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The Effects of Communication Networks and Turnover on
Transactive Memory and Group

Performance

Linda Argote, Brandy L. Aven, Jonathan Kush

To cite this article:
Linda Argote, Brandy L. Aven, Jonathan Kush (2018) The Effects of Communication Networks and Turnover on Transactive
Memory and Group Performance. Organization Science 29(2):191-206.

https://doi.org/10.1287/orsc.2017.117

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ORGANIZATION SCIENCE
Vol. 29, No. 2, March–April 2018, pp. 191–206

http://pubsonline.informs.org/journal/orsc/ ISSN 1047-7039 (print), ISSN 1526-5455 (online)

The Effects of Communication Networks and Turnover on
Transactive Memory and Group Performance
Linda Argote,a Brandy L. Aven,a Jonathan Kushb
a Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; b Charlton College of Business,
University of Massachusetts Dartmouth, North Dartmouth, Massachusetts 0274

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Contact: argote@cmu.edu, http://orcid.org/0000-0002-4149-6968 (LA); aven@cmu.edu, http://orcid.org/0000-0001-7086-5876 (BLA);
jkush@umassd.edu, http://orcid.org/0000-0003-4872-8650 (JK)

Received: May 28, 2015
Revised: March 8, 2016; December 11, 2016;
June 25, 2017; August 17, 2017
Accepted: September 4, 2017
Published Online in Articles in Advance:
March 30, 2018

https://doi.org/10.1287/orsc.2017.1176

Copyright: © 2018 INFORMS

Abstract. We theorize that the effect of membership turnover on group processes and
performance depends on a group’s communication network. We describe two mecha-
nisms through which communication networks affect group performance: (1) the number
of direct communication paths and (2) the clarity of the coordination logic. These mech-
anisms map onto two network dimensions: density, which affects a group’s behavior
through the number of available communication paths, and centralization, which affects a
group’s behavior through the clarity of the coordination logic. We empirically analyze the
effects of turnover on the performance of fully connected all-channel networks and hub-
and-spoke or wheel networks in an experiment of 109 four-person groups performing two
collaborative problem-solving tasks. The greater number of direct communication paths
enabled fully connected groups with stable membership to develop stronger transactive
memory systems (TMSs) and perform better than fully connected groups that experienced
turnover. By contrast, the clear coordination logic of perfectly centralized groups that expe-
rienced turnover facilitated more frequent dyadic communication, which enabled them to
strengthen their TMSs, incorporate the contributions of new members, and improve their
performance. Thus, our results indicate that communication networks condition the effect
of membership turnover on group processes and performance.

Funding: The authors gratefully acknowledge the support of the National Science Foundation [Grants
1111750 and 1459963] and the Center for Organizational Learning, Innovation and Knowledge at
the Tepper School of Business at Carnegie Mellon University.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2017.1176.

Keywords: social networks • turnover • transactive memory • group performance

Introduction
Organizational activity comprises the coordination of
individuals to solve complex problems. Group mem-
bers must identify the expertise of others, access infor-
mation held by different members, and coordinate
that information to accomplish shared goals. Mem-
ber turnover, the exit of an incumbent member and
introduction of a new member, in groups can com-
plicate the identification and coordination of expertise
and information (Arrow and McGrath 1995). Groups
with stable membership are able to learn each other’s
skills and expertise, allocate tasks to the most qualified
members, and coordinate the interdependent activi-
ties of their members. When turnover occurs, however,
incumbent members know little about the expertise
and skills of the new member, and coordination can
become challenging (Lewis et al. 2007). Yet new mem-
bers can nonetheless be a source of new ideas and per-
spectives that improve group performance (Choi and
Thompson 2005).
We theorize that the effect of turnover on group

performance depends on the group’s communication
network. From a network perspective, groups can be

categorized by their structural features—in particular,
their centralization (Katz et al. 2004, Leavitt 1951, Rulke
and Galaskiewicz 2000) and their density (Freeman
1979, Friedkin 1981, Balkundi and Harrison 2006). Cen-
tralization increases for a group as the inequality or
variance in the number of connections group members
have to others increases. A group’s density increases as
the ratio of actual to potential connections increases.

We identify two mechanisms through which a
group’s communication network affects performance:
(1) the number of direct communication paths avail-
able to group members and (2) the clarity of the
coordination logic. The first mechanism maps onto
network density, which affects a group’s behavior
through the number of available communication paths.
High levels of density in communication networks
allow team members to communicate directly with
one another. The second mechanism maps onto net-
work centralization, which affects a group’s behavior
through the clarity of the coordination logic. Central-
ized communication networks direct how information
is shared and inform how members coordinate (Blau
1974, Bunderson and Boumgarden 2010).

19

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mailto:argote@cmu.edu

http://orcid.org/0000-0002-4149-6968

mailto:aven@cmu.edu

http://orcid.org/0000-0001-7086-5876

mailto:jkush@umassd.edu

http://orcid.org/0000-0003-4872-8650

https://doi.org/10.1287/orsc.2017.1176

Argote, Aven, and Kush: The Effects of Turnover on Group Performance
192 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

Figure 1. Fully Connected and Perfectly Centralized
Communication Networks

(a) (b)

A

C

DB

A
C

B D

Notes. Panel (a) represents the fully connected communication net-
work. Panel (b) represents the perfectly centralized communication
network. C is the central member, whereas A, B, and D are peripheral
members.

We focus on the two network structures that max-
imize one structural dimension while minimizing the
other: a perfectly centralized or “hub-and-spoke” net-
work that has the highest degree of centralization and
minimizes density (see Figure 1), and a fully con-
nected, “all-channel” network that maximizes den-
sity and minimizes centralization. These two network
structures not only allow the investigation of cen-
tralization and density in tandem but are also struc-
tures common in organizational settings. For exam-
ple, programming groups’ communication networks
have often been found to be fully connected, where
members can communicate directly with any other
member in an open-source setting (Tsay et al. 2014).
By contrast, particularly in proprietary software, pro-
gramming groups generally have a perfectly central-
ized communication network where one central mem-
ber acts as the “software architect,” and other members
can communicate directly only with this central mem-
ber (Kruchten 2008, Bosch and Bosch-Sijtsema 2010). In
addition, even though project groups in organizations
commonly communicate in a fully connected manner,
groups engaged in covert projects typically communi-
cate in a centralized fashion (Aven 2015).

In the case of stable group membership, high cen-
tralization, such as in our perfectly centralized groups,
limits direct communication among members through
their restricted communication network, which hin-
ders members from learning about others’ expertise.
Centralized communication networks also force mem-
bers to coordinate in a particular manner, irrespective
of members’ preferences and abilities. Rather than pro-
vide members with the opportunity to customize their
coordination logic, centralized group members must
channel information to the central member(s), who
then orchestrate the group’s activities. Hence, the coor-
dination logic in centralized groups is independent of
the particular members and their attributes.

When turnover occurs, the communication network
of perfectly centralized groups improves the group’s

ability to integrate a new member. The coordina-
tion logic of perfectly centralized networks is read-
ily discernible by both incumbent and new members,
which enhances their ability to contribute to the group
(Bunderson and Boumgarden 2010, Morrison 2002).
Because members’ roles in perfectly centralized groups
are not customized to individual members, it is more
likely that the new member can adequately perform the
activities of the departing member in centralized than
in decentralized groups. Finally, the restricted com-
munication pathways in centralized networks require
reliance on the few existing pathways to coordinate,
which reduces the likelihood that any communica-
tion pathway and its respective member are neglected.
These factors enable a perfectly centralized group to
incorporate the contributions of a new member and
improve group outcomes for tasks involving problem
solving and creativity (e.g., Wells and Pelz 1966, Choi
and Thompson 2005).

