THE IMPACT OF FAMILY SUPPORT ON YOUNG NASCENT ...THE IMPACT OF FAMILY SUPPORT ... More recent theoretical perspectives include institutional theory that argues new ventures can ...

St. Petersburg State University

Graduate School of Management

WORKING PAPER

T. Manolova, G. Shirokova, T. Tsukanova, L. Edelman

THE IMPACT OF FAMILY SUPPORT

ON YOUNG NASCENT ENTREPRENEURS’

START-UP ACTIVITIES: A FAMILY EMBEDDEDNESS PERSPECTIVE

# 2 (E)–2014

Saint Petersburg

2014

T. Manolova, G. Shirokova, T. Tsukanova, L. Edelman. The Impact of

Family Support on Young Nascent Entrepreneurs’s Start-Up Activities: A

Family Embeddedness Perspective. Working Paper # 2 (E)–2014. Graduate

School of Management, St. Petersburg State University: SPb, 2014.

Keywords and phrases: nascent entrepreneurs, family capital, student en-

trepreneurship, start-up activities, embeddedness, cohesiveness, GUESSS

Study

Abstract: In this paper we explore the factors associated with the

scope of start-up activities among young nascent entrepreneurs. Taking a

family embeddedness perspective, and drawing on literature from nascent

entrepreneurship, start-up activities, and student entrepreneurship, we hy-

pothesize that the scope of start-up activities of young nascent student en-

trepreneurs is positively associated with family support in the form of dif-

ferent types of family capital; financial, social, human, and physical. We

further argue that the effect of family support on young nascent entrepre-

neurs’ start-up activities is positively moderated by the level of family co-

hesiveness. We test our hypotheses using data from the 2011 Global Uni-

versity Entrepreneurial Spirit Students Survey (GUESSS) which covers

93,625 students from 26 countries. We find that the effects of family sup-

port on young nascent entrepreneurs’ start-up activity are complex and

multi-faceted. Implications for theory, public policy, and managerial prac-

tice are discussed.

Research has been conducted with financial support from Russian Sci-

ence Foundation grant (project No. 14-18-01093)

Tatiana Manolova — Associate Professor, Business Policy and Strategy,

Bentley University

e-mail: [email protected]

Galina Shirokova — Professor, Director of the Center for Entrepreneur-

ship, Graduate School of Management, St. Petersburg State University

e-mail: [email protected]

Tatyana Tsukanova — Assistant Professor, Graduate School of Man-

agement, St. Petersburg State University

e-mail: [email protected]

Linda Edelman — Associate Professor, Business Policy and Strategy,

Bentley University

email: [email protected]

Contents

Introduction ..................................................................................................... 5

Theory and Hypotheses ................................................................................... 7

Method ............................................................................................................ 16

Results ............................................................................................................. 24

Discussion ....................................................................................................... 28

References ....................................................................................................... 35

5

The Impact of Family Support on Young Nascent Entrepreneurs’ Start-Up Activities:

A Family Embeddedness Perspective

Introduction

In the fourth quarter of 2012, youth unemployment across Europe was 23.3% of the popu-

lation, more than twice the 9.3% unemployment rate in the European adult population. This is

problematic because high rates of youth unemployment can lead to a lifetime of lower earnings,

migration as young people leave their country of origin in search of employment, or in more ex-

treme cases, social unrest. Issues around youth unemployment affect more than just youth, they

also have a major impact on society and the economy as a whole. This suggests that the problem

of youth unemployment cannot be separated from larger questions around promoting economic

growth.

New venture development and self-employment can begin to address the youth unemploy-

ment problem. Entrepreneurship can foster individual self-empowerment and serve as an engine

of job creation, economic growth, innovation and constant reinvigoration of economic life (Kelly

et al., 2011; Audretsch, 2007). A long-term supply of potential, well-educated entrepreneurs is

crucial to a well-functioning economy (Carey, Flanagan, & Palmer, 2010).

Young people are particularly well positioned to engage in entrepreneurship. Inc. Maga-

zine’s 2012 survey of the Inc. 500 CEO’s, finds that on average, CEO’s started their first new

venture when they were 27. This is consistent with Lévesque & Minniti, (2006, 2011) who

found that the majority people who start a business are between 25 and 34. Other researchers

suggest that the education and technological savvy of university graduates helps to equip them to

start growth-oriented new ventures (Lüthje & Franke, 2003; Mowery & Shane, 2002). Therefore,

if new venture development is an essential weapon in the battle against youth unemployment, it is

critical to gain an understanding of those factors that influence the ability of young people to start

a new business.

One area that has received little attention in the entrepreneurship literature is the role played

by the family in new venture creation. This is surprising given that families are an important

source of early stage, seed funding (Bygrave, Hay, Ng & Reynolds, 2003), and mentoring (Sulli-

van, 2000). Aldrich and Cliff (2003), in their work on family embeddedness, suggest that this

lack of attention is more due to academic institutional arrangements, where family and business

are studied in different departments or colleges, than to practice. A family embeddedness per-

spective acknowledges that entrepreneur’s businesses and families are inextricably intertwined,

6

rather than the separate entities as they are so often treated as in the entrepreneurship literature

(Jennings & McDougald, 2007). Family businesses comprise between 80 and 90% of all busi-

ness enterprises in North America (Astrachen & Shanker, 2003). Given that entrepreneurs are

embedded in social relationships (Aldrich & Zimmer, 1986; Larson & Starr, 1993), families play

an important role in early stage entrepreneurial decisions.

In this paper, we examine the start-up activities of young, nascent entrepreneurs. In partic-

ular, we focus on the start-up activities and family support received by university students who

are in the process of starting up their own business. Prior research has focused on the entrepre-

neurial intentions of university students (Kolvereid, 1996; Autio et al., 2001; Krueger, Reilly &

Carsrud, 2000; Audet, 2001; Kennedy et al., 2003; Turkey & Selcuk, 2009; Boissin, Branchet,

Emin & Herbert, 2009; Carey, Flanagan & Palmer, 2010; Iakovleva, Kolvereid & Stephan, 2011;

Zellweger, Sieger & Halter, 2011). Family influences on entrepreneurship have been examined

in a number of literatures: family business research (e.g., Chang, Memili, Chrisman, Kellermanns

& Chua, 2009; Rodriguez, Tuggle & Hacket, 2009; Koropp, Grichnik & Kellermanns, 2013),

social network research (Dubini & Aldrich, 1991; Larson, 1991; Shane & Cable, 2002; Gross-

man, Yli-Renko & Janakiraman, 2012; Semrau & Werner, 2013; Newbert, Tornikoski &

Quigley, 2013), and intergenerational transfer of entrepreneurship (Barnir & McLaughlin, 2011;

Litz, 2010; Laspita, Breugst, Heblich & Patzelt, 2012). However, there appears to be a “missing

link” in the literature regarding the relationship between the family support provided to young

nascent entrepreneurs and their start-up activities. This paper addresses that gap.

To examine the relationship between family support and the start-up activities of young,

nascent entrepreneurs, we use the “Global University Entrepreneurial Spirit Students Survey”

(GUESSS) dataset. The GUESSS dataset is a panel study of student of university students. Data

is currently collected in 34 countries. GUESSS systematically records the founding intention and

activity of students on a long-term basis. For this study, we used the data from the 2011

GUESSS survey. In that year, 93,625 students from 26 countries joined the survey, which repre-

sents approximately a 9.4% response rate. Our study only looks at those students who are in-

volved in the process of starting up a business, or 21987students.

