PERFORMANCE BENEFITS OF RECIPROCAL VICARIOUS LEARNING IN TEAMS

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r Academy of Management Journal
2021, Vol. 64, No. 3, 926–947.
https://doi.org/10.5465/amj.2018.0875

                      PERFORMANCE BENEFITS OF RECIPROCAL VICARIOUS
                                  LEARNING IN TEAMS
                                                                    CHRISTOPHER G. MYERS
                                                                    Johns Hopkins University

                          Team members’ vicarious learning from other members’ knowledge and experience is a
                          critical component of learning and performance in interdependent team work contexts.
                          Yet, our understanding of vicarious learning among individuals in teams is still quite
                          limited, as this learning is often oversimplified (as one-way knowledge-sharing) or aggre-
                          gated (as a collective, team-level property), resulting in incomplete and inconsistent
                          findings. In this paper, I extend these views by exploring the underlying distribution of
                          dyadic vicarious learning relationships in teams, specifically using a network approach
                          to examine the consequences of reciprocity in team members’ vicarious learning with
                          one another (i.e., where both individuals in a given dyad learn vicariously from each
                          other’s knowledge and experience). Using a novel method for calculating weighted
                          reciprocity in networks in a study of MBA consulting project teams, I demonstrate that
                          greater team vicarious learning reciprocity is associated with greater team performance,
                          and also moderates the performance consequences of teams’ external learning efforts,
                          offering a potential reconciliation of conflicting results in prior research. In doing so,
                          this paper advances research on vicarious learning in teams, while also providing
                          conceptual and empirical tools for studying learning and other interpersonal workplace
                          interactions from a network perspective.

  As work tasks, and the expertise required to                                                    (Myers, 2018)—forms a core component of team
perform them, become increasingly interdependent                                                  learning (Argote & Gino, 2009), reflecting how
and distributed among members of cross-functional                                                 individuals share, receive, and integrate knowledge
teams in organizations, successful performance re-                                                and experiences in their respective networks of
quires individuals to effectively learn from and inte-                                            relationships with others in the team (e.g., Glynn,
grate the knowledge and experience shared by other                                                Lant, & Milliken, 1994).
team members (Edmondson, Dillon, & Roloff, 2007).                                                    Yet, prior research has tended to present an overly
This vicarious learning among team members—                                                       simplified view of vicarious learning at work, often
defined as an individual’s learning from a process of                                             building from an assumption, for instance, that this
absorbing and interpreting another’s knowledge and                                                learning is unidirectional—that there is a “sharer” of
experiences in order to expand their repertoire of re-                                            knowledge and a “learner” who receives the knowl-
sponses for future tasks or performance challenges                                                edge, and that these roles are stable within a learning
                                                                                                  relationship (implicitly assuming that a “sharer”
                                                                                                  would never learn from the “learner”). This assump-
   I would like to thank Associate Editor Jessica Rodell                                          tion can be seen in the tendency for prior work to
and three anonymous reviewers for providing construc-                                             examine either what might drive a person (often an
tive, developmental feedback throughout the review pro-                                           expert team member) to share knowledge with some-
cess. I am also grateful for feedback, advice, and support                                        one (e.g., a novice team member) or what leads to the
from Kathleen Sutcliffe, Jane Dutton, Scott DeRue, Amir                                           seeking of knowledge by these more novice members
Ghaferi, Jim Westphal, Ashley Hardin, Kira Schabram,
                                                                                                  from experts (e.g., Hofmann, Lei, & Grant, 2009;
and Colleen Stuart; for assistance with data access and
                                                                                                  Levin & Cross, 2004; Osterloh & Frey, 2000). By fo-
analysis from Chen Zhang and Lillian Chen; and for help-
ful comments from session participants at the 2016 Acad-                                          cusing on only a knowledge sharer or recipient, these
emy of Management meetings, where an earlier version of                                           studies have implicitly committed to the “one-way”
this paper received the Managerial and Organizational                                             learning assumption, as they have made ex ante con-
Cognition Division’s Best Student/Dissertation Paper                                              ceptual and empirical determinations of individuals’
Award. Any remaining errors are my own.                                                           roles—for instance, equating “expert” with “sharer”
                                                                                        926
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2021                                                       Myers                                                       927

