Scholarly article on topic 'Entrepreneurial talent and venture performance: A meta-analytic investigation of SMEs'

Entrepreneurial talent and venture performance: A meta-analytic investigation of SMEs Academic research paper on "Economics and business"

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{Entrepreneur / "Economic growth" / Talent / Meta-analysis / SME / Performance}

Abstract of research paper on Economics and business, author of scientific article — Katrin Mayer-Haug, Stuart Read, Jan Brinckmann, Nicholas Dew, Dietmar Grichnik

Abstract As the broad link between small and medium-sized firm activity and key policy goals such as employment or economic growth has become generally accepted, the conversation has focused on a more nuanced understanding of the entrepreneurial engines of economic activity. A significant body of research looking at antecedents to venture performance has identified that entrepreneurial talent variables account for meaningful differences in venture performance and that significant heterogeneity exists across performance measures. These are important issues for institutions and policy makers seeking to achieve specific economic goals (e.g., survival or growth of ventures, employment or revenue). Using meta-analysis, we integrate this work to view connections between aspects of entrepreneurial talent and different performance outcomes. Our investigation includes 50,045 firms (K of 183 studies) and summarizes 1002 observations of small and medium-sized firms. Analysis of these data yields an unexpectedly weak connection between education and performance. Furthermore, growth, scale (number of employees) and sales outcomes are significantly related to planning skills, while profit and other financial and qualitative measures are strongly connected with the network surrounding the firm founders. Moreover, we observe that entrepreneurial talent is more relevant in developing economies.

Academic research paper on topic "Entrepreneurial talent and venture performance: A meta-analytic investigation of SMEs"


Entrepreneurial talent and venture performance: A meta-analytic investigation ofSMEs

Katrin Mayer-Haug3'*, Stuart Readb1, Jan Brinckmannc'2, Nicholas Dewd 3, Dietmar Grichnike'4

a WHU - Otto Beisheim School of Management, Burgplatz 2,56179 Vallendar, Germany b ¡MD, Chemin de Bellerive23, P.O. Box 915, CH-1001 Lausanne, Switzerland c ESADE, Avenida Pedralbes, 60-62, 08034 Barcelona, Spain d Naval Postgraduate School, 1 University Circle, Monterey, CA 93943, United States e University of St. Gallen, Dufourstrasse 40a, CH-9000 St. Gallen, Switzerland


As the broad link between small and medium-sized firm activity and key policy goals such as employment or economic growth has become generally accepted, the conversation has focused on a more nuanced understanding of the entrepreneurial engines of economic activity. A significant body of research looking at antecedents to venture performance has identified that entrepreneurial talent variables account for meaningful differences in venture performance and that significant heterogeneity exists across performance measures. These are important issues for institutions and policy makers seeking to achieve specific economic goals (e.g., survival or growth of ventures, employment or revenue). Using meta-analysis, we integrate this work to view connections between aspects of entrepreneurial talent and different performance outcomes. Our investigation includes 50,045 firms (K of 183 studies) and summarizes 1002 observations of small and medium-sized firms. Analysis of these data yields an unexpectedly weak connection between education and performance. Furthermore, growth, scale (number of employees) and sales outcomes are significantly related to planning skills, while profit and other financial and qualitative measures are strongly connected with the network surrounding the firm founders. Moreover, we observe that entrepreneurial talent is more relevant in developing economies.

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Article history:

Received 19 October 2011

Received in revised form 10 February 2013

Accepted 2 March 2013

Available online 11 April 2013

Keywords: Entrepreneur Economic growth Talent

Meta-analysis SME


1. Introduction

According to the Organisation for Economic Co-operation and Development (OECD) (2006), small and medium-sized enterprises (SMEs) represent over 95% of all businesses and account for 60-70% of all new jobs created in OECD member countries. Coming out of the recent recession, startups have historically provided a dominant engine of durable new job creation (see e.g., Stangler, 2009) and economic growth (see e.g., Foster, 2010). This emphasizes why SMEs are considered to be an economy's backbone in terms of employment as well as innovation (OECD, 2006). As institutions and policy makers have devoted effort and investment to the development of firms at the diminutive end of the spectrum (see e.g., Audretsch et al., 2009), so have academics devoted research

* Corresponding author. Tel.: +49 175 318 3642; fax: +49 711 255 3643. E-mail addresses: (K. Mayer-Haug), (S. Read), (J. Brinckmann), (N. Dew), (D. Grichnik).

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attention to the connection with economic growth (e.g., Audretsch et al., 2007; Carree and Thurik, 2010; Naude, 2011; Schumpeter, 1976).

Prior work motivates this paper, as scholars in the area clearly identify the supply and allocation of entrepreneurial talent in an economy as being central to its vitality (Baumol, 1990, 2010). Moreover, prior work suggests meaningful variance within the dependent level of firm performance outcomes (e.g., Chaganti and Schneer, 1994; Venkatraman and Ramanujam, 1985, 1986; Zou et al., 2010). We expand on this analysis of entrepreneurship by bringing together empirical data on variance in the nature of entrepreneurial talent with variance in outcomes of the enterprises entrepreneurs lead (SMEs). From a policy perspective, a better understanding of which element of entrepreneurial talent is associated with which venture performance dimension is of utmost importance in the efficient deployment of scarce resources. If the connections were well understood, funds could be targeted to foster entrepreneurial talent aspects that have the highest impact on desired venture performance outcomes, since different outcome constructs (such as survival, growth, employment and profit) might not evenly relate to each other (see e.g., investigation of entrepreneurship and different outcomes on a macro-economic level by Nystrom, 2008). Moreover, prior work suggests that

Table 1

Definitions of independent variable measures.

Experience and skills Education Planning Team size Network

Acquisition experience Academic title Business plan formalization Board size Alliances

Alliance experience Accounting education Business planning Founding team size Behavioral integration

Average number of prior positions Business class taken Complete plan Number of firm Benevolence based trust

for the team Business degree Complete planning founders Bridging ties

Broad experience CEO education Developed models Number of founders Business network

Business experience College education Elaborative and proactive Number of owners Coefficient variation of team tenure

Business knowledge Degree planning Number of partners Collaboration

Business similarity experience Education Export planning One-man startup Collaborative networks suppli-

Chief Executive Officer (CEO) Education abroad Formal plans at startup Product development ers/customers/competitors/research

tenure Education (masters) Formal/written plan group size organizations

China experience Education of CEO Length of time planning has Resources of the top Compatible goal

Collaborative experience Engineering degree been employed management team Competence based trust

Creative intelligence Graduate education Level of plan detail (TMT) Cooperation with customer or

Entrepreneurial experience Higher education Operational planning Team founding supplier/large firms/universities

Entrepreneurial knowledge High school education Operations planning Team size Downstream alliances

Entrepreneurial skills Human capital at IPO Overall planning TMT size Educational differences partners

Executive experience Human capital (education) Planning Educational diversity

Experience Level of education Planning for the future Encouragement

Experience in cooperative R&D/in Master of Business Planning index Extent of formal/informal

public companies team Administration (MBA) degree Planning sophistication interaction with TMT

Experience (not as founder) Marketing education Prepared plan Extent of trusting relationships in

Experience of CEO Non-formal education Resource planning TMT

Expertise Other degree Sophisticated planning External sources/tech resources

Explicit knowledge PhD degree Startup business plan Family firm

Finance experience PhD among Management Strategic planning Firm network heterogeneity

Financial skills Primary education Target planning Firm trust

Founding team experience Technology degree Use ofbusiness plan Foreign alliances

Founding team international TMT education Written business plan before Formal coupling (alliance behavior)

experience TMT educational level startup Founding team functional

Founding team startup experience TMT management education heterogeneity

Human capital assets Undergraduate education Friends/parents in business

Industrial experience Functional diversity

Industry experience Generalized reciprocity

Innovation skills Goal congruence

Insider tenure Horizontal alliances

International experience Joint ventures

IT knowledge Knew partner beforehand

Knowledge Linkages to university

Leadership experience Management functional diversity

Managerial experience/skills Manufacturing/marketing

Management capabilities cooperative arrangements

Management experience/skills Marketing alliance

Management industry experience Network capabilities

Manager's tenure with firm Network family friends

Manufacturing experience Networking

Market pioneering know-how Network structure

Marketing experience/skills Number alliances

New resource skill Number of advisors

Number of startups founded Number of alliance partners

Operations skills Number ofcooperators

Opportunity recognition skills Number ofemployed generations

Partner-specific experience Number offamily employees

Portfolio entrepreneur Number ofpartners with repeated

Practical intelligence ties

Previous entrepreneurial New venture team tenure

experience Overall team tenure

Prior entrepreneurial/ Prior relationship

international/ Product innovation group process

management/ownership/ Prominent alliances

startup experience R&D cooperative arrangements

Product innovation skills Relational assets/capital

R&D capabilities/experience Relationship quality

Serial entrepreneur Shared goals

Similar industry experience Shared organizational vision

Skills Similar experience

Startup experience Social capital

State owned enterprise experience Strategic consensus

Strategic skills Strong ties

Supervisory experience Supplier involvement

Tacit knowledge Support of family/friends

Task similarity Team affinity

Technical experience Team cohesion Team collaborative behavior Team completeness Team tenure

Table 1 (Continued )

Experience and skills



Team size


Technological experience Technological know-how Tenure of CEO TMT biotech experience TMT experience TMT functional experience TMT industry experience TMT international experience TMT management experience TMT pharma experience TMT prior executive experience TMT prior startup experience TMT startup experience Western experience Work experience

Years of full time work experience Years of industry/internet related experience of Chief Marketing Officer (CMO)/CEO

Tie intensity/strength TMT age heterogeneity TMT educational heterogeneity TMT functional heterogeneity TMT group cohesiveness TMT heterogeneity TMT major heterogeneity TMT mean tenure TMT social integration TMT tenure

TMT tenure heterogeneity Trust

Trust based governance

Trust (customer/supplier)


Upstream alliances

Work experience differences of


cultural and economic context (Baumol, 1968) influence the availability and deployment of entrepreneurial talent (Zhang et al., 2010). Hence understanding the impact of these contextual factors on the entrepreneurial talent-SME performance relationship can also be beneficial for policy makers around the globe.

Significant academic effort has generated an enormous cache of data that investigates how a variety of antecedent variables relates to different venture performance outcomes. We aggregate these data using meta-analysis. This systematic, evidence-based approach (Hunter and Schmidt, 2004; Lipsey and Wilson, 2001; Rosenberg and Donald, 1995) seeks to identify elements of entrepreneurial talent that economic policy can influence to foster entrepreneurship and inform the macro-economic understanding of the entrepreneurship phenomenon (van Praag and Versloot, 2008). But while Baumol views the components of entrepreneurial talent as a black box of unaccounted variance (Baumol and Blinder, 2010), our meta-analysis aims to enhance understanding by piecing apart different aspects of entrepreneurial talent to determine their connection with different performance outcomes. Thus, our meta-analysis responds to the old saw about an economist being someone who worries about proving that "something that works in practice works in theory" (Baumol et al., 2007, p. 125) with an inductive approach to identifying policy implications around SME performance.