We argue that families play a critical role in university students’ nascent entrepreneurial ac-

tivity. Our contention is that families provide tangible and intangible resources that are instru-

mental in the start-up of the new venture, particularly for young, resource constrained entrepre-

7

neurs. We further argue that the degree of family cohesiveness positively moderates this rela-

tionship.

Our study provides a number of important contributions. First, the study contributes to the

overall entrepreneurship literature by increasing our understanding of how different types of re-

sources influence the start-up activities of young nascent entrepreneurs. Second, our study high-

lights the importance of family in entrepreneurship, by highlighting the direct and indirect effects

of family on new venture creation process and in doing so providing an important link between

the nascent literature and the literature on family business influences.

The paper proceeds as follows. We start by presenting our conceptual framework and hy-

potheses. We then move to a description of our sample, methodology and present our empirical

findings. Next we discuss our findings and then conclude with the implications and limitations of

our research.

Theory and Hypotheses

The Nascent Entrepreneur and Entrepreneurial Start-up Activities

Organizational emergence is a central activity in the field of entrepreneurship (Katz &

Gartner, 1988; Aldrich, 1999; Shane & Delmar, 2004). Entrepreneurship researchers agree that

organizational emergence is a process made up of multiple start-up activities (Carter et al., 2004:

313; Gartner et al., 2004a: 285). Individuals who initiate organizing activates intended to culmi-

nate in a viable business start-up are referred to as nascent entrepreneurs (Reynolds & White,

1997; Aldrich, 1999). The organizing activities in which nascent entrepreneurs are engaged in-

volve setting up routines and structures that are goal directed and establishing boundaries and

systems of activities (Aldrich, 1999). Those activities require plentiful and diverse resources

(Hanlon & Saunders, 2007; Aldrich, 1999; Semrau & Werner, 2013).

Perspectives on organizing activities that influence the probability of start-up have a variety

of theoretical roots (Shane & Delmar, 2004). Early work described the process as a change mod-

el, whereby entrepreneurs accumulate external resources and technology necessary to transform

their ideas into a reality by creating a new business unit (Van de Ven, Angle, & Poole, 1989).

More recent theoretical perspectives include institutional theory that argues new ventures can

survive by achieving legitimacy through the organizing process (Zimmerman & Zeitz, 2002); the

resource-based view which posits that development of unique resources can lead to opportunity

exploitation (Choi & Shepherd, 2004); and evolutionary theory that argues external stakeholder

8

relationships help ventures to overcome their liability of newness (Stinchcombe, 1965; Aldrich,

1999).

Concurrent with the emergence of theories explaining new venture start-ups, researchers

have conducted studies examining start-up processes. Early work by Reynolds and Miller (1992)

examined a sample of nascent entrepreneurs and found that start-up activities did not have a uni-

tary logical progression. Following this research, Gatewood, Shaver and Gartner, (1995) ex-

plored whether cognitive factors as well as entrepreneurial activities led to the formation of a

business while Carter, Gartner and Reynolds, (1996) examined specific start-up activities such as

personal commitment, financial support, hiring and activities which developed the structure of

the business.

While these early studies showed that the activities of nascent entrepreneurs who started a

business are different from those of nascent entrepreneurs who did not, they suffered from prob-

lems of retrospective bias, lack of generalizability and small sample size. These data collection

issues were part of the impetus for the creation of the Panel Study of Entrepreneurial Dynamics

(PSED) dataset(s) (see Gartner, Shaver, Carter & Reynolds, 2004b and Reynolds & Curtin,

2009), which specifically examines the start-up activities of nascent entrepreneurs. Building off

of PSED I and II data that were either collected in the US or internationally, a number of more

recent empirical studies examine the connection between start-up activities and the probability of

start-up.

Shane and Delmar (2002) examined planning, legitimacy and market activities and their ef-

fect on the probability of starting a firm in 223 Swedish new ventures. They found that planning

and legitimacy were significantly correlated with the probability of starting a new venture but

that market activities had no effect. Lichtenstein, Carter, Dooley, & Gartner, (2007) drew on

chaos theory in their study of dynamic patterns in start-up activities in US nascent organizations,

findings that new organizations emerge when the rate of start-up activities is high, start-up activi-

ties are spread over time and firms are more likely to emerge when start-up activities are concen-

trated later in the start-up phase, rather than earlier. Brush, Manolova and Edelman, (2008), use

the Katz and Gartner (1988) framework in their empirical examination of the properties of

emerging organizations finding that all four properties are necessary for firm survival in the

short-term, and those firms that organize more slowly are more likely to continue to organize.

In sum, with the advent of PSED I and II, the study of organizational emergence and nas-

cent entrepreneurship has become both an important and a well-studied branch of entrepreneur-

9

ship research. Within this branch are a number of notable studies that look at start-up activities.

From these studies we can conclude that while the order of the startup activities does not matter,

firms that engage in more activities are less likely to disband. This finding is consistent with

Carter et al (1996) as well as with Brush et al (2008). In both the PSED I and the PSED II da-

tasets, firms which engaged in more activities were more likely to continue the organizing effort.

This suggests that entrepreneurs who are actively engaged in the process of starting the venture

are likely to end up with a viable new venture in the short run.

Family Embeddedness

The acquisition of resources is a key challenge in the entrepreneurial process, especially for

new ventures (Grichnik, Brinckmann, Singh, Manigart, 2014). Resource-based logic suggests

that firms build competitive advantage by utilizing unique sets of resources (Wernerfelt, 1984;

Barney, 1991). Resources are heterogeneous, and include all assets, capabilities, processes, and

knowledge controlled by a firm. Sets of firm-specific resources enable organizations to conceive

and implement unique, inimitable strategies, thereby improving overall effectiveness (Barney,

1991; Grant, 1991). Young firms typically have insufficient resources, which limits their range

of feasible strategic alternatives (Hofer & Sandberg, 1987).

The embeddedness literature argues that economic action is embedded social structures

which consist of enduring networks of interpersonal relationships, and that these social relation-

ships both facilitate and constrain economic action (Granovetter, 1985; Wiklund et al., 2013).

Building on the embeddedness logic, Aldrich and Cliff (2003) propose a family embeddedness

perspective, arguing that the family has the potential to exert a substantial influence on the firm.

They go on to suggest that the characteristics of entrepreneurs' family system, such as family re-

sources, norms and values, can influence the processes involved in venture creation.

Much of the empirical research on resources in new ventures is grounded in a social em-

beddedness perspective. These studies emphasize the importance of the founders’ social ties

when building the firm’s initial stock of resources (see Brush et al., 2001 for a review). Howev-

er, in addition to founders, families play a central role in the resource mobilization process during

start-up. Families are an important source of early stage financing (Bygrave, et al., 2003), and

families influence entrepreneur’s decisions particularly around issues such as ownership and tran-

sition (Wiklund, Nordqvist, Hellersterdt & Bird, 2013).

Some scholars have highlighted the role of family contacts. Aldrich (1999) and Aldrich

and Zimmer (1986), for example, argued in support of the resources provided by the ‘‘strong

10

ties’’ of family members. Similarly, Starr and MacMillan (1990:81) suggested that kinship ties,

in addition to previous work relationships, volunteer connections, and community ties, ‘‘lay the

groundwork for new ventures’’. More recently, Chrisman et al. (2003) asserted that family rep-

resents a critical and often used resource for startups. Nevertheless, few studies have explored

the role that families play in resource mobilization, particularly with respect to youth entrepre-

neurship.

Youth entrepreneurs are different from more experienced entrepreneurs (Sarasvathy, 1998).