When membership is stable, high density in com-
munication networks, such as in the fully connected
groups, enables members to establish a strong trans-
active memory system (TMS), a collective system for
encoding, storing, and retrieving information (Wegner
1987, Lewis and Herndon 2011). Because of fully
connected group members’ greater ability to communi-
cate directly with other members, group members are
able to learn about each other’s expertise and, in turn,
develop a shared cognitive map of expertise. In addi-
tion, fully connected networks allow members to tailor
the group’s coordination logic to account for each mem-
ber’s preferences and abilities.

Although fully connected communication networks
facilitate group performance by encouraging the devel-
opment of a strong TMS, fully connected networks
can also hinder the integration of new group members
when turnover occurs. New members often do not have
the same attributes and knowledge as departing mem-
bers, which makes the substitution of a new member
challenging for these groups because their coordina-
tion logic is tailored to the original members’ unique
abilities and characteristics. Furthermore, the group’s
coordination logic cannot be readily observed by new
members, which impairs their ability to contribute to
the group.

Communication Networks and Transactive
Memory Systems
A group’s communication network serves to channel
information among group members and coordinate
their activities (Leavitt 1951, Faucheux and Macken-
zie 1966). In a dense, fully connected network (see
Figure 1(a)), all of the members share the same number
of communication pathways as all of the other mem-
bers. By contrast, in the perfectly centralized group
(see Figure 1(b)), only member C has communication

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS 193

pathways to the other members (A, B, and D), and
members A, B, and D have only one pathway each to
member C. To both communicate and coordinate activ-
ity in the centralized group, the peripheral members
(A, B, and D) must rely on the central member C. Thus,
the perfectly centralized communication network both
minimizes the number of available communication
pathways and maximizes the clarity of the coordina-
tion logic for the group: all peripheral members com-
municate to the central member, and the central mem-
ber dispatches information to peripheral members. By
contrast, the fully connected network maximizes the
number of communication paths but reduces the clar-
ity of the coordination logic.
The greater number of communication pathways in

fully connected networks promotes information shar-
ing and fosters an understanding of member expertise.
When group members learn information about other
members and their expertise, the group’s transactive
memory system develops (Moreland and Myaskovsky
2000). The transactive memory systems of fully con-
nected groups become tailored to the different skills,
knowledge, and preferences of group members.

Within perfectly centralized groups, the communi-
cation network prevents group members from directly
interacting with one another and developing a coordi-
nation logic that is well suited to the particular mem-
bers of the group. Instead, the communication network
of perfectly centralized groups requires that certain
members relay information to others and thus imposes
a particular coordination logic on the group (Gouldner
1954, Weber 1947). The limited number of communi-
cation pathways also hinders members from directly
learning about each other’s areas of expertise, which in
turn inhibits the development of a strong transactive
memory system.

Transactive memory systems have been found to
improve group performance (see Ren and Argote 2011
for a review) on outcomes such as time to complete
tasks (Faraj and Sproull 2000), errors (Liang et al. 1995),
and creativity (Gino et al. 2010). Transactive memory
systems can be distinguished from related constructs
such as shared mental models, because the knowledge
held by group members with a strong TMS is differ-
entiated through specialization (Lewis and Herndon
2011). In groups with a strong TMS, members have
different information about how to complete their sub-
tasks, whereas in groups with shared mental models,
members have the same information (DeChurch and
Mesmer-Magnus 2010).

Transactive memory systems develop from expe-
rience working together. As individuals work with
the same group members, they discern each other’s
skills and abilities, which allows them to special-
ize their expertise, fine-tune their division of labor,
and coordinate effectively (Wegner 1987). The strength

of a group’s TMS is affected by its communications
(Hollingshead 1998, Yuan et al. 2010). We predict that
differences in communication networks influence the
strength of a group’s TMS. Furthermore, we predict
that the effect of a group’s communication network
depends on whether turnover occurs.

Turnover

Turnover, or membership change, is a common occur-
rence within work groups (Kush et al. 2012). In some
instances, turnover can benefit the group by invigorat-
ing it with information and perspectives from the new
member, which improves its performance (Choi and
Levine 2004, Choi and Thompson 2005, Hancock et al.
2013). The positive effect of turnover on group perfor-
mance is most likely to occur on tasks involving cre-
ativity and innovation (Choi and Thompson 2005). For
example, Wells and Pelz (1966) found that turnover in
groups of scientists improved group performance. The
introduction of a new member, even a novice, into an
existing group can enhance the group’s performance
(Ferriani et al. 2009, Uzzi and Spiro 2005). For new
members to benefit a group, however, they must be
integrated into the group by existing members.

Membership change can hinder group outcomes
because the new member might not have the same
skills as the departing member, understand the coor-
dination logic of the group, or be integrated into
the group (Hausknecht and Holwerda 2013). Lewis
et al. (2007) found that when only one group mem-
ber was replaced in a group, those groups performed
worse than either groups whose membership was sta-
ble or groups whose membership changed totally. The
advantages conferred on fully connected groups by
their strong TMSs when membership is stable are
undermined when turnover occurs. Because TMS is a
cognitive division of labor, the group’s implicit coor-
dination logic is not easily observed by the new mem-
ber (Wegner 1987). Hence, for a fully connected group,
a new member would have difficulty contributing
because he or she would not understand how the
group communicates or organizes its activities. When
a group has developed a strong TMS, the incumbent
members of the group tended to ignore the contribu-
tions of a new member and expect him or her to fill the
role of the departing member (Lewis et al. 2007). Thus,
we anticipate that turnover will harm the performance
of fully connected groups.

The features that undermine TMS formation in per-
fectly centralized groups with stable membership—
limited communication pathways and mandated coor-
dination logic—aid in the integration of new members
when turnover occurs. New members in perfectly cen-
tralized groups can easily discern the simple organiz-
ing principle: all members contribute their information
and views to the central member(s), who then integrate

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
194 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

the information for the group (Guetzkow and Simon
1955). In turn, understanding the group’s coordina-
tion logic helps new members to work effectively with
other members, which is critical to group performance
(Chao et al. 1994, Morrison 2002). Moreover, the lim-
ited connectivity of perfectly centralized groups also
requires greater reliance on the extant pathways for
members to communicate. Frequent communication
improves the new member’s knowledge of how to per-
form his or her tasks and provides clarity in terms of
the responsibilities associated with the position, which
expedites the new member’s ability to contribute to the
group (Morrison 2002). When the group incorporates
the new member’s ideas and perspectives, the group is
able to perform its task more effectively (Levine et al.
2003). In addition to increasing the creative problem
solving of incumbent members (Choi and Thompson
2005), new members can also introduce new insights
that improve group performance.
We theorize two mechanisms through which net-

work structure affects performance: (1) the number of
direct communication paths available to group mem-
bers and (2) the clarity of the coordination logic. We
have argued that the first mechanism is more impor-
tant when membership is stable and that the second
mechanism is more important when turnover occurs.
Thus, we expect that fully connected groups have
stronger TMSs and better performance under stable
membership than when turnover occurs, because of
their greater number of communication paths. The
greater number of paths, however, undermines their
ability to integrate a new member when turnover
occurs. We anticipate that the clarity of the commu-
nication logic of perfectly centralized groups enables
them to integrate the new member, benefit from his or
her new ideas and perspectives, and thereby improve
their performance when turnover occurs. Thus, we pre-
dict the following.

Hypothesis 1. Communication networks and turnover in-
teract to affect group performance: fully connected groups
perform better when group membership is stable than when
turnover occurs, whereas perfectly centralized groups per-
form better when turnover occurs than when group member-
ship is stable.

Hypothesis 2. Communication networks and turnover in-
teract to affect transactive memory systems: fully connected
networks have stronger transactive memory systems when
group membership is stable than when turnover occurs,
whereas perfectly centralized networks have stronger trans-
active memory systems when turnover occurs than when
membership is stable.

