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Venture Capital Research Review and Directions

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VENTURE CAPITAL RESEARCH: REVIEW AND MIXED METHODS

DIRECTIONS

ABSTRACT

This paper reviews venture capital (VC) literature. After briefly presenting the mixed

methods research, which uses quantitative and qualitative data, and exploratory and

confirmatory analysis in one study, we suggest potential mixed methods-related directions for

future VC research. These include gaps in the relational, learning, and related aspects under

research streams, but also a cross-disciplinary mixed methods-related approach to VC

research. Finally, we provide a short critical discussion on both methods and practices. In

doing so, we hope to stimulate entrepreneurship scholars’ interest in these underutilized

methods.

Keywords: venture capital; review; entrepreneurship; mixed methods; finance.


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INTRODUCTION

Venture capital (VC) is ‘an important source of capital for entrepreneurship […]’ (Cumming,

2010, p. 1) and the literature on this topic is significant, including many reviews (e.g., Da

Rin, Hellmann, and Puri, 2013; Drover et al., 2017; Gompers and Lerner, 2001; Gompers,

2007; Jääskeläinen, 2012; Large and Muegge, 2008; Manigart and Wright, 2013;

Rosenbusch, Brinckmann, and Müller, 2013; Sahlman, 1990; Wright, Pruthi, and Lockett,

2005). Regrettably, relatively few research articles have focused on the relational (e.g.,

Huang and Knight, 2017), learning, and other aspects of the venture capitalist‒entrepreneur

relationship. In this article, we not only argue that these aspects should not be neglected, but

we also demonstrate that using mixed methods may be useful here. Indeed, mixed methods

research, which uses quantitative and qualitative data and exploratory and confirmatory

analysis in the same study, allows researchers to (1) create and test theory in the same

research project, (2) make stronger inferences by capitalizing on each methodology’s

strengths, and (3) shed light on conflicting results/findings. This, in turn, can ultimately lead

to new theoretical evaluations (Teddlie and Tashakkori, 2009, pp. 33–36). In VC research,

entrepreneurship scholars could specifically unlock major new insights into the relational-,

knowledge-, learning-, and resource-related aspects of the relationship between an

entrepreneur and one or more venture capitalists, and their resulting effects on investment

performance, because, during the qualitative interviews, they would gain (processual)

understanding into the reasons behind this performance. Further, building upon Drover et

al.’s (2017, p. 1844–1846) recent paper, we contend that using mixed methods in follow-up

(especially unstructured) interviews (e.g., Starbuck, 2016) would help entrepreneurship

scholars to achieve a better understanding of important outliers. This would lead them to have

a modified (i.e., less “averaged”) and fuller perception of the VC investment process ‒ that is,

a process including VC investments that result in extreme profits and losses. Moreover, in


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addition to addressing the partial understanding of the VC investment process, these methods

could be used in follow-up interviews with key informants of other investor groups (business

angels, corporate VCs, crowdfunding teams etc.) to explore the (processual)

interrelationships (e.g., Hellmann and Thiele, 2015) among them and, in turn, of the

financing landscape. Thus, mixed methods would also expand the current partial

understanding (because of their focus on one mechanism) of the financing landscape.

However, only a few entrepreneurship studies have used the mixed methods approach

and, overall, quantitative methods have dominated the field (i.e., 76 percent or 178 out of 235

empirical articles, according to Cameron and Molina-Azorín, 2011, p. 266; see also Crook et

al., 2010; Molina-Azorín et al., 2012). Indeed, Cameron and Molina-Azorín (2011, p. 266)

reported that, between 2003 and 2007, only 8 percent (20/235) of the empirical articles

published in the Journal of Business Venturing and Entrepreneurship Theory and Practice

used mixed methods. By comparison, scholars in adjacent research fields, such as strategic

management, have used this research design more extensively, as 14.6 percent of the articles

published in the Strategic Management Journal between 1997 and 2006 used mixed methods

(see Molina-Azorín, 2011, p. 12). Therefore, in this review, we specifically build the case for

mixed methods for entrepreneurship research. We argue that this approach would be

beneficial as it would provide entrepreneurship scholars with the potential to (1) address

broader research questions and achieve a deeper understanding of the ‘multi-faceted’

(Busenitz et al., 2003, p. 298) entrepreneurial phenomenon, (2) offer more complete

knowledge (Johnson and Onwuegbuzie, 2004; Ketchen, Boyd, and Bergh, 2008; Short et al.,

2010a),1 (3) conduct more process- and context-oriented entrepreneurship research (Molina-

Azorín, 2011; Molina-Azorín et al., 2012), (4) obtain practically significant results (Aguinis

1 The stronger the underlying methodological practices, the more solid the conclusions are

(see Ketchen et al., 2008; Short et al., 2010a).


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et al., 2010), and (5) have their articles cited more often (see Molina-Azorín, 2012).2 In

addition, we show why mixed methods would be useful for VC research. The VC context is

an appropriate setting for entrepreneurship scholars interested in these methods, because

mixed methods allow them to conduct more context- (Molina-Azorín, 2011) and process-

oriented (Molina-Azorín et al., 2012) research, VC research being contextualized (Dimov and

Shepherd, 2005) and process-oriented (Jääskeläinen, 2012).

This review article makes three main contributions. First, we review the past

accomplishments in the VC literature, organized around research streams (including

relational, learning, and related aspects). Second, after briefly presenting mixed methods

research (e.g., on mixed methods, see Creswell and Plano Clark, 2007; Creswell et al., 2003;

Molina-Azorín and Fetters, 2016; Molina-Azorín, 2007, 2012; Molina-Azorín et al., 2012;

Teddlie and Tashakkori, 2009; Tunarosa and Glynn, 2017; Turner, Cardinal, and Burton,

2017; Williams and Shepherd, 2017), we propose ideas for future VC studies, including

identified knowledge gaps that might best be approached through mixed methods, and a

cross-disciplinary mixed methods approach to VC research. Third, we offer a short critical

discussion on methods and practices. Our article is novel because, beyond our review of VC

literature, we develop a series of arguments on the potential power of mixed methods for

entrepreneurship scholars interested in VC research. Indeed, we further contend that VC

insights will be enhanced by mixed methods research in other non-management and

management-related subfields. We thus hope that the proposed directions, including the

cross-disciplinary mixed methods approach, will become research trajectories for

entrepreneurship scholars.

2 Indeed, as Molina-Azorín (2012, p. 40) wrote, ‘[…] the variable for mixed methods was

positively and significantly correlated to article length, number of other articles, and citation

count of other articles.’


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The remainder of this article is structured as follows. The second section includes a

presentation of our review method. Then, a third section reviews the past accomplishments of

the VC literature. Next, we briefly present mixed methods. A fifth section provides possible

mixed methods-related directions for future research. The following section briefly and

critically discusses methods and practices. Finally, we conclude our article in the last section.

METHOD

Here, we provide an overview of our review method. Echoing Cacciotti and Hayton (2015),

we followed a specific review process (see Figure 1). Because we focused on classic or

professional venture capitalists (VCs), we did not consider works on other major sources of

equity finance, such as business angels or corporate VCs (see De Clercq et al., 2006). In line

with seminal articles (e.g., Sahlman, 1990), we followed Gompers (2007, p. 483) and defined

VC as ‘independent and professionally managed, dedicated pools of capital that focus on

equity or equity-linked investments in privately held, high growth companies.’

