coactive vicarious learning: toward a relational …approached vicarious learning from a...

Q Academy of Management Review2018, Vol. 43, No. 4, 610–634.https://doi.org/10.5465/amr.2016.0202

COACTIVE VICARIOUS LEARNING: TOWARD A RELATIONALTHEORY OF VICARIOUS LEARNING IN ORGANIZATIONS

CHRISTOPHER G. MYERSJohns Hopkins University

Vicarious learning—individual learning that occurs through being exposed to andmaking meaning from another’s experience—has long been recognized as a driver ofindividual, team, and organizational success. Yet existing perspectives on this criticallearning process have remained fairly limited, often casting vicarious learning assimply an intrapersonal, one-way process of observation and imitation. Largely absentin prior perspectives is a consideration of the relational dynamics and underlying be-haviors by which individuals learn vicariously through interacting with others, ren-dering these perspectives less useful for understanding learning in the increasinglyinterconnected work of modern organizations. Integrating theories of experientiallearning and symbolic interactionism, I offer a theoretical model of coactive vicariouslearning, a relational process of coconstructed, interpersonal learning that occursthrough discursive interactions between individuals at work. I explore how these in-teractions involve the mutual processing of another’s experience; are influenced bycharacteristics of the individual, relational, and structural context in organizations; andlead to growth not only in individuals’ knowledge but also in their individual and re-lational capacity for learning and applying knowledge. I close by discussing the im-plications of this conceptual model for the understanding and practice of vicariouslearning in organizations.

In the 1980s, before electronic document systemsbecame fashionable, managers at Bain developeda large paper-based document center at its Bostonheadquarters; it stored slide books containingdisguised presentations, analyses, and informa-tion on various industries. The library’s purposewas to help consultants learn fromworkdone in thepast without having to contact the teams that didthe work. But as one partner commented, “Thecenter offered a picture of a cakewithout giving outthe recipe.” The documents could not convey therichness of the knowledge or the logic that hadbeen applied to reach solutions—that under-standing had to be communicated from one personto another (Hansen, Nohria, & Tierney, 1999: 113).

Learning from the experience of others has longbeen recognized as critical for individual and or-ganizational success (Argote & Ingram, 2000;Bresman, 2013; Davis & Luthans, 1980; Manz &Sims, 1981). Using others’ successes and failures

to avoid “reinventing the wheel” allows organi-zations and their employees to alter their learningcurves, reduce inefficiencies, and improve out-put quality (Argote, Gruenfeld, & Naquin, 2001;Bledow, Carette, Kuhnel, & Bister, 2017; Bresman,Birkinshaw, & Nobel, 1999; KC, Staats, & Gino,2013; Kim & Miner, 2007). Building on the founda-tional work of Albert Bandura (i.e., social learningtheory; Bandura, 1977a) organizational scholarshave advanced the notion of vicarious learning tocapture these learning and performance benefits,defining the term as the process by which “anobserver learns from the behavior and conse-quences experienced by amodel rather than fromoutcomes stemming from his or her own perfor-mance attempts” (Gioia & Manz, 1985: 528).As evident in this definition, existing perspec-

tives on vicarious learning have centered on thelearner’s ability to attentively observe the expe-rience of a model (e.g., another individual in thelearner’sworkplace) and subsequently retain andimitate the observed actions, with the learnermotivating their own behavior toward alignmentwith that of the model (Bandura, 1977a). Corre-spondingly, research on vicarious learning hasforegrounded learners’ abilities to identify andimitate what they have observed, either directly(i.e., watching a model perform an action and

This work benefited significantly from the advice andfeedback of Kathleen Sutcliffe, Scott DeRue, Jane Dutton, AmirGhaferi, and James Westphal, as well as from helpful com-ments by Sharon Kim, Michael Pratt, and participants at the2013 May Meaning Meeting. I am also grateful for generativeconversations with Yemeng Lu-Myers, Marybeth Myers, Mat-thew Karlesky, Suntae Kim, and members of the University ofMichigan Management and Organizations area, and I thankguest editor Gary Ballinger and three anonymous reviewersfor an insightful, constructive review process.

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https://doi.org/10.5465/amr.2016.0202

experienceconsequences) or symbolically, throughwritten or pictorial representations of models’actions that can be viewed by learners at a latertime (such as written case summaries or mediarecordings; Bandura, 1986). For example, re-searchers have examined how employeesreplicate actions by directly observing others’behavior (e.g., using behavioral modeling in or-ganizational training; Taylor, Russ-Eft, & Chan,2005), as well as by using data repositories orknowledge management systems (Davenport, DeLong, & Beers, 1998).

Uniting these research findings is an emphasison learners’ independent, one-way observation ofthe model; in each case the model is a non-participant in the learner’s processing of theirobservations. For instance, in the caseof symboliclearningprocesses, the “model” is often simply anartifact of a prior actor’s performance, captured ina document or recording that precludes any formof interaction between model and learner. Thisone-way observation perspective presupposesthat the learner understands the underlying goalsof a model’s actions (e.g., why they acted ina particularmanner) and knowswhat elements ofthe action to emulate (e.g., knowing which ele-ments are critical to successful performance andwhich are unrelated “noise” in the observation ofa particular individual). Yet there is growing evi-dence that this assumption of vicarious learningasan independent, one-wayprocessmaynot holdin the types of interactive, socially embeddedwork of modern organizations.

Organizations have evolved from places whereworkers focused on individual, isolated tasks tosites of coordinated work conducted via networksof relationships among individuals (Adler, Kwon,& Heckscher, 2008; Gittell & Douglass, 2012),drastically changing the social context for learn-ing vicariously from others at work (Myers &DeRue, 2017). While one-way processes of vicari-ous learning through observation and imitationmay have been well-suited to the work of earliereras, such as high-volume manufacturing firms(where “best practices” could be more easilymodeledand replicated; seeTucker, Nembhard, &Edmondson, 2007), or to the domain of childhoodeducation (where Bandura originally developedhis social learning theory; e.g., Bandura, Ross, &Ross, 1963), the increasingly interdependent workthat characterizes the “knowledge economy” hassubstantially altered the ways individuals learnand perform at work (Noe, Clarke, & Klein, 2014;

Powell & Snellman, 2004). Indeed, observingothers at work is far more difficult in the distrib-uted or virtual work arrangements common tomodern organizations (see Hinds & Bailey, 2003),and learning in these settings often involvesprocessing complex experiences laden with bothexplicit and tacit knowledge (Lipshitz, Friedman,& Popper, 2007; Miller, Zhao, & Calantone, 2006;Nonaka, 1994) that vary in their “learnability”(McIver, Lengnick-Hall, Lengnick-Hall, & Ram-achandran, 2013) and in the degree to which theycan be codified and formally disseminated in or-ganizations (Hansen et al., 1999).Existing approaches to vicarious learning that

emphasize independent processing of a model’sexperience make implicit assumptions that whatis to be learned is overtly observable (in the caseofdirect observation of a model) or symbolicallycodifiable (in the case of learning from writtensummaries ormedia recordings). Yet, asHofmann,Lei, and Grant (2009: 1261) have noted, work or-ganizations are sites of substantial “ambiguity,equivocality, anduncertainty,”evokingquestionssuch as “What’s the story here? What does itmean? What do I do next?” that motivate sense-making and learning through interpersonalcommunication and interaction (Weick, Sutcliffe,& Obstfeld, 2005). For instance, in cataloguing thevicarious learning behaviors of pharmaceuticaland aerospace product development teams,Ancona and Bresman (2005) noted that these be-haviors included a number of interpersonal in-teractions, such as inviting experienced others todiscuss past mistakes and talking to others aboutimprovement efforts, in addition to directly ob-serving others’ work. Discursive interaction thusseems to be a common tool for learning fromothers in the (increasingly common) types ofknowledge-based work of modern organizations,requiring scholars to reconsider existing theoriesand develop a more robust understanding of in-terpersonal vicarious learning interactions, wherelearning occurs through the active engagement ofnot only learners but also those from whom theyare trying to learn.The time thus seems ripe for an integrated

theoretical account of thismore interactive formofvicarious learning that specifies the underlyingmicroprocesses of these interactions, as well astheir contextual antecedents and developmentalconsequences. Therefore, I introduce a concep-tual model of coactive vicarious learning (CVL)that reflects a more relational, coconstruction

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perspective on vicarious learning, challenging andcomplementing the prevailing view of vicariouslearning as an independent, one-way process ofobservation and imitation. I integrate perspectivesfrom symbolic interactionism (Mead, 1934) and ex-periential learning (Kolb, 1984; Schon, 1987) to ad-vance this interpersonalmodelofvicarious learning,characterizing learning as the process of makingmeaning out of workplace experiences (which thenenables changes in beliefs and future behavior;Kolb, 1984) but emphasizing the potential for thismeaning to be coconstructed through a dyad’s joint,situated understanding (e.g., Dionysiou & Tsoukas,2013).

In doing so I offer a conceptualization that isexplicitly grounded at the relational level ofanalysis, positing that vicarious learning in-teractions are embedded in ongoing relation-ships and, thus, that the process and outcomes oflearning vary across idiosyncratic dyads. Impor-tantly, this relational model of vicarious learningarticulates a fundamentally different mechanismfor vicarious learning, shifting away from priormechanical conceptualizations where vicariouslearning was viewed as a process of observationand duplication or as a process of knowledgeflowing from “reservoirs” through various trans-fer “conduits” (e.g., Argote & Ingram, 2000). In-stead, the model advanced here moves towarda more developmental conceptualization focusedon enhancing learning capacity (the ability of anindividual or dyad to learn, both in the currentcontext and in future learning opportunities), di-rectly acknowledging the potential of theselearning interactions to have an impact beyondjust the simple transfer of knowledge.

