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Social Capital and Career Success
A SOCIAL CAPITAL THEORY OF CAREER SUCCESS
SCOTT E. SEIBERT and MARIA L. KRAIMER
Cleveland State University
Department of Management & Labor Relations
1860 East 18th Street
Cleveland, OH 44114-3610
Phone: (216) 687-4749
Fax: (216) 687-9354
e-mail: [email protected] and [email protected]
ROBERT C. LIDEN
University of Illinois at Chicago
Department of Managerial Studies, MC 243
601 South Morgan Street
Chicago, IL 60607-7123
Phone: (312) 996-2739
Fax: (708) 479-4697
e-mail: [email protected]
Data was collected for this study while the first author was in the Management Department at the
University of Notre Dame. Partial support was provided by the Alumni Office of the University of Notre
Dame. The current investigation is part of a larger study of career success.
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ABSTRACT
A SOCIAL CAPITAL THEORY OF CAREER SUCCESS
A theoretical model that integrates previously competing theories of social capital with the
literature on career success was developed and tested with a sample of 448 employees in a range of
occupations and organizations. Social capital was conceptualized in terms of network structure (weak
ties and structural holes) and social resources (contacts in other functions and contacts at other
organizational levels). Results of a SEM showed that network structure was related to social resources
and that the effects of social resources on career success were fully mediated by three network benefits:
access to information, access to resources, and career sponsorship.
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Social Capital and Career Success
A Social Capital Theory of Career Success
Organizational researchers have begun to develop increasingly comprehensive models of career
success using demographic, human capital, work/family, motivational, organizational, and industry
variables (e.g., Dreher & Ash, 1990; Judge & Bretz, 1994; Judge, Cable, Boudreau, & Bretz, 1995;
Kirchmeyer, 1998). While this work has provided considerable evidence regarding the determinants of
career outcomes, the role of informal interpersonal behaviors have not been fully explored (Judge &
Bretz, 1994; Pfeffer, 1989). Popular advice for “getting ahead” in one’s career rarely fails to mention
the importance of “networking” for the achievement of career goals (e.g., Bolles, 1992; Kanter, 1977).
Indeed, Luthans, Hodgetts, and Rosenkrantz (1988) found that the most successful managers in their
study spent 70 percent more time engaged in networking activities and 10 percent more time in routine
communication activities than their less successful counterparts. Recent advances in social capital theory
(Coleman, 1990) have begun to provide a more fine-grained analysis of the ways individuals' social
networks affect their careers in organizations (Burt, 1992, 1997; Ibarra, 1995; Sparrowe & Popielarz,
1995; Podolny & Baron, 1997). This theoretical perspective has the potential to considerably enhance
our knowledge of the role of social processes in career success.
The first purpose of the current study is to provide an integration of the current conceptualizations
of social capital as they pertain to career success. Three different theoretical approaches -- weak tie
theory (Granovetter, 1973), structural holes theory (Burt, 1992), and social resource theory (Lin, 1990)
-- focus on different network properties as representations of social capital. However, these theories all
propose that the key explanatory variables for the effect of social capital on career mobility are greater
access to information, resources, and sponsorship or social credentialing. To date, these explanatory
variables have not been included in empirical tests. The main contribution of the current investigation to
the social network literature on careers is to provide a conceptual integration of the three major social
capital theories followed by an empirical test that includes the proposed explanatory mechanisms.
A second purpose of this study is to model social capital effects on a full set of career outcomes
after controlling for other processes known to be determinants of career success. Social network
approaches to career success, growing out of a sociological research tradition, tend to focus on
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Social Capital and Career Success
occupational status or job mobility as the primary career outcome (e.g., Burt, 1992, 1997; Granovetter,
1973; Ibarra, 1995; Lin, Ensel, & Vaughn, 1981a; Sparrowe & Popielarz, 1995). These studies have
often been limited to single organizations, small sample sizes, a limited number of control variables, or
outcomes assessed over short time spans. Reasearch in the organizational literature, on the other hand,
tends to use large and diverse samples and model a broad set of career processes. A model of career
success that does not account for these other processes may provide a biased estimate of the effect of
social capital on careers.
The organizational research has also moved toward the use of a set of extrinsic and intrinsic
outcomes as measures of career success (e.g., Judge & Bretz, 1994; Judge, et al., 1995; Kirchmeyer,
1998; Seibert, Crant, & Kraimer, 1999; Turban & Dougherty, 1994; Wayne, Liden, Kraimer, & Graf,
1999). Extrinsic career outcomes are objectively observable achievements such as salary and
promotions while intrinsic career success refers to the individual’s subjective feelings of accomplishment
and satisfaction with his or her career (London & Stumpf, 1982). Career scholars have argued that
these are related but distinct constructs (Aryee, Chay, & Tan, 1994; Hall, 1976, Wayne et al., 1999)
and that both measures are important because together they reflect not only conventional standards of
success, but feelings of success relative to the individual's own goals and expectations (Judge & Bretz,
1994; Judge et al., 1995; London & Stumpf, 1982; Seibert et al., 1999). By linking the social capital
literature with this career success literature, we hope to provide a rigorous demonstration of the role of
social capital in career success.
The third purpose of this study is to integrate research on social network structure with the
literature on mentoring and careers. Mentoring has been defined as a developmental relationship in
which a less experienced organizational member receives help and guidance from a more experienced
member with the aim of improving the career opportunities and growth of the junior person (Kram,
1985). Research has explored the origin and progress of mentor relationships and the types of activities
taking place within the mentor - protégé relationship (e.g., Chao et al, 1992; Noe, 1988; Turban &
Dougherty, 1994). The literature has also emphasized the important benefit that having a mentor may
have on one's career success (e.g., Dreher & Ash, 1990). Kram (1985) has suggested that it is
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important to understand the full constellation of developmental relationships in which a protégé can be
involved. However, little research has explored (1) the simultaneous impact of multiple developmental
contacts, (2) the way in which the organizational location of these developmental contacts affects their
contribution to career success, or (3) how the structure of one's social network facilitates access to
developmental relationships in different parts of the organization. In this paper we use social capital
theories to extend the mentoring literature by specifying the types of network structures that are likely to
provide the most career benefits. At the same time the social capital literature is enhanced through its
link with the extensive literature on developmental relationships and the benefits they bring to one's
career. We begin with a review of the theories of social capital followed by the development of our
model of social capital and career success.
Theories of Social Capital
Coleman (1990) defined social capital as any aspect of social structure that creates value and
facilitates the actions of the individuals within that social structure. Just as the creation of physical capital
involves changes in materials so as to facilitate production, and human capital involves changes in an
individual's skills and capabilities, social capital is created when the relations among persons change in
ways that facilitate instrumental action (Coleman, 1990).
Social network researchers have taken the lead in formalizing and empirically testing theories
related to the concept of social capital. Social network researchers regard relationships or “ties” as the
basic data for analysis. A “network” can be defined as the pattern of ties linking a defined set of persons
or social actors. Each person can be described in terms of his or her linkages with other people in the
network. The focal person in such an analysis is referred to as "ego" (who is usually the person
supplying the data) while those he or she is tied to are "alters" (Knoke & Kuklinski, 1982).