Systematic reviews of previous research are important (e.g., Macpherson and Holt, 2007) and meta-analysis is of specific relevance to policy makers as a basis for addressing a key issue highlighted by Frese et al. (2012, p. 42): "There are, of course, public policies for fostering entrepreneurship in most countries but there is up to this point, relatively little evidence-based public policy." While other science fields like medicine rely heavily on meta-analytic techniques to aggregate empirical results (Hunter and Schmidt, 2004), this powerful approach has only recently caught the attention of management researchers (Brinckmann et al., 2010; Daltonet al., 2003; Kirca et al., 2011; Read et al., 2009; Rosenbusch et al., 2011; Shea-Van Fossen et al., 2006; Song et al., 2008; Unger et al., 2011). A number of previous meta-analyses in the management and entrepreneurship literature analyze the effect size of one specific antecedent derived from theory against performance (e.g., Unger et al. (2011) investigate the relationship between human capital and firm performance). But to the best of our knowledge, there is no integrated work of relevance to policy makers that seeks to bring together a variety of independent variables associated with entrepreneurial talent while at the same time unpacking the broad construct of performance.

Our analysis organizes and summarizes these data so that different SME performance outcomes relevant to policy makers can be meaningfully examined against different entrepreneurial talent aspects that can be influenced by policy makers. Furthermore, we investigate the moderating effects of economic development and cultural attitude toward uncertainty on the entrepreneurial talent-SME performance relationship. This investigation reveals useful insights for policy makers seeking to influence the entrepreneurial landscape, as well as researchers seeking to understand the role of entrepreneurial talent in SME performance. Our unique view into the diverse dependent variables associated with SME performance begins to expose the various levers associated with firm scale (in number of employees) and sales versus financial performance (such as profit or aggregated financial measures) versus qualitative outcomes (such as survival or perceived success). While these categories reflect SME performance at a certain point in time, we also separate out performance specific to growth in order to contribute insights related to dynamic outcomes such as increase in employment or revenues.

One of the results especially pertinent to policy economists and policy makers is that we clearly show that investment in human capital in the form of education - a fundamental input for many models of economic growth (e.g., Becker and WoRmann, 2009) - has a weak connection with SME performance, particularly in advanced economies. Therefore, from a policy perspective, we find limited justification for investing in general education as a route to economic growth via entrepreneurship. In contrast to education, we find that human capital derived from the network that surrounds the firm's founders has the most robust connection with profit, other financial measures and non-financial venture outcomes ranging from venture survival to perceived success. Furthermore, we find that activities focused on planning have a strong connection with firm scale, sales and growth.

Our enquiry follows five main steps. First, we identify two categories of constructs (entrepreneurial talent and venture performance) from the academic literature. Second, we amass studies from 1990 to 2010 including correlates of different performance measures and entrepreneurial talent aspects, and third, we examine it using meta-analysis. Fourth, after analyzing the main effects, we investigate the moderating effects of economic context and cultural attitude toward uncertainty. We close with conclusions for policy makers looking to achieve certain goals and academics interested in the nature of performance and entrepreneurial talent.

2. Scope of our study

Our aim is to provide policy makers and institution builders with an overview of how various aspects of entrepreneurial talent, which they can influence, affect different SME performance outcomes. As such, we begin by specifying aspects of both independent and dependent variables for inclusion in our study.

2.1. Independent variables

Fundamentally, we seek to understand the relationship between entrepreneurial talent (Baumol, 1990) and various performance measures of SMEs. Baumol (1990) introduced the term entrepreneurial talent, but laments even 20 years later that although we can assume that the return on entrepreneurial talent is the profit above market interest rates, we can neither really define entrepreneurial talent nor can we teach it in schools (Baumol and Blinder, 2010). At the same time, other researchers have built on Baumol's salient work and define entrepreneurial talent as "the ability to discover, select, process, interpret and use the data necessary to take decisions in an uncertain world and, then, to exploit market opportunities" (Ferrante, 2005, p. 169). Following the resource-based view literature, we theoretically bound entrepreneurial talent according to the criteria of it being VRIN (valuable, rare, difficult to imitate and non-substitutable) (Barney, 1991). Thus our work encompasses an additional contribution to the resource stream of research as we specify entrepreneurial talent boundaries based on the resource-based view and empirically synthesize their connection with different performance outcomes. Guided by this theoretical perspective, we searched the literature and identified five entrepreneurial talent elements5 that met the resource-based view criteria and have been the subject of sufficient prior empirical studies as to provide an input to a meta-analysis. See Table 1 for detailed information on entrepreneurial talent oper-ationalizations as well as the following elaborations on each aspect.

2.1.1. Experience and skills

Following Ferrante (2005), a founder's experience offers an element contributing to entrepreneurial talent and has been identified in numerous empirical studies as a distinct correlate of performance (e.g., Song et al., 2008). As the variety of tasks involved in creating and operating a venture includes everything from generating sufficient funding for the business to hiring employees (Carter et al., 1996), we include any experience relevant to this variety of tasks, such as managerial experience, industry experience, previous entrepreneurial experience related to the founder or the founding team, etc., as well as knowledge and skills since these can be considered as an outcome of the human capital investment associated with experience (Becker, 1964; Unger et al., 2011). Understanding the construct of experience and its impact on venture performance is necessary to anyone considering policies that

5 It may be worth offering a note at this point about why personality traits like intelligence, creativity, passion, tenacity or perseverance/persistence or situation specific motivation such as vision, future orientation or self-efficacy are not part of our operationalization of talent. We acknowledge that some of these personality traits are part of Ferrante's (2005) description of factors influencing entrepreneurial talent. However, as traits or psychological measures are expected to be more or less stable over time (e.g., Shane, 2003, p. 97) and cannot be influenced by policy makers, we operationalize entrepreneurial talent by focusing on human capital measures (consistent with Ferrante's (2005) argumentation), skills and close net-work(Ferrante (2005) also highlights the importance of knowledge embedded in the environment). This is consistent with Baumol's initial depiction of entrepreneurial talent. Hence, we acknowledge that human capital is one important aspect in the broader phenomenon of entrepreneurial talent, and we incorporate the close environment (team and close network partners) as elements that also contribute to entrepreneurial talent.

might directly encourage the creation of programs fostering relevant experience and skills or serial entrepreneurship. Further, these insights are also relevant to interventions that might indirectly influence experience by providing policy tools that improve environmental conditions for SMEs (e.g., Audretsch et al., 2009), leading to a continued accumulation of entrepreneurial experience and hence the development of relevant skills.

2.1.2. Education

Similar to experience, formal education is suggested as a factor influencing the ability to successfully discover and exploit an entrepreneurial opportunity (Ferrante, 2005; Unger et al., 2011). Education constitutes an aspect of the founder's talent, which policy makers might also influence both directly and indirectly. The provision of educational opportunities at reasonable cost can be within the reach of the policy maker, as can the targeted selection of inducements to uniquely educated individuals, if desired. A number of previous studies suggest a positive connection between the educational level of the entrepreneur and firm performance (e.g., Jo and Lee, 1996; Mengistae, 2006), but other findings are equivocal (e.g., Lange et al., 2007). Our operationalization of education is broadly based, including education measures related to the founder or the founding team.

2.1.3. Planning

The value of planning and its relation to performance has been long debated in strategic management (e.g., Ansoff, 1991; Mintzberg, 1994). Formal planning involves the determination of goals, the generation and evaluation of different scenarios and strategies as well as implementation control (Armstrong, 1982). Planning scholars argue that planners perform better because they are more efficient in decision-making (Ansoff et al., 1970; Ansoff, 1991) and because they are able to reduce the uncertainty of outcomes (Ansoff et al., 1970). In entrepreneurship the debate on the value of planning is active (Brews and Hunt, 1999; Brinckmann et al., 2010; Delmar and Shane, 2003; Wiltbank et al., 2006), at least somewhat due to the inherent uncertainty of the context (Knight, 1921). One of the primary vehicles of entrepreneurship education is teaching how to prepare a business plan (e.g., Honig and Karlsson, 2004) and a plan is considered by numerous external stakeholders, such as venture capitalists, to be a key venture requirement (Lange et al., 2007). Supporters argue that by simulating future situations, a business plan can enable faster decision-making and can help to overcome bottlenecks (Delmar and Shane, 2003). Hence, acquiring skills in preparing business plans can be considered an ability that facilitates new venture creation and enhances venture performance, which represents an aspect of entrepreneurial talent. Instructors running business planning courses and competitions, policy makers, educators and other actors in the new venture ecosystem have influenced thinking around the business planning process (e.g., Honig and Karlsson, 2004). A recent investigation summarized a positive, yet contextual connection between business planning and the performance of new and established small firms (Brinckmann et al., 2010). We use the existence of a business plan as well as planning activities and sophistication as proxies for basic skills in planning. This allows us to compare the specific skill of business planning with other entrepreneurial talent aspects like experience or education.

2.1.4. Team size

The management or founding team has been identified as another element connected to venture performance (e.g., Song et al., 2008). According to the Panel Study of Entrepreneurial Dynamics (PSED), only about half the ventures in the United States are created by sole founders (Reynolds and Curtin, 2008). We consider a management or founding team to be an accumulation of

entrepreneurial talent. Hence, the team size measure offers an additional aspect to contribute to entrepreneurial talent (Penrose, 1959) and fiscal policy can influence it directly (e.g., by providing differential tax benefits to founding teams instead of individual founders). As team members with complementary competencies are added, the individual founder's cognitive and managerial capacity expands (e.g., Brinckmann and Högl, 2011). Although the positive effect of team size on performance has been indicated (Cooper and Bruno, 1977; Eisenhardt and Schoonhoven, 1990; Penrose, 1959), greater team size does not guarantee performance (Wheelan, 2009), as challenges of coordination and communication arise (Bales and Borgatta, 1962). Hence, it is important to understand whether empirical evidence can help resolve the discussions on team size and its impact on venture performance.

2.1.5. Network

An entire stream of literature in management research has been devoted to theory around networks and depicting insights generated in the field of sociology (e.g., Granovetter, 1973, 1985). This thinking has subsequently been projected onto new ventures to explain how entrepreneurs and founding teams reach outside the boundaries of the firm to gain access to information, advice, talent, capital, resources and partnerships, etc. (for reviews on network-based research in entrepreneurship, see e.g., Hoang and Antoncic, 2003; Slotte-Kock and Coviello, 2010). Entrepreneurial firms face many challenges upon startup, and researchers have investigated and identified the liability of newness (Stinchcombe, 1965) and the liability of smallness (Aldrich and Auster, 1986) as two reasons for the mortality of new and/or small ventures (Freeman et al., 1983). The liability of newness encompasses network-related aspects such as a lack of stable ties and fewer relations characterized by a high level of trust forcing new firms to rely more heavily on strangers (Stinchcombe, 1965). The liability of small-ness describes many resource disadvantages small firms face in comparison with larger firms (Aldrich and Auster, 1986). Utilizing networks or certain relationships has been identified as one way to overcome resource constraints (e.g., Stuart et al., 1999 showed that young, private biotechnology ventures can overcome resource constraints by partnering with larger or more prominent firms). To address liabilities faced by entrepreneurial firms, the creation and maintenance of entrepreneurial networks are sometimes supported by political institutions (Audretsch et al., 2009). Founder and firm networks are an attempt to facilitate knowledge gains, provide additional resources and enhance venture performance (Davidsson and Honig, 2003). In terms of entrepreneurial talent, we focus on strong ties (Bian, 1997) to reflect those elements more directly related to extending a small firm's entrepreneurial talent beyond the boundaries of the founding team member(s). Also, from a resource-based view, we conclude that strong ties meet the VRIN criteria, whereas weak ties are neither rare nor difficult to imitate -especially in today's world with numerous social media networks available. Further, our literature review persuades us that network quality reflects an aspect of entrepreneurial talent, thus we include variables such as diversity or heterogeneity of team and network partners (e.g., Beckman et al., 2007).

