A Social Capital Theory of Career Success
Author(s): Scott E. Seibert, Maria L. Kraimer, Robert C. Liden
Source: The Academy of Management Journal, Vol. 44, No. 2 (Apr., 2001), pp. 219-237
Published by: Academy of Management
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c
Academy of Management Journal
2001, Vol. 44, No. 2, 219-237.
A SOCIAL CAPITALTHEORYOF CAREERSUCCESS
SCOTT E. SEIBERT
MARIA L. KRAIMER
Cleveland State University
ROBERT C. LIDEN
University of Illinois at Chicago
A model integrating competing theories of social capital with research on career
success was developed and tested in a sample of 448 employees with various occupations and organizations. Social capital was conceptualized in terms of network structure and social resources. Results of structural equation modeling 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.
tial to considerably enhance scholars' knowledge of
the role of social processes in career success.
The first purpose of the current study was to
integrate the current conceptualizations of social
capital as they pertain to career success. Three diftie theory
ferent theoretical approaches-weak
(Granovetter, 1973), structural hole theory (Burt,
1992), and social resource theory (Lin, 1990)focus on different network properties as representations of social capital. However, in all these theories, the key explanatory variables for the effect of
social capital on career mobility are greater access
to information, resources, and sponsorship. 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 was to model
social capital effects on a full set of career outcomes, with other processes known to be determinants of career success controlled. Social network
approaches to career success, growing out of a sociological research tradition, tend to focus on 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 and small samples, have had few control variables, or have assessed outcomes over short time spans. Research in
the organizational literature, on the other hand,
tends to have large and diverse samples and models
with broad sets of career processes. A model of
career success that does not account for determi-
Organizational researchers have begun to develop increasingly comprehensive models of career
success using demographic, human capital, workfamily, motivational, organizational, and industry
variables (e.g., Dreher & Ash, 1990; Judge & Bretz,
1994; Judge, Cable, Boudreau, & Bretz, 1995; Kirchmeyer, 1998). Although this work has provided
considerable evidence regarding the determinants
of career outcomes, the roles 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 engaged in routine communication activities than
their less successful counterparts. Recent advances
in social capital theory (Coleman, 1990) have begun
to provide a finer-grained analysis of the ways individuals' social networks affect their careers in
organizations (Burt, 1992, 1997; Ibarra, 1995;
Podolny & Baron, 1997; Sparrowe & Popielarz,
1995). This theoretical perspective has the poten-
Datawere collected and the manuscriptwas submitted
and processed while Scott E. Seibert was in the Management Department at the University of Notre Dame and
Maria L. Kraimerwas a graduate student at the University of Illinois at Chicago. Support for this project was
provided by the ManagementDepartmentat the University of Notre Dame and the Alumni Office of the University of Notre Dame. The currentinvestigation is part of a
larger study of career success.
219
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Academy of ManagementJournal
nants other than social capital may provide a biased estimate of the effect of the latter 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, Grant, & Kraimer, 1999; Turban & Dougherty, 1994; Wayne, Liden, Kraimer, & Graf, 1999).
Extrinsic career outcomes are objectively observable achievements such as salary and promotions,
and intrinsic career outcomes refer to individuals'
subjective feelings of accomplishment and satisfaction with their careers (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
also feelings of success relative to an 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 hoped to
provide a rigorous demonstration of the role of
social capital in career success.
The third purpose of this study was to integrate
research on social network structure with that on
mentoring and careers. Mentoring has been defined
as a developmental relationship in which a less
experienced organization member receives help
and guidance from a more experienced member
whose intent is to improve the career opportunities
and growth of the junior person (Kram, 1985). Research has explored the origin and progress of mentoring relationships and the types of activities taking place within the mentor-prot6ge relationship
(e.g., Chao, Walz, & Gardner, 1992; Noe, 1988; Turban & Dougherty, 1994). The literature has also
emphasized the important effect that having a mentor may have on a protege's career success (e.g.,
Dreher & Ash, 1990). Kram (1985) suggested that it
is important to understand the full constellation of
developmental relationships in which a protege
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 an employee's social
network facilitates access to developmental relationships in different parts of an organization. In
this study, we used 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
April
the extensive literature on developmental relationships and the benefits they bring to careers.
THEORETICAL BACKGROUND
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
people 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 links with other people in the network.
