Strategy Frameworks in Media Organizations

Strategic Media Venturing: Corporate Venture Capital Approaches of TIME Incumbents Tim C. Hasenpusch and Sabine Baumann

Jade University, Germany

ABSTRACT Media firms act in rapidly changing and converging envir- onments characterized by new entrants and increasing com- petition from related industries. As a reaction to this, the incumbents of the telecommunication, information technol- ogy, consumer electronics, media, and entertainment indus- try have increased their corporate venture capital activities. Corporate venture capital activities are a popular approach for gaining access to new innovative ideas and opportu- nities. Despite this practical relevance, the theoretical under- pinning of corporate venture capital and the corporate venturing activities of media firms are poorly understood. Therefore, the purpose of this article is to close this gap by defining corporate venture capital as a bundle of dynamic capabilities (“organizational drivetrain”) and revealing the differences and commonalities of telecommunication, infor- mation technology, consumer electronics, media, and enter- tainment incumbents’ corporate venture capital approaches as response to the ongoing convergence of a technology- driven business environment. To do so, we conducted an exploratory study of 3,145 transactions by 68 telecommuni- cation, information technology, consumer electronics, media, and entertainment incumbents in 2,163 start-up companies between 2002 and 2015, detecting, describing, and comparing their corporate venture capital approaches. The findings reveal a taxonomy of three different types of corporate investors, namely “aggressive,” “attentive,” and “dispersive.” While the aggressive approach covers the most active investors of the sample, who invest primarily in early-stage ventures, attentive investors show a more conservative investment behavior, focusing on their core business within their local proximity. In contrast, dispersive investors disproportionately fund established businesses in a broad array of industries. Hence, the study highlights a sector-dependent usage with incumbents of each telecom- munication, information technology, consumer electronics, media, and entertainment sector preferring a different investment approach indicating the influence of previous path, positions, and processes.

CONTACT Tim C. Hasenpusch [email protected] College of Management, Information, and Technology, Jade University, Friedrich Paffrath-Str. 101, Wilhelmshaven D-26389, Germany.

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 2017, VOL. 19, NO. 1, 77–100 http://dx.doi.org/10.1080/14241277.2017.1280040

© 2017 Institute for Media and Communications Management

Introduction

Media organizations operate in a high-velocity business environment (Faustino & Ribeiro, 2016; Oliver, 2012; Ots, Nyilasy, Rohn, & Wikström, 2015). Emerging technologies allow new market entrants, blur established market and industry boundaries, and lead to increasing competition between established media firms (print, TV, radio, etc.), as well as between new and old media sectors (Mierzejewska & Shaver, 2014; Sullivan & Jiang, 2010). Additionally, start-ups use technology developments to target customers that once primarily belonged to mass media (Van Weezel, 2010), whereas estab- lished media firms still struggle to adapt to the new and rapidly changing media environment (Hirt & Willmott, 2014; Picard, 2011). One such example is the success of content-sharing platforms and social networks, which shows that media firms often misinterpret changes in technology, even if these challenge their traditional business models. Instead, competitors, often from related telecommunication, information technology, consumer electronics, media, and entertainment (TIME)1 industries, use technology developments to create new media businesses (Baumann & Hasenpusch, 2014). In summary, media firms need to overcome their technology-averse behavior and search continually for new opportunities and innovative ideas in response to the challenges and threats of a technology-driven and converging industry (Engel, 2011; Knyphausen-Aufseß, 2005; Mierzejewska & Shaver, 2014; Wadhwa & Kotha, 2006).

One approach gaining access to new innovative ideas and opportunities are equity investments into entrepreneurial start-ups, for example, corporate venture capital (CVC). In 2015, CVC investments were at an all-time high in the post- millennial era, with corporate groups being involved in more than one in five venture deals (National Venture Capital Association, 2016). Previous studies have already shown that CVC activities are especially used by incumbents with a strong resource profile (e.g., free internal cash-flow, strongmarketing resources) acting in business environments characterized by changing technology and increasing competitiveness (Basu, Phelps, & Kotha, 2011; Dushnitsky, 2012). Subsequently, it is not surprising that media firms and incumbents from adjacent TIME sectors launchnew, or intensify existing, CVCprograms (Hasenpusch&Baumann, 2016). CVC activities “have to do with the forward strategy and plans” (European Commission, 2001, p. 9) and recently became a vital part of the corporate strategy to identify and capture the strategic value of emerging technologies and new markets (Battistini, Hacklin, & Baschera, 2013; Napp & Minshall, 2011). Due to the sensitivity to industry turbulence and parent firm characteristics, we see CVC as a strategic development tool to alter an incumbent’s resource base with the purpose of adjusting existing operations, as well as developing new business. Building on the dynamic capability (DC) approach, we define CVC programs as an “organizational drivetrain” (Di Stefano, Peteraf, &Verona, 2014b) consisting of

78 T. C. HASENPUSCH AND S. BAUMANN

simple rules guiding investment activities and complex routines absorbing the value of new technologies and markets.

The purpose of this explorative study is to reveal differences and com- monalities in guiding the CVC activities of TIME incumbents to cope with the ongoing convergence of a technology-driven business environment. The study aims to detect, describe, and compare the CVC approaches of TIME incumbents to suggest that there might be different strategies and objectives of such activities. While investing in new businesses mostly appears expan- sive from the outside, the underlying approach is often rather defensive to deal with anticipated losses in the core business (Hass, 2011). In an earlier study, Dennis and Ash (2001, p. 31) stated that new media businesses are often created by established media firms “as insurance against outside digital dominance.” Thus, media incumbents might use CVC investments mainly to digitalize their core business as a way of protecting it against competitors from the related industries. In turn, information technology (IT) incumbents may apply a more expansive approach using their digital resources to diver- sify into new (media) businesses. Hence, the underlying research questions of this study are two-fold: First, what CVC investment approaches exist within the TIME industry? And second, what, if any, differences occur when the CVC investment approaches across are compared across TIME sectors?

To answer these questions, we studied a sample of 68 international TIME incumbents that had invested in 2,163 start-ups between 2002 and 2015. The large-scale design of this study differs from that of previous media venturing studies, which have mostly employed a case-study design to investigate the venturing activities of media firms (e.g., Fang & Chan-Olmsted, 2002; Hass, 2011; Hipp, 2003), as well as CVC research where most samples focus on earlier waves of CVC and are restricted to Fortune 500 companies (e.g., Dushnitsky, 2012; MacMillan, Roberts, Livada, & Wang, 2008; Yates & Roberts, 1991). To detect investment patterns within our data-set, we used an explorative and deterministic cluster analysis.