Youth entrepreneurs have little, if any, business knowledge, few social relations and little experi-

ence in how to make sense of the entrepreneurial process (Nielsen & Lassen, 2012). In addition,

youth entrepreneurs lack the necessary start-up capital to start a new venture, and typically face

liquidity constraints making borrowing start-up funds difficult (Evans & Jovanovic, 1989). The

lack of social capital coupled with a lack of financial capital lead young entrepreneurs to seek

resource and emotional support from their families in order to start a new business. In the next

section we will explore some of the ways that families deploy their resources in support of youth

entrepreneurship.

Family financial capital

Financial capital is critical for the new firms. Financial capital provides entrepreneurs with

the flexibility to undertake a wider range of start-up activities (Pena, 2002). It can act as a buffer

against random external shocks, allowing entrepreneurs to pursue more capital-intensive strate-

gies, which are better protected from imitation (Cooper, Gimeno-Gascon., & Woo, 1994). How-

ever, due to their young age and lack of collateral and credit history, most of traditional channels

for getting early stage financial capital, such as credit cards or loans, are not available for univer-

sity students (Ozgen & Minsky, 2013).

Financially, nascent entrepreneurs typically acquire early stage funding from friends and

family (Bird, 1989; Blechman & Levinson, 1991; Fenn, 1999; Granovetter, 1985; Schell, 1996;

Van Auken & Neeley, 1996; Winborg & Landstrom, 2001). When looking for early stage financ-

ing, young, student entrepreneurs are able to benefit from family financing and family connec-

tions. A recent study shows that there is a positive influence of family involvement on new ven-

ture debt financing (Chua, Chrisman, Kellermanns, Wu, 2011). Parents may also assist younger

generation family entrepreneurs by using their own connections to provide input with extended

credit to the new firms being launched by their offspring (Colombatto & Melnick, 2008). Exist-

ing literature on family finance assumes that family members and friends have access to private

11

information about a new venture based on their proximity to the venture’s founder (Parker,

2009). Specifically, friends and family are likely to have information about the founders’ work

ethic and dedication to the start-up, which affect the start-up’s value. This private information

implies that friends and family investment in a new venture is a signal to external investors of the

quality of the founder (Conti, Thursby & Rothaermel, 2013).

Family-provided finance is likely the greatest source of financial support for young entre-

preneurs (Steier, 2003). Thus we suggest,

H1a: The higher the family support, in the form of financial capital, the greater the

scope of start-up activities undertaken by the young nascent entrepreneur.

Family Social Capital

Social capital refers to networks of relationships in which personal and organizational con-

tacts are closely embedded (Bastie, Cieply, & Cussy, 2013). Through these relationships, social

actors can gain access to information, resources, and social approbation (Hoang & Antoncic,

2003; Stuart & Sorenson, 2007; Newbert, Tornikosli, & Quigley, 2013). However, the likelihood

of an exchange of resources, channeling of information, or ascribing legitimacy is a function of

the quality of network relationships, measured in the strength of relationship ties (Hoang & An-

tonic, 2003; Newbert et al., 2013). Strong ties tend to be long-standing relationships based on

frequent contacts such as those existing among family members, friends, or tightly- knit commu-

nities (Coleman, 1988). In contrast, weak ties tend to be short-term relationships based on infre-

quent interactions and exchange (Granovveter, 1973). Closely related to the distinction between

strong and weak social ties is the distinction made between “bonding” and “bridging” social capi-

tal (Adler & Kwon, 2002; Gedajlovic et al., 2013). Bonding social capital refers to a collective’s

internal ties and the substance of network relations within that collective (Coleman, 1988; Sand-

ers & Nee, 1996); whereas “bridging” social capital refers to an individual’s external social ties,

with a focus on how external contacts and relationships can be used for personal gain (Burt,

1982; Adler & Kwon, 2002; De Carolis & Saparito, 2006).

Researchers refer to the special form of internal social capital developed through the dy-

namic and trusting relationships of family members and available only to family members as

family social capital (Hoffman et al., 2006; Salvato & Melin, 2008; Chang et al., 2009; Dyer,

Nenque & Hill, 2014). Family social capital may have a strong influence on the venture creation

process, even when the family is not directly involved in the entrepreneurial initiative (Aldrich &

Cliff, 2003; Steier, 2007; Renzulli, Aldrich, Moody, 2000). In addition to the direct effect of fam-

12

ily members being introduced into existing social networks of their kin, family social capital fa-

cilitates the mobilization of other resources needed for successful start-up. Thus, affiliation with a

well-respected family is often interpreted as a signal of personal traits and ascribed status. In ad-

dition, families take responsibility for the obligations and actions of its members. This allows so-

cial actors to “borrow” the family’s established social capital. For example, Chua et al. (2011)

documented that family involvement was instrumental in the acquisition of debt financing by ex-

ploiting previously established relationships between family members and resource holders.

Empirical evidence from the student entrepreneurship literature shows that students with

family business backgrounds stem from a particular familial context that may influence their fu-

ture career intentions (Zellweger, Sieger, Halter, 2011; Laspita, Breugst, Hebilich, Patzelt, 2012).

For example, children may access the social capital of parents-entrepreneurs, including contacts

with suppliers, business partners, customers etc., and they may benefit from their parents’ net-

work when trying to establish a new business (Laspita et al., 2012). Very often, parents’ social

capital could also help their children gain information about new market opportunities (Sorensen,

2007). Formally:

H1b: The higher the family supports in the form of social capital, the greater the scope

of start-up activities undertaken by the young nascent entrepreneurs.

Family Human Capital

The concept of human capital is rooted in the idea that people possess skills, experience

and knowledge that have economic value for them and their firms (Cetindamar, Gupta, Karaden-

iz, Egrican, 2012). Many scholars argue that human capital is the most critical resource that eco-

nomic actors possess (Hitt et al., 2001). The entrepreneurship literature has found that nascent

entrepreneurs with prior entrepreneurial experience have knowledge regarding the various activi-

ties associated with starting a firm, including how to develop contacts with customers and finan-

ciers, how to gather and allocate resources, how to organize internal processes and structures, and

how to attract and retain employees (Dimov, 2010; Delmar & Shane, 2006; Grichnik, Brinck-

mann, Singh, Manigart, 2014). Young nascent entrepreneurs typically lack both entrepreneurial

experience and managerial experience. Therefore, young entrepreneurs look to the family as a

way to overcome their human capital deficits (Hoang & Antoncic 2003).

Family human capital is defined as the knowledge, skills and abilities of individual family

members (Carney, 2005; Coleman, 1988; Danes et al., 2009; Salvato & Melin, 2008). Stocks of

family human capital represent a potential resource advantage for new venture creation and for

13

the firm development (Sorenson & Bierman, 2009). One form of family human capital is men-

toring. Research suggests that decision to start up a business is positively correlated with having

parents who are or were entrepreneurs (Chlosta, Patzelt, Klein, Dormann, 2010; Parker, 2009;

Dunn & Holtz-Eakin, 2000). Moreover, the recent study on entrepreneurial role models suggests

that “entrepreneurial role models tend to be next-door examples rather than more remote ‘icons’”

and role models with a mentoring function are more often sourced from ‘strong tie’ relationships

including family members (Bosma et al., 2012:422). Hence,

H1c: The higher the family supports in the form of human capital, the greater the scope

of start-up activities undertaken by the young nascent entrepreneurs.

Family physical capital

The last form of family capital to be considered is physical capital. Physical capital in-

cludes family assets such as use of the family home as the business office, family vehicles,

phones, and computers that may be used to start a new business (Dyer, Nenque, Hill, 2014).