The focal person in such an analysis (who is usually the person supplying the data) is referred to as
"ego," and 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
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 those with friends, advisors,
and coworkers). The information possessed by any
one member of the clique is likely to be either
shared 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 (that is, 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).
2001
Seibert, Kraimer, and Liden
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 an individual 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 throughout the social system. Burt (1992) critiqued 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" (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
of social capital is social reconceptualization
sources theory (e.g., Lin, Ensel, & Vaughn, 1981a,
1981b). Social resources theory focuses on the
nature of the resources embedded within a network. Lin and coauthors (1981a) argued that it is
not the weakness of a tie 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 development advice and support
are the relevant social resource when considering
an 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 (that is, weak ties reach higherstatus 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).
221
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 ties; structural holes theory focuses on the
pattern of the ties among alters; and social resource
theory focuses on the characteristics of the alters
contacted. The two later theories are each claimed
to supersede the earlier theory (e.g., Burt, 1992; Lin
et al., 1981a), and competitive model testing has
been performed in an effort to empirically 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 networks and the nature of the social
resources embedded in networks and to thus draw
a distinction between their form and their content
(see Lin, 1999). Weak tie theory and structural hole
theory each focuses on the structure of a network.
Social resources theory focuses on the content of a
network. These theories are not mutually exclusive, as competitive model testing implies, but can
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 that 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?"
A MODEL FOR A SOCIAL CAPITAL THEORY
OF CAREER 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, the number of
contacts in other functions and the 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 entire career, and career satisfaction. A full
set of variables drawn from the organizational literature were used as controls in model tests but are
not shown in Figure 1.
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Academy of Management Journal
April
FIGURE 1
of
Social
Model
Capital Effects on Career Successa
Hypothesized
Network Structure
Social Resources
Network Benefits
CareerSuccess
a Control variables and their
paths are not shown for the sake of clarity. Current salary was a logarithm.
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. Maintaining consistency with previous research on social networks
and promotions (Burt, 1992, 1997; Podolny &
Baron, 1997), we focused on intraorganizational
ties because contacts within an employee's own
organization were the ones we expected would
provide the kinds of benefits discussed in social
capital theories (information, resources, and
sponsorship) and would thus influence her or his
success within the organization. We conceptualized social resources as developmental contacts
in other functions and at higher organizational
levels because, within formal organizations,
functional or technical specialty and hierarchical
level are likely to impose salient social boundaries between organization 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 worldviews (Ibarra, 1995;
Kanter, 1977; Lincoln & Miller, 1979). Developmental contacts who are members of social identity groups other than ego's 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
a strong tie to act as a bridge between social cliques,
providing an organization member with contact
with people of different social groups or statuses
(Granovetter, 1973, 1982). An assumption of social
network approaches to social capital theory is that
a person 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, the individual 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 to a given individual
is better than a strong tie to the same person.
Rather, we seek to relate the number of weak ties, a
structural property of an 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, people who choose to invest their
social energy in developing large numbers of weak
ties will have greater access to social groups other
than their own. Although some empirical evidence
has suggested that bridging is more likely with
weak ties than with strong ties (Friedkin, 1980),
surprisingly little empirical research has specifi-
2001
Seibert, Kraimer, and Liden
cally examined the extent to which weak ties
bridge relatively unconnected social groups. Hypotheses la and lb state that, within a career development 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
an organization:
Hypothesis la. The number of weak ties in a
network will be positively related to the number of ego's contacts in other organizational
functions.
Hypothesis lb. The number of weak ties in a
network will be positively related to the number of ego's contacts at higher organizational
levels.
A structural hole is said to ,exist between two
alters when they 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, in that he or she may be able to
trade information gathered from one alter to the
other. Ego provides added value to the organization
through his or her ability to provide information
and coordinate activities among separated alters
(Burt, 1997; Galbraith, 1977). Further, to the extent
that the two alters possess similar resources, ego
can play them off against each other, seeking to set
up social (or material) exchanges with the alter who
offers the best return.
