social capital activation, uncertainty, and organizational...

Social Capital, Uncertainty, Restructuring

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Social Capital Activation, Uncertainty, and Organizational Restructuring

Sameer B. Srivastava

Joint Program in Organizational Behavior & Sociology

Harvard University

Word Count: 13,479

Running Head: Social Capital, Uncertainty, Restructuring

November, 2011

Keywords: social capital; uncertainty; network activation; organizational structure; collective

attachments; social exchange; organizational change


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Social Capital Activation, Uncertainty, and Organizational Restructuring

Abstract: How do events transform social structure? This article illuminates the

microsociological mechanisms through which the uncertainty of organizational

restructuring shifts intraorganizational network structure. It helps resolve an

important conceptual puzzle about uncertainty and networks: one perspective

suggests that uncertainty leads actors to decrease network range, while another

implies the opposite effect. The article clarifies how these opposing forces play

out during the uncertainty of restructuring. The author develops theoretical

propositions about how the uncertainty of restructuring alters network range

across formal subunits and cross-cutting work groups. These propositions are

tested using a unique data set that includes the period before, during, and after a

major restructuring in an information services firm. Analyses of 40 weeks of

electronic communications among 114 employees reveal that, during periods of

heightened uncertainty, there was: (1) an increase in network range across formal

subunits; and (2) a decrease in network range across the myriad work groups to

which actors belonged. The study contributes to research on social capital,

collective attachments in social exchange, and the dynamics of organizational

structure during times of change.

November, 2011


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Sociological research has long studied the role of events in transforming social structure

(Sewell 2005). Transformative events, such as an organizational restructuring, tend to breed

uncertainty for individual actors – for example, about their structural position or resources. This

uncertainty can, in turn, prompt actors to mobilize social capital – i.e., to seek resources such as

information, influence, and social support that are accessible through social connections (Lin

2001; McDonald and Westphal 2003; Mizruchi and Stearns 2001). As Pescosolido (1992: 1105)

writes, “Events set into motion a specific process of coping with uncertainty….They can be seen

as a ‘shock’ to a network, reverberating through it and altering the overall system of relations.”

Just because valuable resources are available through social relations does not, however,

mean that they will be tapped. Trust-based barriers (Smith 2005), cognitive recall of

relationships (Smith, Menon, and Thompson in press), and interpersonal affect (Casciaro and

Lobo 2008) all can constrain the set of contacts with whom people exchange resources. That is,

during many events, people activate only a subset of the relations to which they have access.

Existing theory poses an important puzzle about uncertainty’s effects on the range of

activated networks – i.e., the diversity of individuals to whom a person initiates contact (Burt

1983; Reagans and McEvily 2003). One theoretical perspective suggests that uncertainty will

lead people to activate ties to socially proximate contacts (e.g., Hurlbert, Haines, and Beggs

2000; McDonald and Westphal 2003), with whom they are more likely to have trust-based

relationships based on a history of past exchange (Cook and Emerson 1978). Another view

suggests that uncertainty will trigger the activation of ties to socially distant contacts, as people

search for novel information (e.g., Burt 2000; Friedkin 1982) and seek to influence or form

coalitions with those on whom they depend (Pfeffer 1989, 1992; Stevenson and Greenburg


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2000).1 The former perspective predicts a decrease in the range of activated networks, while the

latter suggests an increase. These choices are important to understand because

intraorganizational network activation can have consequences for individual attainment (Burt

1992; Podolny and Baron 1997; Seibert, Kramer, and Liden 2001) – especially when, as in a

restructuring event, power and resources are in flux (Pfeffer 1989). It can also influence the

level and quality of social support that people obtain to withstand the stresses of uncertain times

(Cohen and Wills 1985; House, Umberson, and Landis 1988; Swanson and Power 2001).

This article helps resolve the conceptual puzzle about uncertainty’s effects on the range

of activated networks. I draw on theories of social capital (Lin 2001), the dynamics of

restructuring (Gulati and Puranam 2009; Huy 2002; Nickerson and Zenger 2002), and

individual-to-collective attachments (Lawler, Thye, and Yoon 2009) to derive propositions about

the effects of uncertainty on two dimensions of intraorganizational network range – across

formal subunits (e.g., departments) and cross-cutting work groups (e.g., project teams consisting

of members from different departments). Next I report on a study that takes advantage of the

quasi-exogenous shock of restructuring to identify the effects of uncertainty on network

activation. The analysis draws on a longitudinal data set – spanning 40 weeks and including the

electronic communication logs of 114 employees, company-wide email distribution lists,

archived employee communications memos, and human resource records – that provides a rare

look into an organization before, during, and after a spell of uncertainty. I also report on semi-

structured interviews with a subset of these employees. Findings from this investigation

contribute to research on social capital, collective attachments in social exchange, and the

dynamics of organizational structure during times of change.

1Following Putnam (2000: 22-23), the former perspective can be thought of as emphasizing “bonding” social capital

– i.e., looking inward and mobilizing solidarity among homogeneous groups – and the latter as focusing on

“bridging” social capital – i.e., looking outward and encompassing people across diverse social cleavages.


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THEORY

Restructuring and Uncertainty

Restructuring is defined as the addition, deletion, or recombination of formal subunits (Samina

2006); these changes are often accompanied by downsizing and changes in reporting

relationships. Even when restructuring is well anticipated, it can produce uncertainty for

organizational actors because one set of changes can trigger a cascade of other realignments that

are difficult to predict (Hannan, Polos, and Carroll 2003a, 2003b). In many cases, even the

initial changes of restructuring are poorly anticipated. That is, employees in a restructuring

organization are often left wondering whether they will remain employed, to whom they will

report, and how their job roles might change (for a review of the effects of restructuring on

workers, see Kalleberg [2009]).

Social Capital Activation

The uncertainty of restructuring can lead people to seek resources from their social contacts. At

any given time, many of these ties will be latent – that is, people will have pre-existing

relationships but no current interaction with a set of individuals. In the wake of events, such as

restructuring, individuals convert some latent ties into active ones – that is, they initiate contact

with individuals with whom they have a pre-existing relationship. For example, organizational

actors might seek out supervisors, mentors, and colleagues in other units who can provide

valuable information or influence their outcomes. Following Hurlbert, Haines, and Beggs (2000:

599), who examine resource mobilization through social contacts following a natural disaster, I

define social capital activation as the choice to initiate contact with certain individuals among the

set of actors in one’s pre-existing network (see also Renzulli and Aldrich [2005], Smith [2005]).2

2Social capital activation has also been used to refer to the cognitive recall of contacts in response to a situational

prime (Smith, Menon, and Thompson forthcoming). Because the distinction between recall and the choice to initiate


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Social Exchange under Uncertainty

Intraorganizational networks can be conceptualized as exchange relations (Cook and Whitmeyer

1992; Uehara 1990) – i.e., repeated transactions of valued resources, such as information and

influence, among the same actors over time (Emerson 1981). Prior research yields contradictory

expectations about the consequences of uncertainty for the choice of exchange partner.3 On one

hand, uncertainty promotes relational commitment: a tendency toward continued exchange with

longstanding, trusted partners (Cook and Emerson 1978; Kollock 1994; Molm, Peterson, and

Takahashi 2000; Podolny 1994). This inclination can lead to a preference for socially proximate

partners, with whom a person is more likely to have a trust-based relationship based on a history

of prior exchange (Buchan, Croson, and Dawes 2002; Krackhardt 1992; Macy and Skvoretz

1998). As Macy and Skovretz (1998: 651) conclude from a series of simulation experiments,

“The earliest trust rule is based on social distance—trust neighbors but not outsiders.”

Uncertainty, for example, can prompt CEOs to seek advice from contacts with the same

functional background and in the same industry rather than contacts with different functional

backgrounds or in different industries (McDonald and Westphal 2003). The former are more

socially proximate to the CEO than the latter. Similarly, bankers operating in uncertain settings

seek information from close contacts when seeking advice on and support for deals (Mizruchi

and Stearns 2001). Finally, when uncertainty is associated with crisis (Hermann 1963), it can

contact with a recalled contact is not of conceptual relevance to my line of reasoning, I focus on the latter in my

usage of the term. My definition also differs from that used by Smith [2005], who defines activation to include both

an individual’s choice to seek resources from a contact and the contact’s choice to provide instrumental or

expressive aid to the help seeker. Because my arguments pertain only to the choices of the focal actor, I define

activation to include only the first of these steps; i.e., the choice to initiate contact. 3Restructuring creates uncertainty for organizational actors, for example by raising questions about their

employment status or departmental affiliation. These unknowns in turn breed social uncertainty, or the likelihood of

concluding satisfactory exchange with colleagues (Cook and Emerson 1984). Social uncertainty stems from the fact

that people become less sure of the resources, power, and positions they will hold once the restructuring has

concluded. For example, informal negotiations among department heads about resource sharing, personnel

transfers, or collaborative projects can become difficult if the departments are at risk of being dissolved or

undergoing budget cuts. Because restructuring-related uncertainty and social uncertainty tend to co-occur, I use the

term, uncertainty, for both forms unless the distinction is materially relevant.


