Transactive Memory Systems, Learning, and Learning Transfer

Organization Science

informs

Vol. 16, No. 6, November–December 2005, pp. 581–598 issn 1047-7039 eissn 1526-5455 05 1606 0581

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doi 10.1287/orsc.1050.0143 © 2005 INFORMS

Transactive Memory Systems, Learning, and Learning Transfer Kyle Lewis, Donald Lange, Lynette Gillis

Department of Management, University of Texas at Austin, 1 University Station B6300, Austin, Texas 78712-0210 {[email protected], [email protected], [email protected]}

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nowledge embedded in a group’s structures and processes can be leveraged to create sustainable advantage for organizations. We propose that knowledge embedded with a transactive memory system (TMS) helps groups apply prior learning to new tasks and develop an abstract understanding of a problem domain, leading to sustained performance. We present a framework for understanding TMSs as learning systems that affect group learning and learning transfer, and we test the major outcomes of the framework in an empirical study. We found that groups with a prior TMS and experience with two tasks in the same domain were more likely to develop an abstract understanding of the principles relevant to the task domain—a critical factor for learning transfer in general. We did not, however, find strong support for our contention that a TMS facilitates learning transfer after experience with only a single task. Further examinations of our findings showed that the extent to which members maintained expertise across tasks influenced the degree of learning transfer, especially for groups whose members had previously developed a TMS with another group. Our findings show that a TMS has broader benefits beyond the task for which it first developed because a TMS affects members’ ability to apply prior learning and develop a collective, abstract understanding of the task domain. More generally, our study demonstrates that TMSs influence group learning and learning transfer. We discuss our study’s implications for practice and for TMS and group learning theories. Key words: learning; transactive memory

1.

Introduction

knowledge, coordinate members’ interactions more effectively, and perform at higher levels than do groups without a TMS (Liang et al. 1995; Moreland 1999; Moreland et al. 1996, 1998; Moreland and Myaskovsky 2000). These laboratory studies were instrumental in bringing the TMS concept and its effects to the attention of researchers and practitioners. An objective of these studies was to show how a TMS enhances task performance on the same task for which the TMS first developed. However, that focus does not capture the fact that most organizational workgroups perform a variety of tasks, either in the context of a single project, or across sequential streams of projects over time (Waller et al. 2002). Knowing whether the effects of a TMS persist in dynamic task environments is critical to understanding the real impact of TMSs in organizations. Several recent field studies (Austin 2003, Faraj and Sproull 2000, Lewis 2003) provide early evidence that TMSs may have longterm value in ongoing groups, but none of these studies specifically examines whether the effects of a TMS extend beyond the task for which it first developed. We offer an explanation for the positive effects of a TMS on group performance that generally has been overlooked by group TMS research: TMSs help members learn, both individually and collectively. We conceptualize TMSs as learning systems that affect group

Group performance in contexts as varied as product development, consulting, research and development, and top management depends on the collaborative processes members use to combine and integrate their unique knowledge. As members collaborate, they encode, interpret, and recall information together, and in so doing they create knowledge that becomes embedded in a group’s structures and processes (Moreland 1999). Embedded knowledge is difficult to recognize and measure, but it is also difficult to imitate, making it a key point of leverage for organizations (Argote and Ingram 2000). The goal of this study is to explain how embedded knowledge can be leveraged to create sustained group performance. We examine knowledge embedded with a group’s transactive memory system (TMS), which we argue influences group learning and performance across several tasks. A TMS is a collective memory system for encoding, storing, retrieving, and communicating group knowledge (Hollingshead 2001, Wegner 1986). TMSs develop over time as group members communicate, observe each other’s actions, and come to rely on one another to be responsible for different but complementary areas of expertise. Laboratory studies of TMSs demonstrate that in groups that develop a TMS, members collectively remember and apply a greater amount of task-critical 581

Lewis et al.: Transactive Memory Systems, Learning, and Learning Transfer

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Organization Science 16(6), pp. 581–598, © 2005 INFORMS

learning and learning transfer to produce sustained group performance. Drawing on TMS theory (Wegner 1986, Wegner et al. 1985), and on learning and learning transfer theories (e.g., Reeves and Weisberg 1994, Singley and Anderson 1989), we develop a framework to explain: (1) how a TMS promotes cycles of learning that produce not only knowledge that is relevant for the current task, but also transferable knowledge that can be applied to other tasks in the same domain, and (2) how a TMS helps members collectively apply prior knowledge to benefit performance in new task contexts. This learning perspective is useful for understanding the value of TMSs in organizations, especially those organizations in which leveraging prior knowledge by transferring learning across contexts or to different customers is critical to firm performance (Argote 1999). For such firms, leveraging experience gained on one task to produce efficiencies and higher-quality products and services is critical to both winning new business and increasing the likelihood that future activities are profitable. In sections that follow, we present a framework for understanding TMSs as learning systems that includes predictions about the effects of TMSs on group learning, learning transfer, and performance, and we present the results of an empirical study designed to test our Figure 1

predictions. We conclude by discussing the implications of these results and by offering recommendations for capitalizing on the value of TMSs in organizations.

2.

TMS-Learning Framework

Our TMS-learning framework shows that a TMS produces cycles of learning with effects that extend beyond the task for which the TMS first developed, to other tasks that a group performs. We adopt Argote’s (1999, p. 131) definition of group learning as a process wherein members share their own knowledge, generate new knowledge, and evaluate and combine this knowledge. Our use of the term learning transfer is consistent with Singley and Anderson (1989) and Cormier and Hagman (1987); learning transfer is defined as occurring when knowledge acquired in one situation affects learning or performance in other situations. We refer to learning that occurs as a consequence of having developed a TMS as TMS learning. We refer to the learning transfer facilitated by a TMS as TMS-learning transfer. The TMS-learning framework is depicted in Figure 1. The framework describes the learning processes, knowledge outcomes, and transfer mechanisms for a group whose members have no prior history together, as the group performs several tasks.