2.2. Dependent variables

Our main ambition is to analyze relationships of different aspects of entrepreneurial talent against a range of venture performance measures of interest to policy makers. Researchers investigating venture performance recognized long ago that it is a multidimensional construct (Venkatraman and Ramanujam, 1986), that performance measurement is a difficult task (Brush and VanderWerf, 1992) and that choice of performance measures is a critical issue in research (Cooper, 1993). Commenting on the state

of the art at the time, Cooper (1993, p. 241) lamented, "Previous research has also used a variety of performance measures, making comparisons across studies more difficult. Little has been done to determine whether the factors that enhance one measure of performance, such as survival, are the same as those that lead to others, such as growth or profitability." Cooper et al. (1994) subsequently provided one of the first studies to examine the impact of various aspects of human capital separately on failure, marginal survival and high growth among a sample of 1053 new ventures. Subsequent studies (e.g., Zahra, 1996) continued this trend of utilizing several different performance measures in their research.

It is now possible to improve this situation using the contemporary expansion in entrepreneurship research. Not only has the sophistication of studies increased, but also an avalanche of entrepreneurship research has appeared driven by: (a) interest in entrepreneurship by policy makers, as the topic re-emerged as a key item on the agenda among economic policy makers (van Praag and van Ophem, 1995; Wennekers et al., 2002); and (b) development of the field of entrepreneurship as a legitimate scholarly paradigm (Venkataraman, 1997). Our organization of performance variables builds on earlier analyses that segment performance items (e.g., Cooper et al., 1994) and distinguishes different performance effects.

We operationalize five static performance categories. The category of scale encompasses measures related to number of employees. The category of sales consists ofvariables that represent sales, revenues and turnover. Furthermore, we introduce a specific financial performance category called profit, which contains measures such as return on sales, net income and profit. A further category was created and named "other financials." This category is broader in order to determine how much variance goes unaccounted for or is differentially accounted for if a specific financial performance measure is not present. It describes all financial performance measures that do not fall into the categories profit or sales and includes measures such as liquidity or overall financial measures, which are a combination of different financial measures. We included a category for non-financial performance measures, which encompasses firm outcomes such as survival and perceived success as well as individual measures such as continuance intention or knowledge acquired, since individual-level dependent variables have been argued to contribute to venture performance measures (e.g., Tiwana and Bush, 2005). Finally, we also established a sixth category to capture the dynamic aspect of growth, reflecting outcomes such as increase in employment or revenues. See Table 2 for information on performance operationalizations.

2.3. Moderating variables

Contingency theory argues that the "optimal" way to organize or lead a company depends on the context or respectively the situation (Burns and Stalker, 1961; Lawrence and Lorsch, 1967). Guided by both prior literature (Baumol, 1968; Hayton et al., 2002) and identifying variables of interest to policy makers, we operational-ized two moderating variables from the design of the underlying studies: economic context (advanced or developing economy), and owing to the uncertain nature of the entrepreneurial context (Knight, 1921), cultural attitude toward uncertainty (Hofstede and Hofstede, 2005).

3. Sample

As a first step in our literature search, we conducted an extensive database query of EBSCO to identify all relevant studies published between 1990 and the end of 2010 in multiple target journals (Academy of Management Journal, Administrative Science Quarterly, Entrepreneurship Theory and Practice, IEEE Transactions

Table 2

Definitions of dependent variable measures.


Scale (number of employees)


Other financials

Qualitative performance

Asset growth

Business growth last 3 years Employee growth Employment growth Firm growth Firm growth (sales) Growth

Growth sales and employment Growth in employees Growth in sales Growth (mix of measures) Growth of employees Internal organic growth Market share growth Net profit growth rate Past growth

Performance (changes in gross

revenues in 2 consecutive years)

Performance (mix of growth measures)

Profit growth

Rapid growth

Revenue growth

Sales growth

Employees Employment Firm size

Firm size (number of employees) International joint venture (IJV) size (number of employees/log of employees) Number of employees at IPO

Subsidiary size (number of employees)

Firm sales

Firm size (in terms of sales)

Firm size (log of sales)

Made a sale

Moving average of



Sales per employee Revenues Year 1 (log)

After tax profits Income

Log of annual profit Net income Profit

Profitability Return on sales (ROS)

Cash flow

Financial performance Financial performance (various measures) IPO


Percentage point spread between the closing price and IPO price

Pre-money valuation Return on assets ROA (3 years average) Return on cash flow (RCF) Return on equity Return on investment (ROI) Shareholder return Stability of profit Valuation

Adhering to budget

Alliance performance

Alliance performance/success

Chief information officer (CIO) role effectiveness

(educator, information, integrator, relational, strategy,


Continuance intention Financial management Financial management knowledge acquired Firm survival

Human resource management knowledge acquired International performance (qualitative) Marketing knowledge acquired Market performance Market share

Outcomes of cooperative R&D contributed to sales growth

Out of business (reverse coded) Overall performance (mix of measures) Overall performance versus competitors Past performance

Past performance (combination of measures) Perceived chance of new venture success Perceived performance Performance

Performance (mix of measures) Performance versus competitor Performance versus stated objectives Profitability compared to competitor index Progress performance Revenue performance versus competitor R&D product development knowledge acquired Speed

Speed to market

Speed to product

Securing long-term survival



on Engineering Management, Journal of Applied Psychology, Journal of Business Venturing, Journal of Management, Journal of Management Studies, Journal of Small Business Management, Long Range Planning, Management Science, Organization Science, Research Policy, Small Business Economics, Strategic Management Journal and Technovation). In order to capture all relevant studies, we used a variety of keywords for performance: performance, "return on investment," ROI, "sales growth," survival, "return on assets," ROA, "return on equity," ROE, "employee growth," growth, profitability, profit, "net income," success, underpricing, "market capitalization," and valuation. For our five entrepreneurial talent aspects, we searched with the key words: experience, education, "human capital," planning, plan, "business plan," "business planning," team, partners, "partnership team," network, parents, friends, "social resources," "social capital," "personal network," underwriters, "number of university links," linkages, advisors, "network capabilities," "outside members of the board," "number of venture capital (VC) board seats," alliances, "partners' equity ownership," "cooperative partnerships," and cooperative. We then proceeded to review every abstract returned from our keyword search.

In a second step, we manually searched two entrepreneur-ship publications not included in the EBSCO database: Frontiers ofEntrepreneurship Research and Strategic Entrepreneurship Journal. In a third step, we added cross-referenced studies identified from the reference lists in previous related meta-analytic and review papers. In a fourth step, we searched the Social Science Research Network (SSRN) and the Proquest dissertations database to identify unpublished dissertations, papers from conference proceedings or unpublished working papers, against our keyword criteria.

From these results, we selected studies based on two criteria. The first criterion was studies investigating SMEs. The definition of SMEs varies across countries and typically the upper limit for SMEs in terms of size ranges between 100 and 500 employees (Ayyagari et al., 2007). As a universal SME definition does not exist, we used 500 employees as the cut-off criteria. This categorizes small versus large firms in the majority of sectors in the United States (SBA, 2010) and has been used by other researchers in the past as the upper size limit for SMEs (e.g., Beck et al., 2005; Dickson et al., 2006; Rosenbusch et al., 2011). The second criterion was studies including a correlation matrix (Song et al., 2008) that contains at least one measure of venture performance and at least one of the described entrepreneurial talent elements.

After applying the selection criteria, our sample included 183 studies described in 175 papers or publications. In four cases (Delmar and Shane, 2003, 2004; Florin, 2001, 2005; Li, 1998; Li and Zhang, 2007; Matthews, 1990; Matthews and Scott, 1995), we recognized that the same sample or sub-sample was used in both studies. However, as each of the studies in these pairs contained different variable relationships of interest, we included both in the pair, paying careful attention not to include duplicate relationships, or combined studies where necessary in order not to unreasonably increase the weight of these studies in the overall meta-analysis (see Appendix 1 for details).

4. Method

Meta-analysis provides a systematic approach to reviewing an existing body of literature (Lipsey and Wilson, 2001) and follows an evidence-based research approach to synthesizing prior empirical studies (Hunter and Schmidt, 2004; Rosenberg and Donald, 1995). This methodology can provide unique insight in areas with conflicting findings and limited sample sizes (Geyskens et al., 2009; Lipsey and Wilson, 2001) and goes beyond a review of past research, as it allows testing of relationships which cannot be addressed by individual studies, estimating effect-strength and

identifying moderating relationships (Hunter and Schmidt, 2004; Lipsey and Wilson, 2001). It can thus also provide direction for future research and theory building (Hunter and Schmidt, 2004). In view of the unique benefits of meta-analysis, the technique has become increasingly popular in management literature in recent years (Geyskens et al., 2009).

4.1. Variable coding

We coded independent and dependent variables according to the definitions in Tables 1 and 2. One advantage of meta-analysis is the correction of idiosyncratic study artifacts (Hunter and Schmidt, 2004). In order to perform these corrections, we recorded construct reliability measures (typically Cronbach's alpha) for perceptual variables (often measured through surveys using a Likert scale). Furthermore, to conduct moderator analyses, we recorded the geography of the study based on data availability and assigned countries to either advanced or developing economies following contemporary management research (e.g., Kirca et al., 2011) and drawing from the detailed country groupings of the International Monetary Fund (IMF) (2010). We also used the geography of the study to assign a value for the cultural uncertainty avoidance (Hofstede and Hofstede, 2005) to the respective study. In cases where studies included a population of firms that made assignment ambiguous, either because the study did not sufficiently describe the sample or because the sample included more than one geography, we excluded the study from the moderator analyses.

4.2. Variable correction

We applied the meta-analytic procedures from Hunter and Schmidt (2004) and corrected for reliability of perceptual measures before conducting the analyses. We used Hunter and Schmidt's (2004) correction for attenuation and corrected for variable measurement error in correlation by applying the following formula:

(V^T x V°2)

where: r represents the corrected correlation coefficient; r0 represents the extracted raw Pearson correlation coefficient between the independent and the dependent variable; aT represents the observed Cronbach's a for reliability of the independent variable; a2 represents the observed Cronbach's a for reliability of the dependent variable.