Moreover, to obtain a better understanding of VC research, we followed the lead of

Busenitz et al. (2003), Short et al. (2010b), and Drover et al. (2017), and considered 137

articles (see Table 1) published in the following entrepreneurship, management, finance, and

sociology journals: Journal of Business Venturing (JBV; 50 articles), Entrepreneurship

Theory and Practice (ETP; 17 articles), Strategic Entrepreneurship Journal (SEJ; 5 articles),

Journal of Management Studies (JMS; 7 articles), Journal of Management (JOM; 1 article),

Management Science (MS; 2 articles), Administrative Science Quarterly (ASQ; 7 articles),

Academy of Management Journal (AMJ; 11 articles), Academy of Management Review

(AMR; 1 article), Organization Science (OS; 2 articles), Strategic Management Journal

(SMJ; 7 articles), Strategic Organization (SO; 1 article), Journal of International Business

Studies (JIBS; 3 articles), Journal of Financial Economics (JFE; 5 articles), Journal of


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Finance (JOF; 12 articles), Review of Financial Studies (RFS; 5 articles), and American

Journal of Sociology (AJS; 1 article).

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Insert Table 1 about here

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We used the leading ABI/Inform and EBSCO electronic databases for this purpose,

because they include a large number of generalist and specialist journals, such as those above.

We also used journal websites (JBV, JFE, SEJ, and JOM) for verification purposes. We used

‘venture capital,’ ‘venture capitalist,’ ‘VC,’ ‘syndication’ (i.e., when a group of venture

capitalists invests in a portfolio company), and ‘syndicate’ as search terms in the title,

abstract, or keywords to include the most relevant articles in VC literature in our final

sample.

We also checked the reference sections of these articles to identify other potentially

relevant works (i.e., articles, but also some books). This process yielded 25 additional

references (see Table 2). However, we excluded working papers, because they had not

undergone peer review. Relatedly, following other reviews (Delgado García, De Quevedo

Puente, and Blanco Mazagatos, 2015), we excluded dissertations and conference proceedings.

===================


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We examined all articles (titles and abstracts) to evaluate to what extent the search

terms were employed in a relevant manner. When doubt on whether a work should be

included occurred, we examined it in full to explicate its relevance for this specific research

domain. Finally, we obtained a final sample of 162 references (137 + 25 additional

references; all denoted with a ‘*’ in the references section), published between 1985 and

2017 (see Figure 1).

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Insert Figure 1 about here

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Finally, our coding scheme3 included the following themes: determinants of VC

funding decisions and investment performance; roles of VCs; investment strategies and

geographical scope; syndication; networking and reputational dimensions of the VC–

entrepreneur dyad; relational, learning, and other aspects of the VC‒entrepreneur

relationship; and institutional influences on the VC‒entrepreneur dyad.

PAST ACCOMPLISHMENTS IN VC RESEARCH: AN ENGAGED REVIEW

Several VC literature reviews attest the accomplishments in this field (e.g., see Fried and

Hisrich, 1988; Manigart and Wright, 2013). However, research has thus far emphasized only

the financial aspects of VC (Admati and Pfleiderer, 1994; Barry et al., 1990; Gompers, 1995;

Kaplan and Strömberg, 2003, 2004; Megginson and Weiss, 1991; Sahlman, 1990),

overlooking its relational and related knowledge-, learning-, and resource-related aspects.

However, there is a need to focus on the latter aspects because of conflicting results. Indeed,

Bygrave and Timmons (1992, p. 207) argued that ‘classic venture-capital investing is at its

best when the suppliers and users of capital work in partnership,’ whereas Kaplan, Klebanov,

and Sørensen (2012, p. 994) found more recently that ‘teamwork tends to be negatively

related to performance.’ Our twofold goal is thus to review VC literature (including the

stream related to relational, learning, and other aspects of the VC‒entrepreneur relationship)

and offer a blueprint for ‘better’ future inquiry by exploiting the potential of mixed methods.

We therefore propose mixed methods-related directions. Below, we review VC literature

before briefly presenting mixed methods and suggesting possible mixed methods-related

directions (inside and outside this stream) for future VC research.

Determinants of VC funding decisions and investment performance

The determinants or drivers of VC funding decisions and investment performance continue to

receive a great deal of attention. As early as the mid-1980s, MacMillan, Siegel, and

3 Due to length limitations, the categorization of VC articles is not included here, but is

available upon request.


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Narasimha (1985) found that the qualities of entrepreneurs, such as their experiences (see

also Riquelme and Rickards, 1992; Schefczyk and Gerpott, 2001) and personalities, are the

most important determinants (see also Muzyka, Birley, and Leleux, 1996). Interestingly, the

findings of Petty and Gruber’s (2011) longitudinal qualitative study indicated that the

management team is not an important criterion for rejection decisions, a finding contrary to

that of MacMillan et al. (1985) and Walske and Zacharakis (2009). Relatedly, Chen, Yao,

and Kotha (2009) found that an entrepreneur’s perceived preparedness, as reflected in the

business plan (see also Kirsch, Goldfarb, and Gera, 2009), influences VC’s investment

decision, whereas Drover, Wood, and Payne (2014b) provided evidence that the VC’s

perceived control over the entrepreneur is positively related to the willingness to fund a

venture. Bengtsson and Hsu (2015) focused on entrepreneur and VC ethnicity, and reported

that an investment match based on co-ethnicity (shared ethnicity) is correlated with more

(less) successful firm outcomes. From a different perspective, Hellmann and Puri (2000)

showed that innovation is also a determinant, with innovator firms being more likely to

obtain VC funding and bring products to market faster (for the positive relationships between

VC and (1) innovation and (2) commercialization, see also Dutta and Folta, 2016). Murnieks

et al. (2011) showed that similarity in the decision-making processes of investors and

entrepreneurs is also a determinant, while Matusik, George, and Heeley (2008) found that

value homophily is a positive moderator of the (1) technical degree possession‒ and (2) start-

up experience‒founder quality evaluation relationships, whether negative or positive. Finally,

contrary to Chemmanur, Krishnan, and Nandy (2011), who found a positive relationship

between VC backing and efficiency, Alperovych, Hübner, and Lobet (2015) investigated how

public and private VCs affect post-investment efficiencies in their portfolio companies and


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reported that Belgian government VC-funded companies are associated with substantially

lower efficiencies.4

Dimov and Shepherd (2005) focused on human capital, and found a positive

association between education in the sciences and humanities, and the number of portfolio

companies going public, whereas Zarutskie (2010, p. 169) found that ‘task-specific and

industry-specific human capital are the strongest predictors of venture capital fund

performance.’ Florin (2005) suggested that the resources and funding prior to an initial public

offering are greatly enhanced when ventures have strong VC backing. Drawing from earlier

research (Dimov and Shepherd, 2005), Bottazzi, Da Rin, and Hellmann (2008) investigated

investor activism (see also Jackson, Bates, and Bradford, 2012), and found two successive

positive relationships between human capital (i.e., partners with prior business experience),

investor activism (i.e., recruitment of senior and outside management, portfolio company

fundraising and interaction), and portfolio company success. Relatedly, results from Ewens

and Rhodes-Kropf (2015) suggested that a partner’s human capital impacts performance.

Recently, Gerasymenko, De Clercq, and Sapienza (2015, p. 90) found that business model

change experience positively impacts portfolio company performance. Finally, echoing the

work of Dimov and Shepherd (2005), Florin (2005), and Colombo and Grilli (2010),

Rosenbusch et al. (2013) performed a meta-analysis of the benefits of VC funding, providing

evidence that VC investment is positively related to funded venture performance. However,

the performance effect is small and disappears after controlling for industry selection effects.

Roles of venture capitalists

The numerous roles of VCs have continued to attract scholarly attention. For example,

Gorman and Sahlman (1989) found that VCs are involved in financing, strategic planning,

4 Although not discussed in detail here, there exists a more negative view of VC decision-

making, with decision accuracy being negatively impacted by (1) overconfidence (Zacharakis

and Shepherd, 2001) and (2) a greater amount of information (Zacharakis and Meyer, 1998,

2000; Zacharakis and Shepherd, 2001).


