturing industry and negative in the service industry. I
believe this study has important implications for understanding the value capture of young firms.
118
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Abstract (if available)
Abstract
In this dissertation, I explore the interorganizational relationships of young entrepreneurial firms. In the first essay, I examine how the relationship of a young entrepreneurial firm with a corporate venture capital (CVC) affects the firm’s R&D investment strategy. In the second essay, I investigate how a strategic alliance formed by a young entrepreneurial firm influences the strategic benefits of a CVC firm. In the third essay, I examine how relationships of a young entrepreneurial firm with major customers affect the profitability of the firm. By exploring the interorganizational relationships of young entrepreneurial firms, this dissertation attempts to understand better how young entrepreneurial firms interact with stakeholders surrounding them. ❧ In the first essay, I examine the effects of CVC ownership and founder incumbency on R&D investment strategy of a young entrepreneurial firm. R&D investment strategy is one of the most important resource allocation decisions that investors and top-level managers make, and it is particularly important for young entrepreneurial firms in technology-intensive industries. Although prior studies have examined how different types of ownership affect R&D investment strategy in large public corporations, we still know little about corporate governance–related determinants of R&D investment strategy in young entrepreneurial firms. To fill this research gap, I focus on the most significant stakeholders in young entrepreneurial firms (i.e., venture capital firms, corporate venture capital firms, and founders) and argue that CVC ownership and founder incumbency positively affect R&D investment strategy in entrepreneurial firms. I found empirical evidence supporting my hypotheses. ❧ In the second essay, I explore when a strategic alliance formed by a portfolio company creates strategic benefits for its CVC firm. Prior studies show that CVC investments create value for investing firms by providing learning opportunities and a window on new technologies. Focusing on the conditions which facilitate knowledge transfer from a portfolio company to a CVC firm, I argue that strategic benefits that a CVC firm captures from a strategic alliance formed by a portfolio company is positively associated with the industry relatedness between the CVC and its portfolio company. Moreover, I argue this effect will be more salient 1) as a CVC’s absorptive capacity increases and 2) when a CVC and its portfolio company are located geographically close because the CVC’s absorptive capacity provides the foundation upon which the CVC learns better from the portfolio company and proximity allows a CVC to capture more effectively knowledge from its portfolio company. In a sample of alliances formed by CVC-backed U.S.-based startups, I found evidence. This study expands the scope of sources for strategic benefits in CVC investments. ❧ In the third essay, I study the relationship between a young firm and its major customers. In particular, I examine how the major customer dependence of a young firm affects the profitability of the young firm. From a bargaining power perspective, a high degree of dependence on major customers can be an impediment to supplier profitability. However, as a nascent player who faces resource limitation, a young firm may take advantage of the beneficial effects in the relationship with major customers. Taking these counter balancing effects into account, this paper explores the effect of major customer concentration on the profitability of young entrepreneurial firms and argues that this effect may differ depending on the type of markets in which the firm competes. More specifically, I argue that major customer concentration is positively associated with the profitability of a young entrepreneurial firm in the manufacturing industry while it is negatively associated in the service industry. In the manufacturing industry, young firms take advantages of scale economies and learning opportunities from the relationship with major customers. However, in the service industry, young firms experience diseconomies of managing for extensive maintenance and follow-up services for major customers. This study underscores the importance of the customer-side in understanding the profitability of young firms.
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Woo, Heejin
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Three essays on young entrepreneurial firms
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Marshall School of Business
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Degree Program
Business Administration
Publication Date
06/19/2015
Defense Date
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