The first approach to the conceptualization of social capital, weak tie theory (Granovetter, 1973),
focuses on the strength of the social tie used by a person in the process of finding a job. Granovetter
(1973) argued that ties among members of a social clique are likely to be strong (defined as emotionally
intense, frequent, and involving multiple types of relationships such as friendship, advice, and coworker). The information possessed by any one member of the clique is likely to be either shared
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quickly or already redundant with the information possessed by the other members. However, ties that
reach outside of one’s social clique are likely to be weak (i.e., not emotionally intense, infrequent, and
restricted to one narrow type of relationship) rather than strong. According to Granovetter (1973),
weak ties are often a bridge between densely interconnected social cliques and thus provide a source of
unique information and resources. Indeed, Granovetter (1973) found that weak ties were more likely
than strong ties to have been the source of information about job openings for the sample of job
incumbents he interviewed. Subsequent research has provided mixed support for the weak tie
hypothesis (Bridges & Villemez, 1986; McPherson, Popielarz & Drobnic, 1992; Murray, Rankin &
Magill, 1981).
Burt’s (1992) structural holes approach to social capital focuses not on the characteristics of ego's
direct ties, but on the pattern of relations among the alters in ego's social network. A structural hole is
said to exist between two alters who are not connected to each other. According to structural holes
theory, it is advantageous for ego to be connected to many alters who are themselves unconnected to
the other alters in ego’s network. According to Burt’s theory (1992; 1997), networks rich in structural
holes provide ego with three primary benefits: more unique and timely access to information, greater
bargaining power and thus control over resources and outcomes, and greater visibility and career
opportunities for ego throughout the social system. Burt (1992) critiques weak tie theory pointing out
that the structural hole concept gets at the bridging property of ties more directly than the weak tie
concept and therefore provides a “… stronger foundation for theory and a clearer guide for empirical
research” (Burt, 1992: 28). Initial empirical evidence has been supportive of structural holes theory, but
has also provided a number of boundary conditions limiting the range of the theory’s application (Burt,
1992; 1997; Podolny & Baron, 1997; Sparrowe & Popielarz, 1995). To date, the role of the proposed
explanatory processes, access to information, bargaining control, and referral have not been empirically
examined.
The third major theoretical approach to the conceptualization of social capital is social resources
theory (e.g., Lin, Ensel & Vaughn 1981a; 1981b). Social resources theory focuses on the nature of the
resources embedded within the network. Lin et al. (1981a) argued that it is not the weakness of the tie
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per se that conveys advantage (nor, by extension, is it the bridging property of weak ties), but the fact
that such ties are more likely to reach someone with the type of resource required for ego to fulfill his or
her instrumental objectives. An alter who possesses characteristics or controls resources useful for the
attainment of the ego's goals can be considered a social resource. For example, alters who provide
career developmental advice and support are the relevant social resource when considering ego's
pursuit of instrumental career goals. Lin’s research showed that tie strength was negatively related to the
occupational prestige of the alter contacted (i.e., weak ties reach higher status alters), and that the
alter’s occupational prestige was in turn positively related to the prestige of the job secured by ego (Lin
et al., 1981a; 1981b; see also De Graaf & Flap, 1988; Marsden & Hurlbert, 1988).
Integration of Social Capital Theories
As the literature review above reveals, controversy exists regarding the proper conceptualization of
social capital. Weak tie theory focuses on the nature of the tie, structural holes theory focuses on the
pattern of ties among alters, and social resource theory focuses on the characteristics of the alters
contacted. The two later theories each claim to supersede the earlier theory (e.g., Burt, 1992; Lin, et al.,
1981a), and competitive model testing has been performed in an empirical effort to determine the best
conceptualization (Sparrowe & Popielarz, 1995).
Despite this controversy, a fruitful integration of the differing conceptualizations of social capital is
possible. The key to this integration is to recognize an analytical distinction between the structural
properties of the network and the nature of the social resources embedded in the network; essentially a
distinction between the form and the content of ego's network (see Lin, 1999). Weak tie theory and
structural hole theory each focus on the structure of the network. Social resources theory focuses on the
content of the network. These theories are not mutually exclusive, as competitive model testing implies,
but may function together because they focus on different points in the process of accumulating social
capital. According to our conceptual integration, the overarching social capital construct is best thought
of as both the different network structures which facilitate (or impede) access to social resources and
the nature of the social resources embedded in the network. The key empirical question then becomes
“what network structures lead ego to have more (or less) access to important social resources?”
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A Model for a Social Capital Theory of Care er Success
Figure 1 presents the model of social capital and career success tested in the current study.
According to the model, two measures of social network structure, weak ties and structural holes, are
related to two forms of social resources, number of contacts in other functions and number of contacts
at higher organizational levels. Social resources effects on career success are in turn mediated by three
types of network benefits: access to information, access to resources, and career sponsorship. Career
success is assessed in terms of current salary, the number of promotions received over the individual’s
entire career, and career satisfaction, controlling for a full set of variables drawn from the organizational
literature.
-----------------------------------------------------------------------------------------------Insert Figure 1 about here
-----------------------------------------------------------------------------------------------We examined the network of career developmental relationships maintained by ego within his or
her organization in order to capture the social resources relevant for the instrumental objective of career
success. Consistent with previous research on social networks and promotions (Burt, 1992, 1997;
Podolny & Baron, 1997), we focus on intraorganizational ties because contacts within one's own
organization are those that we expect to provide the kinds of benefits discussed in social capital theories
(information, resources, and sponsorship) and thus to influence ego's success within the organization.
We conceptualized social resources as developmental contacts in other functions and at higher levels of
the organization because, within formal organizations, functional or technical specialty and hierarchical
level are likely to impose salient social boundaries between organizational members (Burt, 1992; Ibarra,
1993). These organizational identity groups are likely to form their own interaction cliques based on
shared interests, values, training, socialization or world views (Ibarra, 1995; Kanter, 1977; Lincoln &
Miller, 1979). Developmental contacts who are members of other social identity groups are likely to
provide unique information, resources, and influence useful to ego.
Social Network Structure and Social Resources
The fundamental tenet of weak tie theory is that a weak tie is valuable because it is more likely than
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a strong tie to act as a bridge between social cliques, providing contact with people of different social
groups or statuses (Granovetter, 1973, 1982). An assumption of social network approaches to social
capital theory is that one has a finite amount of time and energy to invest in social relationships. Given
that, by definition, strong ties require a greater investment of time and energy, ego must make a strategic
decision to invest his or her social energy in either maintaining a relatively small number of strong ties or
in developing a relatively large number of weak ties (Podolny & Baron, 1997). Our argument is not that
a weak tie is better than a strong tie to the same given individual. Rather, we seek to relate the number
of weak ties, as a structural property of ego's network, to the number of valuable social contacts in
ego's network. A social network characterized by many weak ties is more likely to provide access to
critical social resources. That is, an ego who chooses to invest his or her social energy in developing a
large number of weak ties will have greater access to contacts in other social groups. While some
empirical evidence suggests that bridging is more likely with weak ties than with strong ties (Friedkin,
1980), surprisingly little empirical research has specifically examined the extent to which weak ties
bridge relatively unconnected social groups. Hypothesis one tests the proposition that, within a career
developmental network, the number of weak ties will be related to the number of contacts in other
social identity groups defined by the formal properties of the organization:
Hypothesis 1a: The number of weak ties will be positively related to the number of contacts
in other organizational functions.
Hypothesis 1b: The number of weak ties will be positively related to the number of contacts
at higher organizational levels.
A structural hole is said to exist between two alters when those alters are unconnected to each
other (Burt, 1992). An ego who is connected to two alters who are not connected to each other is, by
definition, a bridge between those alters. According to Burt (1992), this structural position conveys
certain advantages to ego such that ego may be able to trade information gathered from one alter to
which the other alter has no direct access. Ego provides added value to the organization through his or
her ability to provide information and coordinate activities among separated alters. Further, to the
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extent that the two alters possess similar resources, ego can play them off against each other, seeking to
set up social (or material) exchange with the alter who offers the best return.