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also lead to increased commitment to one’s formal subunit and reduced cooperation with other

formal subunits (Krackhardt and Stern 1988). Thus, when facing the uncertainty of

restructuring, people may seek contact with organizationally proximate colleagues whom they

are likely to know well and can trust for information, advice, and support.

On the other hand, under conditions of uncertainty, the resources held by organizationally

distant colleagues can become more valuable (Burt 2000; Pfeffer 1989; Pfeffer 1992). For

example, colleagues in other parts of the organization may possess crucial non-redundant

information (Friedkin 1982), such as knowledge of job vacancies in other subunits that may be

created by restructuring. Similarly, past supervisors, potential future supervisors, and mentors in

other parts of the organization may wield important influence over decisions about whether an

actor will remain employed by the organization and, if so, about the job role the person will

assume. Thus, people will be motivated to strengthen political coalitions with actors in other

parts of the organization (Pfeffer 1989; Pfeffer 1992). These factors can promote a preference

for exchange with organizationally distant colleagues. In sum, the former set of arguments

suggests that restructuring-induced uncertainty will lead to a decrease in network range (i.e., the

activation of ties to organizationally proximate colleagues), while the latter implies an increase in

network range (i.e., the activation of ties to organizationally distant colleagues).

Dimensions of Intraorganizational Network Range

To resolve this conceptual puzzle, I suggest the need to distinguish uncertainty’s effects on two

facets of intraorganizational network range. Range can be defined along multiple dimensions,

for example size, complexity, density, and diversity (Campbell, Marsden, and Hurlbert 1986). In

organizational settings, a key dimension of range is diversity – based on affiliations to collective

units, such as departments or project teams (Ancona and Caldwell 1992; Krackhardt and Stern


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1988; Reagans and McEvily 2003). For example, a person with a high proportion of ties to

colleagues outside her department has broader network range than a person with ties primarily

within her department. I follow Ibarra (1992: 166) in distinguishing between two kinds of

collective units inside organizations: (1) formal subunits, which are defined by “specified

relationships between superiors and subordinates and among functionally differentiated groups

that must interact to accomplish an organizationally defined task;” and (2) cross-cutting work

groups, such as “committees, task forces, teams, and dotted-line relationships that are formally

sanctioned by the firm” but do not correspond to the organizational chart.4 In most

organizations, people belong to a handful of formal subunits, based on the hierarchical reporting

relationships in which they are embedded, and to many different work groups, based on the

workflows and decision processes in which they participate. An increase in range across formal

subunits can occur through an increase in contact with colleagues in different subunits, a decline

in contact with people in the same subunit, or a combination of both effects. The same is true for

range across work groups.

Network Range across Formal Subunits

For three reasons, I expect that restructuring-induced uncertainty will lead to an increase in

network range across formal subunits. First, colleagues in other departments are more likely to

possess non-redundant information about the restructuring (Friedkin 1982), such as which

individuals and groups are likely to be affected by the organizational change and what job

vacancies are likely to be created. Second, uncertainty can trigger the exercise of power and

influence tactics. Organizational actors are apt to use these tactics during situations “like

4Soda and Zaheer (forthcoming) follow a similar approach, distinguishing between “authority relationships” and

“workflow relationships.” Of course, a formal subunit can also be thought of as a work group, and work groups can

be nested within formal subunits. I focus on those work groups that do not correspond to formal subunits – for

example, project teams consisting of members from different departments. For brevity, I hereafter refer to them

simply as work groups.


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reorganizations and budget allocations…and in instances where there is likely to be uncertainty

and disagreement” (Pfeffer, 1992: 37). If the uncertainty of restructuring means that current

reporting relationships and departmental affiliations may not persist, people will instead direct

attention toward colleagues in other organizational subunits who can advocate on their behalf –

for example, keep their names off employee layoff lists and lobby decision makers to help them

secure coveted positions. Finally, in many organizations, periods of restructuring are governed

by strong communication norms. For example, managers are instructed to communicate only

officially sanctioned messages, hew to pre-specified communication timetables, and refrain from

‘leaking’ information to subordinates (for an illustration of prescribed communication protocols

for managers leading organizational change, see Klein [1996]). As a result, the opportunity

structure for exchange within formal subunits can become constrained (Marsden 1983), resulting

in the constriction of communication within formal subunits. Taken together, these arguments

suggest:

Hypothesis 1: An increase in uncertainty will lead to an increase (decrease) in contact

among colleagues in different (the same) formal subunits. That is, an increase in

uncertainty will lead to an increase in range across formal subunits.

Network Range across Work Groups

While the uncertainty of restructuring can be expected to increase network range across formal

subunits, I theorize the opposite effect for range across work groups. Whereas the search for

non-redundant information will lead people to activate ties to colleagues in different

departments, there will be no corresponding preference for interaction with colleagues in


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different work groups. Because work groups often consist of people from different formal

subunits, colleagues in the same work group are still likely to possess non-redundant

information. Similarly, because they are often outside the potentially shifting formal subunit

structure, they are likely to wield different sources of power and influence than colleagues in the

same subunit. Finally, whereas normative constraints can limit communication within formal

subunits, there are few such limitations to the exchange of information and gossip within work

groups (Balogun and Johnson 2004; Isabella 1990). Thus, to fill the information void within

formal subunits, people are apt to seek information from trusted colleagues in their work groups.

Further support for the notion that the uncertainty of restructuring will lead people to

activate ties to proximate colleagues in the work group structure comes from the affect theory of

social exchange, which posits that “when people interact with others, they tend to experience

mild, everyday feelings, and under some conditions people associate these feelings with a shared

group affiliation or membership” (Lawler, Thye, and Yoon 2009: 9). These affective

attachments can draw a person into exchange with individuals who belong to the same collective

unit. This exchange need not be limited, however, to expressive resources such as social

support; it can also include instrumental resources such as information or influence (Lin 2001).

The theory suggests that individual-to-collective attachments are most likely to occur when

relations in the collective unit are characterized by productive exchange5 – i.e., they involve joint

activity, mutual interdependence, shared control over outcomes, and collective rewards for group

outcomes (Lawler, Thye, and Yoon 2000; Lawler 2001; Lawler, Thye, and Yoon 2008). Formal

subunits vary in the extent to which their members engage in productive exchange. For example,

a manufacturing department with an integrated production line has more interdependent activity

5Other basic forms of social exchange include negotiated, involving explicit and binding agreements; reciprocal,

involving sequences of unilateral giving between a pair of actors; and generalized, involving unilateral exchange in

which givers and receivers are not matched in pairs (Lawler, Thye, and Yoon 2000: 617).


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and shared accountability than does a regional sales office with independent sales agents. Yet

neither of these formal subunits has as much interdependence or shared accountability as a work

group, such as a cross-functional product development team (for a detailed discussion of these

examples, see Lawler, Thye, and Yoon [2009: 62-63]). Thus, even in stable times, individual

attachments to work groups are likely to be stronger than those to formal subunits.

For two reasons, I argue that individual attachments to work groups will remain solid,

and even grow stronger, during the uncertainty of restructuring, even while attachments to

formal subunits erode. First, during times of organizational change, the work group structure

tends to remain more stable than the formal subunit structure. For example, project teams, task

forces, and decision making bodies often remain intact even when departmental lines are

redrawn. Moreover, any changes to the work group structure typically lag behind those to

formal subunits (Gulati and Puranam 2009; Lamont, Williams, and Hoffman 1994; Nickerson

and Zenger 2002). Thus, work groups will tend to remain salient collective entities during times

of uncertainty, and individuals’ relatively strong attachments to work groups will tend to persist.

Second, during periods of radical organizational change, managers have been shown to increase

their emotional commitment to work groups, such as project teams responsible for implementing

change initiatives (Huy 2002). These commitments will further draw individuals into exchange

with colleagues in the same work group. Taken together, these arguments suggest:

Hypothesis 2: An increase in uncertainty will lead to a decrease (increase) in contact

among colleagues in different (the same) work groups. That is, an increase in uncertainty

will lead to a decrease in range across work groups.