Sirmon and Hitt (200: 343) argue that family “survivability capital can help sustain the business

during poor economic times or, for example, after an unsuccessful extension or new market ven-

ture”. Without such family support student nascent entrepreneur have to find the other source of

these tangible resources. Therefore,

H1d: The higher the family supports in the form of physical capital, the greater the

scope of start-up activities undertaken by the young nascent entrepreneurs.

The Moderating Role of Family Cohesiveness

Cohesiveness refers to the degree of connectedness and emotional bonding that family

members experience within the family (Lansberg & Astrachan, 1994; Olson & Gorall, 2003;

Laspita et al., 2012). Families with high cohesiveness are characterized by shared norms, behav-

iors, understanding and emotionally intense relationships (Granovveter, 1992). There some evi-

dence in the literature those emotionally intense ties among family members provide access to

resources, often at below-market rates, due to an inherent sense of obligation (Witt, 2004). Also

evidence suggests that nascent entrepreneurs will seek out individuals with whom they have a

strong emotional attachment for various forms of support during new venture creation process

(Renzulli et al., 2000; Ruef et al., 2003; Newbert et al., 2013). For university student launching a

new business, family cohesiveness is critical as young entrepreneurs lack not only entrepreneurial

experience but also strong-tie relationships with business people, and so must rely on family con-

nections. Thus,

14

H2a: The stronger family cohesiveness, the stronger the relationship between fami-

ly financial support and the scope of start-up activities undertaken by the young nascent

entrepreneurs.

H2b: The stronger family cohesiveness, the stronger the link between family social

capital and scope of start-up activities undertaken by the young nascent entrepreneurs.

H2c: The stronger family cohesiveness, the stronger the link between family human

capital and scope of start-up activities undertaken by the young nascent entrepreneurs.

H2d: The stronger family cohesiveness, the stronger the link between family physi-

cal capital and scope of start-up activities undertaken by the young nascent entrepre-

neurs.

The complete theoretical model is presented in Figure 1.

15

Figure 1: Conceptual Framework and Hypotheses

Family Support: Financial

Capital

Family Support: Social

Capital

Family Support: Human

Capital

Family Support: Physical

Capital

Family Cohesiveness

Controls: age, gender, number of

start-up partners, level of com-

mitment to start-up, entrepreneur-

ship courses taken, field of study,

educational institution, country of

origin

Scope of Start-Up Activi-

ties

H2a

H1b

H2b

H2c

H2d

H1d

H1c

H1a

Method

Data collection & sample

We used panel data from the “Global University Entrepreneurial Student Spirit Survey”

(GUESSS) project. GUESSS was initiated by the Swiss Research Institute of Small Business and

Entrepreneurship at the University of St. Gallen in 2003 and currently includes 34 countries. The

data are collected biannually using an online survey. Every participating country is represented

by one coordinator who is responsible for data collection. The coordinator contacts different uni-

versities in the respective country with an invitation to take part in the survey. If the universities

agree, they complete a registration form which indicates how many students will get the link to

the survey.

The GUESSS project has three primary goals: 1) to observe systematically the entrepre-

neurial intentions and activities of students; 2) to identify the antecedents and boundary condi-

tions in the context of new venture creation and entrepreneurial careers in general; and, 3) to ob-

serve and evaluate universities’ activities and offerings related to the entrepreneurial education of

their students (for more details see Sieger et al., 2011). Data from the GUESSS project have been

used, for example, to explore the career choice intentions of students with family business back-

ground (Zellweger, Sieger, & Halter, 2011), or the intergenerational transmission of entrepre-

neurial intentions (Laspita et al., 2012).

We used data from the 2011 GUESSS survey. In that year, 93,625 students from 26 coun-

tries completed the survey, to a response rate of approximately 9.4%1. As the interest of the pre-

sent study is in young nascent entrepreneurs, we selected only the students who were born in or

after 1975 (i.e. not more than 36 years of age at the time of the survey), and were “intentional

founders”, i.e. individuals who had been thinking about founding their own company or were in

the process of establishing it, but had not founded the company yet. To allow for within-country

and within-university variability, we excluded the cases where the respondent was the sole partic-

ipant from a university and/or there were fewer than fifty respondents per country. This resulted

in a final usable sample size of 21,987 students from 23 countries (Argentina, Austria, Brazil,

Chile, China, Estonia, Finland, France, Germany, Hungary, Ireland, Liechtenstein, Luxembourg,

Mexico, Netherlands, Portugal, Romania, Russia, Singapore, South Africa, Switzerland, and the

UK). The students in our sample were on average 24.25 (SD=3.86) years old, and 44.27% of

1 The estimation of the response rate is approximate because the nature of the sampling procedure does not allow an

exact calculation. We do not know exactly whether the number of links reported in the registration form was

achieved by local universities’ representatives. There is also a chance that students shared the link to the survey

among themselves.

17

them were female. Over a third of the students (38.88%) reported that their parents (at least one)

had been entrepreneurs at some point in time.

Variables

Dependent variable

Start-up activities are events, behaviors, and the accomplishments of individuals that lead

to the emergence of new businesses (Carter, Gartner & Reynolds, 2004). They were measured by

ten self-reported dichotomous variables indicating whether or not the young nascent entrepreneur

had engaged in a particular activity prior to or at the time of the survey. The choices included: 1 -

“nothing done so far”, 2 - “thought of first business ideas”, 3 - “formulated business plan”, 4 -

“identified market opportunity”, 5 - “looked for potential partners”, 6 - “purchased equipment”, 7

- “worked on product development”, 8 - “discussed with potential customers”, 9 - “asked finan-

cial institutions for funding, 10 - “decided on date of founding”. Respondents who had checked

choice 1 – “nothing done so far” were excluded from the analysis as the answer implied the stu-

dent had not engaged in any start-up activities. Respondents who had checked choice 10 – “de-

cided on date of founding” were also excluded, as the answer implied that the company was al-

most established, whereas our interest is in the process of setting up a new venture. We next

summed up the tallies to obtain a measure of the scope of start-up activities, ranging between 1

and 8. Among the “intentional founders” in our sample, 40.08% had undertaken at least one start-

up activity, and 21.84% had undertaken at least two. Barely 0.25% (54 students) had pursued all

eight activities tracked by the survey.

Independent variables

In order to capture the different aspects of family support, we constructed four measures,

based on the “Family Support” section of the questionnaire. In this section, students were asked

to indicate to what extent a set of statements concerning family support for their entrepreneurial

activity applied to them, using a 7-point Likert-type scale ranging from 1 = “not at all” to 7 =

“very much”, with 4 as the neutral point. The statements referred to specific types of capital, as

follows: financial capital (3 items), social capital (2 items), human capital (3 items) and physical

capital (2 items).

To construct the scales, we used principal component analysis with varimax rotation. The

four scales exhibited good internal consistency and reliability, with factor loadings of .5 and

above, Eigen values above 1.736 and Cronbach’s Alphas of .79 or higher. Table 1 reports the re-

sults from the factor analysis.

18

Financial capital was measured using three questions: my parents/family provide me with

debt capital; my parents/family provide me with equity capital; the capital provided by my par-

ents/family has favorable and flexible conditions. Social capital was measured using two ques-

tions: my parents/family provide me with contacts to people that might help me with pursuing an

entrepreneurial career; my parents/family introduce me to business networks, providing contacts

to potential business partners and/or customers. The human capital measure was comprised of

three items: my parents/family offer me general knowledge about how to run a business; my par-

ents/family offer me industry-related knowledge on how to produce services and products; my

parents/family coach/mentor me in my entrepreneurial activities. Finally, physical capital was

captured by two items: my parents/family provide me with locations/facilities for my entrepre-

neurial activities; my parents/family provide me access to a distribution network for my intended

company.