Dyadic Communication Frequency
Dyadic communication frequency represents the total
communication that occurs in a group relative to

the number of available communication paths in the
group. The greater number of communication path-
ways in fully connected relative to perfectly central-
ized groups increases the demands on members’ time
and attention and reduces the frequency of commu-
nication along any one pathway. As opposed to fully
connected groups, perfectly centralized groups must
rely on fewer pathways to communicate, increasing
group members’ dependence on each individual path-
way, potentially strengthening one-on-one relation-
ships. Thus, the communication constraints of per-
fectly centralized groups increase members’ reliance
on the available pathways and result in greater dyadic
communication frequency than in less restricted, fully
connected groups.

Dyadic communication along each pathway is espe-
cially important in integrating a new member. The
fewer paths available to perfectly centralized groups
make each connection more salient to group mem-
bers. The greater amount of dyadic communication in
perfectly centralized groups conveys the group’s coor-
dination logic, which is critical in integrating a new
member, and allows members to update the group’s
knowledge of who knows what and to perform bet-
ter when turnover occurs. By contrast, when turnover
occurs in fully connected groups with strong TMSs,
incumbent members neglect to communicate with the
new member, which impairs their performance. For-
mally, we predict the following.

Hypothesis 3. Dyadic communication frequency mediates
the interaction of communication network and turnover on
transactive memory systems, and the mediation is stronger
when turnover occurs than when group membership is
stable.

Scope Conditions
Given that our primary interest is in the interactive
effect of communication networks and turnover on the
processes and performance of small groups, we focus
on perfectly centralized groups and fully connected
groups of four members. Small groups are a predom-
inant means by which tasks are accomplished within
organizations as well as an important unit of analy-
sis for organizational research (Katz et al. 2004, Leavitt
1996, Uzzi and Spiro 2005). In addition, because we
are interested in communication networks, we selected
interdependent tasks that require coordination, so that
we could observe how information was shared in the
group. We focus on nonroutine, complex tasks; there-
fore, our theory applies to complex problem-solving
tasks that require coordination and creativity. Exam-
ples of such tasks include product design, prototype
development, engineering design processes, and soft-
ware engineering. Last, because our tasks involve cre-
ative problem solving, we expect that the contribu-
tions of the new member have the potential to improve

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS 195

group performance (e.g., see Choi and Thompson
2005). We have hypothesized that whether the group
benefits from the new member depends on the group’s
communication network.

Methods
Participants
One hundred and nine four-person groups composed
of 503 individuals (49% female) were recruited from
a mid-Atlantic American university participant pool.1
Participants received $20 or course credit for their par-
ticipation, and an additional reward of $20 per person
was given to the members of the best performing group
in each condition. The ages of participants ranged from
18 to 37, with an average age of 22.0 years. Forty-nine
percent of the participants were Asian or Indian, 37%
were Caucasian, and 14% were other ethnicities.

Tasks
Because our focus is interdependent problem-solving
tasks, we used two tasks that fell in the conceptual
cooperative quadrant of McGrath’s (1984) circumplex
model. Both tasks required creative problem solving.
The first task was a programming task with the pos-
sibility of errors; the second was an idea-generating
task. Both tasks were based on an online graphical
programming interface. This interface allowed partici-
pants to design programs—called pipes—that collect,
manipulate, organize, and filter information from the
Internet to create a desired output. Although similar-
ities exist between this interface and other program-
ming languages, knowledge of other programming
languages would not provide direct benefits because
of the unique graphical programming interface of our
tasks. The preponderance of participants were unfa-
miliar with this interface: 94.2% rated themselves as
unfamiliar or very unfamiliar with the interface in a
postexperiment survey.
The programming task required each group to work

together to create a pipe that (1) sorted selected news
items by publication date, (2) removed any repeated
articles, and (3) allowed a user to specify a search key-
word. The task required the use of five specific mod-
ules. Although working on separate computers, par-
ticipants had access to a virtual work environment,
allowing them to contribute and to see the contribu-
tions of their group mates simultaneously. All mem-
bers could access and make changes to the pipe. In the
idea-generating task, participants were asked to gen-
erate collectively as many new features or functional
improvements as possible to enhance the pipe that they
had created in the programming task. The new features
had to be both novel and feasible to implement, and
therefore required the module expertise of all of the
members. For both tasks, group members never inter-
acted face-to-face and communicated only through an
instant messaging client.

Manipulations
We manipulated two variables in the experiment: com-
munication network and turnover. We manipulated
the communication network by controlling who could
communicate with whom through an instant messag-
ing client. In the perfectly centralized condition, three
peripheral members could contact only one central
member who could communicate with all the periph-
eral members. In the fully connected condition, all
members could communicate with all other members
(see Figure 1). In both conditions, group members
could send only one message to one member at a time.
Throughout both tasks, the communication network
remained unchanged.

We manipulated turnover by replacing a randomly
selected member from each group after the practice
task with another participant who acquired the net-
work position of the departing member. Participants
were not warned that their group might experience
turnover. The new member received the same train-
ing and training materials as the departing member
but had not previously worked with the graphical pro-
gramming tool or with a group. In fully connected
groups that experienced turnover, a randomly chosen
member was selected to be replaced. In the perfectly
centralized groups that experienced turnover, either a
peripheral member or a central member was replaced.
For example, in the centralized (see Figure 1(b)) periph-
eral turnover condition, a peripheral member (A, B,
or D) was randomly selected and replaced. In the cen-
tralized central turnover condition, the central mem-
ber (C) was replaced. We did not predict that turnover
of the central member would have a significantly dif-
ferent effect than turnover of peripheral members, but
we allow for the possibility in our design by including
both types of centralized turnover.

Procedures
After arriving at the laboratory, participants were ran-
domly assigned to one of four isolated rooms, each
associated with a member ID and a position within the
network. We allowed members to communicate using
only an instant text messenger and limited their oppor-
tunities to see one another. The participants attended a
training phase where they received information about
the programming task as well as instructions for the
experiment. The materials explained how to commu-
nicate with other members through the instant mes-
senger, directed participants to a short video demon-
strating the creation of a sample pipe, and provided
detailed information about the programming mod-
ules available. Additionally, we provided each group
member with different, randomly assigned, special-
ized information about one of the modules necessary
for completing the task to ensure that group members
were interdependent. We did not tell members that

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each had specialized information. For groups experi-
encing turnover, the new member received the same
specialized information as the member whom he or
she replaced.
Once all the participants had read the training mate-

rials, they were given 30 minutes to practice the task
together. After they finished the practice task, group
members completed the first survey. At this point,
for groups in the turnover condition, an announce-
ment was made: “One new member will be joining
your group, and another will be randomly chosen to
leave.” A randomly chosen group member was then
removed from the communication network and dis-
missed from the experiment, and a new member was
introduced to the group. The new member occupied
the same position in the communication network as
the departing member and received the same special-
ized information during training. Groups were then
given instructions for the programming task, which
they had 30 minutes to complete. The programming
task was followed by the 5-minute feature-generating
task.2 Group members then completed the second sur-
vey. Finally, we debriefed and thanked participants.