With this article, we make the following contribution to literature. We high- light the entrepreneurial behavior of TIME incumbents through their CVC investments. Thus, we contribute to the underdeveloped topic of media ventur- ing (Hang & Van Weezel, 2007) by characterizing three different types of corporate investors—namely, aggressive, attentive, and dispersive—which are unevenly distributed among TIME sub-sectors, indicating sector-dependent CVC strategies. Furthermore, by conceptualizing CVC as an organizational drivetrain, we add to the theoretical underpinning of CVC (Maula, 2007; Narayanan, Yang, & Zahra, 2009).

The remainder of the current article is structured as follows: To frame CVC as a strategic tool of media management, we first describe the diverse aspects of CVC programs. Afterward, we show how CVC fulfills the recently developed organizational drivetrain concept consisting of simple rules and

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 79

complex routines. Later, we describe the data collection and cluster analysis used to answer the research questions. In the following section, we present the results of the cluster analysis. Next, we discuss and highlight the implica- tions of the article. Finally, we provide an outlook for further investigation and discuss the limitations of this study.

Theoretical foundations

To address the theoretical underpinning of CVC, the purpose of this section is to provide a holistic framework for the use of CVC as a strategic tool for dealing with the challenges of a high-velocity environment. Our theoretical framework is based on previous research on DCs and CVC. In the following sections, we discuss both concepts and link them by characterizing CVC as an “organizational drivetrain” (Di Stefano et al., 2014b). By referring to the drivetrain concept, the article considers the diverse aspects of CVC and provides a classification of the processes assisting to benefit from and execute CVC. This categorization provides the foundation for the empirical analysis.

CVC

Corporate entrepreneurship encompasses all processes used by mid-size and large organizations to facilitate the efforts of corporations to innovate and rejuvenate their businesses to achieve and perpetuate competitive superiority (Cooper, Markman, & Niss, 2000; Ireland, Covin, & Kuratko, 2009; Morris, Kuratko, & Covin, 2007). A central construct of an incumbent’s entrepre- neurial efforts is CVC (Corbett, Covin, O’Connor, & Tucci, 2013). CVC, for example, minority equity investments in entrepreneurial start-up companies, is an external venture type of established corporations (Dushnitsky, 2012; Napp & Minshall, 2011). Compared with other external venture forms, such as acquisitions and alliances, CVC investments offer higher flexibility and smaller risks due to the reduced investment amounts (Benson & Ziedonis, 2009; Lee & Kang, 2015). Thus, CVC allows incumbents to react in a timely manner to emerging business opportunities, which makes it a suitable ven- ture form dealing with the challenges of a high-velocity business environ- ment (Basu et al., 2011; Hass, 2011).

In contrast to venture capitalists who focus solely on financial objectives, incumbents pursue a mixed set of strategic and financial objectives with CVC (Hill, Maula, Birkinshaw, & Murray, 2009; Knyphausen-Aufseß, 2005).2 Previous studies comparing the relative importance of CVC objectives show that no single goal appears to be consistentlymost important, indicating the existence of different CVC strategies (Maula, 2007). However, a pendulous movement between the importance of financial and strategic goals can be observed. For example, prior to the Internet bubble, Siegel, Siegel, and MacMillan (1988) identified return on

80 T. C. HASENPUSCH AND S. BAUMANN

investment as the main objective of CVC investment, whereas in a more recent study,MacMillan et al. (2008) showed that 65% of all CVC programsmainly focus on strategic objectives. More recently, Battistini et al. (2013) define strategic objectives as the underlying rationale for establishing a CVC program, whereas meeting financial criteria is a necessary condition for sustaining a CVC program. The CVC activities of most incumbents appear to be targeted at exploiting on the entrepreneurial capabilities of young and innovative ventures (Benson&Ziedonis, 2009; Poser, 2003). The most cited strategic objectives are, in no specific order: identifying business opportunities and relationships; windows on new technolo- gies andmarkets; identifying possible acquisition objects; and commercializing idle resources (Ernst & Young, 2009; MacMillan et al., 2008; Sykes, 1990).

The targeted objectives determine the CVC investment approach of an incumbent which is defined by four typical elements: industry, technology, development stage, and region (Keil, Zahra, & Maula, 2004; Poser, 2003). For example, a CVC program implemented as an early-alert system might apply a wider investment focus covering a broad array of technologies and industries, while a CVC program aiming to enhance the core business of an incumbent will adopt a more focused approach. In a study during the hype of the new economy, Kann (2001) showed that incumbents investing for the purpose of external research and development mainly invest in their core segment, whereas incumbents aiming to enter new markets focus primarily on adja- cent and emerging industries. Besides the targeted objectives, an investment approach is moderated by the number of investment opportunities existing in specific industries and regions (Poser, 2003). Historically, the United States and Europe are the largest investment areas for corporate investors (BCG, 2012). In 2015, most venture capital (VC) investments were in start- ups relating to the software, biotechnology, and media and entertainment (M&E) sector (National Venture Capital Association, 2016).

To benefit from CVC investments incumbents need to create an entrepre- neurial culture and routines to stimulate resource exchange and knowledge transfer between the venture and the incumbent. Thus,

. . .although CVC investments are a valuable strategy for searching new technolo- gies and business opportunities, firms without the capabilities to absorb the new technologies and recombine them with its existing knowledge base might find that the benefits for them are limited. (Lee & Kang, 2015, p. 369)

The integration of CVC investments within the overall company structure and an intensive exchange of information between, the venture, the incumbent and the CVC unit as agent to coordinate and execute CVC activities are critical to the implementation of CVC as a long-term business development tool (Covin & Miles, 2007). Hence, the most strategic benefits can only be realized through close interaction between an incumbent’s business units, CVC unit, and the start- up firm (Poser, 2003; Sykes, 1990). Interaction can be in the form of presentations

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 81

(board) meetings, sponsorships, and/or direct working relationships (Freese, Keil, & Teichert, 2007; Keil et al., 2004; Souitaris, Zerbinati, & Liu, 2012). Overall, the intensity of interactions determines the strategic benefits of an incumbent (Röper, 2004). Consequently, Wadhwa and Kotha (2006, p. 819) showed that “when investor involvement is high, [. . .] an increase in investments boosts innovation.” Nevertheless, partnerships that are too close may harm the way an incumbent is perceived as an investor and can frighten potential ventures. In particular, closely related start-ups fear imitation and, therefore, may avoid a CVC investor (Katila, Rosenberger, & Eisenhardt, 2008; Maula, Autio, & Murray, 2009), a situation Dushnitsky and Shaver (2009, p. 1046) refer to as “the paradox of CVC.”