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and management recruiting. MacMillan, Kulow, and Khoylian (1989) corroborated their

results, but also highlighted that VCs would prefer to be more involved in other activities,

such as formulating business strategies and marketing plans, and replacing the management

of their funded ventures. This result was found in Elango et al.’s (1995) study, confirming the

importance of evaluation and recruitment of the management team for early-stage VC

investors. Referring to prior literature (Rosenstein et al., 1993), Fried and Hisrich (1995)

reported that VC firms that serve as lead investors are actively involved in providing access

to networks, moral support, general business knowledge, and experience, whereas Amit,

Brander, and Zott (1998), also referring to prior literature (Tyebjee and Bruno, 1984),

demonstrated mathematically that VCs are skilled at choosing and monitoring entrepreneurial

projects. Hellmann and Puri (2002) also focused on the scope of VC involvement, finding it

goes beyond financing the professionalization of start-ups to include activities such as

formulating HR policies, adopting stock option plans, hiring sales and marketing personnel,

and replacing founders. Finally, Baum and Silverman (2004) indicated that VCs pick start-

ups that promise superior performance during the pre-investment stage and then build them

post-investment by providing management expertise and connections (see also Fitza,

Matusik, and Mosakowski, 2009; Lungeanu and Zajac, 2016).

De Clercq et al. (2006), taking extant literature (e.g., Gifford, 1997; Sapienza, 1992;

Sapienza, Manigart, and Vermeir, 1996) into account, compared the issues faced by venture

capitalists, business angels, and corporate VCs during the pre-, post-, and exit investment

phases, highlighting the strategic, financial, networking, interpersonal, reputational, and

disciplinary roles of VCs. Large and Muegge (2008) proposed an eight-category typology of

the nonfinancial value-adding inputs of VC firms (legitimizing, outreaching, recruiting,

mandating, strategizing, mentoring, consulting, and operating). Gerasymenko and Arthurs

(2014) underlined the advisory and CEO replacement roles of VCs, and found that the


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stronger the perception for a portfolio company to have an initial public offering (IPO), the

more likely its CEO would be replaced: the expected time to exit plays a moderating role

(i.e., the shorter the time, the higher the probability). Finally, according to Celikyurt, Sevilir,

and Shivdasani (2014, p. 99), ‘VCs play a significant role in enhancing R&D and innovation

at mature public firms, even long after the firm’s IPO stage.’

Investment strategies and geographical scope

Investment strategies and geographical scope continue to receive significant attention (e.g.,

see Fulghieri and Sevilir, 2009). As early as the 1990s, Gupta and Sapienza (1992)

demonstrated that VCs specialized in early-stage investments are likely to invest in ventures

that are less diversified and narrower in geographic scope. In line with their study, Lerner

(1995) found that the directors of ventures funded in geographical proximity are more likely

to be VC firm board members. Dimov and Martin De Holan (2010) indicated that VCs with

broader investment experience (see also Franke et al., 2008; Shepherd, Zacharakis, and

Baron, 2003) are more likely to enter new markets in close proximity. De Clercq et al. (2001)

observed that VCs diversify their investments by growth stage and geographical scope as

they become more experienced.

Conversely, Norton and Tenenbaum (1993) suggested that VC firms control risks5

(see also Amit, Glosten, and Muller, 1990; Fiet, 1995; Lu, Hwang, and Wang, 2006) by

prioritizing specialization and information-sharing strategies, while, referring to prior

literature (Robinson, 1987), Manigart et al. (2002) found support for the resource-based view

hypothesis that specialized VCs ask for lower returns for specialized investments. Patzelt, Zu

Knyphausen-Aufseß, and Fischer (2009) investigated the influence of top management team

(TMT) composition on the portfolio strategies of VC firms, and found that TMT members

with science and engineering degrees and entrepreneurial experience increase the probability

5 However, we do not discuss contracts in detail here (e.g., see Burchardt et al., 2016;

Cumming, 2008; Fiet, 1995; Kaplan and Strömberg, 2003, 2004; Li and Mahoney, 2011).


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of investing in early-stage ventures. Lastly, Matusik and Fitza (2012) highlighted the U-

shaped relationship between VC diversification and performance (on diversification, see also

Buchner, Mohamed, and Schwienbacher, 2017).

Syndication

VC syndication has always been a popular research area. Bygrave (1987) showed that sharing

expertise and knowledge is the main reason for co-investing or syndicating (Brander, Amit,

and Antweiler, 2002; Gompers and Lerner, 2000; Manigart et al., 2006) under high

uncertainty. Evolving this networking perspective, Bygrave (1988) pointed out that VCs that

invest in highly innovative technology companies are more interconnected than those

investing in their less innovative counterparts. Lerner (1994) observed that VCs in late-stage

syndicated investments team up with their peers and less experienced VCs, whereas Lockett

and Wright (2001) demonstrated that the resource-based view better explains early-stage

syndicated investments. Relatedly, Hochberg, Ljungqvist, and Lu (2010, p. 831) found that

‘there is less entry in VC markets in which incumbents are more tightly networked with each

other, as evidenced by their past syndication patterns.’ Brander et al. (2002) provided

evidence that VCs add value beyond merely selecting the best syndicated investments.

Finally, De Clercq and Dimov (2004) showed that financial- and knowledge-related

hypotheses are equally valid in explaining syndication behavior, whereas, referring to Wright

and Lockett (2003), Manigart et al. (2006) found a positive correlation between a VC’s non-

lead investor role and its value-adding motive to syndicate.

Mäkelä and Maula (2006) created a grounded model and propositions, focused on

commitment between organizations in syndicated cross-border VC investments,6 and

included such moderators as distance, embeddedness, and financial importance, whereas

Meuleman et al. (2017, p. 134) recently validated the hypothesis stating that ‘[p]rospective

6 We herein focus on syndicated cross-border VC investments/cross-border VC syndicates.


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foreign VC partners that have a higher number of previous direct ties with the pre-existing

members of the syndicate will have a higher likelihood of joining a cross-border syndicate.’

Jääskeläinen, Maula, and Seppä (2006) also suggested that syndication frequency positively

moderates the inverted U-shaped relationship between the number of companies and VC

performance, and Dimov and De Clercq (2006) found that specialized expertise (investment

syndication) is negatively (positively) correlated with portfolio failure (on VC entrepreneurs’

perceptions of venture failure, see also Zacharakis, Meyer, and DeCastro, 1999). Drawing

from network finance literature (Hochberg, Ljungqvist, and Lu, 2007), Guler (2007) found

that the higher a VC firm’s status is, the higher its investment termination probability will be

(on termination, see also Devigne, Manigart, and Wright, 2016). De Clercq and Dimov

(2008) validated the hypothesis of a positive relationship between a VC firm’s industrial

knowledge and its investment performance, while, referring to prior works (Mäkelä and

Maula, 2008), Liu and Maula (2016, p. 1414) validated the hypothesis that an ‘[i]nternational

experience in the host country is positively related to VC firms’ propensity to partner with

local VC firm(s) rather than invest alone or with other foreign VC firm(s) when investing in a

foreign venture.’ Dai and Nahata (2016, p. 144) found that ‘[s]yndicate participation of local

VCs, or of non-local VCs with investment experience in the host country, or of non-local

VCs from a culturally similar country (as the host country), is associated with increased

aggregate VC funding provided to portfolio companies’ (for a review on cross-border VC

investments, see Wright et al., 2005). Lastly, Vedula and Matusik (2017) found that

geographical proximity is twice more important than syndication for a VC firm’s decision to

internationalize.

Dimov and Milanov (2010) built on earlier research (Guler, 2007; Hochberg et al.,

2007; Sorenson and Stuart, 2001, 2008), and observed that new VC investments are also

likely to be more syndicated among high-status VCs. Guler and Guillén (2010) found that the


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social status of a VC in its home-based network is positively correlated with its entry onto

overseas markets: home-based co-investors from overseas moderate the relationship between

network advantages (social status and brokerage) and market entry. More recently, Ozmel,

Reuer, and Gulati (2013) found that the positive relationship between the affiliation of a new

venture with a prominent VC firm and the formation of future alliances decreases with the

alliance network prominence of the new venture, whereas Hopp and Lukas (2014) referred to

earlier findings (Ferrary, 2010) and confirmed that the positive relationship between partner

investment experience and lead VC’s probability of collaboration is positively moderated by

a reciprocated history of syndicating activities, signaling activity frequency, and the portfolio

company’s development stage.