According to structural hole theory, an alter who
is already connected to other alters within ego's
network is "redundant" (Burt, 1992) and does not
convey the kinds of benefits to ego that a nonredundant alter would. Parallel to our argument regarding weak ties is the argument that 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 in his or her
network. The addition of a nonredundant alter introduces another set of structural holes into 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
223
between two unconnected individuals. Alters who
are members of unconnected social groups are
therefore uniquely beneficial to an ego. An ego
whose 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. Hypotheses 2a and 2b
allow tests of this proposition:
Hypothesis 2a. The extent of structural holes in
a network will be positively related to ego's
number of contacts in other organizational
functions.
Hypothesis 2b. The extent of structural holes in
a network will be positively related to ego's
number of contacts at higher organizational
levels.
Social Resources and Network Benefits
The notion that social resources embedded in
networks will provide benefits to actors 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 a social system.
However, previous researchers have not incorporated these constructs in their models. The overall
purpose of the remaining set of hypotheses was to
allow us 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 worldviews, 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 contain
the assumption that different functional groups
need to share information across functional boundaries because the groups hold different information
and views. This work suggests that an employee's
contact with members of other organizational functions will provide access to information not available within his or her own functional group. Although other functions may also possess unique
resources, these resources are less likely than is
information to be 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
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status and influence to provide sponsorship to an
employee's career. Accordingly,
Hypothesis 3. The number of contacts in other
functions in a network will be positively related to ego's 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 organization design is that higher-level positions convey more authority than lower-level positions (March & Simon, 1958; Massie, 1965; Weber, 1946). Ideally, higher positions also convey a
broader perspective on issues relevant to an organization and greater access to information upon
which incumbents can base decisions (Galbraith,
1977; March & Simon, 1958). According to the classical principles of formal organizations, those in
positions at higher levels also have greater formal
decision-making authority over the allocation of
resources than do those in 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 relations with developmental contacts at
higher levels should therefore provide an actor
with greater access to these benefits.
Hypothesis 4a. The number of an individual's
contacts at higher organizational levels will be
positively related to access to organizational
information.
Hypothesis 4b. The number of an individual's
contacts at higher organizational levels will be
positively related to access to resources.
We expect contacts at higher organizational levels to also 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, one of the primary functions fulfilled by a mentor (Kram, 1985; Noe, 1988),
involves providing a protege with favorable and
timely exposure, opportunities to engage in chal-
April
lenging assignments, and career advice and coaching. 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 receiving career sponsorship.
Hypothesis 4c. The number of an individual's
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) argued that individuals able to use their network positions to fill
a broker or boundary spanner role within an organization add greater value to the organization. In
fact, centrality in the advice network has been
linked to job performance (Sparrowe, Liden, Wayne,
& Kraimer, 2001). The information-processing approach to organizational design also emphasizes
the value added to organizations by individuals
who play key boundary-spanning, liaison, conflict
resolution, and coordination roles (e.g., Galbraith,
1977). Improved work performance and adding
value should enhance an individual's 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 an 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 her or his actual 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 increase feelings both of
control and competence at work (Gist & Mitchell,
1992) and of psychological empowerment (Spreitzer, 1996). Psychological empowerment in general and self-determination and competence in par-
2001
Seibert, Kraimer, and Liden
ticular are extensions of job design theory (Kraimer,
Seibert, & Liden, 1999; Spreitzer, 1995, 1996), according to which 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.
Theory supports the existence of a relationship between access to information and career satisfaction
and between access to resources and career satisfaction, but 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. An employee's access to information will be positively related to current salary, independent of other career outcomes.
Hypothesis 5b. An employee's access to information will be positively related to the number
of promotions received over his or her career,
independent of other career outcomes.
Hypothesis 5c. An employee's access to information will be positively related to his or her
level of career satisfaction, independent of
other career outcomes.
Our discussion also leads to these hypotheses
regarding access to resources:
Hypothesis 6a. An employee's access to resources will be positively related to current
salary, independent of other career outcomes.
Hypothesis 6b. An employee's access to resources will be positively related to the number
of promotions received over her or his career,
independent of other career outcomes.
Hypothesis 6c. An employee's access to resources will be positively related to her or his
level of career satisfaction, independent of
other career outcomes.
Although 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, Hinings, Lee, Schneck, & Pennings, 1971; Pfeffer, Salancik, & Leblibici, 1978).
225
Possessing relevant organizational information
would give individuals the ability to develop
means of acquiring necessary resources other than
going through formal channels. In essence, information expands one's options for acquiring resources.