In order to frame these aspects of CVC within the strategic management literature, we turn to the recently developed organizational drivetrain concept.

DC approach (“organizational drivetrain”)

Organizational capabilities are hierarchically categorized into operational (or ordinary) capabilities and higher-order DCs (Helfat & Winter, 2011; Teece, 2014). An operational capability enables a firm to make a living on a daily basis (Winter, 2003), for example, aim to maintain the status quo by using existing techniques to support and produce current products and services for an existing customer target group (Helfat & Winter, 2011). In contrast, DCs comprise the “capacity of an organization to purposefully create, extend, or modify its resource base” (Helfat et al., 2007, p. 4). Thus, a DC describes all the capacities that “enable a firm to alter how it currently makes a living” (Helfat & Winter, 2011, p. 1244).

Despite this general understanding of DCs, many different, and even con- trary, definitions of what constitutes a DC exist (Barreto, 2010; Di Stefano, Peteraf, & Verona, 2010a). The roots of these conflicting views lie in the two seminal papers of Teece, Pisano, and Shuen (1997) and Eisenhardt and Martin (2000). In the view of Teece et al. (1997), DCs are characterized as idiosyncratic, detailed, analytic, and complex routines that depend on the paths, positions, and processes of an organization, whereas Eisenhardt and Martin (2000, p. 1106) characterize DCs as “simple, experiential, unstable processes” that show com- monalities across firms “to match and even create market change” (p. 1107). Thus, while still “idiosyncratic in their details and path dependent in their emergence,” best practices exist (Eisenhardt & Martin, 2000, p. 1105). Despite these differences, both articles have a similar chain of logic, defining DCs as processes that create competitive advantages by altering the resources and ordinary capabilities of a firm (Helfat & Peteraf, 2009).

In an attempt to overcome these conflicting views, Di Stefano et al. (2014b) integrate both approaches into a holistic and dynamic system (i.e., “organizational drivetrain”). The underlying foundation of this reconceptualization is that “each

82 T. C. HASENPUSCH AND S. BAUMANN

(of the two conflicting views) focused on a different part of a larger, intercon- nected, and more fully dynamic system” (Di Stefano et al., 2014b, p. 318). Thus, according to Di Stefano et al. (2014b), DCs are interrelated sub-processes of an “organizational drivetrain,” which depend on the previous paths, processes, and positions of an incumbent. Hence, the system consists, on the one hand, of simple, experiential, and unstable rules that provide and change the direction of the system, and, on the other hand, of complex, detailed, and analytical routines that initialize and manage change within an organization. To illustrate this point, Di Stefano et al. (2014b) use the metaphor of the drivetrain of a bicycle, where the crankset represents DCs as simple rules and the freewheel characterizes the “numerous set of complex routines that the organization deploys internally to create and manage change” (Di Stefano et al., 2014b, p. 319). In other words, the structure at the top acts as a constraint for the complex, idiosyncratic, and internal routines within the organization that alter the resources and ordinary capabilities of a firm. Both perspectives are linked to each other through several mechanisms, so adjustments on one side will affect the outcome of the entire system.

Hence, in line with the operationalization framework of DCs by Teece (2007), the aim of the “organizational drivetrain” is to sense and shape opportunities and threats, to seize opportunities (i.e., tomake timely investments), and to continually reconfigure or transform current tangible and intangible assets. These purposes are all linked to each other. Thus, opportunity identification (sensing) and seizing these opportunities via investments fosters the reconfiguration and transforma- tion of the organization (Helfat & Peteraf, 2009). The purpose of the organiza- tional drivetrain then is to preserve and defend competitive advantages by modifying existing as well as creating new businesses.

Based on the drivetrain concept, CVC programs can be defined as an organiza- tional drivetrain consisting of, on the one hand, simple rules defined in the investment approach (e.g., a focus on Internet start-ups within the core industry) and, on the other hand, complex routines (e.g., working partnerships) aimed at absorbing the new technologies and/or resources. Furthermore, even when the investment approach determines the search corridor for potential investments, a set of complex and analytical routines is necessary to evaluate the venture according to its long-term strategic gains. Due to this duality of simple rules and complex routines combined with the intended forward strategy, CVC activities represent both sides of the organizational drivetrain, and, as such, consist of a set of interrelated and connected DCs. Consequently, we conceptualize CVC as an “organizational drivetrain.”

With the aim to detect, describe, and compare the CVC investment approaches of TIME incumbents, the study focuses on the crankset of CVC as an organizational drivetrain. In other words, the article concentrates on the simple rules underlying the seizing of investment opportunities to explore new technologies and businesses as a reaction to the high-velocity and converging environment. Moreover, by focusing on the crankset, this study is one of a few

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 83

that focus on the external perspective within DC research. According to Di Stefano et al. (2010a), previous research in the domain of DCs is dominated by articles with a focus on organizational learning (Zollo & Winter, 2002), absorp- tive capacity (Zahra & George, 2002), and/or rent generation (Makadok, 2001).

Method

Based on the Thomson Reuters Private Equity Database,3 we implemented an explorative and quantitative data-mining project. This database was selected because it is widely used within quantitative CVC scientific research and is seen as representative of the CVC market (Dushnitsky & Lavie, 2010; Zipser, 2008). Overall, the data-mining project follows the cross-industry standard process of data-mining (CRISP-DM). The CRISP-DM is a non-proprietary, well-documented, and free data-mining process developed by industrial companies and data-mining experts (Shearer, 2000).