According to structural holes theory, an alter who is already connected to other alters within ego's
network is "redundant" and does not convey the kinds of benefits to ego that a non-redundant alter
would. Parallel to our argument regarding weak ties, ego must make the strategic choice either to invest
in maintaining a relationship with a redundant alter or to invest in developing a relationship with an alter
who is not redundant with other alters within his or her network. The addition of a non-redundant alter
introduces another set of structural holes in ego's network. Because members of the same social clique
or social identity group are likely to be strongly connected to each other (e.g., Festinger, Schacter, &
Back, 1950; Homans, 1950; Popielarz, 1994), structural holes are likely to be found between alters
who are members of different social groups such as those defined by functional and hierarchical
boundaries within organizations (Burt, 1992; Ibarra, 1993). The fact that ego is acting as a bridge
between two unconnected social groups amplifies the benefits derived from acting as a bridge between
two unconnected individuals. Alters who are members of unconnected social groups are therefore
uniquely beneficial to ego. An ego who's network is rich in structural holes is therefore likely to have
greater access to social resources as defined here - contacts in other functions and at higher
organizational levels. Hypothesis two tests this proposition:
Hypothesis 2a: The extent of structural holes will be positively related to the number of
contacts in other organizational functions.
Hypothesis 2b: The extent of structural holes will be positively related to the number of
contacts at higher organizational levels.
Social Resources and Network Benefits
The notion that social resources embedded in networks will provide benefits to the actor is central
to all three network approaches to social capital. In general, these benefits include greater and more
timely access to information, greater access to financial or material resources, and greater visibility,
legitimacy, or sponsorship within the social system. However, previous researchers have not
incorporated these constructs in their models. The overall purpose of the remaining set of hypotheses is
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to demonstrate that the effects of social capital on career success are through the three explanatory
mechanisms.
The notion that different functional units within an organization have differing perspectives,
contrasting world views, and unique information is axiomatic to the information processing perspective
on organizations (e.g., Galbraith, 1977; March & Simon, 1958). The literatures on matrix or lateral
organizational designs (Davis & Lawrence, 1977; Galbraith, 1994) and overlapping or cross-functional
teams (Clark & Fujimoto, 1991) also presuppose the need for sharing information across functional
boundaries due to differences in the information and views held by different functional groups. This work
suggests that contact with members of other organizational functions will provide access to information
not available within one’s own functional group affiliation. While other functions may also possess unique
resources, these resources are less likely to be both available for transfer and of use across functional
boundaries. And unless they are at higher organizational levels, developmental contacts in other
functions are not likely to have the status and influence to provide sponsorship to one in his or her
career. Accordingly,
Hypothesis 3: The number of contacts in other functions will be positively related to access
to organizational information.
We expect that contacts at higher levels of the organization will also be beneficial to an individual in
a number of ways. A fundamental principle of rational organizational design is that higher level
organizational positions have more authority than lower level positions (March & Simon, 1958; Massie,
1965; Weber, 1946). Ideally, higher level positions also have a broader perspective on issues relevant
to the organization and greater access to information upon which to base their decisions (March &
Simon, 1958; Galbraith, 1977). According to the classical principles of formal organizations, positions
at higher levels of the organization also have greater formal decision making responsibility over the
allocation of resources than do lower level positions. This legitimate basis of social power (French &
Raven, 1968) means that higher level individuals have greater formal power, influence, and control over
resources. They may also be members of dominant coalitions within the organization and therefore enjoy
informal bases of power, influence, and control over resources as well (Thompson, 1967). Positive
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relations with developmental contacts at higher levels should therefore provide an actor with greater
access to these benefits.
Hypothesis 4a: The number of contacts at higher organizational levels will be positively
related to access to organizational information.
Hypothesis 4b: The number of contacts at higher organizational levels will be positively
related to access to resources.
We expect contacts at higher organizational levels to be related to the third network benefit, career
sponsorship. Social network theorists have variously referred to visibility, legitimacy, social
credentialing, and inclusion in career opportunities (Burt, 1992; Lin, 1999) as a benefit of specific
network structures and social resources. Social network researchers have, however, overlooked the
conceptual overlap of this construct with the notion of career sponsorship developed in the literature on
mentoring and careers (Kram, 1985; Noe, 1988). Career sponsorship is one of the primary functions
fulfilled by a mentor (Kram, 1985; Noe, 1988) and involves favorable and timely exposure for the
protégé, opportunities to engage in challenging assignments, and career advice and coaching from the
mentor. Because mentors are found among individuals at higher organizational levels (Kram, 1985), we
expect relations with developmental contacts at higher organizational levels to be related to the receipt
of career sponsorship.
Hypothesis 4c: The number of contacts at higher organizational levels will be positively
related to career sponsorship.
Network Benefits and Career Success
There are two reasons to expect access to information and access to resources to each be related
to objective career success. First, greater access to information and resources should enhance individual
work performance. Information and resources have been noted as contextual factors that empower
employees, leading to higher levels of motivation and performance (e.g., Hackman & Oldham, 1980;
Spreitzer, 1996). Burt (1992, 1997) argues that individuals able to use their network position to fill a
broker or boundary spanner role within the organization add greater value to the organization. In fact,
centrality in the advice network has been linked to job performance (Sparrowe, Liden, Wayne, &
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Kraimer, 1997). The information processing approach to organizational design also emphasizes the
value added to the organization by individuals who play key boundary spanning, liaison, conflict
resolution, and coordination roles (e.g., Galbraith, 1977). This improved work performance and added
value to the organization should lead to greater returns for the individual in terms of objective career
outcomes (Burt, 1992, 1997; London & Stumpf, 1983; Medoff & Abraham, 1981). Second,
information and resources are fundamental bases of social power (French & Raven, 1968). Greater
access to information and resources will increase the individual’s organizational reputation (Kilduff &
Krackhardt, 1994; Tsui, 1984) and the individual will be perceived as more powerful or influential in the
organization (Brass, 1984; Brass & Burkhardt, 1993). These perceptions should make the individual
better able to secure valuable organizational rewards independent of their actual level of performance
(Ferris & Judge, 1991; Luthans, et al., 1988).
We also expect access to information and access to resources to be positively related to career
satisfaction. Having access to relevant organizational information and to resources such as funds,
materials, and space, should allow one to feel more control and competence in their work (Gist &
Mitchell, 1992) as well as a greater sense of psychological empowerment (Spreitzer, 1996).
Psychological empowerment in general, and self-determination and competence in particular, are
extensions of job enrichment theory (Kraimer, Seibert, & Liden, 1999; Spreitzer, 1995, 1996) which
posits that enriched jobs are more satisfying to individuals (Hackman & Oldham, 1980; Spreitzer,
1996). Thus, those who feel greater psychological empowerment with respect to their careers should be
more satisfied with their career progress. While theory supports the existence of a relationship between
access to information and career satisfaction and between access to resources and career satisfaction,
we are not aware of empirical research directly testing these propositions.
The above discussion leads to the following hypotheses regarding access to information;
Hypothesis 5a: Access to information will be positively related to current salary,
independent of the other career outcomes.
Hypothesis 5b: Access to information will be positively related to the number of
promotions received over one’s career, independent of the other career outcomes.
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Hypothesis 5c: Access to information will be positively related to the level of career
satisfaction, independent of the other career outcomes.
and the following hypotheses regarding access to resources:
Hypothesis 6a: Access to resources will be positively related to current salary,
independent of the other career outcomes.