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METHODS

Research Setting

A major information services company, hereafter referred to as InfoCo, served as the research

site for the study. It employed over 8,000 employees and generated over $3 billion in revenue.

The company’s formal structure consisted of global business divisions, regional marketing and

sales units, a global product development unit, and shared support functions (e.g., Finance). In

addition, there were numerous work groups, such as project teams, task forces, and governance

and decision making bodies.

Declining financial performance led InfoCo’s management team to undertake a major

restructuring. There were three major organizational changes. First, InfoCo created global

“solution lines,” which combined product development and marketing resources from different

regions into newly formed formal subunits that had global responsibility for the profitability of a

set of related products and services. Next, InfoCo consolidated the sales and marketing subunits

and downsized redundant personnel in these two functions. Finally, a new global marketing

subunit was established to set standards and ensure consistent implementation across regions.

Many people they lost their jobs, moved departments, or changed supervisors during this period.

Study Participants

The study included all 114 US-based members of the InfoCo’s senior leadership group.6 They

were mostly male (67.5%), white (84.2%), and geographically concentrated: 38.6% in a

Midwestern city, 19.3% in New York City, and the rest were distributed among smaller sites.

They spanned three salary bands (in ascending rank): 7.5% operational leaders, 80.3% tactical

leaders, and 12.2% executive leaders. I collected archived electronic communications and

6Approximately thirty individuals outside the US were also members of the senior leadership team. It was not

possible to include them in the study because of privacy laws and company policies regarding the use of employee

emails in certain countries.


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human resource data on all of these individuals and conducted semi-structured interviews with a

subset (see Appendix A; further details below). Because of concerns about how rank-and-file

employees might react if they somehow became aware of the study (e.g., distraction, distrust of

senior management), it was not possible to include a broader cross-section of employees.

The choice to focus on a relatively senior employee population involves clear tradeoffs.

On the one hand, they all had pre-existing ties to one another through their involvement in the

senior leadership group. Thus, they were an appropriate sample for the study of network

activation (rather than new tie formation). In addition, although they were fairly senior, they had

little ex ante knowledge of the restructuring, which the CEO implemented with limited input

from this group. That is, they experienced uncertainty during restructuring. During the time that

archival data were being collected, they were also unaware of this study.7 Moreover, the

qualitative evidence (see Appendix B) suggests that lower level employees experienced greater

levels of uncertainty than the people included in the study. Thus, the focus on senior employees

probably provides a conservative test of the proposed hypotheses; however, it also raises

questions about the extent to which the findings can be generalized to other employee groups.

Further implications of this choice are explored in the Discussion and Conclusion section below.

Before describing the data collected, I present evidence that the people included in the

study did, in fact, experience a high level of uncertainty during restructuring. First, these

individuals were significantly affected by the restructuring: 43 (37.7%) had a change in

supervisor, 15 (13.6%) moved to a different InfoCo division, and 13 (11.4%) exited the

company.8 Some experienced multiple such changes. Second, the qualitative evidence suggests

7The head of human resources and a handful of his staff knew about the study. Knowledge was kept to this small

group to minimize distraction and keep people from altering their communication patterns in response to the study. 8 I included in the quantitative analysis the thirteen who exited because, during the periods that they were employed

by InfoCo, they were at equal risk of exchanging messages as their colleagues who remained throughout the entire


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that they felt uncertain and that the restructuring represented an exogenous shock. 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 director in charge of a marketing support unit recalled:

There was a peer of mine who lost his job. I literally landed at [City] Airport,

checked my messages, and saw that I was invited that afternoon to a call with the

CEO and a strange list of other people. I sent a note to my boss to find out what

this was all about. He called me to say, “Find a place to sit down.” He then told

me that they had eliminated my peer’s position, and I was assuming responsibility

for his group. Within two hours, I had to get ready for a meeting with the CEO

and [everyone] who would now be reporting to me. I had a ton of questions about

this. Was this part of a broader set of changes that would affect me, or was this

the only shoe to drop? Had we just been acquired by another company?

A product development manager reported: “[L]eaders were given a certain number of

slots to fill. We had to go through a process of assessing and ranking 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.” Similarly, a division general

manager stated, “Our reorganizations tend to be big surprise events when they are unveiled.”

Even some of InfoCo’s well-orchestrated efforts to communicate with people about the

changes and help reduce uncertainty often managed to backfire. A director of product

engineering reported:

Soon after we got the memo [announcing the reorganization], they [i.e., senior

management and HR staff] put a bunch of us in a room and told us about the

‘global solutions journey’ we would soon be embarking on. I still remember this

chart in which they put the pictures of the engineering people at the bottom. I

remember thinking, ‘We are the furthest from Heaven in your chart even though

observation period. I dropped them for the periods that they were no longer employed by InfoCo. There were no

significant differences between those who stayed and those who exited on observable characteristics such as tenure,

rank, gender, or ethnicity.


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we are most important to delivering on the strategy.’ What does that say about

how you value us and what these changes will mean for us?

Data Collection

I compiled four kinds of archival data: (1) internal communication memos, which I used to

establish the period of greatest uncertainty stemming from the restructuring; (2) email logs

(spanning a period of 40 weeks) of 114 InfoCo employees9; (3) extracts from InfoCo’s Human

Resource (HR) system – including time-invariant measures (e.g., sex, ethnicity, date of hire) and

time-varying measures (e.g., departmental affiliation, office location); and (4) extracts of

InfoCo’s email distribution lists to identify shared work groups among employees (i.e., based on

list co-membership in a given week). Based on these communications and the interviews, I

established that the period of greatest uncertainty commenced in Week 9, when the first of

several communications providing details of the new organizational structure was released.

Additional memos – announcing the formation of global solution line units, the consolidation of

other organizational units, and the appointment and departure of key personnel – were sent every

couple of weeks until Week 18. By Week 18, all of the changes to the organizational structure

had been made, all key positions had been filled, and all departing employees had exited. Thus,

although there was uncertainty throughout the observation period, Weeks 9 through 18

represented the period of peak uncertainty. I conducted an additional analysis to corroborate the

restructuring timeline, which is depicted in Figure 1. I worked with the Human Resources staff

to identify restructuring-related keywords, such as “organizational announcement,”

“organizational appointment,” “departure,” “open position,” and several internal code names

used for the restructuring. I then identified restructuring-related email communication through a

keyword match of email subject lines. The volume of these restructuring messages rose to a

9Prior 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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peak in Week 9 and began tapering off after Week 18.10

Having established the timeline, I turn

next to a discussion of the relative merits of two data sources: email logs and distribution lists.

– Figure 1 about here –

Data – Email Logs

Analyses of email communication are becoming increasingly common in organizational research

(Allatta and Singh 2011; Hinds and Kiesler 1995; Kossinets and Watts 2006; Menchik and Tian

2008). Consistent with the ethical standards used in prior studies, I took three steps to protect the

privacy and confidentiality of InfoCo employees (for a discussion of ethical issues in

organizational network analysis, see Borgatti and Molina [2005] and Kadushin [2005]). First, all

identifying information (e.g., names, email addresses) was encrypted using an irreversible

algorithm. Second, email logs did not contain message content – just the trace of who sent a

message to whom, at what date and time, and with what subject line. Finally, I only collected

data on messages exchanged among InfoCo employees – i.e., external messages were excluded.

Electronic communications 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. Moreover, 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 (e.g., by eliminating measurement error created by variation in interviewer

techniques or the context in which surveys are filled out) and limiting the risk that people drop

10

The results reported below were robust to slight (i.e., 1-2 week) variations in the choice of the period of greatest

uncertainty.


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out of the study due to survey fatigue. Finally, email logs contain fairly complete records of

electronic communication. At InfoCo, interviewees reported that they routinely used company

email even for personal communication. At the time of the restructuring, it was uncommon for

InfoCo employees to use instant messaging or personal email services at work. In addition,

emails sent from personal digital assistants also went through the company servers.

These benefits are counterbalanced by at least three limitations. First, the trace of email

communication between two colleagues does not always signify purposive interaction between

them. For example, email messages can sometimes be automatically generated – e.g., “Out of

Office” message sent when a recipient is on holiday. In addition, emails are sometimes sent to

pre-determined distribution lists (e.g., all employees in the company or on a project team) or

routinely copied to colleagues who have little interest in or need to know the information. I

addressed these shortcomings by eliminating all emails that included the phrase “Out of Office”

in the subject line and by restricting the analysis to emails sent only to a single recipient – i.e.,

mass emails, “cc” or “bcc” messages, and “reply all” messages were all excluded.