TMS-Learning Framework

Activities: Develop TMS Learning Cycle 1 (Section 2.1)

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Perform Task 1 Learning Cycle 2 (Section 2.2)

TMS

Perform Task 2 Learning Cycle 3 (Section 2.3)

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3

TMS Learning

TMS Learning

Shared location information More individual lower-order information (expertise) TMS processes for encoding, storing, retrieving Shared higher-order information

Refined location information Additional shared higher-order information Elaborated, contextualized knowledge Patterns for communicating, retrieving information

Performance Mechanisms

Transfer Mechanisms

Availability of task-relevant expertise Retrieval, coordination, utilization of expertise

Transferable knowledge structures Recognition, retrieval, and mapping of transactive knowledge

Evidence of TMS learning: Task 1 performance (Tested by H1)

Perform subsequent tasks

Evidence of TMS learning transfer: Task 2 performance (Tested by H2 and H4)

TMS Learning Increasingly abstract higherorder knowledge Understanding of underlying principles of task domain

Learning/Transfer Mechanisms Interactive cueing that facilitates analogical encoding Shared higher-order knowledge that facilitates collective induction

Evidence of abstract learning/ transfer: Strategic knowledge of task domain (Tested by H3 and H5)

Lewis et al.: Transactive Memory Systems, Learning, and Learning Transfer Organization Science 16(6), pp. 581–598, © 2005 INFORMS

Our TMS-learning framework applies to those groups for which TMSs are especially helpful—groups that perform complex, divisible tasks that require considerable knowledge (Moreland et al. 1996). Divisible tasks (Steiner 1972) allow members to divide the cognitive labor for the task and integrate knowledge possessed by different members. More generally, our framework applies to task-oriented workgroups whose members share responsibility for producing group outcomes, and whose performance depends on coordinating and integrating the various skills, knowledge, and activities of group members. Some examples of groups where our framework applies include crews, product development teams, consulting and other project teams, research and development teams, self-managing teams, and top management teams. Our framework does not apply to workgroups that have loosely defined membership, no definable collaborative task, or low coordination and specialization needs. Groups with one or more of these attributes include informal groups, interest groups, advice groups, ad hoc committees, and communities of practice. The TMS-learning transfer effects described by our framework are bounded by the limits on learning transfer in general. Learning transfer is limited to settings in which the tasks are functionally similar (Singley and Anderson 1989)—that is, when the tasks share similar task elements and when the strategies used to perform one task are applicable to the other. Furthermore, learning transfer is possible only when an individual’s prior knowledge is in some way relevant to the transfer task. Thus, our framework applies to tasks that are functionally similar, and to tasks for which members’ learning can be relevant. Finally, because our specific interest is in the effects of TMSs on learning and performance, other social or attitudinal factors that may influence group processes and performance are not explicitly integrated into the framework. The TMS-learning framework is represented in terms of three TMS-learning cycles, each of which is described below. 2.1. Learning Cycle 1: Initial TMS Learning The first learning cycle produces a TMS, consisting of both an initial TMS structure—an organized store of knowledge that is contained within members’ memories—and a set of transactive processes that members use to encode, store, and retrieve that knowledge (Wegner et al. 1985). A TMS begins to develop when group members start to associate individual members with specific areas of knowledge. Information about members’ expertise is stored in the TMS structure as location information (Wegner et al. 1985).1 Take, for example, a group tasked with managing a software product, composed of members Joanne, Tim, and Mina. Members might come to associate Joanne with information about software and design—Joanne would then be

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known as the location for software engineering information. Similarly, Tim might come to be known as the location for information about product sales and marketing, and Mina might be known as the location for customer support information. We return to this example throughout this section. Also stored in the TMS structure are the specific facts and details, or lower-order information, that each member possesses about a particular topic (Wegner et al. 1985). In the product management group example above, lower-order information in the TMS structure might include particulars about recently implemented functionality and bug fixes (lower-order information possessed by Joanne), data about product sales performance for the last two quarters in each customer market (possessed by Tim), and information relevant to complaints received from customers (possessed by Mina). The location information and lower-order information that make up the initial TMS structure affect what and how much each member decides to learn. In particular, an understanding of others’ expertise affects a member’s choice to learn in an area other than those already associated with another member (Hollingshead 2001, Wittenbaum et al. 1998). As a result, individual members come to specialize in different areas, and the group’s knowledge becomes more differentiated. Furthermore, when members rely on other members to be responsible for information in their respective specialty areas, each member is free to develop more knowledge in his or her own specialty area (Hollingshead 1998, Wegner et al. 1991). In these ways, the initial TMS structure affects the content and extent of each member’s learning. Transactive processes (Wegner et al. 1985), the second component of a TMS, are established during Learning Cycle 1 through a group’s early interactions. Transactive processes function through the interaction and communication among members to encode, store, and retrieve knowledge relevant to the group’s task. When members first communicate, they rely on the initial TMS structure to establish transactive processes. For example, members query others about information they presume to be associated with each member and allocate new information encountered by the group to the appropriate member. Using location information, a member can retrieve information quickly and efficiently when needed for the task, without having to possess that information him or herself. Transactive processes, in turn, affect the TMS structure. First, communicating helps members gain a more accurate understanding of what members know (or do not know), and over the course of repeated interactions, makes members’ location information more similar and more accurate. Second, interacting can lead to what Wegner refers to as integrations of members’ knowledge (Wegner et al. 1985). Integrations result when members discover links between members’ knowledge and

