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How Social Capital, Normative Prescriptions, and Enduring Links Promote Staying:
Extending Job Embeddedness Theory with a Multifaceted Conceptualization of Links
Peter Hom and Kristie Rogers
Department of Management
Arizona State University
David Allen
Department of Management
University of Memphis
Mian Zhang
Department of Human Resources and Organizational Behavior
Tsinghua University
Cynthia Lee
Management and Organizational Development Department
Northeastern University & Hong Kong Polytechnic University
Hailin (Helen) Zhao
Department of Management & Marketing
Hong Kong Polytechnic University
We thank Hillary Reitan, Michael Jennings, Jesyka Simpson, Sarah Vergin, Amanda Christensen,
Chen Zhang, Guadalupe Roa, Ana Sanchez, Barbara Gao, and Mindy West for their invaluable assistance
in data collection, survey development and administration, programming, and marketing of the project
to managers and executives. We also thank Zhen Zhang, Robert Steel, and Zhiang (John) Lin for their
comments on the paper. Financial support came from the Society for Human Resource Management
Foundation Grant and National Natural Science Foundation of China.
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How Social Capital, Normative Prescriptions, and Enduring Links Promote Staying:
Extending Job Embeddedness Theory with a Multifaceted Conceptualization of Links
Abstract
This investigation extends job embeddedness theory, the preeminent perspective on why
people stay. Drawing from theories of social capital, normative control, and turnover contagion,
we proposed an overarching integrative framework that expands a central embedding dimension
known as “links”—or number of workplace and community relationships. Going beyond
standard notions of embedding links, our theoretical approach articulates how other facets of
links—namely, their affective strength, network closure, network centrality, normative
prescriptions, and stability can increase staying. We used egonet and whole-network
methodologies to test this extended model of job embeddedness with nearly 900 employees from
America, Hong Kong, and China. Sustaining a multifaceted link conceptualization, network
centrality, link defections, link strength, and link social pressure improved predictions of quit
propensity beyond that of job embeddedness.
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Since the turn of the century, organizational scientists increasingly explore why job
incumbents remain (Holtom, Mitchell, Lee, & Eberly, 2008; Hom, 2011). This emerging inquiry
closes a conspicuous gap in understanding organizational participation (March & Simon, 1958)
for endemic, perennial scholarship on turnover poorly explains the disparate psychological
processes and motives behind staying (Mitchell, Holtom, Lee, Sablynski, & Erez, 2001; Steel &
Lounsbury, 2009; Westaby, 2005). Redressing this imbalance, Mitchell et al. (2001) promulgated
“job embeddedness” to embody three distinct forces binding people to jobs: fit to job and
community; links (social ties within and outside workplaces); and sacrifice (corporate and
neighborhood amenities relinquished upon leaving). Their elaboration of varied non-economic,
off-the-job constraints deepens insight into staying, going beyond traditional loyalty bases (e.g.,
unemployment, exit costs) long emphasized by turnover and commitment models (Hom &
Griffeth, 1995; Klein, Molloy, & Brinsfield, 2012). Besides this, burgeoning generalizations of
Mitchell et al.’s (2001) theory are illuminating why people become embedded in occupations,
colleges, or host-countries during expatriation (Allen & Moffitt, 2004; Ng & Feldman, 2009;
Tharenou & Caulfield, 2010).
Apart from greater comprehension of the decision to participate (March & Simon, 1958),
Mitchell et al.’s (2001) model elucidates how embedding forces shape employees’ decision to
perform, disputing long-held views that these decisions are largely independent (T.W. Lee,
Mitchell, Sablynski, Burton, & Holtom, 2004). Indeed, this framework can help differentiate
among types of stayers, notably, those who are engaged at work versus those psychologically
detached (Burris, Detert, & Chiaburu, 2008; Hom, Mitchell, Lee, & Griffeth, in press).
Furthermore, embeddedness perspectives are identifying ways by which organizations can mute
or deflect the deleterious effects of “shocks”—jarring events prompting deliberations about
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leaving (Lee & Mitchell, 1994)—on organizational participation and contribution (Burton et al.,
2010; Mitchell & Lee, 2001). Such practical intelligence aids attrition management for shocks
initiate most quits and often arise from less controllable external causes, such as spousal
relocations and unsolicited job offers (T.W. Lee, Mitchell, Holtom, McDaniel, & Hill, 1999;
T.W. Lee, Mitchell, Wise, & Fireman, 1996; Weller, Holtom, Matiaske, & Mellewigt, 2009).
Given its theoretical and practical import, Mitchell et al.’s (2001) paradigm shift has thus
instigated numerous inquiries corroborating their formulation’s capacity for predicting turnover
(and other exit forms) beyond that of traditional antecedents in diverse fields, demographic
subpopulations, and cultures (Hom et al., 2009; Mallol, Holtom, & Lee, 2007; Ramesh &
Gelfand, 2010; Smith, Holtom, & Mitchell, 2011).
All the same, omitted or deficient representation of key relational embedding forces by
this preeminent viewpoint impedes progress clarifying why incumbents stay, perform well, or
resist shocks. Construed as the number of social ties, embeddedness theorists presume that links
promote job incumbency by supplying social capital—or expressive or instrumental resources
that “lie[s] in the structure and content of the actor’s social relations” (Adler & Kwon, 2002; p.
23) (Holtom, Mitchell, & Lee, 2006). Social capital theorists however maintain that strength of
network ties and structure (configuration of relationships or position within that configuration)
capture more or different network resources than totality of network ties (AKA network size or
degree centrality; Balkundi & Kilduff, 2006; Fang, Duffy, & Shaw, 2011; Siebert, Kraimer, &
Liden, 2001). Because strong bonds and central network positions can underpin staying, current
preoccupation with link size underestimates how social capital embeds employees (Feeley &
Barnett, 1997; Hom & Xiao, 2011; Mossholder, Settoon, Henagan, 2005).
Moreover, embeddedness theory understates how normative prescriptions from links
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shape participation decisions, though Mitchell and Lee (2001) claimed that the “sheer number of
links puts pressure on the individual to stay” (p. 217). Deeming prescriptions as proximal causes
of behavior (including leaving; Van Breukelen,Van der Vlist, & Steensma, 2004), prevailing
attitude-behavior theories however imply that merely knowing how many referents surround a
person offers few clues about what they prescribe or whether their prescriptions agree (Ajzen &
Fishbein, 2005; Bagozzi & Warshaw, 1990). As initially conceived, links thus imprecisely proxy
normative prescriptions, downplaying their effects. Yet leaving often hinges on social influence
according to inquiries into why collectivists exit (Maertz, Stevens, & Campion, 2003; Wasti,
2003), expatriates repatriate (Bhaskar-Shrinivas, Harrison, Shaffer, & Luk, 2005), and spouses
quit to trail relocating partners or assume family care duties (Shafer, 2011; Shauman, 2010).
Indeed, recent embeddedness studies found that family demands affect leaving independently of
job or host-country embeddedness (Ramesh & Gelfand, 2010; Tharenou & Caufield, 2010).
Until now, traditional embeddedness theory has also overlooked “link defections”—or
disappearing links—as links that bind can prompt leaving when they end (Ballinger, Lehman, &
Schoorman, 2010). While “turnover contagion” has long been known (Feeley & Barnett, 1997;
Krackhardt & Porter, 1986), Felps et al. (2009) recently determined that coworker job search or
exits can explain additional turnover variance beyond that of conventional embedding forces. Put
differently, embedded or stable colleague links reinforce staying. All the same, the exodus of
other key links—notably, relocating spouses and exiting supervisors—can also spur quits
according to turnover studies (Ballinger et al., 2010; T.W. Lee et al., 1996). Given nonexistent or
deficient portrayal of these link dimensions, embeddedness theory incompletely explicates why
incumbents stay, limiting its potential for enlightening the sundry aforementioned phenomena.
Toward Greater Understanding of Staying
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Heeding growing calls to enrich embeddedness thought with social network and other
relational antecedents (Holtom et al., 2008; Hom & Xiao, 2011; Mitchell & Lee, 2001), we
extend this prominent theory by formulating a multifaceted conceptualization of links that can
advance the emerging literature that quit decisions are not made by individuals alone but also
mirror the social environments in which they live (Bartunek, Huang, & Walsh, 2008). Going
beyond current views of links as network size (Holtom et al., 2008), we contend that network
structure (relationship pattern and network niche) and network tie strength (Balkundi &
Harrison, 2006; Fang et al., 2011; Siebert, Kraimer, & Liden, 2001) can bolster loyalty by
expanding instrumental or expressive resources or their accessibility (Bauer, Erdogan, Liden, &
Wayne, 2006; Mossholder et al., 2005; Nishii & Mayer, 2009). Moreover, our expanded
embeddedness outlook recognizes that stable or “embedded” links sustain staying. Building on
Felps et al.’s (2009) inquiry into co-worker embeddedness, we investigate how the exodus of
other links (and their attendant resources), such as leaders and life partners, can undermine
staying (Ballinger et al., 2010; T.W. Lee et al., 1996; Maertz & Campion, 2004).
From perspectives on behavioral decision-making (Ajzen & Fishbein, 2005; Westaby,
2005) and normative (or collective) regulation over leaving (Maertz & Griffeth, 2004; Shauman,
2010; Tharenou & Caulfield, 2010), our framework further recognizes how social demands or
obligations shape participation decisions. While Mitchell and Lee (2001) suggest that links can
transmit normative influences, this conceptualization neglects how links can issue disparate—if
not opposing—demands for staying. Because intercultural, family migration, and repatriation
studies report differential and conflicting demands from referent others (Eby & Russell, 2000;
Maertz et al., 2003; Maertz & Griffeth, 2004; Tharenou & Caulfield, 2010), we advance
participation prescriptions as embedding force distinct from (albeit not independent of) links.
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Besides direct influence, we postulate that social pressures translate the effects of these link
dimensions, extending Mitchell and Lee’s (2001) thesis that links invoke staying pressures. After
all, network contacts from whom one derives social resources expect repayment—if not ongoing
loyalty (Hom & Xiao, 2011), while departing contacts may advise one to exit (beyond giving job
leads or denigrating the job; Bartunek et al., 2008; Mowday, Porter, & Steers, 1982).
In sum, our expanded embeddedness formulation contributes to the scholarly literature in
several ways. From theories on social capital, normative regulation, and turnover contagion, we
identify additional dimensions of embedding links (besides their number) that reinforce staying
via social resources they deliver, their social pressures, and their stability (vs. defection; Felps et
al., 2009). Secondly, we broaden social network models of attrition that omit external network
contacts, given their exclusive focus on organizational networks (Feeley & Barnett, 1997;
Krackhardt & Porter, 1986; Mossholder et al., 2005). Further, we elucidate how link dimensions
affect staying by positing normative pressures as a prospective (partial) mediator of their effects
(Mitchell & Lee, 2001). All told, incorporating these long-neglected link features can more fully
delineate how relational forces embed the 21th century workforce increasingly deployed in team
or collectivist milieus (Hom & Xiao, 2011; Ramesh & Gelfand, 2010).
An Expanded Theory of Job Embeddedness
Based on embeddedness and motivational forces theories (Maertz & Campion, 2004;
Mitchell & Lee, 2001), our theory partitions links into on- or off-the-job links. Embeddedness
theory and work indicate that internal and external links contribute independently and
differentially toward embedding incumbents (Hom et al., 2009; T.W. Lee et al., 2004).
Motivational forces theorists nevertheless contend that internal and external links can work in
opposition (e.g., constituent vs. normative forces) (Maertz & Griffeth, 2004). Similarly, studies
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of collectivists and expatriates report that family members can counteract embedding effects of
on-the-job or host-country links (Ramesh & Gelfand, 2010; Tharenou & Caulfield, 2010).
Within each context, we further subdivide links based on the social capital they provide
and whether their defection (vs. staying) would impact leaving. Following social capital research
on career success (Siebert et al., 2001) and newcomer socialization (Morrison, 2001), we thus
distinguish between workplace advisors (e.g., superiors, veteran colleagues) and friends, who
furnish information versus emotional support, respectively (although some offer both resources).
Advisors or friends—if they left—may also induce incumbents to leave by depriving them of
expressive or instrumental resources (Ballinger et al., 2010; Felps et al., 2009; Shah, 2000). For
external links, we further differentiate family, community friends, and professional contacts.
Social capital theory and work on job attainment (Lin, 2001) and career development (Higgins,
2001) suggest that such contacts offer job leads and career advice about staying or leaving (Burt,
1997b; Higgins & Thomas, 2001). Family and friends often represent the most influential ingroups (especially in collectivist cultures) whose participation prescriptions are readily heeded
(Ramesh & Gelfand, 2010; Wasti, 2003), while spouses relocating domestically or abroad may
induce partners to quit and follow them (Mäkelä, Känsälä, & Suutari, 2011; Shaffer & Harrison,
2001; Shaumann, 2010).