Next, email exchanges reflect only a subset of the interactions among people. Face-to-

face meetings, phone calls, and informal gatherings at the water cooler are not captured in email

logs. In this setting, however, the email system was linked to the electronic calendars that

virtually all employees used to maintain their daily schedules. Email logs therefore included a

record of all electronically scheduled meetings. One interviewee explained:

Of course you wouldn’t talk about very sensitive topics over email. You will

generally want to have the sensitive discussions in a face-to-face meeting. But

you might see this kind of interaction reflected in email scheduling traffic. If I

were feeling anxious about my own situation, I would schedule a one-on-one

meeting with my mentor or some other trusted colleague. The other way this kind

of interaction will be reflected in email is through organization-wide

communications. Those messages tend to get cascaded down the organization.


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Especially when you get further down the organization, people will often forward

those messages to someone they know or trust to help them clarify what it means.

Third, email traffic may simply reflect routine communication (e.g., negotiating a

convenient time to meet), rather than meaningful efforts to exchange resources such as

information or influence. I addressed this limitation by taking into account the date and time

stamp of messages. In particular, I separately analyzed messages sent outside business hours

(i.e., early mornings, late evenings, weekends, and holidays). The qualitative evidence suggested

that email traffic outside of normal business hours was more likely to reflect the exchange of

social resources. As a sales executive stated, “The off-hours communication tend to blur the

business and the social a lot more. Your laptop computer can swing from being strictly business

to strictly personal in a matter of minutes.” A vice president with young kids reported: “I’ve got

pretty hefty family obligations, so I try not to do a lot of email over the weekend. But the people

I tend to be in touch with at those times are my tighter-knit group.”

These limitations notwithstanding, it is worth noting that people often feel less inhibited

in email communication than in face-to-face communication (Sproull and Kiesler 1986) and that,

over time, computer-mediated teams can develop levels of trust that are comparable to those in

face-to-face teams (Wilson, Straus, and McEvily 2006). Thus, it is likely that email exchanges

included at least an important subset of sensitive communication about the exchange of social

resources. Still, email communication likely represents a conservative indicator of overall shifts

in network range during politically sensitive periods such as restructuring.

Data – 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 work group structure. Widely used across

organizations and readily accessible, distribution lists encapsulate the myriad collective units that


19

exist within an organization. Some lists, of course, trace the formal organizational structure. For

example, a department head might create a list for all department members. Yet, in a typical

organization, email lists also exist for various standing cross-departmental teams, ad hoc task

forces, and professional interest groups that are active. Colleagues with a large proportion of

lists in common are therefore likely to be more proximate in the work group structure than are

colleagues with few shared lists. Furthermore, changes in list membership reflect movement

within the work group structure. Distribution lists are not, however, a perfect data source. Not

every work group in the organization has a corresponding list. Moreover, in the data I collected

at InfoCo, list names were encrypted for confidentiality reasons and could therefore not be

separated into those corresponding to formal subunits, to work groups, and to social groups.

In principle, the work group structure could overlap significantly with the formal

organizational structure, for example if work groups were entirely nested within the formal

subunits. In practice, however, the two measures appeared to reflect non-overlapping

dimensions of network range. There were over 2,300 distribution lists in use during a typical

week in this observation period; the mean number of lists to which an employee belonged was

12.2. The number of distribution lists in use far exceeded the number of departments in which

the sample of 114 employees worked. Moreover, based on a median split of distance in the work

group structure (defined below), only 4.3% of dyads in the sample were both proximate in the

work group structure and in the same department. Thus, at least in this setting, distance in the

work group structure was relatively independent of distance across formal subunits. Interviews

with InfoCo employees further indicated that these lists were used primarily for work groups,

especially project teams that did not correspond to formal subunits. A product development

manager explained, “I use [distribution lists] for very specific project-related activity. People


20

have gotten so weary of email that we’ve had a push to narrow distribution lists to work-related

projects that are active at a given point in time.” Another interviewee explained, “I maintain

distribution lists for my direct reports, my ‘extended direct reports’ in other departments, and

[the product development team I lead].” A female vice president reported, “The only list I use

that isn’t about project-related work is the Women’s Network. The rest are projects.”

Data – Supplemental Semi-Structured Interviews

To help address the limitations of the archival data sources described above, I conducted

supplemental semi-structured interviews with 23 study participants after the restructuring

concluded. These individuals were selected from stratified sub-samples of people who

experienced high and low levels of uncertainty during the restructuring but remained employed

by InfoCo. Legal concerns kept the company from granting me access to those who had exited.

Interviews lasted between 30 and 45 minutes and were recorded and transcribed. The purpose of

the interviews was to validate the timeline of events, assess the level of uncertainty people

experienced, understand how and why people activated their networks during the restructuring,

and determine how they used electronic communication media in this organizational setting. I

used a software tool – Atlas.ti – to code and analyze the responses. I paid particular attention to

the network activation choices described by respondents, coding the kinds of people who were

contacted (e.g., same department or different department; shared work group), the resources

sought in these interactions (e.g., information, influence, social support), and other factors that

promoted or inhibited communication (e.g., normative constraints, socially awkward situations).


21

Measures

The response variable was a count of the number of one-to-one email messages sent in a given

week, t, between a dyadic pair, i and j.11

For the reasons noted above, I also separated out

messages sent outside of business hours (i.e., weekday messages sent before 6 am or after 8 pm

and all weekend and holiday messages). Time zones were not materially relevant, given that

over 90% of the sample were located in the eastern United States.

Given the conceptual focus on network range – i.e., the diversity of actors to which one is

connected – explanatory variables were all expressed as differences (e.g., different sex) or

distance between a pair of actors rather than as similarities (e.g., same sex) or proximity. The

time-varying indicator variable of range across the formal subunits was: Different Departmentt

(set to 1 if i and j were in different departments in week t and to 0 otherwise). For range across

work groups, I considered the distance between two actors based on the number of email

distribution lists to which they both belonged. I employed one of the most widely used distance

measures, Jaccard’s distance (Sneath and Sokal 1973)12

:

Distance in Work Group Structuret = 1 – Si,j / (Ni+Nj-Si,j)

Where: Si,j = Shared distribution lists between i and j in week t

Ni = Number of lists to which i belonged in week t

Nj = Number of lists to which j belonged in week t

This measure has a theoretical range from 0, for a pair of actors who belong to distribution lists

that perfectly overlap one another, to 1, for a pair of actors who have no shared lists. Because

11

Comparable results to those reported below were obtained when the response variable was (undirected) messages

exchanged between dyads rather than (directed) messages sent. I also varied the mass email threshold to include up

to four recipients per message. The results reported below were materially unchanged. Finally, an alternative way

to operationalize network range is to consider the conditional log-odds of any contact (i.e., a dichotomous response

variable) between a dyadic pair, rather than the intensity of contact (see, for example, Reagans and McEvily [2003]).

Logit models using a dichotomous indicator produced comparable results to the Poisson models reported below. I

prefer the Poisson framework because it better accounts for the fact that resource exchange likely occurs over the

course of multiple messages. 12

This covariate is centered when included in regression analyses (Aiken and West 1991) but left uncentered in the

descriptive statistics.


22

the results reported below were robust to the use of alternative distance measures, such as one

based on Dice’s coefficient (Dice 1945)13

, I report only those based on Jaccard’s distance.

To identify the effects of uncertainty on network range, I included two interaction terms:

Uncertainty x Different Departmentt and Uncertainty x Distance in Work Group Structuret.

Positive coefficients for these interaction terms indicate an increase in network range during the

period of uncertainty, while negative coefficients suggest a decrease in range.

Because various individual differences, such as the need for cognitive closure (Webster

and Kruglanski 1994) and the personal need for structure (Neuberg and Newsom 1993), are

known to shape reactions to uncertainty, I included fixed effects for every message sender and

every message receiver to account for such unobserved heterogeneity. To capture temporal

variation in communication exchange (e.g., dips during holiday weeks), I included period (i.e.,

week) fixed effects.14

I also included a number of dyad-level control variables: (a) Different

Locationt; (b) Different Salary Gradet; (c) Different Sex; (d) Different Ethnicity15

; (e) Different

Cohort (i.e., hire dates separated by more than one year); and (f) Different Age (i.e., difference of

more than three years).

Estimation

I constructed a dyad-level panel data set of messages sent between i and j in week, t. Analyses

of such data must contend with the clustering (i.e., non-independence) of observations. The

failure to control for clustering can lead to under-estimated standard errors and over-rejection of

hypothesis tests. I addressed this issue by using a variance estimator that enables cluster-robust

inference when there is two-way or multi-way clustering (Cameron, Gelbach, and Miller 2011).

