Sameer B. Srivastava University of California Berkeleyhaas.berkeley.edu/faculty/papers/srivastava_restructuring.pdf · 2 Organizational Restructuring and Social Capital Activation
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Organizational Restructuring and Social Capital Activation1
Sameer B. Srivastava
University of California – Berkeley
May, 2013
Keywords: organizational restructuring; social capital; uncertainty;
coping; organizational change; network dynamics
1Direct all correspondence to Sameer B. Srivastava, University of California – Berkeley, Haas School of Business,
545 Student Services, #1900, Berkeley, CA 94720-1900; srivastava@haas.berkeley.edu; 510-643-5922. I thank
Jason Beckfield, Chris Muller, Frank Dobbin, Roberto Fernandez, Heather Haveman, Ed Lawler, Ming Leung, Peter
Marsden, Erin Reid, Misiek Piskorski, Eliot Sherman, Toby Stuart, András Tilcsik, Cat Turco and participants of the
MIT-Harvard Economic Sociology Seminar, the Dobbin Research Group, the MIT Economic Sociology Working
Group, and Harvard’s Work, Organizations, and Markets Seminar for helpful comments and suggestions on prior
drafts. Any remaining errors are my own.
© Copyright 2013, Sameer B. Srivastava. All rights reserved. This paper is for the reader's personal use only. This
paper may not be quoted, reproduced, distributed, transmitted or retransmitted, performed, displayed, downloaded,
or adapted in any medium for any purpose, including, without limitations, teaching purposes, without the Author's
express written permission. Permission requests should be directed to srivastava@haas.berkeley.edu.
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Organizational Restructuring and Social Capital Activation
Abstract: This article examines how people within organizations cope with the
uncertainty of restructuring by activating social capital. The process of coping
with uncertainty leads people to search for non-redundant social resources and to
seek interaction with trusted colleagues. This dual response poses a conceptual
puzzle about what kinds of network ties will be activated during restructuring:
weak ties tend to wield non-redundant resources, while strong ties are associated
with trust. My theoretical account helps to resolve this puzzle by highlighting the
role of cross-unit work groups, which represent a nexus of strong bridging ties.
Thus, an increase in job-related uncertainty leads to the activation of more ties to
colleagues who are co-members of cross-unit work groups. At the same time,
potentially shifting departmental affiliations and normative constraints on
communication within the formal organizational structure lead to the activation of
fewer ties to colleagues within the same subunit. Support for these hypotheses
comes from analyses of archived electronic communications and semi-structured
interviews in a firm that underwent a major restructuring. Implications for
research on social resource mobilization and the structural dynamics of
organizational change are discussed.
May, 2013
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Periods of transformative organizational change, such as a restructuring (Balogun &
Johnson, 2004), an initial public offering (Fischer & Pollock, 2004), or major technological shift
(Burkhardt & Brass, 1990), often spark high levels of personal uncertainty for employees. As
they cope with heightened uncertainty, people seek social resources from their contacts (Ashford,
1988; Pescosolido, 1992). Research on how people obtain social resources through networks has
followed two main trajectories. One stream has emphasized the role of strong, homophilous ties
in obtaining various forms of social support (Cohen & Wills, 1985; House, Umberson, & Landis,
1988; Wellman & Wortley, 1990), while another has highlighted the role of weak, heterophilous
ties in gaining access to non-redundant information and novel opportunities (Granovetter, 1995;
Lin, Ensel, & Vaughn, 1981; Yakubovich, 2005).
This article seeks to integrate these two research traditions (e.g., Reagans & Zuckerman,
2001) by focusing on a disruptive episode – organizational restructuring – that is increasingly
common in organizational life (Cappelli et al. 1997; Goldstein, 2012; Kalleberg, 2009). As they
cope with the uncertainty of restructuring, people seek non-redundant information and influence
from their social ties but also seek to interact with close, trustworthy contacts (Ashford, 1988;
Nadler, 1982; Pfeffer, 1992). Although weak ties are often associated with the transmission of
non-redundant resources (Granovetter, 1973), strong ties often prove more effective than weak
ties in channeling instrumental resources in the intraorganizational context (Balkundi, Bentley, &
Kilduff, 2012; Granovetter, 1983; Levin & Cross, 2004). Moreover, organizational actors are apt
to turn to strong, rather than weak ties, when they feel uncertain or insecure (Krackhardt, 1992;
Krackhardt & Stern, 1988). Given the tendency for people experiencing disruptive organizational
change to seek non-redundant resources and to prefer obtaining these resources from trusted,
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strong tie contacts, this article addresses the question: Which workplace ties do people activate in
response to the uncertainty of restructuring?
I theorize that coping responses to uncertainty and facets of organizational structure will
jointly produce distinct patterns of social capital activation within organizations. Core to the
argument is the construct of cross-unit work groups – for example, project teams consisting of
people from different departments. These quasi-formal entities are a pervasive feature of
differentiated organizations (Devine, Clayton, Philips, Dunford, & Melner, 1999). They also
represent a nexus of strong bridging ties, which afford access to heterogeneous information and
opportunities but in the context of dense, trustworthy relationships (Cummings, 2004; Reagans,
Zuckerman, & McEvily, 2004; Reagans & Zuckerman, 2001). I argue that the search for non-
redundant information and influence from trusted, strong-tie contacts will lead people to activate
more ties to colleagues who are co-members of cross-unit work groups. At the same time,
potentially shifting departmental affiliations and normative constraints on communication within
the formal organizational structure will lead people to activate fewer ties to colleagues within
their own subunit. I test these propositions using data from a firm that underwent a significant
restructuring, which produced significant job-related uncertainty. The analyses draw on a
longitudinal data set that spans 40 weeks and includes the electronic communication logs of 114
employees, company-wide email distribution lists, employee communications memos, and
human resource records. These data provide a rare look into social network dynamics before,
during, and after a restructuring. I also report findings from semi-structured interviews with a
subset of employees. Findings from this investigation contribute to research on social resource
mobilization and the structural dynamics of organizational change and also have important
implications for managerial practice.
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ORGANIZATIONAL RESTRUCTURING AND UNCERTAINTY
I follow prior research in defining organizational restructuring as “any major reconfiguration of
internal administrative structure that is associated with an intentional management change
program” (McKinley & Scherer, 2000). The arguments developed below pertain to restructuring
that involves changes to the formal organizational structure – for example, the creation,
dissolution, or integration of subunits – and to the quasi-structure of cross-unit work groups – for
example, the formation, termination, or change in membership of cross-functional teams.
Restructuring of this kind often breeds uncertainty: it makes it difficult for people to predict the
implications of impending organizational changes (Milliken, 1987). Uncertainty can be
experienced even when organizational restructuring is well anticipated because one set of
organizational changes can trigger a cascade of other realignments that are difficult to predict
(Hannan, Polos, and Carroll 2003ab, 2003b). For example, a study of restructuring in a
telecommunications firm found:
“The most frequently cited psychological state resulting from large-scale
organizational change is that of uncertainty….Employees react most strongly to
uncertainty about how a change will affect their careers and daily activity…[and
how it might lead to] potential terminations, transfers, and the need to survive
under a new and relatively unknown supervisor” (Ashford, 1988: 20).