Data sample, sample criteria, and selection

Due to the high-velocity media environment, a widely accepted and distinct classification that differentiates the TIME sectors does not exist (Hess, 2014; Mierzejewska & Shaver, 2014). Thus, assigning incumbents to distinct industry sectors is challenging. In line with other studies (e.g., Wadhwa & Kotha, 2006), we used industry codes (Nomenclature statistique des activités économiques dans la Communauté européenne [NACE]) to define the TIME sectors.4

To construct the sample, we extracted a list of CVC firms from the Thomson Reuters Private Equity Database that we searched for the corporate parents according to the TIME definition to match each CVC firm with its parent company. We extracted 70% from the Orbis database.5 For the remaining 30%, we conducted extensive web research using Google, Bloomberg, and Crunchbase as the main sources of information.

Finally, in line with other CVC studies (Dushnitsky& Shaver, 2009; Gompers & Lerner, 1998; Sykes, 1990), we limited CVC investments according to several criteria: First, we followed the U.S. definition of CVC, restricting CVC to equity investments of the seed, along with early-, expansion-, and later-stage phases (Poser, 2003).6 Each developmental stage describes a specific period of the start- up lifecycle (Appendix A). Second, we limited the data-set to direct CVC invest- ments, excluding all venture capital partnerships7 and investments allied to pen- sion funds or evergreens. Third,we included only incumbents that undertakeCVC activities regularly.8 Firms that make only one investment in an entrepreneurial venture during the timeframe most likely have no explicit strategic objectives of their CVC initiative (Chesbrough & Tucci, 2004; Poser, 2003). Furthermore, one- time investments hint at the absence of an explicit CVCprogram. Fourth, the study covered only CVC activities from 2002 to 2015, which are mainly characterized by

84 T. C. HASENPUSCH AND S. BAUMANN

strategic investment decisions according to changes in technology and the market environment (BCG, 2012; Dushnitsky, 2011).

Overall, the data-set contained 68 incumbents—32 from M&E, 20 from telecommunications, 10 from IT, and 6 from CE—that had invested in 2,163 start-ups through 3,145 transactions between 2002 and 2015. Most of the incumbents’ headquarters were located in the United States (40), followed by Western Europe (13), and East Asia (12).

Variables

The creation of variables to determine the diversification strategy is a con- troversial and much discussed topic within scientific research (Montgomery, 1982; Palepu, 1985). Following Montgomery (1982) and previous media studies (e.g., Jung & Chan-Olmsted, 2005), we used industry codes and sub-locations to determine the diversification approach of each incumbent. Furthermore, the study relied on different variables covering transaction data (e.g., number of transactions; investment round), as well as corporate and venture characteristics. Besides the descriptive variables such as location, technological application, and industry sector, we extended the variable set using product and regional diversification measures (see Table 1).

Analyzing technique

As a modeling technique, we conducted an explorative and deterministic cluster analysis. In the past, cluster analysis was widely used in strategic research (e.g., Goyanes & Dürrenberg, 2014; Ng, Westgren, & Sonka, 2009) to examine organizational configurations, such as strategic groups, taxo- nomies, and archetypes (Ketchen & Shook, 1996). Thus, cluster analysis is a suitable method for grouping TIME incumbents according to the simila- rities of their CVC investment approaches. To interpret the cluster results, we conducted significance tests (Kruskal-Wallis) and contingency tables.

Despite its extensive use, cluster analysis is widely criticized because of the researchers’ influence and the possibility of artificial groupings (Barney & Hoskisson, 1990; Chan-Olmsted, 2006). The most critical issues are (Ketchen & Shook, 1996, p. 451): “selecting clustering variables; [. . .] selecting appropriate clustering algorithms; determining the number of clusters; and validating clus- ters.” Several remedies for each issue exist, all coming with different associated costs (Ketchen & Shook, 1996). We addressed each issue in turn, weighting the remedies to ensure a high-quality and traceable analysis. Additionally, we used within-method triangulation to increase the validity of the results.

Selecting cluster variables According to Poser (2003), CVC investment approach of the incumbents is defined by the four typical elements of a CVC investment: industry, region,

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 85

Ta bl e 1.

Va ria bl e de sc rip

tio n

Va ria bl e

D es cr ip tio

n

Pa re nt

Co m pa ny

In du

st ry

Se ct or

D is tin

ct io n be tw ee n M & E, Te le co m m un

ic at io n (T el ec om

), In fo rm

at io n an d IT ,a nd

Co ns um

er El ec tr on

ic s (C E)

→ TI M E In du

st ry ;

W or ld

Su b- Lo ca tio

n Lo ca tio

n of

co rp or at e pa re nt ’s he ad qu

ar te rs .D

is tin

ct io n be tw ee n:

N or th

Am er ic a, Ce nt ra lA

m er ic a, So ut h Am

er ic a, Ca rib

be an ,E as te rn

Eu ro pe ,N

or th er n

Eu ro pe ,S ou

th er n Eu ro pe ,W

es te rn

Eu ro pe ,C

en tr al Af ric a, Ea st er n Af ric a, N or th er n Af ric a, So ut he rn

Af ric a, W es te rn

Af ric a, Pa ci fic ,C

en tr al As ia ,E as t As ia ,

So ut he rn

As ia ,S ou

th ea st

As ia ,M

id dl e Ea st .

In ve st m en t Ap

pr oa ch

Co m pa ny

O pe ra tin

g Se ct or

D is tin

ct io n be tw ee n M & E, Te le co m ,I T, CE

an d O th er s;

Co m pa ny

Pr im ar y Cu

st om

er Fo cu s

Th e ty pe

of cu st om

er w ho

w ill bu

y or

us e th e co m pa ny ’s pr od

uc t( s) .D

is tin

ct io n be tw ee n Bu

si ne ss -t o- Co

ns um

er (B 2C ), Bu

si ne ss -t o- Bu

si ne ss

(B 2B ), Bu

si ne ss -

to -G ov er nm

en t (B 2G

), an d Bu

si ne ss -t o- Al l( B2 A)

as ge ne ra lt er m

fo r co m pa ni es

w ith

m or e th an

on e “p rim

ar y”

cu st om

er fo cu s;

Co m pa ny

Te ch no

lo gy

Ap pl ic at io n

A cl as si fic at io n fo r a te ch no

lo gy

us ed

by th e st ar t- up

co m pa ny .D

is tin

ct io n be tw ee n in te rn et - an d no

n- in te rn et -r el at ed

co m pa ni es ;

Co m pa ny

Re gi on

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su b- lo ca tio

n of

th e co m pa ny ’s he ad qu

ar te r;