13

This measure is computed by dividing shared lists by the sum of lists to which each member of a dyadic pair

belongs. As noted above, this measure produced comparable results to those reported below. 14

Week fixed effects subsume the main effects of the period of uncertainty. Including an indicator for uncertainty

period (Weeks 9-18), instead of week fixed effects, did not have any material effect on the results reported below. 15

Because 84% of the population was white, I considered only two categories of ethnicity: white and non-white.


23

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.16

This technique is appropriate for the analysis of dyadic

network data, including panel data, and compares favorably in simulation studies to alternative

methods, such as the Quadratic Assignment Procedure (Lindgren 2010). Following this

approach, and because the response variable was a count of messages sent between dyadic pairs

in a given week, I estimated fixed effect Poisson regressions (Cameron and Trivedi 1986) with

three-way clustering of standard errors: by sender, by receiver, and by week.

RESULTS

Quantitative Analysis

Table 1 reports descriptive statistics and a correlation matrix for the main variables of interest.

As expected, there was a negative correlation between messages sent and various measures of

dissimilarity between dyads (e.g., Different Departmentt and Different Locationt).

– 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 communication

16

Each of the first three matrices clusters in one of the dimensions. Because some observation pairs are in the same

cluster in two dimensions, considering only these three matrices would result in double counting. The technique

then clusters on the three combinations of two dimensions and subtracts the resulting matrices. 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] for a detailed explanation). This approach also controls for potential over- or under-dispersion

in the data. I implemented it in STATA using the “clus_nway” script (Kleinbaum, Stuart, and Tushman 2011).


24

volume during the weeks of uncertainty, this change was not statistically significant. The

proportion of messages sent between colleagues in different departments was 0.484 in the period

of uncertainty and 0.432 in the period of relative stability (p<.001). This pattern is consistent

with Hypothesis 1 – that uncertainty will increase network range across the formal structure.

The correlation between Distance in Work Group Structuret and messages sent was -0.148 in the

period of uncertainty and -0.124 in the period of relative stability. This result is consistent with

Hypothesis 2 – that uncertainty will decrease range across the work group structure.


Table 3 reports the results of the Poisson regression models used to formally test

Hypotheses 1 and 2. Models 1 and 2 show baseline results for all messages sent and for

messages sent outside of business hours, respectively. Different Locationt, Different Sex,

Different Departmentt, and Distance in Work Group Structuret have negative coefficients that are

statistically significant. The negative coefficients for Different Departmentt and Distance in

Work Group Structuret are consistent with prior research indicating a tendency for workplace

networks to hew to the formal organizational structure and work flow (Han 1996; Hinds and

Kiesler 1995; Ibarra 1992). It is also worth noting that Different Salary Gradet has a positive

and significant coefficient. One explanation for the lack of a negative relationship with Different

Salary Gradet is that the study population was relatively homogeneous in organizational rank.

Models 3 and 4 add the interaction terms of interest: Uncertainty x Different Departmentt

and Uncertainty x Distance in Work Group Structuret. The former has a positive coefficient that

is significant at the 95% confidence level in both models, while the latter has a negative

coefficient that is significant at the 95% confidence level in both models. These effects were


25

more pronounced in the models including only off-business hour communication.17

These

results provide support for both hypotheses. Considering that changes in email communication

probably represent a conservative indicator of overall shifts in network activation, these effects

were sizable: In the period of uncertainty relative to stability, there was a 21% decline in the

predicted number of messages sent between colleagues in the same department and a 7%

increase in the predicted number of messages sent between colleagues in different departments.

For dyads at the median of the Distance in Work Group Structuret measure, there was an 18%

decline in the predicted number of messages sent in the period of uncertainty relative to stability.


Table 4 reports results that help establish that these effects were in fact driven by the

uncertainty of restructuring. I worked with representatives from InfoCo’s Human Resources

department to identify the job roles that, at the time of the restructuring announcement, seemed

to be at greatest risk of being affected by the changes. For example, a person responsible for

product marketing faced greater uncertainty from the impending creation of global solution lines

than a person responsible for legal compliance (which was largely unaffected by the announced

changes). I identified 27 job roles that involved high levels of uncertainty. Of the 12,882 dyads,

5,742 (45%) included at least one member who held one of these 27 job roles. These dyads

experienced greater uncertainty from restructuring than the 7,140 (55%) dyads in which neither

member held one of these roles. I used seemingly unrelated post-estimation to compare the size

of coefficients in Model 5, which included dyads experiencing low uncertainty, to those in

Model 6, which included dyads experiencing high uncertainty. Uncertainty x Different

17

I used seemingly unrelated post-estimation to compare the size of the coefficients in a model including only off-

business hour communication with those in a model including only business hour communication. The increase in

network range with respect to the formal structure was more pronounced in off-business hour communication

(p<0.05), and the decrease in network range with respect to the work group structure was also more pronounced,

though marginally significant, in off-business hour communication (p<0.10).


26

Departmentt was significantly more positive (p<.01) and Uncertainty x Distance in Work Group

Structuret was significantly more negative (p<.01) in the model with dyads experiencing high

uncertainty than in the model with dyads experiencing low uncertainty.18

Thus, it appears that

the observed shifts in network range occurred in response to heightened uncertainty.


Finally, to understand whether communication patterns reverted to their original state

when the uncertainty period ended, I used a three-period model, with interactions for the period

of uncertainty (e.g., Uncertainty x Different Departmentt) and the period after uncertainty (e.g.,

Post-Uncertainty x Different Departmentt). In the three-period model, Uncertainty x Different

Departmentt was positive and marginally significant (beta=.236, p=.086), and the linear

combination of Uncertainty x Different Departmentt – Post-Uncertainty x Different Departmentt

was positive and significant (beta=.358, p=.031). That is, different department communication

appeared to increase during the period of uncertainty but then wane as uncertainty receded. By

contrast, Uncertainty x Distance in Work Group Structuret was negative and significant (beta=-

1.040, p=.023), while the linear combination of Uncertainty x Distance in Work Group Structuret

– Post-Uncertainty x Distance in Work Group Structuret was of the expected (negative) sign but

not significant (beta=-0.626, p=.149). Thus, it appeared that uncertainty had a transient effect on

network range across formal subunits and a more lasting effect on range across work groups.

Supplemental Qualitative Analysis

The semi-structured interviews helped reveal the motivations underlying these communication

shifts. People reported increasing contact with colleagues in other departments to gather

18

Comparable results were obtained when I instead created an indicator variable for the dyads experiencing more

uncertainty and used three-way interaction terms, e.g., Uncertainty Period x Different Departmentt x Experienced

More Uncertainty, and relevant two-way interaction terms to estimate the effect. Similarly, results were robust to

the use of messages exchanged, rather than sent, as the response variable.


27

intelligence about the restructuring and to position themselves politically for favorable career

outcomes. In choosing whom to contact outside their department, they reported selecting

colleagues with whom they had a trust-based relationship based on a history of working together.

In many cases, they explicitly indicated reaching out to colleagues from project teams with

whom they had built a strong connection on the basis of prior productive exchange – for

example, working together on a difficult assignment. These interactions tended to be driven by a

search for trustworthy information, as well as for social support.19

Finally, the decline in within-

department communication was the result of normative constraints on supervisor-subordinate

communication, as well as socially awkward situations created by restructuring. Table 5

summarizes these points and includes representative quotes.


ROBUSTNESS CHECKS

I conducted additional robustness checks to address potential alternative explanations for these

results. First, the increase in contact across formal subunits could be explained by anticipated

role transitions – for example, a person reaching out to a likely known future supervisor – or

shifting task interdependencies – such as a person beginning to perform new job responsibilities

prior to a changing roles or completing tasks from a prior role after making the transition. To

account for these transitions, I controlled for lagged and future departmental affiliations and

distance measures and also included a control for the person’s departmental at the end of the

observation period (i.e., week 40).20

These analyses produced comparable results to those

19

People also reported turning to past colleagues with whom they did not necessarily have a current working

relationship, friends within and outside the organization, and family members for social support. 20

That is, I included Different Departmentt-1, Different Departmentt-2, Different Departmentt-3, Distance in Work

Group Structuret-1, Distance in Work Group Structuret-2, Distance in Work Group Structuret-3, Different


28

presented in Table 3. Second, I analyzed the content of email subject lines to address the

possibility that people somehow suspected their email was being monitored by senior

management and shifted their communication patterns accordingly (i.e., reducing the volume of

frivolous messages sent to supervisors or same-department colleagues). I coded messages as

frivolous based on their subject lines – e.g., those including phrases such as “Beer?” “Play ball!”