Moderating variable

Family cohesiveness refers to the degree of connectedness and emotional bonding within

the family (Lansberg & Astrachan, 1994; Olson & Gorall, 2003; Laspita et al., 2012). Students

were asked to indicate the level of agreement with the following statements: 1) “Family together-

ness is important”; 2) “Family members feel very close”; 3) “When family gets together, every-

one is present”; 4) “Family members ask each other for help”. Each statement was evaluated by a

7-point Likert-type scale (“completely disagree” to “completely agree” with a defined neutral

point). The four items were subjected to principal component analysis and loaded on a single fac-

tor with loadings of 0.45 or better. The scale demonstrated good internal consistency (single fac-

tor extracted, Eigen value of 2.752) and reliability (Cronbach’s Alpha = 0.8444).

Control variables

We controlled for students’ age (calculated based on the self-reported year of birth), gender,

country of origin (23 dummy variables), number of partners participating in the new venture

(self-reported count), level of commitment (self-reported percent of student’s average weekly

working time he/she planned to invest in his/her company), number of entrepreneurship courses

(self-reported count), and field of study (four dummies, denoting Business and Economics, Natu-

ral Sciences, Social Sciences, and Other).

The descriptive statistics and zero-order correlations of all variables entered into the regres-

sion estimation are reported in Tables 2 and 3, respectively.

Table 1: Factor and Reliability Analysis of Multi-Item Scales

Family Support

Family

Cohesiveness

Financial Capital

Social

Capital

Human

Capital Physical Capital

Item Loading* Item Loading* Item Loading* Item Loading* Item Loading*

Debt capital 0,577

Social

contacts 0,707

General

knowledge 0.577

Locations and

facilities 0.7071

Family

togetherness 0,451

Equity capital 0,549 Social

networks 0,707

Industry-

specific

knowledge

0,578 Distribution

network 0.7071

Family members

feel close 0,540

Favorable financing

conditions 0,603

Mentorship 0,576

Everyone present 0,507

Family members

ask for help 0,497

Proportion of

variance explained 2.139

1.831

2.475

1.736

2.752

Eigen-value* 2.139

1.831

2.475

1.736

2.752

Cronbach's Alpha 0,795 0,907 0,893 0,846 0,844

* Confirmatory factor analysis, single factor extracted.

Table 2: Descriptive Statistics

Variable n Mean S.D. Min Max Frequences*

Categories Percent

Dependent Variable

Scope of start-up ac-

tivities 21987 2,31 1,45 1 8

Controls

Age 21987 24,25 3,86 16 35

Gender 21987 0,44 0,50 0 1 Female 44,27

Number of partners 21987 1,06 0,98 0 4

Number of

entrepreneurship courses 21987 4,26 3,69 0 8

Level of commitment 21987 53,22 28,26 0 100

Field of Study 21987 2,21 1,18 1 4

Business &

Economics 36,97

Natural

Sciences 29,85

Social

Sciences 8,70

Others 24,47

Family Support

Financial capital 21987 8.04e-09 1,46 -1,52 3,61

Social capital 21987 1.83e-08 1,35 -1,57 2,44

Human capital 21987 -1.24e-08 1,57 -1,83 3,18

Physical capital 21987 -1.50e-08 1,32 -1,22 3,16

Family Cohesiveness 21987 -2.77e-09 1,66 -6,21 1,79

* Categorical variables only

Table 3: Correlations

N Variable

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1

Scope of start-up

activities 1,00

2 Age 0,07* 1,00

3 Gender -0,15* -0,05* 1,00

4

Number of

partners 0,13* -0,08* -0,06* 1,00

5

Number of

entrepreneurship

courses 0,10* -0,04* -0,02* 0,03* 1,00

6

Level of

commitment 0,05* 0,018* 0,01 0,00 0,02* 1,00

7

Field of Study:

Business and Eco-

nomics 0,00 -0,09* 0,02* -0,01 0,30* 0,02* 1,00

8

Field of Study:

Natural Sciences 0,03* 0,04* -0,18* 0,02* -0,17* -0,02* -0,5* 1,00

9

Field of Study:

Social Sciences -0,04* 0,07* 0,14* -0,02* -0,15* -0,04* -0,23* -0,20* 1,00

10

Field of Study:

Others 0,00 0,02* 0,08* 0,01 -0,06* 0,02* -0,44* -0,37* -0,18* 1,00

11 Financial capital -0,02* -0,14* -0,03* 0,02* 0,07* 0,00 0,04* -0,01 -0,05* 0,00 1,00

12 Social capital 0,02* -0,20* 0,03* 0,03* 0,10* 0,03* 0,06* -0,04* -0,05* 0,01 0,54* 1,00

13 Human capital 0,01 -0,21* 0,05* 0,00 0,11* 0,03* 0,06* -0,03* -0,05* 0,00 0,53* 0,74* 1,00

14 Physical capital 0,01 -0,15* 0,00 -0,01 0,1* 0,00 0,04* -0,02* -0,04* 0,00 0,59* 0,67* 0,74* 1,00

15

Family

cohesiveness -0,02* -0,05* 0,12* 0,03* 0,06* 0,05* 0,04* -0,01 -0,04* 0,00 0,15* 0,23* 0,25* 0,20* 1,00

* Significant at p<.05 or better

22

Statistical Procedure

Our dependent variable is a count (the number of start-up activities), therefore we speci-

fied a hierarchical Poisson regression, utilizing the STATA procedure. Prior to specifying the

regression, we ran a number of diagnostics. First, we checked for potential over-dispersion of the

dependent variable, in which case a negative binomial specification would have been the appro-

priate statistical procedure. Since the mean of the dependent variable was higher than the vari-

ance, we chose the Poisson specification. Next, we tested for multicollinearity. At 3.08, the high-

est variance inflation factor (VIF) among the independent variables was below than the con-

servative cut-off value of 5.0 (Studenmund, 1992), assuring us that multicollinearity should not

be a concern.

In the first step of hierarchical analysis we included only the control variables (Model 1);

in the second step we added financial, social, human and physical capital as independent varia-

bles (Model 2), in the third step we added “family cohesiveness” (Model 3), and in the fourth

step we added the four interaction terms of different forms of family capital with “family cohe-

siveness” (Model 4). The results are reported in Table 4.

Table 4: Hierarchical Poisson Regression Estimates of the Effects on the Scope

of Start-Up Activities

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Controls

Age 0.013*** 0.013*** 0.014*** 0.014*** 0.013*** 0.013*** 0.013***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Gender -0.176*** -0.179*** -0.176*** -0.176*** -0.177*** -0.177*** -0.176***

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)

Number of partners 0.068*** 0.068*** 0.068*** 0.068*** 0.068*** 0.068*** 0.068***

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Number of

entrepreneurship

courses 0.013*** 0.013*** 0.013*** 0.013*** 0.013*** 0.013*** 0.013***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Level of

commitment 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Field of Study: Natu-

ral Sciences 0.028** 0.029** 0.029** 0.028** 0.028** 0.028** 0.028**

(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

Field of Study: So-

cial Sciences 0.016 0.015 0.014 0.014 0.013 0.013 0.013

23

(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)

Field of Study:

Others 0.008 0.008 0.007 0.007 0.007 0.007 0.007

(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)

University YES YES YES YES YES YES YES

Country of Origin YES YES YES YES YES YES YES

Table 4: Hierarchical Poisson Regression Estimates of the Effects on the Scope

of Start-Up Activities (Cont.)