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create new knowledge that no member had previously possessed. For example, suppose Joanne, Tim, and Mina are discussing lagging sales of their software product. During their discussion, members explore their respective sets of lower-order information (i.e., details about software engineering, sales, and customers) that are relevant to information about sales performance. Integrating their views may lead the members to a group-generated solution—for example, the recognition that customers from a particular market segment have been complaining about product functionality alterations in the current product release. As a consequence of their collective discovery, members integrate relevant details about software engineering, sales, and customers, and encode that information into their TMS. Integrated information is encoded as shared higher-order information, defined as the “topic, theme, or gist” of some set of lower-order information (Wegner et al. 1985, p. 264). In the product management group example, a shared higher-order topic, “determinants of declining sales,” might represent members’ newly discovered knowledge about the causes of sales problems in a particular market segment. The shared higher-order topic points to specific lowerorder information about product functionality, sales figures, and complaining customers that all members can retrieve. Thus, relying on an initial TMS structure and set of transactive processes can produce new collective knowledge that is stored as higher-order information in the TMS. Articulating the processes involved in developing a TMS reveals the links between TMSs and individual and collective learning. By the end of Learning Cycle 1, members will have learned who possesses what expertise, developed new member-level knowledge in the form of specialized expertise, and developed new collective knowledge in the form of shared higher-order information. Ultimately, the effects of Learning Cycle 1 are evident in the group’s performance on the task for which the TMS developed. Past TMS research demonstrates that groups perform better when they develop and rely on established TMS structures and processes (e.g., Liang et al. 1995). Learning Cycle 1 produces those structures and processes, as well as TMS learning, to make a greater amount of relevant knowledge available for task processing. To replicate past research findings and lay the foundation for predictions about subsequent learning cycles, we hypothesize that: Hypothesis 1. Groups with a TMS (groups that have completed Learning Cycle 1) will demonstrate higher task performance than will groups with no TMS. 2.2. Learning Cycle 2: Learning by Doing Our learning systems perspective suggests that TMS learning continues after a TMS has developed, and that this learning has effects that extend beyond initial task


performance. TMS learning occurs, for example, while the group performs its task. When a group performs its task, members encode and store new information about the task, other members, and other members’ knowledge (Brandon and Hollingshead 2004). Learning during task performance comprises Learning Cycle 2 of our TMS-learning framework. Learning by doing is especially important when the context is integral to performance (Argote 1999), for example, when learning occurs in the presence of other group members. Learning by doing affects both parts of a TMS—the TMS structure and the set of transactive processes that operate on that structure. Performing a group task affects the information encoded and stored in the TMS structure in at least three ways. First, seeing what works and does not work and observing how individuals perform individually and collectively may cause revisions or refinements to members’ understanding of who knows what (i.e., location information). Second, as members share and discuss information, they may discover new ways that individuals’ lower-order information can be integrated as shared higher-order information (Wegner et al. 1985). Third, observing interactions and taking part in discussions during which knowledge is exchanged helps members develop a more elaborated, contextualized understanding of their own knowledge. A semantically elaborated memory (cf. Anderson and Reder 1979, Wegner et al. 1985) results when a member draws inferences about an item of information and considers its meaning in relation to other information. Observing other members and taking part in group discussions help a member build elaborated knowledge structures that represent how a member’s own knowledge fits with and builds on other members’ task-related knowledge. Marks et al. (2002, p. 4) refer to this type of knowledge as “interrole knowledge,” an understanding of the content of and interrelationships among members’ knowledge. Their research found that when members were aware of one another’s jobs, roles, and expertise, they developed shared conceptualizations of interrole knowledge, which in turn positively influenced group coordination and performance. We expect that by simultaneously refining location information and facilitating a contextualized understanding of members’ knowledge, learning by doing will produce shared conceptualizations of interrole knowledge. In addition to affecting the TMS structure, learning by doing affects transactive processes. Performing the task provides feedback about the efficacy of interactions for retrieving and sharing information and helps set patterns for future interaction. Research on habitual routines in groups suggests that patterned interactions do develop in groups, and that they develop very quickly (cf. Gersick and Hackman 1990, Hackman and Morris 1975). Even while a group performs a single task, there are likely


to be many opportunities to execute patterns of communication and elicitation (Rulke and Rau 2000). For example, a member who repeatedly queries others during task performance might initiate a pattern of interaction characterized by members volunteering information only after being asked. A different pattern might emerge in response to a member who withholds information from his/her expertise area—others might become more aggressive in asking for information from that member, or they might forego interacting with that member altogether. In sum, learning by doing during task performance helps members refine location information and develop an elaborated, contextualized understanding of how their own task-relevant knowledge relates to others’ task-relevant knowledge. Learning Cycle 2 also helps establish patterns for communicating and retrieving information, which reduce uncertainty about how group interactions ought to proceed (Gersick and Hackman 1990). Learning Cycle 2 has the effect of making groups more effective and efficient at performing the same task for which the TMS initially developed. We argue that Learning Cycle 2 also affects tasks other than the task for which a TMS initially developed, by facilitating transfer of learning across tasks. 2.2.1. TMSs and Learning Transfer. Individuals are often unable to transfer learning from one situation to another because they fail to notice the functional similarities and underlying principles common across tasks (Singley and Anderson 1989). Research on individuallevel learning transfer shows that whether a person who has acquired knowledge in one situation applies it to other situations depends largely upon that person’s mental representation of the knowledge (Reeves and Weisberg 1994). Individuals who have developed an abstract mental representation of the problem domain are more likely to recognize when and how prior learning will apply to a novel task. Without an abstract understanding of the underlying principles relevant to a domain, however, individuals are more likely to focus on the superficial features of a task and fail to recognize that previously learned procedures and strategies could be used to solve the problem. Given a group bias for discussing shared information (Stasser and Titus 1985), the more members that fail to notice functional similarities across problems, the more likely it is that the group will favor discussion of the superficial features over the underlying principles relevant to both problems. In sum, the way that prior knowledge is organized affects two preconditions of learning transfer: (1) recognition of functional similarities across problems, and (2) mapping of prior knowledge and learned problem-solving strategies to the new problem (Bassok 1990). We argue that having developed and utilized a TMS on a task helps groups overcome the impediments