“Golf,” and “Gasoline Cartoons.” The proportion of such messages did not vary significantly

across time periods.21

Finally, to account for the role of competition among actors – for

example, a decline in contact if two people were vying for the same job – I used job families, as

defined by the HR system, to construct another control variable: Different Job Familyt.

Individuals in the same job family would presumably be more likely to compete with one

another for the same job. The results reported in Table 3 were materially unchanged with the

inclusion of this control.

DISCUSSION AND CONCLUSION

The goal of this article has been to clarify how the uncertainty of a transformative event – in this

case, restructuring – affects the activation of social capital within organizations. Restructuring-

induced uncertainty exerts two opposing forces on the range, or diversity, of network ties people

activate. On one hand, it leads to a preference for exchange with trusted, socially proximate

partners (e.g., Buchan, Croson, and Dawes 2002). This tendency suggests a contraction in the

Departmentt+1, Different Departmentt+2, Different Departmentt+3, and Distance in Work Group Structuret+1,

Distance in Work Group Structuret+2, and Distance in Work Group Structuret+3 as controls. The interaction terms of

interest, Uncertainty x Different Departmentt and Uncertainty x Distance in Work Group Structuret were significant

and of comparable sign and magnitude to the coefficients reported in Table 3. 21

Because the formal and work group structures are not perfectly orthogonal (e.g., some work groups are nested

within formal subunits), I also tested but found no evidence for a potential three-way interaction; i.e., Uncertainty x

Different Departmentt x Distance in Work Group Structuret was not significant in specifications that included this

term and relevant two-way interactions.


29

range of activated networks. On the other hand, it can draw people into exchange with distant

partners, who wield resources such as non-redundant information and influence that become

more valuable under uncertainty (e.g., Friedken 1982; Pfeffer 1992). This propensity implies an

expansion in the range of activated networks. I reconcile this conceptual tension by

disentangling uncertainty’s effects on range across formal subunits and cross-cutting work

groups in the organization. Drawing on insights about social capital activation (e.g., Smith

2005), the dynamics of organizational structure in times of change (e.g., Gulati and Puranam

2009), and collective attachments in social exchange (e.g., Lawler, Thye, and Yoon 2008), I

argue that uncertainty will lead to an expansion in range across formal subunits because people

will seek non-redundant information and political influence from colleagues in other departments

(Friedkin 1982; Pfeffer 1992). Moreover, organizational norms will constrain exchange within

formal subunits, leading people to reduce communication with departmental colleagues (Klein

1996; Marsden 1983). Thus, an increase in uncertainty will lead to an increase in network range

across formal subunits.

By contrast, I argue that there will be no corresponding preference for interaction with

colleagues in different work groups; rather, people will tend to activate ties to proximate

colleagues in the work group structure. Because work groups often consist of individuals from

different formal subunits, colleagues in the same work group are likely to have access to non-

redundant information. Because these individuals are outside the potentially changing formal

subunit structure, they will also tend to wield different sources of power and influence than

colleagues in the same subunit. In addition, because there are few normative constraints on

communication within work groups during restructuring (Balogun and Johnson 2004; Isabella

1990), people will fill the information void in their formal subunits by turning to colleagues in


30

the same cross-cutting work groups. Further support for the prediction that uncertainty will

increase individual attachments to work groups comes from the affect theory of social exchange

(Lawler, Thye, and Yoon 2009). Because the work group structure tends to be more stable

during restructuring than the formal subunit structure and because any changes to work groups

tend to lag behind those to formal subunits (e.g., Gulati and Puranam 2009), individual-to-

collective ties to work groups will remain strong even while those to departments erode (Lawler,

Thye, and Yoon 2009). Finally, during times of organizational change, managers tend to

increase their emotional commitment to work groups (Huy 2002). These factors all suggest that

an increase in uncertainty will lead to a decrease in this network range across work groups. The

empirical evidence supports these propositions about uncertainty’s effects on activated network

range across formal subunits and work groups.

I turn next to two outstanding questions raised by these findings. First, did network

activation choices during the uncertainty of restructuring affect individuals’ subsequent

outcomes? Given the research design, it was not possible to establish a causal link; however, I

examined the association between network activation during uncertainty and the likelihood of

exit from the firm fourteen months after restructuring. Given that the US economy was in a

significant downturn at the time, it is likely – though not known – that many of these exits were

involuntary. These results, which are reported in Appendix C, suggest that those who sent more

messages with colleagues in different departments than predicted by a baseline model had lower

conditional log-odds of exit. This association should, however, be considered preliminary

because other unobserved factors (e.g., external job prospects) may have influenced exit

decisions. Second, the focus on a relatively senior employee population in a single organization

raises the question about the extent to which these findings can be generalized to other settings.


31

Although the evidence in Appendix B suggests that lower-ranking employees experienced even

greater uncertainty than those involved in this study, further work is needed to establish that

these same patterns hold across different employee populations and other types of organizations.

The findings from this study make three primary contributions. First, they enhance our

understanding of social capital activation. Whereas prior work in this tradition has examined the

job searches through which people gain entry into organizations (Bian 1997; Lai, Lin, and Leung

1988; Lin, Ensel, and Vaughn 1981; Marsden and Hurlbert 1988; Wegener 1991), this study

instead exposes the difficult-to-observe dynamics of intra-organizational network activation. In

addition to the factors – such as trust-based (Smith 2005) and interpersonal affect (Casciaro and

Lobo 2008) – that are known to drive a wedge between the actual and potential resources actors

obtain through networks, this study highlights the role of organizational factors such as norms

that govern supervisor-subordinate relations. In this setting, supervisors sought to maintain

professionalism by not divulging information to just a subset of their subordinates. At the same

time, subordinates felt inhibited in communicating with supervisors whose own career outcomes

were unclear. The net effect was a constriction of contact between subordinates and supervisors,

some of whom could have been potentially useful sources of information, influence, or social

support. At the same time, the study points to the role of work groups as an enabler of network

activation and a potentially valuable conduit for resource exchange during restructuring. In

addition, whereas prior research on network activation has tended to considered ordinary

contexts, this work joins a handful of studies that consider non-routine times. For example, prior

research on the activation choices of people who experienced the effects of Hurricane Andrew

reports that pre-existing core network structure influenced the use of core network ties for

informal support (Hurlbert, Haines, and Beggs 2000). The present study reveals that uncertainty


32

triggers not only the activation of core networks but also of peripheral ties, such as those that

span formal subunit boundaries. Moreover, it uncovers how people activate networks to obtain

not only social support but also instrumental resources such as information and influence.

Second, this study brings to the literature on attachments in social exchange (Lawler

2001; Lawler, Thye, and Yoon 2008; Lawler and Yoon 1998) insight into the dynamics of

individual-to-collective attachments (see also McPherson and Smith-Lovin [2002]). Previous

research has identified the exchange conditions that are most likely to produce micro social

orders – “the recurring patterns of activity that orient people toward members of a social unit”

(Lawler, Thye, and Yoon 2008: 520). These findings suggest that, under conditions of

uncertainty, micro social orders inside organizations vary in their degree of stability. Those

corresponding to formal subunits tend to be relatively fragile, while those defined by other work

groups tend to endure and may in fact be strengthened by uncertainty. Moreover, while the core

insights about individual-to-collective attachments have been developed in laboratory

experiments, this study demonstrates the theory’s applicability in a field setting.

Third, this study has important implications for research on organizational structure and

performance in turbulent times (Davis, Eisenhardt, and Bingham 2009; Krackhardt and Stern

1988; Lin et al. 2006; Rindova and Kotha 2001). At the organizational level, Krackhardt and

Stern (1988) argue that the structure of internal friendship ties within organizations can influence

their ability to survive crisis situations. In particular, 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 game. 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 intraorganizational ties, this study


33

suggests the need to also consider network action, in the form of activated networks. In

particular, it may be inadequate to consider a single E-I index, which remains static over time

and determines an organization’s ability to withstand turbulent times. Instead, we must consider

at least two forms of the E-I index – one based on formal subunits and one based on other work

groups. Conditions of uncertainty can cause the E-I index for formal subunits to increase and the

E-I index for work groups to decrease. It remains to be explored how these shifts in E-I index

influence an organization’s ability to survive uncertain crises. At the individual level, Soda and

Zaheer (forthcoming) examine the performance implications of inconsistencies between an

actor’s informal network and her position in the formal authority and workflow structure of the

organization. Based on a cross-sectional network survey and a mapping of individuals within the

formal authority and workflow structures, they find that consistency between the informal

network and formal authority structure supports attainment, while the effects of consistency

between the informal network and workflow structure vary by type of coordination. Although

their study importantly highlights the interplay of formal structures and social networks and

develops a useful method for examining inconsistencies, it also takes a static view of networks.