Apart from this, we submit that normative pressures to participate or withdraw translate
(in part) link effects because incumbents face normative demands for reciprocation and ongoing
exchanges from network contacts with whom they share resources (Hom & Xiao, 2011;
Sparrowe & Liden, 2005). To illustrate, superiors engaging high leader-member exchanges may
obligate subordinates to reciprocate such generosity with greater fidelity to them, and by
extension, the firm (Sparrowe & Liden, 2005). By comparison, teammates and subordinates, who
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rely on incumbents’ work or their social assets may also impel them to stay (Hom & Xiao,
2011). Similarly, family members often have a major say in participation decisions when their
economic welfare, well-being, or careers depend on incumbents’ remaining in the current job or
community (Greenhaus & Powell, 2006; Mäkelä et al., 2011; Ramesh & Gelfand, 2010). In
what follows, we describe how these various link dimensions can influence staying.
----------------------------------------Insert Figure 1 about here
-----------------------------------------On-the-Job Link Dimensions
Unified social capital formulations universally highlight two prime sources of social
capital: relationship structure and resources (both breadth and depth) (Fang et al., 2011; Higgins
& Thomas, 2001; Siebert et al., 2001). Given their beneficial effects on job and career outcomes,
we believe network configuration and resources can also enhance organizational participation.
We reiterate Friedman and Holtom’s (2002) stance that “the more connected a person is
professionally and socially at work, the more likely it is that they will stay in their organizations”
(p. 417) because the person derives network resources from being so connected. We next discuss
how varied on-the-job social capital forms affect staying.
Network centrality. Social capital authors submit that network centrality—or being
centrally located in social networks (Feeley, 2000)—offers structural advantages, such as more
support, power, and resources (Balkundi & Harrison, 2006; Sparrow, Liden, Wayne, & Kraimer,
2001). Specifically, Feeley et al.’s (2008) network erosion theory asserts that central occupants
in friendship networks obtain greater support and coping resources buffering against stressors.
Given ample expressive resources, central network participants thus remain compared with those
who “‘fall off’ the edges of the social network” (Feeley & Barnett, 1997; p. 374). Moreover,
central incumbents receive information sooner than those on a network’s periphery (Mehra,
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Dixon, Brass, & Roberstson, 2006) or access more novel information when bridging
disconnected parties (Mehra, Kilduff, & Brass, 2001). As a result, they can perform more
effectively and take on valued boundary-spanning roles, earning more rewards and recognition
that entices them to stay (Hom & Griffeth, 1995). Finally, central actors form many network ties
may feel constrained by such ties from departing networks and the workplaces in which they
reside (Balkundi & Harrison, 2006). In support, network inquiries reported that network
centrality prolongs job incumbency (Feeley, 2000; Feeley et al., 2008; Mossholder et al., 2005).
Hypothesis 1: Centrality in internal networks is negatively related to quit propensity.
Affective link strength. We further propose emotional closeness to organizational
constituents as another link dimension as embeddedness theorists recently acknowledged that
“the quality of the ties [may] determine which ones will be more important in making a quitting
decision” (Holtom et al., 2008; p. 257). Social capital research suggests that strong ties can drive
loyalty as they deliver more expressive and instrumental resources. To illustrate, Morrison
(2002) found that deeper affective ties to coworkers and supervisors enhance newcomers’ social
integration (Friedman & Holtom, 2002), while Higgins and Thomas (2001) observed that close
ties to developmental mentors improve protégés’ job satisfaction. Greater social integration and
job satisfaction should thereby promote staying (Hom & Griffeth, 1995). More directly, Feeley et
al. (2008) reported that employees befriending many coworkers tend to stay, while Nishii and
Mayer (2009) documented that subordinates participating in favorable leader-member exchanges
with supervisors are less quit-prone. From this discussion, we posit:
Hypothesis 2: Strength of workplace links is inversely related to quit propensity.
Friendship network closure. Podolny and Baron (1997) theorize that friendship networks
instill a sense of identity and belonging when they are “smaller networks that display high
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closure and cohesiveness, not large networks full of structure holes” (p. 674). Specifically,
network closure yields more expressive resources by fortifying bonds and readily serving
emotional needs. In closed networks, members’ dyadic relationships to others are embedded
within third-party ties. Such “Simmelian” ties (Krackhardt, 1998) become strong and robust as
“repeated contact is more likely between people with…many mutual friends and acquaintances”
(Burt, 2001, p. 622), building trust (Burt, 2007). Unlike disconnected members, interconnected
members also can expeditiously support others when they need them as they can better notice
and meet one another’s need for comfort (Balkundi & Harrison, 2006). All told, network closure
expands the flow of expressive resources to members, in turn committing them to their cohesive
network community (Balkundi & Harrison, 2006) and the organization in which the network is
nested (Sluss & Ashforth, 2008). In support, Burt (2005) found that turnover falls among
bankers’ when their colleague ties are enmeshed in third-party ties, while Hom and Xiao (2011)
noted fewer voluntary quits among Chinese belonging to closed networks. Thus, we predict:
Hypothesis 3: Friendship network closure is negatively related to quit propensity.
Advisor network closure. Apart from friends’ expressive resources, we consider
instrumental resources from “advisors,” who give advice and information that can improve work
and career effectiveness (Burt, 1997b; Morrison, 2002; Podolny & Baron, 1997). In contrast to
friendship networks, advice networks generate more social capital when network members are
sparsely connected (“structural holes,” Burt, 1992, 2005). As Podolny and Baron (1997: 679) put
it, “the most valuable mentor tie would be to an alter who is not tied to ego’s other contacts.”
Participants in sparse networks can gain unique information, ensure their interests are shown in
the best light to others, and take up valued boundary spanning roles (Higgins & Thomas, 2001;
Siebert et al., 2001). Network scholarship attests to such structural benefits, revealing that
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structural holes in career or professional networks increase pay and promotions (Burt, 1997a;
Higgins & Thomas, 2001; Podolny & Baron, 1997). Such inducements may in turn increase
loyalty according to turnover theory (Hom & Griffeth, 1995), yielding the following:
Hypothesis 4: Advice network closure is positively related to quit propensity.
Colleague and advisor defections. Drawing from social capital and embeddedness
viewpoints (Holtom et al., 2006; Shaw, Duffy, Johnson, & Lockhart, 2005), we assert that
incumbents whose corporate friends or advisors leave may lose social capital embedded in those
relationships, weakening their resolve to stay. Exiting compatriots can result in lost friendships
and expressive resources that would otherwise give them reason to stay (Feeley & Barnett,
1997). Mowday, Porter, and Steers (1982) thus noted that “the loss of a close friend or colleague
may be particularly traumatic” (p. 148), while Shah (2000) observed that losing a friend
“eliminates a source of social support” (p. 102). Additionally, departing mentors or supervisors
may deprive employees of social capital that facilitates future career prospects (Ballinger et al.,
2010). Diminished career prospects can attenuate the current job’s expected utility relative to
alternatives (Mobley et al., 1979), or inflate referent cognitions about their availability elsewhere
(Aquino, Griffeth, Allen, & Hom, 1997), bolstering quit propensity. Because exiting work
friends, colleagues, and superiors can impel employees to quit (Ballinger et al., 2010; Feeley &
Barnett, 1997; Felps et al.; 2009; Kacmar, Andrews, Van Rooy, Steilberg, & Cerrone, 2006;
Kirshenbaum & Weisberg, 2002), we propose the following:
Hypothesis 5: Colleague and advisor defections positively relate to quit propensity.
Off-the-Job Link Dimensions
Closure in professional contact networks. Following social capital views on
occupational attainment and career progress (Granovetter, 1983; Higgins, 2001; Higgins &
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Kram, 2001), we believe that structural holes in incumbents’ networks of external professional
contacts (e.g., former boss or teacher, family; Bian, 2002; Burt, 1997b) can increase quit
propensity. Specifically, Granovetter (1983) submits that ties bridging different social circles
yield non-redundant information about job openings (especially in new industries; Brown &
Konrad, 2001), noting that weak rather than strong ties tend to be bridges (Borgatti & Halgin,
2011). Higgins and Kram (2001) similarly declare that ties spanning wide-ranging and
disconnected social systems bring about career and job changes by expanding information,
resources, and access to broader career possibilities. By bridging separated external contacts,
incumbents receive more varied and novel job leads, enabling and motivating them (when
learning about better options) to quit (Griffeth, Steel, Allen, & Bryan, 2005). In support, Higgins
(2001) observed that MBA graduates whose career advisors come from diverse relationship
contexts switch careers, while those whose advisors are disconnected more often switch jobs. On
the basis of the foregoing theoretical and empirical evidence, we put forth:
Hypothesis 6: Professional network closure is inversely related to quit propensity.
Spousal relocations. Corresponding to on-the-job link defections, our formulation
incorporates spousal relocations as a key off-the-job defection. Turnover, expatriation, and
family-migration researchers have oft-noted how spouses’ geographical move can prompt their
life partners to quit (T.H. Lee, Gerhart, Weller, & Trevor, 2008; T.W. Lee, Mitchell, Wise, &
Fireman, 1996; Mäkelä et al., 2011; Shaffer & Harrison, 2001; Shauman, 2010; Taylor, 2007).
Indeed, Lee and Mitchell’s (1994) unfolding model formally recognizes spousal relocation as a
(path 1) turnover-inducing shock. While noting how community links (e.g., spousal
employment) promote staying, customary embeddedness views understate how their
disappearance jeopardizes staying. To preserve their marriage, maintain (or raise) the family’s
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standard of living (if relocating spouses most contribute to the household income), or advance
their partners’ careers, spouses may quit (even incurring unemployment and career setbacks) and
follow partners moving domestically or abroad (Mäkelä et al., 2011; Shaffer & Harrison, 2001;
Shauman, 2010). With these insights in mind, we posit the hypothesis below:
Hypothesis 7: Spousal relocations positively relate to quit propensity.
Normative Pressures: Embedding Force and Mediator
Building on Mitchell and Lee’s (2001) insight that links can issue normative pressures,
we posit that these pressures can embed incumbents but recognize that they can vary in influence
and even conflict (Ajzen & Fishbein, 2005; Maertz & Griffeth, 2004). We however combine
workplace advisors and friends because their prescriptions are more alike (due to shared
organizational affiliation and overlap [given name generators; see below]). Compared with
family, community friends, and external professional contacts (Burt, 1997b; Siebert et al., 2001),
they more likely consult staying (assuming they also stay on the job; Felps et al., 2009; Maertz &
Campion, 2004). Based on the Ajzen-Fishbein (2005) and motivational forces theories (Maertz
& Griffeth, 2004), we thus submit the following:
Hypothesis H8a: Internal pressures to stay are inversely related to quit propensity.
Various perspectives on family embeddedness (Ramesh & Gelfand, 2010), spousal
support (Greenhaus & Powell, 2006; Mäkelä et al., 2011), and collectivist attrition (Hom &
Xiao, 2011; Maertz et al., 2003) underline the crucial role family and friends play in counseling
incumbents about the wisdom of staying or leaving for jobs elsewhere (advancing careers or
satisfying family needs or desires). Similarly, social capital models on career development and
job attainment maintain that professional contacts in relationship contexts outside organizations
can supply job leads and career advice, which can shape participation decisions (Higgins, 2001;
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Lin, 2001). In support, many studies revealed how spouses oft-time coax partners to resign when
the latter’s job creates family hardships (e.g., expatriate assignments; Tharenou & Caulfield,
2010), interferes with child-rearing (Shafer, 2011), or requires protracted family separations
(e.g., military duty; Drummet, Coleman, & Cable, 2003), while network research disclosed how
outside friends and career advisors influence participation decisions (Burt, 1997b; Higgins &
Thomas, 2001). Given those theories and findings, we thus posit (combining community friends
and professional contacts as they can represent the same people [see below]):
Hypothesis 8b: Family pressures to stay are inversely related to quit propensity.
Hypothesis 8c: External pressures to stay are inversely related to quit propensity.
Building upon Mitchell and Lee’s (2001) mediation thesis, we conceive how normative
pressures mediate effects of varied link dimensions (including number of links). Specifically, we
posit that affective strength of internal links boosts internal pressures to stay. If incumbents form
strong ties to colleagues or advisors, their contacts may obligate them to stay to maintain their
ongoing social exchange (or friendships) and retain access to their social resources (Hom &
Xiao, 2011). Moreover, occupants of central network niches may feel more internal pressure to
stay as they have more work friends or subordinates dependent on them (Balkundi & Harrison,
2006; Mitchell & Lee, 2001). Conversely, defecting colleagues or advisors may persuade
incumbents to leave to self-justify why they are leaving (Mowday et al., 1982) or recruit them
away to their new employer (“Pied Piper Effect”), thereby transplanting exchange relationships
elsewhere (Hom & Xiao, 2011). Accordingly, we posit:
Hypothesis 9a: Internal normative pressures mediate how internal affective strength
affect quit propensity.
Hypothesis 9b: Internal normative pressures mediate how network centrality affect quit
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propensity.
Hypothesis 9c: Internal normative pressures mediate how on-the-job link defections
affect quit propensity.