Hypothesis 6b: Access to resources will be positively related to the number of promotions
received over one’s career, independent of the other career outcomes.
Hypothesis 6c: Access to resources will be positively related to the level of career
satisfaction, independent of the other career outcomes.
While the relationships between access to information and career success and between access to
resources and career success are thought to work through similar processes (work performance,
power, reputation, and empowerment), these are considered to be independent constructs having
unique effects on outcomes (Spreitzer, 1996). However, it is also likely that access to information has a
direct influence on access to resources. Information is a basis for social power (French & Raven, 1968)
and access to and control over information can be translated into power and influence in organizations
(Hickson, et al., 1971; Pfeffer, Salancik, and Leblibici, 1978). Possessing relevant organizational
information would allow individuals the ability to develop alternative means of acquiring necessary
resources other than going through formal channels. In essence, information expands one’s options for
acquiring resources.
Hypothesis 7: Access to information will be positively related to access to resources.
The positive effects of mentoring in general and career sponsorship in particular on career
outcomes have been amply demonstrated in the careers literature (Chao, Walz, & Gardner, 1992;
Chao, 1997; Dreher & Ash, 1990; Wayne et al., 1999; Whitely, Dougherty, & Dreher, 1991). For
example, Dreher & Ash (1990) found that, after controlling for several demographic, human capital, and
organizational variables, individuals who reported more extensive mentoring received more promotions,
had higher incomes, and were more satisfied with their total compensation. Chao et al. (1992)
performed a cannonical correlation analysis and found career mentoring to be most strongly associated
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with employees’ intrinsic job satisfaction. Accordingly, the following hypotheses represent a replication
of previous research on career mentoring within an integrated model of social capital and career
success.
Hypothesis 8a: The level of career sponsorship will be positively related to current salary,
independent of the other career outcomes.
Hypothesis 8b: The level of career sponsorship will be positively related to the number of
promotions received over the individual’s entire career, independent of the other career
outcomes.
Hypothesis 8c: The level of career sponsorship will be positively related to career
satisfaction, independent of the other career outcomes.
Control Variables
Organizational researchers have tested relatively comprehensive models of career success. Some
specific findings are that human capital variables (education, years in the workforce, experience in
multiple organizations, and career interruptions), demographic variables (gender, marital status, and
spouse employment status), and organizational characteristics (organization size, metropolitan location,
and industry sector), have effects on salary, promotions, and/or career satisfaction (Judge & Bretz,
1994; Judge et al, 1995; Kirchmeyer, 1998; Seibert et al., 1999; Wayne et al., 1999). In addition,
career satisfaction and salary have been found to vary by occupation. Specific theoretical linkages
between each category of variables and the career outcomes are beyond the scope of this paper (see
Judge et al., 1995) but these variables will be included as controls in the current study.
METHODS
Sample and Procedures
As part of a larger study, a diverse sample of 2781 randomly selected undergraduate business,
MBA, and engineering school students from a large private Midwestern university, received surveys at
their home addresses. The alumni, who had graduated from three to thirty years prior to the date of the
study, were instructed to complete the survey and return it directly to the first author in a postage-paid
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envelope. Confidentiality of survey responses was ensured to all alumni. To encourage responses, we
entered all alumni respondents into a drawing for three prizes of approximately $50 in value. A reminder
postcard was mailed three weeks after the initial mailing.
A total of 773 alumni surveys were returned (28% response rate). Respondents who were not
currently working (N = 73), working only part-time (N = 42), or were self-employed (N = 116) were
eliminated from the analyses because their career outcomes would not be comparable to those with
current full-time employment. After eliminating cases with missing data, the final sample consisted of 448
alumni. T-tests revealed that respondents and non-respondents did not significantly differ with respect to
gender, race, or major (business versus engineering). However, we received fewer responses from
MBA graduates (13%) than were represented in the target sample (25%).
The demographic breakdown of the respondents is as follows: average age was 35.6 years;
average time since graduating from the university was 13.0 years; 65% were male; 72% were married;
96% were Caucasian; 43% had a Bachelor’s degree as their highest degree attained, 9% had a
Master’s degree other than an MBA, 41% had an MBA, 5% had a Law degree, and 2% had a Ph.D.
Average tenure in their current organizations was 6.16 years.
Measures
Social capital. Respondents were asked to list (by initials) “the people who have acted to help your
career by speaking on your behalf, providing you with information, career opportunities, advice or
psychological support or with whom you have regularly spoken regarding difficulties at work, alternative
job opportunities, or long-term career goals.” This network relation was chosen based on theoretical
considerations, as it directly addresses the social resources mobilized in pursuit of instrumental career
goals. Network size is the total number of alters indicated by the respondent.
For alters who were past or current members of the respondents’ organization, the respondent also
indicated the alter’s organizational function (same function as yourself or different function as yourself)
and organizational level (lower than yourself, same as yourself, or higher than yourself). Contacts in
other functions is the number of individuals who were identified as members of a different function. The
number of individuals who were identified as higher in organizational level formed the contacts at higher
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levels variable. Additionally, we asked respondents to indicate how close they felt to the alter and how
close the alters were to each other on a scale where 2 = especially close, 1 = less close, and 0 =
distant. Weak ties was the sum of all ties between the respondent and his/her alters coded as 0 or 1.
Following Burt (1992, 1997), structural holes was calculated as one minus constraint using the ego
network data. The constraint posed by an individual alter j was calculated according to Burt’s formula:
cij = ( p ij + ∑q piq pqj) 2 , for q ≠ i, j where pij is the proportion of i’s relations invested in contact j,
piq is the proportion of i’s relations invested in q, and pqi is the proportion of q’s relations invested in
i. The total in parentheses is the proportion of i’s relations that are directly or indirectly invested in the
connection with contact j. The sum of squared proportions, ∑ cij , is the constraint posed by the entire
j
network. One minus constraint (1− c ij ) is thus the lack of constraint, or degree of structural holes
present in the ego network.
Network research typically relies on single item sociometric questions, which by themselves do not
provide information regarding measurement reliability. To ensure reliability, we pre-tested the survey
instrument and allowed respondents to list a large enough number of individuals to generate an accurate
representation of their networks (Marsden, 1990; Rogers & Kincaid, 1981). Sociometric questions
were also designed to be as specific as possible, to enhance accuracy of recall. Finally, questions were
focused on typical, long-term relationships rather than brief, transient or episodic interactions. Research
has shown that people’s recall of brief, episodic interactions are highly inaccurate (Bernard, Killworth,
Kronenfeld, & Sailer, 1984) but that people are remarkably accurate in recalling typical interactions and
long-term relationships (Freeman, Romney, & Freeman, 1987), such as the developmental relationships
sought in the current study.
Access to information and resources. The access variables were measured using Spreitzer’s
(1996) six-item scale. Three items each were designed to measure access to information and access to
resources and were scaled 1 = strongly disagree to 7 = strongly agree. An exploratory factor analysis
specifying a varimax rotation indicated that 5 of the 6 items formed two clear factors representing
access to resources (α=.89) and access to information (α=.87). One item intended to measure access
to information, “I have access to the strategic information I need to do my job well,” cross-loaded and
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Social Capital and Career Success
was therefore eliminated from further analyses.
Career Sponsorship. The 8 career sponsorship items from Dreher and Ash’s (1990) global
mentoring scale were summed to form a composite (α=.90). This scale assesses the extent to which
senior colleagues have provided sponsorship, exposure and visibility, challenging assignments, and
protection. Responses were made with a five-point scale (1 = very little to 5 = great deal). An example
item is “Gone out of his / her way to promote your career interests.”