This conceptual model emphasizes the uniqueimpact that vicarious learning has on not only theknowledge and awareness of each individual inthe interaction but also their capacity for learningin future experiences, resulting from the influenceof vicarious learning on person- and dyad-specific attributes (e.g., efficacy and trust) andresources (e.g., perspective-taking abilities andshared mental models). In this way, I define theoutcome of vicarious learning as changes in in-dividuals’ response repertoires—when individuals“become able to respond to task-demand or anenvironmental pressure in a different way as a re-sult of earlier response to the same task (practice)or as a result of other intervening relevant expe-rience” (English & English, 1958: 259, as cited inWeick, 1991: 116).Changes to response repertoires

reflect increases in the “stock” of available re-sponses (e.g., behaviors, routines, habits), aswell as in the “capability to recombine portionsof the stock in novel ways” (Christianson,Farkas, Sutcliffe, & Weick, 2009: 846–847). AsSitkin, Sutcliffe, and Weick (1998: 70) noted,adopting this definition (a) allows the concept oflearning to capture a broad range of changes(e.g., in beliefs, practices, and relationships),(b) focuses on the development and elabora-tion of available capabilities (whether or notthey are used), and (c) acknowledges that learn-ing enables the potential for future action butdoes not require the manifestation of that action(e.g., improved performance) to have occurred(see also Huber, 1991, and Wilson, Goodman, &Cronin, 2007).

TOWARD A RELATIONAL THEORY OFVICARIOUS LEARNING

Organizational scholars have traditionallyapproached vicarious learning from a social-cognitive perspective, building on Bandura’swork (1977a, 1986, 1989a). Bandura’s social learn-ing theory (1977a) gave rise to key organizationalperspectives on learning, since organizationswere recognized as frequent sites of individualslearning from others (such as training, establish-ing rules or norms, and learning general workpatterns; Manz & Sims, 1981). This perspectiveasserted that directly observing and modelingothers’ actions at work could help explain therapid spread of behavior in the workplace (Davis& Luthans, 1980), and it posited intrapersonalprocesses of attention, retention, reproduction,andmotivation (Bandura, 1977a) as thekeydriversof individuals’ ability to find and imitate behavior(Manz & Sims, 1981). Bandura further argued thatvicarious learning could also occur throughsymbolic (i.e., written or pictorial) means, such aswritten summaries or video-recorded perfor-mances, noting, for instance, that “theacceleratedgrowth of video technologies has vastly ex-panded the range of models to which people areexposed day in and day out” (1997: 93). The in-clusion of video-recordedmodels draws attentionto the fundamentally intrapersonal nature of thisperspective: vicarious learning is seen as occur-ring entirely within the individual as a result ofone-way observation of a model (without neces-sarily evenphysical colocation) andprocessing ofthe “script” implied in themodel’sactions (Gioia&

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Manz, 1985). In subsequent research on vicariouslearning in organizations, scholars have largelymaintained this perspective, typically focusingon actors (individuals, as well as units or organi-zations) finding and copying the practices ofothers (e.g., Baum, Li, & Usher, 2000; Taylor et al.,2005), guided by the notion that vicarious learningoccurs “essentially at arm’s length” (Bresman,2013: 55).

However, as noted earlier, this solely intra-personal perspective on vicarious learning islimiting, and the assumption that one-way, in-dependent observation and imitation constitutesthe totality of vicarious learning overlooks theincreasingly interpersonal nature of learning inorganizations. I thus follow the broader perspec-tive of situated learning (Lave & Wenger, 1991) inrejecting a purely cognitive view of learning inorganizations, focusing less on “cognition andwhat goes on in individual heads” (Weick &Westley, 1996: 442) and more on experiences andinteractions as the primary inputs to learning.Specifically, in contrast to prior cognitive, in-formation transfer perspectives, I adopt anexperience-based view of learning (Kolb, 1984)and integrate it with a symbolic interactionistperspective (Mead, 1934) to introduce a more in-teractive, relational model of vicarious learningin organizations.

Experiential learning (Kolb, 1984; Schon, 1987)has served as a dominant theoretical frameworkfor studies of individual learning in organiza-tions, casting learning as a cyclical process ofindividual meaning making that occurs throughthe transformation of experience, via reflectiveobservation, into an abstract concept that canthen be refined and applied to future experience(Kolb, 1984). Although typically used in the contextof learning from an individual’s own direct expe-rience, others’ experiences and perspectives canalso be key drivers of an individual’s meaning-making, problem-solving, and learningprocessesin organizations (e.g., Baker, Jensen, & Kolb, 2005;Hoover, Giambatista, & Belkin, 2012).

Despite this recognition of the potential forothers’ experiences to impact individuals’ learn-ing, experiential and vicarious learning per-spectives have remained largely separated(Hoover et al., 2012). Yet viewing experientiallearning as a process of meaning making high-lights a connection between this approach andtheories of symbolic interactionism (Mead, 1934)that allows for a deeper integration of the role of

others in the learning process. The symbolicinteractionist perspective is built on the notionthat individuals act according to the meaningthey make of their experiences and that thismeaning is cocreated through processes of in-terpersonal interaction and continually reinter-preted and modified as the result of furtherinteraction (Blumer, 1969). Consistent with expe-riential learning, symbolic interactionism thusholds a simultaneous focus on both meaning(what Kolb [1984] might refer to as “abstract con-ceptualization”) and action (“concrete experi-ence” in experiential learning), but it emphasizesthe role of others in deriving and modifying thismeaning. As each person in the interactionconsiders the other’s perspective, they developan emergent, shared understanding of themeaning of an object or experience and, corre-spondingly, shared expectations for action (fora discussion in the context of organizationalroutines, see Dionysiou & Tsoukas, 2013).In this sense it is only through the discourse

emerging from interaction with others that in-dividuals come to understand the meaning of anexperience, and this meaning is cocreated withtheir interaction partners, making these inter-actions fundamental to learning. This relationallearning process results in the development ofa joint, situated understanding of an experience,firmly rooted in both the dynamics of inter-personal interaction and the specific nature of theexperience. This integration of symbolic inter-actionism and experiential learning is consistentwith broader arguments of social construction,which have rejected the notion of the individualas the key locus of knowledge, arguing insteadthat knowledge is based in, and a funda-mental property of, discourse generated throughinterpersonal relationships (Gergen, 1997). In-tegrating these perspectives in the context oflearning at work, I use the term coactive vicari-ous learning (CVL) to capture this relational in-teraction of learning from others’ experiences,adopting “coactive” from the literature on com-puter science and engineering, where it has de-scribed processes where agents (human users,algorithms, or a combination of the two) learn anddevelop optimal solutions by sharing examplesand responses with one another (e.g., Grecu &Becker, 1998; Shivaswamy & Joachims, 2015;Sokolov, Riezler, & Cohen, 2015). I therefore for-mally define CVL as a discursive learning processwhere individuals (i.e., a model and learner)

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intentionally share and jointly process a model’swork experience(s) in interpersonal interactions tococonstruct an emergent, situated understandingof the experience(s).

Distinguishing CVL

In introducing this new concept of CVL, I offeran interpersonal conceptualization of vicariouslearning to challenge, but also to complement,prevailing intrapersonal views (which I refer to asindependent vicarious learning [IVL]) underlyingmuch of the extant literature. Each perspective isbroadly focused on understanding the way in-dividuals learn from others (i.e., models), but theyfundamentally differ in the level of involvement ofa model in the learning process. IVL perspectivesview the role of the model as merely providingan individual with an observable experience,casting the remainder of the learning processas occurring within the individual (i.e., the modelwould be involved only in the first stage ofthe experiential learning process, generating abroader set of concrete experiences the individualcan intrapersonally reflect on, conceptualize, andadopt; Kolb, 1984). In stark contrast, CVL empha-sizes the role of the model as a cocreator in thelearning process, engaging with the learner jointlyin the full “cycle” of experiential learning—mutually reflecting on an experience, developingshared abstract understandings, and collaborat-ing to “try out” possible interpretations and appli-cations of this understanding.