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to learning transfer described above. We contend that Learning Cycle 2 influences the degree to which members transfer their prior learning because: (1) learning by doing helps create abstract knowledge structures stored within the TMS structure, and (2) utilizing an established TMS both helps members collectively recognize functional similarities and underlying principles common to tasks, and helps members retrieve and map prior knowledge and problem-solving strategies to the new task. 2.2.2. Transferable Knowledge Structures. Learning Cycle 2 produces three types of transferable knowledge that can be relevant in new task contexts. First, individual knowledge produced from learning by doing is elaborated and contextualized as a result of members learning more about their own specializations and more about how their knowledge relates to others’ knowledge and to the task. Individuals with more elaborate, abstract representations of task-relevant knowledge are more likely to identify when tasks are indeed functionally similar and recognize how their prior knowledge applies in a novel context. Second, shared location information refined during Learning Cycle 2 will remain useful in a new task context as long as membership and expertise specializations remain somewhat stable. Finally, shared higher-order information is likely to remain relevant when members recognize that tasks have underlying principles and elements in common. In our product management group example, market sales trends, software functionality, and customer feedback are key task elements relevant to the group’s initial task. The group members integrated these task elements under the higher-order concept “determinants of declining sales.” If the group recognizes that these same task elements also apply to a different product context, they will be able to draw on the same shared higher-order concept to retrieve specific sales, software engineering, and customer support information to diagnose sales problems with the new product. Thus, knowledge produced by TMS-learning cycles is likely to be useful across tasks that share similar elements and underlying principles. 2.2.3. Recognition, Retrieval, and Mapping of Transactive Knowledge. Utilizing an established TMS affects the degree to which members actually transfer prior knowledge by helping members recognize task similarities and by facilitating retrieval and mapping of prior learning to a new task. Transactive retrieval processes refined in Learning Cycle 2 increase the likelihood that members recognize how prior lower-order and higher-order knowledge apply to the task. Transactive retrieval processes are characterized by interactive cueing of members’ recall, a sequential, iterative process in which partners cue information from the other’s memory (Hollingshead 1998, Wegner et al. 1985). In a group context, interactive cueing involves one member

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cueing recall of another member’s knowledge, which in turn helps members recall different information relevant to the group’s task. Interactive cueing that occurs in a novel task context can help members retrieve knowledge that they would not otherwise recognize as relevant to the new task. For example, suppose one member recognizes that the initial and new task share a common element. When that member uses a commonly understood label for previously encoded information related to the task element, it triggers associations in other members’ minds. Given this cue, other members can locate and retrieve detailed lower-order information relevant to the new task. By relating the new task context to the context in which information was first encoded, interactive cueing processes improve the chances that what members learned on the initial task will transfer (Singley and Anderson 1989). Learning Cycle 2 also establishes patterns of interaction that are likely to persist in a new task context, especially when members perceive that the initial and new tasks are functionally similar. Members are likely to execute the same patterns established on an initial task, even without explicitly discussing their applicability or efficacy (Feldman and Rafaeli 2002, Gersick and Hackman 1990, Louis and Sutton 1991). Interaction patterns thought to have been successful in the past should remain useful for guiding efficient transactive processes and helping members retrieve and share task-relevant knowledge. Having developed a shared conceptualization of interrole knowledge is likely to further reinforce these interaction patterns, because the shared conceptualizations contain knowledge about the sequence of interdependent activities needed to accomplish a task (Marks et al. 2002). Routinized interactions can be problematic if groups execute the same patterns in inappropriate situations and without full deliberation about their probable effects (Gersick and Hackman 1990, Louis and Sutton 1991). When different tasks are functionally similar, however, the transfer of established interaction patterns is not likely to be detrimental. On the contrary, we expect that when members recognize that tasks share common elements and underlying principles, and when they draw on established interaction patterns, they will be more likely to retrieve knowledge critical to the new task. The above arguments suggest that learning by doing in Learning Cycle 2 helps members leverage their TMS learning and transfer what they have learned to a new task context. The effects of prior learning and learning transfer are properly measured by performance on a transfer task (cf. Singley and Anderson 1989). Thus, we hypothesize that: Hypothesis 2. Groups that have previously developed and utilized a TMS on one task will perform better on a subsequent, similar task than will groups with no prior TMS.


2.3.

Learning Cycle 3 and Beyond: Generalizing to the Task Domain TMS learning occurs not only as members perform their initial task, but also as they perform a subsequent (transfer) task. In particular, performing a second task in the same domain creates increasingly abstract knowledge about the principles underlying both tasks. Research on analogical encoding (e.g., Gentner et al. 2003, Loewenstein et al. 1999) shows that comparing two different but analogous problems helps individuals understand the underlying structure common to both. In a recent study, Gentner et al. (2003) found that individuals prompted by researchers to compare two different negotiation problems not only recognized common task features, but also developed an abstract understanding of the underlying principles of the problem domain. By promoting the abstraction of concepts related to the domain, analogical encoding helps individuals recall and transfer prior knowledge across tasks (Gentner et al. 2003, p. 394). Related conclusions can be drawn from the research on collective induction (e.g., Laughlin 1999, Laughlin and Bonner 1999, Laughlin and Hollingshead 1995). Collective induction refers to the processes by which a group infers some general principle or rule from concrete examples of that principle. The process of collective induction involves members observing patterns, regularities, and relationships across tasks in a domain, proposing and evaluating hypotheses to account for those patterns, and eventually converging on the correct principle or rule that underlies the domain tasks (Laughlin 1999). Collective induction research shows that groups tend to be good at inferring general principles from several examples, in part because groups share a conceptual system of ideas that helps members realize when a proposed solution is correct (Laughlin 1999). We propose two reasons that having developed a TMS in the past will facilitate both analogical encoding and collective induction. First, a prior TMS is likely to facilitate analogical encoding because the interactive cueing typical of established TMS processes helps members recognize functional similarities across tasks and, in so doing, prompts members to make comparisons across tasks. Prompting individuals to compare across problems is known to improve analogical encoding (Gentner et al. 2003). Second, prior TMS-learning cycles produce shared higher-order information, an abstract form of knowledge that links each member’s knowledge to specific knowledge about the task. When the same members experience a second task together, their higherorder knowledge becomes further elaborated, as members form associations between what they and others know and between the initial task and the subsequent task. These higher-order knowledge structures are the very types of shared conceptualizations that help groups