Fi na nc ia lS

ta ge

of In ve st m en t

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st ag e at

th e da te

of th e in ve st m en t ro un

d. D is tin

ct io n be tw ee n se ed ,e ar ly st ag e, ex pa ns io n,

la te r st ag e, bu

yo ut s (b uy )/ ac qu

is iti on

s (a cq ) an d

ot he rs ;

Co re

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Pe rc en ta ge

of tr an sa ct io ns

in co m pa ni es

w ith

in th e sa m e in du

st ry

se ct or

of an

in cu m be nt ;

TI M E In ve st m en ts

Pe rc en ta ge

of tr an sa ct io ns

in co m pa ni es

w ith

in th e TI M E in du

st ry ;

To ta lP

ro du

ct D iv er si fic at io n

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da hl

In de x m ea su re

th at

st at es

th e nu

m be r of

tr an sa ct io ns

of an

in cu m be nt

w ith

in di ffe

re nt

in du

st rie s;

1 �

P Pi

2

ðP Pi Þ2 ;P ia sp or ti on

of tr an

sa ct io n w it hi n th e in du

st ry se ct or

i

To ta lR eg io na lD

iv er si fic at io n

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da hl

in de x m ea su re

th at

st at es

th e nu

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tr an sa ct io ns

of an

in cu m be nt

w ith

in di ffe

re nt

re gi on

s;

1 �

P Pi

2

ðP Pi Þ2 ;P ia sp or ti on

of tr an

sa ct io n w it hi n th e w or ld su b lo ca ti on

i Ex te nt

of Re gi on

al D iv er si fic at io n

Pe rc en ta ge

of tr an sa ct io ns

in co m pa ni es

no t w ith

in th e sa m e w or ld

su b- lo ca tio

n of

an in cu m be nt .

Tr an sa ct io n D at a

Av g.

Tr an sa ct

pe r Ye ar

N um

be r of

tr an sa ct io ns

pe r ye ar

of in ve st m en t;

Le ga lE nt ity

(fr eq ue nc y)

N um

be r of

le ga le

nt iti es

of an

in cu m be nt

co nd

uc tin

g CV

C in ve st m en ts .I n ge ne ra l, an

ex te rn al CV

C un

it in di ca te s a hi gh

er co m m itm

en t to w ar d CV

C in ve st m en ts ;

Le ga lE nt ity

(t ra ns ac t.)

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uc t of

th e le ga le

nt iti es

of an

in cu m be nt ;

Bu si ne ss

U ni t (fr eq ue nc y)

N um

be r of

bu si ne ss

un its

of an

in cu m be nt

co nd

uc tin

g CV

C in ve st m en ts ;

Bu si ne ss

U ni t (t ra ns ac t.)

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uc t of

th e bu

si ne ss

un it of

an in cu m be nt ;

N ew

In ve st .

Pe rc en ta ge

of fir st -t im e tr an sa ct io ns

pr og

re ss in g in to

st ar t- up

co m pa ni es

by an

in cu m be nt ;

Fo llo w

In ve st .

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of fo llo w -o n tr an sa ct io ns

pr og

re ss in g in to

st ar t- up

co m pa ni es

by an

in cu m be nt ;

Ro un

d of

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Av er ag e in ve st m en t ro un

d at

w hi ch

a tr an sa ct io n of

an in cu m be nt

ta ke s pl ac e;

Av g.

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Ag e

Av er ag e co m pa ny

ag e (in

m on

th s) at

th e tim

e of

th e tr an sa ct io n.

86 T. C. HASENPUSCH AND S. BAUMANN

technology, and development stage. To avoid multi-collinearity, we con- ducted a principal component analysis with orthogonal rotations to reduce the number of variables. Due to the absence of reliable and valid factor scores representing each element, we then chose at least two variables to represent each element. Moreover, variables with correlation coefficients above 0.9 were excluded (Backhaus, Erichson, Plinke, & Weiber, 2011). Additionally, all variables were standardized, eliminating the effects of scale differences between the variables (Hair, 1998). Overall, the typical elements of a CVC investment are covered by 11 variables (see Table 2).

Number of clusters The methodological literature offers a variety of stopping rules to determine the number of clusters (Hair, 1998; Ketchen & Shook, 1996). As each method has its limitations, we used several techniques, such as inspection of the dendogram, “elbow”-analysis, and the Mojena test. All stopping rules suggested a solution comprising three to six clusters. After comparing all possible cluster solutions, we preferred the three-cluster solution as it identifies the best trade-off between validity, stability, and interpretability.

Clustering algorithms Cluster researchers suggest a two-step analysis using hierarchical and non- hierarchical algorithms to increase the validity of cluster solutions (Ketchen & Shook, 1996; Ng et al., 2009). Following this suggestion, we used the Ward algorithm (hierarchical) and the k-Means algorithm (non-hierarchical). To eliminate outliers, we used the hierarchical single-linkage approach, reducing the sample to 65 incumbents. Overall, the combination of single-linkage, Ward and k-Means algorithms is proven to detect “natural” clusters (Hair, 1998). Similarity between the incumbents was measured by the Euclidean distance.

Table 2. Descriptive statistic of the cluster variate. Variable Type N Min Max M SD Element

Core Investments (%) # transactions 68 0.00 1.00 0.33 0.29 Industry TIME investments (%) # transactions 68 0.11 1.00 0.68 0.18 Industry Total PD* Herfindahl Type 68 0.00 0.84 0.43 0.18 Industry Extent of RD* (%) # transactions 68 0.00 1.00 0.31 0.34 Region Total RD* Herfindahl Type 68 0.00 0.70 0.25 0.22 Region Early Stage (%) # transactions 68 0.00 0.88 0.37 0.23 Development Expansion (%) # transactions 68 0.00 0.73 0.36 0.14 Development Later Stage (%) # transactions 68 0.00 0.86 0.23 0.20 Development Internet-related (%) # transactions 68 0.09 1.00 0.86 0.17 Technology B2C (%) # transactions 68 0.00 0.88 0.24 0.22 Technology B2B (%) # transactions 68 0.00 0.94 0.49 0.24 Technology

*Note: PD: Product diversification; RD: Regional diversification.