Career success. Career success
was measured in terms of promotions, salary, and career satisfaction. Alumni reported the number of
promotions they had received over their entire careers. Promotions were defined as “any increases in
level and/or any significant increases in job responsibilities or job scope.” Alumni also indicated their
current annual salaries (including bonuses and other direct income). Self-reports of income have been
shown to correlate highly with archival, company records (Judge et al., 1995; Turban & Dougherty,
1994). Because the z-test on the skewness statistic indicated a non-normal distribution for salary (z =
36.5, p < .001), we followed Gerhart and Milkovich’s (1989) recommendation and used a natural
logarithmic transformation of salary for all analyses. Career satisfaction was measured using Greenhaus,
Parasuraman, and Wormley’s (1990) five items (scaled from 1=very dissatisfied to 5=very satisfied)
which were summed to form a composite (α=.83). An example item is “The progress I have made
toward meeting my goals for advancement.”
Control variables. Measured in the alumni questionnaires, these variables were gender (0 = male, 1
= female); marital status (0 = not married, 1 = married); spouse employment status (0 = spouse not
employed, 1 = spouse employed); whether they had an MBA degree (1 = yes), whether they had an
employment gap and if so the number of weeks of that gap; whether they live in a major metropolitan
city (population over 1 million; 1 = yes); the number of organizations that they had worked for over their
careers; and whether their occupation was categorized as general management (1 = yes)1. The number
of employees in the respondent’s firm was scaled 1 = less than fifty employees to 5 = more than 1000
employees. Number of years in the workforce was measured by subtracting the alumni’s year of
graduation from the year the study was conducted. Network size was also used as a control variable in
order to examine the unique effects of developmental contacts in other functions and at higher levels as
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Social Capital and Career Success
distinct from the sheer number of developmental contacts.
Analyses
The hypothesized structural equations model was tested using LISREL 8 (Joreskog & Sorbom,
1993) with the covariance matrix as input. Because no conventional estimate of error is available for the
single-item measures, we assumed no error in the measurement model for the four social capital
variables, the single-item control variables, and the two single-item dependent variables. The assumption
of no error provides a conservative test of the model. In order to reduce the sample size to parameter
estimate ratio, single scale score indicators were used to measure the other latent constructs in the
structural model. For access to information, access to resources, career sponsorship, and career
satisfaction, the measurement path estimates were set equal to one in order to scale the latent variables
(Bollen, 1989), and the error variance was set equal to the scale variance times one minus the reliability
in order to account for measurement error (Hayduk, 1987). We also allowed the error terms for the
three endogenous latent constructs for salary, promotions, and career satisfaction to be correlated in
order to account for the correlations among these three constructs.
In testing the theoretical framework, we fitted several nested models to the data, each
incorporating different assumptions about the model parameters. Comparisons with reasonable
alternative models are recommended in order to show that the hypothesized model is the best
representation of the data and is considered to be an important part of assessing model fit (Bollen,
1989; Kelloway, 1998). The first alternative model estimated only the direct paths from the control
variables to the career outcome variables. This “control variables only” model provided a base-line fit
for an assessment of the incremental contribution of the additional paths in the theoretical model. The
relationships tested in the control variables only model were based on prior research findings. The
“hypothesized model” estimated both the control variable paths and the set of paths hypothesized in this
study.
The remaining alternative models directly tested the adequacy of the hypothesized model, (with the
effects of network structure on network benefits fully mediated by social resources and with the effects
of social resources on career outcomes fully mediated by network benefits) with that of partially
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Social Capital and Career Success
mediated and non-mediated models, as recommended by Kelloway (1998). We examined three
partially mediated models that assessed both the direct and indirect effects between our sets of
constructs. The partially mediated models posit that our hypothesized mediated effects are better
represented as direct and indirect effects. We also examined two non-mediated models in order to
assess whether the effects of social capital on career success were only direct, with no indirect effects
through the network benefits. All of these alternative models included the control variable paths.
RESULTS
Preliminary Analyses
To assess the degree to which common method bias may have presented a problem, all the scale
items for the variables used in this study were subjected to a principal components analysis using
varimax rotation (Harman, 1967). From this analysis, six clear factors emerged representing the
expected constructs: career sponsorship, career satisfaction, access to resources, social capital,
objective career success, and access to information. The average item loading on the intended construct
was .75 and, of the 120 potential cross-loadings, only three were above .30 with the largest crossloading equal to .40. The lack of cross-loadings across the items for social capital, network benefits,
and career success provides confidence that common method bias was not a problem for this study.
Additionally, the fact that the access to resources, access to information, and career sponsorship items
loaded on three separate factors provide evidence of discriminant validity among our network benefits
constructs.
Hypothesized Model
Table 1 reports the means, standard deviations, and intercorrelations among the study variables.
-----------------------------------------------Insert Table 1 about here
-----------------------------------------------Our hypothesized model fit the data well (χ2 = 191.11, df = 88, p < .01; RMSEA = .05; AGFI = .90;
NFI = .91; CFI = .95). Utilizing the change in chi-square test (Bentler & Bonett, 1980), we compared
our hypothesized model with a number of nested models (Table 2). The first comparison showed that
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Social Capital and Career Success
the hypothesized model provided a significantly better fit than did the “control variables only” model
(∆χ2 = 498.83, ∆df = 18, p < .01). The second comparison was between the hypothesized model and
the first partially mediated model (partially mediated 1). Partially mediated 1 estimated the paths in the
hypothesized model as well as the direct paths from the network structure constructs (weak ties and
structural holes) to the network benefits (access variables and sponsorship). The change in chi-square
test showed that this alternative model was significantly better than the hypothesized model (∆χ2 =
25.82, ∆df = 6, p < .01).
Partially mediated 1 was therefore retained as the best-fitting model and was then compared to the
second partially mediated model (partially mediated 2) and the third partially mediated model (partially
mediated 3). Partially mediated 2 estimated the same paths as partially mediated 1 and also estimated
the direct paths from the social resources constructs (contacts across functions and at higher levels) to
career outcomes. Partially mediated 3 tested all direct paths from network structure to the network
benefits, from network structure to the career outcomes, and from social resources to career outcomes,
in addition to the hypothesized mediated effects. The change in chi-square tests revealed that partially
mediated 2 and 3 were not significantly better than partially mediated 1 and were less parsimonious.
Lastly, the non-mediated model test comparisons were conducted. In the first non-mediated
model, the paths from network benefits to career outcomes were constrained to zero, but the paths
from social resources to career outcomes were freed. The second non-mediated model also constrained
the paths from network benefits to career outcomes to zero, but allowed the paths from network
structure to career outcomes to be estimated. Because the non-mediated models were not nested within
partially mediated 1, but were nested within partially mediated 3, the non-mediated models were
compared to partially mediated 3. Both non-mediated models fit significantly worse than partially
mediated 3. Table 2 reports the change in Chi-square for all nested model comparisons and other fit
indices for all seven comparative models.
The nested model comparisons indicated that the model estimating the hypothesized effects and
direct effects from network structure to network benefits (partially mediated 1) was the best fitting, most
parsimonious model. Thus, this model was retained as the best fitting model and is interpreted below in
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Social Capital and Career Success
order to examine the hypothesized relationships.