4.3. Analysis

After correcting for artifacts and obtaining the average effect size per study, we used the Comprehensive Meta-Analysis software (Borenstein et al., 2005) to compute a mean effect size (Hunter and Schmidt, 2004; Lipsey and Wilson, 2001). Starting by weighting each study with the inverse of its variance, which encompasses the within-study variance and between-studies variance, we employed a random effects model (Borenstein et al., 2007):

EWcYc £ Wc

where: Y represents the weighted mean effect size across studies in the analysis; Wc represents the weight assigned to each study (which is the reciprocal of individual within-study and the between-studies variance); Yc represents the individual study effect size.

Table 3

Main effect sizes of independent variables to performance categories.

Dependent variable Independent variable Number of firms Number of Point estimate 95% confidence interval Test of null (two-tail)

studies (random effects)

Lower limit Upper limit z-value p-value

Growth Experience and skills 11,808 36 0.054 0.014 0.093 2.642 0.008

Education 9830 26 0.092 0.046 0.138 3.920 0.000

Planning 2454 10 0.203 0.129 0.275 5.286 0.000

Team size 2812 11 0.083 0.036 0.129 3.469 0.001

Network 4720 21 0.095 0.035 0.154 3.094 0.002

Scale (number of employees) Experience and skills 16,078 54 0.055 0.015 0.094 2.712 0.007

Education 15,069 36 0.081 0.038 0.123 3.711 0.000

Planning 3605 17 0.198 0.071 0.317 3.071 0.002

Team size 3585 16 0.180 0.115 0.244 5.319 0.000

Network 9768 35 0.097 0.046 0.147 3.734 0.000

Sales Experience and skills 12,171 28 0.088 0.034 0.143 3.158 0.002

Education 12,298 19 0.011 -0.045 0.068 0.384 0.694

Planning 1450 7 0.173 0.053 0.288 2.814 0.005

Team size 4639 9 0.157 0.063 0.248 3.268 0.001

Network 7688 11 0.110 0.035 0.184 2.882 0.004

Profit Experience and skills 8309 17 0.065 0.019 0.111 2.790 0.005

Education 9557 13 -0.011 -0.078 0.056 -0.334 0.739

Planning 999 6 0.090 0.000 0.179 1.958 0.050

Team size 1590 6 0.054 -0.034 0.142 1.202 0.229

Network 2250 10 0.090 0.014 0.164 2.310 0.021

Other financials Experience and skills 8906 17 0.048 0.002 0.094 2.057 0.040

Education 8749 8 0.039 -0.007 0.085 1.675 0.094

Planning 789 3 -0.026 -0.199 0.148 -0.294 0.769

Team size 1565 6 0.014 -0.036 0.064 0.554 0.580

Network 1721 8 0.148 0.071 0.224 3.719 0.000

Qualitative Experience and skills 4983 32 0.180 0.103 0.256 4.534 0.000

Education 5866 18 0.038 0.003 0.073 2.147 0.032

Planning 1517 8 0.204 0.036 0.361 2.366 0.018

Team size 2948 17 0.004 -0.050 0.058 0.150 0.881

Network 6936 25 0.243 0.153 0.329 5.190 0.000

5. Results

We computed 30 main effects, presented in Table 3, representing each of the six performance categories with respect to the five aspects of entrepreneurial talent. We present the results in the same order as we introduced the performance categories.

5.1. Main effect results

Starting with the category of performance variables related to growth, planning presents the strongest mean effect size (effect size = 0.203, p < 0.001) among our entrepreneurial talent variables. Similarly, planning exhibits the strongest relationship with the two categories of outcome variables measuring firm size, reflecting scale in number of employees (effect size = 0.198, p = 0.002) and sales (effect size = 0.173, p = 0.005). Turning to the performance category of profit, network emerges as the more stable relationship (effect size = 0.090, p = 0.021) of the two entrepreneurial talent variables that share the same effect size against that outcome. The main effect between planning and profit exhibits a comparable effect size (effect size = 0.090, p = 0.050) as network and profit (effect size = 0.090, p = 0.021), but the robustness tests (see Section 5.3) display that the connection between planning and profit is not as stable as the one between network and profit. The only other entrepreneurial talent aspect with a connection to profit differing significantly from zero is experience and skills (effect size = 0.065, p = 0.005). Against performance outcomes included in the "other financials" category, we find that network has the highest connection (effect size = 0.148, p < 0.001). For qualitative performance measures, we also observe that network presents the highest effect size (effect size = 0.243, p < 0.001).

Although we group independent and dependent variables, we do not presume to represent distinctive constructs. Instead we offer insight as to where interrelationships may lie with point estimates based on a random effects model to provide correlation estimates between independent and dependent variables in Tables 4 and 5. We observe no significant relationship above 0.116 for the independent variables (Table 4). We find one significant correlation greater than 0.5 for the dependent variables (Table 5). This strong correlation between the dependent variables offers reassurance to the validity of our underlying data in that the two firm size measures (scale in number of employees and sales) are highly correlated.

5.2. Moderator analyses

There are alternative methods for determining the presence of moderation in meta-analytic data. Hunter and Schmidt (2004) suggest the potential presence of subgroups that may moderate main effect data if the sampling error is responsible for less than 75% of the observed variability. Additionally, King et al. (2004) add a test from Koslowsky and Sagie (1993) analyzing the width of the 90% credibility intervals for values larger than 0.11 as this width indicates the presence of potential heterogeneity within the main effects. We followed King et al. (2004), using both tests and requiring a positive result to both in order to indicate potential moderation. These tests proved positive for our overall main effect, so we proceeded to investigate two moderators of interest to policy makers and of relevance to new venture research that could be operationalized in our dataset. To explore moderator variables, we used weighted meta-regression (Lipsey and Wilson, 2001) in order to control for the differential effects of various outcome variables indicated by our main effects analyses, and investigated

Table 4

Correlation estimates of independent variables.

1. Experience and skills 2. Education 3. Planning 4. Team size 5. Network

1. Experience and skills 15,923 3198 8754 8157

2. Education 0.029 2420 4169 6018

3. Planning 0.044 0.004 1246 522

4. Team size 0.070* 0.069* 0.064* 6377

5. Network 0.065** 0.067*** 0.210 0.116***

Note. Values in the lower diagonal reflect point estimates; values in the upper diagonal reflect the number of firms.

Correlations are taken from the original studies, not corrected for artifacts, averaged on a study level for the calculation of the displayed point estimates based on a random effects model. * p <0.05. ** p<0.01. *** p<0.001.

Table 5

Correlation estimates of dependent variables.

1. Growth

2. Scale

3. Sales

4. Profit

5. Other financials

6. Qualitative

1. Growth

2. Scale

3. Sales

4. Profit

5. Other financials

6. Qualitative


0.098* 0.126 0.163* 0.138* 0.068

6134 10,309

0.577** 0.222** 0.058 0.099**

6712 6241 5652

0.450* 0.028 0.283**

6824 7510 6781 5618


1673 5052 3252 971 576

Note. Values in the lower diagonal reflect point estimates; values in the upper diagonal reflect the number of firms.

Correlations are taken from the original studies, not corrected for artifacts, averaged on a study level for the calculation of the displayed point estimates based on a random effects model. * p <0.05. ** p<0.01. *** p<0.001.

the impact of potential moderating variables on the elements of entrepreneurial talent included in our main effects analyses. The baseline model is included in Table 6 as Model 1.

unstandardized coefficient, indicating that the connection between the entrepreneurial talent variables in our study is significantly smaller in advanced economies than in developing economies.

5.2.1. Economy: advanced versus developing

To our baseline model, and for every study in which the data was available and specific, we included a binary variable reflecting advanced (1) versus developing (0) economy depending on where data were gathered. The addition of the variable to Model 2 generated significant R2 change of 0.025 (p < 0.001) over Model 1, and the analyses revealed a negative (-0.066) and significant (p < 0.001)

5.2.2. Uncertainty avoidance

Generally measured at the societal level, uncertainty avoidance reflects a culture's (in)tolerance for uncertainty and ambiguity, and the extent to which people within that culture are (un)comfortable in uncertain situations (Hofstede and Hofstede, 2005). This measure is an indication of how much people in a society minimize uncertainty through rules, safety and security (Hofstede and Hofstede,

Table 6

Meta-regression models with the moderating impact of economic context and level of uncertainty.

Model 1 Model 2 Model 3

Baseline Economic context Economy and uncertainty avoidance

Unstandardized Standard error Unstandardized Standard error Unstandardized Standard error

coefficient coefficient coefficient

(Constant) 0.047*** 0.006 0.104*** 0.009 0.058*** 0.012

Growth binarya -0.038*** 0.008 -0.037*** 0.008 -0.037*** 0.008

Sales binary 0.003 0.007 0.006 0.007 0.008 0.007

Profit binarya -0.039*** 0.008 -0.041*** 0.008 -0.036*** 0.008

Financial binary3 -0.050*** 0.008 -0.051*** 0.008 -0.050*** 0.008

Qualitative binarya 0.019* 0.009 0.017» 0.009 0.016» 0.009

Planning binaryb 0.093*** 0.011 0.101*** 0.011 0.093*** 0.011

Experience and skills binaryb 0.047*** 0.006 0.051*** 0.006 0.047*** 0.006

Network binaryb 0.081*** 0.008 0.082*** 0.007 0.085*** 0.007

Team binaryb 0.064*** 0.009 0.070*** 0.009 0.071*** 0.009

Economy Adv/Dev. -0.066** 0.008 -0.072*** 0.008

Uncertainty avoidance 0.001*** 0.000

R2 (adjusted) 0.117(0.100)*** 0.143(0.124)*** 0.156 (0.135)***

R2 change (adjusted) 0.025 (0.024)*** 0.013(0.011)**

a As the performance category variables are coded as dummies, scale is excluded as the baseline variable against other performance binaries. b As the entrepreneurial talent variables are coded as dummies, education is excluded as the baseline variable against other talent binaries. t p<0.10.

* p <0.05.

** p<0.01.

*** p<0.001.

2005). The positive, significant (p<0.001), unstandardized coefficient of 0.001 for uncertainty avoidance in Model 3 indicates that the connection between the entrepreneurial talent variables in our study and performance increases in cultures with a higher level of uncertainty avoidance. Model 3 generated a significant (p = 0.009) R2 change of 0.013 over Model 2.