and “novice” with “learner or recipient.” This is a                understand team behavior and performance (Hum-
potentially troublesome simplification, because                    phrey & Aime, 2014: 444). For example, network con-
when individuals learn vicariously from others they                cepts and analysis have been applied to understand
do not simply “consume” a sharer’s experience (Mat-                team interdependence and communication patterns
zler & Mueller, 2011: 318) but rather often “feed back             in the face of team member change and turnover,
questions, amplifications, and modifications that                  with studies examining how changes in team mem-
add further value” (Quinn, Anderson, & Finkelstein,                bership can differentially impact performance as a
1996: 8), helping to motivate a “mutual give-and-                  function of structural features of the team’s network,
take” of knowledge (Ipe, 2003: 346). As a result, our              and the departing individual’s place within it (e.g.,
understanding of these vicarious learning relation-                Argote, Aven, & Kush, 2018; Stuart, 2017). However,
ships among individuals at work, and particularly                  this microdynamics approach has yet to be as fully
the consequences of different distributions of these               incorporated in the domain of team learning, with ex-
dyadic relationships among members of a group or                   tant research tending to conceptualize team learning
team, is still quite limited (see Myers, 2018).                    as a unitary, team-level property (Edmondson, 2002;
   In this paper I aim to broaden our understanding                Vashdi, Bamberger, & Erez, 2013; for a review see
of vicarious learning by examining the consequences                Wilson, Goodman, & Cronin, 2007) varying only in
of differing levels of reciprocity in team members’ vi-            amount, and thus implicitly assuming that learning is
carious learning relationships (i.e., the extent to                distributed uniformly and equivalently between all
which person A learns from the experiences and in-                 individuals in the team (rather than as a network of
sight shared by person B, and B in turn learns from                differently distributed knowledge [Espinosa & Clark,
those shared by A). In contrast to the prevailing one-             2014]). However, each team member may vary in
way assumption described above, reciprocity may be                 their participation in vicarious learning with other
a particularly relevant characteristic of team mem-                members, as a function of their differing background
bers’ learning relationships, as the cross-functional,             and position in the team learning network (Singh,
interdependent team contexts increasingly found in                 Hansen, & Podolny, 2010), and may draw different
organizations offer significant opportunities for bidi-            lessons from another’s shared experiences based on
rectional learning among peers on the team. Indeed,                the nature of their relationship with the sharer (see
reciprocity has been identified as a feature of work-              Myers, 2018). This potential for differential learning
place learning, with evidence indicating that consid-              can be better reflected and understood by adopting a
erations of reciprocity play a part in motivating                  network-based model of learning in teams that cap-
greater engagement in knowledge-sharing among                      tures the structure and distribution of vicarious learn-
organizational units (e.g., Dyer & Nobeoka, 2000;                  ing relationships among team members.
Schulz, 2001), as well as explaining patterns of                      Additionally, considering team vicarious learning
advice-seeking among teachers (Siciliano, 2016).                   reciprocity opens avenues for better explaining the
Building on this nascent recognition of reciprocity’s              consequences of teams’ learning, and in particular
role in workplace learning, I hypothesize and test the             resolving discrepant findings in earlier research
performance consequences of more reciprocal vicari-                regarding team’s engagement in learning outside of
ous learning in teams. Using a sample of MBA con-                  the team’s boundaries (external learning [Ancona &
sulting project teams, I find empirical support for a              Bresman, 2005]). Teams’ engagement in external
direct effect of vicarious learning reciprocity on team            learning has been shown in prior research to comple-
performance, as well as a moderating effect of this                ment their internal learning (the learning with and
reciprocity in explaining the performance benefits                 from other team members discussed above), posi-
(or costs) of teams engaging in external learning.                 tively interacting with internal knowledge develop-
   Advancing a model of vicarious learning reciproci-              ment in ways that enhance performance (Bresman,
ty in teams offers several key contributions to studies            2010), and also to conflict with this internal learning,
of teams and learning in modern organizations. First,              such that engaging in external learning in addition to
in contrast to earlier team research that has focused              internal learning overtaxes team members and harms
on stable, collective attributes of teams (i.e., relying           performance (Wong, 2004). These conflicting results
on aggregated, static team-level constructs to explain             invite a question of whether team members’ internal
outcomes), I contribute to a growing body of research              learning may unfold in fundamentally different
that has used a network approach to explore the mi-                ways across teams, such that some teams benefit
crodynamics of team processes “below the surface”                  from engaging in external learning and others do not.
of these collective-level constructs in order to better            Yet, because existing research has neglected to
928                                          Academy of Management Journal                                         June

explore the underlying patterns of team members’               repertoire of possible responses to future events—
learning from one another, these differences in how            focusing less on whether the particular lessons drawn
learning is occurring in a team have been overlooked           from others’ experience are “right” or “wrong” (as
(in favor of aggregated measures of how much learn-            this determination depends on how lessons are ap-
ing occurs). Incorporating underlying structural fea-          plied to unknown future challenges) and instead on
tures of a team’s network of learning relationships,           the benefits that accrue from greater awareness of
and in particular reciprocity (as a capacity-building          others’ knowledge and experience and a more robust
characteristic that could influence team members’              set of responses an individual could apply in the face
ability to absorb additional learning from outside             of future task challenges (see Myers, 2018).
sources), provides a means for disentangling these                In this way, vicarious learning allows individuals
effects and resolving these conflicting findings.              to enhance their own experiential learning processes
   Finally, given the theoretical promise of reciprocity       (i.e., reflecting on and making meaning of their own
for broader studies of teams and organizations, this           idiosyncratic set of work experiences) by reflecting
work also contributes by advancing methodological              on and drawing lessons from others’ experiences,
tools for conceptualizing and measuring reciprocity            gained through inherently interpersonal processes of
as a characteristic of networks at work. Prior ap-             observation, discussion, and interaction with others
proaches to studying reciprocity in networks within            in their work environment. Indeed, learning has long
work organizations have typically relied on binary             been seen as a social phenomenon in organizations
conceptualizations of reciprocity, or have looked only         that involves action at both the individual and col-
at the relative balance of weighted ties (for recent ex-       lective level (Argyris & Sch€  on, 1978; Weick, 1979,
amples, see Caimo & Lomi, 2014; Kleinbaum, Jordan,             1995), leading scholars to consider network-based
& Audia, 2015; Lai, Lui, & Tsang, 2015), making                approaches as a means of understanding organiza-
a number of critical simplifications that have limited         tional learning (i.e., viewing this learning as built on
the field’s understanding of reciprocity. In contrast,         a network of interpersonal connections in organiza-
my approach (drawing upon recent developments in               tions [e.g., Glynn et al., 1994; Weick & Roberts,
network methods from scholars in the physical scien-           1993]). In line with this approach, network scholars
ces [Squartini, Picciolo, Ruzzenenti, & Garlaschelli,          have examined how the distribution and characteris-
2013]) attends not only to the presence of reciprocal          tics of particular relationships (e.g., the strength or
ties but also to their strength. The findings of this          embeddedness of a tie [Granovetter, 1973; Uzzi,
study thus contribute not only to literature on indi-          1997]) influence learning in organizations (Levin &
vidual and team learning by adopting a network-                Cross, 2004). For instance, weak ties were shown in
based view of team members’ vicarious learning from            one study to facilitate the search for diverse informa-
one another, but also to the organizational literature         tion in a consulting firm (Hansen, 1999), while Uzzi
more generally by advancing reciprocity as a key fea-          and Lancaster (2003) found that embedded ties allow
ture of individuals’ networks of relationships at work         for the transfer of more tacit knowledge and experi-
and providing a robust set of empirical tools for              ence between bank loan officers and clients.
assessing this fundamental network characteristic.                Whereas the embeddedness of a tie is one feature of
                                                               a network relationship, another key feature is wheth-
                                                               er the relationship is reciprocal. Though broadly asso-
      RECIPROCITY IN VICARIOUS LEARNING
                                                               ciated with the strength of a tie (e.g., Granovetter,
           RELATIONSHIPS AT WORK
                                                               1973), the reciprocity of a tie reflects a distinct focus
   Learning vicariously from others’ experiences in            on the tendency of a given pair of individuals to de-
the workplace has long been considered a valuable              velop mutual connections with each other (rather
process for innovation and performance in organiza-            than just a one-way connection [Newman, 2010]). In
tions (Manz & Sims, 1981). Vicarious learning occurs           terms of vicarious learning, a reciprocal relationship
through individuals being exposed to, and making               can thus be considered one in which each individual
sense of, others’ experience and outcomes (gained              learns from the experiences and knowledge of the
through passive means, such as observation, or more            other (i.e., in a mutual give-and-take of knowledge
active and discursive means, such as storytelling) in          [Ipe, 2003]). This can be contrasted with a nonreci-
their work setting (Myers, 2018). This perspective             procal (one-way) relationship, where one person
views others’ experience as beneficial for individual          shares knowledge and experience with the other but
learning insofar as making meaning of another’s ex-            the reverse is not true. Research in communication
perience helps refine and expand the individual’s              (e.g., Rogers & Kincaid, 1981) has noted reciprocity to
2021                                                        Myers                                                          929