Ma, Rhee, and Yang (2013) investigated the impact of power source mismatch on the

effectiveness of VC syndication, while Zhelyazkov and Gulati (2016) examined the relational

and reputational impacts of withdrawing from VC syndicates on successful syndication

behaviors, reporting VCs are less likely to syndicate with directly (relational) and historically

(reputational) withdrawn co-investors. Relatedly, Gompers, Mukharlyamov, and Xuan (2016,

p. 629) showed that ‘venture capitalists who share the same affinity-based characteristics are

more likely to syndicate with each other and that this homophily has a detrimental effect on

the probability of investment success […].’ Referring to prior works (e.g., Gu and Lu, 2014),

Ter Wal et al. (2016) found that the best configuration for venture success is when syndicates

have open-specialized or closed-diverse networks. Lastly, Chemmanur, Hull, and Krishnan

(2016, p. 576) found that ‘[e]ntrepreneurial firms backed by syndicates of both international

and local VCs are more successful than those backed by syndicates consisting of purely

international VCs or purely local VCs, both in terms of exit likelihood and post-IPO

operating performance.’

Networking and reputational dimensions of the VC–entrepreneur dyad


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Research on VC‒entrepreneur networks and reputation has grown substantially, especially

over the past decade (e.g., see Nahata, 2008; Sørensen, 2007). Drover, Wood, and Fassin

(2014a) investigated the reputational dimensions of the dyad, and noted that a funding

rejection (enhancing the fear of failure) may lead entrepreneurs to accept teaming up with an

unethical VC. Referring to prior works (e.g., Nahata, 2008; Sørensen, 2007), Cumming and

Dai (2013) showed that a portfolio company that has improved its perceived quality is more

likely to change to a new lead VC with a better reputation, despite the risk of obtaining lower

investments and valuations in subsequent financing rounds. Wang (2016) also found that

social network ties are more important in the early (attention/awareness) stages of the VC

investment process. Referring to prior works (Steier and Greenwood, 1995), Shane and Cable

(2002) documented that ex ante direct ties to an entrepreneur are positively related to

investors’ decisions to invest in new ventures, this effect disappearing when the

entrepreneur’s reputation is considered. Dimov, Shepherd, and Sutcliffe (2007) observed that

the status of the VC reinforces the negative relationship between TMT’s financial expertise

and investment selection, and Vanacker and Forbes (2016) subsequently found that financial

resource attraction by a VC-funded company depends more on the industry-specific

experience of the lead VC than its media prominence. Stuart, Hoang, and Hybels (1999)

reported a positive relationship between uncertainty regarding the quality of the affiliated

venture and reliance on external evaluators for the key dimensions of affiliates (i.e.,

successful innovation, and work and evaluation experience), whereas Hallen and Pahnke

(2016) found that VC firm prominence is negatively related to an entrepreneur’s accurate

evaluation of its track record. Moreover, referring to prior literature on reputation (e.g.,

Gompers, 1996; Hallen, 2008; Petkova, Rindova, and Gupta, 2013), Petkova et al. (2014)

found that reputable VCs are more likely to invest in the emerging clean energy sector and,

complementing prior works (Ozmel and Guler, 2015; Pahnke, Katila, and Eisenhardt, 2015),


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Pahnke et al. (2015, p. 1348) found that ‘if the firm ha[d] a high-status VC, gaining an

indirect competitor tie decreas[ed] product introductions […] by 43%.’ Milanov and

Shepherd (2013, p. 732) found that ‘the reputation of a newcomer’s initial partners […] [had

a] positiv[e] influence [on] its future status.’ Recently, referring to prior literature (Hsu,

2006), Ragozzino and Blevins (2016, p. 995) also validated the hypothesis that ‘[t]he higher

the prominence of the VC, the greater the likelihood that the new venture will go public after

its founding.’ Unifying previous studies on reputation and status (including Hsu, 2004; Lee,

Pollock, and Jin, 2011; Milanov and Shepherd, 2013), Pollock et al. (2015) found that the

effect of reputation on status increases with the age of the firm. In their combined

investigation of social ties and status hierarchies in VC decision making, Wuebker, Hampl,

and Wüstenhagen (2015, p. 170) notably validated the hypothesis that ‘[u]nder conditions of

market uncertainty, personal ties have a higher relative influence than status hierarchies in

venture capital investment decisions.’ Lastly, Bermiss et al. (2017) looked at how socially

salient actors/actions (beacons) influence entrepreneurial activity, and identified two types.

Endorsing beacons are closely related to status and reputation, because their influence comes

from the reputation of exceptional insight. Conversely, demonstrating beacons are less reliant

on reputation or status because they garner attention by their performance in the market.

Relational, learning, and other aspects of the VC‒entrepreneur relationship

Numerous complex and important issues regarding the relational-, knowledge-, learning-, and

resource-related aspects of VC research remain unexplored, but considerable progress has

nevertheless been made. For example, grounded in earlier contributions (Rosenstein, 1988),

Barney et al. (1996) emphasized that new venture teams (NVTs) generally have a strong

desire to learn, but their evaluations of VC learning assistance diverge considerably. Their

study provided evidence of two negative relationships between an NVT assessment of VC

business management advice and the team’s venture industry experience and tenure in the


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venture. The authors also found two other negative relationships between NVTs’ assessment

of VC operational assistance and these two dependent variables, and one positive relationship

with venture’s technological innovativeness as the dependent variable.

Contrary to the research finding of a negative correlation between a VC firm’s trust in

the portfolio company and VC firm learning (De Clercq and Sapienza, 2005), De Clercq and

Sapienza (2006) provided evidence that a VC firm’s trust (on trust, see Harrison et al., 1997)

in social interactions with goal congruence and commitment to a portfolio company are

positively related to positive outcomes, such as the VC’s perception of the company’s

performance. In line with earlier findings (Sweeting, 1991), Cable and Shane (1997)

proposed that time pressure, communication, social relationships, relational demography,

congruence in work values, relative power, payoff, bonding mechanisms, generosity,

penalties, and staged capital payouts impact whether entrepreneurs and VCs cooperate. The

research of De Clercq and Sapienza (2001), inspired by several seminal articles (Dyer and

Singh, 1998; Lane and Lubatkin, 1998), found that knowledge-based routines are positively

correlated with relational rents, and that trust and knowledge relatedness moderate this

positive relationship. Shepherd and Zacharakis (2001) built a model related to partner

cooperation (on cooperation, see also Harrison et al., 1997; Zacharakis, Erikson, and George,

2010), which included trust and further proposed that open and frequent communication

between VC firms and entrepreneurs leads to more effective trust generation mechanisms.

Lastly, referring to prior works (Busenitz et al., 1997), Busenitz et al. (2004) validated the

hypothesis that procedural fairness between venture capitalists and NVT members is

positively correlated with venture performance.

By referring to prior studies on the feedback‒decision commitment relationship

(Sapienza and Korsgaard, 1996) and to single- and double-loop learning (Argyris and Schön,

1978), Chugh, Nicolaou, and Barnes (2011) suggested that single-loop learning mediates the


18

VC feedback‒start-up commitment escalation relationship, whereas inhibited double-loop

learning mediates the VC feedback‒start-up exit relationship. Finally, from an organizational

learning perspective, Dimov et al. (2012) found that the accumulation, familiarity, and

shaping of decisional experience positively influences a VC’s decision on early investments,

while the decay of decisional experience related to dormant decisions has a negative

influence.