5.3. Robustness checks

5.3.1. Validity test: random versus fixed effect model

A fixed effect model assumes that studies used in the metaanalysis are functionally homogenous, and thus the "true effect size" of the studies is the same and resulting differences stem only from sampling error (Borenstein et al., 2007; Lipsey and Wilson, 2001). Consequently, researchers have argued for the use of a random effects model when combining studies from different researchers and contexts in meta-analysis (e.g., Erez et al., 1996) as it assumes heterogeneity between the studies due to a sampling error as well as an additional variability component that is assumed to be randomly distributed (Borenstein et al., 2007; Lipsey and Wilson, 2001). As our meta-analytic database covers 183 studies encompassing a variety of industries and geographies, applying a random effects model appears appropriate. To validate our results, we replicated our random effects model analyses by also using a fixed effects model (Read et al., 2009) and found our results robust and substantially the same, except point estimates of five of the 30 main effects, with four related to planning.6

The effect size between planning and scale in number of employees decreased from 0.198 (random effects model; p = 0.002) to 0.132 (fixed effect model; p <0.001), while the effect size for team size and against scale increased from 0.180 (random effects model; p <0.001) to 0.195 (fixed effect model; p<0.001). This is due to the fact that the studies with a larger sample size such as Burke et al. (2010) and Matthews et al. (2001), which have a low correlation between planning and scale, are relatively higher weighted in a fixed effect model compared with a random effects model, where the weights are more balanced and larger size studies are less dominant (Borenstein et al., 2007). In addition, the effect size of experience and scale increased from 0.055 (p = 0.007) in the random effects model to 0.113 (p <0.001) in the fixed effect model, which primarily results from one study (Muse et al., 2005), showing a high correlation between experience and scale. This study is based on secondary data and is large (4637 firms) in comparison with numerous survey-based studies in our data set; hence, it is weighted higher in the fixed than in the random effects model (Borenstein et al., 2007).

A similar difference was evidenced against the outcome variable of firm size in terms of sales, where again team size displaced planning (random effects model effect size = 0.173, p = 0.005; fixed effect model effect size = 0.155, p<0.001) as the strongest effect against the outcome, using a fixed effect model. Effect size between team size and sales increases from 0.157 (random effects model, p = 0.001) to 0.223 (fixed effect model; p < 0.001). We analyzed the underlying data and found that the main difference stems from one study (Mollick, 2010) with a large sample size (1522 firms) in comparison with other studies in our data set and a high correlation between team size and sales.

In the case of profit, in the random effects model the effect sizes of planning and network are similar but differ in terms of

6 Due to different assumptions in the validity and robustness tests, it is natural that small changes in terms of significance level and effect sizes occur for nearly all calculated relationships. With consideration to article length and overall relevance ofthose smaller differences, we describe in the text only the meaningful differences that impact the results we discuss in this article. This applies to all validity and robustness tests in this section.

significance. However, in the fixed effect model, planning (effect size = 0.098) displaces network (effect size = 0.078, p<0.001) and increases in significance (p = 0.002) as the fixed effect model, with its different underlying assumption, produces narrower confidence intervals (Borenstein et al., 2007).

With regard to the qualitative performance measures, using a random effects model, planning had a higher effect size (effect size = 0.204, p = 0.018) than experience and skills (effect size = 0.180, p < 0.001). This effect size decreased for planning in the fixed effect model to 0.122 (p < 0.001) because the study of Dencker et al. (2009), which had the largest sample size in this sub-group analysis and a negative correlation, was weighted relatively higher in the fixed effect model. The effect size of network also remained the highest in the fixed effect model (effect size = 0.215, p < 0.001), followed by experience and skills (effect size = 0.148, p<0.001).

The fact that only five of 30 results are meaningfully different in the fixed effect model, compared with the random effects model reassures us that our results are broadly similar across models. However, in the specific case of planning, the variation within these results suggests contingency endogenous to the variable of planning that merits closer investigation (Brinckmann et al., 2010), an issue we take up in Section 6.3.

5.3.2. Validity test: unit of analysis

Our collection of prior work yielded studies conducted at the individual, team and firm units of analysis. We developed an approach for including this variety of work while at the same time reducing the risk of systematic bias, which might result from differences in the level of analysis of the different studies. In order to standardize data, we captured both the number of firms and the number of individuals reported in every study. If a study only reported the number of firms, we used the description of the sample to estimate the value of the unreported individual N, and did the same to estimate the number of firms if the study only provided the number of individuals. We report our analyses using an N that reflects the number of firms in a study. However, we were concerned that standardizing based on the firm level might offer excess statistical power to studies that looked at the smallest firms, so we validated all our analyses by running them again using the individual unit of analysis. The 30 main effect results remained largely unchanged, except one. Network emerges clearly as the entrepreneurial talent aspect having the strongest relationship with profit (effect size = 0.109) differing significantly from zero (p = 0.005) as the effect size of the planning and profit relationship only marginally changes (effect size = 0.098), but experiences a decrease in significance (p = 0.077). With only one substantially differing result with regards to the effect sizes, the validation test gives us additional assurance that standardizing the unit of analysis did not generate a systematic bias in our meta-analyses, and offers an approach for future researchers using meta-analysis to combine studies of different units of analysis.

5.3.3. Validity test: reliability

Scholars with significant experience in meta-analytic methods have suggested that observed variables (not latent constructs) might not be 100% reliable. In order to conduct a test that assumes there is a measurement error in our observed variables, we recalculated all 30 correlations between dependent and independent variables using an assumed average accuracy of 0.80 for all the observed variables (Dalton et al., 2003) and re-ran the random effects models. While point estimates and significances shifted marginally, the entrepreneurial talent variable changed position in only two cases. In the growth category, education displaced network as the talent variable with the second highest relationship to growth (education effect size = 0.121, p <0.001; network effect size = 0.115, p = 0.001), still leaving planning with the strongest

K. Mayer-Haug etal./ Research Policy 42 (2013) 1251-1273 Funnel Plot of Standard Error by Fisher's Z

00 jff Sggp o o

0,0 Fisher's Z

Fig. 1. Funnel plot (random effects model).

connection to growth (effect size = 0.237, p <0.001). For the profit performance measures, experience and skills displaced planning as the second highest mean effect size differing significantly from zero (effect size = 0.078, p = 0.005) as the significance level of planning decreased (effect size = 0.112, p = 0.059), leaving network still with the strongest and significantly different from zero connection (effect size = 0.108, p <0.000) to profit. With only two cases showing a meaningful change in results, this analysis gives us some assurance that observed variable measurement accuracy did not generate a systematic bias in our meta-analyses.

5.3.4. Validity test: firm size

In our operationalization of SME firm size, we set a maximum of 500 employees (e.g., Beck et al., 2005; Dickson et al., 2006; Rosenbusch et al., 2011). However, it is arguable whether the effect of entrepreneurial talent remains the same for a firm of 500 employees versus 50 employees. Hence, as a robustness test of our analysis, we carried out all main effect correlations for small firms with 50 employees or less, and compared those correlations with our previous results that included firms with up to 500 employees. The main difference was that the strength of the connection between planning and performance is lessened for smaller firms. In terms of scale in number of employees, team size with an effect size of 0.212 (p <0.001) overtakes planning (effect size = 0.173, p = 0.039). For sales of small firms, network shows the highest main effect significant from zero (effect size = 0.144, p = 0.005) compared with the insignificant effect size of planning (effect size = 0.162, p = 0.088). For profit, we observe that planning loses effect size and significance level (effect size = 0.025, p = 0.731), leaving network as the strongest connection with profit (effect size = 0.120, p = 0.006), closely followed by experience and skills (effect size = 0.096, p = 0.002). With regard to "other financials" and qualitative performance measures, our findings do not change, with network remaining the entrepreneurial talent variable with the strongest connection. Generally speaking, these analyses suggest that researchers investigating planning should be conscious of the stage and size of the populations of firms under investigation.

5.4. Publication bias

One of the benefits of meta-analysis is the possibility of assessing whether publication bias may be present. Of the 183 studies included in the meta-analyses, 20 are unpublished studies (doctoral dissertations, working papers, conference proceedings). We tested with a mixed effect model to determine whether there is a significant difference between the effect sizes of published versus

unpublished studies. We were reassured that our study faces only limited publication bias, as due to overlapping confidence intervals, no significant difference (p >0.05) was observed between the main effects from published versus unpublished studies. In addition, we used a funnel plot to assess possible publication bias (see Fig. 1). Following Borenstein et al. (2005), a publication bias can be observed from the funnel plot if the studies at the bottom -where studies with a smaller sample size are located - are clustered on one or the other side of the mean. Studies with a smaller sample size, at the bottom of the plot, clustered largely different from the mean, suggest a greater than average effect size, which increases the likelihood of meeting statistical significance criteria and being published. This is not the case in our funnel plot. Furthermore, by applying the file drawer technique to our sample (Hunter and Schmidt, 2004; Rosenthal, 1979), our analysis revealed that 9158 studies with a null-effect are needed to cause an insignificance of our overall results, which exceeds the tolerance level suggested by Rosenthal (1979) by nearly 10 times: 5 x 183 studies+ 10, which equals, for our meta-analysis, 925 cases, and further increases our confidence that publication bias is limited in our analysis.

5.5. Limitations

Beyond the results of our robustness tests, we highlight three additional limitations. First, although our meta-analysis covers 183 studies, during our literature search we identified numerous additional studies of interest that we were not able to include as the papers lacked the data necessary (e.g., statistics such as a correlation table) - a common complaint of meta-analysis authors (Read et al., 2009). Second, meta-analyses share limitations inherent to the underlying studies (Robertson et al., 1993). A case in point in the present study is potential endogeneity in business planning. The business planning of organizations may reflect a broader set of strategic choices that they make. However, this endo-geneity is hardly ever controlled for in the underlying studies we meta-analyzed; therefore, this concern cannot be eliminated in our meta-analysis. A third, and perhaps related limitation of the method concerns granularity, since the underlying studies are typically not designed for the research question under investigation (Robertson et al., 1993). While meta-analysis offers extraordinary power to bring a large body of diverse extant work to a research question, it does not afford insight into follow-on questions suggested by the data, such as why some firms undertake business planning while other similar firms do not. There are many nuanced elements in the venture performance thesis, which might profitably be explored with investigation using alternative methods. As such,

Size Sales Profit Other financials

Fig. 2. Plotted summary model of research findings.

this meta-analysis does not seek to be the final point of the scholarly discussion, but rather aims to synthesize extant evidence and provide guidance and orientation for future research.

6. Discussion and conclusion

While the quantitative results are presented in Table 3, Fig. 2 displays the main effects in a clustered bar chart to provide a graphical illustration summarizing the main findings of our work. The richness and breadth of these data offer many potential avenues for discussion and conclusions, but we focus our attention on five elements in particular.

6.1. Moderating effects of economic development and uncertainty avoidance

Our moderator analyses revealed that entrepreneurial talent is more strongly connected with performance in developing economies than in advanced economies. As this finding may encourage policy makers in developing countries to consider ways of enhancing the relevant entrepreneurial talents of individuals, we explore related research and possible underlying explanations. Carayannis and von Zedtwitz (2005) build on a similar premise, assuming that startup incubators are more valuable in less developed economies since their functionalities of bridging knowledge or increasing the access to different resources can have more impact than in already developed countries. The resource-based view offers insight into why this may be, starting with the assumption that entrepreneurial talent is unevenly distributed across individuals entering entrepreneurship (Barney, 1991). Compounding that effect, individual entrepreneurial talent is likely to vary more in developing than in advanced economies, owing to a higher and more consistent education level across developed economy populations (e.g., Lerner et al., 1997). Furthermore, in developing economies, more individuals may enter entrepreneurship out of necessity, a situation that changes with economic development (Kelley et al., 2012; Venkataraman, 2004), adding to the heterogeneity of active entrepreneurial talent. As our results support these arguments of previous researchers, further efforts to unpack the mechanisms and causality underlying the relationship between entrepreneurial talent and performance in developing economies should be encouraged.