-------------------------------Insert Table 2 about here
-------------------------------Examination of the standardized parameter estimates indicated that 14 of the 17 hypothesized
relationships were significant and in the predicted directions (see Figure 2) while accounting for the
control variables. Specifically, Hypothesis 1 related weak ties positively to contacts in other functions
(1a) and at higher levels (1b). The statistically significant parameter estimates (b = .14 and .44,
respectively, p < .01) indicated support for Hypotheses 1a and 1b. Hypothesis 2 positively related
structural holes to contacts in other functions (2a) and at higher levels (2b). A statistically significant
parameter estimate was found between structural holes and higher level contacts (b = .31, p < .01)
providing support for 2b, but was only marginally significant between structural holes and cross function
contacts (b = .09, t = 1.94). Overall, the hypotheses relating network structure to social resources were
supported.
-------------------------------Insert Figure 2 about here
------------------------------Hypothesis 3 was supported as a statistically significant parameter estimate was found between
contacts in other functions and access to information (b = .15, p < .01). Hypothesis 4 related contacts
at higher levels to access to information (a), access to resources (b), and career sponsorship (c). A
statistically significant parameter estimate was found from contacts at higher levels to access to
information (b = .14, p < .05) and to career sponsorship (b = .29, p < .01), but not to access to
resources, indicating support for Hypotheses 4a and 4c, but not 4b.
Hypothesis 5 positively related access to information to salary (5a), promotions (5b), and
career satisfaction (5c). The results indicated support for Hypotheses 5b (b = .11, p < .05) and 5c (b =
.14, p < .05), but failed to support Hypothesis 5a. Respondents who indicated greater access to
information reported more promotions and greater career satisfaction. Hypothesis 6 positively related
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Social Capital and Career Success
access to resources to salary (6a), promotions (6b), and career satisfaction (6c). The results indicated
support for Hypotheses 6a (b = .11, p < .05) and 6c (b = .28, p <.01). Respondents who indicated
greater access to resources reported higher salaries and greater career satisfaction. Hypothesis 7 was
supported as a significant positive parameter estimate was found from access to information to access to
resources (b = .56, p < .01). Fully supporting Hypotheses 8a, b, and c, alumni who reported greater
career sponsorship reported higher salaries, more promotions, and greater career satisfaction as all
three parameter estimates were statistically significant and positive (b = .12, .17, and .32, respectively,
all p < .01)..
Although not hypothesized, there were two other significant path estimates pertaining to social
capital in partially-mediated model 1: A significant negative parameter estimate was found from weak
ties to access to information (b = -.14, p < .05) and from weak ties to career sponsorship (b = -.28, p
< .01). None of the paths from structural holes to the network benefit variables were statistically
significant.
Finally, consistent with previous findings in the careers literature, several of the control variables
were significantly (p < .05) related to the career success outcomes. Log salary was predicted by years
since graduation (b = .41), marital status (b = .22), spouse employment (b = -.14), MBA degree (b =
.22), employment gaps (b = -.18), size of employing organization (b = .12), metropolitan area (b =
.16), and management occupation (b = .16). The number of promotions was predicted by years since
graduation (b = .33), marital status (b = .11), MBA degree (b = .17), employment gaps (-.20), number
of employing organizations (b = .18), and management occupation (b = .14). Career satisfaction was
predicted by network size (b = .11), gender (.11), and the size of the employing organization (b = .11).
The social capital and control variables together explained 47% of the variance in current salary, 34% of
the variance in promotions, and 36% of the variance in career satisfaction. The explained variance in the
career outcomes was greater in the alternative hypothesized model than the control variables only
model: The control variables only model explained 42% of salary, 29% of promotions, and 5% of
career satisfaction.
DISCUSSION
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Social Capital and Career Success
Support of our hypothesized model revealed the importance of social capital to career success.
Specifically, our results demonstrated that two measures of network structure, weak ties and structural
holes, positively related to the level of social resources embedded in a person’s network, measured as
the number of developmental contacts in other functional areas and at higher levels in the organization.
Social resources were in turn positively related to current salary, number of promotions over one’s
career, and career satisfaction through their positive relationships with three measures of network
benefits; access to information, access to resources, and career sponsorship. The veridicality of these
results was strengthened by the control of a diverse set of demographic, human capital, and
organizational variables as well as network size. This was clearly demonstrated by the significantly better
fit of the model including the social capital and control variables over the alternative model which
contained only the control variables as predictors. Our findings have implications both for the social
capital and careers literatures.
Social Capital
The current results provide support for the model of social capital used in the current study and
help to resolve a growing controversy in the literature regarding the conceptualization of social capital.
While different scholars have tended to focus on only one aspect of the social network as the defining
element of social capital, the current study demonstrates the analytic utility of separately defining social
resources and social network structure and empirically examining the ways in which network structure
influence the level of social resources embedded in the network.
Specifically, the results of this study show that the number of weak ties and the level of structural
holes in the actor’s ego network each have independent effects on the level of social resources. As
indicated by the larger parameter estimates, the weak ties measure appears to have the stronger and
more robust effect on social resources. However, weak ties were significantly negatively related to two
of the network benefit measures, access to information and career sponsorship. These results supply
additional support for the traditional emphasis placed on the value of strong ties in providing information
and social support (Festinger, Schacter, & Back, 1950; Krackhardt, 1992). That is, people with whom
one has a strong relationship are likely to provide one with more information and assistance. Overall,
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Social Capital and Career Success
these results redress the overemphasis in weak tie theory on the strength of the tie per se, rather than on
its function as a bridge for social benefits. Our results suggest that, from a practical standpoint, it may be
best for a person to invest in the development of weak ties to increase the level of social resources
embedded within his or her network, but then to invest (perhaps selectively) in strengthening those ties
to increase the benefits actually mobilized on his or her behalf.
Previous research has demonstrated direct relations between network structure and several
organizational outcomes, such as promotions (Burt, 1992, 1997), influence (Brass & Burkhardt,
1993), and turnover (Krackhardt & Porter, 1986). The effects of social capital on these outcomes have
often been theoretically explained (but not empirically tested) as occurring because of the access to
information, resources, and sponsorship opportunities resulting from social contacts (e.g., Blau & Alba,
1982; Burt, 1997). The current study was unique for its inclusion of these mediating variables that aid in
understanding why social capital affects outcomes. Specifically, we found support for the role of access
to information, resources, and career sponsorship as fully mediating the relations between social capital
and career success. Support for the mediated rather than direct paths from social network variables to
career outcomes suggests that mere “schmoozing” with individuals outside of one’s work unit will not
affect career outcomes unless one is able to reap resources and sponsorship from these contacts (cf.
Wayne & Liden, 1995).
Findings of the current investigation also suggest that the value of social capital may vary
with respect to the nature of the contacts that comprise one’s social network. Specifically, we
found that developmental contacts in other functions were related to access to information and
indirectly to access to resources, but were not significantly related to career sponsorship.
Developmental contacts at higher organizational levels were related to access to information and
career sponsorship, and indirectly to access to resources. Thus, location of developmental
contacts appears to be differentially related to the nature of the network benefits provided.
Consistent with traditional mentoring research, higher level contacts provided more career
sponsorship than did those across functions. With respect to resource access, it is likely that
contacts in other functions did not have formal authority to directly provide resources to those in
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Social Capital and Career Success
other units. It was, however, surprising that contacts at higher organizational levels did not
directly relate to access to resources, but only indirectly provided resources by providing more
information to their junior colleagues. Future research is needed to determine if the location of
exchange contacts affects access to information, resources, and sponsorship opportunities
differently at each stage in one’s career. For example, close contacts with the immediate
superior and peers may be most beneficial during early socialization (Liden, Wayne, & Stilwell,
1993; Major, Kozlowski, Chao, & Gardner, 1995), but after being assimilated into the
organization, contacts with individuals at higher levels and other functions may prove most
useful.