Hypothesis 7. An employee's access to information will be positively related to his or her
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, 1997; Chao et al., 1992;
Dreher & Ash, 1990; Wayne et al., 1999; Whitely,
Dougherty, & Dreher, 1991). For example, Dreher
and Ash (1990) found that, after they controlled 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 and colleagues (1992)
performed a canonical correlation analysis and
found career mentoring to be most strongly associated with employees' intrinsic job satisfaction. Accordingly, the following hypotheses were formulated to allow replication of previous research on
career mentoring within an integrated model of
social capital and career success:
Hypothesis 8a. The level of an employee's career sponsorship will be positively related to
current salary, independent of other career
outcomes.
Hypothesis 8b. The level of an employee's career sponsorship will be positively related to
the number of promotions received over her or
his entire career, independent of other career
outcomes.
Hypothesis 8c. The level of an employee's career sponsorship will be positively related to
her or his career satisfaction, independent of
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's employment status), and organizational
characteristics (organization size, metropolitan location, and industry sector) have effects on salary,
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Academy of ManagementJournal
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 links between each category of variables and the career
outcomes discussed above were beyond the scope
of this study (see Judge et al., 1995) but these variables were included as controls.
METHODS
Sample and Procedures
A diverse sample of 2,781 randomly selected undergraduate business, master of business administration (M.B.A.), and engineering school alumni of
a large, private, midwestern university received
surveys at their home addresses. The alumni, who
had graduated from 3 to 30 years prior to the date of
the study, were instructed to complete the survey
and return it directly to the first author in a postagepaid 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 surveys were returned (a 28 percent response rate). Respondents who were not currently working (n = 73), working only part-time
(n = 42), or self-employed (n = 116) were eliminated from the analyses because their career outcomes would not be comparable to those of alumni
with current full-time employment. After cases
with missing data had been eliminated, the final
sample consisted of 448 alumni. T-tests revealed
that respondents and nonrespondents did not significantly differ with respect to gender, race, or
major (business versus engineering). However, the
percentage of responses from M.B.A. graduates
(13%) was lower than the percentage they represented in the target sample (25%).
The demographic breakdown of the respondents
was as follows: Their average age was 35.6 years;
the average time since their graduation from the
university was 13.0 years; 65 percent were male; 72
percent were married; and 96 percent were Caucasian. Forty-three percent had a bachelor's degree as
their highest degree attained, 9 percent had a master's degree other than an M.B.A., 41 percent had an
M.B.A., 5 percent had a law degree, and 2 percent
had a Ph.D. Average tenure in their current organization was 6.16 years.
April
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." We chose
this description of a network relation on the basis of
theoretical considerations, as it directly addresses the
social resources mobilized in pursuit of instrumental
career goals. Network size was the total number of
alters indicated by a respondent.
For alters who were past or current members of a
respondent's organization, the respondent also
indicated the alter's organizational function relative to him or herself ("same function" or "different
function") and organizational level ("lower," "same,"
or "higher"). 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 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 was "especially close,"
1 was "less close," and 0 was "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), we calculated structural holes, using the ego
network data, as 1 minus the calculated value of the
constraint posed by an individual alter j: cij = (Pij +
piqpqj)2,for q + i, j, where Pi is the proportion of i's
relations invested in contact j, Piq is the proportion of
i's relations invested in q, and pq is the proportion of
q's relations invested in j. 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 network. One minus constraint
(1 - cij) is thus the lack of constraint, or the 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 pretested 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 on brief, transient, or episodic
interactions. Research has shown that people's re-
2001
Seibert, Kraimer, and Liden
call of brief, episodic interactions is 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)
like the developmental relationships analyzed in
the current study.
Access to information and resources. The access variables were measured using Spreitzer's
(1996) six-item scale. Three items were designed to
measure access to information, and three were designed to measure access to resources (1 = "strongly disagree" to 7 = "strongly agree"). An exploratory factor analysis specifying a varimax rotation
indicated that five of the six items formed two clear
factors representing access to resources (a = .89)
and access to information (a = .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 was therefore eliminated from further analyses.
Career sponsorship. The eight career sponsorship items from Dreher and Ash's (1990) global
mentoring scale were summed to form a composite
(a = .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 = "a 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.
Respondents 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." They 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 a Z-test on the skewness statistic indicated
a nonnormal 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 (1 = "very
dissatisfied" to 5 = "very satisfied"), which were
summed to form a composite (a = .83). An example
item is, "The progress I have made toward meeting
my goals for advancement."