We also believe that colleague and advisor network closure augments internal pressures
to stay. Closed networks generate more collective resources as dense ties foster trust among
network members (Coleman, 1990; Podolny & Baron, 1997). Trusting members in turn more
freely contribute to the collective resource pool for they believe that they will be repaid (Oh,
Labianca, & Chung, 2006). To safeguard communal resources, mutually affiliated members
develop shared norms of loyalty to retain members’ resources and ensure that members repay
their debt to the collective (Lin, 2001). Through peer monitoring and sanctions, dense
communities can also better enforce such norms as mutually interacting members can send
uniform (and stronger) normative messages for staying (Friedkin, 2001) and collectively sanction
individuals from defecting (Buchan, Croson, & Dawes, 2002; Yamagishi, Cook, & Watabe,
1998). Thus, we predict:
Hypothesis 9d: Internal normative pressures mediate how network closure in friendship
and advisor networks affect quit propensity.
Finally, we propose that family pressure to stay mediate the influence of spousal
relocations. Given how offshore assignments, family migration, and turnover can impact the
family (e.g., spousal careers, family standard of living, children’s upbringing), studies of dualincome couples revealed how spouses (especially those earning higher pay, employed in
prestigious jobs, or working long hours) can influence their partners’ decisions to accept
expatriate assignments, relocate, or quit (Mano-Negrin & Kirschenbaum, 2000; Shafer, 2011;
Shaffer & Harrison, 2011). Given these findings and that spousal relocation is a well-
17
documented cause of turnover (T.W. Lee et al., 1996; Shauman, 2010), we thus propose:
Hypothesis 9e: Family pressures to stay mediate how spousal relocations affect quit
propensity.
The above theoretical and empirical evidence thus imply these mediation hypotheses,
though we argue that mediation by normative demands is partial as other link dimensions and job
embeddedness can have direct unmediated effects. To illustrate, normative pressures from links
would not fully mediate the impact of job embeddedness as employees stay because in addition
to the influence of links, employees may fit the job or would sacrifice valued job benefits if they
exit. Similarly, job incumbents may stay due to their strong affective ties to colleagues (Maertz
& Griffeth, 2004), though the latter may advise them to leave for “greener pastures” elsewhere.
STUDY OVERVIEW
We investigated our extended embeddedness model with two studies applying different
but complementary network methodologies. Study 1 collected ego-network data from employees
in multiple industries (financial services, manufacturing, etc.) and societies (US, China, Hong
Kong), while Study 2 collected “whole-network” data to assess network centrality from an
American restaurant chain. While respondents can reliably describe their direct contacts,
perceptual ego-network data poorly capture their centrality within networks and actual
interconnections between contacts to whom they are tied—especially those outside their
immediate circle of contacts (Mehra et al., 2001).
STUDY 1: EGONET TEST OF EXTENDED MODEL
Method
US sample and procedure. From a random sample (290) of a financial services firm’s
workforce, 136 financial representatives completed surveys with Network Genie (Hansen, Reese,
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Bryant, Bishop, Wyrick, & Dyreng, 2008), an online survey to collect network and survey data.
This 46.9% response rate is higher than the typical response rate (34%) to electronic surveys
(Shih & Fan, 2008). A vice-president initially issued an email request for participation, which
was voluntary and confidential. Three more reminders over several weeks generated 136
participants. During our study, the Great Recession and housing crisis greatly diminished quits
and relocations. Six months after the initial survey, only four participants quit. Following Hom et
al. (2009), we also resurveyed respondents about their lagged withdrawal cognitions for our
criterion, a year after the first survey. Eighty-one people participated, which represented a 66.4%
response rate as 122 of the initial participants still worked in the firm.
Respondents were Caucasian (97.0%); 78.6% completed bachelor’s degrees; 55.4% had
worked five years or less for the firm; 91.1% were married; 47.3% had young children living at
home; and 44.6% had lived in their community for over 20 years. Respondents and
nonrespondents were alike in age, tenure, sales performance, and gender.
Hong Kong sample and procedure. We recruited two samples: working part-time MBA
students and employees from a multinational manufacturing firm based in Hong Kong (hereafter
known as ENERGIZER). We delivered an egonet survey to MBA students during class sessions,
while circulating an internet survey to the ENERGIZER workforce. To earn gift certificates
(worth $100 HK), respondents completed two surveys, of which one was lagged to capture
withdrawal cognitions. Bilingual Hong Kong faculty translated surveys into Chinese and backtranslated them. The ENERGIZER sample filled out a survey presenting questions in both
English and Chinese, whereas the MBA sample received an English-language survey as they are
more proficient in English (as they are instructed in English and must prove proficiency to enroll
in college and MBA programs).
19
One hundred and sixty-three MBA students completed both surveys (lag time between
surveys was a week), while 147 ENERGIZER workforce completed both surveys (lag time
between surveys was four weeks; 242 filled out the first survey, while 168 filled out the second).
The HRM vice-president at ENERGIZER initially solicited participation via email from units
having historically higher turnover. We also circulated e-mail reminders for each successive
wave. MBA students provided cell phone numbers for survey matching, whereas the
ENERGIZER employees provided their names for this purpose.
Among MBA students, 68.1% were managers (11.9% of the total sample were senior
managers), while the rest had no supervisory responsibilities. Forty-four percent were women
and 60% were single. Their mean age was 32.08 years and they worked an average of 5.86 years
for their employer. They also belonged to firms whose workforce averaged 9,057 employees.
Forty percent worked in privately owned firms, 28.7% in foreign-owned firms, and 16% in stateowned firms. Among ENERGIZER participants, 29.4% were supervisors or managers. They
held primarily professional or administrative posts (e.g., accountants, human resources manager,
senior analyst programmer). Their mean age was 35.46 years, and mean tenure was 7.63 years.
Sixty-four percent were women and 49.7% were married.
Beijing sample and procedure. We recruited multiple classes of 231 employees enrolled
in part-time and executive MBA programs at a Beijing university. They completed two surveys
separated by a week (the second assessing withdrawal cognitions) and furnished cell phone
numbers to permit survey matching. Seventy-eight percent were married, and 42.9% had a child.
Seventy-five percent were men, and their mean age is 33 years. Eight-six percent were managers
(ranging from 34% entry-level to 10% senior-level managers) and averaged 6.57 years of firm
tenure. Forty-six percent worked in state-owned enterprises, 33% in foreign-owned firms, and
20
16.7% in privately owned businesses. Seventy-eight percent are official Beijing residents (or
hukou, which entitles them to state-sponsored benefits, such as children’s education and health
care; Canaves, 2010), while 84% own their homes. They averaged 13.86 years of Beijing
residency and averaged 4.80 years in their neighborhood.
Measurement of Theoretical Constructs
Job embeddedness. Following standard practice customizing job embeddedness items to
“incorporate[d] unique ‘enmeshing’ opportunities available to…employees within the host
organization and its local community” (T.W. Lee et al., 2004; p. 716), we adopted and adapted
items from previous scales (Hom et al., 2009; T.W. Lee et al., 2004; Mitchell et al., 2001). We
also added context-specific items based on pilot work. Thus, we surveyed employees from the
same firm or society about what makes them fit the job or community, what job and community
links they have, and what they would lose if they left the job or community. Hong Kong and
Chinese faculty also reviewed U.S. embeddedness items for relevancy and made changes to
improve meaningfulness. We standardized items before creating subscales and an overall
composite as we used different rating formats for questions about demographic traits (e.g.,
marital status, estimated number of dependent coworkers) and attitude-like constructs (e.g., fit)
(e.g., Likert ratings; T.W. Lee et al., 2004) (α = .80).
Colleague network closure. Responding to a name generator from Podolny and Baron
(1997), respondents listed at least three workplace colleagues (alters) who are their friends—
those with whom they are comfortable discussing sensitive matters. Respondents (egos) then
answered a series of questions in which they rated every pair of alter-alter relations with
dichotomous rating about whether or not a particular alter is close or not to another alter (cf.
Thomas, 2005). Finally, we used UCINET 6 (Borgatti, Everett, & Freeman, 2002) to compute
21
colleague network closure (Burt, 1992) from these ratings. High scores indicate closed networks
with few structural holes, while low scores indicate sparse networks full of holes.
Internal advisor network closure. Following Morrison (2002) and Ibarra (1992, 1993),
we had respondents identify organizational contacts (“advisors”) who furnish information or
advice that facilitate their effectiveness. As before, they rated ego-alter and alter-alter relations
with a dichotomous closeness scales and we used UCINET to compute advisor network closure.
Colleague/advisor affective strength. After listing names (cf. Thomas, 2005),
participants described their emotional closeness to colleagues and advisors with 3-point rating
scales that were then averaged (Higgins, 2001; Ibarra, 1995).
External professional contact network closure. Adapting Siebert et al.’s (2001) name
generator, respondents listed people outside of the organization who speak on their behalf, offer
job leads, or provide advice about career opportunities. They next reported how close these alters
are to one another and we used UCINET to generate professional contact network closure. Our
hypothesis however centers on structural holes (the inverse of network closure) in professional
contact networks (Burt, 1992, 2005) because social capital perspectives on career advancement,
developmental networks, and job attainment posit that non-redundant ties confer more external
social capital (Granovetter, 1983; Higgins & Kram, 2001; Siebert et al., 2001).
Anticipated colleague/advisor defection. Following Kirschenbaum and Weisberg
(2002), research participants described the likelihood that named workplace friends and advisors
will quit in the near future, using a 5-point likelihood scale. These ratings were averaged.
Anticipated spousal relocation. Respondents reported whether or not their spouse
planned to relocate in the near future.
Normative pressures for participation. Participants reported normative expectations to
22
stay from each contact listed in the three name generators noted above. To fully capture external
normative forces (Maertz & Griffeth, 2004), we also asked participants to name three or more
friends in the community and had them describe normative pressures from that source too. We
used Van Breukelen et al.’s (2004) question: “To what extent does your [contact] think you
should remain employed at your firm” (1 = not at all; 2 = don’t care; 3 = wants you to stay; 4 =
wants you to stay very much). Because they were highly correlated by locale (and some contacts
appear in more than one name generator; Burt, 1997b), we averaged ratings for the sets of
internal contacts (pooling perceptions of colleagues and advisors) and external contacts (pooling
perceptions of professional contacts and community friends) to reduce multicollinearity. A
separate question from Prestholdt, Lane, and Mathews’ (1987) captured family pressure to stay
as well.
Other turnover antecedents. Embeddedness research generally controls turnover
antecedents based on March and Simon’s (1958) notions of movement desirability and ease—
namely, job attitudes and perceived alternatives—to estimate incremental validity of job
embeddedness (Lee et al., 2004; Mitchell et al., 2001). We thus measured job satisfaction with
Price and Mueller’s (1986) index and affective company commitment with Meyer & Allen’s
(1997) items and combined them to capture overall job attitudes (Harrison, Newman, & Roth,
2006) (α = .85) . We measured perceived opportunity with Griffeth et al.’s (2005) subscale: α =
.71.
Quit propensity. We used two indices of quit propensity: intentions and behaviors.
Respondents rated withdrawal cognitions, using Hom and Kinicki (2001) and Bluedorn’s (1982)
questions (α = .92). Such cognitions are typically the best single turnover predictors (Griffeth et
al., 2000) and can foreshadow pent-up turnover that arises when an economy recovers (Allen,
23
Bryant, & Vardaman, 2010). To illustrate, a recent poll discloses that 60% of all workers plan to
quit once the job market recovers (Light, 2010), whereas another poll reveals that 36% of
employees hope to work for a different employer in the next 12 months (Berry, 2011). Moreover,
tracking withdrawal cognitions allows managers to proactively address prospective leavers’
reasons for leaving (Iverson & Roy, 1994). After all, it is more difficult to reverse the decisions
of those who have already quit. Further, this criterion merits scrutiny in its own right as people
planning to leave—or “psychologically detachment” (Burris et al., 2008)—can express more job
dissatisfaction (Doran, Stone, Brief, & George, 1991) and other dysfunctional responses
besides—or before—leaving (e.g., other withdrawal acts; Harrison et al., 2006). Indeed, polls
cited above suggest that the segment of the workforce involuntarily trapped in a job is growing
and can pose a motivational challenge for employers (Schiemann, 2009).
Due to the Great Recession (and housing crisis) in America during this study, only four
U.S. respondents voluntarily quit six months later. For the ENERGIZER sample in Hong Kong,
we collected turnover data from personnel records a year after the first survey. Twenty-four
voluntarily quit, whereas six involuntary left (a 14.7% quit rate). We also collected follow-up
data from 81 of the original 129 MBA students first surveyed in Summer classes. Six months
later, we resurveyed them a third time about their employment status when they returned to
attend classes. Nine quit since the first survey—an 11.1% quit rate.
Statistical Analysis
Corresponding to a meta-analysis, we pooled all samples together as measures were
similar—if not— identical. For Chinese participants, we used a 6-point agreement-disagreement
scale for some questions to reduce central tendency bias as they tend to avoid extremes due to
the Confucian “doctrine of the mean” value (Hui et al., 2004). Consequently, we standardized all
24
measures before combining subsamples.