Institutional determinants of the VC‒entrepreneur dyad

The influence of institutions on the VC‒entrepreneur dyadic relationship has long been

considered as an important research area. Scholars have generally investigated VCs and

institutions by building on Scott’s (1995) tri-dimensional (normative, cognitive, and

regulatory) conceptualization of the institutional context. For instance, Bruton, Fried, and

Manigart (2005) did so and proposed that the networking role of VCs is more important in

strongly networked (i.e., Asian) countries. Based on earlier work (Bruton and Ahlstrom,

2003; Bruton, Ahlstrom, and Singh, 2002), Ahlstrom and Bruton (2006) conducted 65 semi-

structured interviews with VCs around East Asia, and their data revealed that supportive

networks and weak legal environments make it unlikely for a portfolio company’s CEO to be

replaced. Zacharakis, McMullen, and Shepherd’s (2007) study of the U.S., South Korean, and

Chinese economies indicated that Chinese VCs emphasize human capital (market) factors

more than the U.S. (South Korean) VCs. Further, by using a case study methodology to

compare Latin American and Asian VC contexts, Bruton, Ahlstrom, and Puky (2009)

observed that community-oriented normative and cognitive institutions supportive of VC

activity are not present in Latin America, while, focusing on VC investments in Latin

America, Khoury, Junkunc, and Mingo (2015, p. 814) found ‘a negative relationship between

the level of political hazards in the target venture’s home country and the amount of VC

investment within a round.’ More recently, Li and Zahra (2012) found that uncertainty


19

avoidance or collectivism weakens the positive relationship between the national level of

institutional development and VC activity (on the potential importance of national VC market

development, see also Croce, Marti, and Murtinu, 2013).

Critical conclusion

Overall, although scholars have investigated topics such as syndication, investment strategies,

and the roles and activities of venture capitalists, only a few articles7 have focused on trust,

knowledge, relational rents, communication, fairness, learning, and procedural justice in the

VC‒entrepreneur relationship. However, this problem might easily be resolved because

mixed methods (Creswell and Plano Clark, 2007; Greene, Caracelli, and Graham, 1989;

Johnson and Christensen, 2004; Molina-Azorín, 2012; Tashakkori and Teddlie, 1998) have

the potential to unlock major new insights into these aspects of VC research. Therefore, we

briefly present mixed methods below before suggesting research directions based on these.

PRESENTATION OF MIXED METHODS

Here, we briefly present mixed methods.

Definition

Despite several notable definitions of mixed methods (Creswell and Plano Clark, 2007;

Greene et al., 1989), we begin with the succinct yet comprehensive definition of Creswell et

al. (2003):

A mixed methods study involves the collection or analysis of both quantitative and/or

qualitative data in a single study in which the data are collected concurrently or

sequentially, are given a priority, and involve the integration of the data at one or

more stages in the process of research. (p. 212)

7 Related references are Barney et al. (1996), Busenitz, Moesel, and Fiet (1997), Busenitz,

Fiet, and Moesel (2004), Cable and Shane (1997), Chugh et al. (2011), De Clercq and Dimov

(2004, 2008), De Clercq and Sapienza (2001, 2005, 2006), Dimov and Martin De Holan

(2010), Dimov and Shepherd (2005), Dimov, Martin de Holan, and Milanov (2012),

Harrison, Dibben, and Mason (1997), Liu and Maula (2016), Lockett and Wright (2001),

Matusik and Fitza (2012), Sapienza and Korsgaard (1996), Shepherd and Zacharakis (2001).


20

We agree with this definition because mixed methods research uses both quantitative and

qualitative data, and exploratory and confirmatory analysis in the same study (Walsh et al.,

2015).

Complementarity: An important purpose

Greene et al. (1989) suggested that mixed methods designs can be used for several purposes

(e.g., triangulation, development,8 initiation, expansion), although other authors have

suggested that complementarity (i.e., when researchers seek the ‘elaboration, enhancement,

illustration, [and] clarification of the results from one method with the results from the other

method,’ Greene et al., 1989, p. 259; see also Bryman, 2009; Johnson and Christensen, 2004)

is a key purpose (e.g., Molina-Azorín, 2007, 2012; Molina-Azorín et al., 2012; Tashakkori

and Teddlie, 1998; Turner et al., 2017). For example, Molina-Azorín et al. found that

complementarity was the second most important purpose, representing approximately 22

percent of mixed methods research in the entrepreneurship stream (2012, p. 435) and 13

percent in strategic management (Molina-Azorín, 2012, p. 43). Further, qualitative methods

usually serve to clarify quantitative findings (Molina-Azorín, 2012, p. 44). Lastly, because

complementarity ‘is typically used’ in exploratory design (Molina-Azorín et al., 2012, p.

448), we focus below on this design.9

Explanatory design: Definition, purpose, and advantages

Creswell (2003) drew on the works of Morgan (1998), Morse (1991), and Creswell et al.

(2003), among others, and suggested the explanatory design10 is

8 For a study illustrating development purpose, see Guler (2007). 9 Creswell and Plano Clark (2007, p. 85) explained that ‘[i]f one phase is followed by another

phase, the first phase is quantitative, quantitative methods or data are emphasized, the second

phase is connected to the results of the first phase, and the intent is to explain these results

using qualitative data as a follow-up, then the choice of design is the Explanatory Design

[…].’ For a detailed presentation of other mixed methods designs, see, for example, Creswell

and Plano Clark (2007). 10 Although we herein focus on explanatory design, researchers can also implement an

exploratory mixed methods design. This design ‘is characterized by an initial phase of


21

the collection and analysis of quantitative data followed by the collection and

analysis of qualitative data. The priority typically is given to the quantitative data,

and the two methods are integrated during the interpretation phase of the study. […]

The purpose of […] [this] design typically is to use qualitative results to assist in

explaining and interpreting the findings of a primarily quantitative study (p. 215).

Lastly, many authors have highlighted the advantages of an explanatory mixed

methods design, among which its straightforwardness (Creswell, 2002, 2003; Creswell et al.,

2003).11

POSSIBLE MIXED METHODS DIRECTIONS FOR FUTURE VC RESEARCH

Here, we highlight the need for the greater use of mixed methods in future VC research. We

also propose possible research questions and improvements that link mixed methods and VC

research. We also present a cross-disciplinary mixed methods-related approach to VC

research to inspire entrepreneurship scholars to pursue mixed methods research by pointing

potential contributions that can be made.

The need for the greater use of mixed methods in future VC research

We contend that, despite substantial progress, many complex and important issues and

knowledge gaps regarding the relational-, knowledge-, learning-, and resource-related aspects

of VC research remain unexplored. In doing so, we echo the arguments of Combs et al.

(2011) on franchising research. We therefore focus on these aspects and, following Teddlie

and Tashakkori (2009, pp. 33–36), suggest how mixed methods might be applied to future

VC research.

First, we suggest that mixed methods would enhance VC research by allowing

researchers to create and test theory, make stronger inferences, and shed light on conflicting

qualitative data collection and analysis, which is followed by a phase of quantitative data

collection and analysis. Therefore, the priority is given to the qualitative aspect of the study.

The findings of these two phases are then integrated during the interpretation phase. At the

most basic level, the purpose of this strategy is to use quantitative data and results to assist in

the interpretation of qualitative findings. Unlike the […] explanatory approach, which is

better suited to explaining and interpreting relationships, the primary focus of this […]

[approach] is to explore a phenomenon’ (Creswell, 2003, p. 215; for a detailed presentation,

see also Creswell and Plano Clark, 2007). 11 For methodological guidelines, see Turner et al. (2017).