This key feature of IVL—a one-way process oflearners observing and adopting a model’s behav-ior or knowledge—extends not only to literatureexplicitly using the terms vicarious or observa-tional learning but also to studies of knowledgetransfer (as well as associated topics, such as theseeking or sharing of knowledge or information)in organizations (e.g., Argote, Ingram, Levine, &Moreland, 2000) and to studies of communities ofpractice (e.g., Brown & Duguid, 1991), which relyon similar assumptions of independent vicari-ous learning. For instance, studies of knowl-edge transfer tend to focus on the availabilityor characteristics of particular “conduits” forsharing knowledge—such as a close network tie(Hansen, 2002), knowledge management data-base (Davenport et al., 1998), or personnel rotationsystem (Kane, 2010)—inherently emphasizing in-dividuals’ mere exposure to others’ knowledgevia the conduit and assuming that, once exposed,

individuals can effectively adopt that knowledgeon their own.Likewise, research on communities of practice

emphasizes individual learners’ efforts to partic-ipate more “fully” in a group of skilled practi-tioners (Lave & Wenger, 1991) as the primarydrivers of learning. This perspective highlightshow learners absorb key ideas and informationby watching competent practitioners from a van-tage point on the “periphery” of work, arguing, forinstance, that “if training is designed so thatlearners cannot observe the activity of practi-tioners, learning is inevitably impoverished”(Brown & Duguid, 1991: 50). As Bailey and Barleyhave noted, these perspectives afford little activerole for models in the learning process, since theyhave “downplayed explicit teaching and arguedthat most knowledge was transmitted subtly andunintentionally through newcomers’ participa-tion in community activities” (2010: 283). Similarly,although other work in the domain of grouplearning has affirmed the importance of in-dividuals sharing knowledge interactively inteams or workgroups (see Wilson et al., 2007), re-search and theory in this domain tend to treatlearning as agroup or teamproperty (e.g., Vashdi,Bamberger, & Erez, 2013). In doing so this workfocuses primarily on how individuals collectivelyreflect on a shared event or experience (one thatthe group encountered together), rather than anindividual’s vicarious processing of a unique ex-perience communicated by a particular teammember.These descriptions highlight a further differ-

ence between CVL and existing IVL-based ap-proaches, in that each perspective views learningas occurring via a different activity or opportunityin organizations. CVL emphasizes interpersonalinteractions as the key site of vicarious learning,focusing on the joint, interactive processing of anexperience between individuals (i.e., learnersand models), whereas observational approachesfocus on individuals’ opportunities to observe thebehavior of a model, viewing observation andimitation as the key activities by which in-dividuals learn from others at work. By compari-son, knowledge transfer perspectives prioritizethe existence and utilization of conduits for shar-ing knowledge (typically between organizationalunits; Argote, 2015), viewing the use of these con-duits as the key learning activity and exploringfactors that make this use more or less likely oreffective (e.g., examining the impact of a shared


identity on the effectiveness of a personnel rota-tion system; Kane, 2010). Finally, as evident in thedescription above, communities of practice re-search emphasize individual participation as thekey learning activity, casting individuals’ learn-ing from others as emerging from their “fuller”participation and socialization into the commu-nity (Brown & Duguid, 1991; Lave &Wenger, 1991).

Beyond these fundamental differences in howthe vicarious learning process unfolds, CVL canbe further distinguished from existing perspec-tives by comparing them in terms of the assump-tions theymakeabout the intentionality ofmodelsand learners in the learning process and aboutthe content to be learned. CVL assumes a discur-sive, cocreation process of learning, correspond-ingly incorporating the view of both the learnerand model as active, intentional participants inan interaction. However, although theories of ob-servational learning generally conceptualizelearners as intentionally observing and adoptingmodels’behavior,muchempirical research in thisdomain affords relatively little attention to theseintentional efforts (e.g., viewing learners as un-aware mimics in behavioral observational train-ing), and this perspective tends to viewmodels asentirely unintentional participants in the learningprocess (as evident in the use of recorded or tele-vised models in observational training). On theother hand, research on knowledge transfer hasrecognized that learners intentionally make useof various conduits to seek knowledge or in-formation fromothers (often judging thevalueandaccessibility of a potential model’s knowledge;e.g., Borgatti & Cross, 2003). As with CVL, theseperspectives also afford models a high degree ofintentionality in the process, recognizing thatmodels have their own motivations for sharingknowledge (which can be distinct, but comple-mentary to, the motivation of learners; e.g.,Quigley, Tesluk, Locke, & Bartol, 2007) or may in-tentionally hide knowledge from others (Cerne,Nerstad, & Dysvik, 2014). In contrast, research oncommunities of practice overtly assumes lowintentionality of both learners and models, artic-ulating an automatic diffusion process where“teaching and learning are often unintentional”(Bailey & Barley, 2010: 263).

Finally, in terms of the content of learning(i.e., what might be gleaned through engaging invicarious learning), CVL assumes that this con-tent is situated in the discourse between modeland learner, emerging from idiosyncratic joint

processing of experience. Communities of prac-tice research makes a similar assumption—thatlearning is inherently situated in the learner’sengagement in community activities—viewingthe content to be learned as the tacit awarenessand expertise that emerge through increasingparticipation in the work of a particular commu-nity. However, theories of observational learningand knowledge transfer generally treat the con-tent to be learned as observable or codifiable(e.g., able to be documented in a knowledgemanagement system) and, thus, implicitly as-sume that this content can be prescribed or de-fined ex ante. Indeed, conducting behavioralmodeling-based training (e.g., showing a videoof a model displaying behavior desired by theorganization; Taylor et al., 2005) or utilizinga knowledge-sharing conduit (e.g., determiningwhat knowledge to share in a knowledge man-agement system or what information to seek froma network contact) requires a preemptive defini-tion of what behavior or knowledge should bemodeled, shared, or sought. Some studies in theknowledge transfer literature have explored howconduits could be used for transferringmore tacit,situated knowledge; however, they generallyconclude that the degree to which knowledge ismore tacit (i.e., less codified) impedes transferbetween individuals in different units or organi-zations (Zander & Kogut, 1995), and that person-to-person connections (e.g., strong or trusted ties;Hansen, 1999; Levin & Cross, 2004) are necessaryfor sharing tacit knowledge. As Reagans andMcEvily summarized, “Transferring tacit knowl-edge is more sensitive to having the right personwith the right connection at the right place”(2003: 264).Table 1 collates these differences between CVL

and existing perspectives on how individualslearn from others at work, highlighting the con-stellation of characteristics distinguishing CVLfrom these prior theories, as well as areas ofcommonality with existing perspectives. Impor-tantly, although CVL does share particular as-sumptionswith prior theories (i.e., assumptions ofintentionality with knowledge transfer researchand of emergent content with communities ofpractice perspectives), these assumptions arethemselves necessary but insufficient elements(Podsakoff, MacKenzie, & Podsakoff, 2016) of theconcept of CVL.In addition toarticulating theunique features of

CVL, comparing this new conceptual model to

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existing approaches highlights the explanatoryvalue that CVL can offer for understandinglearning in dynamic, socially interactive, andknowledge-intensive organizations. For instance,learning in these organizations is increasinglyreliant on both interpersonal interactions and thesharing of tacit knowledge (Noe et al., 2014).Yet although research has acknowledged theimportance of social relationships as conduits forsharing knowledge and learning (e.g., Argote,McEvily, & Reagans, 2003; Ingram&Roberts, 2000;Uzzi & Lancaster, 2003), it generally has focusedon the presence or absence of these relationships(e.g., whether or not people share a friendship orembedded tie) and has been largely silent onwhat occurs in learning interactions within therelationship. This may be driven by the pre-dominant use of structural analyses of socialnetworks in these studies (e.g., Hansen, 1999;Levin & Cross, 2004; Reagans & McEvily, 2003),which limits their capacity to examine in-terpersonal microprocesses. Likewise, a sizableportion of this research has been conducted at theunit or organization level of analysis (often at-tributing the act of learning directly to the unit ororganization itself; e.g., Argote, 2015),masking theunderlying interpersonal interactions that con-stitute vicarious learning in these settings.

In this way, existing views can be said to be“social but not relational,” as described by BaileyandBarley (2010: 283),whohighlightedhowextantresearch tends to underemphasize “teaching-learning interactions” (and specifically the roleof the teacher), demonstrating that these in-teractions varied significantly across different

communities of professional engineers. In thesekinds of knowledge work environments (e.g., en-gineering), interactivity and two-way discussionseem necessary for understanding what can belearned vicariously from others’ experiences,since individuals are likely unable to deduce the“lesson” of another person’s experience withoutdiscussion of the person’s thoughts and decision-making processes (i.e., the underlying judgmentsand tacit understanding guiding their actions).Thus, although independent modes of vicariouslearning certainly can be effective for learning inparticular circumstances, introducing a coactiveperspective on vicarious learning adds sub-stantial nuance and clarity to our knowledge ofhow individuals workwith one another to learn inthe increasingly interdependent and knowledge-intensive work of modern organizations.

CVL Interactions

The defining characteristic of CVL lies in itsgrounding in the relational interactions be-tween models and learners. Yet relatively little isknown about what actually occurs in theseinteractions—the various interactional “moves”and microprocesses that constitute vicariouslearning (Bresman, 2010; Darr, Argote, & Epple,1995)—since existing approaches have tended toemploy the simplified causal imagery of “findand copy,” without an understanding of theinterpersonal behaviors underlying the vicari-ous learning process. Building on the construc-tionist perspectives articulated earlier (Blumer,1969; Gergen, 1997; Mead, 1934), I theorize that

TABLE 1Distinguishing CVL

Characteristics CVLObservationalVicarious Learning Knowledge Transfer

Communities ofPractice

Learning process:Learning generallyviewed as . . .

Interpersonal process ofjoint, coconstructedmeaning making

Within-person process of knowledge or behavior adoption after exposureto model

Learning activity:Learning generallyoccurs via . . .

Interpersonalinteractions

Observation andimitation

Conduits fortransferringknowledge

Fuller participation incommunity

Learning intentionality:Engagement inlearning isgenerally . . .

Intentional Intentional orunintentional

Intentional Unintentional

Learning content:What is learned isgenerally . . .

Situated (emergingfrom discourse)

Defined ex ante (inbehavior to beobserved)

Defined ex ante (inexisting knowledgeor practice)

Situated (emergingfrom participation incommunity work)


vicarious learning interactions are relational sitesof discursive, coconstructed meaning making, andthat through the joint processing of a model’s ex-perience (e.g., dialogue focused on reviewing ac-tions, comparing them to prior experiences, etc.),both learnerandmodelareable togenerate insight.When a model offers their experience, the learnermay respond with a question or observation aboutthe experience or may reciprocally share an expe-rience that complements or contradicts themodel’sexperience, and these contributions can help boththe learner and themodel develop amore nuancedunderstanding of the experience.