We used LISREL (Jõreskog & Sõrbom, 2006) to test the structural model predicting
withdrawal cognitions, using maximum likelihood estimation (ML). Although 694 participated
in surveys, 616 participants provided complete data on withdrawal cognitions. Because predictor
data were missing for members of this latter subgroup, we used multiple imputation to fill in
missing data, which outperforms conventional methods for handling missing data (Schafter &
Graham, 2002). We note that LISREL analyses using listwise deletion of missing data (based on
an N of 551) yielded similar results. Because ML estimation yields biased estimates when
endogenous variables (i.e., turnover) are dichotomous (Kline, 2011), we analyzed polychoric
correlations when testing the model predicting turnover. For this LISREL analysis, we used
listwise deletion of missing data, yielding an N of 373. Our models allowed disturbance terms for
all normative prescriptions (endogenous variables) to correlate as their covariation may reflect
other causal influences besides shared causal antecedents represented in the model.
We gauged model fit with the comparative fit index (CFI), standardized root mean square
residual (SRMR), and Root-Mean-Square Error of Approximation (RMSEA; Hu & Bentler,
1998). CFIs exceeding .90 indicate good model fit, while SRMR less than .10 signal good fit
(Kline, 2011). RMSEA of less than .05 indicate close fit, while values between .05 and .08
indicate reasonable fit (Kline, 2011).
Figure 2 shows the operational model predicting withdrawal cognitions. We tested this
model by incorporating pathways specified by hypotheses but also included perceived job
opportunities, job attitudes, and job embeddedness to demonstrate how extra link dimensions
improve upon their predictive validity. As they are not the prime focus of the current research,
Figure 2 portrays these constructs and their pathways with dashed ellipses and arrows,
25
respectively. Following the March and Simon (1958) paradigm, standard turnover models
specify that job attitudes and perceived job opportunities should influence quit propensity (Hom
& Griffeth, 1995; Mobley et al., 1979; Price & Mueller, 1986). Embeddedness scholars also find
that job embeddedness explains additional variance in leaving beyond that explained by those
antecedents (Mitchell et al., 2001). Moreover, job embeddedness should increase normative
pressures to remain because incumbents have more workplace and community links (e.g.,
outside friends) that obligate them to stay (Mitchell & Lee, 2001). Embedded employees also
receive corporate amenities (e.g., medical coverage, housing for state employees in China; Hom
& Xiao, 2011) that benefit families and thus expand family pressures to stay (Ramesh &
Gelfand, 2010). Further, sparse external professional networks increase perceived job
opportunities by offering more new leads (Bian, 2002; Granovetter, 1983; Brown & Konrad,
2001), while outside friends and professional contacts counseling that one should quit (normative
pressure to quit; Burt, 1997b) may offer job leads to facilitate compliance with their advice.
Excepting Hypothesis 1 (which can only be tested with whole-network methodology,
see Study 2), we tested all other hypotheses with the structural model in Figure 2.Demonstrations
of significant direct or indirect effects (via mediators) on quit propensity by each link dimension
corroborates its unique explanatory contribution. We tested social capital mediation via
normative prescriptions by inspecting statistical significance of structural coefficients making up
a mediational pathway (Kenny, Kashy, & Bolger, 1998). MacKinnon, Lockwood, Hoffman,
West, and Sheets (2002) conclude that the joint significance test (JST) of each path in a
mediational chain yields an optimal trade-off between Type I and II errors.
----------------------------------------Insert Figures 2 and 3 about here
-----------------------------------------STUDY 1 RESULTS
26
SEM Test Predicting Withdrawal Cognitions
The structural model predicting lagged withdrawal cognitions closely fit data according
to all three fit measures: CFI = .95; SRMR = .047; and RMSEA = .073 (2 = 110.15, df = 25, p
< .05). Figure 2 reports standardized parameter estimates for this model and their statistical
significance. In line with traditional turnover research, perceived job opportunities (β = .10, p <
.05) are positively related to withdrawal cognitions, while job attitudes (β = -.31, p < .05) are
negatively related. Moreover, external pressures to stay were inversely related to perceived job
opportunities, though professional network closure was unrelated to those perceptions. Job
embeddedness (β = -.16, p < .05) also displayed unique effects on withdrawal cognitions, beyond
traditional antecedents and the new link dimensions.
We did not find internal affective strength, advisor network closure, or colleague network
closure to affect withdrawal cognitions (disputing Hypotheses 2, 3, and 4). In support of
Hypotheses 5 and 7, respectively, we observed that anticipated colleague defections and spousal
relocations were inversely related to withdrawal cognitions. Professional network closure
however did not impact those cognitions, disputing Hypothesis 6. Furthermore, family and
external pressures to stay were inversely related to withdrawal cognitions (affirming Hypotheses
8b and 8c), though not internal pressures to stay (rejecting Hypothesis 8a). Sustaining Mitchell
and Lee’s (2001) thesis, we found evidence that normative pressures (via family and external
contacts) mediate (in part) how job embeddedness improves retention propensity as job
embeddedness is positively related to all three types of normative pressures to stay. As internal
pressures to stay were unrelated to withdrawal cognitions, our findings reject Hypotheses 9a, 9c,
and 9d that it mediates effects of other link dimensions. By comparison, we corroborated family
pressure to stay as mediating the influence of spousal relocation (consonant with Hypothesis 9e).
27
LISREL Test Predicting Voluntary Turnover
The structural model predicting turnover also precisely explained observed covariances
according to all three fit measures: CFI = .95; SRMR = .05; and RMSEA = .076 (2 = 90.84, df =
25, p < .05). Figure 3 displays standardized parameter estimates. Job embeddedness is inversely
related to turnover (β = -.16, p < .05), while perceived job opportunities is positively related to
leaving (β = .12, p < .05). Attesting to Hypotheses 2 and 5 respectively, internal affective
strength decreases the likelihood of leaving (β = -.14, p < .05), while defecting on-the-job links
increases that likelihood (β = .21, p < .05). What is more, colleague network closure also lowered
the odds of leaving (β = -.10, p < .05), sustaining Hypothesis 3. Conversely, we found no support
for the effects of advisor and professional network closure (contesting Hypotheses 4 and 6,
respectively). Further, prospective spousal relocations were inversely related to leaving
(disputing Hypothesis 7).
Consistent with Hypotheses 8a to 8b, internal and family pressures to stay were inversely
related to quits. External pressure to stay had no impact on quits (repudiating Hypothesis 8c). In
conjunction with significant effects of job embeddedness on these three sources of normative
pressures, we uncovered empirical support for Mitchell and Lee’s (2001) thesis that normative
pressures would mediate how job embeddedness would affect turnover. Finally, we determined
that internal pressures to stay mediate affective strength of internal ties (supporting Hypothesis
9a) but not the other internal link dimensions (rejecting Hypotheses 9c and 9d). Family pressure
also did not translate the effects of spousal relocations, contravening Hypothesis 9e.
STUDY 1 DISCUSSION
Sustaining nascent research expanding basic job embeddedness theory (Felps et al., 2009;
Ramesh & Gelfand, 2010), we found that other dimensions of links besides their number can
28
account for unique variance in withdrawal cognitions or behaviors. Excepting advisor and
professional network closure, these added link dimensions extended the validity of
embeddedness theory for explaining withdrawal criteria. In particular, we showed that affective
strength of internal ties was negatively related to turnover. This finding supports Holtom and
associates’ (2008) recognition that totality of links may inadequately capture their quality. This
finding also extends prevailing turnover research that attachments to coworkers and superiors
reinforce staying (Griffeth et al., 2000) by illustrating that these effects can occur independently
of job embeddedness or total number of links.
In line with past network research (Burt, 2005; Podolny & Baron, 1997), we observed
that closed friendship networks in the workplace reduce turnover. This finding extends Hom and
Xiao’s (2001) research that closed networks comprising external and internal contacts who
supply varied social resources (beyond expressive resources) can induce Chinese managers to
stay. While structural holes in various kinds of social networks expedite career progress
(especially in the West; Siebert et al., 2001), this result corroborates Podolny and Baron’s (1997)
view that certain types of network closure are beneficial. They found that closed “buy-in”
networks (among contacts having fate control over ego) engender higher job satisfaction and
upward mobility, while we established that closed friendship networks can sustain job loyalty.
We also showed that anticipated defections by colleagues and/or advisors increase
withdrawal cognitions and turnover. This result extends previous research on turnover contagion
(Felps et al., 2009; Krackhardt & Porter, 1986) by considering close colleagues, not simply
members of one’s work unit. Departing colleagues with whom one is not close may prompt
leaving because their job searches or quits communicate saliency and viability of alternatives
(Felps et al., 2009). However, the loss of friendships may represent a different mechanism for
29
driving leaving. Moreover, this observation goes beyond Ballinger et al.’s (2010) finding that
exiting immediate superiors can induce leaving among followers with whom they have positive
leader-member exchanges because we assessed a broader array of advisors. Losing members of
one’s advice network—not necessarily one’s immediate superior—can also propel leaving.
Further, we observed that anticipated spousal relocation can boost the likelihood of quits
even if individuals are otherwise embedded. This observation also goes beyond past work on
turnover contagion by demonstrating that off-the-job defections can motivate leaving (Felps et
al., 2009). Turnover scholars have long recognized that spousal relocations are prime reasons for
leaving from exit interview data (Abelson, 1987; Griffeth & Hom, 2001) and recently deemed
them as critical events prompting mental deliberations about leaving (“shocks”; Holtom,
Mitchell, Lee, & Inderrieden, 2005; TW Lee et al., 1996). Excepting Kammeyer-Meuller et al.
(2005) who measure such critical events prior to actual quits, few turnover researchers tried to
capture the prospects of spousal relocations in advance of quits. By comparison, research on
dual-career couples has forecasted whether spouses quit based on partners’ occupational (e.g.,
pay, job prestige) and personal traits (e.g., education; Mano-Negrin & Kirschenbaum, 2000;
Shafer, 2011; Shauman, 2010).
We also found that normative pressures can translate the effects of job embeddedness
onto withdrawal criteria, verifying Mitchell and Lee’s (2001) longstanding mediation thesis.
Nonetheless, we go beyond their thesis by demonstrating that normative expectations can affect
withdrawal outcomes independently of job embeddedness, suggesting that links—currently
construed as number of social ties—do not fully capture social influence. Rather, our study
underscores the benefits of integrating theory and work on normative regulation of turnover
(Maertz & Campion, 2004; Prestholdt et al., 1987; Wasti, 2003) with embeddedness theory to
30
more fully reflect how social prescriptions affect quits as well as how they vary and even conflict
across links (Ajzen, 1991; Ajzen & Fishbein, 1980). To illustrate, we documented that off-thejob links influence withdrawal decisions more than do on-the-job links (cf. TW Lee et al., 2004).
Contrary to prediction, we found no effects for network closure among advisors or
professional contacts on withdrawal criteria. Over the years, Burt’s (1997a) network closure
index has widely predicted career success (Podolny & Baron, 1997; Xiao & Tsui, 2007). Our
operationalization however focused on one name generator at a time (whereas prior
methodologies used multiple ones simultaneously to identify a host of contacts offering diverse
resources) and interrelationships among contacts from one subsystem (whereas earlier work also
assessed ties between organizational and extra-organizational contacts; Hom & Xiao, 2011).
Such differences in operationalization may have accounted for our weaker findings for network
closure (or structural holes), though they raise questions about why cross-system ties would
facilitate incumbents’ career advancement within a firm (Burt, 1997a; Podolny & Baron, 1997).
Study 1 unearthed evidence that social capital, normative influence, and enduring links
are related to withdrawal criteria over and above job embeddedness, job attitudes, and perceived
job opportunities. Even so, advisor and professional network closure were not related to
withdrawal. The low turnover rates may partially account for some of these non-significant
findings by attenuating predictor-turnover relationships. Future research on network closure
during more favorable economic conditions or in higher turnover contexts would thus be
valuable. Also, we focused on ego networks which, although vital to understanding an
individual’s perceived social network, do not provide the same information or resources as whole
networks (and ability to examine network centrality). In Study 2, we extend our study of social
network processes with whole-network methodology.
31
STUDY 2: WHOLE-NETWORK TEST IN RESTAURANTS
To assess network centrality, we collected whole-network data from six restaurants
belonging to a national U.S. chain. To more fully capture social capital in networks, we focus on
both informational and friendship networks because an individual’s centrality may not be
consistent across networks. For example, an individual centrally located in information
networks—who lies in the “mainstream of information flow in the network” (Feeley, 2000; p.
265)—would receive instrumental resources. The same individual may be peripherally situated
in friendship networks and thus deprived of expressive resources. Therefore, measuring
friendship centrality would neglect the social capital that this individual gains from information
centrality. In support, various scholars find that friendship and advice centrality are modestly or
moderately correlated (Ibarra, 1995; Feeley et al., 2008). To fully capture the social capital
influences on staying, we must consider incumbents’ centrality in both networks, as some
network researchers have done (Feeley, 2000; Feeley, 2008).
We also computed different network centrality measures as they capture different ways
by which actors become central in networks (Feeley, 2000) and are imperfectly correlated
(Valente, Coronges, Lakon, & Costenbader, 2008). Given a particular type of network (e.g.,
advice vs. friendship), we chose centrality measures that best capture how resources become
available from central locations and were not redundant with common links proxies (that
resemble out-degree centrality indices). Specifically, we examined two measures for friendship
networks that best assess expressive resources: in-degree centrality and eigenvector centrality.