Restructuring produces three distinct forms of uncertainty for organizational actors –
strategic, structural, and job-related (Bordia et al. 2004). Strategic uncertainty refers to
uncertainty at the organizational level – for example, the reasons for the change and how the
external environment might evolve over time. Structural uncertainty pertains to the inner
workings of the organization – such as reporting relationships, the configuration of subunits, and
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work processes. Job-related uncertainty is about an individual’s job security, expected future
role, and advancement opportunities. Although these forms of uncertainty are interrelated, it is
job-related uncertainty that produces the strongest coping response from people living through a
restructuring (Ashford, Lee, & Bobko, 1989; Bordia et al., 2004).
COPING WITH UNCERTAINTY BY ACTIVATING SOCIAL CAPITAL
Social capital consists of “resources embedded in a social structure that are accessed and/or
mobilized in purposive actions” (Lin, 2001: 29). At any given time, many network ties that are
potential sources of social resources are latent – that is, people have pre-existing relationships
but no current interaction with a set of individuals.2 In the wake of events such as restructuring,
people convert some latent ties into active relationships (Levin, Walter, & Murnighan, 2011;
Mariotti & Delbridge, 2012; Pescosolido, 1992). Social capital activation is therefore defined as
the choice to initiate contact with certain individuals among the set of actors in one’s pre-existing
network (Hurlbert, Haines, & Beggs, 2000: 599).
The onset of job-related uncertainty produces psychological strain, which leads people to
cope (in part) by managing the sources of uncertainty (Billings & Moos, 1981; Hall &
Mansfield, 1971; Pearlin & Schooler, 1978). A major component of this response is information
seeking. Information is valuable insofar as it enhances the predictability of a situation (Ashford,
1988); therefore, people coping with job-related uncertainty are apt to seek non-redundant
information – for example, about who is likely to exit and create a job vacancy or how the
content of a given job role might change – to increase the predictability of restructuring.
Communication flows within organizations tend to hew to the formal organizational
structure (Han, 1996; Hinds & Kiesler, 1995; Lazega & van Duijn, 1997). As Allen (1977: 211)
2Both strong and weak ties can be active or latent at a given time.
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stated, “The real goal of formal organization is the structuring of communication patterns.”
Because of this fact, colleagues outside of a person’s subunit are likely to know different
information than the focal actor, while colleagues within the same subunit are likely to know the
same information. During a restructuring, the non-redundant information held by colleagues
outside one’s subunit that could potentially reduce job-related uncertainty will become more
salient and therefore more valuable.
People coping with job-related uncertainty also seek political influence from colleagues
in order to “increase their sense of control and confidence” (Ashford, 1988: 22; Pfeffer, 1989,
1992). Because the uncertainty of restructuring means that current departmental affiliations and
reporting relationships may not persist, political support from one’s own supervisor and
colleagues within the same subunit will become less reliable. Instead, people will seek to shore
up distal alliances and obtain support from colleagues outside of their subunit. In sum, the
process of coping with uncertainty will lead people to seek non-redundant information and
influence, and these resources are more likely to reside outside, rather than inside, their subunit.
Although weak ties are often thought to be the best conduits for the flow of non-
redundant information (Granovetter, 1973), strong ties may be more effective than weak ties in
channeling instrumental resources within organizations (see Balkundi et al., [2012] for a meta-
analysis that supports this contention). Indeed, Granovetter (1995: 150-151; emphasis added)
suggests that weak ties are only likely to be beneficial “if they reach individuals…whose
resources are different, in that they pertain to organizational or institutional settings different
from one’s own.” That is, weak ties are most valuable when they connect actors across
organizations rather than when they connect actors within a single organization. Moreover, under
conditions of uncertainty, when they feel insecure, people are especially likely to turn to strong,
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rather than weak, ties for instrumental resources such as information about job vacancies
(Granovetter, 1983). During disruptive episodes, organizational actors seek social resources from
trustworthy sources and “strong ties constitute a base of trust that…[provides] comfort in the
face of uncertainty” (Krackhardt, 1992: 218; Krackhardt & Stern, 1988).
Organizational actors coping with job-related uncertainty thus face a dilemma. On one
hand, they seek to obtain non-redundant resources, which are more likely to exist among contacts
outside their subunit. On the other hand, they prefer to interact with trusted, strong-tie
colleagues, who are less likely to have access to non-redundant resources. I argue that this
dilemma will be resolved by activating ties to colleagues who are co-members of cross-unit work
groups. These work groups – for example, cross-functional teams, task forces consisting of
people from different divisions, and governance bodies that bring together representatives from
different geographic units – are part of an organization’s quasi-formal structure (Blau & Scott,
1962; Ibarra, 1992b). Whereas subunits, such as departments, divisions, and functions that define
reporting relationships, provide a means for differentiating the organization, work groups that
span subunits serve as a means for integration (Blau, 1970; 1967).3 Indeed, survey research
indicates that cross-unit work groups are widely used across a range of organizations and are
especially likely to exist in differentiated organizations that contain many subunits (Devine et al.,
1999). Within such organizations, people typically belong to a handful of subunits, based on
their reporting relationships; however, they may be embedded in dozens of cross-unit work
groups – depending on their role in the workflow and decision processes of the organization
(Cummings & Haas, 2012).
3Not all work groups span subunits. For example, some project teams consist of people from only one department. In
large, differentiated organizations, however, work groups are frequently put in place to coordinate activity across
subunits (see, for example, Ancona and Caldwell [1992] and Nadler and Tushman [1997]). This argument pertains
to the latter kind of work group.
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Thus, one can conceptualize a space of cross-unit work groups. Colleagues are more
proximate in this space when they have more overlap in membership of such work groups; they
are more distant when they have fewer cross-unit work groups in common. For three reasons, I
contend that coping with uncertainty will lead people to activate ties to colleagues who are
proximate in this work group space. First, these colleagues are likely to be trustworthy providers
of social resources because they have frequent contact and a history of prior exchange with the
focal actor (Kollock, 1994; Krackhardt, 1992; Podolny, 1994). Second, because these colleagues
tend to work in different subunits than the focal actor, the resources they hold are likely to be
non-redundant (Friedkin, 1982). Third, these colleagues are embedded in a dense social structure
(i.e., the work group) that facilitates “the formation of common knowledge and shared
meanings…and promote[s] the cooperation and coordinated actions that are necessary to
integrate and take advantage of diverse sources of knowledge” (Tortoriello & Krackhardt, 2010:
168). That is, people will more readily cooperate with, and digest information received from,
colleagues in shared work groups. Taken together, these arguments suggest:
H1: An increase in job-related uncertainty stemming from organizational
restructuring leads people to activate more ties to colleagues who are co-members of
cross-unit work groups.