The varying effects of the mediating variables on career outcomes also beckons additional
research. Although simple correlations between the three mediating variables and the three
career outcomes were all positive and significant, path estimates within the model showed that
career sponsorship was the most important network benefit in terms of career success. The
importance of career sponsorship relative to resource and information access should be verified
in future research. Future research should also examine the effects of other types of networks
such as friendship and interorganizational ties on career success.
Careers
The results of the current investigation strongly suggest the relevance of integrating social capital
theory with research on careers. Previous research demonstrating the influence of social capital on
career success variables such as promotions (Brass, 1984; Burt, 1992) has not been linked to the
literature on careers. Similarly, most studies in the careers literature have not considered social capital as
an antecedent of career success (Judge & Bretz, 1994; Judge et al., 1995; Wayne et al., 1999).
Investigations of career success that have acknowledged the importance of social capital have typically
not used social network methods (e.g., Gould & Penley, 1984; O’Hara, Beehr, & Colarelli, 1994). To
our knowledge an attempt has not been previously made to examine social capital within the context of
the careers literature.
In the current study, an explicit attempt was made to merge the literatures on social capital and
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Social Capital and Career Success
careers. Specifically, many of the variables included in comprehensive investigations of career success
(Judge et al., 1995) were tested as controls in the current study. Thus, the added contribution of social
capital, operating through the mediating variables, was clearly demonstrated. Additionally, most of the
research on networks and careers has focused on promotion rates or mobility. By incorporating both
objective and subjective measures of career success, the social capital effects on career success can be
more readily compared to other career success research in the organizational literature. These results
provide strong incentive for future research to consider social capital as a key variable of both objective
and subjective career success.
The findings of the current study also have important implications for a key subfield of the careers
literature, mentoring. It is assumed in some mentoring studies that protégés have only one mentor at a
time (Chao et al., 1992; Judge & Bretz, 1994; Tepper, 1995). In other studies (Dreher & Ash, 1990;
Turban & Daugherty, 1994), the amount of mentoring received has been assessed without differentiating
between respondents with a single or multiple mentors. Our results show that individuals with multiple
mentors reap greater career benefits than those having only one mentor. Furthermore, the results
indicate that the more contacts established at high levels of the organization, the more gained from
career sponsorship. Kram’s (Kram, 1985; Kram & Isabella, 1985) interviews suggested that peers
play an important role in career development, but the current investigation is unique because it relates a
full set of developmental relationships involving superiors and peers situated in different parts of the
organization to the level of career success. In essence, we have expanded the study of mentorship to
include a network of mentors who should be located in various functions and at higher levels in the
organization.
The notion of “mentoring networks” suggests many topics for future research. For example, a
superior may be beneficial not only for the mentoring that they personally provide, but also for
introducing followers to influential members of the organization who subsequently play a role in
mentoring the follower (Sparrowe & Liden, 1997). Longitudinal research could assess the way in which
a newcomer to the organization develops a mentoring network. Future research might also examine the
type of mentoring provided by each mentor. For example, one mentor might provide emotional support
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Social Capital and Career Success
and spiritual guidance. Another mentor might help the protégé with task and technical advice. Yet
another mentor might assist the protégé in coping with organizational politics. Discovering that each
mentor does indeed provide a unique type of advice or assistance would explain our finding that the
larger the mentoring network, the more beneficial to the protégé. In essence, the more mentors, the
greater the coverage of different types of advice and support. Conversely, our results demonstrate the
constraining weakness of multiplex ties in which an individual is in the precarious position of relying on
the same tie(s) for multiple contents (Podolny & Barron, 1997).
In summary, the current study makes several contributions to the literature: 1) Research on social
networks was enhanced by integrating contending theories of social capital, testing an analytical
distinction between network structure and network resources and testing for their interrelationship; 2)
The social capital and careers literatures were extended by testing for the effects of social capital on a
full set of career outcomes after controlling for other variables related to careers; 3) our integrated social
capital theory of career success was further supported by the findings that information, resource access,
and sponsorship play a mediating role in the relation between social network variables and career
outcomes; and 4) The mentoring literature was augmented by examining the influence that multiple
developmental relationships have on career success and by assessing the way in which the location of
the developmental contact in the organization influenced the protégé’s access to information, resources,
and career sponsorship.
Limitations
Although the large sample and numerous control variables represent strengths of the study, a
limitation is that, due to the cross-sectional design, it was not possible to make inferences concerning
causal direction for the paths tested in our model. For example, rather than higher level contacts
providing information to focal individuals, perhaps focal individuals who have gained access to
information are more attractive to contacts at higher organizational levels. Another limitation associated
with the design is that valid variance in career outcomes explained by differences in organizations,
culture, or norms was treated as error. An ideal study would include a large sample of individuals within
a large sample of organizations. This would enable the researcher to examine organization type, and
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Social Capital and Career Success
accompanying culture and norms, as multiple level effects in a comprehensive model of career success.
A second limitation of the study is the possible problem of common method bias, which can inflate
relationships among variables. Several steps were taken in order to minimize the problem in this study,
including separation of the items for the independent and dependent variables into different sections of
the survey instrument, and the use of different question formats for each set of variables. Because the
social capital variables are behavioral in nature while the career outcome variables are factual or
attitudinal, common method bias should be minimized.
A third limitation is that although the initial response rate of 28% is typical for mailed surveys, the
effective response of 17.3% after removing self-employed and part-time employed individuals and
missing data is somewhat low. Although nonrespondents did not differ from respondents on
demographics or major in school, it was found that the response rate for MBA graduates was lower
than for undergraduates. It is therefore possible that the sample is not as representative of the population
of MBA’s as is true for the undergraduate sample. A final limitation of our research is the use of single
item scales in measuring some of the variables. Although the use of single-item scales is common in
social network research, it would be preferable in future research to use three or more items for each
measure so that reliability can be estimated.
In sum, the path model tested in the current investigation represents an attempt to test an integrated
theory of social capital and career success. Support for our hybrid model suggests that both the social
capital and careers literatures are enhanced through integration. It follows that future research on career
success would benefit from the inclusion of social capital variables.
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Social Capital and Career Success
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In Press - Academy of Management Journal 37
Social Capital and Career Success
Footnote
1
As previous research has found differences in reported salary, promotions, and/or career satisfaction
based on different occupations and industries, we asked respondents to indicate which of 11 categories
best described their occupations, and which of 12 categories best described their industries. We
dummy-coded each of those variables such that “manufacturing” was the comparison category for all
industry categories and “accounting” was the comparison category for all occupation categories. Rather
than include every dummy-code variable in the hypothesized model, we ran MANOVAs using career
satisfaction, promotions, and salary as the dependent variables, and industry type and occupation type
as the independent variables. We then included only the statistically significant industry and occupation
types in the LISREL analysis. The results of the MANOVA revealed that the only statistically significant
occupation category was “general management.” Thus, the dummy-code for general management is
included in the hypothesized model as a control variable. While two industry categories (non-profit and
finance) were significant in the MANOVA, their paths were not statistically significant in the initial
LISREL analysis of the hypothesized model, thus, we deleted them from the hypothesized model before
making further model comparisons.
In Press - Academy of Management Journal 38
Social Capital and Career Success
TABLE 1 a
Means, Standard Deviations, and Correlations
Variable
Mean
S.D.