Reflecting the number of people nominating an individual as friend, in-degree centrality assesses
how well-liked that individual is (Mossholder et al., 2005). A high in-degree centrality thus
captures the willingness of others to provide social support, which proxies amount and
32
availability of expressive resources (Mehra et al., 2006), and thus sustains staying (Mossholder et
al., 2008). Eigenvector centrality, on the other hand, reflects connections to popular network
contacts. Such centrality confers social capital because one can indirectly benefit from popular
contacts’ vast friendship networks and their inherent resources that become available through the
friendship chain. Mehra et al. (2006) thus found that group leaders high in eigenvector centrality
in friendship networks became more effective leaders and developed favorable reputations.
For the task-advice network, we examined betweenness and closeness centrality.
Betweenness refers to the “frequency with which a position falls between pairs of other positions
in a network” (Feeley, 2000; p. 265). Such “go-betweens” can access more novel information
and can control information flow between separated parties (Mehra et al., 2001). As a result, this
niche accords occupants more influence (increasing work effectiveness; Sparrowe & Liden,
2005) and the ability to facilitate resource flow and knowledge-sharing across the workplace
(Mehra et al., 2001). Higher performance and boundary spanning activities in turn would boost
their pay, promotion, and recognition, inducing them to stay (Hom & Griffeth, 1995).
By contrast, closeness “refers to the extent that one is close to all others in the network” and
requires fewer links to communicate to all other network members (Feeley, 2000; p. 267).
Situated close to others, “actors high on closeness measures are able to efficiently transmit
information and have independence in the sense that they do not need to seek information from
other more peripheral actors” (Valente et al., 2008; p. 18). Such structural advantages can help
employees perform their job (by lessening role ambiguity), while allowing them to communicate
more widely and quickly (extending their influence). Greater role clarity and influence in turn
enhance performance which accrues more organizational inducements for staying (Hom &
Griffeth, 1995). To avoid redundancy with links measures, we measured in-closeness, which is
33
based on others’ choice of an actor as an information source. Feeley (2000) found that
betweenness and closeness centrality indices both predict turnover and are correlated at .61.
We tested an abbreviated version (see Figure 4) of our model for several reasons. For
one, the restaurant workforce predominantly comprised students and unskilled Mexican
immigrants. For the former, food preparation, cooking, or serving customers represent temporary
or part-time jobs they take to finance their schooling rather than pathways to long-lived careers.
For the latter, they rarely have advancement opportunities within restaurants (or corporate
headquarters) given their limited English proficiency and education. Given restaurant work (and
distance between headquarters and restaurant locale), we omitted advisor status as hourly
employees would rarely (if ever) interact with higher corporate officials, being limited to
immediate store managers and supervisors. We also did not examine ties to external professional
contacts (and thus network closure and normative pressures) as student workers primarily leave
to return to school rather than assume jobs elsewhere. Finally, we tested an abbreviated model
because whole network surveys are cognitively demanding and time consuming, especially for
respondents who are not highly literate in English or Spanish. Given their greater relevancy for
this workforce, Study 2 thus assesses spousal relocations and various forms of network
centrality, focusing on Hypotheses 1, 7, 8a, and 8b. Like Study 1, we tested a model in which
link antecedents have direct effects on withdrawal criteria as well as indirect effects through
normative prescriptions, while “controlling” for job embeddedness (whose effects are partially
mediated by normative pressures), job attitudes, and perceived job availability.
----------------------------------------Insert Figures 4 and 5 about here
-----------------------------------------Sample and Procedure
34
Of 230 employees, 224 participated (response rates ranged from 78% to 95% across
restaurants). Forty-seven percent were White, while 46% were Hispanics (or Mexicans). Sixtytwo percent completed some high school, while another 26.8% completed some college. Average
firm tenure was 2.01 years, mean age was 26.8 years, and 51.3% were single. We also created a
Spanish-language version of the survey. Two Mexican nationals at the university translated the
survey into Spanish, which was then checked by two other university staff fluent in written
Spanish. We surveyed employees during break times and offered them $10 gift certificates.
Measures
Job embeddedness. Given respondents’ limited reading proficiency, we used items from
Crossley, Bennett, Jex, & Burnfield’s, (2007) index of global job embeddedness and Mitchell et
al.’s (2001) community embeddedness items (α = .83, nine items).
Network centrality. We provided a name roster to respondents who checked off names of
friends (friendship network) and those to whom they go for “help or advice about work-related
matters” (advice network), adapting Ibarra’s (1995) questions. Using UCINET, we computed
centrality indices, reporting normed indices to ensure comparability across restaurant networks
varying in size and density (Hanneman & Riddle, 2005; Mehra et al., 2006).
Normative pressure. Using Van Breukelen et al.’s (2004) question, respondents
rated the extent to which their family thinks they should remain. The above name roster also had
respondents check off names of organizational contacts who think they should stay, modeled
after Burt’s (1997b) name generator whereby respondents named contacts with whom they
would discuss leaving. We counted the number of checked names and divided this sum by the
number employed (as workforce size varied across facilities) to compute an index of internal
pressure to stay.
35
Defecting links. Respondents described the chances their spouse or partner will relocate
to a distant community in the near future.
Job attitudes. We used Cammann, Fichman, Jenkins, & Klesh’s (1983) 3-item
satisfaction index, and 3 items from Meyer-Allen (1997) affective commitment index (α = .55).
Perceived job opportunities. We used 3 items from Griffeth et al.’s (2005) index (α =
.64).
Quit propensity. We used Hom and Kinicki’s (2001) questions about thoughts of
quitting, search intentions, and quit intentions (α = .86). Personnel records revealed that 39
participants voluntarily quit 6 months later, while six were fired.
Statistical Analyses
As before, we used LISREL to evaluate models predicting withdrawal cognitions and
turnover (correcting for turnover dichotomy). Because respondents were nested within
restaurants, we replicated LISREL estimates of direct predictor effects with Hierarchical Linear
Modeling (HLM, Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2004). Though unable to test
a causal model in its entirety, HLM can adjust for the biasing effects of nonindependent
observations. We thus estimated a series of unconditional random-coefficients regression models
that include predictors in a level-1 equation predicting each endogenous variable but no level-2
predictors. Simulation work however shows that decreasing group-level sample sizes (especially
less than 30 groups; Kreft & De Leeuw, 1998) weakens statistical power for assessing fixed
effects (Scherbaum & Ferreter, 2009). Maas and Hox’s (2005) simulation research finds that
regression coefficients can be accurately estimated with as few as ten groups, though standard
errors are biased. To offset low statistical power in part, we used restricted maximum likelihood
and interpreted 1-tail significance tests (Maas & Hox, 2005; Scherbaum & Ferreter, 2009).
36
STUDY 2 RESULTS
LISREL Test of Model Predictions of Withdrawal Cognitions
The structural model predicting withdrawal cognitions closely fit the data according to all
three fit measures: CFI = 1.00; SRMR = .015; and RMSEA = .000 (2 = 3.97, df = 9, p > .05), as
Figure 4 reports this model’s standardized parameter estimates and their statistical significance.
In line with expectations, job attitudes (β = -.30, p < .05) and job embeddedness (β = -.23, p <
.05) are negatively related to withdrawal cognitions. Countering Hypothesis 1, only betweenness
advice centrality predicted withdrawal cognitions (β = -.11, p < .10). Moreover, spousal
relocation was unrelated to withdrawal cognitions, contrary to Hypothesis 7. That internal
normative pressure was positively (rather than negatively) related to withdrawal cognitions
disputes both Hypotheses 8a and 9b. Further, family pressures lessened withdrawal cognitions
(upholding Hypothesis 8b) and mediated effects of job embeddedness (affirming Hypothesis 9e).
SEM Test of Model Predictions of Turnover
The model predicting turnover attained good fit: CFI = 1.00; SRMR = .013; and RMSEA
= .000 (2 = 3.47, df = 9, p >.05). Figure 5 reports the standardized parameter estimates. For the
most part, we found support for Hypothesis 1. Specifically, three of the four network centrality
measures—namely, in-degree friendship, eigenvector friendship, and betweenness centralities—
predicted turnover. In accord with Hypothesis 7, spousal relocation increases turnover (β = .19,
p < .10). Contrary to Hypotheses 8a and 8b, normative pressures did not predict turnover and
thus did not mediate the effects of job embeddedness or other link dimensions (dispute
Hypotheses 9b and 9e).
HLM tests in Table 1 also found that family pressures to stay and betweenness advice
centrality decreased withdrawal cognitions. Moreover, these findings indicate that spousal
37
relocation increased turnover but that in-degree friendship centrality decreased leaving.
--------------------------------------------------Insert Table 1 about here
---------------------------------------------------STUDY 2 DISCUSSION
Study 2 provided additional support for theoretical expansion of job embeddedness
theory. Network centrality and expected spousal relocation explained unique variance in
withdrawal criteria beyond that accounted for by job embeddedness (and job attitudes and
perceived alternatives). Importantly, these whole-network findings complimented earlier egonet
findings. Extending past examinations of constituent forces (which stress spousal influence;
Maertz & Griffeth, 2004; Prestholdt et al., 1987), this test—like Ramesh and Gelfand’s (2010)—
found that parental pressures to stay or leave can strongly dictate continued employment among
student workers. Further, we generalized prior work on job embeddedness by demonstrating their
validity for part-time or temporary workers.
Study 2 demonstrated the utility of whole-network methodology for studying
embeddedness, revealing that network centrality can explain additional turnover variance beyond
job embeddedness. The original theory underscores the perceived totality of direct connections,
not necessarily their “reality.” Yet we found that in-degree and eigenvector centrality in
friendship networks as well as incloseness advice centrality were negatively related to turnover,
though conventional wisdom holds that the perceived rather than actual network structure most
influence withdrawal (Krackhardt & Porter, 1985). Like Mossholder et al. (2005), we discerned
that incumbents who are popular among their workmates are prone to stay. Even those who have
few friends but are nonetheless popular with others can reap expressive resources. Our
eigenvector centrality results also expand network models of attrition (Feeley et al., 2008;
38
Mossholder et al., 2005) by showing that being connected to popular colleagues (even if ego
lacks many friends) is embedding.
In addition to refining job embeddedness theory, Study 2 extends the theory of network
erosion (Feeley, 2000; Feeley et al., 2008) by demonstrating that betweenness centrality in
advice networks was related to lower withdrawal cognitions and behavior. Such findings are
consonant with Burt’s (1992) structural hole theory, as incumbents bridging disconnected parties
receive better and more varied information and thereby can assume more valued bridging
functions as well acquiring more power as a gatekeeper of information flow (Balkundi &
Harrison, 2006; Lin, 2005). Such incumbents may remain as they would be reluctant to give up
their network niche and the social capital it brings (Holtom et al., 2006).
Affirming Mitchell and Lee (2001), this study also showed that job embeddedness was
positively related to workplace and family pressures to stay, though only family pressures to stay
translated its impact on withdrawal cognitions. Interestingly, both friendship centrality indices
were inversely related to internal pressures to stay. Quite likely, organizational contacts
pressuring incumbents to stay are not necessarily their close friends, who can still retain their ties
to incumbents even when they leave (Krackhardt & Porter, 1985). As the classic McDonald’s
network study attests (Kilduff & Krackhardt, 2008), most fast-food restaurant workers will quit –
many to return to school full-time. If incumbents’ friends are eventual turnover cases, they may
not necessarily be pressuring incumbents to stay—or stay until they too leave. In contrast, other
contacts to whom they are connected, such as supervisors or long-term (older) employees, may
express desires for them to stay. Alternatively, our measure of the number of contacts who think
one should stay rather than intensity of this internal pressure may explain why internal pressures
were not inversely related to withdrawal criteria.
39
GENERAL DISCUSSION
Our investigation closes a conspicuous gap in the proliferating scholarship on job
embeddedness (Holtom et al., 2008; Hom, 2010) by formulating an expanded multifaceted
conceptualization of how embedding links sustain staying. Drawing from comprehensive
accounts about how social networks advantage and influence people, we examine how crucial
features of network structures and ties that have long been shown to promote assimilation
(Morrison, 2002), career progress (Higgins & Thomas, 2001; Siebert et al., 2001), and job
attainment (Lin, 2001) can affect job incumbency. Following rising scholarship on how the
complex social environment impacts quit decisions (Bartunek et al., 2008; Hom & Xiao, 2011),
we sought to enrich job embeddedness theory’s original notion of links as the sheer number of
ties individuals possess. Specifically, we elucidated additional ways links may embed people by
delivering social capital, link connectivity, social influence, and link endurance (vs. defection).
Four key findings in particular warrant conceptual expansion of job embeddedness
theory. One, we found in Study 1 that employee expectations that on-the-job links may disappear
shaped withdrawal decisions. Current conceptualizations and studies of job embeddedness
implicitly treat embeddedness as a somewhat static attribute. Our findings suggest that
employees are aware that their position in a network can shift over time as other network
participants exit, and that these employees may proactively respond to anticipated shifts that
could reduce their social capital by contemplating their own departure from the organization.