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Family Support

Financial capital -0.022*** -0.021*** -0.022*** -0.021*** -0.021*** -0.021***

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Social capital 0.013** 0.014*** 0.014*** 0.013** 0.014*** 0.014***

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Human capital 0.006 0.008 0.008* 0.008 0.007 0.008*

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Physical capital 0.006 0.007 0.006 0.006 0.006 0.005

(0.005) (0.005) (0.006) (0.006) (0.006) (0.006)

Family Cohesiveness -0.010*** -0.009*** -0.008*** -0.008*** -0.008***

(0.003) (0.003) (0.003) (0.003) (0.003)

Cohesiveness x

Financial Capital 0.002

(0.002)

Cohesiveness x Social

Capital 0.006***

(0.002)

Cohesiveness x Human

Capital 0.005**

(0.002)

Cohesiveness x

Physical Capital 0.005**

(0.002)

Regression Function

_cons 0.256*** 0.259*** 0.251*** 0.252*** 0.252*** 0.252*** 0.252***

(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)

Pseudo r2 0,0222 0,0228 0,0229 0,0229 0,0230 0,0230 0,0230

N 21987 21987 21987 21987 21987 21987 21987

Poisson regression; p>chi2=0,000 for all models; *p<0.10, ** p<0.05, ***p<0.01

24

RESULTS

The control variables were included in the Model 1. The patterns largely confirmed our ex-

pectations. The individual age effect was significant and positive: age was positively associated

with the scope of start-up activities (b=0.013, p<0.01). The coefficient for the gender was -0.176

(p<0.01), indicating a lower scope of start-up activities for female young nascent entrepreneurs.

The number of partners was positively associated with the scope of start-up activities (b=0.068,

p<0.01). Students who had taken more entrepreneurship classes had undertaken a higher number

of start-up activities (b=0.013, p<0.01). The level of commitment was also positively associated

with the scope of start-up activities (b=0.001, p<0.01). Compared to the business students, stu-

dents with specialization in natural science were involved in a higher scope of start-up activities

(b=0.028, p<0.1). There were also significant country-of-origin and university effects.

The next step of analysis tested the main effects of family financial, social, human, and

physical capital (Model 2). Social capital had a statistically significant and positive relationship

with the scope of start-up activities of young nascent entrepreneurs: i.e., the higher the family

support in the form of social capital (b=0.013, p<0.05), the greater the scope of start-up activities

undertaken by young entrepreneurs. These findings provided support for Hypothesis H1b. Finan-

cial capital had statistically significant but negative relationships with start-up activities. The

higher the family support in the form of financial capital (b=-0.022, p<0.01), the lower the scope

of start-up activities. Thus, H1a was rejected. Human capital and physical capital had both insig-

nificant relationships with start-up activities, thus our Hypotheses H1c and H1d were not sup-

ported. In Model 3, “family cohesiveness” was significantly and negatively associated with the

scope of start-up activities (b=-0.010, p<0.01).

Models 4-7 included sequentially the interaction terms. The interactions between social

capital and family cohesiveness (b=0.006, p<0.01), human capital and family cohesiveness

(b=0.005, p<0.01) and physical capital and family cohesiveness’ (b=0.005, p<0.01) were all pos-

itive and statistically significant, rendering support to our hypotheses H2b-H2d. The interaction

between family financial capital and family cohesiveness was not significant. Thus, hypothesis

H2a was not supported.

25

Robustness tests

As previously discussed, the GUESSS survey encompasses different countries and differ-

ent universities within these countries. Thus, the structure of these data is multilevel because the

respondents are nested within universities, which, in turn, are nested within different countries.

The observations, therefore, are not independent from each other because potential similarities

may occur among students in a particular country or university. To address this challenge, we

implemented a multilevel mixed-effect (hierarchical) modeling approach (Laspita et al., 2012) as

a robustness test. Mixed model estimation allows to account for the nested data structure and to

take into account the cross-level interactions and Poisson distribution. As a starting point for the

analysis, we ran ANOVA to determine what portion of the variance in individual start-up activi-

ties is due to cross-country and cross-university difference as compared to individual differences.

These statistical tests demonstrated that the means of groups were not equal, confirming that the

observed variance can be partly explained by different countries of origin and different educa-

tional institutions.

We separated the variance at each level: individual (level 1), university (level 2) and coun-

try (level 3). The results are reported in Table 5. First, we considered separately the model at the

country level (Model 1) and at the university level (Model 2). There were 21987 level-1 units,

and 23 level-3 units (countries). In the fixed effect part, the estimate of start-up activities was

0.9, meaning that the average number of start-up activities across individuals and countries is

around one. But only about 2.2% of the variance in the scope of activities was due to differences

across counties, with the remaining almost 97.8% attributable to individual differences. There

are 281 level-2 units (number of groups that represent universities) in Model 2. The estimate of

start-up actions was also around one. Universities accounted for 4% of the variance in the activi-

ties. Model 3 combined the three levels: individual, country and university.

26

Table 5: Multi-level Mixed-effect Poisson Regression Estimates of the Effects on the

Scope of Start-Up Activities

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Controls

Age

0.013*** 0.013*** 0.013*** 0.014*** 0.013*** 0.013*** 0.014***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Gender

-0.18*** -0.18*** -0.18*** -0.18*** -0.18*** -0.18*** -0.18***

(0.009) (0.009) (0.009) (0.009) (0.01) (0.01) (0.01)

Number of

partners

0.069*** 0.07*** 0.07*** 0.07*** 0.07*** 0.07*** 0.07***

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Number of

entrepreneurship courses

0.015*** 0.014*** 0.014*** 0.014*** 0.014*** 0.014*** 0.014***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Level of commitment

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Field of Study: Natural Sciences

0.027** 0.027** 0.028** 0.027** 0.027** 0.027** 0.027**

(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)

Field of Study:

Social Sciences

0.02 0.02 0.018 0.018 0.017 0.017 0.017

(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)

Field of Study:

Others

0.019 0.019 0.019 0.019 0.018 0.018 0.018

(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)

Family Support

Financial

capital

-0.02*** -0.02*** -0.02*** -0.02*** -0.02*** -0.02***

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Social capital

0.013** 0.014*** 0.014*** 0.013** 0.014*** 0.014***

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Human capital

0.007 0.008* 0.008* 0.008 0.007 0.008

(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Physical capital

0.007 0.007 0.006 0.006 0.006 0.005

(0.005) (0.005) (0.005) (0.005) (0.005) (0.006)

Family

Cohesiveness

-0.01*** -0.01*** -0.008*** -0.008*** -0.009***

(0.003) (0.003) (0.003) (0.003) (0.003)

27

Table 5: Multi-level Mixed-effect Poisson Regression Estimates of the Effects on the

Scope of Start-Up Activities (Cont.)