For instance, studies of stories in organizationshave noted that storytelling (one potential form ofCVL interaction) affords the individual in the lis-tener role an active, coproducing part (see Garud,Dunbar, & Bartel, 2011), as the listener engages indialogue with the storyteller, asking for clarifi-cation or expressing emotional responses aboutthe story and ultimately changing how the story-teller views and understands the experience(Sawyer, 2003; Tsoukas, 2009). The benefits of sto-rytelling in organizations stem from this “perfor-mance” of the story (i.e., the enacted telling of andlistening to the story; Boje, 1991), with the nar-rative formallowing formultiple interpretationsandmore flexible communication of meaning ofan event to be expressed (Browning, 1992;Weick, 1987). Although stories are often viewedas a tool for cultural reproduction or mainte-nance (e.g., Dailey & Browning, 2014), there isemerging evidence of the value of them asa means of vicarious learning, arising from thedialogue between storyteller and listener (fora discussion in the nursing context, see Roberts,2010).

Tsoukas (2009) theorized that dialogical pro-cesses are central to individual knowledge crea-tion in organizations (facilitating the ability todistance oneself from previously held perspec-tives on a prior experience), and I argue that thisdialogue facilitates individuals’ vicarious learn-ing from others’ experience as well. To developa more thorough understanding of this dialogueembedded in CVL interactions, I draw from theo-ries of narrative and discourse—specifically,Hernadi’s (1987) notion of the hermeneutic triad(see Czarniawska, 1999)—to articulate three keyelements involved in CVL interactions. Hernadi(1987) asserted that individuals interact witha narrative through explication (“standing under”the narrative to understand what occurred),

explanation (“standing over” the narrative andanalyzing what happened), and exploration(“standing in for” and connecting with the teller).Blending this perspective with similar ap-proaches in studies of speech acts and storytell-ing in organizations (e.g., Boje, 1991), I argue thatthere are three corresponding discursive ele-ments involved in aCVL interaction—experience,analysis, and support—that enable learning.Moreover, these three elements help individualslearn and alter their response repertoires (Sitkinet al., 1998) inuniquebut complementaryways.Asdescribed below, sharing experiences duringa CVL interaction exposes individuals to newideas and knowledge by making them aware ofan event or occurrence; engaging in analysis re-fines knowledge by examining the experienceand testing possible interpretations or connec-tions; and demonstrating support builds thenecessary capacity (both individually and in-terpersonally) to engage with the knowledge andupdate the individuals’ repertoires for respondingto future situations.Experience. Experience reflects the “raw mate-

rial” of vicarious learning: a learner’s exposure toamodel’s current or prior experience provides thenecessary content for engaging in a CVL in-teraction and changing response repertoires. Im-portantly, exposure to this experience can takea variety of forms, including more observationalstrategies like “shadowing” (see Leonard, Barton,& Barton, 2013) or apprenticing, as well as morenarrative strategies like storytelling or in-personsimulation (e.g., where models guide learnersthrough a reenactment of a particular experience,as in the “staff rides” of wildland firefighters;Useem, Cook, & Sutton, 2005; Weick & Sutcliffe,2007). These strategies parallel the direct andsymbolic exposure methods described in IVL(e.g., observation or reviewing awritten summaryor video recording of behavior), but they allow forlearners and models to interact during or imme-diately after exposure to engage in discussionaboutwhat occurred (e.g., discussingwhat a learnerobservedwhen shadowing amodel, rather than justpassively observing an unaware model).Becoming aware of a greater number of expe-

riences (e.g., others’ experiences one was pre-viously unaware of) provides individuals witha greater stock of material on which to reflect,facilitating the development of new (and poten-tially more nuanced) knowledge and expandingtheir response repertoire. While independent

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approaches to vicarious learning cast this learn-ing as occurring solely within the learner(i.e., taking in amodel’s experience and reflectingon it internally), CVL interactions involve in-terpersonal sharing and reflecting on experience,allowing the interaction to include the sharing ofmultiple experiences from the perspectives ofboth the model and the learner. Indeed, in CVLinteractions the learner may also add an experi-ence of their own into the discourse, in response tothe model’s experience. These additional experi-ences can serve to challenge or complement themodel’s experience, creating a broader base ofexperiences on which the model and learner canreflect and enabling them to generate greaterknowledge and a more robust understanding ofwhat might be applied to future situations.

Analysis. The second component of CVL in-teractions is the analysis offered by the learner ormodel (or both) on the experience during an in-teraction. This analysis may take the form ofa probing question, comment, or request for clar-ification, among others. Although the simplesharing of another’s experience may increase in-dividuals’ awareness of alternatives, by engag-ing in analysis of the experience in discussionwith one another, individuals are able to evalu-ate, reinterpret, or compare their emerging un-derstanding of the experience. This analysisallows individuals to develop new knowledge, aswell as to refine their existing stocks of knowl-edge (e.g., by analyzing a model’s experience incomparison to some other prior experience,whichmay yield a new understanding of the prior ex-perience), thereby both expanding and reshapingtheir response repertoire.

The interactive nature of CVL allows learners totest their interpretation of models’ experiences byasking questions or making comments and, corre-spondingly, allows models to clarify or reexaminetheirowninterpretationofanexperience.AsCohen,Hilligoss, and Amaral (2012) have suggested,a learner’s questions can cause models to take thelearner’s perspective, leading them to potentiallyprovidedifferent informationor tomodify their storyof the experience (e.g., Connell, Klein, & Meyer,2004). This process is iterative, and a model mayalso offer questions or analysis, leading to anemergent discourse where both parties engage insensemaking through their reactions and re-sponses (Garud et al., 2011; Weick, 1995).

Support. The final component of a CVL in-teraction is support, referring to statements or

behaviors in the interaction that communicatesocial or emotional assistance in processing anexperience. Viewing learning as the updating ofresponse repertoires involves not only the devel-opment of new knowledge or abilities but alsochanges in relationships or beliefs (Sitkin et al.,1998) that could impact an individual’s potentialfor future action. Providing support during a CVLinteraction (e.g., exchanging encouraging state-ments when discussing a model’s challengingexperience) thus directly enhances individuals’response repertoires by helping to develop indi-vidual beliefs (e.g., building the learner’s self-efficacy for engaging in a similar experience;Bandura, 1977b) and interpersonal relationships(e.g., creating a relational support system) inways that enhance their capacity to respond toa future situation.At the same time, demonstrating support also

contributes indirectly to individuals’ repertoiresby facilitatingmoreeffectivesharingofexperienceand analysis. Research on relational embedded-ness and knowledge sharing (e.g., Ingram &Roberts, 2000; Uzzi, 1997; Uzzi & Lancaster, 2003)has shown that exchange relationships embeddedin social attachments (e.g., friendships) facilitatesharing of more tacit, private knowledge. Thesupportive behaviors involved in these informalsocial relationships help develop a sense of trustand set different norms of interaction (compared toformal arm’s-length ties) that encourage learning.Although this research examines ties between in-dividuals in different firms (e.g., among differenthotels or between banks and their clients; Ingram& Roberts, 2000; Uzzi & Lancaster, 2003), engagingin these supportive behaviors with others in anindividual’s own work environment can similarlyhelp to “smooth”communicationand interaction inways that enhance learning (see Lipshitz et al.,2007). For instance, a learner who is discussinga model’s failed experience might express emo-tional support to the model, building a sense ofcamaraderie in the relationship that facilitatesmore open, thorough discussion and generatesmore insightful analysis of the experience.

Enacting CVL

These three elements—experience, analysis,and support—serve as multiplicative compo-nents of CVL interactions such that high levels ofall three facilitate greater learning in the in-teraction and lower levels of any one element can


inhibit learning. Each serves a unique purpose:experience provides the necessary foundation ofmaterial fromwhich to learn, analysis constitutesthe structure of understanding built on this foun-dation, and support serves as the scaffolding thatenables the coconstruction of this understanding.For instance, a learner offering an analysis ofa model’s experience in the absence of anyexpressed support may come across as criticizingand may make the model less likely to share fulldetails of the experience, inhibiting the learner’sknowledge of the experience and reducing thepotential benefit of the learner’s feedback andanalysis for the model’s own understanding.Likewise, an interaction with high levels ofexpressed support for a model’s experience, butwithout any analysis, can inhibit learning sinceunderlying issues or tacit elements of the experi-ence are less likely to be surfaced.

As a clarifying example of these elementsenacted in a CVL interaction, consider the case ofan individual who has recently been transferredto a technologydivision of a consulting firmand isshadowing an established member of his newteam. The model (in this case the establishedteammember) provides the learnerwith an initialexperience to observe (such as watching themodel interact with a client who has very specificdemands), after which the learner asks questionsof the model to better understand the observed

experience, such as “Is that how all of the clientsusually are?” In doing so the learner is remindedof a prior experience working with a client, whichhe then brings up to share with the model (tocontrastwith thecurrent experience): “I remembera client meeting where the client was not at allwhat we expected. They had no clue what theywanted!”Afterhearing the learner’sdescription ofthe experience, the model expresses support forthe learner’s experience and offers a story of herown similar experience, saying, “That must havebeen frustrating, but I’m sure you handled it well.It reminds me of a client meeting we had wherethey came in with nothing—no ideas, no plan;they just knew they wanted some help, but theydidn’t really know for what.” This story promptsanother question from the learner: “So sometimesthe clients don’t know what they want, but othertimes they are like this one and have very specificdemands?” The model offers her own analysis inresponse: “I guess youare right. Thinkingabout it,it’s usually the biotech folks that know exactlywhat they want—you know, they’re all formerchemists and engineers. But for some of the otherfirms, theyhave lessof a specific ideaofwhat theyneed.” This leads to a statement of support fromthe learner: “That must be kind of exciting, butalso a bit difficult, to have to be ready for sucha wide variety of clients.” This exemplar of a CVLinteraction is displayed in Figure 1.