This process may also broaden our understanding of how turnover and why turnover contagion
occurs (Felps et al. 2009). Future research that examines how individuals form these
expectations and the conditions under which these expectations do and do not influence
withdrawal would be valuable.
40
Two, we found in Study 2 that network centrality is related to withdrawal over and above
job embeddedness. Current conceptualizations of embeddedness focus on perceived volume of
direct ties. Yet network studies find that social ties are not always reciprocated (especially
instrumental relationships; Ibarra, 1993) and that in- and out-degree centrality measures are
modestly related. For example, Feeley et al. (2008) report that in 70% of the reported links, alters
did not report the same level of relationship as the ego, and that in-degree and out-degree
centrality measures correlate between .32 and .39. In short, our findings suggest that we can gain
deeper insight into the social capital embedded in links by considering objective links, different
types of links (e.g., friendship vs. advice), and different structures by which individuals are
linked to others (e.g., whether they are close to others, whether they are linked to popular others,
or whether they link disconnected others).
Three, we found across studies that normative pressures, both internal and external to the
organization, can affect staying independently of job embeddedness. The idea that normative
influences are important is not new; turnover research based on theories of reasoned and planned
behavior (Ajzen, 1991; Ajzen & Fishbein, 1980) emphasize normative expectations as important
drivers of turnover (Maertz & Campion, 2004; Prestholdt et al., 1987). However, our account
provides a foundation for explaining how and why social influence in the form of participation
prescriptions arise. Job embeddedness and other link dimensions, such as affective tie strength
and link defections can shape normative pressures to stay, integrating job embeddedness and
social network constructs with the Ajzen-Fishbein theories. Further, findings regarding
normative pressures confirm that the conceptualization of links should go beyond mere number
to incorporate the normative expectations embedded in those links as well. We also affirm
Mitchell and Lee’s (2001) supposition that normative pressures (partly) mediate how links
41
impact staying, though revealing that normative demands from outside links carry much more
weight in employees’ participation decisions, which accords with theories of normative
regulation of turnover (Maertz & Griffeth, 2004; Ramesh & Gelfand, 2010; Wasti, 2003).
Four, we also found across studies that anticipated spousal relocation is related to
withdrawal, again expanding our understanding of social influences on turnover. Although the
idea that individuals might quit jobs to trail a moving spouse is familiar (cf. T.W. Lee et al.,
1996), our findings suggest that the anticipation that a spouse could relocate may encourage
employees to proactively consider withdrawal, and that this anticipation is important over and
above job embeddedness, job attitudes, perceived job opportunities, and network characteristics.
This also supports Felps et al.’s (2009) recent suggestion that job embeddedness theory may
need to expand the conceptualization of the role of links to incorporate expectations about future
duration of links. It also suggests that, whereas turnover research often treats kinship
responsibilities as control variables, future research that delves into the nuances of how and
when family obligations influence turnover would be valuable.
In addition to these recommended expansions to job embeddedness theory, our efforts
begin to offer some answers to Mitchell et al.’s (2001) long-standing question about when job
embeddedness drives leaving. In line with their hypothesis, we found that incumbents “wellconnected” to external friends and professional contacts (from whom they seek career
opportunities or job leads) heed their opinions about the wisdom of staying and leaving more
than they do that of their colleagues at work (cf. Burt, 1997b; Higgins & Thomas, 2001). Social
influence in the form of internal and external normative pressures, and the prospect of potential
social capital losses in the form of anticipated network changes over time, may lead even highly
embedded employees to consider leaving.
42
Practical Implications
Our findings suggest several practical ways to embed personnel. Rather than merely
increasing the number of links (Mitchell et al., 2001), we suggest that employers might also
strengthen links. According to Gallup studies (Harter, Schmidt, & Keyes, 2003), having a few
close relationships at work—not necessarily many—is essential for retention and job
engagement. Moreover, managers should attend to employees’ structural positions in social
networks and boost centrality of those situated on the network periphery. To foster friendship
centrality, they may design work in teams, physically arrange the workspace to promote
interaction, and offer opportunities for socializing. To promote advice centrality, managers might
identify cohesive self-contained cliques within workplaces and assign members the role of
bridging disconnected cliques.
We also noted that normative pressures from family and friends were most related to
withdrawal propensity. Given their significance in shaping withdrawal decisions, employers
could build network ties between office and community links (e.g.,“bring your child to work”)
and enhance their external reputations (inducing family pride; Ramesh & Gelfand, 2010). They
can also strengthen family prescriptions to stay by subsidizing home purchases or helping
employees’ spouses find new (or better) employment (Holtom et al., 2006). Moreover,
sponsoring social and recreational activities and involving employees’ families and friends may
breed interfamily ties too (Hom & Xiao, 2011). Ramesh and Gelfand’s (2010) research endorses
such practices as they found that family embeddedness (comprising family views about firms,
family-coworker ties, work benefits for family) fortifies loyalty in collectivist and individualist
societies.
Along these lines, organizations might supply referral incentives to recruit employees’
43
community friends and relatives and capitalize on multiplex ties to bind incumbents more tightly
to jobs. Similarly, recognizing the influence of external links, employers might recruit employees
locally (Holtom et al., 2006) who are embedded in the community and likely face normative
pressures to stay from circles of friends and relatives. Because embedded colleagues stabilize
employment (Felps et al., 2009), so might embedded community members who become
workmates (Hom & Xiao, 2011. Further, companies can structure benefits packages and use
other methods to lessen work interference with family activities and responsibilities (and
schooling for Study 2 restaurant workers) (e.g., reducing exorbitant or inconvenient work hours),
which often underpin family pressures to quit (Hom & Kinicki, 2001).
Our demonstration of the embedding effects of network centrality suggests a new tool for
corporate retention toolkits. In addition to traditional methods for forecasting and understanding
turnover, such as attitudes surveys and exit interviews, invaluable intelligence can be gleaned
from systematically mapping employees’ social networks. Analyzing such networks may enable
managers to identify potentially problematic areas in the organization, such as sparsely
connected work groups, key individuals at high risk of leaving due to peripheral network
positions, or key personnel who might trigger a chain reaction if they exit. Along with recent
observations about how leadership succession (Ballinger et al., 2010) and attrition (Kacmar et
al., 2006) can foment follower turnover, our egonet studies disclosed that exiting advisors
(including superiors) can initiate thoughts of leaving. Firms might foster greater embeddedness
among superiors and popular mentors to sustain retention among the rank and file (Ballinger et
al., 2010; Kacmar et al., 2006).
It is worth noting that turnover is not always bad; Allen and colleagues (2010) identify a
number of circumstances under which turnover can be functional for organizations. Analysis of
44
internal social networks may help organizations more carefully target their retention efforts. For
example, turnover of a moderately performing manager on the periphery of a social network may
be more functional than turnover of a moderately performing manager who is central to that
network (Shaw et al., 2005), thus warranting a different approach from the organization.
Turnover is desirable for employees as well for overly embedded incumbents fail to develop new
social capital over time (Ng & Feldman, 2010).
Study Limitations and Future Research Directions
Our studies suffered from methodological limitations. No doubt, the Great Recession and
housing crisis greatly reduced domestic job and geographic movements. In particular, quits
among American respondents fell dramatically during our study, attenuating predictive strength
of our embeddedness model. More than this, our studies may have underestimated external social
capital effects by overlooking affective strength to professional contacts and network range of
those connections (Higgins, 2001; Lin, 2001). Further, we sampled contacts narrowly—situated
in one subsystem and furnishing one kind of social capital (e.g., task or career advice), which
may have attenuated the effects of structural holes (or network closure) on staying. Previous
work sampled contacts from various contexts and who provide varied resources, such as political
and social support (Burt, 1997a; Podolny & Baron, 1997; Siebert et al., 2001).
Our extended view of job embeddedness would benefit from future research exploring
moderators and mediators. One potential moderator is cultural differences, such as collectivism,
which may magnify the effects of relational antecedents (such as links and network closure; Hom
& Xiao, 2011; Ramesh & Gelfand, 2010) and may partly explain some of our null findings. To
consider this possibility, we carried out a multiple-group path analysis comparing path models
between U.S. and Chinese samples in Study 1. For the model predicting withdrawal cognitions,
45
affective strength of internal links more strongly predicted job attitudes and normative pressures
to stay (having positive relationships) among the Chinese than Americans. For the model
predicting turnover, anticipated colleague defections more highly predicted (positively) turnover
of the Chinese than Americans. Following Ramesh and Gelfand (2010), future research would
sample multiple nationalities from diverse cultures (matching samples by industry or occupation)
as well as explicitly measuring cultural values. What is more, unemployment rates likely
moderate how community links, sacrifice, and network closure impact relocations. Conceivably,
strong local job markets weaken community embeddedness effects for people can switch jobs
without giving up community benefits or links (Allen, 2006). Further inquiry might include
shocks in our expanded embeddedness theory and determine whether our model can buffer
against negative workplace shocks (another push force to leave; Burton, Holtom, Sablynski,
Mitchell, & Lee, 2010) and unsolicited job offers (a pull force to leave; Mitchell & Lee, 2001).
Finally, we recommend more research on whether employees truly lose social capital when their
powerful or influential advisors quit (Ballinger et al., 2010) and whether they can maintain this
capital by following them to a new workplace.
In conclusion, we provided a broad extension of the preeminent perspective on staying,
assessed key sources of resources embedded in network relationships and structure that have
been identified by many unified models of social capital. Our preliminary test revealed that
integrating social capital and job embeddedness literatures can improve understanding and
prediction of why people stay. We established that the totality of links do not fully capture how
social capital can drive decisions to stay or leave. Rather, embeddedness thinkers might consider
the strength and durability of links, whether links span disconnected social circles, and normative
prescriptions from links.
46
REFERENCES
Abelson, M.A. 1987. Examination of avoidable and unavoidable turnover. Journal of Applied
Psychology, 72: 382-386.
Abrams, D., Ando, K., Hinkle, S. (1998). Psychological attachment to the group: Cross-cultural
differences in organizational identification and subjective norms as predictors of workers’
turnover intentions. Personality and Social Psychology Bulletin, 24, 1027-1039.
Adler, P.S., & Kwon, S-W. 2002. Social capital: Prospects for a new concept. Academy of
Management Review, 27: 17-40.
Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50: 179-211.
Ajzen, I., & Fishbein, M. 1980. Understanding attitudes and predicting social behavior.
Englewood Cliffs, NJ: Prentice-Hall.
Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. Albarracín, D.
Johnson, B., Zanna, M. (Eds), The handbook of attitudes, (pp. 173-221). Mahwah, NJ,
US: Lawrence Erlbaum Associates Publishers.
Allen, D. G. 2006. Do organizational socialization tactics influence newcomer embeddedness
and turnover. Journal of Management, 32: 237-256.
Allen, D., Bryant, P. & Vardaman, J. 2010. Retaining talent: Replacing misconceptions with
evidence-based strategies. Academy of Management Perspective, 24: 48-64.
Allen, D., & Moffitt, K. 2004. Applying organizational retention theory and research methods to
student retention. Report to University Retention Committee, Management Department,
Fogelman College of Business and Economics, University of Memphis, Memphis, TN.
47
Aquino, K., Griffeth, R.W., Allen, D.G., & Hom, P.W. 1997. Integrating justice constructs into
the turnover process: A test of a referent cognitions model. Academy of Management
Journal, 40: 2308-1227.
Bagozzi, R., & Warshaw, P. (1990). Trying to consume. Journal of Consumer Research, 17,
127-140.
Balkundi, P. & Harrison, D. 2006. Ties, leaders, and time in times: Strong inference about
network structure’s effects on team viability and performance. Academy of Management
Journal, 49: 49-68.
Balkundi, P., & Kilduff, M. (2006). The ties that lead: A social network approach to leadership.
Leadership Quarterly, 17, 419-439.
Ballinger, G., Lehman, D., & Schoorman, D. 2010. Leader-member exchange and turnover
before and after succession events. Organizational Behavior and Human Decision
Processes, 113: 25-36.
Bartunek, J., Huang, Z., & Walsh, I. 2008. The development of a process model of collective
turnover. Human Relations, 6: 5-38.
Bauer, T., Erdogan, B., Liden, R., & Wayne, S. (2006). A longitudinal study of the moderating
role of extraversion: leader-member exchange, performance, and turnover during new
executive development. Journal of Applied Psychology, 91, 298-310.
Bentler, P.M., & Wu, E.J.C. 2005. EQS 6.1 structural equation modeling software for
Windows, Multivariate software inc, Encino, CA.
Berry, J. 2011, Jun 19. Ruthless economy is eroding job loyalty. Arizona Republic, pp. A.1-A.1.
Retrieved from
http://login.ezproxy1.lib.asu.edu/login?url=http://search.proquest.com/?url=http://search.proques
t.com/docview/872383283?accountid=4485.
48
Bhaskar-Shrinivas, P., Harrison, D., Shaffer, M., & Luk, D. (2005). Input-based and time-based
models of international adjustment: Meta-analytic evidence and theoretical evidence.
Academy of Management Journal, 48, 257-281.
Bian, Y. 2002. Chinese social stratification and social mobility. Annual Review of Sociology,
28: 91-116.