Measures
Several variables in this study were behavioral mea-
sures, including our two dependent measures: the
number of errors in the programming task and the
number of functional improvements in the feature-
generating task. The dyadic communication between
group members was also a behavioral measure based
on the messages sent on the instant messaging client.
The remaining measures were collected from a sur-
vey that measured the group’s TMS, perceptions of the
communication network, and group member demo-
graphics and experience, such as their familiarity with
the programming interface.
Errors. We calculated the number of errors in the pipe
that each group submitted. Errors included both cases
when the group used incorrect modules and cases
when the group omitted modules required for the pro-
gram to function properly. To calculate the errors, we
categorized errors into four comprehensive categories:
missing modules, incorrect settings, incorrect modules,
or unconnected modules. The number of errors for a
module was based on the number of settings in that
module. For example, if a group did not include a par-
ticular module in their program and that module had
three required settings, three errors would be recorded
for the group. If a module was included but one of
the settings was wrong, the group was coded as hav-
ing one error. These errors were summed into a single
measure for each group. A portion of the groups had
their errors assessed by two coders (n � 70). A Cohen’s
kappa of 0.72 (p < 0.001) indicated good agreement between coders (Cohen 1960). A single coder then coded errors for the remaining groups.

New Features. During the feature-generating task,
participants responded to the question, “Think of any
ways that you think the pipe you just built could be
improved by creating new features. What other ways
could this pipe be more helpful to an end user, be
more simply designed, etc.?” Two coders coded a sub-
set of the groups (n � 40), assessing each new fea-
ture on whether it introduced novel functionality and
was feasible to implement. Two coders attained very
good reliability, as indicated by a Cohen’s kappa of 0.88
(p < 0.001). Any disagreements between coders were addressed and resolved. A single coder then coded the number of new features for the rest of the groups.

Transactive Memory Systems. Lewis’s (2003) 15-item
survey measure was used to measure the groups’
transactive memory systems. The survey instrument
was administered at the end of the study, after groups
had completed both tasks. The overall reliability was
acceptable (Cronbach’s alpha�0.82). The average inter-
group reliability (rw g) was 0.95, indicating that it is
appropriate to aggregate the individual-level measures
to the group level. The intraclass correlation coefficient
(ICC(2)) value was 0.61, indicating acceptable reliabil-
ity of the measure. These reliability statistics provide
evidence for general agreement among group mem-
bers and the appropriateness of aggregating to the
group level (LeBreton and Senter 2008). The ICC(1)
value, which indicates the extent to which the variabil-
ity in individual responses can be predicted by group
membership, was 0.28. Values of 0.25 or higher are con-
sidered to be large effects (Murphy and Myors 1998).

Dyadic Communication Frequency. We calculated the
dyadic communication frequency based on group
members’ instant text messages. As our theory is about
the integration of new members, we chose to use a
measure that would capture the strength of the com-
munication relationships between individuals in the
group. Dyadic communication frequency was calcu-
lated as the sum of all group messages exchanged
between members during the task performance period,
divided by the total available dyadic communication
paths in the group (three for perfectly centralized and
six for fully connected). This value was then divided
by the time to task completion to account for varia-
tion in the groups’ completion times. We calculated
this variable separately for the programming task and
the feature-generating task. Analyses predicting errors
used the dyadic communication frequency during the
programming task, and analyses predicting new fea-
tures used the dyadic communication frequency dur-
ing the feature-generation task. For Table 2, we used
cumulative dyadic communication frequency, the com-
bination of dyadic communication frequency across
both task periods, to predict the effect of the network
on communication and on TMS (Models 6 and 7),

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because TMS development should be affected by all
prior communication in the group, not just communi-
cation relevant to a particular task. We present analyses
for communication to and from the new member for
groups in the turnover conditions in the online sup-
plement. Because these measures of communication to
and from the new member do not apply for groups that
did not experience turnover, we use dyadic communi-
cation frequency in analyses involving both turnover
and no-turnover groups. Dyadic communication fre-
quency includes communication to and from the new
member for groups that experienced turnover.

Results
We begin this section by discussing the effectiveness
of the communication network manipulation. We then
present results for errors and new features. Next,
we provide mediation analyses indicating that dyadic
communication frequency explains the effects of the
communication network and turnover on TMSs. Last,
we provide a test of the overall model implied by
Hypotheses 1–3. The section concludes with a presen-
tation of robustness checks.

Communication Network Manipulation Checks
First, we determined whether members correctly as-
sessed their group’s communication network. Each
group member was asked which communication path-
ways existed among members of the group. Group
members correctly identified the group’s communica-
tion pathways 86% of the time. Fully connected groups
were slightly more accurate (88%) than perfectly cen-
tralized groups (84%) in identifying communication
pathways (p < 0.1). These results suggest that group members were aware of their available communication pathways in both the centralized and fully connected conditions. Second, although it was not possible for the perfectly

centralized groups to send messages in a decentral-
ized pattern, it was possible for the fully connected
groups to send messages in a centralized pattern. To
explore this possibility, we calculated Freeman’s (1979)
degree centralization measure based on the commu-
nication messages shared within each group. Degree
centralization is a group-level measure of the disper-
sion of group members’ degree centrality scores. Given
that degree centralization can be calculated only for
dichotomous networks, we included a communication
pathway only if two members communicated over a
certain threshold. The threshold for inclusion was one
standard deviation below the group’s mean level of
messages.3 Using a threshold for tie inclusion is com-
mon in network research, and the threshold we applied
provides a reasonable distribution of degree centraliza-
tion values within the sample (Borgatti et al. 2013). The
degree centralization measure was highly correlated

with the manipulation of the communication network
(r � 0.81, p < 0.001), which suggests that our commu- nication manipulation influenced the pattern of mes- sages exchanged among group members, as expected.4

Performance
We predicted that the communication network and
turnover would interact to affect group performance
(Hypothesis 1) such that fully connected groups would
perform better when there was no turnover than when
there was, and perfectly centralized groups would per-
form better when turnover occurred than when mem-
bership was stable. Next, we hypothesized that the
communication network and turnover would inter-
act to predict transactive memory systems such that
fully connected groups would have stronger TMSs
when membership was stable than when turnover
occurred, and perfectly centralized networks would
have stronger TMSs when turnover occurred than
when membership was stable (Hypothesis 2). Finally,
we proposed that dyadic communication would medi-
ate the interaction between the communication net-
work and turnover in predicting the TMS and that the
mediation would be stronger when turnover occurred
than when it did not (Hypothesis 3). A stronger medi-
ation would mean that more of the effect of the net-
work on the TMS was due to the network’s effect
on dyadic communication frequency when the group
experienced turnover than when it did not.

We present descriptive statistics and correlations in
Table 1. Table 2 presents the ordinary least squares
estimates predicting errors based on the communi-
cation network, turnover, and their interaction.5 In
Model 1 of Table 2, the interaction of communication
network and turnover was negative and significant
(B � −2.31, p < 0.05), as predicted by Hypothesis 1. Additionally, we found a significant positive interac- tion between communication network and turnover on the number of new features (B � 2.64, p < 0.05) in Model 3 of Table 2. These interactions suggest that, for both errors and new features, fully connected groups performed better when group membership was sta- ble than when turnover occurred, whereas perfectly centralized groups performed better when turnover occurred than when membership was stable. This pat- tern can be seen in Figure 2, which depicts the mean numbers of errors (left side) and new features (right side) as functions of communication networks and turnover. This pattern of results supports Hypothesis 1.