collectively induce general principles underlying tasks in the domain (Laughlin 1999). In sum, a prior TMS leverages prior learning, making it more likely that members encode more abstract knowledge relevant to the domain and more likely that members recognize when the same general principles apply across different tasks. Therefore, we hypothesize that, given experience on tasks in the same domain: Hypothesis 3. Groups with a prior TMS will be more likely to demonstrate abstract, generalized knowledge about the underlying principles relevant to the domain than will groups that have never developed a TMS.

3.

Interference in TMS Learning and Transfer

Thus far, our arguments have assumed that group members will have full access to the two components of a TMS—a TMS structure and a set of transactive processes. We argued that once a group has developed an efficient TMS structure and effective TMS processes, members are better able to learn and transfer taskrelevant information. Suppose instead that a group’s TMS structure is relatively inefficient, as might be the case when group members do not possess common location information. In that case, TMS processes would also be relatively inefficient. Without a shared understanding of who is responsible for what, new information encountered by the group might be encoded by more members than necessary, or might never be encoded (Wegner 1986). Discovering which members possess what expertise and deciding on the appropriate allocation of new information takes time, reducing the efficiency of TMS encoding processes. Furthermore, until members understand which members possess what expertise, they will be less efficient at retrieving information and communicating about task elements that had previously been organized as shared higher-order information. Members must again develop shared higher-order concepts before they can efficiently retrieve and coordinate what members know. Changes to the TMS structure, if severe enough, could also cause groups to abandon their habitual routines and force members to learn new patterns of interaction (Gersick and Hackman 1990). Disruptions to the TMS structure, then, should interfere with members’ learning and learning transfer. Indeed, TMS research examining TMS encoding and retrieval processes demonstrates that, when an existing TMS structure is changed, a prior TMS does interfere with learning and reduces group performance. In a study comparing the encoding and retrieval processes of intimate couples with those of stranger couples, Wegner et al. (1991) found that intimate couples (who had presumably already developed an implicit structure for learning and recalling information) performed worse than stranger couples on a knowledge recall test when

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the experimenters explicitly assigned responsibility for learning in specific areas of expertise. Imposing a new division of cognitive labor on couples that had already developed an implicit TMS structure seemed to impede how much information they were able to learn and later recall. An interesting laboratory study by Baumann (2001) suggests that the impact of a disruption to the TMS structure may depend on the extent of that disruption. Baumann found that when groups were constructed to preserve expertise categories and the distribution of expertise across task trials, groups whose membership had changed after developing a TMS were able to quickly learn which members possessed what expertise and construct a new TMS. Because the task as well as the division of cognitive labor remained constant across trials, members may have been able to apply some prior TMS learning even though group membership had changed. Together, the above studies suggest that significant disruptions to the TMS structure, defined as changes that affect location information and redefine the division of cognitive labor, are likely to affect members’ learning and, consequently, learning transfer across tasks. We note that when the task has also changed, it is likely that even minor disruptions to the TMS structure will affect members’ ability to map prior knowledge. Therefore, we hypothesize that: Hypothesis 4. Groups that experience a disruption to an established TMS structure will perform worse on a transfer task than will groups that have never developed a TMS. Disruptions to an existing TMS structure are also likely to interfere with members’ higher-order learning about the task and task domain. Members are less apt to develop contextualized knowledge about how their own knowledge fits with other members’ task-relevant knowledge, because members will no longer be certain what knowledge other members actually possess. Furthermore, because disruptions prevent or delay access to lower-order information, integrations that produce shared higher-order information will occur slowly, or not at all. Without contextualized knowledge at the individual level, and without shared conceptual knowledge at the group level, members are unlikely to identify and recognize when tasks are functionally similar, and are unlikely to be able to abstract common principles underlying the tasks. Therefore, we predict that: Hypothesis 5. Groups that experience a disruption to an established TMS structure will be less likely to demonstrate abstract, generalized knowledge about the underlying principles relevant to the task domain than will groups that have never developed a TMS. We have argued that TMSs are learning systems that produce transferable knowledge, help members recognize the functional similarities and common principles


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across tasks, and facilitate retrieval and mapping of prior knowledge across tasks. We further argued that, given additional task experience, groups with a TMS are more likely to develop an abstract understanding of the underlying principles of the task domain. If a TMS structure is significantly disrupted, however, having developed a TMS in the past is expected to reduce members’ learning transfer and hamper the development of abstract knowledge about the task domain. We test our hypotheses in an empirical study that is described next. Our study examines the key outcomes of our TMS-learning framework: (1) whether a TMS developed on one task facilitates learning transfer across tasks; (2) whether a prior TMS, combined with multiple task experiences, helps groups develop abstract, generalized knowledge about the underlying principles relevant to the task domain; and (3) the conditions under which having developed a TMS in the past hampers, rather than facilitates, learning and transfer.

4.