At the same time, for two reasons I expect to see the opposite response with respect to
colleagues in the same subunit. First, as noted above, the uncertainty of restructuring indicates
that current departmental affiliations and reporting relationships may not persist. Thus, people
will be less inclined to rely upon political support from their supervisor or colleagues within the
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same subunit. Second, communications norms that are prevalent in differentiated organizations
will tend to curtail social capital activation within subunits. In a restructuring, managers are often
expected to communicate only officially sanctioned messages to their departments, adhere to
pre-specified communication timetables, and refrain from ‘leaking’ information to subordinates
(Klein, 1996). Indeed, surveys of employees who experienced restructuring routinely report high
levels of dissatisfaction with the volume and quality of communication received through the
formal structure – for example, from supervisors or colleagues within the same subunit
(Goodman & Truss, 2004). These communication norms serve to constrain the opportunity
structure for interaction within subunits (cf. Marsden, 1983), whereas there are fewer
prohibitions about what can be communicated through work groups (see, for example, Balogun
& Johnson, [2004]; Isabella, [1990]).These arguments suggest:
H2: An increase in job-related uncertainty stemming from organizational
restructuring leads people to activate fewer ties to colleagues who are in their own
subunit.
METHODS
Research Setting
A major information services company, hereafter referred to as InfoCo, served as the research
site for the study. Declining financial performance led InfoCo’s management team to undertake a
major restructuring. The restructuring involved the creation of new subunits, such as a global
marketing function; the combination of existing subunits, such as “solution lines” that integrated
product development and marketing; and the elimination of certain other subunits and job roles.
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It also resulted in changes in the work group structure. The broad thrust of the changes –
centralization of certain functions, regionalization of others, and downsizing to reduce costs –
was largely consistent with the forms of restructuring experienced by workers across a wide
range of US corporations (Capelli, 2008; Cappelli et al., 1997; Osterman, 2000).
Study Subjects
The study included all 114 US-based members of the InfoCo’s extended leadership group.
Because members of this group all had pre-existing ties to one another, they were well-suited to
a study of social capital activation. The group was mostly male (67.5%) and white (84.2%). A
significant portion (58%) worked in one of two main regional offices; the rest were distributed
among smaller sites. They spanned three salary grades (in ascending rank): 7.5% were
“operational leaders,” 80.3% were “tactical leaders,” and 12.2% were “executive leaders.”
The company granted access to data on the extended leadership group but did not permit
data collection on lower level employees (given concerns about how rank-and-file employees
might react if they learned that their email traffic – even if stripped of identifying information
and content – was being tracked for research purposes). The focus on a relatively senior
employee population raises questions about the generalizability of findings from this study. In
this particular case, however, the restructuring was implemented in a manner that mitigates this
concern. By all accounts, the CEO kept details of the restructuring close to his vest until the
changes were announced. Thus, even this senior group of employees experienced a discrete
period of heightened job-related uncertainty. The qualitative evidence suggests that most
understood the strategic rationale for the change. Thus, most did not face significant strategic
uncertainty during this time. In the period before the new organizational structure was
announced, many seemed to know that some form of restructuring was imminent but remarkably
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few knew the details (e.g., which subunits would be created, merged, or dissolved; who would
report to whom) or what it would mean for them personally. So in the period before the
announcement, they faced considerable structural uncertainty but limited job-related uncertainty.
Once the new structure was announced, the structural uncertainty was mostly resolved, but there
was a spike in job-related uncertainty as people scrambled to understand what the new structure
would mean for them. Virtually everyone experienced this increase in job-related uncertainty,
and it is this form of uncertainty that is most likely to produce the coping responses theorized
above (Bordia et al., 2004). Indeed, by the time restructuring concluded, many study participants
had been significantly affected: 43 (37.7%) had a change in supervisor, 15 (13.6%) moved to a
different InfoCo division, and 13 (11.4%) exited the company.4 (Some experienced more than
one of these changes.) Those who experienced these changes did not learn of them until after the
initial announcement, and those who were not affected did not know so until considerably later.5
Semi-structured interviews (see details below) provided further evidence that these
individuals experienced an increase job-related uncertainty. As one marketing director reported,
“The announcement happens, and then I get a call from HR and the guy who was my boss at the
time. They say, ‘We’re eliminating your role, and you’re not going to get job you thought you
were going to get.’ Then they offered me another job that I really didn’t want. I was stunned.”
Similarly, a marketing support director, who participated in the process of identifying which
people were let go and who was selected for what open position, described the period as follows:
It was just a terrible, terrible time….All of the leaders were given a certain
number of slots to fill. We had to go through a process of assessing and ranking
4There were no statistically significant differences between those who exited and those who stayed on observable
characteristics such as gender, tenure, or salary band. 5During the time that archival data were being collected, this group was also unaware that it was involved in a
research study. Knowledge of the study was kept to a small group (e.g., CEO, head of HR) to minimize distraction.
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people – for example, eleven people might be ranked for a job role with ten open
slots. The eleventh person was laid off. If the job role was redefined, we had to
tell all incumbents that they were laid off and had to interview to get their job
back. Everyone was feeling insecure. In my area, news leaked that 40% of the
staff would be let go.
In sum, the individuals included in this study were well-suited to the study of social capital
activation as a coping response to job-related uncertainty.
Data Collection
Four kinds of archival data were collected for the study: (1) internal communication memos,
which were used to construct the timeline of restructuring events; (2) email logs (spanning a
period of 40 weeks) of the extended leadership group6; (3) extracts of InfoCo’s email distribution
lists, which were used to identify shared work groups among employees (based on list co-
membership in a given week); and (4) extracts from InfoCo’s human resources system.
Internal communication memos (and semi-structured interviews) indicated that the period
of greatest job-related uncertainty commenced in Week 9, when the CEO released the first of
several communications that provided details of the new organizational structure. Additional
memos – announcing the formation of new subunits, the consolidation of other units, and the
appointment and departure of personnel – were sent intermittently until Week 18. All changes to
the organizational structure had been made, key positions had been filled, and departing
employees had all exited by Week 18. Thus, Weeks 9 to 18 represented the period of heightened
job-related uncertainty (see Figure 1).
– Figure 1 about here –
6Prior research indicates that this time period is appropriate for the study of employee reactions to restructuring
(Brockner, Tyler, and Cooper-Schneider 1992; Shah 2000).
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Email logs represented a second key information source. Analyses of email
communication are becoming increasingly common in organizational research (Allatta & Singh,
2011; Hinds & Kiesler, 1995; Kossinets & Watts, 2006). Consistent with the ethical standards
used in prior studies (Borgatti & Molina, 2005; Kadushin, 2005), identifying information (e.g.,
email addresses) was encrypted using an irreversible algorithm, email logs did not contain
message content, and only messages internal to InfoCo were collected. Although these choices
helped protect the privacy of study subjects and the confidentiality of company data, they also
restricted the ability to analyze the meaning embedded in email content.
