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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).

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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.”

41

Figure 1: Restructuring Timeline

[Figure included in separate Powerpoint file.]