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Validation To ensure a valid and natural cluster solution, we used the RUNT test of Hartigan and Mohanty (1992) and compared several cluster solutions by using different cluster algorithms as well as modifying the data-set. As a test statistic, we used the RAND index (Rand, 1971). Except for the test on a split data-set, all results were within the range of theoretical and practical accep- tance. With a value of 18, the RUNT test indicates that, under the assump- tion of normality between the objects and a significance level of 95%, a natural cluster structure exists (Hartigan & Mohanty, 1992).

Results

The cluster analysis detected three distinct CVC approaches of TIME incum- bents that fulfilled the criteria of similarity among, and dissimilarity between, each cluster. Therefore, all variables of the cluster variate contributed sig- nificantly to the cluster solution. In comparison, most significant differences occurred between cluster one and two, as well as between cluster two and three (see Table 3).

Based on the similarities within each cluster and the specifications of the cluster variate, we labeled each cluster according to its predominant characteristic.

Aggressive cluster (C1)

The first cluster covers 17 incumbents from the M&E sector (53%) and IT (41%) sector. Most of these firms are located in East Asia (35%), West Europe (35%), and North America (16%).

Regarding their investment approach, the incumbents focus mainly on early-stage start-ups with an internet-related and B2C-oriented business model. The average round of transactions as well as the low average company age indicates a focus on early-stage start-ups. Most incumbents invest in the first or second round and the average company age is less than three years. Consequently, most transactions (81%) are the first-time an incumbent invests into that specific start-up firm. Concerning the organizational mode, nearly all incumbents within this cluster established explicit external CVC units. Ninety- four percent of all transactions were conducted via an external unit. The high penetration rate of external units, combined with the number of transactions per year, indicates a high commitment toward CVC investments.

The emphasis on young start-ups pursuing an Internet-based business model shows that the incumbents are willing to take high but calculable risks to shape the digital future of their company. Thus, in combination with the high commitment of the incumbents, we labeled this CVC investment approach as aggressive.

88 T. C. HASENPUSCH AND S. BAUMANN

Attentive cluster (C2)

The second cluster is the largest within the study, containing 26 incumbents. Most of these incumbents are classified as M&E (69%) or telecommunication (23%) firms with headquarters located in the United States (92%).

Considering their CVC investment approach, the incumbents are mainly characterized by investing in their own core business as well as the TIME industry. Furthermore, most investments are within the same world sub-

Table 3. Descriptive statistics, F- and t-values, and significance tests of the three-cluster solution Aggressive (C1) Attentive (C2) Dispersive (C3)

M F t M F t M F t Sig.1 Pairs2

Core .35 1.3 .01 .49 .8 .55 .16 .4 −1.0 .001 C2-C3*** ICT .67 1.2 -.08 .78 .5 .83 .59 .7 -.70 .000 C2-C3*** Total PD .49 .9 .31 .31 .9 -.70 .53 .4 .84 .000 C1-C2***

C2-C3*** Extent RD .36 .6 .20 .11 .4 −1.0 .51 1.3 .54 .000 C1-C2***

C2-C3*** Total RD .40 1.0 .66 .12 .4 −1.0 .32 .8 .32 .000 C1-C2***

C2-C3*** Early Stage .64 .4 1.92 .36 .6 -.17 .22 .3 −1.3 .000 C1-C2***

C1-C3*** C2-C3*

Expansion .25 .9 -.87 .41 .9 .42 .38 .5 .25 .000 C1-C2*** C1-C3***

Later Stage .05 .1 −3.4 .20 .8 -.11 .37 .7 .99 .000 C1-C2*** C1-C3*** C2-C3**

Internet-related .95 .2 1.1 .93 .3 .65 .77 1.4 -.71 .000 C1-C3*** C2-C3***

B2C .46 1.2 .96 .20 .5 -.24 .11 .3 −1.15 .000 C1-C2*** C1-C3***

B2B .20 .4 −1.8 .54 .5 .31 .64 .5 .92 .000 C1-C2*** C1-C3***

Avg. Transact. per Year 6.6 2.2 .26 3.8 .3 -.24 5.6 .8 .08 .159 - Legal Entity (frequency) 1.5 1.5 .49 .7 0.7 -.43 .7 .6 -.05 .011 C1-C2***

C1-C3*** Legal Entity (transact.) .94 .3 .64 .54 1.1 -.29 .59 1.0 -.16 .002 C1-C2***

C1-C3** Business Unit (frequency) .10 .2 −1.54 .80 1.0 .18 80 1.2 .28 .002 C1-C2***

C1-C3*** Business Unit (transact.) .06 .3 −1.15 .46 1.1 -.27 .41 1.0 -.16 .002 C1-C2***

C1-C3** New Invest. .81 .6 .89 .67 .8 -.14 .62 1.1 -.37 .002 C1-C2**

C1-C3*** Follow Invest. .19 .6 -.89 .33 .8 .14 .38 1.1 .37 .002 C1-C2**

C1-C3*** Round of Transact. 1.84 .1 −2.86 3.21 .7 .12 3.93 .6 .97 .000 C1-C2**

C1-C3*** Avg. Company Age 31 .4 −1.5 49 .9 .20 54 .6 .64 .000 C1-C2***

C1-C3*** 1Significance was tested by the non-parametric Kruskal-Wallis-Test for random samples. 2The alpha error was corrected by the Bonferroni correction. 3Only significant pairs are listed: p < 0.1*, p < 0.05*, p < 0.01***.

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 89

location and in Internet-based start-ups. Hence, the incumbents of this cluster seem to be alert to digital developments taking place within their core and local business environment. This statement is supported by the data on the number of transactions per year and the proportion of business units in charge of CVC investments. Overall, incumbents within this cluster con- duct the smallest number of CVC transactions per year, and their business units execute most of these transactions. Thus, incumbents within this cluster show moderate commitment to CVC.

In summary, incumbents within this cluster seem to be more observant, cautious, and risk-averse than incumbents belonging to the first cluster. Therefore, we labeled this investment approach as being attentive to the changes and new market participants within the incumbent’s business environment.

Dispersive cluster (C3)

The last cluster is the second largest, containing 22 incumbents from the telecommunications (59%), consumer electronics (18%), M&E (18%), and IT (5%) industries. Regarding the regional distribution, the incumbents are located in the United States (50%), West Europe (27%), and East Asia (18%).