22

results/findings and knowledge gaps regarding how variations in collective and strategic

versus collective and operational knowledge-sharing routines (e.g., variations in

specialization, transfer, and recombination; see Dyer and Singh, 1998) impact dyadic

expertise and, in turn, relational rents (Dyer and Singh, 1998). Future mixed methods VC

research could examine situations, with phenomenological data suggesting how statistical

results should be interpreted. Moreover, other mixed methods analyses could focus on how

large knowledge bases and the related flows (De Clercq and Dimov, 2008) affect uncertainty

reduction (De Clercq and Dimov, 2004) and the investment performance of single (De Clercq

and Dimov, 2008) and multiple (Jääskeläinen et al., 2006) VCs through syndication and

external knowledge sources. Mixing quantitative and qualitative methods could also help

scholars gain a deeper understanding of the role of perceived and achieved relational quality

in learning (De Clercq and Sapienza, 2005). Lastly, future mixed methods VC research could

create and test theory, provide stronger inferences, and shed light on the conflicting

results/findings and knowledge gaps on the role of feedback (e.g., see Sapienza and

Korsgaard, 1996) in each stage of the VC investment process: deal origination, screening,

evaluation, structuring, and post-investment (Eckhardt, Shane, and Delmar, 2006; Elango et

al., 1995; Robinson, 1987; Tian, 2011; Tyebjee and Bruno, 1984; Wang, 2016).

Second, mixed methods research on fairness has the potential to elucidate the ethical

(Drover et al., 2014a) and reputational dimensions (e.g., Bermiss et al., 2017; Dimov and

Milanov, 2010; Dimov et al., 2007; Lee et al., 2011; Milanov and Shepherd, 2013; Nahata,

2008; Petkova et al., 2013, 2014; Pollock et al., 2015; Ragozzino and Blevins, 2016;

Sørensen, 2007; Vanacker and Forbes, 2016; Zhelyazkov and Gulati, 2016; Zott and Huy,

2007) of VC investment. Scholars could focus on the fair (Busenitz et al., 1997) and unfair

decisions, and actions and techniques (e.g., lean or total quality management techniques) that

impact professional relationships, procedures (e.g., see Busenitz et al., 2004), and, ultimately,


23

ongoing relationships in dyads and syndicates, as well as the investment performance of

single (De Clercq and Dimov, 2008) and multiple (Jääskeläinen et al., 2006) VCs. In this

regard, a better understanding of the role of trust (De Clercq and Sapienza, 2005; Harrison et

al., 1997; Lockett and Wright, 2001; Shepherd and Zacharakis, 2001; Manigart et al., 2006)

would be particularly important.

Third, future VC research could use mixed methods to test theories and inferences or

explain the conflicting results/findings and knowledge gaps in the VC firm‒portfolio

company relationship by examining their prior experiences (De Clercq and Dimov, 2004,

2008; Dimov and Martin De Holan, 2010; Dimov and Milanov, 2010; Dimov and Shepherd,

2005; Dimov et al., 2012; Hopp and Lukas, 2014; Patzelt et al., 2009; Petty and Gruber,

2011; Sørensen, 2007) and learning dynamics (Barney et al., 1996; De Clercq and Sapienza,

2005; Dimov et al., 2012). Both dyadic and syndicated relationships could be explored as

well as the past, present, and future investment performances of single (De Clercq and

Dimov, 2008) and multiple (Jääskeläinen et al., 2006; Liu and Maula, 2016) VCs.

Fourth, another potentially fruitful area for mixed methods VC research is the impact

of contact (Sapienza, 1992) and communication (Busenitz et al., 2004; Sapienza, 1992;

Shepherd and Zacharakis, 2001) on dyadic and syndicated relationships and perceived and

realized investment performance (De Clercq and Dimov, 2008; De Clercq and Sapienza,

2006; Jääskeläinen et al., 2006). In this regard, theory, inferences, and conflicting

results/findings and knowledge gaps on the attitudes and emotions involved in and resulting

from communication specifics (e.g., communication frequency and channels) provide fertile

grounds for research.

Finally, mixed methods VC research could focus on key moderators of the VC firm‒

portfolio company relationship. Although our goal is not to develop a full list, Table 3


24

presents some examples of some moderators that, we believe, can help researchers provide a

richer and more nuanced analysis of this relationship.

===================


===================

Possible mixed methods questions

We now focus on sequential descriptive design and propose possible mixed methods research

questions. According to Onwuegbuzie and Leech (2006), sequential descriptive studies

include a descriptive component (i.e., the quantitative part that generates the independent

variable) that informs a phenomenological component (i.e., the qualitative part that generates

the dependent variable). However, due to space constraints, we will not repeat here the three

benefits of mixed methods with regard to theory, inferences, and conflicting results/findings

(see Teddlie and Tashakkori, 2009, pp. 33–36).

First, as previously mentioned, scholars of the relational-, knowledge-, learning-, and

resource-related aspects of VC research might focus on differences in knowledge-sharing

routines (e.g., variations in specialization, transfer, and recombination; see Dyer and Singh,

1998) within dyads or syndicates that generate high versus low relational rents (De Clercq

and Sapienza, 2001). Longitudinal sequential descriptive studies could consider questions

such as: (1) in terms of expertise, what are the differences between dyads or syndicates that

share high versus low levels of knowledge; (2) regarding the creation of rents, what are the

differences between dyads or syndicates with stronger versus weaker expertise? Herein,

variations in routines (independent variable at t) could be used to inform the expertise of the

dyadic or syndicated group (dependent variable at t; independent variable at t + 1) and, in

turn, relational rents (dependent variable at t + 1).

Second, this type of research design could be used to investigate differences in terms

of dyadic or syndicated fairness (Busenitz et al., 1997, 2004) between high- and low-

performing VC investments (De Clercq and Dimov, 2008; Jääskeläinen et al., 2006). Indeed,


25

questions that could be examined using longitudinal sequential descriptive designs include:

(1) what are the procedural differences between dyads or syndicates that make or plan to

make fair decisions, as opposed to those that do not proceed fairly; (2) what are the

differences, in terms of fairness and procedural justice (Sapienza and Korsgaard, 1996;

Shepherd and Zacharakis, 2001) between, for example, dyadic or syndicated groups that

implement such procedures (e.g., several versus numerous); (3) In terms of fairness and

procedural justice, what are the differences in VC investment performance between dyadic

and larger syndicated groups? Herein, fair and unfair decisions (independent variable at t)

could be used to shed light on the procedures (dependent variable at t; independent variable at

t + 1) and, in turn, the dyad or syndicate (dependent variable at t + 1; independent variable at

t + 2) and VC investment performance (dependent variable at t + 2).

Third, it might be useful to determine the differences between prior experiences (De

Clercq and Dimov, 2004, 2008; Dimov and Martin De Holan, 2010; Dimov and Milanov,

2010; Dimov and Shepherd, 2005; Dimov et al., 2012; Hopp and Lukas, 2014; Liu and

Maula, 2016; Patzelt et al., 2009; Petty and Gruber, 2011; Sørensen, 2007) and learning

dynamics (Barney et al., 1996; De Clercq and Dimov, 2004, 2008; De Clercq and Sapienza,

2005; Dimov and Shepherd, 2005; Dimov et al., 2012; Liu and Maula, 2016) of dyads or

syndicates that yield high versus low investment performance (De Clercq and Dimov, 2008;

Jääskeläinen et al., 2006). Longitudinal sequential descriptive studies could address the

following questions: (1) how do positive and negative experiences affect learning in dyads or

syndicates; (2) how do dyads or syndicates that learn more versus less dynamically and

efficiently differ; (3) how do differences in prior experience and learning dynamics in dyadic

or syndicated groups affect VC investment performance? Herein, past experience

(independent variable at t) could be used to gain a better understanding of the learning

dynamics (dependent variable at t; independent variable at t + 1) and, in turn, the dyad or


26

syndicate (dependent variable at t + 1; independent variable at t + 2) and VC investment

performance (dependent variable at t + 2).