Methods

We tested our hypotheses by conducting a longitudinal experiment in which three-person groups performed electronic assembly tasks. In a series of three sessions, each separated by one week, groups were trained on an assembly task (Week 1), performed that task (Week 2), and then a week later performed a different assembly task and a knowledge task (Week 3). The participants, tasks and procedures, design, manipulations, and measures are described next. 4.1. Participants Participants were undergraduate students from a large southwestern U.S. university who earned extra credit toward their course grades by taking part in the study. We began with 434 students randomly assigned to conditions in groups of three. Over the course of the threeweek study, 47 participants were lost to attrition and 87 were excused because a member of their group did not show up to one of the three sessions (if even a single member was absent, the group to which that member was assigned became unusable). Excused students received full credit for participating. Three hundred participants in 100 groups completed the entire study. Attrition did not differ across conditions, nor were there any demographic differences among students who were excused, dropped out, or finished the experiment. The final sample averaged 21 years of age and was approximately 50% males and 50% females. Of the participants, 59% were Caucasian, 20% were Asian, 8% were Hispanic, 5% were African-American, and 8% did not report their ethnicity. 4.2. Tasks and Materials We selected tasks for this study with two criteria in mind. First, the initial (learning) task had to be one on

which groups could develop a TMS. Second, the initial and subsequent tasks had to be different from each other in terms of superficial features, and yet be functionally similar, such that they had task elements in common and such that the strategies used to complete the learning task were appropriate to the other tasks. For prior learning to transfer, members would have to recognize the common features of the tasks and ignore the superficial differences (Singley and Anderson 1989). For the learning task we chose an off-the-shelf electronics assembly kit—a telephone kit—that is comparable in complexity to electronics-oriented kits used in past TMS research (e.g., Liang et al. 1995, Moreland and Myaskovsky 2000), so we were confident that participants would be able to develop TMSs on the task. The telephone kit was composed of 47 parts, including circuit boards, wires, screws, and buttons. We chose another off-the-shelf electronics assembly kit—a personal stereo tape player—for our transfer task. The personal stereo kit was composed of 31 parts, including earphones, play/stop buttons, tape guides, circuit board, screws, wires, and battery connections. We used a third kit— an electronic stapler—as the basis for testing whether groups with experience with the learning and transfer tasks developed abstract, generalized knowledge about the underlying principles relevant to the task domain. The electronic stapler kit was composed of 45 parts, including a circuit board, wires, buttons, a small motor, and screws. To confirm that these three tasks differed superficially but were indeed functionally similar, we analyzed the tasks using frameworks proposed in the literature (e.g., McGrath 1984, Steiner 1972, Wageman 1995). The tasks differ in terms of superficial features in two ways. First, while the kits have many common parts (e.g., screws, brackets, circuit boards, and wires), some parts differ across kits. For instance, the telephone assembly has no motorized parts, while the personal stereo and stapler have gears and belts that regulate operation. Second, the tasks differ in terms of what Miller (1973, 1974) called goal image, or a mental picture of the task’s end state. Participants likely have a different goal image for each of the three products (telephone, personal stereo, and stapler) and are consequently likely to have preconceptualizations of the tasks that make them seem somewhat dissimilar (Fleishman and Quaintance 1984). In spite of their surface differences, the three tasks are functionally similar in several ways. First, all three tasks can be categorized as divisible rather than unitary (Steiner 1966, 1972), meaning that each task has the potential to be accomplished through a genuine division of labor. Second, all three tasks lend themselves to interdependent rather than independent work by group members (Wageman 1995). Indeed, when we reviewed pretest videotapes of groups completing the tasks, we observed members working interdependently, rather than working


in isolation or relying on a single member to apply his or her expertise to accomplish the entire assembly. The degree of interdependence needed to successfully complete the tasks requires the type of interaction that typifies a TMS (Hollingshead 2001). Other functional similarities among the tasks can be assessed using McGrath’s (1984) task classification scheme. McGrath organized tasks into six types along two dimensions, cooperative versus conflictual, and conceptual versus behavioral. All three of our tasks correspond to the intersection of the cooperative and behavioral categorizations, and map well onto the two tasks that McGrath associated with that intersection— planning and performance/psychomotor activities. Planning. Groups performing each of the three tasks might benefit from devising an action-oriented plan for completing the assembly of the electronics kit. Assembly planning that applies to all three kits might include formulating a rough theory of operation, defining what suboperations comprise the whole theory of operation, identifying the assembly actions required to achieve suboperations, outlining the necessary sequence of actions, considering how to organize parts, and planning the coordination of member actions and interactions. Performance/Psychomotor Activities. All three kits have small parts that can be described as fasteners, buttons, circuit boards and wiring, moving parts, or stationary parts. The assemblies require similar ordered actions, such as placing and fastening an array of smaller stationary parts onto larger parts, placing small moving parts onto larger parts so that they move and interact to perform their designed functions, placing and fastening parts that secure the moving-parts assemblies, fastening subassemblies to bases, and snapping and fastening casings. All the kits involve assembly with the use of small screwdrivers and screws. All told, the three tasks have functional similarities that permit us to expect that expertise gained on one task will be applicable to the other tasks. At the same time, the task content of the kits differs substantially enough to allow us to infer whether learning transferred across tasks. 4.3. Design and Manipulations To determine whether a TMS that developed on the learning task influenced learning transfer and the subsequent development of domain knowledge, we designed an experiment with three tasks (learning task, transfer task, knowledge task) performed in sequence. Each task was performed by two types of groups, created by either reassigning members to new groups before they performed a task, or by keeping members in the same groups in which they completed the previous task, resulting in a 2 × 2 × 2 factorial design. We intended to do a series of planned comparisons, which required that the