We turn now to a test of Hypothesis 2. As can
be seen from Model 5 of Table 2, the interaction of
the network and turnover was positive and signifi-
cant in predicting TMS (B � 4.71, p < 0.05), consis- tent with Hypothesis 2. Fully connected groups devel- oped stronger TMSs when membership was stable than when turnover occurred (55.1 versus 52.4), and

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198 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

Table 1. Means and Correlations

Mean SD 1 2 3 4 5 6

1. Errors 2.04 2.87
2. New Features 4.39 2.63 −0.33∗∗∗
3. TMS 52.84 5.17 −0.34∗∗∗ 0.19

†

4. Dyadic Communication Frequency During Programming Task 1.53 0.68 −0.24∗ 0.15 0.26∗∗
5. Dyadic Communication Frequency During Feature Generation Task 0.87 0.85 −0.05 −0.01 0.26∗∗ 0.63∗∗∗
6. Dyadic Communication to New Member During Prog. Task 0.83 0.51 −0.30∗ 0.21† 0.36∗∗ 0.85∗∗∗ 0.51∗∗∗
7. Dyadic Communication from New Member During Prog. Task 0.69 0.43 −0.27∗ −0.01 0.35∗∗ 0.76∗∗∗ 0.53∗∗∗ 0.67∗∗∗

†p < 0.1; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

perfectly centralized groups developed stronger TMSs
when turnover occurred than when membership was
stable (50.9 versus 52.9).
Although not explicitly hypothesized, we examined

whether transactive memory systems mediated the
effect of communication networks on performance. As
can be seen from Table 2, when TMS was included
as a predictor of errors in Model 2, its coefficient was
negative and significant. Furthermore, the compari-
son of results between Model 1 and Model 2 reveals
that the interaction of communication network and
turnover was no longer significant when TMS was
included, suggesting that TMS accounted for the effect
of the interaction on errors. Similarly, when TMS was
included as a predictor of new features (see Model 4), it
was positive and marginally significant and the inter-
action of network structure and turnover became less
informative (p < 0.05 to p < 0.1).

Next, we investigated the role of dyadic communi-
cation frequency in explaining TMS. As can be seen

Table 2. Models of Errors, New Features, Dyadic Communication Frequency, and Transactive Memory Systems

Dyadic
New New Communication

Errors Errors Features Features TMS Frequency TMS

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Centralized Network 0.67 −0.14 −1.76∗ −1.28 −4.24∗∗ 0.33† −5.26∗∗∗
(0.87) (0.85) (0.80) (0.83) (1.56) (0.18) (1.49)

Turnover 0.72 0.21 −1.28 −0.98 −2.68† −0.17 −2.15
(0.86) (0.82) (0.79) (0.79) (1.55) (0.18) (1.46)

Centralized×Turnover −2.31∗ −1.42 2.64∗ 2.09† 4.71∗ 0.48† 3.28†
(1.13) (1.10) (1.04) (1.07) (2.05) (0.23) (1.96)

TMS −0.19∗∗∗ 0.10†
(0.05) (0.05)

Dyadic Communication 3.11∗∗∗
Frequency (0.80)

R2 0.06 0.17 0.08 0.11 0.07 0.24 0.18
N 109 109 109 109 109 109 109

Notes. Values in parentheses are standard errors. Models 3 and 4 include a control for groups that inadvertently received additional time on
the task; 1 � received 10 minutes, and 0 � received 5 minutes. The results are consistent if the groups who received 10 minutes are removed
from the analysis. Cumulative dyadic communication frequency, which is used in Models 6 and 7, is the dyadic communication frequency for
the combination of the programming task and the feature-generating task.
†p < 0.1; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

from Model 6 of Table 2, the interaction of the commu-
nication network with turnover in predicting dyadic
communication was marginally significant and pos-
itive (B � 0.48, p < 0.1). The mean levels of dyadic communication as a function of communication net- work and turnover can be seen in Figure 3. Fully con- nected groups had somewhat more dyadic commu- nication when they did not experience turnover than when they did, whereas perfectly centralized groups had somewhat more dyadic communication when they experienced turnover than when they did not. These results are in line with Hypothesis 3.

Subsequently, we examined how communication
networks, turnover, and dyadic communication fre-
quency affected TMS. Model 7 in Table 2 adds dyadic
communication frequency as a predictor of TMS and
shows that dyadic communication frequency had a
significant and positive effect on TMS. Also, relative
to Model 5, the interaction between network struc-
ture and turnover became marginally significant when

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Figure 2. Mean Errors and New Features as Functions of Communication Network and Turnover

0
1
2
3
4
5

Fully
connected

Perfectly
centralized

E
rr

or
s

0
1
2
3
4
5

N
ew

f
ea

tu
re

s
6
7
Fully
connected
†
Perfectly
centralized

*

†
*

*

No turnover

Turnover

Note. Error bars are 95% confidence intervals.
† p < 0.1; ∗ p < 0.05.

dyadic communication frequency was included in
Model 7. These results indicate that dyadic communi-
cation partially explained the effects of the interaction
of network structure and turnover on the development
of TMS, in support of Hypothesis 3.
Figure 3 also shows the amount of communication

that was directed to the new member (see the darker
bands in the turnover condition bars) compared with
the other three members. As can be seen from the

Figure 3. Dyadic Communication Frequency by Network
and Turnover During the Programming Task

0

0.5

1.0

1.5

D
ya

di
c

co
m

m
un

ic
at

io
n

fr
eq

ue
nc

y

2.0

2.5

No turnover Turnover No turnover Turnover

Fully connected Perfectly centralized

†
†

Notes. Within each group that experienced turnover, the darker sub-
section is the portion of the communication within the group that
went to the new member. The rest of the bar represents the com-
munication to the other three members. Error bars are 95% con-
fidence intervals on the group’s dyadic communication frequency.
Planned contrasts are on dyadic communication frequency. Planned
contrasts were also performed comparing the proportion of dyadic
communication that went to the new member in perfectly central-
ized versus fully connected groups. Perfectly centralized groups had
significantly more communication to their new members than fully
connected groups (p < 0.001). † p < 0.1.

figure, the proportion of communication directed to
the new member was much higher in perfectly cen-
tralized than in fully connected groups. The difference
in the amount of communication to the new mem-
ber was significant using planned contrasts (p < 0.001), indicating that perfectly centralized groups communi- cated more to their new members than fully connected groups. We also replaced the communication directed to the new member with the communication from the new member and found a similar pattern: the new member communicated more in perfectly centralized than fully connected groups.

In addition, we analyzed communication to or from
the new member as a predictor of TMS (see the online
supplement for more details). Dyadic communication
(B � 3.59, p < 0.001), communication to the new mem- ber (B � 4.52, p < 0.001), and communication from the new member (B � 4.55, p < 0.01) were all associ- ated with increases in the strength of a group’s TMS. When both communication to and from the new mem- ber were included, only communication to the new member was significant (B � 3.20, p < 0.05). Thus, the effect of incumbent members communicating to the new member is a more robust predictor of TMS than communication from the new member for groups that experienced turnover.

As a supplement to our quantitative results, we pro-
vide examples from groups’ transcripts to illustrate the
differences in how fully connected and perfectly cen-
tralized groups integrated the new member. The first
quote is from a fully connected group that experienced
turnover. Incumbent 1 wrote, “Are you working on it?
Just checking.” Incumbent 2 responded, “I think [New
Member] is doing random shit” (Group 55). This quote
illustrates that the incumbent group members in the
fully connected groups did not directly communicate
with the new member, whom they seemed to ignore
and assumed would not contribute.

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200 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

In a perfectly centralized group with peripheral
member turnover, we see encouragement between
incumbents and the new member. Incumbent 1 wrote,
“We figured out up to part c, [but] we can’t figure
that out.” After the new member works on the shared
screen, the new member responded, “[That] should be
[the solution to part c].” Incumbent 1 answered with
“mmm . . . let me ask the others . . .you are amazin [sic]”
(Group 70).
In a perfectly centralized group with central member

turnover, incumbent members informed the new mem-
ber about the organizing logic in the group. Incumb-
ent 1 wrote, “[C]ould you speak with A and B???” The
new member responded, “[Y]eah [I] think so. I can talk
to B and B said he/she can only talk to me.” Incum-
bent 1 answered with “you [perform] as a communica-
tor for the group” (Group 70). These quotes reinforce
the quantitative results indicating that perfectly cen-
tralized groups communicated more with their new
members and incorporated their contributions better
than fully connected groups did.