Email data of this kind have several advantages over traditional network surveys. First,
they can be collected unobtrusively, which can be useful in observing network dynamics during
a politically sensitive time such as an organizational restructuring. Next, they provide a window
into peripheral ties, which network surveys typically do not seek to measure. They can also yield
more reliable indicators of interaction than surveys, which can suffer from various forms of
recall and self-report bias (Marsden, 2011). For longitudinal network analysis, they have the
added benefit of allowing for consistent data collection over time – for example, by avoiding
measurement error that can arise from variability in interviewer techniques and eliminating the
sample attrition that can occur in repeated surveys. At InfoCo, interviewees reported that they
routinely used company email even for personal communication, including messages sent via
personal digital assistants. At the time of the restructuring (early 2008), it was uncommon for
InfoCo employees to use instant messaging or personal email services at work.
These benefits are counterbalanced by certain limitations. First, the trace of email
communication does not always signify purposive interaction. For example, email messages can
sometimes be automatically generated, sent to pre-determined distribution lists, or mindlessly
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copied to peripheral actors. To address these shortcomings, emails including the phrase “Out of
Office” in the subject line and mass emails (those sent to more than one recipient) were
excluded.7 Next, email logs contain only a subset of communications. At InfoCo, the email
system was linked to electronic calendars and therefore included a record of all formally
scheduled meetings. Nevertheless, given that people likely communicated through a mix of
scheduled meetings, email exchanges, and informal, unscheduled communication, email logs
provide an incomplete window into the communications that took place in this period. Because
more sensitive communication was likely to take place in informal face-to-face and phone
communication, email logs likely represent a conservative indicator of social capital activation in
response to job-related uncertainty.
In addition to email logs, I collected email distribution lists. Just as the choice to use
email data involves tradeoffs, so too does the decision to use email distribution lists to locate
individuals in the space of cross-unit work groups. Widely used across organizations, distribution
lists encapsulate the myriad collective units that exist within an organization but that are often
poorly documented or kept updated. Because list names were encrypted in the data available
from InfoCo, it was not possible to distinguish among different list types. Semi-structured
interviews suggested that InfoCo’s lists primarily corresponded to work groups rather than
subunits. Moreover, in a typical week during the observation period, there were over 2,300 active
distribution lists – far more than the number of subunits to which subjects belonged. Although
the lists obtained from InfoCo likely reflected a mix of cross-unit work groups, work groups
nested within subunits, and subunits themselves, findings from other studies that have used email
7The results reported below were robust to different mass email thresholds – for example, including messages sent to
up to five recipients.
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distribution lists and had access to list names suggest that the vast majority represent work
groups (Liu, Srivastava, & Stuart, 2013).
To derive from these data a measure of distance in the space of cross-unit work groups,
two additional steps were taken (see below for details on the measure): weighting lists by size
(assuming that small lists are more likely to reflect meaningful work groups in which strong,
trustworthy ties form while large lists are more likely to indicate diffuse work groups or
subunits) and weighting lists by the diversity of subunits represented on the list.
Finally, repeated extracts from InfoCo’s human resource systems were used to construct
time-varying measures of positions in subunits and cross-unit work groups and to identify
sociodemographic characteristics.
Measures
The response variable was a count of the number of one-to-one email messages exchanged in a
given week, t, between a dyadic pair, i and j. Explanatory variables included Same Departmentt
(set to 1 if i and j were in the same department in week t and to 0 otherwise), Both Men, Both
Women, and Distance in Cross-Unit Work Group Spacet.8 In principle, work groups can overlap
significantly with subunits, for example if they are entirely nested within subunits. The measure
of distance in cross-unit work group space was therefore adjusted by the level of subunit
diversity represented on each list (Blau, 1977) and by list size.9 The resulting measure is a
variant of Jaccard’s distance, a widely used distance measure that has a theoretical range from 0
to 1 (Sneath & Sokal, 1973)10
:
8The restructuring affected both subunit and cross-unit work group structure – for example, 6% of dyads
experienced a change in reporting relationship and about 5% of dyads experienced more than a half standard
deviation change in distance in cross-unit work group space. 9The diversity measure was based on the eleven divisional groups (collections of departments) that were represented
on lists. More diverse lists included people from a broader range of subunits than less diverse lists. 10
All continuous covariates are mean-centered in regression analyses. The results reported below were robust to the
use of alternative distance measures, such as one based on Dice’s coefficient (Dice 1945).
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∑
∑
∑
∑
Where: i, j index members of the dyad
k indexes distribution lists
si,j = 1 if i and j belong to list k; 0 otherwise
mi = 1 if i belongs to list k; 0 otherwise
mj = 1 if j belongs to list k; 0 otherwise
dk = 1 – sum of squared proportions of divisional groups represented on list k
Nk = size of list k
Only 5.2% of dyads that were below the median of this measure were also in the same
department, suggesting that this measure is more or less orthogonal to formal structure.
To identify increases or decreases in social capital activation during the period of
heightened job-related uncertainty, an indicator, Uncertaintyt, set to 1 for Weeks 9 through 18
(from the time structural changes were first announced to when they were fully implemented),
and the following interaction terms were used: Uncertaintyt x Same Departmentt and
Uncertaintyt x Distance in Cross-Unit Work Group Spacet. Hypotheses 1 and 2 predict
significant and negative coefficients, respectively, for the linear combinations: (1) Uncertaintyt
+Uncertaintyt x Distance in Cross-Unit Work Group Spacet and (2) Uncertaintyt +Uncertaintyt x
Same Departmentt.
Given the well-documented tendency in social networks toward various forms of
homophily and propinquity (McPherson, Smith-Lovin, & Cook, 2001), the following controls
were also included: (1) Same Locationt, set to 1 for dyads in the same building and floor (e.g.,
Allen, 1977); (2) Same Salary Gradet, set to 1 for dyads at the same hierarchical rank (e.g., Han,
1996); (3) Same Cohort, set to 1 for dyads hired within one year of each other (e.g., Wagner,
Pfeffer, & O'Reilly, 1984); (4) Same Age, set to 1 for dyads with an age difference of less than
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four years (e.g., Burt, 2000)11
; (5) Same Ethnicity, set to 1 for dyads listed in the human resource
system as having the same ethnicity – White, Asian, African American, Latino / Hispanic, or
Other (e.g., Mehra, Kilduff, & Brass, 1998); and same gender (Ibarra, 1992a), which was
decomposed into (6) Both Women and (7) Both Men.
Estimation
A dyad-level panel data set of email messages exchanged between i and j in week, t, was
constructed. Analyses of such data must contend with the clustering (i.e., non-independence) of
observations. Error terms in regression analyses will be correlated across observations, a problem
referred to as network autocorrelation. The failure to control for clustering can lead to under-
estimated standard errors and over-rejection of hypothesis tests. To address this issue, a variance
estimator that enables cluster-robust inference when there is multi-way clustering was
implemented (Cameron, Gelbach, & Miller, 2011). This situation arises when – as in this study –
there is clustering at both the cross-sectional and temporal levels. In the case of two-way
clustering, the technique produces three different variance matrices: for the first dimension, for
the second dimension, and for the intersection of the two. The first two matrices are added
together and third subtracted. In the case of three-way clustering, the analogous technique results
in the creation and combination of seven one-way cluster robust variance matrices.12
Thus, I
estimated Poisson regressions with standard errors clustered by sender, by receiver, and by week.