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full factorial be carried out. We chose this design for three reasons, described next. First, to test the effects of a TMS on transfer and learning, we had to create a TMS in some groups and not in others. Comparing these groups on the learning task would allow us to confirm the effects of Learning Cycle 1 (Hypothesis 1), and comparing these groups on later tasks would allow us to test our predictions about the extent to which a TMS does or does not facilitate learning transfer (Hypotheses 2 and 4) and contribute to the development of abstract domain knowledge (Hypotheses 3 and 5). We created a TMS with a training manipulation described in Moreland et al. (1996, 1998). We trained all members on the learning task in groups of three, expecting that group training would help TMSs to develop (Moreland 1999), and then we “disabled” the TMSs of half of the trained groups by reassigning members to new groups before they performed the learning task. Groups remaining intact after training would have full access to their training TMS, while groups whose members were reassigned after training would no longer have access to elements of the TMS structure and processes that were previously associated with other members. This manipulation created two comparison groups—those with a training TMS and those without access to the TMS developed during training (for simplicity, we refer to this comparison group as having “no prior TMS”). A second reason for our design choice is that we had to isolate the effects of the training TMS on learning and learning transfer across two subsequent tasks. This meant that we had to control for the possibility that groups might develop a useful TMS while performing one of the tasks, even if they had not developed a TMS during training (Baumann 2001, Hollingshead 1998). To demonstrate that transfer and subsequent learning effects were caused by the training TMS, and not by task learning or a newly developed TMS, we would have to control for the confounding effects of task and group experience. Task experience was controlled for by having all groups perform the same tasks, in the same sequence. Group experience was controlled for by reassigning members to new groups before they performed a subsequent task. Reassignment should render any TMS that developed in a prior group less relevant to subsequent task performance (Moreland et al. 1996, 1998), and keeping membership intact should provide a group with the full advantages of its training TMS. Thus, we controlled for group experience on the telephone task by reassigning half of the members to new groups before they performed the stereo (transfer) task, and we controlled for group experience on the stereo task by reassigning half of the members to new groups prior to performing the stapler (knowledge) task. Reassignment enabled us to isolate the effects of having developed a prior TMS on learning transfer (Hypothesis 2) and

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on the subsequent development of abstract knowledge (Hypothesis 3). Finally, our design had to allow us to test whether disruptions to an established TMS structure impeded learning transfer (Hypothesis 4) and the development of abstract knowledge (Hypothesis 5). This requirement is satisfied by our 2 × 2 × 2 design because the reassignment manipulation is itself a significant disruption to the established TMS structure. Reassigning members to new groups effectively disrupts any preexisting cognitive division of labor and makes members’ prior location information less relevant for task processing. Reassignment allows us to compare groups that have experienced a disruption to a prior TMS structure with those groups whose full TMS structure remained intact. Thus, our longitudinal 2 × 2 × 2 design: (1) produces a TMS in some groups but not others, (2) controls for alternative influences on transfer and learning (i.e., by controlling for group and task experience) while isolating the effects of TMS learning, and (3) maintains or disrupts a group’s established TMS structure—all of which are necessary for testing our hypotheses about TMS learning and transfer. 4.4. Procedures We conducted each of the three experimental sessions in a large classroom. The room was equipped with tables spaced far enough apart that groups could not see other groups’ materials or hear their conversations. At the start of the experiment, participants were told that the study was being done to investigate how groups work together. They were told that there would be three sessions run in three consecutive weeks. Participants were not told that some of them would be reassigned to new groups in subsequent sessions because group members who expect turnover may decide not to rely on transactive memory. Groups were randomly assigned to an experimental condition, and participants were randomly assigned to their initial groups by blindly drawing a group number from a hat. In subsequent sessions, the member composition of groups in the reassignment conditions was also determined by random assignment, constrained such that no members were regrouped with people they had worked with before. During the first session, groups received training on the learning task (telephone assembly). First, participants completed a short survey asking for demographic information and previous experience with electronics kit assembly. A graduate assistant helping us with the experiment then performed a 15–20 minute task demonstration in full view of all of the groups. The trainer used a script and a demonstration model to describe the step-by-step procedures for constructing the telephone assembly kit. The script was used to ensure that all participants received identical training instructions. Talking among participants and note taking was prohibited;


participants were only allowed to watch and listen to the demonstration. The experimenters then directed groups to their separate work areas and issued each group a telephone assembly kit on which to practice for approximately 30 minutes. Members were free to discuss the task within their own groups and to call the trainer over to their work area for private questions. At the end of the practice time the experimenters dismissed the participants and reminded them to return one week later. The same participants returned one week later to perform the learning task (telephone assembly) under timed conditions. At the start of this second experimental session, participants either remained in their training groups or were randomly reassigned to new groups in the manner described earlier. The experimenters issued each group an unassembled telephone kit identical to the one they had completed the week before and directed groups to complete the assembly to the best of their ability in a maximum of 30 minutes. Upon finishing the group task, each participant completed a questionnaire with items about group cohesiveness and motivation (Liang et al. 1995), items measuring the extent to which a training TMS had indeed developed (Lewis 2003), and a fillin-the-blank question asking members to describe each member’s expertise. Participants were reminded to return one week later and were dismissed. In the first part of the third and final session, groups performed the transfer task (stereo assembly). Participants either remained in their learning-task group or were randomly assigned to new groups before starting the transfer task. The experimenters gave each group a preassembled personal stereo kit and allowed them one minute to examine the assembly and components. Participants were not allowed to disassemble the stereo kit or alter the preassembled kit in any way. This was the first opportunity that the participants had to examine the assembled personal stereo kit. The groups received no other training or instruction. After the one-minute examination period, the experimenters collected the preassembled kits, issued each group an unassembled personal stereo kit, and instructed each group to assemble the kit to the best of their ability in a maximum of 30 minutes. Upon completing the transfer task, participants were given a questionnaire asking them to once again describe each member’s expertise (the questionnaire also included items about task difficulty and group processes that were not analyzed for this study). The second part of Session 3 was devoted to testing the extent to which groups had developed a generalized understanding of the underlying principles of the electronics kit assembly domain (abstract knowledge test). To control for the possibility that groups developed a TMS while performing the transfer task, we once again reassigned half of the participants to new groups before asking groups to complete the knowledge test. The abstract knowledge test consisted of examining, but


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not assembling, another electronics kit (electronic stapler). Experimenters issued each group an assembled electronic stapler and a survey that asked the group to articulate a strategy for assembling the stapler. To complete this task well, participants would have to abstract the underlying principles common to both of the prior two tasks and recognize the general task strategies that apply to all three tasks. After the surveys were completed, experimenters thanked the participants and then dismissed them. To avoid the possibility that participants who had recently completed the experiment would discuss it with classmates who had not yet completed the three-session series, we did not immediately debrief participants about the study hypotheses. Instead, we invited participants to debriefing sessions held at the end of the semester. A small percentage of the participants (less than 5%) ultimately chose to attend these sessions.2 4.5.