be an integral part of information-sharing, because it              directions between node pairs in the network (out of
helps refine and shape emerging insights from shared                the total weight of all realized ties in the network).1
knowledge, suggesting that it is key to realizing the                  In this sense, a team’s vicarious learning reciprocity
learning benefits of a workplace relationship (Adler &              is distinct from other characteristics of the team’s vi-
Kwon, 2002; Kang, Morris, & Snell, 2007). Indeed,                   carious learning network, such as its density (New-
considerations of reciprocity have been tied to organi-             man, 2010). The density of teams’ networks of
zation- and unit-level knowledge-sharing in several                 different work relationships (e.g., task, advice, or hin-
prior studies (Dyer & Nobeoka, 2000; Hall, 2001).                   drance relationships) has been shown to influence
Studies have found, for instance, that organizational               team outcomes (e.g., Sparrowe, Liden, Wayne, &
units receiving knowledge from others tended to                     Kraimer, 2001; Tr€   oster, Mehra, & van Knippenberg,
share their own knowledge with those other units                    2014). Yet, whereas density reflects the proportion of
(Lai et al., 2015; Schulz, 2001), while other work has              total realized dyadic ties in the network (or total tie
found that perceptions of learning reciprocity are as-              weight, in a weighted network) out of all possible ties,
sociated with improved chronic illness care in medi-                reciprocity emphasizes the directionality of these ties,
cal clinics (Leykum et al., 2011; No€       el, Lanham,             specifically capturing the proportion of bidirectional
Palmer, Leykum, & Parchman, 2013).                                  ties out of all realized ties. In the context of vicarious
  Though these studies have generally been con-                     learning relationships, density can thus be broadly
ducted at collective levels of analysis, this underlying            considered as the amount of vicarious learning hap-
concern for reciprocity applies to individuals’ dyadic              pening within the team (including vicarious learning
vicarious learning at work as well. Sharing knowledge               that is reciprocated as well as unreciprocated), where-
with others can be risky (as others can potentially ex-             as reciprocity is a feature of the distribution of this
ploit the information without sharing anything in re-               overall amount of vicarious learning in the team.
turn [e.g., Empson, 2001]), and so individuals’                        At the same time, reciprocity is also distinct from
expectations of reciprocity and trust with another per-             other forms of the distribution of these ties, such as
son can drive their motivation to share experiences for             their centralization. The most basic, and often-used,
the other’s learning (e.g., Reinholt, Pedersen, & Foss,             characterization of network centralization comes
2011), helping to overcome several sources of ob-                   from Freeman’s (1978) degree centrality, which in-
served hesitancy among individuals for seeking                      volves capturing the number of ties a given node is in-
knowledge from and sharing knowledge with others at                 volved in (as a way of assessing which nodes are
work, such as the fear of ceding “ownership” of                     more “in the thick of” a set of ties), and then compar-
knowledge (Davenport & Prusak, 1998; Hansen, Mors,                  ing the distribution of these centrality values among
& Løvås, 2005; Quigley, Tesluk, Locke, & Bartol, 2007)              nodes in the network (i.e., whether most nodes are
or the risk of “feeling incompetent or embarrassed”                 similar in their degree centrality, relative to the most
(Hofmann et al., 2009: 1262) from seeking knowledge.                central node in the network). In a team network, de-
                                                                    gree centralization thus captures the extent to which
Distinguishing Vicarious Learning Reciprocity                       ties (or tie weight) are dispersed more evenly across
                                                                    team members versus concentrated among one or
   Applied to the context of team learning, reciprocity             more central members, and has been demonstrated as
thus reflects a distinct characteristic of the various              an important characteristic of critical knowledge-
member–member dyadic ties that make up a team’s                     sharing structures (Huang & Cummings, 2011), as
vicarious learning network, with unique implications                well as a determining factor in the way teams commu-
for team processes and outcomes relative to other                   nicate, develop transactive memory systems, and per-
characteristics of the network. I therefore formally de-            form in the face of turnover (Argote et al., 2018). In
fine team vicarious learning reciprocity as a composi-              contrast to this focus on the distribution of tie weight
tional construct, consisting of the proportion of                   at the team member node level (i.e., the evenness or
reciprocated vicarious learning ties between team                   unevenness of team members’ number or strength of
members (out of all realized ties in the team network).             ties, relative to other team members), reciprocity fo-
Though this definition refers to the simple case of un-             cuses on the nature of ties between any given pair of
weighted ties (i.e., focusing only on the presence or               team members (i.e., the extent to which a given pair
absence of ties), it is easily adapted to network studies
considering tie weight. Specifically, reciprocity of                  1
                                                                        I discuss this weighted reciprocity definition (and the
weighted vicarious learning ties can be defined as the              novel method used for measuring vicarious learning reci-
proportion of the total tie weight present in both                  procity in this study) further in the Methods section.
930                                           Academy of Management Journal                                         June