Most incumbents of this cluster largely ignore their own core business and search for investment opportunities in different world sub-locations. Furthermore, their CVC activities are focused on mature companies in their later stages pursuing a Business-to-Business (B2B)-oriented business model. The later-stage focus is further emphasized by the comparatively long duration for which the target companies have been in business. Additionally, the CVC activities target a broader set of industries. Hence, we labeled this investment approach as dispersive.

In summary, the cluster analysis was successful in identifying three distinct CVC investment approaches relating to TIME incumbents. Thus, three types of corporate investors—aggressive, attentive, and dispersive—can be observed within the investment data. However, the descriptions reveal that, due to the unbalanced sample, incumbents of the M&E or telecommunications sectors dominate each investor type. Hence, half of the incumbents within each cluster are from theM&E or telecommunications sectors. Despite the dominance of the two sectors, sector-specific preferences exist (see Table 4).

Incumbents from the M&E sector prefer an attentive approach, whereas telecommunication and CE firms mainly follow a dispersive investment approach. In contrast, four in five of the IT incumbents follow an aggressive CVC approach. However, 29% of the M&E firms apply an aggressive approach. In summary, more than half of the incumbents of each TIME sub-sector follow a similar investment approach. Hence, we found evidence for sector-dependent CVC strategies when facing high-velocity and conver- ging business environments.

90 T. C. HASENPUSCH AND S. BAUMANN

Discussion

After defining CVC as a strategic tool that can be used to cope with the challenges and threats of a high-velocity and converging environment, our aim was to identify, describe, and explain different types of CVC investment practices utilized by TIME incumbents. Therefore, the article focused on the crankset of the CVC drivetrain to seize investment opportunities. Through an explorative and deterministic cluster analysis (q-type), we found three distinct, but almost equally popular investment approaches within the TIME industry—namely, “aggressive,” “attentive,” and “dispersive.” In line with the notion of CVC revealing “the forward strategy and plans” (European Commission, 2001, p. 9) of a firm and the crankset representing its direction, such as the search corridor for CVC investment opportunities, the article highlights three approaches to tackling the issues of a converging business environment. Firms pursuing an aggressive CVC investment approach powerfully tackle challenges with a clear focus on becoming a digital leader within the TIME industry. Their approach centers on the early identification of new business opportunities. In contrast, the constraints within the atten- tive approach reveal a more cautious and observant attitude toward ongoing developments. Hence, the main driver of this approach seems to be a window on new technology and markets arising within the core and adjacent areas. Finally, firms with a dispersive CVC approach seem to be anxious about missing out on lucrative opportunities and therefore prefer not to “put all their eggs into one basket.” Regarding the later-stage focus of the dispersive

Table 4. Contingency table: TIME sector x cluster solution. ICT Industries Aggressive Attentive Dispersive Total

M&E N 9 18 4 31 Expected N 8.1 12.4 10.5 31 % within cluster 53 69 18 - % within M&E 29 58 13 100 Standardized residuum 0.3 1.6 −2.0 -

Telecom N 1 6 13 20 Expected N 5.2 8.0 6.8 20 % within cluster 5 30 65 - % within Telecom 6 23 59 100 Standardized residuum −1.8 −0.7 2.4 -

IT N 7 1 1 9 Expected N 2.4 3.6 3.0 9 % within cluster 41 4 5 - % within IT 78 11 11 100 Standardized residuum 3.0 −1.4 −1.2 -

CE N 0 1 4 5 Expected N 1.3 2.0 1.7 5 % within cluster 0 4 18 - % within CE 0 20 80 100 Standardized residuum −1.1 −0.7 1.8 -

Total N 17 26 22 65 % within total 26 40 34 100

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approach, a strategic objective other than finding a “window on new mar- kets” might be the identification of new acquisition opportunities. Even though the exploitation of new businesses is on the strategic agenda of all TIME incumbents (Röper, 2004), the taxonomy of TIME incumbents’ CVC approaches reflects the differences in how they use CVC as a strategic tool. In other words, within the TIME industry, businesses employ the CVC drive- train along three different routes to observe and access new innovative ideas and opportunities. Thus, the taxonomy represents the different mindsets of TIME incumbents toward coping with the challenges and threats of a high- velocity and converging business environment.

The majority of incumbents linked to each sub-sector rely on one of the three approaches, indicating sector-dependent CVC strategies. The article shows existing commonalities between incumbents of the same sector. As indicated by the linkingmechanisms between both perspectives of the CVC drivetrain, the previous paths, positions, and processes of an incumbent affect its CVC approach to seize investment opportunities. In particular, the majority of M&E incumbents pursue an attentive CVC investment approach, revealing the traditionally technology-averse and observant behavior of media firms. Nevertheless, about one-third of media firms follow an aggressive CVC approach, developing new business and initializing change processes toward a more technology-driven and entrepreneurial business culture. Thus, it seems that, while a few M&E firms use CVC to change their entrepreneurial culture and try to become leaders at the forefront of digital developments, the majority ofM&E incumbents still act in amore risk-aversemanner, using CVC as an alert signal for digital developments within their core business. In contrast, we found that nearly all IT incumbents in this sample use their digital competencies to extend their business. Thereby, IT incumbents focus heavily on media start-ups and attack M&E incumbents’ core business.

Incumbents of the telecommunications and consumer electronics sectors mainly follow a dispersive CVC approach. Incumbents of both industry sectors invest in a broader set of industries, with later-stage companies pursuing a B2B business model. The dispersive investment approach reflects the multi-level business structure of both sectors. Thus, these incumbents offer, on the one hand, products and services for the end consumer (e.g., vod or social TV), while on the other hand, they need to have a state-of-the art infrastructure and technology base to enhance their core business (e.g., network infrastructure).

Besides these sectoral distinctions, we observed regional differences. Incumbents of Western Europe and East Asia over-proportionally pursue an “aggressive” approach, whereas most U.S. incumbents follow an attentive approach. One explanation is that over 70% of all IT incumbents in our sample are located in East Asia. Another explanation lies in the dominance of the U.S. venture capital market. In particular, Silicon Valley is a hotbed for media start-ups (Kaye & Quinn, 2010) offering plenty of investment

92 T. C. HASENPUSCH AND S. BAUMANN

opportunities. Thus, while non-U.S. incumbents might invest in more risky and early-stage ventures in order to become recognized as trustworthy investors, U.S. firms might use their existing reputation and local proximity to establish other non-equity-based relationships in the early stages.