Control variables. These variables were gender
(0 = "male," 1 = "female"); marital status (0 = "not
married," 1 = "married"); spouse employment sta-
227
tus (0 = "spouse not employed," 1 = "spouse employed"); having an M.B.A. degree (1 = "yes"),
having had an employment gap and, if so, the number of weeks it lasted; residence in a major metropolitan city, defined as one with a population over
1 million (1 = "yes"); the number of organizations
worked for over the career; and having an occupation categorized as general management (1 =
"yes").1 The number of employees in a respondent's firm was scaled from 1, "less than 50 employees," to 5, "more than 1,000 employees." Number of years in the workforce was measured by
subtracting the year of graduation from the year the
study was conducted. We also used network size as
a control variable in order to examine the unique
effects of developmental contacts in other functions and at higher levels as distinct from the sheer
number of developmental contacts.
Analyses
The hypothesized structural equations model
was tested using LISREL 8 (Joreskog & S6rbom,
1993), with the covariance matrix as input. Because
no conventional estimate of error was 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 provided a conservative test of the
1 As previous research has shown differences in reported salary, promotions, and/or careersatisfaction that
are 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 so that manufacturing was the
comparison category for all industries and accounting
was the comparison category for all occupations. Rather
than include every dummy-coded variablein the hypothesized model, we ran multivariate analyses of variance
(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 LISRELanalysis. The results of
the MANOVArevealed 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.
Although two industry categories (nonprofitand finance)
were significant in the MANOVA, their paths were not
statistically significant in the initial LISRELanalysis of
the hypothesized model; thus, we deleted them from the
hypothesized model before making further model comparisons.
228
Academy of ManagementJournal
model. In order to increase sample size relative to
the parameter estimates, we used single-scale score
indicators 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 1 in order to scale the latent variables
(Bollen, 1989), and the error variance was set equal
to the scale variance times 1 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 as a means of showing that a hypothesized model is the best representation of the data
and are considered to be an important part of assessing model fit (Bollen, 1989; Kelloway, 1998). The first
alternative model specifies only the direct paths from
the control variables to the career outcome variables.
This control-variables-only model provided a baseline 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 specifies both the control variable paths and the set of paths hypothesized in this
study.
With the remaining alternative models, we compared the fully mediated hypothesized model with
the partially mediated and nonmediated 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 are tests
of whether hypothesized mediated effects are better
represented as direct and indirect effects. We also
examined two nonmediated 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 might present a problem, we subjected all the
scale items for the variables used in this study to
a principal components analysis using varimax
April
rotation (Harman, 1967). From this analysis, six
clear factors representing the expected constructs
emerged: 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 3
were above .30, with the largest equal to .40. The
absence of cross-loadings among 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 provides evidence of discriminant validity among our network benefits constructs.
Hypothesized Model
Table 1 reports the means, standard deviations, and
correlations among the study variables. Our hypothesized model fit the data well (X = 191.11, df = 88,
p < .01; root mean square error of approximation
[RMSEA] = .05; adjusted goodness-of-fit index
[AGFI] = .90; normed fit index [NFI] = .91; comparative fit index [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
the hypothesized model provided a significantly better fit than did the control-variables-only model
(AX2= 498.83, Adf = 18, p < .01). The second comparison was between the hypothesized model and the
first partially mediated model (partially mediated
model 1). Partially mediated model 1 specified 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 chisquare test showed that this alternative model was
significantly better than the hypothesized model
(AX2= 25.82, Adf = 6, p < .01).
Partially mediated model 1 was therefore retained as the best-fitting model and was then compared with the second partially mediated model
(partially mediated model 2) and the third partially
mediated model (partially mediated model 3). Partially mediated model 2 specifies the same paths as
partially mediated model 1 and also the direct
paths from the social resources constructs (contacts
across functions and at higher levels) to career outcomes. Partially mediated model 3 includes all direct paths from network structure to network benefits, from network structure to career outcomes,
and from social resources to career outcomes, in
addition to the hypothesized mediated effects. The
TABLE 1
Means, Standard Deviations, and Correlationsa
Variable
1. Contacts in other
functions
2. Contacts at higher
levels
3. Access to information
4. Access to resources
5. Career sponsorship
6. Salaryb
7. Promotions
8. Career satisfaction
9. Weak ties
10. Structural holes
11. Network size
12. Years since graduation
13. Female
14. Married
15. Spouse employment
16. M.B.A.
17. Employment gap in
weeks
18. Number of employees
19. Metropolitan area
20. Number of employers
21. General management
an = 448.
b Natural
logarithm.