FIGURE 1Exemplar of a Vicarious Learning Interaction

Initial

awareness

Coconstructed learning

(Initial) Model(Initial) Model

(Initial) Learner

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This cycle of interaction can carry forward intofuture learning—for instance, if the model takesthe learner to observe a different meeting, thismay prompt a return to this discussion anda continuation of the learning process, or it maygenerate an entirely new learning interaction. Akey point to note, however, is that the learningfrom this process is coconstructed by both themodel and the learner. While the initial experi-ence offered by the model (observing a clientmeeting) gave rise to an initial awareness ofwhatwas to be learned (i.e., “What is it like interactingwith clients in this division?”), the learning thatresulted from reflecting on the experience (i.e.,that biotech clients know exactly what they wantand other clients are more open-ended) occurredthrough the joint processing of the experienceand comparisons drawn to prior experiences,facilitated by statements of support that estab-lished a common ground and strengthened therelationship between the two individuals. Indeed,communicated support strengthens subsequentactions by each person (indicated by the in-creased bolding of the lines in Figure 1), buildingstronger connections that promote further sharingof experience and analysis. Notably, this supportmay also be embedded in other interactional“moves,” beyond just discrete statements. Forinstance, simply sharing a similar experience(such as a failed client interaction) may commu-nicate support for the experience of the otherparty by establishing a commonbackgroundandcommunicating that they are not alone in theirexperience.

Also noteworthy is the notion that the roles ofmodel and learner can fade somewhat after a fewsequences of interaction (as was the case in thisexample); as each brings up relevant experiencesto consider in the interaction, the distinction be-tween model and learner becomes less overt.However, it is still useful to use the labels “model”and “learner” (even if only for the initial stages ofthe interaction) in order to clarify each person’srole in undertaking the interaction (i.e., thelearner as “knowledge seeker” and model as“knowledge sharer”) and to tie these concepts toprior findings in extant literature. Finally, theseinteractions are not strict “tit for tat” exchangesbut, rather, can unfold somewhat unpredictablyand organically; sometimes experience is re-ciprocated with another experience (as in thecommunal sharing of “war stories” among tech-nicians; Orr, 1996), while other times a single

experience is met with increasingly detailedanalysis (as in firefighters’ staff rides; Useemet al., 2005; Weick & Sutcliffe, 2007).

CVL IN CONTEXT

With a definitional understanding of thesediscursive CVL interactions, I turn now to thecontextual factors that can increase individuals’engagement in CVL, in terms of not only likeli-hood or frequency but also intensity (i.e., engag-ing in CVL interactions that involve greaterexchange of experience, analysis, and support,allowing them to more deeply probe the lessonsof particular experiences). Understanding howthese contextual factors give rise to CVL in orga-nizations is important, given the informal natureof vicarious learning that makes these processesdifficult to promote simply through managerialor institutional means (relative to more formallearning activities, such as organizational train-ing). Since CVL is focused explicitly on the in-teractive construction of learning embeddedwithin particular model-learner relationships, Ifocushere on three facets of context: the relationalcontext between individuals involved in the in-teraction, the structural context of the setting inwhich these interactions are embedded, and theindividual context of relevant characteristicseach person (i.e., both learner and model) bringsto the interaction (as shown in Figure 2).

Relational Context

Elements of the relational context between in-dividuals provide the relational “backdrop” fortheir engagement in CVL interactions, unique toany given dyad. This relational context capturesthe qualitative nature of a pair’s relationship andprior interactions, and it can influence the natureof their CVL interactions—causing, for instance,a learner to engage with a model’s experiencein different ways than would another personresponding to the same experience as a functionof the idiosyncratic relationship and history be-tween the learner and model. This relationalcontext between individuals can be seen as con-sisting of their relationship quality (also termedexchange quality in certain bodies of literature;Colquitt, Baer, Long,&Halvorsen-Ganepola, 2014;Cropanzano &Mitchell, 2005), the affective tone oftheir relationship, and their familiarity and his-tory of interactionwith one another. The quality of


the relationship between individuals consistsof the degree of their mutual respect, trust, andobligation (Blau, 1964; Colquitt et al., 2014),generating greater exchange of informationalresources between individuals (as in models ofhigh-quality leader-member exchange;Graen&Uhl-Bien, 1995) that can improve knowledgedissemination (Lipshitz et al., 2007) and perfor-mance in complex, changing situations (Carter,Armenakis, Feild, & Mossholder, 2013). Indeed,more trustful relationships facilitate greaterinformation sharing, particularly of more pri-vate or proprietary information (Levin & Cross,2004; Uzzi & Lancaster, 2003), and can encouragepeople seek help from their colleagues (espe-cially when the colleague has a high degree ofexpertise; Hofmann et al., 2009).

A pair’s relational context also consists of theirinterpersonal affect, particularlywhether the toneof this affect is positive or negative. Affectivesentiments toward others are distinct from judg-ments about their competence (e.g., respect fortheir expertise) and can shape the extent to whichindividuals engage in task interactions witha particular person such that when individualsperceive some degree of negative affect in theirrelationship with the other person, they are less

likely to interact with them, even when the otherperson is highly competent (relying on thesecompetence judgements only in the presence ofmore positive affect; Casciaro & Lobo, 2008). Be-yond just engaging in task interactions, thepositive or negative appraisal of individuals’relationships with a potential knowledge pro-vider (i.e., as an ally or threat) can fundamentallyalter how they obtain and interpret the knowl-edge (e.g., Menon & Blount, 2003), suggestingthat these appraisals influence engagementin learning interactions as well.At the same time, individuals’ engagement in

CVL interactions likely depends also on theirhistory of interaction with one another and thedegree of familiarity they have developed. Re-search has demonstrated that greater familiar-ity and experience working together facilitateknowledge integration and enhanced perfor-mance in teams (Gardner, Gino, & Staats, 2012),enabling individuals to pool unique informationto come to a shared, multifaceted understandingofa scenario (e.g.,Gruenfeld,Mannix,Williams,&Neale, 1996). In this way, the relational contextbetween two people involves not only judgmentsor perceptions of the relationship in the presentbut also the past history of interaction, and these

FIGURE 2Contextual Antecedents and Developmental Outcomes of CVL

Developmental outcomesContextual antecedents

Relational context Relationship qualityAffective toneFamiliarity and historyof prior interactions

Individual knowledge

CVL interaction(s)

Individual context Learning orientationand motivation to learn Prior knowledge stockKnowledge configuration

Structural context Proximity Role structureTask structure

Individual capacity Transactive memory Perspective takingSelf-efficacy

Shared mental modelsTrustAffective commitment

Relational capacity

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three elements together (relationship quality, af-fective tone, and familiarity) coalesce to providethe relational context of aCVL interaction inwaysthat are more or less helpful for overcoming thepotential psychological and social barriers toengaging in CVL. Specifically, engaging in vi-carious learning can be a risky process, since itinvolves exposing a prior experience to analysis,as well as sharing perspectives with and seekingopinions from another person in a way that canadmit a lack of knowledge (Borgatti & Cross,2003). Engaging in the interactive analysis of anexperience also creates the potential for sharingnegative feedback on another’s performance orhandling of a prior experience. In poor relationalcontexts, this negative feedback can often beperceived as stressful or anxiety inducing, creat-ing a feeling of threat that can result in rigidityand restriction in information processing (Staw,Sandelands, & Dutton, 1981). Yet when the re-lational context between individuals is charac-terized by high-quality relationships, positiveaffect, and familiarity, this type of feedback maybe perceived as less threatening (e.g., Menon &Blount, 2003) and engagement inCVL interactionscan be significantly enhanced.

Proposition 1: A relational context be-tween individuals characterized by (a)higher relationship quality, (b) morepositive affective tone, and (c) greaterfamiliarity and history of prior inter-actions increases individuals’ engage-ment in CVL.