In summary, the discussion shows how the incumbents of each TIME sector use CVC to develop new business as a response to the challenges and threats of a converging environment by relying on their previous paths, positions, and processes. Moreover, this research supports recent work by Hass (2011) who states that media firms adopt rather defensive entrepre- neurial activities when faced with anticipated losses in the core business. However, a minority of media firms seem to imitate the more aggressive and expansive investment practice of IT incumbents, focusing on digital sectors such as e-commerce or special interest networks and Websites.

Conclusion

The article detected, described, and compared different CVC investment approaches within the TIME industry and contributed to the theoretical under- pinning of CVC investments. The sector-specific distribution of the detected investment approaches supports the premise that CVC investments are con- ducted mainly for strategic reasons. Furthermore, the results of similar CVC approaches (simple rules) that are moderated by the industry sector and region of an incumbent (i.e., similar path, positions, and processes) backs the definition of CVC as an “organizational drivetrain” consisting of interactive and comple- mentary DCs. Thus, the study supports the view of Di Stefano et al. (2014b) and might add to the interaction between the contrary views within DC research. Additionally, by focusing on the crankset of the drivetrain concept, we extend the, thus far, neglected external perspective on DC research.

Furthermore, we contribute to the CVC literature by presenting a new taxon- omy of corporate investors. Previous classifications are based on observable differences in the commitment to CVC (Siegel et al., 1988), set-up structure of CVC programs (Dushnitsky, 2012), locus of investment (Hill & Birkinshaw, 2014), or investment practice (Souitaris & Zerbinati, 2014). Finally, by highlight- ing the entrepreneurial behavior of TIME incumbents through their investing approaches, we contribute to the overlooked topic of media venturing (Hang & VanWeezel, 2007). More precisely, we extended recent work by Hass (2011) that focused on corporate venturing as a defensive entrepreneurial activity of estab- lished media firms.

Limitations and further research

Limitations of the current article mainly relate to the sources of data collection and the applied methodology. Despite the representability of the Thomson

INTERNATIONAL JOURNAL ON MEDIA MANAGEMENT 93

Reuters database, it does not contain all equity investments and covers the U.S. market more accurately than it does other regions. Furthermore, the TIME sectors are unevenly represented due to the combination of different databases and constraints within the databases. Additionally, in line with the explorative character of the study, we used industry codes to differentiate between the TIME sectors and reduce our subjectivity regarding what constitutes a TIME firm. However, using different definitions might lead to other results.

As modeling technique, we used a deterministic cluster analysis. Although we applied measures to reduce the researchers’ influence and to address weaknesses of this method, each remedy comes with trade-offs. For example, all variables in the study were standardized, allowing each variable to contribute equally. However, this has reduced differences between each incumbent to ease the interpretation of the results. Furthermore, as suggested by Ketchen and Shook (1996), a time-serial analysis would enhance the results of studies using a cluster analysis technique.

While the study covers past CVC investments and highlights strategic directions, it does not show single strategic CVC objectives as a rationale for setting up a CVC program. A primary data collection methodology (e.g., interviews, survey) combined with a case-study design would enhance the study’s results. Furthermore, future research could apply a case study design to focus on the linking mechanism between the freewheel and the crankset of the CVC drivetrain, as well as the reconfiguration processes to convey the new knowledge into the organization.

Notes

1. Telecommunication, Information Technology, Consumer Electronics, Media, and Entertainment.

2. Besides financial and strategic objectives, CVC has a third objective: social responsi- bility. The reasoning is that the increasing availability of CVC leads to greater employ- ment. This macroeconomic point-of-view is not part of this study.

3. Previously known as VentureXpert. 4. According to the European Venture Capital Association’s industry classification and

the Nomenclature statistique des activités économiques dans la Communauté européenne (NACE), the TIME industry in the present article is defined as: telecom- munication (46.52, 47.42, 95.12, 26.3*, 61.**), Internet technology (63.11, 63.12, 62.09), consumer electronics (26.2*, 26.4*, 47.43, 95.21, 26.8*, 46.51, 47.41), and media and entertainment (18.**, 58.**, 59.**, 60.**, 63.9*, 73.1*).

5. The Orbis database is one of the worldwide leading databases for general company information. In total, the database contains financial information relating to more than 170 million companies worldwide aligned with industry reports, current ownership structure, country profiles, press reports, and rumors about M&A activities, as well as information about board members, directors, and managers.

6. In contrast to the U.S. definition, Europeans often include specific buyout types such as MBOs or MBIs when defining CVC.

94 T. C. HASENPUSCH AND S. BAUMANN

7. Even so, dedicated funds financed by parent companies and managed by venture capitalists might be strategically relevant; we believe that the strategic value of indirect investments is insignificant compared with that of direct investments. Furthermore, only 10% of all CVC deals are indirect investments according to MacMillan et al. (2008).

8. At least six transactions within the timeframe.

Acknowledgments

The authors would like to thank Andreas Will (Technische Universität Ilmenau) for his valuable suggestions and contributions that greatly improved the quality of the article. Further, we would like to thank the editors and review team for their insightful comments. An earlier version of the current article was presented at the Technology, Innovation, and Entrepreneurship Conference 2016 of the Verband der Hochschullehrer für Betriebswirtschaft e.V. in Copenhagen (Technical University of Denmark).

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Appendix A: Developmental Stage

Development Stage Definition

Seed Stage In this stage, a relatively small amount of capital is provided to an inventor or entrepreneur to prove a concept. This involves product development and market research as well as building a management team and developing a business plan.

Early Stage This stage provides financing to companies in the phase of completing product development. In some cases, products have only just been made commercially available. Companies are in the process of organizing or have been in business for 3 years or less.

Expansion Stage

This stage involves working capital for the initial expansion of a company. Companies may or may not be already showing a profit. Some of the capital may be used for plant expansion, marketing, working or product improvement. Institutional investors are more likely to invest. Overall, the venture capitalist’s role in this stage evolves from a supportive role to a strategic one.

Later Stage Capital in this stage is provided for companies that have reached a stable growth rate and are not growing as fast as the rates they attained in the expansion stages. Again, these companies may or may not be profitable, but are more likely to be profitable than in previous stages. Other financial characteristics of these companies include positive cash flow. This stage also includes companies considering an IPO.

Source: Based on National Venture Capital Association (2014, p. 115)

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