each have vicarious learning ties with one another).            gain no new awareness about what the receiver may
Reciprocity can therefore be distinguished from these           know. Conversely, in a reciprocal vicarious learning
other characteristics of a team’s network of learning           relationship, each person develops their understand-
relationships, inviting a consideration of its unique           ing and knowledge about the other, affording them a
implications for team performance.                              more robust “map” of the expertise in the group,
                                                                which has been shown to significantly enhance group
CONSEQUENCES OF RECIPROCAL VICARIOUS                            performance (Hollingshead, Gupta, Yoon, & Brandon,
  LEARNING FOR TEAM PERFORMANCE                                 2012; Moreland & Myaskovsky, 2000).
                                                                   Moreover, the knowledge or description of experi-
   Organizations have long used teams as a vehicle for          ence shared by a team member in a reciprocal vicari-
channeling individuals’ knowledge into performance              ous learning relationship is likely to be richer, more
outcomes, and the effectiveness of this performance             complete, and potentially more honest, as the mutual
is driven by teams discerning and incorporating the             vulnerability and disclosure of this reciprocal rela-
relevant experience of each team member (Littlepage,            tionship yields a stronger sense of trust and closeness,
Robison, & Reddington, 1997; Thomas-Hunt, Ogden,                overcoming some of the knowledge-sharing barriers
& Neale, 2003). Though most perspectives on team                described earlier and encouraging the sharing of more
learning conceptually recognize this interpersonal              detailed or private information (Uzzi & Lancaster,
sharing of experience, it is typically empirically lost         2003). In this sense, the learning value of the shared
in aggregation at the group level (concluding that the          knowledge and experiences in a more reciprocal vi-
group engages in simply a greater or lesser amount of           carious learning dyad is likely to be greater (relative
learning). However, understanding how this learning             to an unreciprocated vicarious learning relationship),
is distributed across individuals in the team is critical       both because the knowledge shared is likely to be
for understanding the performance effects of team               more nuanced and complete (thereby facilitating
learning (e.g., Argote & Ophir, 2002). Importantly, the         more accurate, robust mental maps of others’ knowl-
consequences of how learning is distributed in teams            edge) and because the reciprocal sharing of experien-
(including the reciprocity of these learning relation-          ces and knowledge in these dyads allows for greater
ships) may vary across different task settings, depend-         dialogue and comparative analysis of the shared ex-
ing on the extent to which successful team outcomes             periences (see Myers, 2018). Indeed, the creation of a
depend on integrating and coordinating efforts among            more complete shared mental model hinges on indi-
team members. However, as organizational teams                  viduals’ interaction and dialogue, as team members
continue to face more complex, knowledge-intensive,             develop their understanding of the others’ perspec-
and service-oriented work that incorporates the ef-             tives through repeated discussion and exchange of in-
forts of diverse team members (vs. more disjunctive             formation (e.g., Cannon-Bowers, Salas, & Converse,
tasks where a single individual can drive outcomes),            1993), promoting enhanced future interactions and
the impact of different distributions of these interper-        performance (Mathieu et al., 2000).
sonal learning relationships is likely to be of high im-           Other empirical evidence has supported the per-
portance for understanding team performance.                    formance-enhancing benefits of greater shared men-
                                                                tal models and transactive memory as well; for
Direct Performance Effects of Vicarious                         instance, Lim and Klein (2006) observed that combat
Learning Reciprocity                                            teams with more shared mental models (regarding
                                                                their taskwork and teamwork) had higher levels of
   Vicarious learning is fundamentally a process of un-         performance, while Gardner, Gino, and Staats (2012)
derstanding others’ experiences, and so directly con-           found that greater familiarity and experience work-
tributes to an individual’s sense of who knows what,            ing together (relational resources that serve as key
or who has done what, in the organization—that is,              antecedents to transactive memory) facilitate team
transactive memory (Brandon & Hollingshead, 2004;               knowledge integration and team performance. Build-
Moreland & Myaskovsky, 2000)—and helps individu-                ing on these prior findings, greater reciprocity of vi-
als develop a shared way of seeing the world—that is,           carious learning should thus have a direct, positive
a shared mental model (Mathieu, Heffner, Goodwin,               effect on team performance, as it reflects team mem-
Salas, & Cannon-Bowers, 2000). However, though one-             bers’ greater understanding of each other’s knowl-
way vicarious learning or knowledge-sharing allows              edge and mental models, beyond the simple transfer
the receiver to become more aware of what the sharer            of knowledge and awareness that would occur in a
knows, the reverse is not true—a sharer is assumed to           one-way vicarious learning interaction. Therefore,
2021                                                       Myers                                                        931

beyond just the amount of vicarious learning occur-                those in which the team learns about its task from in-
ring in the team, greater reciprocity of this vicarious            dividuals outside the team) increased team perfor-
learning among team members should be positively                   mance, particularly for teams engaging in more
related with team performance.                                     internal learning activities (consistent with argu-
                                                                   ments for absorptive capacity [Cohen & Levinthal,
  Hypothesis 1. Greater reciprocity of vicarious
                                                                   1990]). Results supported both hypotheses, with in-
  learning among team members is positively
                                                                   ternal and external team learning complementing
  associated with team performance.
                                                                   one another to enhance performance.
                                                                      Though there are certainly contextual differences in
Vicarious Learning Reciprocity and the Effects of                  the setting of the two studies that can partially explain
External Learning                                                  the divergent findings (e.g., pharmaceutical teams be-
   Beyond this direct influence of vicarious learning              ing in a more dynamic, fluctuating environment where
reciprocity on team performance, reciprocity in team               external and internal learning are less at odds vs. more
members’ vicarious learning relationships can also                 mature task settings [Bresman, 2013]), the underlying
impact performance by altering the performance ef-                 conceptual tension—that team members’ internal
fects of teams’ engagement in external learning. In                learning from one another can enable the team to better
addition to their internal learning with and from oth-             understand and adopt knowledge from outside of the
er members within the team, team members often en-                 team (enhancing performance), but can also tie up lim-
gage in learning beyond the external boundaries of                 ited cognitive resources that are then exhausted by en-
their team (Ancona & Bresman, 2005), through pro-                  gaging in external learning (leaving no resources for
cesses such as team member rotation or knowledge                   performance)—remains. This tension touches on a
transfer through a team member’s outside relation-                 fundamental challenge of learning, noted by Bunder-
ships (e.g., Kane, 2010; Uzzi & Lancaster, 2003). Prior            son and Sutcliffe (2003), in that learning is simulta-
work has suggested that both internal and external                 neously performance-enabling, because it promotes
learning can potentially help a team develop knowl-                adaptability and ongoing improvement (Argote,
edge and perform effectively—by sharing and refin-                 Gruenfeld, & Naquin, 2001), and performance-inhibit-
ing existing ideas or capabilities (internal learning)             ing, because it consumes resources and diverts atten-
and discovering new ideas or capabilities (external                tion away from performance (e.g., March, 1991; Singer
learning) (Ancona & Caldwell, 1992; Hansen, 1999).                 & Edmondson, 2008). Considering a team’s vicarious
However, research has reported conflicting results                 learning reciprocity may help address these competing
regarding teams’ engagement in this external learn-                tensions (and reconcile conflicting findings), as teams
ing in addition to their ongoing internal learning (Ar-            with greater vicarious learning reciprocity should
gote & Miron-Spektor, 2011).                                       have greater capabilities for integrating knowledge and
   Most notably, Wong (2004), in a study of 73 teams               communicating efficiently among members, allowing
from a variety of industries (financial services, health           them to be both more adaptive and more resource-effi-
care, technology, and industrial), found support for               cient in their use of internal and external learning.
the hypothesis that greater internal learning promot-                 As noted above, greater reciprocal vicarious learn-
ed greater efficiency (exploitation), while greater ex-            ing among team members can generate unique bene-
ternal team learning promoted greater innovation                   fits (relative to unreciprocated vicarious learning),
(exploration). However, she also found that high ex-               such as enhanced trust and greater understanding of
ternal learning reduced the effect of internal learning            others’ experiences, that reflect team members’
on teams’ efficiency (with no corresponding interac-               stronger relational capacity for future learning (i.e.,
tion of internal and external learning on innovation),             the enhanced ability of team members to learn from
indicative of a detrimental overall performance ef-                new information shared by one another in the fu-
fect of engaging in high levels of external learning on            ture, resulting from prior engagement in vicarious
top of internal learning (Wong, 2004), potentially be-             learning [Myers, 2018]). In other words, team mem-
cause engaging in both forms of learning draws                     bers who have engaged in more reciprocal vicarious
heavily on the team’s cognitive, temporal, and atten-              learning with one another possess greater relational
tional resources (detracting from the resources avail-             resources with which to absorb external knowledge
able for performance [Singer & Edmondson, 2008]).                  or information brought in by another team member,
In contrast, Bresman (2010) explored a similar ques-               which should enhance their knowledge integration
tion in 62 pharmaceutical teams, hypothesizing that                capability—that is, the capability to create “novel
external vicarious learning activities (specifically               combinations of different strands of knowledge,
932                                               Academy of Management Journal                                          June