Moderated Mediation Analyses
We tested the moderated mediation proposed in Hy-
pothesis 3 using PROCESS (Hayes 2013). Figure 4 pro-
vides a theoretical model of the moderated mediation
we predict. The use of bootstrap sampling in PROCESS
allows for tests of mediation and moderation that have
more statistical power and violate fewer assumptions
than other tests of mediation (Hayes 2013). For all of the

Figure 4. Theoretical Framework and Statistical Models Predicting Errors by Turnover Condition

Full moderated serial mediation

(a)

(b) Groups that did not experience turnover (c) Groups that experienced turnover

+

+

_

+

+
+

Centralized
network

Performance

Dyadic
communication

frequency

Transactive
memory systems

Turnover
+

Hypothesis 1
Hypothesis 2
Hypothesis 3

_

No turnover serial mediation

– 0.20*

– 5.09**

2.30

0.37*

Centralized
network

Errors

Dyadic
communication
frequency
Transactive
memory systems

– 0.38

Just turnover serial mediation

– 2.82†

3.59***

– 0.15*0.92***

Centralized
network
Errors
Dyadic
communication
frequency
Transactive
memory systems

– 0.91

Notes. Panel (a) shows the full statistical framework. Panel (b) shows coefficients for groups that did not experience turnover (n � 42), and
panel (c) shows coefficients for groups that experienced turnover (n � 67) for errors.
†p < 0.1; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

analyses, we used 50,000 bias-corrected bootstrap sam-
ples. In the overall model, we used dyadic communica-
tion frequency because it occurs for both turnover and
no-turnover groups. The results of the PROCESS anal-
ysis indicated that dyadic communication frequency
mediated the relationship between the communication
network and transactive memory systems, and that
the relationship was stronger when turnover occurred
(B � 2.45; 95% confidence intervals (CIs): 1.14, 4.28)
than when it did not (B � 1.02; 95% CIs: 0.01, 2.69).
The index of moderated mediation test (Hayes 2015)
was significant (95% CIs: 0.20, 3.28), indicating that the
strength of the mediation was significantly stronger for
the turnover than no-turnover groups, providing addi-
tional support for Hypothesis 3.

We used the partially standardized indirect effect to
determinetheeffectsizeofthenetworkvariableonTMS
through dyadic communication frequency (Preacher
and Kelley 2011). The partially standardized indirect
effect can be interpreted the same as a Cohen’s d—
the number of standard deviations of change in TMS
that occur as a result of the effect of the network on
dyadic communication frequency. For groups that did
not experience turnover, perfectly centralized groups
had TMSs that were 0.22 standard deviations stronger
than fully connected groups (95% CIs: 0.01, 0.56),
due solely to centralized groups communicating more
than fully connected groups. Because perfectly cen-
tralized groups communicated even more when they
experienced turnover, centralized groups had TMSs
that were 0.57 standard deviations stronger than fully

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connected groups when turnover occurred (95% CIs:
0.27, 0.91), because of their communication. Cohen
(1960) described 0.22 as a small effect and 0.57 as a
medium effect. These calculations demonstrate that the
importance of dyadic communication in explaining the
network’s effect on TMS increases substantially when
groups experienced turnover.
We now turn to a test of a full “moderated serial

mediation” model as seen in Figure 4. The analysis
for errors indicated that for groups that did not ex-
perience turnover, the effect of communication net-
work on errors was partially mediated by dyadic
communication frequency and TMS (B � −0.21; 95%
CIs: −0.66, −0.03); however, the majority of the effect
was accounted for by TMS alone (B � 0.98; 95% CIs:
0.41, 1.92). By contrast, for groups that experienced
turnover, the strength of the effect of communication
network through dyadic communication frequency
and TMS was larger in magnitude (B �−0.52; 95% CIs:
−1.08,−0.21) than that of the effect through TMS alone
(B � 0.44; 95% CIs: 0.0002, 1.1386). The index for mod-
erated mediation (Hayes 2015) indicated that the effect
of having a perfectly centralized network on errors
through dyadic communication frequency and TMS
was stronger in turnover versus no-turnover groups
(95% CIs: −0.68, −0.05). The effect of having a perfectly
centralized network on errors just through TMS did
not differ in magnitude based on turnover (95% CIs:
−1.35, 0.16).6

For groups that did not experience turnover, TMS
explained the majority of the effect of the communi-
cation network on errors: perfectly centralized groups
had weaker TMSs than fully connected groups, which
led to more errors (see Figure 4(b)). There was a
countervailing effect of the communication network
through dyadic communication frequency—perfectly
centralized groups communicated more than fully
connected groups, which improved their TMSs and
thereby reduced errors. But this second effect was
smaller than the first when group membership was sta-
ble. Hence, when there was no turnover, the net effect
of having a perfectly centralized network increased
errors (−0.21 + 0.98 � 0.77). When groups experi-
enced turnover, however, the magnitudes of the effects
changed (see Figure 4(c)). For groups that experi-
enced turnover, the effect of having a perfectly central-
ized network on dyadic communication frequency was
much stronger than for groups that did not experience
turnover, and the effect of dyadic communication fre-
quency on TMS was also stronger. When groups expe-
rienced turnover, the effect of perfectly centralized net-
works reducing errors through their effect on dyadic
communication frequency and TMS exceeded the per-
fectly centralized network’s direct effect on increasing
errors through TMS alone (−0.52 + 0.44 �−0.08).

To provide a sense of the size of these effects, we
present the effect sizes for the moderated serial medi-
ation. For groups that did not experience turnover, the
partially standardized indirect effect of the network on
errors through dyadic communication frequency and
TMS was −0.07 (95% CIs: −0.23, −0.01), and there was
also a significant mediation just through TMS (0.35;
95% CIs: 0.14, 0.68). The interpretation of these signif-
icant effect sizes is that for groups that did not expe-
rience turnover, there was a 0.28 (−0.07 + 0.35) SD
increase in the number of errors for perfectly central-
ized versus fully connected groups. This effect reversed
for those that experienced turnover, with a 0.18 SD
(95% CIs:−0.37,−0.07) decrease in errors due to dyadic
communication and TMS.

We tested the same moderated serial mediation with
new features as the dependent variable. The serial
pathway of the centralized network effect on new fea-
tures through dyadic communication frequency and
TMS was not significant for groups with stable group
membership (B � 0.02; 95% CIs: −0.04, 0.16) but was
positive and significant for groups that experienced
turnover (B � 0.09; 95% CIs: 0.01, 0.28). TMS by itself
acted as a significant mediator of the effect of hav-
ing a centralized network on new features for groups
with stable membership (B � −0.55; 95% CIs: −1.34,
−0.09) but not for groups that experienced turnover
(B � −0.03; 95% CIs: −0.33, 0.26).7 These results are
similar to the results for errors; however, neither of
the mediations was moderated by turnover. In these
analyses, we found general support for our hypotheses
and for the overall model in Figure 4(a). Results were
stronger for errors than for new features.