Bluedorn, A. C. 1982. A unified model of turnover from organizations. Human Relations, 35:
135-153.
Bollen, K.A. 1989. Structural equations with latent variables. New York: Wiley.
Borgatti, S., Everett, M., & freeman, L. 2002. UCINET for Windows: Software for social
network analysis. Cambridge, MA: Analytic Technologies.
Borgatti, S.P., & Halgin, D.S. (2011). On network theory. Organization Science, 22, 11681181..
Borgatti, S.P., Mehra, A., Brass, D.J., & Labianca, G. 2009. Network analysis in the social
sciences. Science, 323: 892-895.
Brown, D.W., & Konrad, A.M. 2001. Granovetter was right: The importance of weak ties to a
contemporary job search. Group & Organization Management, 26: 434-462.
Buchan, N., Croson, R., & Dawes, R. (2002). Swift neighbors and persistent strangers: A crosscultural investigation of trust and reciprocity in social exchange. American Journal of
Sociology, 108, 168-206.
Burris, E. R., Detert, J. R., & Chiaburu, D. S. 2008. Quitting before leaving: The mediating
effects of psychological attachment and detachment on voice. Journal of Applied
Psychology, 93: 912-922.
Burt, R. 1992. Structural holes. Cambridge, MA: Harvard University Press.
49
Burt, R. (1997a). The contingent value of social capital. Administrative Science Quarterly, 42,
339-365.
Burt, R. (1997b). A note on social capital and network content. Social Networks, 19, 355-373.
Burt, R. 2001. Attachment, decay, and social network. Journal of Organizational Behavior, 22:
619-643.
Burt, R. 2005. Brokerage and closure. Oxford: Oxford University Press.
Burt, R. 2007. Secondhand brokerage: Evidence on the importance of local structure for
managers, bankers, and analysts. Academy of Management Journal, 50: 119-148.
Burton, J., Holtom, B., Sablynski, C., Mitchell, T., & Lee, T. 2010. The buffering effects of job
embeddedness on negative shocks. Journal of Vocational Behavior, 76: 42-51.
Cammann, C., Fichman, M., Jenkins, G.D., Jr., & Klesh, J.R. 1983. Assessing the attitudes and
perceptions of organizational members. In S.E. Seashore, E.E. Lawler, III, P.H. Mirvis, & C.
Cammann (Eds.), Assessing organizational change: A guide to methods, measures, and
practices: 71-138. New York: Wiley.
Cauchon, D., & Overberg, P. (2009). Housing crisis has cut mobility in U.S. this year. USA
Today, December 24, 5A.
Canaves, K. 2010. Beijing is urged to ease migration. Wall Street Journal, March 2: A. 12.
Coleman, J.S. 1990. Foundations of social theory. Cambridge, MA: Harvard University Press.
Crossley, C., Bennett, R., Jex, S., & Burnfield, J. 2007. Development of a global measure of job
embeddedness and integration into a traditional model of voluntary turnover. Journal of
Applied Psychology, 92: 1031-1042.
Dalton, D.R., Todor, W.D., & Krackhardt, D.M. 1982. Turnover overstated: The functional
taxonomy. Academy of Management Review, 7: 117-123.
50
Doran, L.I., Stone, V.K., Brief, A.P., & George, J.M. 1991. Behavioral intentions as predictors of
job attitudes: The role of economic choice. Journal of Applied Psychology, 76: 40-45.
Drummet, A., Coleman, M., & Cable, S. 2003. Military families under stress: Implications for
family life education. Family Relations, 52: 279-287.
Fang, R., Duffy, M.K., & Shaw, J.D. 2011. The organizational socialization process: Review and
development of a social capital model. Journal of Management, 37: 127-152.
Feeley, T. 2000. Testing a communication network model of employee turnover based on
centrality. Journal of Applied Communication Research, 28: 262-277.
Feeley, T., & Barnett, G. 1997. Predicting employee turnover from communication networks.
Human Communication Research, 23: 370-387.
Feeley, T., Hwang, J., & Barnett, G. 2008. Predicting employee turnover from friendship
networks. Journal of Applied Communication Research, 36: 56-73.
Felps, W., Mitchell, T., Hekman, D., Lee, T., Harman, W., & Holtom, B. 2009. Turnover
contagion: How coworkers’ job embeddedness and coworkers' job search behaviors
influence quitting. Academy of Management Journal, 52: 545-561.
Friedkin, N.E. 1993. Structural bases of interpersonal influence in groups: A longitudinal case
study. American Sociological Review, 58: 861-872.
Friedkin, N.E. 2001. Norm formation in social influence networks. Social Networks, 23: 167189.
Friedman, R.A., & Holtom, B. 2002. The effects of network groups on minority employee
turnover intentions. Human Resource Management, 41: 405-421.
Granovetter, M. 1983. The strength of weak ties: A network theory revisited. Sociological
Theory, 1: 201-233.
51
Greenhaus, J.H., & Powell, G.N. 2006. When work and family are allies: A theory of workfamily enrichment. Academy of Management Journal, 31: 72-92.
Griffeth, R., & Hom, P. 2001. Retaining valued employees. Thousand Oaks, CA: Sage.
Griffeth, R., Hom, P., & Gaertner, S. 2000. A meta-analysis of antecedents and correlates of
employee turnover. Journal of Management, 26: 463-488.
Griffeth, R., Steel, R., Allen, D., & Bryan, N. 2005. The development of a multidimensional
measure of job market cognitions: The Employment Opportunity Index (EOI). Journal of
Applied Psychology, 90: 335-349.
Haggerty, J. (2012). U.S. loses high-tech jobs as R&D shifts toward Asia. Wall Street Journal,
January 18, p. B1, B2.
Hanneman, R., & Riddle, M. 2005. Introduction to social network methods. Riverside, CA:
University of California, Riverside.
Hansen, W., Reese, E., Bryant, K., Bishop, D., Wyrick, C., & Dyreng, D. 2008. Network genie:
https://secure.networkgenie.com. Tanglewood Research: Greensboro, NC.
Harrison, D., Newman, D., & Roth, P. 2006. How important are job attitudes?: Meta-analytic
comparisons of integrative behavioral outcomes and time sequences. Academy of
Management Journal, 49: 305-326.
Harter, J.K., Schmidt, F.L., & Keyes, C.L.M. 2003. Well-being in the workplace and its
relationship to business outcomes. In C. L.M. Keyes & J. Haidt’s (eds.), Flourishing: The
positive person and the good life (Chapter 9, pp. 205-224). Washington, DC: American
Psychological Association.
Higgins, M. 2001. Changing careers: The effects of social context. Journal of Organizational
Behavior, 22: 595-618.
52
Higgins, M. & Kram, K. 2001. Reconceptualizing mentoring at work: A developmental network
perspective. Academy of Management Review, 26: 264-28.
Higgins, M. & Thomas, D. 2001. Constellations and careers: Toward understanding the effects
of multiple developmental relationships. Journal of Organizational Behavior, 22: 223-247.
Holtom, B., Mitchell, T., & Lee, T. 2006. Increasing human and social capital by applying job
embeddedness theory. Organizational Dynamics, 35: 316-331.
Holtom, B., Mitchell, T., Lee, T., & Eberly, M. 2008. Turnover & retention research: A glance at
the past, a closer review of the present, and a venture into the future. The Academy of
Management Annals, 2: 231-274.
Holtom, B.., Mitchell, T., Lee, T., & Inderrieden, E. (2005). Shocks as causes of turnover: What
they are and how organizations can manage them. Human Resource Management Journal,
44, 337-352.
Hom, P. 2010. Organizational exit. In S. Zedeck, H. Aguinis, W. Cascio, M. Gelfand, K. Leung,
S. Parker, & J. Zhou (Eds.), Handbook of Industrial/Organizational Psychology, Vol. 2.
Washington, DC: American Psychological Association.
Hom, P. & Griffeth, R. 1995. Employee turnover. Cincinnati, OH: South-Western College
Publishing.
Hom, P. & Kinicki, A. 2001. Toward a greater understanding of how dissatisfaction drives
employee turnover. Academy of Management Journal, 44: 975-987.
Hom, P., Mitchell, T., Lee, T., & Griffeth, R. (in press). Reviewing employee turnover: Focusing
on proximal withdrawal states and an expanded criterion. Psychological Bulletin.
Hom, P. W., Tsui, A. S., Wu, J. B., Lee, T. W., Zhang, A. Y., Fu, P. P., & Li, L. 2009.
Explaining employment relationships with social exchange and job embeddedness. Journal
53
of Applied Psychology, 94: 277-297.
Hom, P. & Xiao, Z. 2011. Embedding social capital: How guanxi ties reinforce Chinese
employees’ retention. Organizational Behavior and Human Decision Processes, doi:
10.1016/j.obhdp.2011.06.001.
Hu, L., & Bentler, P.M. 1998. Fit indices on covariance structure modeling: Sensitivity to
underparameterized model misspecifications. Psychological Methods, 3: 424-453.
Hui, C., Lee, C., & Rousseau, D. 2004. Psychological contract and organizational citizenship
behavior in China: Investigating generalizability and instrumentality. Journal of Applied
Psychology, 89: 311-321.
Hunter, J.E., & Schmidt, F.L. 2004. Methods of meta-analysis: Correcting error and bias in
research findings. Thousand Oaks, CA: Sage.
Ibarra, H. 1992. Homophily and differential returns: Sex differences in network structure and
access to an advertising firm. Administrative Science Quarterly, 37: 422-447.
Ibarra, H. 1993. Network centrality, power, and innovation involvement: Determinants of
technical and administrative roles. Academy of Management Journal, 36, 471-501.
Ibarra, H. 1995. Race, opportunity, and diversity of social circles in managerial networks.
Academy of Management Journal, 38: 673-703.
Ibarra, H., & Andrews, S.B. 1993. Power, social influence, and sense making: Effects of network
centrality and proximity on employee perceptions. Administrative Science Quarterly, 38:
277-303.
Insch, G.S., McIntyre, N., & Napier, N.K. 2008. The expatriate glass ceiling: The second layer of
glass. Journal of Business Ethics, 83: 19-28.Iverson, R., & Roy, P. 1994. A causal model
of behavioral commitment: Evidence from a study of Australian blue-collar employees.
54
Journal of Management, 20: 15-41.
Jõreskog, K. & Sõrbom, D. (2006). LISREL 8.80. Lincolnwood, IL: Scientific Software.
Kacmar, K. M., Andrews, M. C., Van Rooy, D. L., Steilberg, R. C., & Cerrone, S. 2006. Sure
everyone can be replaced … but at what cost? Turnover as a predictor of unit level
performance. Academy of Management Journal, 49: 133-144.
Kammeyer-Mueller, J.D., Wanberg, C.R., Glomb, T.M., & Ahlburg, D. 2005. The role of
temporal shifts in turnover processes: It’s about time. Journal of Applied Psychology, 90:
644-658.
Kanter, R.M. 1977. Work and family in the United States: A critical review and agenda for
research and policy. New York: Russell Sage Foundation.
Kenny, D.A., Kashy, D.A., & Bolger, N. 1998. Data analysis in social psychology. In D.T.
Gilbert, S.T. Fiske, & G. LindzeyC (eds.), Handbook of social psychology (4th ed.). New
York: Oxford University Press.
Kilduff, M. & Krackhardt, D. 2008. Interpersonal networks in organizations. New York:
Cambridge University Press.
Kirschenbaum, A., & Weisberg, J. 2002. Employee’s turnover intentions and job destination
choices. Journal of Organizational Behavior, 23: 109-125.
Klein, H., Molloy, J., & Brinsfield, C. (2012). Reconceptualizing workplace commitment to
redress a stretched construct: Revisiting assumptions and removing confounds. Academy of
Management Journal, in press. Retrieved from
http://journals.aomonline.org/InPress/pdf/done/994.pdf
Kline, R.B. 2011. Principles and practices of structural equation modeling (3rd Ed.). New
York: Guilford Press.
55
Krackhardt, D. 1998. Simmelian ties: Super strong and sticky. In R. Kramer & M. Neale (eds.),
Power and influence in organizations: 21-38. Thousand Oaks, CA: Sage.
Krackhardt, D. & Porter, L. 1985. When friends leave: A structural analysis of the relationship
between turnover and stayers’ attitudes. Administrative Science Quarterly, 30: 242-261.
Krackhardt, D. & Porter, L. 1986. The snowball effect: Turnover embedded in communication
networks. Journal of Applied Psychology, 71: 50-55.
Kreft, I.G.G., & De Leeuw, J. 1998. Introducing multilevel modeling. Newbury Park, CA: Sage.
Lee, T.H., Gerhart, B., Weller, I., & Trevor, C. 2008. Understanding voluntary turnover: Pathspecific job satisfaction effects and the importance of unsolicited job offers. Academy of
Management Journal, 51: 651-671.
Lee, T.W. & Mitchell, T. 1994. An alternative approach: The unfolding model of voluntary
model of voluntary employee turnover. Academy of Management Review, 19: 51-89.
Lee, T.W., Mitchell, T., Sablynski, C., Burton, J., & Holtom, B. 2004. The effects of job
embeddedness on organizational citizenship, job performance, volitional absences, and
voluntary turnover. Academy of Management Journal, 47: 711-722.