which have utility for solving organizational prob-                    Returning to the conflicting case examples, though
lems, from component knowledge sourced from with-                   few details were made available about the teams in
in and beyond the organization” (Zahra, Neubaum, &                  Wong’s (2004) study, the description provided by
Hayton, 2019: 10–11). This is consistent with prior                 Bresman (2010) of the pharmaceutical teams noted
findings that stronger relational resources (such as                their rich, dense interactions involving significant
familiarity or shared work experience) are key predic-              discussion and feedback (e.g., after “trial and error”),
tors of teams’ knowledge integration capability, par-               as well as their established working relationships as
ticularly under uncertainty (Gardner et al., 2012).                 “core team members” who had been involved with
Applied to external learning—where knowledge is                     the entire duration of the project—all elements that
likely to be more uncertain, unfamiliar, and potential-             would seem to support the presence of more recipro-
ly divergent from the team’s existing knowledge (giv-               cal vicarious learning relationships, potentially ex-
ing rise to the beneficial effects of this learning on              plaining the positive results found in his study.
innovation [Wong, 2004])—a team’s knowledge inte-                   Though these assertions are purely post hoc interpre-
gration capability is likely to be particularly impor-              tations of the sample description, when combined
tant for effectively translating and incorporating this             with the arguments above they provide a measure of
knowledge in performance-enhancing ways.                            anecdotal support for the notion that teams’ engage-
   At the same time, greater reciprocity of vicarious               ment in external learning (in addition to their inter-
learning within the team should aid teams’ knowledge                nal learning) may harm performance, but not in cases
communication efficiency, allowing for this knowl-                  where there is more reciprocal vicarious learning
edge integration to occur in less resource-intensive                among team members. Indeed, by enhancing knowl-
ways. Teams with greater vicarious learning reciproci-              edge integration and communication among team
ty, as noted earlier, should have pairs of team members             members, and therefore allowing for more effective
with greater shared mental models and understanding                 and efficient learning, teams with greater vicarious
of each other’s perspectives—developed through mu-                  learning reciprocity should experience greater perfor-
tual sharing of experience. This team-level compilation             mance gains from this external learning.
of dyadic understanding of others’ perspectives is
well-described by Huber and Lewis’s (2010) notion of                   Hypothesis 2. Teams’ vicarious learning reci-
cross-understanding. As these authors suggested,                       procity moderates the relationship between
cross-understanding can allow team members to learn                    their external learning and team performance.
and perform both more effectively and more efficient-                  Specifically, when vicarious learning reciproci-
ly—so that they can devote less time and energy to                     ty is higher (lower), greater external learning
communicating knowledge:                                               will be more positively (negatively) associated
                                                                       with team performance.
  Cross-understanding increases the effectiveness of com-
  munication by enabling members to choose concepts
  and words that are maximally understandable and mini-                                   METHODS
  mally off-putting to other group members. … Without an            Sample and Procedure
  understanding of one another’s mental models, members
  are apt to make arguments or proposals concerning                    I tested my hypotheses (summarized in Figure 1) in
  group processes and products that are technically, politi-        the context of MBA consulting project teams, which
  cally, or otherwise unacceptable to those whose mental            consisted of MBA students from a large university
  models they do not understand, thus contributing to con-          in the midwestern United States who traveled and
  fusion, conflict or stalemate. (Huber & Lewis, 2010: 10)          worked full-time in 4–6-person consulting teams over
   Thus, by allowing team members to more quickly                   seven weeks on projects for different client organiza-
and easily communicate to share knowledge with one                  tions around the world. Client organization sponsors
another, greater vicarious learning reciprocity should              provided teams with a current project or challenge
enable teams to engage in internal and external learn-              faced by the organization and the student teams worked
ing more efficiently, freeing up resources (i.e., time              as consultants to gather data, conduct analysis, and offer
and attention) to translate this learning into team per-            recommendations for the client organization. As part of
formance. This is consistent with evidence that teams’              a broader data collection effort (used to support multiple
shared mental models benefit team performance,                      studies, including prior research using variables includ-
in part, through team internal interactional processes              ed here as controls [i.e., De Stobbeleir, Ashford, &
such as coordination, cooperation, and communica-                   Zhang, 2019; Zhang, Nahrgang, Ashford, & DeRue,
tion (Mathieu et al., 2000).                                        2020]), students completed multiple surveys over the
2021                                                       Myers                                                        933