Robustness Checks
Because the central and peripheral turnover conditions
did not differ in their errors (1.22 and 1.23, respectively;
p � 0.99), new features (4.52 versus 5.00, respectively;
p � 0.54), or the strength of their transactive mem-
ory systems (52.2 versus 53.6, respectively; p � 0.37),
these two centralized turnover conditions were com-
bined into a single centralized network turnover con-
dition for the analyses shown in Table 2. We repeated
these analyses but included only one form of central-
ized turnover at a time. When either of the central-
ized turnover conditions (central member or periph-
eral member turnover) was removed from Model 1
in Table 2, the interaction of network structure and
turnover on errors was in the same direction and
marginally significant (p < 0.1). This reduction in sig- nificance is likely due to a reduction in power. No dif- ferences were detected when Model 3 in Table 2, pre- dicting new features, was run with only one of the perfectly centralized turnover conditions included in the analysis at a time. Second, we reran all moderated mediation analyses with data from only one of the two

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
202 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

perfectly centralized turnover conditions included at
a time and did not find any significant differences for
turnover based on whether the peripheral or central
member left the group.
We created two variables that measured the imbal-

ance of messages sent and received among the group
members, using the Herfindahl–Hirschman index of
communication corrected for the number of pathways
as an alternative explanation of our findings. These
indices measure imbalances, and they increase to 1 as
more communication is either sent or received by a
single individual and decrease to 0 if there is perfect
equality in the number of messages sent or received.
Although the imbalance of sent messages (B �−14.89,
p < 0.001) and the imbalance of received messages (B � −18.73, p < 0.1) were both negatively related to TMS, neither measure was statistically significant when dyadic communication frequency was added to the model, and dyadic communication frequency was significant and positive (p < 0.01) in both cases. These results suggest that dyadic communication frequency is a more robust predictor of TMS than imbalanced communication.

Discussion and Conclusion
Our results indicate that perfectly centralized com-
munication networks can reverse the negative effect
of turnover in groups. When turnover occurred in
fully connected groups, incumbents tended to ignore
new members who could not readily understand the
group’s implicit coordination logic or their roles within
the group. By contrast, the explicit coordination logic
of perfectly centralized groups enabled them to make
use of the contributions of new members. In central-
ized groups, the means of coordination, which were
largely determined by the communication structure,
were simple and explicit. The high dyadic communi-
cation frequency in the perfectly centralized groups
reduced the likelihood that the incumbent members
ignored the new member. The active integration of
the new member not only fostered the formation of
a strong TMS but also improved the creativity and
problem-solving capabilities of centralized groups.
The experimental manipulation of communication

networks we use here provides two distinct method-
ological advantages. First, identifying the direct causal
effects of networks has proven challenging in field
settings, particularly because of endogeneity issues
(Ferriani et al. 2009, Lee et al. 2014, Uzzi and Spiro
2005). Our study design provides insight into commu-
nication networks and group processes and is unham-
pered by endogeneity concerns (see Croson et al.
2007 for a discussion of the advantages of experimen-
tal research). The experimental design and random
assignment of participants to communication networks
and to positions in the networks also enabled us to

attribute effects to the communication network rather
than to conditions that led to the formation of the net-
works or to qualities of individuals who might gravi-
tate to certain positions in the networks (Sasovova et al.
2010). Thus, directly manipulating the communication
network allows us to make causal claims about the
effects of communication networks on group perfor-
mance (Manski 1993). Second, where possible, we used
behavioral measures, such as the amount of commu-
nication, which are more objective than self-reported
variables (Spector 1994). In particular, self-reports of
communication and social networks have been found
to be heavily biased by social factors, such as individ-
ual status (Bernard et al. 1984).

In addition to providing insights difficult to obtain
by other methods, the design of our study maps well to
organizational phenomena. The two tasks we used—
the programming task and the idea-generating task—
parallel many tasks found in organizational settings.
Within organizations, groups often work on complex
tasks, which involve both problem solving and creativ-
ity (Devine et al. 1999). Thus, our realistic tasks provide
external validity. In addition, we trained individuals on
different information to increase task interdependence
among the group members, which captures the chal-
lenge of integration and coordination among group
members with specialized members. Group members
also communicated through computers located in
separate rooms and thus operated as a distributed
group. This arrangement is analogous to contempo-
rary project-based work conducted in organizations
via email (Kleinbaum et al. 2013, Aven 2015). This fea-
ture of our experiment not only represents a condition
under which many groups operate in organizations
today but also allows us to control the communication
network and to capture all communication that occurs
among group members. Although the features of our
experimental design were chosen to reflect character-
istics of real organizations, these design features also
present boundary conditions to which our findings are
most likely to generalize. Therefore, we anticipate that
our findings will generalize to small groups in which
members perform complex interdependent tasks and
communicate in a distributed manner.

Another boundary condition of our findings is the
experience of the new member. The new member in
our study received the same individual training as the
departing member but did not work with a group pre-
viously. We chose this study design because we felt that
new members to groups typically would have received
some training but would have limited group experi-
ence. Understanding the effect of the prior experience
of the new member on group processes and perfor-
mance is an interesting issue for future research.

Although we did not hypothesize differences in the
effects of turnover in central and peripheral mem-
bers in perfectly centralized groups, we included both

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS 203

types of turnover in our design to be able to ascer-
tain whether any differences observed were due to
the structure, as we hypothesized, or to the position.
To accomplish this, we randomly assigned individu-
als to networks and to positions rather than allow-
ing individuals to gravitate to the networks or posi-
tions that they preferred or for which they believed
they were especially qualified. Furthermore, the new
member received the same training as other members
and the same specialized information as the depart-
ing member. These features of the design enabled us
to investigate the causal effects of the communication
network, a major goal of the study. We did not find dif-
ferences between turnover in the central or peripheral
positions in dyadic communication, TMS, or perfor-
mance. It is important to note that turnover of central
and peripheral members might have different effects
in groups where members’ positions are correlated
with other characteristics, such as experience or skills,
which is often the case in organizations. By using ran-
dom assignment, our study enabled us to determine
the effect of network structure, independent of these
individual factors, on group outcomes.
One limitation of our study was that the feature-

generating task always followed the programming
task. We thought that this was the most realistic
sequence: it would be more natural for groups to
identify “ways that the pipe you just built could be
improved by creating new features” after rather than
before they had built the pipe. The correlation between
errors in the programming task and new features in
the feature-generating task was −0.33, which indicates
a moderate relationship between the two tasks: as
errors on the programming task decreased, the number
of new features typically increased. Thus, performing
well on the first task did not constrain members from
being able to identify ways the pipe could be improved
on the second task. Nonetheless, it would be useful
in future work to develop an idea-generating task that
could occur before or after the programming task and
thus allow the order of tasks to be counterbalanced.
This paper contributes to several literatures as well

as highlights their intersections. In a review of the
transactive memory literature, Ren and Argote (2011)
recommended greater research on how social networks
affect the development of transactive memory systems.
Our research shows that the communication network
is an important predictor of transactive memory sys-
tems. In addition, our findings demonstrate that com-
munication networks condition the effect of turnover
on TMS development. Previous work has shown that
membership change disables a group’s TMS, which
impairs its performance (Lewis et al. 2007, Moreland
et al. 1996). Our study demonstrates that perfectly cen-
tralized communication networks can reverse the neg-
ative effect of turnover of one member. Relative to fully

connected groups, incumbent members of centralized
groups communicate more with the new member,
which enables them to update their TMS and improve
their performance. Our results suggest that perfectly
centralized groups are more receptive to newcomers
and more likely to incorporate the ideas of new mem-
bers than fully connected groups.

Our findings also offer insights into the effects of
the communication network structure on group-level
outcomes, such as group performance (Casciaro et al.
2015). Although there is a robust literature on the
effects of communication networks on individual out-
comes (Ahuja et al. 2003, Borgatti and Cross 2003),
our work provides new insights into the effects of net-
work configurations on group-level outcomes. In this
study, we empirically established that, under stable
membership, a fully connected communication net-
work enables the development of a TMS, which in turn
leads to positive performance. When turnover occurs,
a perfectly centralized network enables the develop-
ment of a TMS, which in turn improves performance.
In addition, our findings begin to unpack how commu-
nication networks interact with membership change to
affect group processes.