Structural Context

Beyond these relational elements of context,engagement in CVL interactions can also beinfluenced by more structural features of a pair’swork context. The structural context of individuals’work—the properties or characteristics of a workarrangement that shape, prescribe, or constrainbehavior and task accomplishment—can affecttheir pattern of interactions (altering, for instance,the opportunity to interact with beneficiaries oftheir work; Grant, 2007), providing both bound-aries and opportunities for interpersonal learninginteractions. Indeed, information-seeking or-sharing interactions in organizations typicallymirror the formal structures of the organization(see Bailey & Barley, 2010). I focus here on threeelements of the structural context linking a given

pair of individuals in the organization: proximity(the distance in physical, hierarchical, or otherorganizational space between individuals), rolestructure (the configuration of individuals’ rolesin terms of their responsibility for helping- orlearning-oriented behavior), and task structure(the nature of the work itself as requiring more orless interaction).With regard to proximity, the networks in-

dividuals use for sharing knowledge at workoften reflect their existing interaction networks(McDermott & O’Dell, 2001), and physical proxim-ity has long been shown to underlie almost allsocial interaction (e.g., Festinger, Schachter, &Back, 1950), specifically impacting informationseeking (Borgatti & Cross, 2003). This effect ofproximity onengagement in learning interactionscan also apply to proximity in an organizationalhierarchy (i.e., “vertical” proximity), as evident indata onknowledge-sharing incidents capturedbySiemsen, Roth, Balasubramanian, and Anand(2009), who noted that the vast majority of theseincidents (for which they could obtain hierarchydata) occurred at the same hierarchical level (al-though seeking and sharing knowledge withthose at a different hierarchical level have alsobeen proven beneficial, particularly for certaintypes of knowledge resources or in certain workenvironments; e.g., Bunderson & Boumgarden,2010; Cross & Sproull, 2004).Likewise, roles that are structured to involve

explicit responsibilities related to learning orhelping others (e.g., formal mentoring roles) canenhance individuals’ engagement in CVL in-teractions, both for individuals in the helping/learning role (as individuals tend to engagemorefrequently in behavior they perceive to be em-bedded in their formal role responsibilities;e.g., Hofmann, Morgeson, & Gerras, 2003) and forothers who may be more willing to seek out orshare knowledge with someone they perceive tobe responsible for learning or helping. For in-stance, Hofmann and colleagues (2009) found thatnurses perceived others in formal helping roles(i.e., nurse preceptors, who serve as resources orconsultants for less experienced nurses) to bemore accessible and possess greater expertiseand, thus, were more likely to seek help fromthem in the face of a complex situation. This rolestructure, along with proximity, can increase op-portunities for communication (e.g., through re-currentmentoring sessions)whilealso influencing(especially for proximity) the richness of the


medium used to communicate. Greater frequencyof communication and the use of richer media(e.g., face-to-face interaction or video versusleanermethods, such as email) can help build thetrust necessary for sharing knowledge at work(Abrams, Cross, Lesser, & Levin, 2003). Contactbetween individuals utilizing a richer medium ofinteraction allows for more effective communi-cation of information and interpretations (Daft &Lengel, 1984), while greater frequency of commu-nication has been related to individuals’ motiva-tion to share knowledge with coworkers (throughits relationship with increased psychologicalsafety; Siemsen et al., 2009).

Finally, task structure (i.e., the nature of thework itself) has been shown to influence how in-dividuals relate to one another in a variety ofways. For instance, tasks can be structured to re-quire more independent or interdependent activ-ities (i.e., simple compilation of individual effortversusa cooperative coordination of effortswhereeach individual’s work is dependent on the workof others), and greater task interdependence hasbeen found to facilitate interpersonal learning inwork teams (Wageman, 1995). Also relevant forlearning, task structures can vary in their timedemands, having more or less slack time and re-sources, and given that engaging in learning re-quires time and energy (often drawing theseresources away from performance; Singer &Edmondson, 2008), tasks structured with greaterslack resources can free individuals’ resources forengaging in vicarious learning with others. En-gaging in CVL involves the investment of signifi-cant time and energy into “unpacking” anexperience with another person, and these fea-tures of the structural context (task structure, aswell as proximity and role structure) can in-crease engagement in these discursive in-teractions by promoting formal and informalencounters (e.g., water cooler interactions), aswell as providing the necessary familiarity andoverlap in goals, vocabularies, andprofessionalorientation for effective learning from others(Lipshitz et al., 2007).

Proposition 2: A structural context ofindividuals’ work characterized by (a)closer proximity, (b) more helping- orlearning-focused role structures, and (c)more interdependent or less time-intensive task structures increases in-dividuals’ engagement in CVL.

Individual Context

While I cast CVL as a fundamentally relationalphenomenon, I also acknowledge that learningis a voluntary, personal activity and theorize thatelements of the individual context—learning-relevant attributes of both the learner andmodel—can impact the frequency and intensity ofCVL interactions. I use the term individual learn-ing attributes to refer to the range of individualcharacteristics that have been described as in-creasing an individual’s learning at work, spe-cifically attending to individuals’ learningorientation (asageneral attribute) andmotivationto learn (as a situation-specific characteristic),their existing stock of knowledge, and the con-figuration of this knowledge (i.e., their degree ofspecialization of knowledge). For example,a strong learning goal orientation—that is, thetendency to pursue goals related to learning andmastery in a work situation (Dweck, 1986;VandeWalle, 1997)—can increase learning by en-couraging greater feedback seeking (VandeWalle,Ganesan, Challagalla, & Brown, 2000), suggestingthat individuals with stronger learning orienta-tionsmightbeparticularly likely toseekoutothers’experience or analysis (in a CVL interaction),which is consistent with findings that learningorientation promotes increased motivation to en-gage in learningactivities (Colquitt & Simmering,1998). Indeed, the motivation to learn (as a moreproximal, situation-specific learning character-istic) should particularly impact engagementin CVL for both learners and models, sincehigh motivation to learn has been associatedwith a broad range of learner behaviors andoutcomes (for a review in the training context,see Colquitt, LePine, & Noe, 2000), and motiva-tional considerations have also been identifiedas key antecedents to individuals’ engagementin knowledge sharing (Osterloh & Frey, 2000;Quigley et al., 2007; Reinholt, Pedersen, & Foss,2011).The extent of individuals’ existing stocks of

knowledge is also an important determinant oftheir engagement in CVL interactions, in partbecause greater preexisting knowledge allowsthem toanalyzeaparticular experience in greaterdepth (i.e., engaging more intensively in a CVLinteraction) andalsomakes themmore likely tobemore frequent participants in these interactions.For instance, a model with a greater stock ofexisting knowledge is likely to be perceived by

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others as an expert and, thus, is more likely to besought out as amentor to “shadow” or as someonefrom whom one can seek stories of past experi-ence (consistent with evidence on informationseeking and help seeking; Hofmann et al., 2009;Morrison & Vancouver, 2000). However, greaterstocks of knowledge can also benefit learnersin CVL interactions, since greater existingknowledge builds their absorptive capacity torecognize and incorporate knowledge gatheredfrom another person (Ko, Kirsch, & King, 2005;Szulanski, 1996). Greater absorptive capacity—asa function of existing knowledge stocks—should therefore cause learners to recognize thevalue in a potentialmodel’s experience, leadingto more frequent and in-depth engagementin CVL.

In addition to the stock (e.g., quantity) of in-dividuals’ prior knowledge, the configuration ofthis knowledge—that is, the tendency for in-dividuals to have knowledge and experience fo-cused inonedomain (specialists) or spreadacrossmultiple domains (generalists)—also serves asan important contextual antecedent of CVL in-teractions. This difference in configuration of pastexperience and knowledge can change the wayindividuals evaluate problems and opportunities(e.g., changing the way executives evaluate stra-tegic acquisitions; Hitt & Tyler, 1991) and canchange how they engage with others in their or-ganization. For instance, teams made up of moregeneralists havebeen shown to engage in greaterinformation sharing and coordination, whereasspecialists engage in this knowledge sharingonly under certain structural conditions (i.e., adecentralized structure; Rulke & Galaskiewicz,2000). When engaging in learning interactions,individuals with highly specialized backgroundsappear to have more difficulty sharing experi-ences and constructing common meanings,whereas individuals with more generalist con-figurations of knowledge (including what hasbeen called “transspecialist” or “T-shaped”knowledge; Leonard-Barton, 1995) are better ableto understand the relation of others’ experiencesand task domains to their own and to explore in-tegration applications (Kang, Morris, & Snell,2007). These differences in engagement likelyextend to CVL interactions as well, making thesedifferent configurations of knowledge, alongsidethe effects of individuals’ motivation or orienta-tion toward learning and extent of existingknowledge, key elements of the individual context

brought by the learner and model to a CVLinteraction.

Proposition 3: An individual contextcharacterized by individuals’ (a) stron-ger learning goal orientation and mo-tivation to learn, (b) greater stocks ofexisting knowledge, and (c) more gen-eralist configurations of knowledge in-creases individuals’ engagement inCVL.

DEVELOPMENTAL CONSEQUENCES OF CVL

I turn now to the repertoire-developing conse-quences of engaging in CVL. In prior research onvicarious learning (as well as learning morebroadly), scholars often used performance or be-havior change to indicate the occurrence oflearning, but as Sitkin and colleagues noted, “Notall learning results in observably changed ac-tions or articulated changes in beliefs”; rather,learning is more broadly a process of “makingsimpler repertoires more robust and general byunderstanding more deeply how to apply andadapt them” (1998: 70). Therefore, I specificallyaddress developmental consequences of CVLthat help individuals build and revise their re-sponse repertoires (inways that would likely leadto improved performance in the face of a futuretask), theorizing about the impact of CVL not onlyon individuals’ knowledge but also on their indi-vidual- and relational-level capacity for futurelearning and application of knowledge.

Individual Knowledge

Aprimary intended outcome of engaging in anyform of vicarious learning is an increase in in-dividuals’ knowledge (e.g., in the form of newbeliefs, awareness, skills, or behaviors) frommaking meaning of a model’s experience. Yet,relative to more independent forms of vicariouslearning, engaging in CVL interactions can par-ticularly enhance this knowledge development intwo key ways. First, because CVL involves dis-cussion, analysis, and cocreation of learning be-tween two people, both the learner andmodel arelikely to gain awareness and knowledge fromCVL as each engages actively in the interactionby discussing, processing, and revising theiremergent understanding of an experience. Thus,beyond just the learner, the model can also learn


from the coconstructed understanding, consistentwith observations that the act of sharing knowl-edge allows sharers to test assumptions andverify their ideas by participating in mutualcodevelopment (Nonaka, 1994).