                         FIGURE 1                                  distribution of learning ties within the entire team) em-
                      Conceptual Model                             ployed within each team. Indeed, while in undirected
                                                                   network studies one individual’s response is often tak-
        Team                                                       en to indicate the presence or absence of a relationship
 Vicarious Learning          Hypothesis 1
    Reciprocity                                                    (even if the other party did not complete the measure),
                                               Team                in a directed network (where each direction of the rela-
              Hypothesis 2
                                            Performance            tionship between two people is treated as indepen-
        Team
                                                                   dent) high response rates are critical (e.g., Burt, 1987;
  External Learning                                                Stork & Richards, 1992). Because the surveys were as-
                                                                   sociated with a major experiential learning program in
                                                                   the school’s MBA curriculum, and were seen as a criti-
                                                                   cal part of the program experience, very high response
course of their consulting project, during which the               rates were attained in this sample. Specifically related
data for this study were gathered. These surveys                   to the vicarious learning measures, only one team
asked students about their own beliefs and behaviors,              provided a response rate lower than 100%; this team
as well as about their other team members. Client                  was excluded from analysis (as noted below).
sponsors completed a survey regarding the team’s
                                                                      Procedure. Prior to beginning the project (“Time
work at the end of the project period.
                                                                   0”), the MBA program office assigned individuals to
   Sample selection. Singleton and Straits (1993) not-
                                                                   teams and participants completed a number of pre-
ed that in order to draw accurate conclusions from a
                                                                   program activities, including a broad survey regard-
survey study, it is necessary to select a sample appro-
                                                                   ing their attitudes toward, and expectations for, the
priate to one’s theory and hypotheses. Given my inter-
est in understanding how team members reciprocally                 project. Approximately halfway through the project,
(or nonreciprocally) learn from one another in the ser-            once teams had experience working together (“Time
vice of team performance, these consulting project                 1”), participants completed a survey that included
teams provided an ideal context for several reasons.               items assessing their prior familiarity with each other
These teams were involved in a realistic, but novel,               team member, the extent to which the team had
business challenge that required them to draw on and               strong norms for learning, and their engagement in
integrate their background knowledge and diverse                   feedback-seeking behavior during the team’s work.
prior experiences. The challenges were not limited to              Finally, around the end of the project period (“Time
a single functional area and team members did not all              2”), participants completed another survey that
come from similar backgrounds, providing both a                    included items assessing their vicarious learning
breadth of prior experiences and the impetus for inte-             relationship with each other team member, as well
grating these prior experiences (i.e., the need to inte-           as assessing the team’s external learning.
grate across different areas) to promote vicarious                    A total of 454 first-year MBA students, assigned to
learning. In this way, the teams’ structure and work               89 different teams, participated in these surveys. As
were emblematic of the increasingly project-based,                 noted above, given the challenges of incomplete data
dynamic team structures of modern organizations, as                for analyzing reciprocity in network ties (as well as
well as of the types of knowledge-based services work              other network parameters [Stork & Richards, 1992]),
of the “knowledge economy” (Powell & Snellman,                     I excluded one team that did not provide complete
2004). Indeed, as the world of work becomes increas-               responses for the vicarious learning measure from all
ingly characterized by ad hoc teaming, contract work,              team members, yielding an initial sample of 88 teams
and shorter job tenures, the features of these MBA pro-            (made up of 450 individuals; on average 27.5 years
ject teams (newly formed teams given a specific pro-               old and 33% female). At the conclusion of the project,
ject and short time duration for integrating knowledge             the company project sponsors (i.e., the client for each
and accomplishing an objective) provide a high degree              consulting project) completed a separate survey about
of relevance and external validity for generalizing this           the project and their perceptions of the team’s
study’s findings to other organizational settings.                 work, which included measures of team performance
   Additionally, utilizing MBA consulting teams al-                (focusing on both team outcomes and team quality).
lowed me to test my hypotheses in a context with high              As not all sponsors completed this survey, team per-
response rates, providing an ideal empirical setting for           formance ratings were only available for 62 teams (for
a study of vicarious learning reciprocity, particularly            an effective sponsor response rate of 70%), limiting
given the whole-network approach (i.e., examining the              the final sample size for my analyses (n 5 62).
934                                            Academy of Management Journal                                            June