We described two mechanisms through which cen-
tralization affects performance: (1) the number of direct
communication paths available to team members and
(2) the clarity of the coordination logic. The first mech-
anism maps onto network density, which affects a
team’s behavior through the number of available com-
munication paths. The second mechanism maps onto
network centralization, which affects a team’s behav-
ior through the clarity of the coordination logic. We
focused on the two network structures that maximize
one structural dimension and minimize the other: (1) a
fully connected, all-channel network that maximizes
density and minimizes centralization and (2) a hub-
and-spoke or perfectly centralized network that max-
imizes centralization and minimizes density. Network
types, however, vary across both density and central-
ization. We suggest that an important issue for future
research is to disentangle the effects of network den-
sity and centralization. Although it is not possible to
make the two dimensions of density and centraliza-
tion orthogonal,8 one could design studies where the
two dimensions were not highly correlated and thereby
investigate their separate effects. On the basis of our
theory and evidence, we would predict that density
is a more important predictor of group performance
when membership is stable and that the clarity of the
coordination logic is more important when turnover
occurs on complex interdependent tasks such as the
ones used here. This theory leads to a variety of pre-
dictions about the effects of particular network typolo-
gies on group processes and performance under con-
ditions of member stability or turnover. For example,

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
204 Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS

one prediction would be that a ring or circle network
would not perform as well as a hub-and-spoke net-
work when turnover occurred, because of the lower
centralization and correspondingly less clear coordi-
nation logic of the circle network. Another research
approach would be to identify networks, such as cer-
tain lattices, that do not maximize one network dimen-
sion and minimize another but that are, rather, inter-
mediate on both dimensions. Although not optimal for
either stable or turnover conditions, such a network
might perform better when there is uncertainty about
whether turnover will occur. Testing these predictions
is an interesting avenue for future research.
Our results suggest that environments where mem-

ber turnover commonly occurs might benefit from en-
couraging centralized communication networks. The
results of our mediation analyses provide further in-
sights into how groups might address membership
turnover, such as promoting communication, specif-
ically to new members. Just as assigning a facili-
tator in brainstorming sessions improves creativity
and efficiency (Sutton and Hargadon 1996), assign-
ing an incumbent group member to communicate
with new members and encourage them to con-
tribute might facilitate the integration of new mem-
bers even in decentralized communication settings.
Alternatively, structured processes or formal proce-
dures that facilitate contributions from all members
could also be effective in offsetting the negative effects
of membership turnover. Future research should exam-
ine additional mechanisms for onboarding new mem-
bers effectively and enabling them to contribute to
group performance.

Our results have implications for organizational
design and contradict the classic advice in the litera-
ture (e.g., see Burns and Stalker 1961) that organiza-
tions in dynamic environments should be organized in
an organic rather than hierarchical manner. Instead, our
study indicates that centralized or hierarchical groups
perform better in dynamic rather than in stable envi-
ronments but fully connected groups do not when the
dynamism is caused by membership change. Thus,
our results suggest that taking a fine-grained approach
to understanding the source of the dynamism will
advance organization design research as well as con-
tribute to a growing body of literature on the benefits
of certain forms of bureaucracy (Bunderson and Boum-
garden 2010).

In their seminal work, Burns and Stalker (1961)
argued that groups can cultivate either efficiency
through constrained, formal, or mandated structures or
creativity by permitting more autonomy and emergent
coordination. Centralized networks are rarely viewed
to be useful for innovation, as their limited pathways
reduce the interconnectedness and, thus, interactions

of unique idea holders, undermining innovation. How-
ever, limited structures may help provide the necessary
communication arrangement to share ideas when the
group’s membership is not stable. Our findings suggest
that turnover provides a means to introduce innovation
and creativity into mandated structures or centralized
networks. Thus, organizations may be able to gain the
benefits of efficiency while also increasing the innova-
tion capabilities of their teams by adopting centralized
structures coupled with the frequent rotation of team
members.

Acknowledgments
This research was presented at the 2015 Academy of Man-
agement meeting in Vancouver, Carnegie Mellon University,
Johns Hopkins University, Massachusetts Institute of Tech-
nology, the University of California at Santa Barbara, the
University of Pennsylvania, the University of Virginia, and
Washington University in St. Louis. The authors wish to
thank participants in these forums, especially Taya Cohen
and Anita Woolley, for their helpful feedback, and members
of the SCALE (Social Coordination Across Large Environ-
ments) project, especially Jim Herbsleb and Anita Sarma, for
their helpful advice. They would also like to thank senior edi-
tor Tiziana Casciaro and the reviewers for their very thought-
ful comments.

Endnotes
1Data from seven groups were dropped from the analysis. Two
groups failed to follow the experiment’s instructions, and five
groups contained members who had previously participated in the
experiment.
2Eleven groups were inadvertently given 10 minutes instead of 5.
There was no main effect of this on new features, but a control vari-
able was included in all predictions of new features (see the notes to
Table 2).
3We used the R (version 3.1.3) package tnet (Opsahl 2012, ver-
sion 3.011) to convert the networks, and we used the R package
igraph (Csárdi and Nepusz 2006, version 0.7.1) to calculate Freeman’s
(1979) centralization.
4Betweenness centralization, variance in how often a member resides
on the shortest communication path between all possible pairs, was
also used as a robustness check. Similar to degree centralization,
betweenness centralization had a positive correlation with central-
ization (0.79, p < 0.001). 5To aid in interpretation, we report coefficients for ordinary least squares models; however, all analyses involving errors and new fea- tures were repeated with Poisson regressions, a version of the gen- eralized linear model appropriate for a dependent variable that is a count. These Poisson regressions produced results very similar to those presented here. 6As a robustness check, we repeated these analyses using just dyadic communication frequency that occurred within the first six minutes of the programming task. Six minutes was the minimum time to completion among all the groups on the programming task. Focus- ing on the first six minutes allowed us to explore variations in dyadic communication frequency without having to account for time to complete the task. Results from these analyses were identical to those using dyadic communication frequency across the entire time groups worked. The serial pathway in the moderated serial media- tion predicting errors was smaller (no turnover, B �−0.13; 95% CIs:

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Argote, Aven, and Kush: The Effects of Turnover on Group Performance
Organization Science, 2018, vol. 29, no. 2, pp. 191–206, © 2018 INFORMS 205

−0.48, 0.05; turnover, B � −0.41; 95% CIs: −0.89, −0.16) though still
significantly moderated by turnover (95% CIs: −0.71, −0.01).
7These analyses were repeated using communications from only the
first five minutes of the task. Five minutes was the minimum time
to completion for the feature-generating task. Results from these
analyses are similar in pattern and direction to those reported here,
indicating that the results are robust.
8The lowest possible network density in any fully connected network
is always greater than the density for a perfectly centralized network.
The formula for the density of a fully connected network with the
lowest possible density would be N/(N · (N −1)/2), where N is the
number of members of the group. The formula for the density of a
perfectly centralized network is similar, but the numerator is N − 1
instead of N. Thus, all fully connected groups have higher density
than their perfectly centralized counterparts.

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Linda Argote is the David M. and Barbara A. Kirr Pro-
fessor of Organizational Behavior and Theory at the Tep-
per School of Business at Carnegie Mellon University, where
she directs the Center for Organizational Learning, Innova-
tion and Knowledge. Her research focuses on organizational
learning, organizational memory, knowledge transfer, social
networks, and group processes and performance. She served
as editor-in-chief of Organization Science from 2004 to 2010.

Brandy L. Aven is an associate professor of organiza-
tional behavior and theory at the Tepper School of Busi-
ness, Carnegie Mellon University. She received her Ph.D. in
sociology from Stanford University. Her research interests
include market and organizational fraud, entrepreneurship,
and social networks.

Jonathan Kush is an assistant professor of management
at the Charlton College of Business, University of Mas-
sachusetts Dartmouth. He received his Ph.D. from the Tepper
School of Business, Carnegie Mellon University. His research
interests include group collaboration, collective cognition,
social networks, and their effects on group performance.

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https://cran.r-project.org/package=tnet