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Cohesiveness x Financial Capital

0.002

(0.002)

Cohesiveness x Social Capital

0.007***

(0.002)

Cohesiveness x

Human Capital

0.005***

(0.002)

Cohesiveness x Physical Capital

0.005**

(0.002)

_cons 0.856*** 0.837*** 0.857*** 0.401*** 0.39*** 0.38*** 0.38*** 0.38*** 0.38*** 0.38***

(0.021) (0.01) (0.022) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037)

Random-effects Parameters

sd(Residual)

Intercept (Nation) 0.093

0.008 0.005 0.005 0.005 0.005 0.005 0.005 0.005

(0.017)

(0.003) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Intercept (University)

0.014 0.006 0.003 0.003 0.003 0.003 0.003 0.003 0.003

(0.002) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Model Fit Statistics

AIC 75064.82 75056.56 75012.76 74070.33 74037.16 74025.88 74026.61 74017.64 74019.6 74022.55

BIC 75080.82 75072.56 75036.76 74158.31 74157.14 74153.85 74162.58 74153.61 74155.57 74158.52

N 21987 21987 21987 21987 21987 21987 21987 21987 21987 21987

*p<0.10, ** p<0.05, ***p<0.01

In models 4-10 we replicated the procedure previously implemented in the hierarchical

Poisson regression models, entering the controls (Model 4), the main effects of the four different

forms of family capital (Model 5), family cohesiveness (Model 6), and the interaction terms

(Models 7-10). In the Model 6 there were 21987 level-1 units, 281 level-2 units (number of

groups that represent universities), and 23 level-3 units (number of groups that represent nations)

and with a log-likelihood=-36996.94. The grand mean estimate for start-up activities was ap-

proximately 0.38 (p<0.001). The likelihood (LR) ratio test statistics in all models confirmed that

the null hypothesis that there is no cross-country and cross-university variation in the scope of

start-up activities could be rejected. The main and interaction effects in the Multi-level regres-

sion were consistent with the Poisson regression, indicating that the results are robust to alterna-

tive regression specifications. In order to estimate how well the model fits the data, the AIC

28

(Akaike Information Criterion) and BIC (Bayes Information Criterion) are usually used, and the

smaller values are better. In the Table 5 these statistics are represented and the Models with our

main independent variables (Models 4-5) are represented better results compared to the Models

1-4 and the addition of interaction terms lead to slight decrease in the AIC and BIC (Models 6-

10) with the exception for the interaction term between the financial resources of the family and

family cohesiveness.

Discussion

In this study, we used insights from the family embeddedness perspective to explore the

start-up process of young nascent entrepreneurs. Our contention was that families provide sup-

port in the form of human, social, financial, and physical capital, and that more cohesive families

facilitate the translation of family capital into an expanded scope of start-up activities. The re-

sults from statistical testing revealed a more nuanced picture of the role of family resources in

the start-up process, as we discuss below.

The role of family social capital

In line with a large body of entrepreneurship literature anchored in the theory of social cap-

ital (Aldrich & Zimmer, 1986; Davidsson & Honig, 2003; Chang et al., 2009; Danes et al., 2009;

Hoffman, Hoelscher, Sorenson, 2006; Zellweger et al., 2012; see also Stam et al., 2014 for a re-

cent meta-analysis of the role of entrepreneurial social capital on small firm performance), we

find that the family social capital, in the form of social contacts and introduction into social net-

works, has a consistently significant positive effect on the scope of start-up activities undertaken

by young nascent entrepreneurs. Our finding extends prior research in this area by documenting

that it is the family’s external ties (social contacts and networks), in particular, that are instru-

mental in the process of nascent entrepreneurs’ venture creation.

Entrepreneurship and family business researchers have traditionally focused on different

aspects of social capital. Prior research in entrepreneurship has emphasized the role of the family

as a locus of “bonding social capital”, or internal “strong ties” (Sanders & Nee, 1996; Da-

vidsson & Honig, 2003; Renzulli & Aldrich, 2005; Kalnins & Chung, 2006). In contrast, family

business research has emphasized the ability of families to pass on to the next generation their

family-specific external social interactions (Sirmon & Hitt, 2003; Salvato & Melin, 2008).

Our study thus bridges the entrepreneurship and family business perspectives by highlight-

ing that “strong ties” within a family are not only a source of internal “bonding social capital”,

29

but also a source of external “bridging social capital”, in the form of passing valuable social con-

tacts and/or entry into the family’s existing social networks. This bridging social capital is in-

strumental in the young entrepreneur’s advancing through the start-up process. A fruitful exten-

sion of our study will be to examine the relative importance of bonding and bridging family so-

cial capital along the start-up trajectory. Another interesting research question is whether the

role of family as a source of bridging social capital is likely to change over time, the rationale

being that as individuals mature and develop valuable social contacts of their own, they may be

less likely to rely on family connections.

The tenuous influence of family financial, human, and physical capital

We expected that, similar to the effect of family social capital, the family financial, human,

and physical capital would likewise be positively associated with the scope of start-up activities

undertaken by young nascent entrepreneurs. However, we found that family financial capital

was, in fact, consistently negatively associated with the scope of start-up activities, whereas fam-

ily physical and human capital had no significant impact. Below, we present our interpretation of

these findings.

Two alternative explanations emerge with respect to the negative effect of family financial

capital on the scope of start-up activities. First, family financial capital may serve as a substitute

for alternative means of capitalizing the nascent venture. Recall that we measured the scope of

start-up activity as a sum of different actions undertaken by nascent student entrepreneurs. These

activities were: ‘thought of first business ideas’, ‘formulated business plan’, ‘identified market

opportunity’, ‘looked for potential partners’, ‘purchased equipment’, ‘worked on product devel-

opment’, ‘discussed with potential customers’, or ‘asked financial institutions for funding. Hav-

ing family support in the form of financial capital alleviates the need to generate sales revenue

fast and hence reduces the pressure to identify and discuss business with potential customers.

Further, having family money reduces the need to look for potential partners, or to look for out-

side sources of funding, which, in turn, lessens the urgency of producing a formal business plan

that typically is required by strategic partners, lenders, or private equity providers. Thus, the

higher the extent of family financial support, the fewer the activities associated with cash genera-

tion and/or mobilization.

The literature on slack resources in entrepreneurial firms (Bradley et al., 2011; Patzelt et

al., 2008) offers an alternative explanation. Most nascent firms battle the high odds of failure

30

under conditions of extreme resource scarcity (Baker & Nelson, 2005). Financial slack, or ex-

cess financial resources, therefore, provides a much needed cushion against environmental

shocks, is liquid and easily convertible into other resources, creates an environment conducive to

innovation, and allows the nascent entrepreneur to continue with the realization of the entrepre-

neurial initiative (Cooper, Gimeno-Gascon, & Woo, 1994). Not surprisingly, financial capital is

considered to be the lifeblood of the new venture. At the same time, financial slack can foster

complacency and actually stifle entrepreneurial behavior (Stevenson & Jarillo, 1990; Bradley et

al., 2011). For example, in their study of the dual effects of financial slack on small firm growth,

Bradley et al., (2011) documented that while financial slack had a significant and positive effect

on sales growth; it had a significant negative impact on entrepreneurial characteristics such as

strategic orientation, growth orientation, or entrepreneurial culture. In other words, the safety

cushion provided by family financial support may take the edge of entrepreneurial urgency and

aspiration, resulting in a slower pace of organizing, i.e. fewer start-up activities. Future research

should ascertain if there is an optimal threshold of financial resources conducive to the success-

ful pursuit of entrepreneurial initiatives, akin to the inverse U-shaped relationship between finan-

cial slack and performance proposed in the context of established firms (George, 2005).

Similar reasoning may apply to the non-significant effect of family physical capital on the

scope of nascent entrepreneurial activities. Property and equipment supplied by the family may

negate the need to secure assets from outside sources. Alternatively, entrepreneurial ventures in

the new economy tend to be asset-light and the proliferation of online social platforms allows for

creative ways of renting and/or sharing costly fixed assets. Thus, one can surmise that the availa-

bility of family physical capital in the form of locations/facilities, or a distribution network, may

not be so critical for the scope of start-up activities undertaken by young nascent entrepreneurs.