*
p < .05
< .01
**p
Mean
s.d.
0.99
1.33
2.83
1.70
5.40
4.88
2.91
4.31
4.76
3.74
1.92
0.35
5.32
13.02
0.35
0.72
0.49
0.41
16.83
4.16
0.69
3.18
0.14
1
2
3
6
7
8
9
10
4
5
.24**
.18**
.13**
.38**
.01
.05
-.02
-.02
-.02
.04
-.02
.01
-.04
.16**
.18**
.38**
-.12*
.04
.05
-.03
.07
.01
-.01
-.05
-.03
.49**
.32**
.23**
.06
.05
.02
.10*
-.01
-.10*
.41**
.47**
-.22** -.16**
.31**
.28**
.03
-.02
.19**
.25**
-.14** -.10*
-.05
.08
.11*
-.03
.13**
.05
.06
-.01
-.02
.31**
.37**
.42**
.09
.07
.02
.05
-.05
.06
.01
-.03
.02
.00
-.04
-.00
.06
-.04
-.09
.13**
.04
-.02
.19**
.15** -.01
.01
-.11*
-.00
.19**
.14**
.30**
.26**
.13**
.02
-.09
.16**
-.02
-.02
.01
.02
11
12
13
14
.27**
.11*
.16**
1.39
.03
.09
.49**
1.33
.13**
0.86
.03
.25**
.10*
.09
.21**
0.54
.18**
.14**
.20**
2.88
.12**
0.71
.35**
.08
.17**
.54** -.03
1.52
.14**
.45**
0.25
.05
.25**
.48**
.07
1.99
7.07
.02
.04
.09*
0.48 -.06
.05
-.01
.06
.01
.08
0.45
-.01
0.50 -.02
-.05
.04
.11*
0.49
.13**
.04
48.13
-.06
.03
.03
1.28 -.07
0.46 -.11*
.03
.12* -.01
2.13
.13**
.05
0.35
.11*
-.04
-.06
.11*
.07
-.00
.10*
.03
-.16**
.02
-.08
-.08
-.07
-.08
-.30**
.33** -.20**
.14**
.04
.03
-.11*
.14**
.09*
.61**
.06
-.09
-
-.03
-.02
-.01
.01
.09
-.03
-.04
.02
.26** -.09*
.20** -.12**
.01
-.07
.04
.04
-
230
Academy of Management Journal
April
TABLE 2
Nested Model Comparisons
Model
2
(df)
AX2 (Adjf
RMSEA AGFI CFI NFI
Model Comparisons
.05
.90
.95
.91
(18)
.11
.74
.69
.67
25.82** ( 6)
.05
.91
.96
.92 Partially mediated model 1 compared to
hypothesized model
6.88
( 6)
.05
.90
.96
.92 Partially mediated model 2 compared to partially
mediated model 1
10.83
(12)
.05
.90
.96
.93 Partially mediated model 3 compared to partially
mediated model 1
297.90** ( 85) -143.44**
( 5)
.08
.84
.89
.86 Nonmediated model 1 compared to partially
mediated model 3 (nested models)
301.25** ( 85) -146.79**
( 5)
.08
.84
.89
.86 Nonmediated model 2 compared to partially
mediated model 3 (nested models)
Hypothesized
191.11** ( 88)
Control variables only
689.94** (106)
Partially mediated 1
165.29** ( 82)
Partially mediated 2
158.41** ( 76)
Partially mediated 3
154.46** ( 70)
Nonmediated 1
Nonmediated 2
-498.83**
Control variables compared to hypothesized model
** p < .01
change in chi-square tests revealed that partially
mediated models 2 and 3 were not significantly
better than the first partially mediated model and
were less parsimonious.
Lastly, the nonmediated model test comparisons
were conducted. In the first nonmediated model,
the paths from network benefits to career outcomes
were constrained to zero, but the paths from social
resources to career outcomes were freely estimated.