1
2
3
4
5
6
7
8
9
10
1. Contacts in other
Functions
.99
1.33
2. Contacts at higher
Levels
2.83
1.70
.27**
3. Access to information
5.40
1.39
.16**
.11*
4. Access to resources
4.88
1.33
.03
.09
.49**
5. Career sponsorship
2.91
.86
.03
.13**
.25**
.24**
6. Salary (natural log)
4.31
.54
.09
.10*
.21**
.18**
.16**
7. Promotions
4.76
2.88
.18**
.14**
.19**
.13**
.18**
.49**
8. Career satisfaction
3.74
.71
.08
.12**
.35**
.38**
.38**
.32**
.23**
9. Weak ties
1.92
1.52
.17**
.54**
-.03
.01
-.12*
.05
.06
-.05
10. Structural holes
.35
.25
.14**
.45**
.05
.05
.04
.02
.10*
.08
.31**
11. Network Size
5.32
1.99
.25**
.48**
.07
-.02
.05
-.10*
-.01
.11*
.37**
.42**
12. Years since graduation 13.02
7.07
.09*
.02
.04
-.02
-.03
.47**
.41**
-.03
.09
.07
11
-.16**
In Press - Academy of Management Journal 39
Social Capital and Career Success
In Press - Academy of Management Journal 40
Social Capital and Career Success
TABLE 1
CONTINUED
Variable
Mean
S.D.
1
2
3
4
5
13. Female
.35
.48
-.06
14. Married
.72
.45
15. Spouse employment
.49
16. MBA
.41
6
7
.05
-.01
-.02
.07
-.22** -.16**
.06
.01
.08
.04
.01
.31**
.50
-.02
-.05
-.01
-.02
-.01
.49
.13**
.04
.11*
.01
8
9
10
11
.13**
.02
.05
.02
.28**
.05
.06
-.05
-.08
-.02
.03
.06
.01
-.03
-.08
-.05
.25**
.19**
-.01
.02
.00
-.07
17. Employment gap
weeks
16.83
48.13 .04
-.06
.03
-.04
-.03
-.14** -.11*
-.02
-.04
-.00
-.08
18. Number of employees
4.16
1.28 -.07
.03
.11*
.06
-.02
.19**
.04
.13**
-.02
.07
-.03
19. Metropolitan area
.69
.46
-.11*
.03
-.04
-.04
.01
.15**
-.01
.02
-.02
-.00
-.02
20. Number of
employers
3.18
2.13
.12*
-.01
-.06
-.09
-.11*
-.00
.19**
-.09
.01
.09*
-.01
21. General management
.14
.35
.13**
.05
.11*
.13**
.14**
.30**
.26**
.16**
.02
.03
.01
In Press - Academy of Management Journal 41
Social Capital and Career Success
TABLE 1
CONTINUED
Variable
12
13
14
15
16
17
18
19
13. Female
-.30**
14. Married
.33**
-.20**
15. Spouse employment
.04
.14**
.61**
16. MBA
.03
-.11*
.06
-.03
17. Employment gaps
.14**
.09*
-.09
-.01
.08
18. Number of employees -.03
.09
.01
.00
.14**
-.09
19. Metropolitan area
-.04
.02
-.07
-.04
.01
-.04
.16**
20. Number of
employers
.26**
-.09*
.04
-.00
.10*
.29**
-.23** -.06
21. General management
.20**
-.12**
.04
-.09
.03
.03
-.01
a
N = 448
* p < .05
** p < .01
-.11*
20
.03
In Press - Academy of Management Journal 42
Social Capital and Career Success
a
TABLE 2
Nested Model Comparisons
Model
Hypothesized Model
W → X → Y → Z ← Controls
Control Variables Only Model
W X Y Z ← Controls
Partially Mediated 1:
W → X → Y → Z ← Controls
Partially Mediated 2:
W → X → Y → Z ← Controls
Partially Mediated 3:
W → X →Y → Z ← Controls
Non-Mediated 1:
W→ X→Y
χ2
df
191.11**
∆χ2
∆df
RMSEA
.05
AGFI
.90
CFI
.95
NFI
.91
.11
.74
.69
.67
Model Comparisons
88
689.94**
-498.83**
106
18
165.29**
25.82**
82
6
158.41**
6.88
76
6
154.46**
10.83
70
12
297.90**
-143.44**
85
5
301.25**
-146.79**
85
5
Control Variables compared
to Hypothesized Model
.05
.91
.96
.92
Partially Mediated 1 compared
to Hypothesized Model
.05
.90
.96
.92
Partially Mediated 2 compared to
Partially Mediated 1
.05
.90
.96
.93
Partially Mediated 3 compared to
Partially Mediated 1
.08
.84
.89
.86
Non-Mediated 1 compared to Partially
Mediated 3 (nested models)
Z ← Controls
Non-Mediated 2:
W→ X→Y
a
.08
.84
.89
.86
Non-Mediated 2 compared to Partially
Mediated 3 (nested models)
Z ← Controls
For simplicity, model comparisons are represented using the following notation: W = network structure variables (weak ties and structural holes);
X = social resources (contacts with other functions and contacts at higher levels); Y = network benefits (access to information, access to
resources, career sponsorship); Z = career success (salary, promotions, and career satisfaction); Controls = control variables.
In Press - Academy of Management Journal 43
Social Capital and Career Success
** p < .01
In Press - Academy of Management Journal 44
Social Capital and Career Success
FIGURE 1
Hypothesized Model of Social Capital Effects on Career Success1
Access to
Information
H: 5
Log Current
Salary
H: 3
# of Weak Ties
H: 1
Contacts in
Other Functions
H: 7
Access to
Resources
Structural Holes
H: 2
Contacts at
Higher levels
# Promotions,
Entire Career
H: 4
Career
Sponsorship
1
H: 6
Control Variables and their paths are not shown for the sake of clarity.
H:8
Career
Satisfaction
In Press - Academy of Management Journal 45
Social Capital and Career Success
FIGURE 2
Structural Equation Model Results
-.14
Access to
Information
.15
# of Weak Ties
Contacts in
Other Functions
.14
.11
Log Current
Salary
.14
.56
.12
.44
-.28
.14
# Promotions,
Entire Career
Access to
Resources
.28
Structural Holes
.31
Contacts at
Higher levels
.15
.29
Career
Sponsorship
.14
.32
Fit: χ 2 = 165.25, df = 82, p < .01; AGFI = .91, NFI = .92, CFI = .96.
Parameter estimates are from the completely standardized solution and are significant at p < .05.
Hypothesized relationships are represented in bold; relationships that were not hypothesized are in normal type.
Hypothesized paths that were not significant were eliminated from the model.
Control Variables and their paths are not shown for the sake of clarity.
Career
Satisfaction
In Press - Academy of Management Journal 46
Social Capital and Career Success
Scott E. Seibert (Ph.D., Cornell University) is an Assistant Professor of Management and Labor
Relations at the James J. Nance College of Business Administration, Cleveland State University. His
interests in personality, interpersonal processes, and social networks inform his research on groups,
mentoring, and career success.
Maria L. Kraimer (Ph.D., University of Illinois at Chicago) is an Assistant Professor of
Management & Labor Relations at the James J. Nance College of Business Administration,
Cleveland State University. Her research interests include career-related issues, employee
adjustment, and the employee-employer relationship.
Robert C. Liden (Ph.D., University of Cincinnati) is Professor of Management at the
University of Illinois at Chicago. His research focuses on interpersonal processes within the
context of such topics as leadership, groups, career progression, and employment interviews.