Our finding that entrepreneurial talent is connected with performance in uncertainty-avoiding cultures adds to the literature in important ways. Previous research has focused on entrepreneurial entry, and at the macro and micro levels generally connects low uncertainty avoidance and entrepreneurial entry (Hayton et al.,

2002), though results and explanations are equivocal (Wennekers et al., 2007). Similarly, at the individual level, previous research shows that across countries, entrepreneurs exhibit lower uncertainty avoidance than non-entrepreneurs (McGrath et al., 1992), and that investigations focusing on entrepreneurial cognition propose lower uncertainty avoidance is positively connected with entrepreneurial cognition (Busenitz and Lau, 1996). Work investigating uncertainty avoidance and performance outcomes has not paralleled that investigating entrepreneurial entry, and the contingent influence of cultural elements on entrepreneurial outcomes has been identified as an under-explored area (George and Zahra, 2002). As we establish the connection between uncertainty avoidance and performance in our data, we offer a speculation regarding the self-selection effects that might be at play, as a means of encouraging future research. It could be that in high uncertainty-avoiding cultures, individuals who are quite sure they have what it takes to be successful in building and managing a venture are ready to choose entrepreneurship with its inherent uncertainty over a secure and more predictable employment. This could contrast with cultures that present a lower level of uncertainty avoidance where individuals of all levels of entrepreneurial talent might just "try" entrepreneurship, with less reflection on whether they have the necessary talents to make their business successful. As these considerations are purely hypothetical, we call out for further research to explore the underlying mechanisms of how cultural context affects the entrepreneurial talent-performance relationship.

6.2. Different outcomes are connected with different entrepreneurial talent aspects

Different firm performance outcomes are not necessarily correlated with each other (e.g., Chaganti and Schneer, 1994; Venkatraman and Ramanujam, 1985, 1986; Zou et al., 2010) and theory often does not provide us with indications on how talent mechanisms differ with regard to various venture performance outcomes (e.g., human capital theory). In this paper, we are able to provide a contribution to theory by synthesizing a large volume of empirical work. Growth and firm size measures (scale in number of employees and sales) are predominantly tied to talents connected with planning - at least for SMEs of a certain size. However, in addition to planning, team size also presents a strong association with scale and sales, supporting the notion that greater management capacity better enables the kind of coordination that is necessary as firms get bigger (Penrose, 1959).

Profit offers a stable connection with the entrepreneurial talent variable of network and to a lesser extent with experience

and skills. In our analysis, the connection between planning and profit is not as robust as the connection between network and profit or experience and profit, as the various robustness tests showed a decrease in the significance level with regard to planning. If SMEs are considered an important vehicle in generating economic surplus, this finding suggests the importance of support for public policies that increase the stock of strong entrepreneurial networks and entrepreneurial experience in an economy. Venture profit is not only important for tax revenues but also for individuals considering entry into entrepreneurship according to rent-seeking theory (Baumol, 1990) and selfish motivation (Weitzel et al., 2010). This implies a case for policy interventions that invest in building or deepening the stock of entrepreneurial networks and entrepreneurial experience in a region or country, beyond promoting startups. This notion is consistent with literature on economic growth that highlights the contribution of different knowledge stocks to growth (Romer, 1990).

Furthermore, we find that network has the highest correlation with the "other financials" performance category. This implies that some aspects of SME performance, ranging from financial alliance performance to initial public offering (IPO) and return on equity (ROE) may likely require a broader and more diverse cast of characters than the founders alone. We also observe the importance of clearly specifying the dependent variable. The connection between entrepreneurial talent and agglomerated performance measures such as "other financials" differs substantially from more narrow measures such as sales. For the qualitative performance category, network emerges as the entrepreneurial talent with the strongest connection. Our finding regarding qualitative measures and overall the finding that network is connected most strongly with three of the investigated performance outcomes are also in line with contemporary network research, which broadly shows positive network performance effects in the entrepreneurial context (Hoang and Antoncic, 2003). From a theoretical point of view, the four mechanisms of social networks in an inter-organizational context identified by Zaheer et al. (2010) provide an explanation as to why network has the strongest relationships with half of the tested performance outcomes. First, according to Zaheer et al.'s (2010) review, social networks are often considered a valuable resource offering access to additional (economic and non-economic) resources from which venture performance can benefit (e.g., Bourdieu, 1986; Nahapiet and Ghoshal, 1998; Portes, 1998). Second, according to Zaheer et al. (2010), they are also a means of generating trusting relationships, which add to performance by reducing transaction costs (e.g., Wu and Leung, 2005). A third mechanism described by Zaheer et al. (2010) refers to inter-organizational networks being a source of power and control that are able to reduce or increase resource dependencies of a focal firm (Pfeffer and Salancik, 1978). The fourth mechanism identified by Zaheer et al. (2010) refers to the signaling effect that can arise from partnering with a high-status company (see e.g., Stuart et al., 1999). These findings related to network and venture performance imply that policy makers need to simplify and encourage networking for (potential) founders. For example, by increasing and institutionalizing mentorship programs in universities or governmental institutions in which an experienced founder acts as a mentor and provides advice on a regular basis to new or potential firm founders, founder networks could be enhanced and hence lead to better venture performance on various dimensions.

Overall, two contributions are generated by our analysis. First, with these data, we are able to do more than demonstrate a differential correlation between outcome variables - we are able to show that entrepreneurial talent inputs associated with growth and firm size (scale in number of employees and sales) are different from those associated with performance outcomes such as profit, IPO and survival. This should further encourage researchers and

policy makers to specify performance measures of interest, theorize more specifically with regard to specific dependent variables, and combine multiple performance measures with care. As enticing as it might be to combine performance variables, unpacking the objective function for both the founder and the policy maker will encourage more surgical, focused interventions that are more likely to generate the intended results.

Second, we show that entrepreneurial talent is associated with growth, scale and sales, but to a lesser extent with financial performance outcomes such as profit. With these findings, we are able to add specificity to Penrose's (1959) theory of the growth of the firm, which argues that a key limitation to enabling organizational growth is the capability of the management team. Meanwhile, the entrepreneurial talent of the entrepreneur or the entrepreneurial team appears to be less important for profitability. This finding is consistent with the broader view of entrepreneurs creating artifacts, which are of value especially to themselves (Benz and Frey, 2008). To this point, we have indications that across the population, entrepreneurs work more hours (Ajayi-Obe and Parker, 2005) and make less money than their employed peers (Hamilton, 2000), while at the same time extracting a number of side-benefits (Carter, 2011). Given that we find substantial variance across the different investigated performance outcomes, we suspect that there will be even greater variance against an even broader slate of dependent variables such as satisfaction, happiness, social progress, financial freedom and making a difference in the world. These variables have begun to be (somewhat grudgingly) accepted in economic circles, largely as a result of political adoption in some European countries (Blanchflower and Oswald, 2011; Stiglitz et al., 2010). So, as much as traditional economists might consider these objective functions irrational or subjective, there are indications that these variables may compose much of what the founder of a small firm is working to accomplish (e.g., Benz and Frey, 2008; Blanchflower et al., 2001). We believe that a clearer understanding of these variables will facilitate a more fruitful relationship between venture founders and policy makers in shaping outcomes.

6.3. Contingency in planning and performance

From a theoretical point of view, the positive relationship between planning and performance can be argued both from the perspectives of having the artifact (a plan) and from the learning that is derived from the process (Brews and Hunt, 1999; Brinckmann et al., 2010; Delmar and Shane, 2003). Expanding this debate, prior research has indicated that planning leads to better venture performance (Delmar and Shane, 2003), a finding reinforced by a recent meta-analysis (Brinckmann et al., 2010). At the same time, other researchers questioned the immediate impact of business planning on performance, with work showing the planning to performance relationship to be largely superficial (Honig and Karlsson, 2004; Kirsch et al., 2009; Powell, 1992). Another view suggests that planning is to some extent endogenous to cognitive ability and human capital (Frese et al., 2007), where planning leads to improved performance, but talented entrepreneurs would also be more likely to plan.

Overall, our data suggests support of the planning school, as the effect size of planning to performance overall is higher than any of our other talent variables (effect size = 0.171; p <0.001). However, two important caveats accompany this result. First, the difference to the next highest talent variable - network (effect size = 0.135, p<0.001) - is not statistically significant (t-value = 0.889; two-tailed p = 0.374). Moreover, the average breadth ofthe 95% confidence intervals around the main effect between performance and planning is 0.134, nearly the size of the effect itself (0.171), and more than 30% larger than the next highest average confidence interval (team size = 0.090). This indicates meaningful

endogeneity in the relationship between planning and performance, perhaps suggesting the presence of contextual moderators. Second, our results highlight the importance of specification of the dependent variable, as we find planning primarily associated with growth, scale and sales measures and to a substantially lesser extent with profitability and other financial measures. Moreover, as we see in a post hoc analysis (Section 5.3), this only applies to SMEs that have achieved a certain size.

The contingencies associated in planning are also illustrated when our results are viewed with those of Brinckmann et al. (2010). Neither their bivariate moderation analysis nor their meta-regression indicated significant differences between the performance impact of having a plan and the planning process. We also coded studies according to whether they measure having a plan or planning (excluding studies where the construct was ambiguous). With our data we do find a significant difference (Q = 5.384; p = 0.020) with regard to the impact on overall performance of planning process (effect size = 0.183, p = 0.000) versus having a plan (effect size = 0.066, p = 0.011). We assume the differences are attributed to the study inclusion criteria of both meta-analyses, but more importantly, we suspect that these findings might be more attributable to a lack of precision in the underlying studies. One issue lies in the difficulty of distinguishing between idiosyncratic planning and process from having a plan. There is a big difference between an entrepreneur who writes a plan once at the beginning of the venture, files it away and only takes it out for discussions with financial investors, and an entrepreneur who has a plan, uses it as a strategic and operational tool and revises it on a constant basis. Hence, it is not surprising that studies investigating only the bare existence of a plan might fail to capture a large part of the variance around planning.

There may also be an issue of measurement within underlying studies at play. Our review of the articles in our dataset that contained planning constructs revealed a meaningful difference. Of the 183 studies, 26% included independent variables measured as dichotomous (representing 36% of the firm population). But of the studies specific to planning, 42% of the firm population represented operationalized business planning as a dichotomous variable. This difference led us to not perform the correction for dichotomous variables (Hunter and Schmidt, 2004), as the correction would have unevenly biased our analyses toward studies measuring planning.7 It also leads us to the question of why planning should be measured as a dichotomous variable at all (the degree to which a plan is developed and/or employed feels important in understanding planning). Our conclusion on this topic is that consumers of academic research demand that scholars investigating planning address a number of key issues with rigorous empirical research prior to making their own plans based on academic investigations of business planning. These include (but are not limited to):

(a) (How) is the business plan actually used in a small enterprise?

(b) Are business planning and adaptation alternatives or orthogonal?

(c) What is the causality between planning and scale?

(d) Do experienced founders use business plans differentially from novices?