The second nonmediated 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 nonmediated models were not nested within
partially mediated model 1 but were nested within
partially mediated model 3, the nonmediated models were compared to partially mediated model 3.
Both nonmediated models fit significantly worse
than the third partially mediated model. Table 2
reports the change in chi-square for all nested
model comparisons and other fit indexes for all
seven comparative models.
The nested model comparisons indicated that the
model including the hypothesized effects and direct effects from network structure to network benefits (partially mediated model 1) was the bestfitting, most parsimonious model. Thus, we
retained this model as the best-fitting model and
interpret it below in order to examine the hypothesized relationships.
Examination of the standardized parameter estimates indicated that 14 of the 17 hypothesized
relationships were significant and in the predicted
directions (see Figure 2) when the control variables
were accounted for. Specifically, Hypotheses la
and lb positively relate weak ties to contacts in
other functions (la) and at higher levels (lb). The
statistically significant parameter estimates (b =
.14 and .44, respectively; p < .01) indicated support for Hypotheses la and lb. Hypotheses 2a and
2b positively relate structural holes to contacts in
other functions (2a) and at higher levels (2b). A
statistically significant parameter estimate was
found for the path between structural holes and
higher-level contacts (b = .31, p < .01) providing
support for Hypothesis 2b, but the estimated path
between structural holes and cross-function contacts was only marginally significant (b = .09, t =
1.94). Overall, the hypotheses relating network
structure to social resources were supported.
Hypothesis 3 was supported, as a statistically
significant parameter estimate was found for the
path between contacts in other functions and access to information (b = .15, p < .01). Hypotheses
4a, 4b, and 4c relate contacts at higher levels to
access to information (4a), access to resources (4b),
and career sponsorship (4c). A statistically significant parameter estimate was found for the path
from contacts at higher levels to access to information (b = .14, p < .05) and from these contacts to
career sponsorship (b = .29, p < .01), but the path
between these contacts and access to resources was
not significant. Thus, support was indicated for
Hypotheses 4a and 4c, but not for Hypothesis 4b.
Hypotheses 5a, 5b, and 5c positively relate access
to information to salary (5a), promotions (5b), and
2001
231
Seibert, Kraimer, and Liden
FIGURE 2
Results of Structural Equation Modela
Career
\
Sponsorship
e
.32
-W
o
Career
Satisfaction J
Fit:X2 = 165.25, df = 82, p < .01; AGFI= .91, NFI = .92, CFI= .96. Parameterestimates are fromthe completely standardizedsolution
and are significant at p < .05. Hypothesized relationships are representedby bold arrows, and relationships that were not hypothesized
are representedby light arrows.Hypothesized paths that were not significant were eliminated from the model. Controlvariablesand their
paths are not shown for the sake of clarity. Currentsalary was a logarithm.
a
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. Hypotheses 6a, 6b, and 6c positively relate 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 for the path from
access to information to access to resources (b =
.56, p < .01). Fully supporting Hypotheses 8a, 8b,
and 8c, respondents 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: significant, negative parameter estimates were found for the path
from weak ties to access to information (b = -.14,
p < .05) and for the path 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. Salary was predicted by years
since graduation (b = .41), marital status (b = .22),
spouse employment (b = -.14), M.B.A. 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),
M.B.A. 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 percent of the variance in current salary, 34 percent of the variance in
promotions, and 36 percent of the variance in career satisfaction. The explained variance in the
career outcomes was greater in the alternative hypothesized model than in the control-variablesonly model: the latter explained 42 percent of
salary, 29 percent of promotions, and 5 percent
of career satisfaction.
DISCUSSION
Support of our hypothesized model revealed the
importance of social capital to career success. Spe-
232
Academy of ManagementJournal
cifically, our results demonstrate that two measures
of network structure, weak ties and structural
holes, positively relate to the level of social resources embedded in a person's network, measured
as the number of developmental contacts in functional areas of an organization other than their own
and at higher levels in the organization. Social resources were in turn positively related to current
salary, number of promotions over the career, and
career satisfaction through their positive relationships with three measures of network benefitsaccess 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 containing 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. Various scholars have tended to focus on
only one aspect of the social network as the defining element of social capital, but 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 influences the level of social resources embedded in a network.