11
A difference of less than four years was also used to define same age in Burt (2000). The results reported below
did not change materially when other age difference cut-offs were used instead. 12
Each of the first three matrices clusters in one dimension. Because some observation pairs are in the same two-
dimensional cluster, considering only these three matrices would result in double counting. So matrices that cluster
on the three combinations of two dimensions are then subtracted. This eliminates double counting but does not
account for pairs that share the same cluster in all three dimensions. So the seventh matrix, which clusters on pairs
sharing the same cluster in all dimensions, is added back (see Cameron, Gelbach, and Miller [2011: 10-11]). This
technique, which also controls for potential over- or under-dispersion in the data, was implemented in STATA using
the “clus_nway” script (Kleinbaum, Stuart, and Tushman 2013).
19
This technique is appropriate for the analysis of dyadic network data, including panel
data (Cameron et al., 2011; Kleinbaum, Stuart, & Tushman, 2013). In simulation studies
(Lindgren, 2010), it performs at least as well as an alternative approach: Multiple Regression
Quadratic Assignment Procedure (MRQAP) with double semi-partialing (DSP) (Dekker,
Krackhardt, & Snijders, 2007). Moreover, it is computationally faster than MRQAP with DSP in
dealing with large data sets (Kleinbaum et al., 2013). Unlike stochastic actor-based models (e.g.,
those estimated using SIENA), this approach does not account for higher-order dependence
structures (e.g., transitive triplets) that may exist in the data. However, stochastic actor-based
models assume a dichotomous response variable and are appropriate when the number of
observations of a network is small – usually less than ten (Snijders, van de Bunt, & Steglich,
2010). Thus, they are not appropriate for this data set, in which the response variable is a count
of email messages exchanged over 40 weeks.
As a robustness check, an alternative approach – estimating Poisson regressions with
fixed effects for every sender and every receiver in the study – was also implemented (Mizruchi,
1989; Reagans & McEvily, 2003). This approach shifts the potentially autocorrelated
disturbances out of the residuals and yields consistent and efficient estimates (Mizruchi, 1989:
421). It also accounts for all time-invariant, unobserved differences among study participants.
The results reported below were materially unchanged with this alternative approach.
RESULTS
Quantitative Analysis
Table 1 reports descriptive statistics and a correlation matrix. As expected, there is a positive
correlation between messages exchanged and various measures of similarity between dyads (e.g.,
20
Same Departmentt and Same Locationt) and a negative correlation between messages exchanged
and Distance in Cross-Unit Work Group Spacet.
– Table 1 about here –
Table 2 provides a comparison of aggregate communication patterns between the periods
of uncertainty and relative stability. Although there was a slight increase in aggregate
communication volume during the weeks of uncertainty, this change was not statistically
significant. Consistent with Hypothesis 1, the correlation between Distance in Cross-Unit Work
Group Spacet and messages exchanged was -0.101 in the period of relative stability and -0.122 in
the period of uncertainty. Similarly, consistent with Hypothesis 2, the proportion of messages
sent between colleagues in the same department was 0.568 in the period of relative stability and
0.516 in the period of uncertainty (p<.001).
– Table 2 about here –
Table 3 reports the results of the regression analyses used for hypothesis testing. Model 1
depicts results from the baseline model. Same Locationt, Same Departmentt, and Both Women
have positive and significant coefficients, while Distance in Cross-Unit Work Group Spacet has
a negative and significant coefficient. The coefficients for Same Departmentt and Distance in
Cross-Unit Work Group Spacet are consistent with prior research indicating a tendency for the
formal and quasi-formal structure to influence communications in the workplace (Allen, 1977;
Hinds & Kiesler, 1995; Srivastava & Banaji, 2011). The positive coefficient for Both Women is
consistent with prior research on homophily in the workplace (Ibarra, 1992a; Kleinbaum et al.,
2013; Lincoln & Miller, 1979), though there is no evidence in this setting for homophily among
men. Unlike prior research (Han, 1996), Same Salary Gradet in this setting has a significant and
21
negative coefficient, perhaps because the senior-most leaders among study subjects were
working more within their divisions across vertical levels than with their peers in other divisions.
Models 2 and 3 test for the significance of the relevant interaction terms: Uncertaintyt x
Distance in Cross-Unit Work Group Spacet and Uncertaintyt x Same Departmentt. In Model 2,
Uncertaintyt represents the effects of job-related uncertainty on colleagues at the mean distance
in cross-unit work group space. It has a slightly negative but not significant coefficient. The
interaction term Uncertaintyt x Distance in Cross-Unit Work Group Spacet, has a negative and
significant coefficient (beta=-0.488; p<.05). In Model 3, Uncertaintyt represents the effects of
job-related uncertainty on colleagues in different departments. It has a slightly positive but not
significant coefficient. The interaction term, Uncertaintyt x Same Departmentt, has a negative
and significant coefficient (beta=-0.216; p<.05). Taken together, Models 2 and 3 indicate
significant uncertainty interaction effects that are consistent with Hypotheses 1 and 2.
Model 4 represents the fully specified model used to conduct more specific hypothesis
tests. The relevant interaction terms are significant and of the expected sign: Uncertaintyt x
Distance in Cross-Unit Work Group Spacet (beta=-0.866; p<.001) and Uncertaintyt x Same
Departmentt (beta=-0.295; p<.01). The linear combination, Uncertaintyt + Uncertaintyt x
Distance in Cross-Unit Work Group Spacet, is negative and significant (beta=-0.789; p<.01).
Thus, there is support for Hypothesis 1. That is, when job-related uncertainty is heightened,
increasing distance in cross-unit work group space tends to suppress the number of messages
exchanged between colleagues. Conversely, colleagues who are more proximate in work group
space are apt to exchange more messages with one another when they face an increase in job-
related uncertainty. Similarly, because the linear combination, Uncertaintyt + Uncertaintyt x
Same Departmentt, is also negative and significant (beta=-0.218, p<.05), there is support for
22
Hypothesis 2. That is, when job-related uncertainty increases, colleagues in the same department
tend to exchange fewer messages with one another.
– Table 3 about here –
Considering that changes in email communication probably represent a conservative
indicator of shifts in social capital activation, these effects were sizable: In the period of
heightened job-related uncertainty relative to stability, there was a 6% increase in the predicted
number of messages among dyads at the 5th
percentile of Distance in Cross-Unit Work Group
Spacet, a 9% decrease in the predicted number of messages among dyads at the 50th
percentile,
and an 11% decrease in the predicted number of messages among dyads at the 95th
percentile.
Turning to subunits, there was a 14% decline in the predicted number of messages exchanged
between colleagues in the same department and a 7% increase in the predicted number of
messages exchanged between colleagues in different departments.