(e) Is business planning in firms primarily a vestigial outcome of education?

(f) When is a business plan a liability?

7 As an additional validity check, we also conducted the calculations including a correction for dichotomy. We observe only one meaningful change compared to the results discussed in this article. In the category of profit, planning slightly overtakes network and emerges as the talent variable with the highest effect size with profit.

We hope that until some clarity can be offered on these and other questions around business planning, policy makers and researchers alike will critically reflect on the application of planning in their specific venture context.

6.4. Re-educate education to foster entrepreneurial performance

Some of our key findings relate to education. Education is distinctive in that it presents the lowest effect size against two of our measured dependent variables (education with sales: effect size = 0.011, p = 0.694, education with profit: effect size = -0.011, p = 0.739) and presents the lowest relationship with all performance measures aggregated of any of our talent variables in direct effects (effect size = 0.060, p < 0.001). This finding is also reflected in the meta-regression (see Table 6), indicating that after controlling for different performance outcomes, every talent variable analyzed in the models demonstrates a significantly stronger relationship to performance than education since education is the excluded variable in the regression models. This persistently weak connection between education and performance may be unexpected because according to the education-growth nexus, it is plausible that societies with more educated populations have more skilled labor forces and should grow faster (Baumol et al., 2007), though Baumol et al. (2007) caution that for economic growth, education is not a sufficient but a necessary condition. One explanation for our finding could lie in the general empirical measurement of education, i.e., the number of years spent in an educational context. Rather, output (i.e., the quantity and quality) of what individuals actually accumulate as knowledge (see e.g., Unger et al., 2011) might provide a more accurate measure relating to economic growth. Further research needs to disentangle the education-growth nexus to provide additional policy implications to foster entrepreneurial talent.

Conversely, it is possible to argue that education in general today is not meant to help people start and run small firms. And although we looked at education in general, taking Baumol's view, this result would be expected to remain substantially the same if we investigated only specific entrepreneurial education. Baumol stated that it may not be feasible to teach entrepreneurial talent in class (Baumol and Blinder, 2010) - at least not in the kind of educational settings that past classrooms have provided.

To this, we strongly encourage the debate on why and suggest moving to how. Clearly, not every curriculum needs to promote entrepreneurship but - broadly speaking - education needs to provide people with the tools for what they want to do in the world. As evidenced by the amount of venture creation activity, one of the things that people want to do in the world is create firms to help themselves fulfill their goals, whatever these may be. The debate we seek to encourage is how education might be reshaped so that it provides a more positive connection to at least some of the objective and subjective functions entrepreneurs pursue when starting and running firms. Policy debates highlight the role of formal educational institutions in developing and socializing individuals (Heckman, 2000), but education might also fulfill a more prominent role in fostering the development of firms. At present, early entrepreneurship education is presumed to occur largely in families. However, skill formation is a dynamic process in which early learning provides foundations for later development (Heckman, 2000) and firms provide a strong source of skill development via on-the-job experience. Therefore, we suggest that there may be unrealized synergies between early (formal) education about entrepreneurship and later experiential skill acquisition in firms. Extant research and analyses summarized by Heckman (2000) point in general to underinvestment in the very young despite the benefits of learning synergies and much longer payoff horizons that such investments yield. We therefore encourage further research that takes a holistic view of the

connections between entrepreneurship-promoting skill formation across the institutions of family, formal education and firms.

6.5. Theoretical conclusions for researchers

For researchers, we raise three theoretical issues arising from our results:

6.5.1. Theory for predicting the relationships affecting performance

Our results underline the importance of a fine-grained analysis of distinct performance outcomes. However, current theoretical research offers little basis for predicting or understanding the relative magnitude of the relationships between the various components of entrepreneurial talent and different indicators of performance (Unger et al., 2011). Therefore, a challenge - and opportunity - now exists for researchers to craft a cohesive and persuasive theory that predicts specific talent variables' differential impact on certain measures of performance.

6.5.2. Conceptualizing talent mixes and profiles

The findings of our study lend support to notions of the mul-tidimensionality of entrepreneurial talent (Federici et al., 2008). This leads us to suggest that future research should develop theory about entrepreneurial talent that recognizes the complexity of talents, including interactions between different aspects of talent. The notion we prefer here is that of talent mixes, resulting in an overall talent profile. There is no necessary one-to-one mapping of talents to an overall profile; dissimilar talents may yield similar overall profiles. Some prior research has highlighted one aspect of talent mixes: the performance impact of generalists ("jack-of-all-trades," balanced portfolio of talents) versus specialists (Hartog et al., 2010; Lazear, 2005). Furthermore, work by Weitzel et al. (2010) has already begun to explore the possible impact of specific talents (creativity and business talent) on selfishness versus altruism, thus highlighting the importance of distinguishing between different talent mixes when considering the impact on an entrepreneur's goals and performance.

6.5.3. Incorporating venture profiles into talent research

Lastly, there is an important modeling issue in the literature on entrepreneurial talent that needs to be addressed by researchers, which is that the talent-performance link is incomplete. Explicit in the economic research on entrepreneurial talent is the notion that persons can be (self) identified or revealed as entrepreneurs (Ferrante, 2005) and that these talents can be directed by appropriate economic policy into more or less productive avenues (Acemoglu, 1995; Baumol, 1990; Murphy et al., 1991). Based on our findings, we argue that one paradox of profiling people in entrepreneur versus non-entrepreneurs is that care has to be taken to go far enough in profiling. Dividing a population of students (for example) into those with entrepreneurial potential and those without it fails to incorporate the issue of what kinds of ventures might work well for individuals with different talent profiles, contingent on their choice to start a venture. Instead of asking whether an individual has the "right stuff" to become an entrepreneur, the next stage of talent research must ask and answer the question, "What kind of venture would be good for a person to start, given their particular constellation of talents?" In other words, future research should develop models of the talent-performance relationship that incorporate a mediating role for the venture profile, whereby the venture is construed as a design task that incorporates the individual's talents, values and aspirations. Researchers may then be able to recommend how venture design can be leveraged to appreciate a person's talents, whatever they may be.


We would like to thank our institutions, WHU - Otto Beisheim School of Management, IMD, ESADE, the Naval Postgraduate School and the University of St. Gallen for their support that enables our work. We appreciate the efforts and thoughtful inputs of our anonymous reviewers and the editor assigned to this manuscript. And finally, we express our appreciation to the authors whose work we draw from, and who enabled us to summarize the literature by providing the descriptive data necessary to conduct a meta-analysis.

Appendix A.

Details on studies included in the meta-analysis

Authors name (year) Sample size Country of Economy Uncertainty

origin avoidance

N firms N ind.**

Aarstadet al. (2010) 20 40 Norway Advanced 50

Agarwal et al. (2004) 59 14,750 n/a n/a n/a

Amason et al. (2006) 174 43,500 U.S. Advanced 46

Ancona and Caldwell (1992) 5 409 n/a n/a n/a

Arthurs et al. (2008) 307 92,100 U.S. Advanced 46

Azriel (2003) 60 1200 U.S. Advanced 46

Bamford et al. (2000) 140 7000 U.S. Advanced 46

Barney et al. (1996) 205 10,250 U.S. Advanced 46

Batjargal (2007) 52 2132 China Developing 30

Batjargal (2010) 159 7473 China, Developing n/a


Baum and Bird (2010) 143 2145 U.S. Advanced 46

Baum and Locke (2004) 229 1438 U.S. Advanced 46

Baum and Silverman (2004) 204 20,400 Canada Advanced 48

Beal and Yasai-Ardekani (2000) 101 9494 U.S. Advanced 46

Becerra et al. (2008) 65 3250 Norway Advanced 50

Beckman et al. (2007) 161 9016 U.S. Advanced 46

Begley (1995) 239 2390 U.S. Advanced 46

Bermanet al. (1997) 161 3220 U.S. Advanced 46

Bingham et al. (2007) 12 70 n/a n/a n/a

Boeker and Wiltbank (2005) 86 25,800 U.S. Advanced 46

Boone and de Brabander(1993) 51 4080 Belgium Advanced 94

Boone and Hendriks (2009) 33 1320 Belgium, Advanced n/a


Boxetal. (1993) 95 4750 U.S. Advanced 46

Boxet al. (1995) 187 28,050 Thailand Developing 64

Branko (2004) 415 83,000 U.S. Advanced 46

Brunninge et al. (2007) 889 17,780 Sweden Advanced 29

Brush and Chaganti (1999) 279 4185 U.S. Advanced 46

Burgers et al. (2009) 240 118,800 Netherlands Advanced 53

Burke etal. (2010) 422 7849 U.K. Advanced 35

Capelleraset al. (2010) 647 17,469 Argentina, Developing n/a


Chile, Peru

Carson et al. (2003) 129 32,250 U.S. Advanced 46

Carter etal. (1996) 71 142 U.S. Advanced 46

Carter etal. (1997)" 144 1440 U.S. Advanced 46

Carter etal. (1997)* 59 590 U.S. Advanced 46

Chaganti and Schneer(1994) 372 372 U.S. Advanced 46

Chaganti et al. (2008) 26 1950 U.S. Advanced 46

Chandler and Hanks (1994) 155 2325 U.S. Advanced 46

Chandler and Jansen (1992) 134 804 U.S. Advanced 46

Chandler and Lyon (2009) 124 62,000 U.S. Advanced 46

Chen et al. (2009) 202 50,500 China Developing 30

Chrisman et al. (2005) 31 31 U.S. Advanced 46

Ciavarella et al. (2004) 111 2220 U.S. Advanced 46

Cliff (1998)" 141 3525 Canada Advanced 48

Cliff (1998)" 88 1056 Canada Advanced 48

Cooper et al. (1997) 391 1799 U.S. Advanced 46

Crusoe (2000) 57 570 U.S. Advanced 46

Davidsson and Honig (2003) 380 380 Sweden Advanced 29

De Carolis et al. (2009) 269 269 U.S. Advanced 46

De Clerq and Sapienza (2006) 298 14,900 U.S. Advanced 46

Delmar and Shane (2003) 211 211 Sweden Advanced 29

Delmar and Shane (2004) 211 211 Sweden Advanced 29

Dencker etal. (2009) 436 436 Germany Advanced 65

Dingkun (2003) 210 5250 U.S. Advanced 46

Doutriaux (1992) 65 325 Canada Advanced 48

Doving and Gooderham (2008) 234 234 Norway Advanced 50

Edelman etal. (2005) 192 384 n/a n/a n/a

Escriba-Esteve et al. (2009) 295 36,875 Spain Advanced 86

Farrell et al. (2005) 38 273 Ireland Advanced 35

Fasci and Valdez (1998) 604 1812 U.S. Advanced 46

Fernhaber and Li (2010) 150 52,500 U.S., Advanced n/a


Florin (2001) 279 90,117 U.S. Advanced 46

Florin (2005) 277 89,471 U.S. Advanced 46

Forbes (2005a) 77 9625 U.S. Advanced 46

Forbes (2005b) 108 1080 U.S. Advanced 46

Freel and de Jong (2009) 594 29,700 Netherlands Advanced 53

Authors name (year) Sample size N firms N ind." Country of origin Economy Uncertainty avoidance index