Second, because CVL interactions allow multi-ple perspectives to be expressed on a given ex-perience, these interactions are likely to revealinsights that could not be obtained alone (i.e., ina one-way IVL process), yielding greater knowl-edge than that created by a single individual’sintrapersonal learning process (see van der Vegt& Bunderson, 2005, on the benefits of and condi-tions for sharing multiple perspectives in thecontext of group learning). Indeed, the discursivenature of CVL interactions enables a form ofsecond-order learning broadly akin to Giddens’(1987) double hermeneutic—namely, that in-dividuals can learn not only from their own in-terpretation of an experience but also from others’analysis of and reaction to that interpretation. Asa result, both models and learners engaging inCVLare likely to leavewith enhancedknowledge.

Proposition 4: Engaging in CVL in-creases individuals’ knowledge.

Individual Capacity

Although existing theories of vicarious learn-ing have generally stressed the knowledgeresulting from these learning processes, I extendbeyond these knowledge creation outcomes toexamine the potential of vicarious learning toimpact individuals’ (both learners’ and models’)capacity for learning, developing, andperformingin the future. For example, at a basic level, en-gaging in CVL increases individuals’ awarenessof what others in the organization know—whathas been termed transactivememory (Lewis, 2004;Lewis, Lange, & Gillis, 2005; Moreland & Argote,2003). CVL interactions expose individuals to theexperiences of others (whether as a learner oras a model hearing about a learner’s experiencethrough their reaction and response), and, as a re-sult, these individuals leave the interaction witha greater awareness of “whoknowswhat” or “whohas done what” in the organization. This trans-activememory enhances individuals’ capacity forlearningmore quickly and effectively in the futureby reducing their need to expend effort searchingfor an individual with relevant experience.

A further consequence of engaging in a CVLinteraction lies in the potential for development of

stronger perspective-taking abilities, sincethese interactions help individuals developa broader, more integrated understanding ofa work experience (a key antecedent of perspec-tive taking; Parker & Axtell, 2001). Developing thisability to see issues from others’ perspectivesenhances future interpersonal interactions (e.g.,Batson, 1991) and, correspondingly, should en-able individuals to more effectively engage infuture interpersonal learning. Likewise, in hiswork on self-efficacy, Bandura (1977b) notedhow vicarious experiences can enhance an in-dividual’s sense of efficacy because greaterawareness and understanding of another’sexperience can boost individuals’ own beliefsabout being able to accomplish the same feat.Indeed, CVL can increase individuals’ self-efficacy for learning both by reinforcing theirown learning abilities (i.e., “I learned from thisinteraction, so I will likely be able to do soagain”) and by exposing them to someone else’slearning from an experience shared during CVL(e.g., a model seeing a learner benefit from theinteraction), enhancing their capacity for futurelearning.

Proposition 5: Engaging inCVL increasesindividuals’ capacity for future learning,specifically increasing their (a) trans-active memory, (b), perspective-takingabilities, and (c) self-efficacy beliefs.

Relational Capacity

In addition to this general enhancement of in-dividuals’ learning capacity, engaging in CVLalso builds capabilities specific to the idiosyn-cratic learner-model dyad that can also enhancefuture learning from one another—which I termthe dyad’s relational capacity for future learning.Building on research defining high-quality con-nections (e.g., Dutton & Heaphy, 2003), this re-lational capacity reflects adyadic connection thatcan both carry greater sentiment or experienceand withstand greater strain (e.g., from changingconditions), and engaging in CVL helps developthis relational capacity in severalways. Althougheach individual (i.e., learner and model) in a CVLinteraction develops their own transactive mem-ory, they also develop shared mental models(shared knowledge structures regarding a con-cept or experience; Cannon-Bowers, Salas, &Converse, 1993) that arise out of the unique in-teraction shared in the relationship. These shared

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mental models have been shown to enhance in-teraction processes (as well as performance;Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000), suggesting that they would en-hance a pair’s capacity to effectively engage infuture CVL owing to the presence of a sharedframework for conceptualizing and communi-cating experiences, analysis, and support. Forinstance, when a pair develops shared mentalmodels, as part of what Huber and Lewis (2010)call “cross-understanding,” they are able toengage inmore efficient and effective learning,with less “wasted” energy spent trying to shareand analyze experiences, because they areable to communicate ideas in language theyknow others will understand and appreciate.This is consistent with prior research showingthat relational ties can lead to the develop-ment of relationship-specific language andunderstandings that allow for the effectivetransfer of more complex and nuanced in-formation (e.g., Uzzi, 1997).

As noted earlier, engaging in CVL involves theexchange not only of information or experiencebut also of support. Supportive statements ina CVL interaction help encourage the sharing ofexperiences that might be considered embar-rassing (such as a failed experience) and facili-tate the questioning and analysis of another’sshared experiences. As such, these interactionsare likely also sites of increasing trust and affec-tive commitment (i.e., positive emotional attach-ment to others at work). Sharing experiences withone another can create positive affect for andcommitment to the other person in the relation-ship, developing emotional bonds from the re-ciprocal exchange of support in the interaction(e.g., Lewis &Weigert, 1985; McAllister, 1995) that,in turn, lead individuals to engage in greaterhelping or citizenship behaviors toward co-workers (see Lavelle, Rupp, & Brockner, 2016).Similarly, through the repeated sharing of expe-rience and analysis of the experience, dyads candevelop not only their own understanding andknowledge but also a sense of dependability,based on the other’s reliable analysis and insightregardinganexperience, facilitating greater trustand commitment in the relationship. Indeed, en-gaging in vicarious learning requires that in-dividuals be willing to be vulnerable with oneanother (since it can expose unsuccessful expe-riences or reveal ignorance), which drives thedevelopment of trust (Mayer, Davis, & Schoorman,

1995), correspondingly reducing the costs of futurelearning (Levin & Cross, 2004).

Proposition 6: Engaging in CVL in-creases a pair’s relational capacity forfuture learning, specifically increasingtheir (a) sharedmental models, (b) trust,and (c) affective commitment.

Impact of Developmental Outcomes on FutureLearning Context

As evident in the description above, aspects ofthese developmental outcomes are inherentlyrelated to the antecedent context for CVL, high-lighting the cyclical, recurrent nature of these in-teractions and their embedding in individuals’and dyads’ ongoing development in organiza-tions. As individuals engage in CVL and buildtheir knowledge and capacity, this correspond-ingly alters the individual and relational contextin which they might engage in a future learninginteraction (e.g., Baker et al., 2005), which isrepresented by the dashed lines in Figure 2.These loops reflect how growth over time in in-dividuals’ knowledge and capacity can alter theindividual context of future CVL interactions(e.g., altering their stock or configuration ofknowledge), and changes in dyad-specific ca-pacity can alter the relational context of thesefuture interactions (e.g., adding to the history ofprior interactions and changing relationshipquality).

DISCUSSION

The concept of CVL provides a challenge toperspectives that have equated vicarious learn-ing with solely observational learning in organi-zational settings, offering a novel coconstructionmechanism for understanding vicarious learningat work. The rise of the knowledge economy(Powell & Snellman, 2004) and the increasinglysocial nature of work suggest that “find and copy”approaches to vicarious learning are likely to beof increasingly limited utility in understandinglearning in today’s complex, relationally embed-ded work organizations. Therefore, the model ofCVL presented here brings explicit attention tothe underlying behaviors and relational in-teractions that constitute vicarious learning inthese interdependent work environments—attention that has been largely absent from prior


research. Indeed, despite scattered attention toparticular elements of these vicarious learninginteractions—for instance, recognizing thatmodel or “source” groups can influence howexperience-seeking groups adapt and learn fromthe sources’ experience (among pharmaceuticaldevelopment teams learning from other teams intheir firm; Bresman, 2013), or that aspects of thecontext or environment (e.g., competition, licens-ing requirements, or technology) influence thenature of teaching-learning interactions (amongdifferent groups of engineers; Bailey & Barley,2010)—this model is the first to provide an in-tegrated account of these vicarious learning in-teractions that simultaneously attends to theircontextual antecedents, specifies their capacity-building consequences, and models their con-stituent behaviors.

In doing so themodel presented here addressesthe long-standing recognition that “organiza-tional learning research using the term vicariouslearning hasbeenagnostic about the activities bywhich it occurs” (Bresman, 2010: 95) and “a greaterunderstanding of the micro processes underlyingthe transfer of knowledge is needed” (Darr et al.,1995: 1761). The concept of CVL moves beyondsimply recognizing what kinds of relationshipsinhibit or encourage transfer and toward an ex-plicit understanding of what actually occurs inthese interactions to facilitate vicarious learning.Drawing on theories of discourse and narrativeanalysis, the model presented here suggestsseveral key components of these interactions (i.e.,experience, analysis, and support) and locates thisrelational process of learning within a broadernetwork of antecedents and consequences.