Qualitative Analysis
To help address the limitations of the archival data sources described above, I conducted
supplemental semi-structured interviews with 23 InfoCo employees, who were selected in
consultation with human resource professionals. They were chosen from sub-samples believed to
have experienced higher and lower levels of uncertainty during the restructuring (based on their
job roles) but who remained employed at the firm. Legal concerns kept the company from
granting me access to those who had exited. The interviews, which occurred several months after
the restructuring concluded, lasted between 30 and 45 minutes and were recorded and
transcribed. The 23 semi-structured interviews included 16 with a subset of the individuals
whose email data were analyzed and 7 with human resource professionals who helped to
implement the restructuring. Interviews lasted between 30 and 45 minutes and were recorded and
23
transcribed. The purpose of the interviews was to validate the timeline of events, assess the
nature of uncertainty people experienced, understand how and why they activated their networks
during the restructuring, and determine how they used electronic communication media.
Each set of interviews was coded to identify whether or not the respondent reported: (1)
feeling uncertain during the restructuring; (2) experiencing strategic uncertainty; (3)
experiencing structural uncertainty; (4) experiencing job-related uncertainty; (5) coping with
uncertainty through social capital activation; (6) activating strong tie contacts; (7) activating
weak tie contacts; (8) activating ties within the respondent’s subunit; and (9) activating ties
outside the respondent’s subunit. These categories were not mutually exclusive – for example, a
person could report activating both strong tie and weak tie contacts. Table 4 below provides
illustrative quotations that were coded as belonging to each of these categories.
– Table 4 about here –
Of the 16 individuals whose responses were coded in this manner, 9 were men and 6
were women. 15 of the 16 reported experiencing some form of uncertainty as a result of
restructuring: 3 experienced strategic, 13 experienced structural, and 13 experienced job-related
uncertainty. Of those who reported encountering one of these forms of uncertainty, 13 engaged
in social capital activation to cope with uncertainty (e.g., searching for information or influence).
11 of these 13 reported reaching out to a strong tie contact (coded as such because terms such as
“trustworthy,” “trust,” “close,” or “friend” were used to describe the relationship). Two reported
activating a weak tie contact or did not specify the nature of the relationship to the contact. No
one indicated that he or she had forged a new tie in response to the uncertainty of restructuring.
Of those who activated ties in coping with uncertainty, 10 activated ties to colleagues outside of
their subunit, while 3 activated ties to either colleagues within their subunit or to a mix of
24
colleagues within and outside their subunit. In sum, the qualitative evidence, though based on a
limited sample, provides corroborating support for the hypothesized mechanisms.
Robustness Checks
Supplemental analyses were conducted to help rule out three alternative explanations. First, it is
possible that changes in social capital activation across subunits and work groups were not a
response to job-related uncertainty but rather a reflection of shifting task interdependencies. For
example, if a person were moving from one subunit to another, there would be a period of
transition as she completed prior assignments and ramped up in her new job role. Similarly, if
she were moving from one work group to another, there would be a period of adjustment from
one group to the other. To account for these shifts, two supplemental analyses were conducted.
First, Model 4 was re-estimated using lagged and leading measures: i.e., Same Departmentt-1,
Same Departmentt+1, and the corresponding four measures for Distance in Cross-Unit Work
Group Space. Including these four dyad-level, time-varying covariates, which controls for
transition time before and after the observed change in subunit or work group, did not materially
change the results: (a) Uncertaintyt + Uncertaintyt x Distance in Cross-Unit Work Group Spacet
(beta=-.874; p<.001); and (b) Uncertaintyt + Uncertaintyt x Same Departmentt (beta=-0.225;
p<.05). Second, an even more conservative test was applied: re-estimating Model 4 for the subset
of dyads in which neither person experienced a move to a different subunit. This subset of dyads
presumably faced little to no change in task interdependency with one another during the
observation period. The linear combinations of interest were not materially changed: (a)
Uncertaintyt + Uncertaintyt x Distance in Cross-Unit Work Group Spacet (beta=-0.908; p<.001);
and (b) Uncertaintyt + Uncertaintyt x Same Departmentt (beta=-0.262; p<.05). Thus, the
alternative explanation of shifting task interdependencies seems unlikely.
25
Second, the decline in communication among people in the same could have resulted
because of competition among actors. For example, if two people with comparable skills were
vying for the same job, they might curtail communication with each other. Re-estimating Model
4 using an indicator, set to 1 for dyads in the same job family (e.g., market planning), did not
materially change the results.
Finally, it is possible that observed changes in social capital activation occurred because
of some other unobserved event (e.g., a financial shock reported in the news during one or more
of the restructuring weeks) rather than in response to restructuring. This alternative explanation
was addressed in three ways. First, Model 4 was re-estimated using time (i.e., week) fixed
effects. Because the main effect of Uncertaintyt is subsumed in the week dummies, one cannot
explicitly test the significance of the linear combinations of interest – e.g., Uncertaintyt +
Uncertaintyt x Same Departmentt. However, since the main effect of Uncertaintyt in Model 4 is
close to zero, the interaction terms in the model with week fixed effects approximate the linear
combinations of interest: Uncertaintyt x Distance in Cross-Unit Work Group Spacet (beta=-
0.859; p<.001); and (b) Uncertaintyt + Uncertaintyt x Same Departmentt (beta=-0.292; p<.05).
Second, a “placebo” regression was estimated for another comparably long but randomly
selected period in the data: Weeks 20-29 (for empirical examples of placebo regressions, see
Leigh & Neill, [2011]; Olsson, [2009]). If the placebo regression produced one or more
comparable results as the regression based on the restructuring period, it would suggest that
factors other than restructuring could also account for the identified effects. When the placebo
period was compared to the rest of the data set (excluding weeks 9-18), none of the relevant
linear combinations – e.g., Placebo Periodt + Placebo Periodt x Distance in Cross-Unit Work
Group Spacet – was significant. Third, I derived an alternative measure of job-related uncertainty
26
using email subject lines. An analysis of a sample of email subject lines across the entire
observation period surfaced 43 subject line fragments that appeared to be associated with the
restructuring. Examples included: “Organizational announcement,” “Resignation,” “Open
Position,” “Appointed,” and “Departure.” The proportion of messages in a given week
containing at least one of these phrases can be thought of as a continuous, time-varying measure
of the level of job-related uncertainty. Although this measure was at its highest levels in Weeks
9-18 (the restructuring period), it was non-zero in most weeks and varied throughout the
observation period. When the measure based on restructuring keywords was substituted for the
uncertainty period (Weeks 9-18) indicator and its associated interaction terms in Model 4,
comparable results were obtained: Proportion Restructuring Messagest x Distance in Cross-Unit
Work Group Spacet (beta = -18.370; p<.01) and Proportion Restructuring Messagest x Same
Departmentt (beta = -6.589; p<.01). Taken together, these analyses bolster the inference that
changes in social capital activation occurred because of the restructuring and not some other
unrelated event.