Frese et al. (2007)" 117 117 South Developing 49


Frese et al. (2007)" 215 215 Zimbabwe Developing n/a

Frese et al. (2007)" 73 73 Namibia Developing n/a

Fung et al. (2007) 2105 324,170 China Developing 30

Gimeno et al. (1997) 1457 1457 U.S. Advanced 46

Gimmon and Levie (2010) 193 193 Israel Advanced 81

Goedhuys and Sleuwaegen (2010) 254 7874 11 African Developing n/a


Gruberet al. (2008) 84 37,800 Germany Advanced 65

Haberand Reichel (2007) 305 38,125 Israel Advanced 81

Hayton (2002) 200 50,000 U.S. Advanced 46

Higashide and Birley (2002) 57 2850 U.K. Advanced 35

Hmieleski (2009) 201 10,050 U.S. Advanced 46

Hmieleski and Baron (2008a) 159 39,750 U.S. Advanced 46

Hmieleski and Baron (2008b) 207 51,750 U.S. Advanced 46

Hmieleski and Carr (2008) 216 54,000 U.S. Advanced 46

Hmieleski and Ensley (2007)" 66 168 U.S. Advanced 46

Hmieleski and Ensley (2007)" 154 1540 U.S. Advanced 46

Holcomb (2007) 632 305,256 U.S. Advanced 46

Honig(1998) 215 250 Jamaica Developing 13

Honig(2001) 64 448 Palestine Developing n/a

Honig and Karlsson (2004) 396 396 Sweden Advanced 29

Hsu (2007) 149 7450 U.S. Advanced 46

Jo and Lee (1996) 48 4800 South Advanced 85

Khavul(2001) 82 1394 Israel Advanced 81

Kim and Higgins (2007) 292 24,820 U.S. Advanced 46

Kishida (2005) 314 942 U.S. Advanced 46

Kor(2003) 73 18,250 U.S. Advanced 46

Kundu and Katz (2003) 47 470 India Developing 40

Lane et al. (2001) 78 5538 Hungary Developing 82

Lange et al. (2007) 330 41,250 U.S. Advanced 46

Larsson et al. (2003) 223 223 Sweden Advanced 29

Lee et al. (2001) 137 17,125 South Advanced 85

Lee and Tsang(2001) 168 3360 Singapore Advanced 8

Lerneret al. (1997) 218 2616 Israel Advanced 81

Lerner and Almor (2002) 220 3300 Israel Advanced 81

Lernerand Haber(2001) 53 424 Israel Advanced 81

Li (1998) 184 9200 China Developing 30

Li and Zhang (2007) 184 9200 China Developing 30

Lin et al. (2006) 125 25,000 Taiwan Advanced 69

Lin et al. (2009) 110 5500 Taiwan Advanced 69

Ling and Kellermanns (2010) 86 5160 U.S. Advanced 46

Lubatkin et al. (2006) 139 8618 U.S. Advanced 46

Lyles et al. (2004) 135 3645 Hungary Developing 82

Manolova et al. (2007) 545 8938 Bulgaria Developing 85

Matthews (1990) 103 2575 U.S. Advanced 46

Matthews and Scott (1995) 130 4160 U.S. Advanced 46

Matthews et al. (2001) 467 467 n/a n/a n/a

McEvily and Marcus (2005) 234 14,742 U.S. Advanced 46

McGee et al. (1995) 210 21,000 U.S. Advanced 46

Meziou (1991) 176 8800 U.S. Advanced 46

Mineret al. (1994) 90 90 n/a n/a n/a

Minguzzi and Passaro (2001) 104 2600 Italy Advanced 75

Mitchell et al. (2008) 220 220 U.S. Advanced 46

Mollick (2010) 1552 55,872 n/a n/a n/a

Morris et al. (1997) 177 8850 U.S. Advanced 46

Mursitama (2006) 1080 54,000 Indonesia Developing 48

Muse et al. (2005) 4637 148,384 U.S. Advanced 46

Nadkarni and Herrmann (2010) 195 80,155 India Developing 40

Niehmetal. (2008) 221 1105 U.S. Advanced 46

Niosi (2003) 60 1560 Canada Advanced 48

Okamuro (2007) 255 32,130 Japan Advanced 92

Orseret al. (2000) 1004 1004 Canada Advanced 48

Oxley and Wada (2009) 548 137,000 n/a n/a n/a

Park (2010) 126 63,000 South Advanced 85

Park and Krishnan (2001) 78 5694 U.S. Advanced 46

Patzelt et al. (2008) 99 4653 Germany Advanced 65

Pena(2004) 114 114 Spain Advanced 86

Pett and Wolff (2003) 149 11,175 U.S. Advanced 46

Powell (1992)" 68 8500 U.S. Advanced 46

Powell (1992)" 45 5625 U.S. Advanced 46

Authors name (year) Sample size N firms N ind." Country of origin Economy Uncertainty avoidance index

Rauch et al. (2000)" 66 1650 Germany Advanced 65

Rauch et al. (2000)" 48 1200 Germany Advanced 65

Rauch et al. (2005) 95 570 Germany Advanced 65

Raz and Gloor (2007) 71 710 Israel Advanced 81

Reuberand Fischer (1994) 43 2924 Canada Advanced 48

Rosenkopfand Almeida (2003) 116 29,000 U.S. Advanced 46

Saffu and Manu (2004) 171 2052 Ghana Developing 54

Sambasivan et al. (2009) 243 12,150 Malaysia Developing 36

Sapienza et al. (2004) 54 6048 Finland Advanced 59

Sarason and Tegarden (2003) 314 7850 U.S. Advanced 46

Schulze et al. (2003) 1464 266,448 U.S. Advanced 46

Senjem (2001) 113 28,250 U.S. Advanced 46

Shrader and Siegel (2007) 198 49,500 U.S. Advanced 46

Sine et al. (2006) 449 2694 U.S. Advanced 46

Smaltz et al. (2006) 100 25,000 U.S. Advanced 46

Soh(2010) 49 12,250 U.S. Advanced 46

Song et al. (2010) 694 52,050 China Developing 30

Stam (2010) 75 375 Netherlands Advanced 53

Stam and Elfring (2008) 87 348 Netherlands Advanced 53

Stam and Wennberg (2009) 647 16,175 Netherlands Advanced 53

Stetz etal. (2005) 865 865 n/a n/a n/a

Stewart (2003) 72 1800 U.S. Advanced 46

Tiwana and Bush (2005) 122 122 n/a n/a n/a

Tornikoski and Newbert (2007) 830 830 U.S. Advanced 46

Tsai (2009) 753 334,332 Taiwan Advanced 69

Ucbasaran et al. (2003) 92 92 UK Advanced 35

Ungeret al. (2009) 90 90 South Developing 49


van Gelder et al. (2007) 91 455 Fiji Developing n/a

van Gelderen et al. (2000) 49 1225 Netherlands Advanced 53

Vissa and Chacar (2009) 84 168 India Developing 40

Walter etal. (2006) 149 2384 n/a n/a n/a

Walters et al. (2010) 494 123,500 U.S. Advanced 46

Watson et al. (2003) 175 1750 U.S. Advanced 46

Weaver and Dickson (1998) 252 12,600 Norway Advanced 50

Wiklund and Shepherd (2003) 326 7172 Sweden Advanced 29

Wiklund and Shepherd (2008) 2253 2253 Sweden Advanced 29

Wincent etal. (2010) 41 861 Sweden Advanced 29

Wright et al. (2008) 349 22,685 China Developing 30

Yang et al. (2008) 105 52,500 Eastern Developing n/a


Yli-Renko et al.(2001) 180 4320 UK Advanced 35

Zahraet al.(1997) 121 10,164 U.S. Advanced 46

Zahra et al. (2007) 384 38,400 U.S. Advanced 46

Zahra and Bogner (2000) 116 5800 U.S. Advanced 46

Zhaoet al.(2010)" 133 1995 China Developing 30

Zhaoet al.(2010)" 75 150 China Developing 30

Zheng etal. (2010) 170 42,500 U.S. Advanced 46

Zollo et al. (2002) 81 20,250 U.S. Advanced 46

Zou et al. (2010) 252 12,600 China Developing 30

* Papers from which multiple studies were extracted are listed multiple times in this table.

** In situations where average firm size of the respective sample was not provided, we estimated the average firm size based on the sample description in order to calculate the number of individuals.


*Aarstad, J., Haugland, S.A., Greve, A., 2010. Performance spillover effects in entrepreneurial networks: Assessing a dyadic theory of social capital. Entrepreneurship Theory and Practice 34,1003-1019.

Acemoglu, D., 1995. Reward structures and the allocation of talent. European Economic Review 39,17-33.

*Agarwal, R., Echambadi, R., Franco, A.M., Sarkar, M.B., 2004. Knowledge transfer through inheritance: spin-out generation, development, and survival. Academy of Management Journal 47, 501-522.

Ajayi-Obe, O., Parker, S.C., 2005. The changing nature of work among the self-employed in the 1990s: evidence from Britain. Journal of Labour Research 26, 501-517.

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Ansoff, H.I., Avner, J., Brandenburg, R.G., Portner, F.E., Radosevich, R., 1970. Does planning pay? The effect of planning on success of acquisitions in American firms. Long Range Planning 3 (2), 2-7.

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Audretsch, D.B., Grilo, I., Thurik, A.R., 2007. Handbook of Research on Entrepreneur-ship Policy. Edward Elgar, Cheltenham, UK-Northampton, MA.

Audretsch, D.B., Grimm, H.M., Schuetze, S., 2009. Local strategies within a European policy framework. European Planning Studies 17,463-486.

Ayyagari, M., Beck, T., Demirguc-Kunt, A., 2007. Small and medium enterprises across the globe. Small Business Economics 29, 415-434.

"Azriel, J.A., 2003. Small, High-Technology Firms and their Larger Strategic Alliance Partners: Entrepreneurial and Resource-Based Perspectives. Doctoral dissertation. University at Albany, State University of New York.

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"Bamford, C.E., Dean, T.J., McDougall, P.P., 2000. An examination of the impact of initial founding conditions and decisions upon the performance of new bank start-ups. Journal of Business Venturing 15, 253-277.

Barney, J.B., 1991. Firm resources and sustained competitive advantage. Journal of Management 17,99-120.

"Barney, J.B., Busenitz, L.W., Fiet, J.O., Moesel, D.D., 1996. New venture teams' assessment of learning assistance from venture capital firms. Journal of Business Venturing 11, 257-272.

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"Baum, J.A.C., Silverman, B.S., 2004. Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups. Journal of Business Venturing 19, 411-436.

"Baum, J.R., Bird, B.J., 2010. The successful intelligence of high-growth entrepreneurs: links to new venture growth. Organization Science 42,397-412.

"Baum, J.R., Locke, E.A., 2004. The relationship of entrepreneurial traits, skill, and motivation to subsequent venture growth. Journal of Applied Psychology 89, 587-598.

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Baumol, W.J., 2010. The Microtheory of Innovative Entrepreneurship. Princeton University Press, New Jersey.

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