This study reveals that, during critical junctures in careers – such as restructuring – the

consistency between networks and different organizational structures can endogenously change.

Further work is needed to assess the implications for individual attainment (see, for example,

Burt [1992; 2005]).

Beyond these three core contributions, this work has implications for two other

literatures. For research on events that transform social structure (Clemens 2007; Sewell 1996;

Sewell 2005), this study underscores the importance of the choice of time intervals, which can

shape whether “cases are understood as episodes of transformation or stretches (in time or space)


34

of coherence and continuity” (Clemens 2007: 543). For example, a recent study of email

communication in a post-merger integration takes four-month communication snapshots and

concludes “worker routines are slow to change even when a transformative event such as an

acquisition occurs” (Allatta and Singh 2011: 1111). Although four-month intervals seem

appropriate to understanding changes in routines, this choice probably also obscured countless

individual reactions to uncertain micro events, such as announcements of key personnel changes.

What appeared as a period of stability in their data may instead have encompassed many spells

of network change. Moreover, if network activation during these micro periods of uncertainty

solidified connections among work groups, then the observed stability in routines at four-month

intervals may even have been reinforced by the cumulative effects of episodic tumult.

Next, this work informs sociological research on the aftermath of organizational

restructuring (e.g., Dencker 2008; DiPrete 1993; DiPrete and Nonnemaker 1997; Tienda, Smith,

and Ortiz 1987), especially regarding downsizing’s effects on workers who remain employed in

an organization (Brockner et al. 1987; Brockner, Tyler, Cooper-Schneider 1992; Parks-Yancey

2004; Shah 2000). A study of survivors’ reactions to layoffs reports that the loss of coworkers

with whom a survivor had friendship ties had a negative effect on the survivor’s attitudes and

organizational commitment, while the loss of coworkers who were structural equivalents had a

positive effect (Shah 2000). The present study highlights the need to consider not only these

person-to-person connections but also the shared group affiliations between survivors and

coworkers who are laid off. For example, an individual’s negative reactions to the loss of a

friend might be stronger if that friend is closely connected in the work group structure but less so

if the friend is only connected through formal subunits. Similarly, work group affiliations may

moderate positive reactions to the loss of a structural equivalent.


35

Finally, the study makes a methodological contribution: suggesting a novel data source

that can be used to “dust the fingerprints of informal organization” (Nickerson and Silverman

2009: 538). This study uses an affiliation matrix derived from email distribution lists to map the

distance between actors in the work group structure (see Liu, Srivastava, and Stuart [2011] for an

illustration of how email lists can be also used to construct an intraorganizational ecology of

individual attainment). Given the widespread availability of email distribution lists, this data

source and the measure used in this article seem to have wide applicability.

Beyond these contributions, the study suggests several avenues for future research. As

noted above, a key next step is identifying the performance implications of network activation

during periods of organizational change. Another useful direction is examining how different

forms of uncertainty influence network activation. For example, uncertainty can be localized to

particular actors or pervasive (Beckman, Haunschild, and Phillips 2004); it can be experienced as

a threat or an opportunity (Jackson and Dutton 1988); or it can be transient or sustained (Michel

2007). How do individuals use their networks differently in these different cases? Third, how

does pre-existing network structure influence network activation (Gargiulo and Benassi 2000;

Hurlbert, Haines, and Beggs 2000; Uehara 1990)? In this setting, the baseline period prior to

restructuring was not long enough to address this question. Future research could profitably

extend the baseline period before the period of change to better measure network structure prior

to an exogenous shock. Fourth, given the nature of the archival data collected for this study, it

was not possible to discern differences in type of formal subunit or work group. For example,

some collective units inside organizations might foster greater competition among members,

while others engender cooperation. Formal subunits with structurally equivalent members or

greater levels of internal competition (Burt 1997) might see sharper declines in within-


36

department communication because members are vying for the same job opportunities.

Although I accounted for potential competitive dynamics using job codes, future research should

include finer-grained measures of equivalence and competition among actors. Finally, an

important next step is to explore differences in reactions to uncertainty across a wider range of

media. Whereas this study considered the effects of uncertainty on email communication, it

would be helpful to know whether the patterns observed in this study carry over to other media

such as informal, face-to-face meetings, phone calls, and text messages.

In summary, this study illuminates the microsociological mechanisms by which macro

events, such as restructuring, transform social structure. It highlights the role of uncertainty in

altering individual commitments to collective units, such as organizational subunits and work

groups, and in the activation of social capital. These insights set the stage for further

investigations of uncertainty’s role as an engine of shifting attachments and network change.


37

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43

Table 1: Descriptive Statistics and Correlation Matrix

Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

1. Messages Sent 0.17 1.28 1.00

2. Different Location 0.97 0.16 -0.12 1.00

3. Different Salary Grade 0.75 0.43 -0.00 -0.02 1.00

4. Different Sex 0.45 0.50 -0.01 -0.02 0.02 1.00

5. Different Ethnicity 0.27 0.44 -0.02 -0.00 -0.05 0.03 1.00

6. Different Tenure 0.85 0.36 -0.01 0.03 0.01 -0.00 -0.06 1.00

7. Different Age 0.58 0.49 -0.01 -0.01 0.01 -0.00 0.03 0.00 1.00

8. Different Department 0.95 0.22 -0.29 0.17 0.08 0.01 0.01 0.01 0.00 1.00

9. Distance in Work Group Structure 0.91 0.08 -0.13 0.29 0.07 0.00 -0.00 0.11 0.01 0.26 1.00

10. Uncertainty x Different Department 0.26 0.44 -0.04 0.03 0.01 -0.01 0.01 0.00 -0.00 0.14 0.10 1.00

11. Uncertainty x Dist. in Work Grp. Str. 0.25 0.41 -0.01 0.02 0.00 -0.01 0.01 0.01 -0.00 0.01 0.11 0.97 1.00

N=472,224; Number of Dyads = 12,882


44

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)

Messages Sent per Week 1,909 2,071 -0.634

(0.530)

Proportion of Messages Sent

between Colleagues in

Different Departments

0.432 0.484 -4.185

(0.000)

Correlation between Messages

Sent and Distance in Work

Group Structuret

-0.148 -0.124 --


45

Table 3: Poisson Regression of Messages Sent Between Dyads on Covariates

Covariates Model 1: All

Messages

Model 2: Off-

Hour Messages

Model 3: All

Messages.

Model 4: Off-

Hour Messages

Different Locationt -0.658** -0.815** -0.661** -0.818**

(0.228) (0.236) (0.228) (0.236)

Different Salary Gradet 0.294** 0.362** 0.293* 0.360**

(0.114) (0.131) (0.113) (0.130)

Different Sex -0.329** -0.310** -0.328** -0.309**

(0.104) (0.114) (0.104) (0.113)

Different Ethnicity 0.218 0.112 0.222 0.117

(0.237) (0.248) (0.239) (0.250)

Different Cohort 0.046 -0.036 0.048 -0.033

(0.197) (0.192) (0.197) (0.191)

Different Age -0.067 -0.088 -0.066 -0.088

(0.099) (0.109) (0.100) (0.109)

Different Departmentt -2.539*** -2.622*** -2.621*** -2.737***

(0.169) (0.181) (0.170) (0.183)

Distance in Work Group Structuret -4.262*** -3.912*** -4.038*** -3.560***

(0.640) (0.641) (0.671) (0.674)

Uncertainty x Different Departmentt 0.316* 0.415*

(0.142) (0.162)

Uncertainty x Distance in Work Group Structuret -0.800* -1.168*

(0.375) (0.533)

Sender Fixed Effects Yes Yes Yes Yes

Receiver Fixed Effects Yes Yes Yes Yes

Period (Week) Fixed Effects Yes Yes Yes Yes

Constant -0.786 -0.701 -0.741 -0.642

(0.426) (0.429) (0.424) (0.428)

Chi2 11608 9403 11847 9743

Prob>Chi2 0.000 0.000 0.000 0.000

Number of Observations 472224 472224 472224 472224

* 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; 12,822 dyads. Fixed effect coefficients not reported.