DISCUSSION
The goal of this article has been to examine how people within organizations cope with the
uncertainty of organizational restructuring through the activation of social capital. Restructuring
breeds high levels of strategic, structural, and job-related uncertainty for organizational actors
(e.g., Bordia et al., 2004). The strain of job-related uncertainty leads people to activate social
capital in the search for social resources (Pescosolido, 1992). Within organizational settings,
people experiencing such uncertainty face a dilemma. On one hand, they seek non-redundant
information and different forms of influence (Ashford, 1988; Pfeffer, 1992), which are most
27
likely to exist outside of their subunit. Yet when organizational actors feel uncertain and
insecure, they are apt to seek social resources from trustworthy, strong tie contacts (e.g.,
Krackhardt, 1992), who are less likely to possess non-redundant resources. The resolution to this
dilemma occurs through the activation of ties to co-members of cross-unit work groups who, on
one hand, wield non-redundant resources and, on the other, represent trustworthy exchange
partners. At the same time, uncertainty about departmental affiliation and normative constraints
on communication within the formal organizational structure lead to the activation of fewer ties
to colleagues within the same subunit. Support for these propositions comes from analyses of 40
weeks of archived electronic communications among 114 employees in an information services
firm that underwent a major restructuring and from semi-structured interviews with a subset of
these individuals.
Contributions to Theory and Research
This study makes three noteworthy contributions. First, it contributes to research on network
dynamics during periods of transformative change, highlighting in particular the distinctive
features of this process when it unfolds inside organizations. It joins a growing body of research
that shows various ways in which strong, rather than weak, ties serve as vital arteries for the
circulation of not only expressive but also instrumental resources within organizations (Balkundi
et al., 2012; Hansen, 1999; Krackhardt, 1992; Levin & Cross, 2004; Nelson, 1989; Reagans &
Zuckerman, 2001; Tortoriello & Krackhardt, 2010). In addition, building on prior research that
emphasizes the importance of understanding “the microstructural context in which [bridging] ties
are embedded” (Tortoriello & Krackhardt, 2010: 168), it highlights the role of a pervasive but
under-theorized feature of differentiated organizations – cross-unit work groups – in the flow of
information and opportunities. These work groups can be thought of as a nexus of strong
28
bridging ties, which afford access to non-redundant resources from a dense cluster of trustworthy
relations (cf. Reagans & Zuckerman, 2001). Finally, it demonstrates that subunits are much less
effective at channeling resources during disruptive episodes such as restructuring. Together,
these findings deepen our understanding of social capital activation (Casciaro & Lobo, 2008;
Hurlbert et al., 2000; Mariotti & Delbridge, 2012; Smith, 2005) by uncovering how aspects of
formal and quasi-formal organizational structure shape which potential network resources are
actually tapped during a disruptive episode such as restructuring.
Second, this study has important implications for research on organizational structure and
performance in turbulent times (Davis, Eisenhardt, & Bingham, 2009; Krackhardt & Stern, 1988;
Lin, Zhao, Ismail, & Carley, 2006; Rindova & Kotha, 2001). This literature has tended to take
internal network structure as given and examined the consequences of different structures for
organizational performance. For example, Krackhardt and Stern (1988) argued that the structure
of internal friendship ties can influence the ability of organizations to thrive in crisis situations.
Firms with a high ratio of cross- to within-subunit friendship ties – i.e., a high External-Internal
(E-I) Index – were more effective at surviving crises in a simulation exercise. Findings from the
present study suggest the need to complicate this account. Whereas the experimentally
manipulated organizations created by Krackhardt and Stern (1988) varied in the structure of
internal ties, this study suggests the need to also consider network action – in the form of social
capital activation. These results suggest that it is inadequate to consider a single E-I index, which
remains static over time and determines an organization’s ability to withstand turbulent times.
Instead, one must consider at least two forms of the E-I index – one based on subunits and the
other on cross-unit work groups. Conditions of job-related uncertainty can cause the former to
29
increase and the latter to decrease. It remains to be explored how these endogenous shifts in E-I
index influence an organization’s ability to survive uncertain crises.
Finally, the study makes a methodological contribution: suggesting a novel data source
that can be used to “dust the fingerprints of informal organization” (Nickerson & Silverman,
2009: 538). This study uses an affiliation matrix derived from email distribution lists to map the
distance between actors in the space of cross-unit work groups (see also Liu et al., [2013]).
Given the widespread availability of email distribution lists and the challenge of identifying the
myriad and constantly shifting work groups that exist in large, differentiated organizations, this
data source and the measure used in this study appear to have wide applicability.
Limitations and Directions for Future Research
This study had certain limitations, which point to avenues for future research. First, because of
privacy concerns, it was not possible to analyze email content. Future studies could benefit from
using content analysis techniques that can infer meaning from email data while preserving
confidentiality (Aral & Van Alstyne, 2011). Second, because the baseline period prior to
restructuring was relatively short, this study could not account for the role of pre-existing
network structure in influencing activation choices (e.g., Gargiulo & Benassi, 2000). Future
research could profitably extend the baseline period before an uncertainty-producing shock.
Third, this study was based on just one form (emails) of employee communication – albeit one
that is correlated with face-to-face and telephone modes of interaction (Kleinbaum, Stuart, &
Tushman, 2008). A useful next step would be to explore differences in reactions to uncertainty
across a wider range of media – such as unscheduled meetings, phone calls, and text messages.
Finally, because distribution list names were masked, it was not possible to study how
30
differences in work group characteristics, such as slack or autonomy, affect activation choices
(Haas, 2006). Future research using distribution lists would profit from access to list names.
Managerial Implications
These findings also have important implications for management practice. Whereas prevailing
wisdom about effective communication during restructuring emphasizes the importance of
timely and coordinated messaging through the formal organizational structure (Herzig &
Jimmieson, 2006; Klein, 1996), this study casts doubt on the appropriateness of this approach.
Organizational leaders who rely on the formal structure to communicate about a restructuring
may be swimming upstream, given that people tend to activate fewer ties to colleagues in the
same subunit during restructuring. Instead, they may gain substantially more traction by
communicating through cross-unit work groups, to which people seem to more naturally turn
during restructuring. In sum, this study deepens our understanding of uncertainty as an engine of
network change and the role organizational structure in conditioning these social dynamics.