This emphasis on understanding the behaviorsenacted by individuals in their learning relation-shipswith one another brings to light another keycontribution of the CVL model—namely, its focuson vicarious learning as a capacity-buildingprocess, rather than as a more mechanical ex-change of knowledge. The CVL model goes be-yond the simple flow of knowledge from oneperson to another in the present interaction,bringing greater attention to the potential for CVLto increase future learning as well, by buildingindividual and relational capacity for learning.This attention to capacity thus provides an ex-planatory mechanism for the observation that vi-carious learning not only creates linearknowledge growth (as would be expected inapurelymechanical exchange of information) but

also can promote exponential learning, funda-mentally altering individuals’ learning curves(e.g., Quinn, Anderson, & Finkelstein, 1996).Anadditional consequenceof rejectingapurely

mechanical view of vicarious learning lies in therecognition that vicarious learning is unique toidiosyncratic dyads of individuals at work (ratherthan unfolding uniformly across individuals). Forexample, Bandura’s (1989b) discussion oftelevision-based models for behavior impliesthat the experience of the model would be equiv-alently understood and learned by all ob-servers. Likewise, perspectives on teammember/personnel rotation (e.g., Kane, 2010), as well as ongroup learning (e.g.,Wilson et al., 2007), generallyassume that when a team member shares an ex-periencewith the rest of thegroup, this experienceis internalized uniformly by all team members(i.e., yielding a shared conceptualization of theexperience among the entire team). In contrast,the model advanced here theorizes that theknowledge and capacity an individual derivesfrom a vicarious learning interaction are contin-gent on the nature of the relationship between themodel and the learner, as well as each in-dividual’s own background. This insight un-derscores the need to consider individuals’learning from others as an integral part of theirongoing relationship dynamic. The more positivea pair’s relationship, the more likely individualswill be to view the experience in a positive lightand see it as a potential learning opportunity.This should, in turn, lead them to expend greatereffort to process and incorporate the experienceinto their own repertoire of knowledge and action,relative to an experience shared by someonewithwhom they share a more negative relationship(which, according to balance theory, would beviewed negatively, to maintain the balance in therelationship; Heider, 1958).In this way, the CVL framework highlights the

importance of focusing on specific, idiosyncraticdyads when trying to understand how learningunfolds in group settings (a focus that is particu-larly important in light of empirical evidence thatworkplace learning interactions occur most oftenamong dyads, as opposed to alone or in groupsettings, at least among communities of engi-neers; Bailey & Barley, 2010), rather than treatinglearning as a group-level property. This insightsuggests that learning in groups might be betterunderstood as constituted, at least in part, by thenetwork of dyadic learning relationships within

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the group (in addition to truly collective learningefforts, such as shared reflection on a commonexperience). Such a perspective recognizes eachindividual learner in the group as engaged ina number of different learning relationships withothers in the team, and it opens avenues for ex-ploring the nature of the distribution of these re-lationships in the group. Indeed, the relationalview implied here posits not only that learningmoves from themodel to the learner, as generallyassumed in studies of vicarious learning andknowledge transfer, but also that these labels areoften only temporary starting points for the in-teraction. The CVL perspective thus providesa two-way learning mechanism, in contrast to themore limited assumptions of one-way learning inprior studies that assume the model and learnerroles as fixed (and often equivalent to “expert”and “novice” roles, or as corresponding with hi-erarchical level in the organization). In the flatter,more specialized work of modern organizations,these roles are likely less stable or associatedwith hierarchy, suggesting that a new perspec-tive, less reliant on one-way assumptions of fixedlearning roles, is needed for making sense of in-dividuals’ learning from others at work.

Directions for Future Research

Building on the perspectives advanced here,future research should continue exploring thenature of individuals’ interactive learning re-lationships at work, and—although certainly notan exhaustive list—I describe here several po-tential directions for future work in this domain.For instance, future work is needed to continueexploring the role of context for engaging in vi-carious learning. The CVL model emphasizesmany of the benefits of individuals possessingstrong, proximate relationships for effectivelylearning from one another, since they wouldpossess the necessary frameworks in which toplace the experiences shared in the interaction(given their similarity), facilitating analysis of theexperience. However, findings from some studiesof group expertise diversity (see Bunderson &Sutcliffe, 2002) have suggested that overly similarindividuals are actually at risk of reducedlearning, given the absence of divergent per-spectives and experiences. These findings sug-gest instead that learning from individuals whohave functionally dissimilar backgrounds helpsgenerate innovation and enhanced performance

(e.g., Bantel & Jackson, 1989). In their review andempirical examination, Bunderson and Sutcliffe(2002) reconciled theseseeminglydivergentviewsby distinguishing intrapersonal functional di-versity (variety of experience within an individu-al) from dominant function diversity (variety ofexperience between individuals), revealing thatthe information-sharing and performance bene-fits of experience diversity were realized onlywhen it was located within the individual.This reconciliation supports the beneficial role

of structural similarity and proximity advancedhere, but future research should further examinethe potential dark sides of these elements as theyrelate to vicarious learning and performance. Forinstance, although proximity and interdependenttask structures provide individuals with similarlanguage and perspectives that can reduce con-fusion in the discussion of an experience, this in-terdependence or similarity in positionmight alsointroduce competitive pressures (e.g., to “standout” on a project and receive a promotion) thatdiminish or alter engagement in CVL. Likewise,the CVL model focuses on the development of in-dividuals’ response repertoires via exposure toothers’ experiences and, thus, makes the as-sumption (consistent with earlier definitions;Sitkin et al., 1998) that this development is gener-ally beneficial, since it prepares individuals withmore—and more robust—responses for futuresituations. Although significant prior researchhas linked the repertoire-developing outcomesdiscussed here (i.e., individual knowledge, aswell as specific elements of individual and re-lational capacity, such as shared mental models;Lim & Klein, 2006) to improved work performance,future research exploring potential negativeconsequences of misinterpreting or misapplyingwhat is learned fromaCVL interaction is certainlywarranted.Additionally, future research is needed to fur-

ther explore the cross-level implications of thedyadicCVL framework. Thesedyadic interactionsmay serve as the mechanism for observed vicar-ious learning at higher levels of analysis (i.e.,between different units or firms); however, thepattern of these underlying dyadic interactionsdeserves greater attention in future work. For in-stance, scholars might examine how longer“chains” of vicarious learning interactions—where an individual shares an experience withmultiple people in sequence, or where individ-uals learn from one person’s experience and, in


turn, share that experiencewith a third person (asin the retelling of a story of another’s experience;Dailey & Browning, 2014)—influence the learningprocess. How might individuals’ early sharing ofanexperience (and their revisedunderstandingofits meaning through these interactions) impacttheir later sharing of that experience?

Likewise, considering collective-level vicari-ous learning as constituted by a series of dyadicvicarious learning interactions raises similarquestions about sequencing and learning chains.For instance, when unit A learns from the experi-ence of unit B, the conceptual model presentedhere suggests that this learning may occurthrough an initial dyadic interaction betweenmembers of each unit (A and B), followed by sub-sequent vicarious learning interaction(s) in-volving members within unit A (to spread theknowledgeof thenewly importedexperience fromunit B and coconstruct an understanding of how itcould be applied). CVL thus provides a means forexploring collective-level learning as a chain ofinterpersonal learning interactions, but futureresearch could no doubt explore a number ofquestions related to the efficacy of these chains(e.g., related to communication medium, physicalproximity, within-unit cohesiveness, etc.) in driv-ing more collective-level changes in learning orperformance.

Finally, future research should consider howthis process might unfold in different situationsand in concert with other learning practices. Forinstance, how might the learning generated inCVL interactions generalize to varying task do-mains? In work settings where individuals havea broader variety of experiences, might compari-sons with others’ experiences enable individualsto develop more robust abstractions that gener-alize more easily? Likewise, future research isneeded to explore the role of context in de-termining the relative importance or weighting ofthe three elements of CVL interactions. Are theresituations where certain elements (i.e., experi-ence, analysis, and support) become more or lesssalient or important to the learning process? Inmore emotionally taxing work environments, forinstance, support may be a more influential as-pect of CVL interactions than in a lower-stresscontext where direct analysis (with less support)may be sufficient. These differing work environ-ments may also yield different combinationsof CVL interactions in concert with other formsof learning. CVL interactions may be more

prevalent, and more powerful, in settings whereindividuals have less certainty about the natureof their future work tasks or where direct accu-mulation of experience ismoredifficult. Similarly,CVL interactions might be employed in concertwith more independent forms of vicarious learn-ing (to varying degrees) in different settings,suggesting a potential future research directionexploring their relative frequencyor interaction indriving learning.

Conclusion

In his foundational work on social learning,Bandura noted that “learning would be exceed-ingly laborious, not to mention hazardous, ifpeople had to rely solely on the effects of their ownactions to inform themwhat to do” (1977a: 22). Thiscaptures the importance of vicarious learningfor organizations—where making mistakes and“reinventing the wheel” can be costly (Bresman,2010). The novel model of coactive vicariouslearning advanced here posits that vicariouslearning occurs not solely through passive observa-tion and imitation but, rather, through engagementin interpersonal interactions and coconstruction ofthe meaning of experience. This relational concep-tualization offers a fundamentally different way ofthinking about how individuals learn from one an-other in organizations—and, in particular, a way ofthinking that is more consistent with the kinds oflearning observed in interdependent, knowledge-based workplaces—advancing the field’s un-derstanding of learning at work and layinga foundation for future research on vicariouslearning in organizations.

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ChristopherG.Myers ([email protected]) is an assistant professor in themanagement andorganization discipline at the Johns Hopkins University Carey Business School. He re-ceived his Ph.D. from the University of Michigan Ross School of Business. His researchexploresprocessesof individual learninganddevelopment in knowledge-intensiveworkorganizations.


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