Specifically, the results of this study show that
the number of weak ties and the level of structural
holes in an actor's network each has 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 et al.,
1950; Krackhardt, 1992). That is, people with
whom one has a strong relationship are likely to
provide one with more information and assistance.
Overall, 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
April
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 in 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 full mediators of 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 a
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, the 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
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,
2001
Seibert, Kraimer, and Liden
and sponsorship opportunities differently at each
stage in a career. For example, close contacts with
the immediate superior and peers may be most
beneficial during an employee's early socialization
in an organization (Liden, Wayne, &Stilwell, 1993;
Major,Kozlowski, Chao, &Gardner,1995), but after
she or he has been 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 invites 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 those composed of
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 litera-
ture on careers. Similarly, most studies in the careers literature have not included social capital as
an antecedent of career success (Judge & Bretz,
1994; Judge et al., 1995; Wayne et al., 1999). Inves-
tigations 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, no previous attempt has been 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
careers. Specifically, many of the variables included in comprehensive investigations of career
success (Judgeet al., 1995) were tested as controls.
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 incorporatingboth
objective and subjective measures of career success, we were able to more readily compare the
social capital effects on career success found here
233
with findings from other career success research in
the organizational literature. These results provide
a strong incentive for future researchersto consider
social capital as a key variable in relation to both
objective and subjective career success.
The findings of the current study also have important implications for a key subfield of the careers research, mentoring. It is assumed in some
mentoring studies that proteges have only one mentor at a time (Chaoet al., 1992; Judge &Bretz, 1994;
Tepper, 1995). In other studies (Dreher & Ash,
1990; Turban&Dougherty, 1994), researchershave
assessed the amount of mentoring received without
differentiating between respondents with single
and those with 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 that people establish at high organizational
levels, the more they gain from career sponsorship.
Kram's (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 organizations to 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 of
and at higher levels in the focal employee's organization.
This notion of a "mentoring network" suggests
many topics for future research. For example, superiors may be beneficial not only for the mentoring that they personally provide, but also for introducing subordinates to influential organization
members who subsequently play a role in mentoring the subordinates (Sparrowe & Liden, 1997).
Longitudinal research could assess the way in
which a newcomer to an 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 and spiritual guidance. Another
mentor might help the protege with task and technical advice. Yet another mentor might assist the
protege in coping with organizationalpolitics. 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 it is to the protege. 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
234
Academy of ManagementJournal
multiple types of assistance (Podolny & Barron,
1997).
In summary, the current study makes several
contributions to the literature: (1) We enhanced
research on social networks by integrating contending theories of social capital, testing an analytical
distinction between network structure and network
resources and testing for their relationship. (2) We
extended the social capital and careers literatures
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) we augmented
the mentoring literature 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
an organization influenced a protege's access to
information, resources, and career sponsorship.
Limitations
Although its large sample and numerous control
variables represent strengths of the study, a limitation is that, owing 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 accompanying culture and norms, as
multiple-level effects in a comprehensive model of
career success.
A second limitation of the study is the possibility
of common method bias, which can inflate relationships among variables. We took several steps to
minimize the problem, including separating the
items for the independent and dependent variables
into different sections of the survey instrument and
using different question formats for each set of variables. Because the social capital variables are behavioral and the career outcome variables are factual or attitudinal, common method bias should
have been minimized.
April
A third limitation is that although the initial response rate of 28 percent is typical for mailed surveys, the effective response rate of 17.3 percent that
pertained after we had removed responses from
self-employed individuals and part-time employees and those with missing data is somewhat low.
Although nonrespondents did not differ from respondents on demographic characteristics or major
in school, it was found that the response rate for
M.B.A. graduates was lower than that for undergraduate alumni. It is therefore possible that the
sample is not as representative of the population of
M.B.A.'s as it is of the population of undergraduates. A final limitation of our research is the use of
single-item scales for some of the variables. Although single-item scales are 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 can be enhanced through integration. It follows that future
research on career success would benefit from the
inclusion of social capital variables.
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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 group decision making, mentoring, and career success. He will be joining the department of managerial
studies at the University of Illinois at Chicago in the fall
of 2001.
Maria L. Kraimer (Ph.D., University of Illinois at Chicago) is an assistant professor of management and 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. She will
be joining the department of managerial studies at the
University of Illinois at Chicago in the fall of 2001.
Robert C. Liden (Ph.D., University of Cincinnati) is a
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.