Fourth, scholars that adopt longitudinal sequential descriptive research could turn

their attention to differences in dyadic or syndicated communications (Busenitz et al., 2004;

Sapienza, 1992) that yield high versus low investment performance (De Clercq and Dimov,

2008; Jääskeläinen et al., 2006) and their effects on firm reputation and status (Dimov and

Milanov, 2010; Dimov et al., 2007; Guler and Guillén, 2010; Guler, 2007; Ma et al., 2013;

Milanov and Shepherd, 2013; Pahnke et al., 2015; Stuart et al., 1999; Wuebker et al., 2015;

Zhang, Gupta, and Hallen, 2016; Zhelyazkov and Gulati, 2016). This triggers the following

research questions: (1) what are the differences between dyads or syndicates that have diverse

and more or less efficient communication methods (e.g., emails, meetings; Shepherd and

Zacharakis, 2001); (2) what are the differences in communications that affect VC investment

performance in dyadic or syndicated groups; (3) what effects do such communications have

on firm reputations and statuses? Herein, communication (independent variable at t) could be

used to inform the dyadic or syndicated (and thus larger) group (dependent variable at t;

independent variable at t + 1) and, in turn, VC investment performance (dependent variable at

t + 1; independent variable at t + 2) and firms’ reputations and statuses (dependent variable at

t + 2).

We believe that longitudinal sequential descriptive designs can provide new insights

in areas generally beyond the reach of monomethods. Researchers have yet to take full

advantage of the many ways in which sequential descriptive mixed methods research (and,

globally, mixed methods) can guide and improve current methodological choices. Therefore,

we follow Johnson and Onwuegbuzie (2004, p. 21) and propose possible improvements.

Possible mixed methods improvements


27

Mixed methods can address complex research questions via a three-process-oriented and

longitudinal (Baum and Silverman, 2004; De Clercq and Sapienza, 2005, 2006; De Clercq et

al., 2001; Florin, 2005; Gerasymenko and Arthurs, 2014; Patzelt et al., 2009; Petty and

Gruber, 2011) study design, instead of the single-question, cross-sectional design. They can

also help researchers avoid issues related to other longitudinal designs. Taking the example of

panel studies (e.g., De Clercq and Dimov, 2004; Dimov and De Clercq, 2006; Dimov and

Milanov, 2010), researchers who use mixed methods can avoid such panel study issues as (1)

long-term sample attrition, (2) best timing concerning the juncture of further data collection,

(3) poor long-term planning of the study, and (4) ‘panel conditioning’ effects (i.e., the impact

of continued participation on participant behavior) (on these problems, see Bryman, 2008, p.

51). Indeed, when researchers use mixed methods, they can avoid these issues by (1) agreeing

on participation before starting the initial data collection process, (2) starting the collection of

further data at the end of the initial data collection process and analysis, (3) planning the

study ex ante, and (4) asking another research member to handle the subsequent data

collection process and analysis.

In fact, with mixed methods, entrepreneurship scholars can gain deeper and better

insight and more complete knowledge (i.e., using various experiences, learning, and group

possibilities). For example, through a three-pronged study that encompasses learning

dynamics, dyadic and larger groups, and VC investment performances, the qualitative

component of longitudinal sequential descriptive research design provides a more fine-

grained and nuanced analysis of the process, overcoming quantitative component weaknesses

by revealing and explaining (De Clercq and Sapienza, 2005; Tunarosa and Glynn, 2017) the

mechanism of the correlations. All these explanations can then serve as a springboard for the

development of (1) stronger (i.e., more triangulated) findings, and (2) more precise and

relevant theoretical and practical implications. Following the lead of Short, Moss, and


28

Lumpkin (2009) and Short et al. (2010b), we propose potentially interesting and more

general research perspectives. Our approach is intentionally generic, with the hope that the

VC firm‒portfolio company dyad and post-investment phase will become genuine research

trajectories for entrepreneurship scholars.

A cross-disciplinary mixed methods-related approach to VC research

With regard to the VC firm‒portfolio company dyadic relationship (Sapienza, 1992) during

the post-investment phase (for a comprehensive overview, see De Clercq et al., 2006) and the

portfolio company’s long-term performance and value creation, Table 4 details some of the

prospects that are, in our opinion, the most promising for entrepreneurship scholars and show

how mixed methods could be used to research them.

===================


===================

DISCUSSION

Having reviewed past accomplishments in VC literature, organized this body of research

around streams, presented mixed methods, and proposed specific directions for future VC

research, we now provide a short critical discussion on methods and practices. A great

advantage of qualitative studies is that they give participants (Aguinis et al., 2010) the

opportunity to discuss quantitative results in more detail. Further, provided that the researcher

reports and shares all quantitative results (i.e., statistically significant and nonsignificant; see

Starbuck, 2016) with study participants, he or she has the opportunity to explicate both (1)

the statistically significant and (2) statistically insignificant results. Therefore, the use of

mixed methods is a great opportunity to improve the level of practical significance (Aguinis

et al., 2010), but only on condition that we cover all results (the online supplement could also

include insignificant results) and embrace and explore all aspects of research rather than

simply musing.


29

For example, entrepreneurship scholars interested in explanatory mixed methods and

ethnography could use subsequent qualitative interviews to investigate (1) the meanings that

participants attach to the quantitative results related to the expertise of venture capitalists and

their portfolio companies, (2) reactions that participants have regarding the quantitative

results related to high versus low relational rents, and (3) reasoning and thinking modes that

participants use when viewing the results as practically significant (i.e., make sense to

practitioners). Therefore, although we are fully aware that this approach is more demanding

because researchers have to manage additional qualitative datasets or consume larger

amounts of temporal resources, as they have to articulate diverse methods (on qualitative

methods, see Rauch, Van Doorn, and Hulsink, 2014; Symon, Cassell, and Johnson, 2016), we

contend entrepreneurship scholars to seriously take mixed methods into consideration in their

research.

CONCLUSION

VC is a major source of venture financing. In this article, our aims were (1) to review the past

accomplishments in VC literature and to organize this body of research around streams

(including the under-researched relational, learning, and other aspects stream), (2) briefly

present mixed methods and propose directions for future VC research using mixed methods,

and (3) provide a short critical discussion on methods and practices. Although some scholars

could argue high implementation costs (e.g., implementation problems, lack of personal

motivation, knowledge, and competence), we contend that collaborative endeavors (e.g.,

Low, 2001) offer a solution, provided that research teams create synergistic environments.

Moreover, the research questions we propose and the cross-disciplinary approach challenge

the maturity of VC research (Edmondson and McManus, 2007) and can potentially revitalize

it. Therefore, echoing Crook et al. (2010, p. 202; see also Chandler and Lyon, 2001), who

wrote that ‘entrepreneurship research that moves beyond relying solely on one data source


30

seems like a fruitful endeavor,’ we invite teams of entrepreneurship scholars to consider

underutilized mixed methods in their future VC, and globally, in their multi-theoretical and

multi-cultural works.


31

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Table 1. Initial 137 references considered for review

JBV ETP SEJ SMJ JOM JMS MS OS JIBS ASQ AMJ AMR SO AJS JFE JOF RFS Total12

Initial search 561 193 85 90 38 67 52 35 54 31 68 5 14 17 152 80 55 1597

Rejection justification:

no clear focus on VC or

overly specific

contribution

189 78 24 35 12 26 23 16 25 8 19 2 6 9 59 27 26 584


already selected

136 39 17 13 5 14 5 5 9 8 22 2 3 4 14 16 11 323


not already selected

186 59 39 35 20 20 22 12 17 8 16 0 4 3 74 25 13 553

Selected articles 50 17 5 7 1 7 2 2 3 7 11 1 1 1 5 12 5 137

12 A detailed document presenting the selection is available upon request.


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Table 2. Additional (25) references considered for review

Additional references Access

Brander et al. (2002) Cited by Hochberg et al. (2007), Sørensen

(2007)

Bruton et al. (2002) Cited by Ahlstrom and Bruton (2006)

Busenitz et al. (1997) Cited by Busenitz et al. (2004)

Bygrave and Timmons (1992) Cited by Hellmann and Puri (2000), Stuart et

al. (1999)

Chugh et al. (2011) Cited by Manigart and Wright (2013)

De Clercq and Dimov (2004) Cited by De Clercq and Dimov (2008)