31
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36
Tables and Figures
Table 1: Descriptive Statistics and Correlation Matrix
Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) Messages Exchangedt 0.33 2.40 1.00
(2) Same Locationt 0.03 0.16 0.12 1.00
(3) Same Salary Gradet 0.25 0.43 0.00 -0.02 1.00
(4) Same Cohort 0.15 0.36 0.02 0.03 0.01 1.00
(5) Same Age 0.27 0.45 0.00 0.00 0.00 0.00 1.00
(6) Same Ethnicity 0.73 0.44 0.02 -0.00 -0.05 -0.06 0.02 1.00
(7) Distance in Cross-
Unit Work Group Spacet -0.00 0.07 -0.11 -0.13 -0.07 -0.09 -0.01 0.00 1.00
(8) Same Departmentt 0.05 0.22 0.31 0.17 0.08 0.01 -0.00 0.01 -0.23 1.00
(9) Both Women 0.11 0.31 0.04 0.03 -0.02 0.01 0.02 -0.05 0.01 0.01 1.00
(10) Both Men 0.44 0.50 -0.01 -0.04 0.03 -0.01 -0.01 0.06 -0.02 0.00 -0.31 1.00
(11) Uncertaintyt 0.27 0.44 -0.00 -0.00 -0.00 -0.00 0.00 -0.01 0.01 0.00 -0.01 0.01 1.00
(12) Uncertaintyt x
Distance in Cross Unit
Work Group Spacet
0.00 0.04 -0.06 -0.06 -0.03 -0.05 -0.00 -0.00 0.53 -0.12 0.01 -0.01 0.01 1.00
(13) Uncertaintyt x Same
Departmentt 0.01 0.12 0.14 0.09 0.04 0.00 -0.01 0.00 -0.11 0.52 0.00 0.01 0.20 -0.22 1.00
N=236,122; Number of Dyads = 6,441
37
Table 2: Comparison of Aggregate Communication Patterns across Time Periods
Period of Relative Stability
(Weeks 1-8; 19-40)
Period of
Uncertainty
(Weeks 9-18)
t-statistic
(p-value)
One-to-One Messages
Exchanged per Week
3,819 4,141 -0.634
(0.53)
Proportion of Messages
Exchanged between
Colleagues in Same
Department
0.568 0.516 4.185
(0.00)
Correlation between
Messages Exchanged and
Distance in Cross-Unit Work
Group Spacet
-0.101 -0.122 --
38
Table 3: Poisson Regression of Messages Exchanged Between Dyads on Covariates
Covariates Model 1:
Baseline
Model 2:
H1
Model 3:
H2
Model 4:
H1 + H2
Same Locationt 0.654**
(0.249)
0.653**
(0.249)
0.655**
(0.249)
0.655**
(0.249)
Same Salary Gradet -0.346**
(0.108)
-0.345**
(0.108)
-0.348***
(0.108)
-0.345**
(0.108)
Same Cohort 0.227
(0.209)
0.226
(0.209)
0.226
(0.209)
0.225
(0.208)
Same Age 0.054
(0.122)
0.055
(0.122)
0.052
(0.122)
0.055
(0.122)
Same Ethnicity 0.332
(0.177)
0.331
(0.178)
0.330
(0.178)
0.330
(0.178)
Both Women 0.587***
(0.165)
0.587***
(0.165)
0.585***
(0.165)
0.584***
(0.165)
Both Men -0.050
(0.205)
-0.050
(0.205)
-0.049
(0.205)
-0.050
(0.205)
Distance in Cross-Unit
Work Group Spacet
-1.707**
(0.599)
-1.569*
(0.618)
-1.707**
(0.598)
-1.446*
(0.630)
Same Departmentt 2.893***
(0.155)
2.894***
(0.155)
2.951***
(0.152)
2.972***
(0.154)
Uncertaintyt -0.062
(0.091)
-0.084
(0.102)
0.077
(0.103)
Uncertaintyt x Dist. in
Cross-Unit Work
Group Spacet
-0.488*
(0.239)
-0.866***
(0.234)
Uncertaintyt x Same
Departmentt
-0.216*
(0.099)
-0.295**
(0.106)
Constant -2.177*** -2.161*** -2.197*** -2.197***
(0.268) (0.275) (0.277) (0.277)
Chi2 1548 1617 1550 1640
Prob>Chi2 0.000 0.000 0.000 0.000
Number of Obs. 236122 236122 236122 236122
* p<0.05, ** p<0.01, *** p<0.001; two-tailed tests; standard errors clustered by
sender, receiver, and time – resulting in seven cluster combinations; number of
dyads = 6,441.
39
Table 4: Qualitative Evidence
Category Illustrative Quotation
Feeling uncertain as a result of restructuring “As part of the restructuring, [Unit A], which I
was leading and [Unit B], which Liz was
leading, were combined. Liz and I were peers.
[Our boss] told us that he was merging the two
units and would decide soon whether one of us
or an external candidate would run the
combined group. We were the only two
internal candidates. I ended up getting the job,
but it was far from clear at the time.”
Experiencing strategic uncertainty “It was unclear in this case what the CEO’s
objectives were. What was known is that he
wanted to improve performance, but it was not
clear what the organizational impediments to
success looked like. The uncertainty was about
not knowing what problem we were trying to
solve.”
Experiencing structural uncertainty “So we had already declared the strategy, but
we hadn’t declared the change in
organization….The biggest question mark was
about what it would mean to depart from a
customer facing structure to a more product-
centric structure.”
Experiencing job-related uncertainty “I felt a lot of uncertainty when [my new boss]
was announced as coming into that role. I
didn’t know what was going to happen. It was
the change in leadership, from [my old boss] to
[my new boss] that made me worried. I had no
idea what [my new boss] would think of me.
Would he value the work I do? Would he keep
me in my role?”
Coping with uncertainty through social capital
activation
“I tried to gather as much information as I
could. I tried to alleviate my fears by getting
more information.”
Activating strong tie contacts
“I reached out to people I had worked with in
the past where integrity remains in our
relationship. People I have faced challenges
with and have overcome challenges with. They
become part of your trust circle. You ask them,
‘Am I reading this wrong? How are you seeing
things?’ You go to these people because
you’ve gone to battle with them in the past.”
40
Table 4: Qualitative Evidence (continued)
Activating weak tie contacts “Five years ago [the CEO] made the decision
to start a management associate program,
hiring people right out of business school to do
rotations. They hire maybe five people per
year. That group seems to be really well
networked. A lot of them had done rotations in
strategy or corporate M&A. I was getting
information from them.”
Activating ties within the respondent’s subunit “When I met with [my departing supervisor,
the former division president], he said to me,
“You know a new division president typically
gets himself a new sales VP. Don’t just sit
there and hope everything works out. Either
leave or go to [my incoming supervisor, the
current division president] and say, ‘What do
you think of me?’ I didn’t do that exactly, but
I did reach out to [my incoming supervisor].”
Activating ties outside the respondent’s subunit “I reached out to everyone in my personal
trusted network, wherever they might
be….When I was in my 20s, my personal
trusted network included mostly people in my
immediate work environment. I was recreating
my college years when my best friends were in
my dorm. At work, they were people in my
department. When I advanced into
management, I learned that it is wise to have a
network that is broader than your immediate
network. For the past few years, when I need
to gather intelligence, I try to reach out to
somebody in the business I work with
regularly, someone in sales or customer
support (to understand what is happening
externally that is driving the change), someone
in strategy or business development (so I can
figure out what we’re going to go after next),
and someone in HR person – if they qualify as
being in the trust circle.”
Activating ties outside the organization “I called up [name]. He used to be head of
strategy [in my division], and I worked for him
doing segment planning. He was made interim
head of [the division], but he ultimately didn’t
get the job on a full-time basis. So he ended up
leaving. But he still knew the organization, so I
did reach out to him during this time.”
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