Measures                                                         order to generate a more logical depiction of the flow
                                                                 or motion of the network (see Borgatti, Everett, & John-
   Unless noted, items were assessed on a 5-point
                                                                 son, 2018: 202), with directional arrows corresponding
Likert-type scale (1–5, with anchors appropriate to
                                                                 to the flow of knowledge and experience between indi-
the scale). Appendix A lists the scale items for all
                                                                 viduals in the network—that is, reflecting the move-
key measures in the study.
                                                                 ment of knowledge and experience from sharer to
   Vicarious learning reciprocity measure. Individ-              learner (see Online Supplementary Material3 for fur-
uals’ vicarious learning was measured via a whole-               ther details of this approach and for the calculations of
network, within-team survey (conducted at Time 2)                these measures in two sample teams).
that asked individuals to rate the extent to which they             Notably, these assessments captured not only the
learned from the experiences shared by each other                presence of a vicarious learning relationship, but more
team member. Building from existing measures that                specifically the extent of each person’s learning from
assess learning or advice relations (e.g., Cross, Borgatti,      the other’s experience (i.e., tie strength or weight, rang-
& Parker, 2001; Leykum et al., 2011), each individual            ing from 0–4). Prior approaches to studying reciprocity
assessed, for their relationship with each other team            in workplace networks have tended to focus solely on
member, the extent to which: “[This person] often                the presence or absence of mutual ties by either direct-
shares his/her prior experiences, expertise, or knowl-           ly measuring ties as present or absent (i.e., 0 or 1) or di-
edge with me to help my learning,” and “I am able to             chotomizing weighted tie measures to 0 or 1 based on
draw meaningful lessons from the experiences and in-             some minimum tie strength threshold (see, e.g., Caimo
formation [this person] shares with me.” These two               & Lomi, 2014; Cross et al., 2001; Kleinbaum et al., 2015).
items were rescaled from the 1–5 rating provided by              Yet, considering reciprocity only as a binary characteris-
respondents to a 0–4 scale to facilitate construction of         tic masks important considerations of relative tie
the network measures described below, and were then              strength that are critical for understanding complex net-
averaged to create a measure of an individual’s vicari-          works of relationships in organizations. For instance, a
ous learning from a given team member (a 5 .89).2                learning relationship in which both individuals report
   This approach generated 1,926 unique assessments              learning from each other to a moderate degree (i.e., each
of individuals’ vicarious learning with other members            reporting 2.5 out of 4) is likely quite different from one
of their team (among the 88 teams in the initial sam-            in which each individual learns from the other to a very
ple). In constructing the network of vicarious learning          great degree (i.e., each reporting 4 out of 4), but both
relationships in the team, I assessed learning from the          may be treated as equivalent in a binary approach (de-
perspective of the recipient of shared knowledge or ex-          pending on the cutoff threshold; see Online Supplemen-
perience (as the sharer may be unaware of whether the            tary Material), to the detriment of our understanding of
other person learned anything from their sharing). A vi-         the flow of knowledge within teams.4
carious learning tie (between two team members) there-              To address this issue, I build on recent developments
fore consists of each member’s assessment of their own           in network methods (Squartini et al., 2013) to consider
learning from the experiences shared by the other. I de-         vicarious learning reciprocity not as the simple
fine the in-degree flows of the tie (the portion coming
in to the focal individual) as the individual’s reported
                                                                    3
vicarious learning from the experiences shared by the                 Online Supplementary Material is available at: https://
other, and the out-degree flows as the amount of vicari-         osf.io/chwpn/?view_only=a4cfdd0ee5414cbcae6b9776bc
ous learning (reported by the other) stemming from the           3e5e80
                                                                    4
focal individual’s sharing of experience. This is some-               Other studies have used an approach that examines
what different from standard approaches to defining              the balance of in-degree and out-degree flows by taking
in- and out-degree flows in survey-based directed net-           the absolute value of in-flows minus out-flows (see, for
works, which generally focus on who provided the rat-            example, Lai et al., 2015). While this approach does not
ing as the determinant of directionality (i.e., self-            dichotomize reciprocity, it suffers from many of the same
                                                                 limitations, recasting reciprocity as the degree of (im)bal-
reported ties are out-degree, and other-reported ties are
                                                                 ance between two nodes and masking the strength of the
in-degree). However, I transpose these tie directions in         relationship. To demonstrate this similarity in limitation,
                                                                 consider that in this approach, a relationship consisting
  2
    Two additional items were included in the survey to          of an incoming tie of weight 3 (out of 4) and an outgoing
assess the validity of this measure of individuals’ vicari-      tie of weight 4 results in an equivalent “balance” measure
ous learning from each other team member. See Appen-             (of 1) as a relationship consisting of an incoming tie
dix B for details.                                               weight of 1 and an outgoing tie weight of 2.
2021                                                              Myers                                                          935

                  FIGURE 2                                                  Team vicarious learning reciprocity was then cal-
Decomposition of Tie Weight in an Asymmetric                              culated using an adaptation of the standard binary
Network Dyad into Fully Reciprocated and Fully                            approach—which calculates reciprocity as the pro-
         Unreciprocated Components                                        portion of the total number of reciprocal ties in the
                                                                          team divided by the total number of ties—to account
  i    3.5                   i                     i                      for weighted ties (again following Squartini et al.,
                                                       1.5
                                                                          2013) by taking the proportion of the sum of all team
                    =            2             +                          members’ reciprocated vicarious learning tie weight
        2    j                          j                    j            out of the total (realized) weight of the network:
                                                                                             Xn         Xn X
  Note: Adapted from Squartini et al. (2013: 2).                                     W$             s$
                                                                                                     i     i51
                                                                                                                      w$
                                                                                                                  jÞi ij
                                                                                          5     i51
                                                                                                       5 Xn X
                                                                                      W         W                     w
presence of bidirectional vicarious learning ties but rath-                                                 i51    jÞi ij
er as the strength of the reciprocated tie, such that dyads                 As this formula suggests, this weighted reciprocity
can have more- or less-reciprocal vicarious learning rela-                measure ranges from 0 to 1 (i.e., from 0 to 100% of tie
tionships (rather than simply reciprocal or not). As an                   weight reciprocated within the network). Empirical-
example, consider the dyad in Figure 2 (adapted from                      ly, the reciprocity values in the final analyzed sam-
Squartini et al., 2013), where individual i reports learn-                ple (n 5 62) ranged from .65–.98, with an average of
ing vicariously from the experience of individual j to a                  .85 (SD 5 .08; see Online Supplementary Material
relatively large extent (3.5) while individual j reports                  for more detail on this reciprocity measure and sev-
learning vicariously from the experiences shared by i to                  eral comparisons to approaches relying on dichoto-
a lesser extent (2). This relationship can be decomposed                  mizing ties to determine reciprocity).
into a fully reciprocated tie of weight 2 and a fully unrec-
                                                                             External learning and performance measures.
iprocated tie (from j to i) with a weight of 1.5.
                                                                          Team external learning was measured by surveying
   More generally, following Squartini and colleagues
                                                                          all team members (at Time 2) regarding the extent to
(2013), any dyadic relationship ðwij , wji Þ can be
                                                                        which the team engaged in information-gathering or
equivalently decomposed as wij$ , wij! , wij , where
                                                                          learning from a variety of sources outside of the
wij$ represents the fully reciprocated portion of the
                                                                          team. Specifically, adapting an approach used in pri-
tie weight, and wij! and wij represent the fully unrec-
                                                                          or research (e.g., Ancona & Caldwell, 1992; Bresman
iprocated portions of the out-degree and in-degree
                                                                          & Zellmer-Bruhn, 2012) to fit the project team con-
weight (for individual i), respectively. This recipro-
                                                                          text, team members were asked to rate the extent to
cated weight between i and j can thus be expressed as
                                                                          which their team learned from five different sources:
wij$ 5min½wij , wji 5wji$ and the unreciprocated
                                                                          faculty, industry experts, other teams, second-year
weight from i to j as wij! 5wij 2wij$ . Notably, if wij! .0
                                                                          MBA students, and personal network contacts.
then wij 50 (and vice versa), reflecting the fully un-
                                                                          Though reliability was somewhat lower than typical
reciprocated nature of this portion of tie weight.
                                                                          thresholds (a 5 .66), standard measures of intrateam
   Using this decomposition, I calculated the recipro-
                                                                          agreement among the 88 teams in the initial sample
cated and unreciprocated tie weights for the dyadic vi-
                                                                          supported aggregating these ratings to the team level.
carious learning relationship between an individual
                                                                          Specifically, median rwg(5), as a measure of interrater
and each other member of their team. I then calculated
                                                                          agreement, was .90 (using a uniform null distribu-
total reciprocated and unreciprocated tie strength for
                                                                          tion; 94% of teams had values above .70), and ICC
each individual, such that each individual’s total   P recip-             values (calculated for unequal group sizes as de-
rocated tie strength (s$   i ) is defined as si 5
                                                 $           $
                                                        jÞi wij
                                                                          scribed in Bliese [2000: 355]), reflecting both inter-
(and P total unreciprocated P tie ! in- and out-strength as
                                                                          rater agreement and interrater reliability (LeBreton &
si 5 jÞi wij , and s!  i 5    jÞi wij , respectively).
                                                      5