De Clercq et al. (2006) Cited by Fitza et al. (2009)

De Clercq et al. (2001) Cited by Dimov and De Clercq (2006)

De Clercq and Sapienza (2001) Cited by Busenitz et al. (2004)

Dimov et al. (2012) Cited by Petkova et al. (2014)

Fried and Hisrich (1995) Cited by Cable and Shane (1997), Sapienza

et al. (1996)

Gompers (1996) Cited by Nahata (2008), Sørensen (2007)

Gompers and Lerner (2000) Cited by Shane and Cable (2002)

Kaplan and Strömberg (2003) Cited by Sørensen (2007)

Large and Muegge (2008) Cited by Drover et al. (2014a)

Lerner (1994) Cited by Gompers (1995), Sorenson and

Stuart (2001)

Lockett and Wright (2001) Cited by Manigart et al. (2006), Wright and

Lockett (2003)


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Mäkelä and Maula (2008) Cited by Liu and Maula (2016)

Manigart and Wright (2013) Cited by Ragozzino and Blevins (2016)

Shane and Cable (2002) Cited by Dimov et al. (2007)

Shepherd and Zacharakis (2001) Cited by Patzelt et al. (2009)

Tyebjee and Bruno (1984) Cited by Shane and Cable (2002)

Wright et al. (2005) Cited by Li and Zahra (2012), Mäkelä and

Maula (2006)

Zacharakis et al. (1999) Cited by Dimov and De Clercq (2006)

Zacharakis et al. (2010) Cited by Manigart and Wright (2013)


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Table 3. Examples of moderators

Moderator Example

Institutional

factors and

frameworks

Dimensions included in the World Governance Index (government

effectiveness, quality of regulatory policies, rule of law and property

rights protection, political stability, voice and accountability, and

control of corruption); mix of political, economic, and contractual rules

(see Li and Zahra, 2012, pp. 100–101)

Level of

rules and

regulations

Product-, management-, legal liability-, and government regulation-

related risk factor (Arthurs and Busenitz, 2006, p. 206)

Firm location Entrepreneurial firm’s headquarters (Tian, 2011, p. 137)

Levels of

uncertainty

and demand

uncertainty

Venture stage, technical, product, and marketing innovation (Sapienza

et al., 1996, p. 455); the difficult assessment of customer preferences for

technology, difficult forecasting of demand for technology, and difficult

prediction of changes in customer preferences for technology

(Townsend and Busenitz, 2015, p. 298)

Investment

stage

Pre-investment (deal origination, screening, evaluation, and structuring),

post-investment, and exit phase (De Clercq et al., 2006, p. 97); seed,

startup, and other early stage; expansion, later/acquisition, and other

stage (Dimov and De Clercq, 2006, p. 213)

Timing Time to first VC investment (i.e., ‘the number of years, since inception,

that it took the new venture to secure VC funding;’ Ragozzino and

Blevins, 2016, p. 1001)

Contract Security, veto, control, voting, board, and exit rights (for a full


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specifications presentation, see Burchardt et al., 2016, p. 28)

Dyadic

ethnicity

Surnames of the VC firm’s partners and portfolio company’s founders

(Bengtsson and Hsu, 2015, p. 343; on ethnicity, see also Zhang, Wong,

and Ho, 2016)

Geographical

distance

‘[T]he miles between a VC’s headquarters and the portfolio firm’s

headquarters’ (Lee et al., 2011, p. 42)

Reputation

and other

network-

related

dimensions

VC’s IPO capitalization share (Cumming and Dai, 2013, p. 1008; see

also Nahata, 2008); VC firm centrality (Ozmel et al., 2013, p. 857)


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Table 4. Cross-disciplinary mixed methods-related approach to VC research

Sub-field Illustrative examinations/investigations

Economics (1) Quantitative examination of economic variables (e.g., GDP, firm profitability, employment rate) and (2) qualitative

and phenomenological exploration of the effects of these variables on the VC firm–portfolio company dyadic

relationship.

Sociology (1) Quantitative focus on norms, networks, institutions, and power variables (Ahlstrom and Bruton, 2006; Li and

Zahra, 2012) and (2) qualitative and phenomenological focus on their effects on the dyadic relationship.

Political

science and

anthropology

Examination of statistical and causal relationships between the VC value creation process and both (a) public policies

and (b) culture (e.g., Li and Zahra, 2012; Sapienza et al., 1996).

Quantitative and correlational focus on and (2) qualitative and phenomenological comparison of the links between the

entrepreneur’s gender (Malmström, Johansson, and Wincent, 2017) and national characteristics, plus their resulting

effects on VC experiences.

Psychology (1) Quantitative investigations of the role of psychological conflicts (i.e., discrepancies in individual visions at group

level) in the VC firm–portfolio company relationship, (2) qualitative interviews focused on those ‘conflictual’

experiences, and (3) empirical comparisons of experimental and control (i.e., no effects on value creation activity)


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groups.

Organizational

behavior

(1) Quantitative investigation of the role of cognition (e.g., Mitchell et al., 2007; Murnieks et al., 2011) and (2)

qualitative investigation of the cognition–dyadic success relationship.

Human

resource

management

(1) Quantitative and (2) qualitative investigations of the dyadic differences in human resource management and human

capital practices with regard to contextual contingencies, such as firm location (e.g., Tian, 2011; Tyebjee and Bruno,

1984; Walske and Zacharakis, 2009).

(1) Quantitative and (2) qualitative investigations of these dyadic differences with regard to (public versus private)

investors.

Operations

management

(1) Quantitative and (2) qualitative investigations of the idiosyncratic dyadic differences concerning the management

of supply chain (a) processes and (b) relationships (e.g., Liker and Choi, 2004).

Accounting (1) Statistical (how much?) and (2) causal (why?) investigations of the role of costs (a) in the dyad during the post-

investment phase and (b) on the portfolio company’s long-term performance.

Finance (1) Quantitative and (2) qualitative investigations on the role of contracts (Burchardt et al., 2016; Cumming, 2008; Fiet,

1995; Kaplan and Strömberg, 2003, 2004) in the (a) portfolio company’s long-term performance and (b) value

creation process.


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(1) Quantitative and (2) qualitative investigations on the role of advisors (e.g., helping overcome inexperience and

funding problems) in the value creation process (Lahti, 2014).

Marketing Examination of statistical and causal significance of the relationships between VC and market orientation of the

portfolio firm strengths/weaknesses and environmental opportunities/threats (SWOT).

(1) Quantitative and (2) qualitative investigations on the links between marketing and reputational benefits (e.g.,

market attractiveness, product differentiation) provided by a VC firm and the portfolio company’s (a) long-term

performance and (b) value creation process.

Family

business

(1) Statistical (how much?) and (2) causal (why?) investigation of the significance of the socioemotional wealth

impact (e.g., Gómez-Mejía et al., 2007, 2011) on (a) the VC firm’s commitment, more generally, (b) dyadic

relationship, and (c) family firm’s (e.g., Gedajlovic et al., 2012; Schulze and Gedajlovic, 2010) long-term performance.

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Setting research objectives

Examine past accomplishments in VC literature

Organize this literature around research streams/themes

Include the stream/theme related to the relational, learning, and other aspects of the VC‒

entrepreneur relationship

Defining conceptual boundary

Focus on classic or professional venture capitalists (VCs)

Setting inclusion criteria

Search boundaries List of search terms Covered period

ABI/Inform and EBSCO databases ‘venture capital,’ ‘VC,’ 1985–2017

SEJ, JOM, JFE, and JBV websites ‘venture capitalist,’

Reference list of identified articles ‘syndication,’ syndicate’

Books

Applying exclusion criteria

Articles that primarily focus on other major sources of equity finance (e.g., business

angels, corporate venture capitalists)

Working papers

Dissertations

Conference proceedings

Final sample (162 references)

Figure 1. Summary of the review process

venture capital research: review and mixed methods ... · venture capital research review and...

Documents