Lee, T.W., Mitchell, T., Wise, L., & Fireman, S. 1996. An unfolding model of voluntary
employee turnover. Academy of Management Journal, 39: 5-36.
Light, J. 2010. More workers start to quit. Wall Street Journal, May 26.
Lin, N. 2001. Social capital: A theory of social structure and action. Cambridge, UK:
Cambridge University Press.
Maas, C.J., & Hox, J.J. 2005. Sufficient sample sizes for multilevel modeling. Methodology:
European Journal of Research Methods for the Behavioral and Social Sciences, 3: 86-92.
MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G., & Sheets, V. 2002. A
56
comparison of methods to test mediation and other intervening variable effects.
Psychological Methods, 7: 83-104.
Maertz, C., & Campion, M. 2004. Profiles in quitting : Integrating process and content theory of
turnover. Academy of Management Journal, 47: 566-582.
Maertz, C., & Griffeth, R. 2004. Eight motivational forces and voluntary turnover : A theoretical
synthesis with implications for research. Journal of Management, 30: 667-683.
Mäkelä, L., Känsälä, M., & Suutari, V. (2011). The roles of expatriates’ spouses among dual
career couples. Cross-Cultural Management: An International Journal, 18, 185-197
Mallol, C., Holtom, B., & Lee, T. 2007. Job embeddedness in a culturally diverse environment.
Journal of Business and Psychology, 22: 35-44.
Mano-Negrin, R., & Kirschenbaum, A. (2000). Spousal interdependence in turnover decisions:
The case of Israel’s medical sector employees. Journal of Family and Economic Issues, 21,
97-121.
March, J., & Simon, H. 1958. Organizations. NY : Wiley.
Marsden, P.V. 1993. The reliability of network density and composition measures. Social
Networks, 15: 399-421.
McPherson, J., Popielarz, P., & Drobnic, S. 1992. Social networks and organizational dynamics.
American Sociological Review, 57: 153-170.
Mehra, A., Dixon, A., Brass, D., & Robertson, B. 2006. The social network ties of group
leaders : Implications for group performance and leader reputation. Organization Science,
17: 64-79.
Mehra, A., Kilduff, M., & Brass, D. 2001. The social networks of high and low self-monitors :
Implications for workplace performance. Administrative Science Quarterly, 46 : 121-146.
57
Meyer, J.P., & Allen, N.J. 1997. Commitment in the workplace: Theory, research, and
application. Thousand Oaks, CA: Sage.
Mitchell, M., Vogel, R., Bennett, R., & Crossley, C. (2011, August). Effects of job alternatives
on employees’ felt frustration, turnover intentions, and behaviors. Paper presented at the
2011 Academy of Management Conference, San Antonio, Texas.
Mitchell, T., Holtom, B., Lee, T., Sablynski, C., & Erez, M. 2001. Why people stay: Using job
embeddedness to predict voluntary turnover. Academy of Management Journal, 44: 11021121.
Mitchell, T. & Lee, T. 2001. The unfolding model of voluntary turnover and job embeddedness:
Foundations for a comprehensive theory of attachment. Research in Organizational
Behavior, 23: 189-246.
Mobley, W.H., Griffeth, R.W., Hand, H.H., & Meglino, B.M. 1979. Review and conceptual
analysis of the employee turnover process. Psychological Bulletin, 86: 493-522.
Morrison, El. 2002. Newcomers’ relationships: The role of social network ties during
socialization. Academy of Management Journal, 45: 1149-1160.
Mossholder, K., Settoon, R., & Henagan, S. 2005. A relational perspective on turnover:
Examining structural, attitudinal, and behavioral predictors. Academy of Management
Journal, 48: 607-618.
Mowday, R.T., Porter, L.W., & Steers, R.M. 1982. Employee-organization linkages: The
psychology of commitment, absenteeism, and turnover. New York: Academic Press.
Ng, T. W. H., & Feldman, D. C. 2009. Occupational embeddedness and job performance. Journal
of Organizational Behavior, 30: 863-891.
Nishii, L., & Mayer, D. 2009. Do inclusive leaders help to reduce turnover in diverse groups?
58
The moderating role of leader-member exchange in the diversity to turnover relationship.
Journal of Applied Psychology, 94: 1412-1426.
Oh, H., Labianca, G., & Chung, M.-H. (2006). A multilevel model of group social capital.
Academy of Management Review, 31, 569-582.
Podolny, J., & Baron, J. 1997. Resources and relationships: Social networks and mobility in the
workplace. American Sociological Review, 62: 673-693.
Prestholdt, P., Lane, I., & Mathews, R. 1987. Nurse turnover as reasoned action:
Development of a process model. Journal of Applied Psychology, 72: 221-227.
Price, J.L., & Mueller, C.W. 1981. Professional turnover: The case of nurses. New York: SP
Medical & Scientific Books.
Price, J.L., & Mueller, C.W. 1986. Absenteeism and turnover of hospital employees.
Greenwich, CT: JAI Press.
Ramesh, A., & Gelfand, M. 2010. Will they stay or will they go? The role of job embeddedness
in predicting turnover in individualistic and collectivistic cultures. Journal of Applied
Psychology, in press.
Raudenbush, S., Bryk, A., Cheong, Y., Congdon, R., & du Toit, M. 2004. HLM 6:
Hierarchical linear and nonlinear modeling. Lincoln Wood, IL: Scientific Software
International, Inc.
Schafer, J., & Graham, J. (2002). Missing data: Our view of the state of the art. Psychological
Bulletin, 7: 147-177.
Scherbaum, C.A., & Ferreter, J.M. 2009. Estimating statistical power and required sample
sizes for organizational research using multilevel modeling. Organizational Research
Methods, 12: 347-367.
59
Schiemann, W.A. 2009. Reinventing talent management: How to maximize performance in
the new marketplace. Hoboken, NJ: Wiley.
Schneer, J.A., & Reitman, F. 2002. Managerial life without a wife: Family structure and
managerial career success. Journal of Business Ethics, 37: 25-38.
Shafer, E.F. 2011. Wives’ relative wages, husbands’ paid work hours, and wives’ labor-force
exit. Journal of Marriage and Family, 73: 250-263.
Shaffer, M., & Harrison, D. (2001). Forgotten partners of international assignments:
Development and test of a model of spouse adjustment. Journal of Applied Psychology,
86, 238-254.
Shah, P. 2000. Network destruction: The structural implications of downsizing. Academy of
Management Journal, 43: 101-112.
Shauman, K.A. 2010. Gender asymmetry in family migration: Occupational inequality or
interspousal comparative advantage. Journal of Marriage and Family, 72:375-392.
Shaw, J.D., Duffy, M.K., Johnson, J.L., & Lockhart, D.E. 2005. Turnover, social capital loss,
and performance. Academy of Management Journal, 48: 594-606.
Shih, T. & Fan, X. 2008. Comparing response rates from web and mail surveys: A meta-analysis.
Field Methods, 20: 249-271.
Siebert, S., Kraimer, M., & Liden, R. 2001. A social capital theory of career success. Academy
of Management Journal, 44: 219-237.
Sluss, D., & Ashforth, B.E. 2008. How relational and organizational identification converge:
Processes and conditions. Organization Science, 19: 807-823.
Smith, D.R., Holtom, B.C., & Mitchell, T.R. 2011. Enhancing precision in the prediction of
voluntary turnover and retirement. Journal of Vocational Behavior, 79: 290-302.
60
Sparrowe, R.T., & Liden, R.C. 2005. Two routes to influence: Integrating leader-member
exchange and network perspectives. Administrative Science Quarterly, 50: 505-535.
Sparrowe, R.T., Liden, R.C., Wayne, S.J., & Kraimer, M.L. 2001. Social networks and the
performance of individuals and groups. Academy of Management Journal, 44: 316-325.
Steel, R.P., & Lounsbury, J.W. 2009. Turnover process models: Review and synthesis of a
conceptual literature. Human Resource Management Review, 19: 271-282.
Taylor, M. 2007. Tied migration and subsequent employment: Evidence from couples in Britain.
Oxford Bulletin of Economics and Statistics, 69: 795-818.
Tharenou, P., & Caulfield, N. 2010. Will I stay or will I go? Explaining repatriation by selfinitiated expatriates. Academy of Management Journal, 53, 1009-1028.
Thomas, A. 2005. Social networks and career paths of urban teachers: Effects of career
decision-related communication networks on teacher retention. Unpublished doctoral
dissertation, University of California, Los Angeles.
Tiano, S. 1994. Patriarchy on the line: Labor gender and ideology in the Mexican maquila
industry. Philadelphia: Temple University Press.
Valente, T., Coronges, K., Lakon, C., & Costenbader, E. 2008. How correlated are network
centrality measures? Connections, 28: 16-26.
Van Breukelen, W., Van der Vlist, R., & Steensma, H. 2004. Voluntary employee turnover:
combining variables from the ‘traditional’ turnover literature with the theory of planned
behavior. Journal of Organizational Behavior, 25: 893-914.
Wang, P., Lawler, J., Walumba, F., & Shi, K. (2004). Work-family conflict and job withdrawal
intentions: The moderating effect of cultural differences. International Journal of Stress
Management, 11, 392-412.
61
Wasti, S. 2003. Organizational commitment, turnover intentions and the influence of cultural
values. Journal of Occupational and Organizational Psychology, 76: 303-321.
Wellman, B., & Wortley, S. 1990. Different strokes for different folks: Community ties and
social support. American Journal of Sociology, 96: 558-588.
Xiao, Z. & Tsui, A. 2007. When brokers do not work: Social capital in high-commitment
organizations. Administrative Science Quarterly, 52: 1-31.
Yamagishi, T., Cook, K., & Watabe, M. 1998. Uncertainty, trust, and commitment formation in
the United States and Japan. American Journal of Sociology, 104: 165-194.
62
Table 1: HLM Robustness Tests
Different Dependent Variables for Different Regression Models
Predictors
Job Attitudes
Perceived Job Opportunities
Job Embeddedness
Family Pressure to Stay
Workplace Pressures to Stay
Anticipated Spousal
Relocation
In-Degree Friendship
Centrality
Eigenvector Friendship
Centrality
Betweenness Advice
Centrality
Incloseness Advice
Centrality
Family Pressure
to Stay
B
0.55**
Workplace
Pressures to Stay
B
Withdrawal
Cognitions
B
Turnover
B
-0.33**
0.07
-0.35**
-0.14**
0.28**
-0.01
0.06
-0.01
0.02
-0.03
0.08
0.05**
-0.06**
-0.03
-0.07**
-0.14**
-0.01
-0.03
0.04**
-0.10**
-0.03
0.04
0.02
0.02
0.08**
-0.03
Note. We used hierarchical generalized linear modeling in HLM to predict turnover with a Bernoulli distribution and logit link.
*p < .10, one-tailed test.
**p <.05,one-tailed test.
63
Figure 1: Extended Theory of Job Embeddedness
64
Figure 2: Study 1 Extended Model of Job Embeddedness Predicting Withdrawal Cognitions
65
Figure 3: Study 1 Extended Model of Job Embeddedness Predicting Turnover
Colleague/Advisor
Affective Strength
Colleague
Network
Closure
Advisor
Network
Closure
-.10**
Job
Embeddedness
-.14**
.14**
.25**
-.01
-.16*
Job
Attitudes
.33**
.05
.26**
-.03
-.06
Anticipated
Colleague/Advisor
Defections
Internal
Pressure
to Stay
.01
.21**
-.09*
Voluntary
Turnover
-.07**
Anticipated
Spousal
Relocation
.07
Professional
Network
Closure
Family
Pressure
to Stay
.03
-.21**
.12**
.04
Perceived Job
Opportunities
-.04
External
Pressure
to Stay
-.11**
66
Figure 4: Study 2 Extended Model of Job Embeddedness Predicting Withdrawal Cognitions
In-Degree
Friendship
Centrality
Eigenvector
Friendship
Centrality
-.04
Job
Attitudes
-.01
-.29**
-.29**
Betweenness
Advice
Centrality
Workplace
Pressure to
Stay
.16**
-.11*
-.30**
.12*
.34**
Incloseness
Advice
Centrality
Withdrawal
Cognitions
.02
.14**
-.19**
-.23**
Job
Embeddedness
*p < .10. **p < .05.
.33**
Family
Pressure
to Stay
-.03
Anticipated
Spousal
Relocation
.09
.06
Perceived Job
Opportunities
67
Figure 5: Study 2 Extended Model of Job Embeddedness Predicting Turnover
In-Degree
Friendship
Centrality
Eigenvector
Friendship
Centrality
-.32**
Job
Attitudes
-.21**
-.29**
-.29**
Betweenness
Advice
Centrality
-.03
.15
Workplace
Pressure to Stay
-.15**
.02
.33**
Incloseness
Advice
Centrality
Voluntary
Turnover
.11
.16**
.02
Family
Pressure
to Stay
-.01
Job
Embeddedness
.40**
.02
Anticipated
Spousal
Relocation
*p < .10. **p < .05.
.15
.19*
Perceived Job
Opportunities