46

Table 4: Poisson Regression of Messages Sent Between Dyads Experiencing Varying

Levels of Uncertainty on Covariates

Model 5:

Less Uncertainty

Model 6:

More Uncertainty

Different Locationt -0.230 -0.572*

(0.229) (0.229)

Different Salary Gradet 0.270 0.393**

(0.174) (0.117)

Different Gender -0.277 -0.038

(0.147) (0.160)

Different Ethnicity -0.198 -0.255

(0.189) (0.181)

Different Cohort -0.118 -0.089

(0.148) (0.224)

Different Age -0.172 -0.144

(0.121) (0.113)

Different Departmentt -2.508*** -2.981***

(0.255) (0.141)

Distance in Work Group Structuret -3.970*** -1.293*

(0.609) (0.643)

Uncertainty x Different Deptt 0.021 0.505**

(0.102) (0.156)

Uncertainty x Distance in Work Group Structuret 0.107 -1.432*

(0.344) (0.488)

Period (Week) Fixed Effects Included Included

Constant -0.601 0.499

(0.366) (0.404)

LR chi2 53364 115083

Prob>Chi2 0.000 0.000

Number of Dyads 7140 5742

Number of Observations 264374 207870

Seemingly Unrelated Post-Estimation: Wald Test that Coefficients are Equal Between Models

Uncertainty x Different Dept. χ2=8.22**

(p=0.004)

Uncertainty x Distance in Work Group Structure

χ2=8.55**

(p=0.004)

* 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; Fixed effect coefficients not reported.


47

Table 5: Motivations underlying Network Activation Choices Choice Motivations Representative Quotes

Initiating Contact

with Colleagues in

Different

Departments

Non-redundant

Information

“I tried to find out what was happening in Marketing, in Sales, and in Product Development. I tried to piece together

what senior management was doing and where the company was going based on what I picked up from the different

functional areas.” – Sales Support Manager

“I had heard rumors that my boss was on the rocks because he was engaged in, shall we say, questionable behavior. I

figured he’d be let go in the restructuring, and he was. I had a guess about who I would end up reporting to instead and

talked to one of the people in that guy’s group. I wanted to get a feel for whether my management style would fit with

my new boss’ style.” – Sales Leader

Influence

“If you want to survive, you have to do some political maneuvering. When new people come in to roles that you have

to work with, you have to let them know that you want to work with them. In a functional support role like mine, you

have to start this dialogue every time a new leader comes in.” – Sales Support Manager

“I reached out to maybe 10 to 15 people to help me get my current position. I chose people who were generally well

thought of and would likely be well positioned in the new structure. People who could advocate on my behalf. They

had to be able to speak to my contributions in the organization.” – Division Leader

Initiating Contact

with Colleagues in

Same Work Group

Trustworthy

Information /

Social Support

“When I need to gather intelligence, I try to reach out to somebody in my trust circle…someone I’ve worked closely

with on projects.” – Product Development Director

“I go to the people on my teams who I have gone in battle with together before.” – Customer Support Director

“I seek out people in adjacent units who I have worked closely with for 10-15 years.” – Marketing Manager

“The people I reached out to for emotional support tended to be peers of mine who worked on the same project team.

That is, people in other functional groups that I had a long work history with.” – Director of Marketing Strategy

Decreasing Contact

with Colleagues in

Same Department

Normative

Constraints on

Supervisor-

Subordinate

Communication

“If you have direct reports, you can’t share information with one that you don’t share with the other. So you can’t share

information with your own staff, but you can with other people you trust. With direct reports, it is a professional

relationship; you have to adhere to certain professional standards.” – Vice President of Corporate Strategic Initiative

“[My boss] was pretty tight-lipped. In general, direct line management did not communicate a whole lot during that

time. What they don’t get is that when they go silent, it actually ups the level of concern and uncertainty. If they do

communicate, it tends to be in formal blast communications that occur at infrequent intervals.” – Division Leader

Socially

Awkward

Situations

“I tried reaching out to my boss, who was eventually let go. He was totally out of the loop: it was painfully obvious he

wasn’t being consulted on major decisions.” – Marketing Communications Manager

“[My boss], who eventually lost her job, went completely dark. I think she just withdrew.” – Marketing Director

“I knew that [my boss] was a contender for a top job. I was sensitive to the fact that he might be sensitive about

discussing his situation.” – Marketing Vice President


48

Figure 1: Timeline of Key Events

0 5 10 15 20 25 30 35 40

Research study

observation

period ends

Weeks 9-18: Period of Greatest

Uncertainty

Research study

observation period

begins

New structure,

open positions

announced;

layoffs begin

Layoffs end;

people selected

and moved into

new roles

New Roles, Processes Defined

Restructuring Timeline: Weeks


49

Appendix A – Interview Schedule

1. Could you tell me about your career history? What is your role at [InfoCo] today? What role

did you have prior to the restructuring event?

2. Think back to the recent reorganization.

a. When did you first learn of these changes? How did you learn of them?

b. How did you think you would be affected? How were you actually affected?

c. How certain were you of the implications for you personally? At what point did the

personal career implications become clear for you?

d. Did you initiate contact with any of your colleagues to discuss the situation? If so,

whom did you reach out to? Why did you reach out to these people? How did you

reach out to those people – i.e., what form of communication?

e. Did others initiate contact with you to discuss the situation? If so, who reached out to

you? Why did they do so? How did they do so?

3. Emails and email distribution lists:

a. What kinds of communication do you tend to have over email? By phone? Face-to-

face?

b. During sensitive times, such as the period of restructuring, how do you use these

different communication media?

c. How do you use email distribution lists in communicating with others? What lists are

you part of? When do you decide to create a new list? When do you get rid of a list?


50

Appendix B – Uncertainty Reactions of Less Senior Employees

Sales vice president: “I had just about everyone from the sales organization stopping by my

office at that time. They kept asking, ‘What’s going on? What does this mean for me?’ If

they had any sense that the changes would impact them directly, you could be sure they were

out there trying to find out about them’.”

Director of product development: “I remember 20-25 junior people coming to my office

during that time. They were trying to figure out if the company was collapsing, if their

manager was going to change, and who was likely to become their new manager.”

Sales support leader: “I was often approached by subordinates or others further down in the

organization because I’ve been through a tremendous amount of change…. It was an

inordinate number of people who came to see me, especially those worried about the threat of

outsourcing.”

Marketing support director: “I think the restructuring was worse for people below

me. It was less unsettling for me because I had confidence in my own marketability

and brand recognition within the company. In the game of musical chairs, I was very

confident that I’d have a chair when the music stopped. And if I didn’t have a chair, I

was pretty marketable on the outside. When you look at people further down, you

find a lot of long-timers, who only know the InfoCo way, or very young people. Both

groups are vulnerable and worried in these times.”

A division general manager: “All of my direct reports had questions and a lot of uncertainty,

which was even more amplified than my own – even though I didn’t know any more than

they did. Because they were a step removed from the decisions, their uncertainty was even

greater.”


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Appendix C: Logit Model of Exit from Firm (Fourteen Months After Observation Period)

Model

C1

Model

C2

Model

C3

Age in Years 0.120** 0.139** 0.125**

(0.039) (0.046) (0.043)

Log Tenure in Years -0.722* -0.712* -0.732*

(0.342) (0.351) (0.345)

Senior Rank -0.041 -0.321 -0.164

(0.861) (0.876) (0.904)

Female -0.744 -0.628 -0.782

(0.606) (0.653) (0.626)

White -0.439 -0.650 -0.356

(0.575) (0.555) (0.600)

Log Communication Volume during Uncertainty -0.381* -0.301 -0.355*

(0.168) (0.162) (0.171)

Messages Sent to Colleagues in Different Departments during

Uncertainty – Residual

-13.954*

(6.669)

Messages Sent to Colleagues in Same Department during

Uncertainty – Residual

-0.455

(0.239)

Messages Sent to Distant Colleagues in the Work Group

Structure during Uncertainty - Residual

0.387

(7.970)

Messages Sent to Proximate Colleagues in the Work Group

Structure during Uncertainty – Residual

-1.533

(2.047)

Constant -3.013 -3.859 -3.203

(1.926) (2.086) (2.035)

Chi2 21.191 20.314 21.076

prob>Chi2 .0016948 .0092104 .0069489

N 114 114 114

* p<0.05, ** p<0.01, *** p<0.001; two-tailed tests; robust standard errors in parentheses.

Note: Residuals are the mean difference, across the weeks of uncertainty, between actual

and predicted messages to different types of colleagues (e.g., those in the same or different

departments). Distant colleagues in the work group structure defined as those above the

median distance across all dyads (i.e., Distance in Work Group Structuret); proximate

colleagues defined as those at or below the mean.

social capital activation, uncertainty, and organizational...

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