  5
     In order to make comparisons at the individual level,                   of potential dyadic ties for any given individual in the
these measures would require adjustment to account for dif-               team network. This choice of scaling has the benefit of
ferences in the number of ties in each team network. Though               creating a measure interpretable as the average tie
not used in the analyses here, comparable scaled measures                 strength (for reciprocated strength, unreciprocated in-
of individual vicarious learning reciprocity can be created by            strength and unreciprocated out-strength, respectively)
dividing each strength measure by one less than the total                 for a given individual (see Online Supplementary
number of team members, which is equivalent to the number                 Material).
936                                              Academy of Management Journal                                         June

Senter, 2007), were: ICC(1) 5 .23, ICC(2) 5 .60.6                  of vicarious learning reciprocity and external learning
Moreover, given that the items of this measure were                on team performance, as described below.
discrete sources of potential external learning, rather
                                                                      Control measures. To better understand the
than alternative measures of an identical concept,
                                                                   effects of team vicarious learning reciprocity, I also
the reliability coefficient (a) is less relevant to the va-
                                                                   control for several other features of teams’ vicarious
lidity of the measure (as noted by Quigley et al.,
                                                                   learning networks. Specifically, I control for team
2007). Teams’ ratings of their external learning
                                                                   vicarious learning density in order to isolate the
ranged from 2.20–4.15, with an average rating of 3.04
                                                                   effects of the distribution of vicarious learning (i.e.,
(SD 5 .42) in the final sample (n 5 62).
                                                                   as more- or less-reciprocated) above-and-beyond the
   Finally, team performance was assessed by the
company project sponsors (i.e., the liaison from the               effects of the overall amount of vicarious learning
client organization for whom the project team was                  in the team. I calculate vicarious learning density as
working) using a composite of five items developed                 the total weight of the vicarious learning ties present
by the university MBA program office for assessing                 in each team’s network, divided by the maximum
the outcomes of the consulting project teams and                   potential weight of vicarious learning ties in
five items developed by the program office for assess-             the network:
                                                                                            Xn X
ing the quality of the team and their work together.                                                     w
                                                                                    W          i51    jÞi ij
The project sponsor was asked to rate, for example,                                      5
the team’s “productivity (i.e., quantity of work com-                             W  max     nðn21Þ3w 
pleted)” and “overall team performance” (team out-                 where n is the number of team members and
comes), as well as “the team’s ability to work                     w  represents the maximum network tie weight.
together” and “the overall quality of the [project]                   I also control for team vicarious learning centrali-
team” (team quality). These two sets of items were                 zation in order to understand the effects of reciproci-
averaged into a single performance measure in order                ty alongside how centralized vicarious learning ties
to capture both outcome- and process-related dimen-                are in the network (as another form of tie distribu-
sions of performance.7 Exploratory factor analysis                 tion). Drawing from Freeman’s (1978) classic charac-
showed that all 10 items loaded on a single factor,                terization of centrality as reflecting individuals who
and the reliability of the composite measure was                   are “in the thick of things,” I focus here on individu-
high (a 5 .94). As noted earlier, ratings were avail-              als’ total degree centrality in the team vicarious
able for only 62 teams, with an average team perfor-               learning network to capture differences across teams
mance rating of 4.33 (SD 5 .66) and a range from                   in the extent to which vicarious learning interactions
1.60–5.00. These values (on a 5-point scale) suggest               were more concentrated among certain team mem-
the presence of an atypical distribution, and in par-              bers. Following Freeman’s definition, as well as
ticular potential right-censoring of the team perfor-              more recent guidance on calculating centralization
mance ratings (such that the score of 5 acted as a                 within asymmetric, weighted networks (see Wei,
threshold and potentially limited ratings that would               Pfeffer, Reminga, & Carley, 2011), I calculate team
have otherwise been higher); thus, tobit regression                vicarious learning total degree centralization by first
analysis was used to model the hypothesized effects                calculating the total degree centrality of each
                                                                   individual in the team (simultaneously accounting
                                                                   for bothPin-degree  P and out-degree tie weights) as
  6                                                                Citotal 5ð jÞi wij 1 jÞi wji Þ. Team total degree cen-
    Simulation studies have shown that the use of shorter
                                                                   tralization can then be defined as the sum of differ-
Likert-type scales and small group sizes can generate sub-
stantially underestimated values for ICC(1) and ICC(2), re-        ences between the team’s most central member and
spectively (Beal & Dawson, 2007; Bliese, 1998). As the             each other member, divided by the maximum poten-
data aggregated in this study come from 5-point Likert-            tial value of this difference:
type scales provided by individuals in 4–6-person groups                            Xn                         
                                                                                               total
(meeting both criteria above), reported ICC values should                                   C         2 C total
                                                                                                          i
                                                                                       Xi51
                                                                                           n                     
be interpreted as potentially underestimated.                                     Max i51 C  total 2 Citotal
  7
     Eight teams received ratings from multiple sponsors
(i.e., 2–3 liaisons from the same client organization) for at      where C  total represents the node with the highest
least some of the items included in this measure, and so           total degree centrality among nodes in the network
scores for each item were averaged across available rat-           (see Online Supplementary Material for more detail
ings before constructing the team performance measure.             on the calculation of this measure).
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