The non-significant effect of family human capital is more puzzling. Our measure of fami-

ly human capital was comprised of three aspects, general knowledge, industry-specific

knowledge, and mentorship. As discussed in the theoretical framing of our study, a long line of

research both in the entrepreneurship and family business literatures has argued for the important

role of parental role-models and mentoring for nurturing entrepreneurship (Chlosta, Patzelt,

Klein, Dormann, 2010; Parker, 2009; Dunn & Holtz-Eakin, 2000). Families create human capi-

tal by handing down the knowledge of “how to do business”, particularly from one generation to

the next (Dyer et al., 2014). Because of the lack of prior entrepreneurial experience and general

31

managerial skills among nascent student entrepreneurs, we surmised that family human capital

may help them to gain needed knowledge through informal conversations with family members,

by watching their parents at work. It may be that family human capital is vital for the trans-

generational transfer of family values and for the nurturing of entrepreneurial intentions (Laspita

et al., 2012), but not so important for the actual realization of the entrepreneurial initiative. Fu-

ture research can further elucidate the complex effects of family human capital at different stages

of the start-up process.

The dual effect of family cohesiveness

We found that family cohesiveness had dual effect on the scope of start-up activities under-

taken by young nascent entrepreneurs. On the one hand, and contrary to our predictions, family

cohesiveness had a consistently negative direct effect on the scope of start-up activities. On the

other hand, and in line with our predictions, family cohesiveness had a positive moderating effect

on three aspects of family support, facilitating the transition of family social, human, and physi-

cal capital into an enhanced scope of start-up activities (the moderating effect on the relationship

between family financial capital and start-up activities was not significant). We interpret this du-

al effect as follows.

With respect to the negative effect of family cohesiveness on the scope of nascent entre-

preneurial activity, previous work by Aldrich and Cliff (2003), Dyer and Handler (1994), and

others has examined how certain “family patterns” can have both positive and negative influence

on entrepreneurial initiatives (Dyer et al., 2014). Indeed, some families may not be supportive of

new venture formation efforts (Arregle et al., 2007), particularly in cultures that place high value

on the stability and prestige associated with working for a high-status employer or the govern-

ment (for a recent overview on the role of cultural values for entrepreneurship, see Krueger et al.,

2013). This lack of support might discourage nascent entrepreneurs from starting businesses in

order to avoid relational conflicts (Dyer & Handler, 1994; Kellermanns & Eddleston, 2004), par-

ticularly in cohesive families with a high level of self-reinforcing mutual moral obligations. Even

if the family is generally supportive of the young entrepreneur’s aspirations, paradoxically, tight-

ly knit families may offer some disadvantages in the start-up process. Work by Renzulli, Al-

drich, and coauthors (Renzulli, Aldrich, & Moody, 2000; Renzulli & Aldrich, 2005) examined

the role of the family, particularly with respect to the activation of ties for access to resources,

and the likelihood of a new business start-up. These authors argued and found that having a

32

greater proportion of kin on someone’s discussion (advice) network lowered the likelihood that

s/he would start a new business, because a high proportion of kin was indicative of inward-

looking social ties and a high level of redundancy in information sources (Renzulli et al., 2000).

Greater family cohesiveness may further reinforce the redundancy in information sources, thus

hindering the scope of start-up activity.

Once the family has committed resources to the support the young individual’s entrepre-

neurial initiative, though, cohesiveness helps. For example, the effect of family social capital in

the form of social interactions and introductions into the family’s external social networks would

likely be strengthened by the opportunities to engage in interactions with family members. Like-

wise, the effect of family human capital in the form of experience, knowledge, and mentorship is

strengthened by opportunities within the family to interact, share knowledge, and learn from

each other. Family cohesiveness also enhances the effect of family physical capital, in the form

of facilities and access to distribution networks on the scope of start-up activities. Overall, our

empirical results support Pearson, Carr, & Shaw (2008) who argued that a family “time and sta-

bility together”, “interdependence”, “interaction”, and “closure” all strengthen family capital.

In sum, we found that the role of family cohesiveness in the entrepreneurial process is

complex and multidirectional. As discussed in the theory development section of our paper, fam-

ily cohesiveness is a relatively unexplored aspect of the family embeddedness perspective. We

call on future research to investigate the critical thresholds of the dual family cohesiveness ef-

fects, as well as their heterogeneity across cultures and institutional settings.

33

Implications and conclusions

Boundaries and Limitations

Our study is not without limitations, which need to be borne in mind when interpreting its

results. First, we didn’t differentiate between types of families. For example, some previous

studies have indicated that entrepreneurs sometimes come from dysfunctional families and they

start new business in order to gain control of their world (Kets de Vries, 1977; 1985; Collins &

Moore, 1964). Future research might focus on the different types of families, such as cohabita-

tion, domestic partners, divorces, extended families, intergenerational families and the implica-

tions of these different family types on the scope and outcomes of start-up activities (Aldrich &

Cliff, 2003). Second, the quantitative instrument utilized in this study was cross-sectional, which

does not allows us to identify causal relationships between family support and scope of student

entrepreneurs’ start-up activities. We call on future panel research to further elucidate the tem-

poral dynamics of family support and young entrepreneur’s start-up activity. Finally, our sam-

pling procedure, as discussed in the Methods section, was not a truly randomized one. Although

the large sample size minimizes the likelihood that the data collection procedures would com-

promise the generalizability of the findings to the population of interest to the study, future re-

search, based on randomized sampling, can offer a robust and generalizable corroboration of our

findings.

Implications

Limitations notwithstanding, our study demonstrates that family social capital provides

critical advantages to potential entrepreneurs. Further, our study demonstrates that family cohe-

siveness influences entrepreneurial activity and self-employment to a significant degree (Dyer et

al., 2014). These findings have important implications for both public policy and aspiring young

entrepreneurs.

The differential access to family capital in different countries and different demographic

groups may affect the business formation and self-employment rate at the country level, includ-

ing the national early-stage entrepreneurial activity rate (Kelley, Singer, Herrington, 2012). As

discussed in the introduction, youth unemployment around the world poses serious concerns, re-

lated to the depletion of the young generation’s human and social capital and its growing social

disenfranchisement. Boosting the level of entrepreneurial activity carries the promise of alleviat-

ing youth unemployment and invigorating economic life with new and innovative products, ser-

34

vices, and organizational forms. To public policy makers interested in encouraging the entrepre-

neurial activity of the young generation, our study suggests that one good way to do so is to pay

closer attention to the role of the family. Programs and tax incentives for enhanced family sup-

port of youth entrepreneurs’ start-up activities may be instrumental in directly and indirectly

stimulating youth entrepreneurship.

To aspiring young entrepreneurs, our study reminds once again that family matters. Young

entrepreneurs, therefore, will be well advised to carefully calibrate the benefits and costs of solic-

iting family support in the process of their new venture creation. In particular, engaging family

members in social interactions and making the maximum of the established family-specific so-

cial connections, is likely to be instrumental in the start-up process.

In conclusion, families have the potential to supply young nascent entrepreneurs with

unique forms of capital that enable them to effectively establish firms. However, we find that the

effect of family embeddedness and family cohesiveness is complex and multifaceted. Our study

starts an interesting conversation on the role of family for youth entrepreneurship. It is our hope

that other researchers will join in and enrich this conversation.

35

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