Measures

Learning-Task Performance. Performance on the learning task (telephone assembly) was measured by assembly accuracy—the number of assembly operations done correctly. Similar accuracy-based measures of performance have been used in prior TMS research (e.g., Liang et al. 1995, Moreland and Myaskovsky 2000). Distinct operations were defined according to steps described in printed instructions included with the assembly kit. We pretested the telephone assembly using the printed instructions and confirmed 38 distinct operations in the telephone task. A trained experimenter examined each group’s completed telephone and counted misplaced or misconnected components according to these instructions. We deducted one point from a group’s performance score for each inaccurate operation, such that higher scores indicate higher learning-task performance. Learning Transfer Transfer-Task Performance. The extent to which groups transferred learning across tasks was measured in terms of transfer-task performance— the number of assembly operations done correctly for the personal stereo assembly kit. Pretests using the kit’s printed instructions indicated that there are 34 distinct operations in the personal stereo task. Points were deducted from a group’s score for each inaccurate operation, such that higher scores indicate higher learning transfer to the stereo task. Abstract Knowledge. We measured the extent to which groups developed an abstract, generalized understanding of the underlying principles common to the tasks by asking groups to articulate a strategy for assembling a third electronics kit (electronic stapler), a task in the same domain as the learning and transfer tasks. The abstract knowledge measure takes into account knowledge about the task and how the group might solve the

problem. Our measure is similar to explanation-based measures used in past learning-transfer research to test for the development of abstract knowledge (e.g., Gentner et al. 2003). Groups that recognize the ways in which the learning and transfer tasks are functionally similar should be better able to apply abstract principles of the task domain to decompose the new problem and plan out a solution for assembling the stapler (Singley and Anderson 1989). Abstract knowledge scores were obtained by rating a group’s response to the question “What sort of strategy would your group develop and utilize if you wanted to conduct the assembly of this kit efficiently and accurately? (How would you go about doing it?).” Two of the coauthors, blind to the group’s experimental condition, rated each group’s written strategy description. We used a five-point scale to rate the quality of each group’s strategy description. The scale was anchored at 1 = trivial and 5 = integrative, where “trivial” was interpreted as relating to strategy descriptions that entailed the superficial, surface elements of the task—elements that would differ between the stapler task and the other two tasks; and “integrative” was interpreted as relating to strategy descriptions that transcended the superficial elements and corresponded to underlying principles relevant to how such problems can be solved. The two raters independently rated the same 20 groups and, after determining that interrater reliability was high (ICC(2) = 098), split up the remaining groups and rated them. The average score of the raters was used for the first 20 groups. A higher strategy-quality score indicates a greater degree of abstract knowledge than does a lower score. Control Variables. We controlled for prior expertise with electronics or electronics kits, anticipating that members who had experience on tasks similar to the tasks we used in our study would be able to achieve higher performance. Prior expertise was measured with two items that appeared on the pretraining survey: “Based on past experience, I would rate my overall knowledge level of electronics as” and “Based on past experience, I would rate my skill level with electronics kit assembly as.” Each of those items had a fivepoint response format, anchored at 1 = beginner and 5 = expert. The interitem correlations r = 069 justified summing the item scores to form a composite. Group scores were computed as the sum of member composite scores in each group, in each condition. Because each member’s prior expertise is independent of other members’ prior expertise, there was no need to check for within-group agreement before summing members’ scores. A higher group score indicates a greater level of prior expertise in the group than does a lower score.

5.

Results

We conducted all hypotheses tests at the group level using ANOVA and planned contrasts. Initial checks

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showed that group gender composition (computed as percent of female members) was significantly negatively related to prior expertise for each of the tasks and significantly negatively related to transfer-task performance. Therefore, we controlled for gender composition in addition to controlling for prior expertise in all analyses. 5.1. Learning-Transfer Check Singley and Anderson (1989) recommend checking that learning transfer is even possible before testing hypotheses about the extent of learning transfer between tasks. Their recommended method compares performance for groups that complete both learning and transfer tasks with the performance of groups that only complete a transfer task. If the performance of the trained groups is higher than that of the untrained groups, transfer has occurred from the learning to the transfer task. We tested for learning transfer using a holdout sample from the same population as our study participants. A total of 93 students in 31 groups comprised the holdout sample, 16 of whom were trained on the learning task and performed the transfer (stereo) task, and 15 of whom received no training before performing the transfer task. The difference between performance means for the transfer task was significant, F 1 29 = 2031, p < 001 (M = 2525 for trained groups versus M = 1666 for untrained groups). Higher transfer-task performance for groups that received training on the learning task relative to those that did not is evidence that transfer occurred between the learning task and the transfer task. 5.2. Manipulation Check We created a TMS in some groups and not others by keeping half of the participants in their training groups and by reassigning the other half of the participants to new groups. We expected that groups remaining intact after training would have full access to their TMS, and therefore higher TMS scores, than would groups whose members had been reassigned. We measured TMSs with a 15-item scale developed by Lewis (2003) and computed a TMS composite score for each member by summing scores on the 15 items = 083. To confirm that members’ scores could be aggregated to the group level, we evaluated the rwg statistic (George 1990), which measures the degree to which individual ratings within a group are interchangeable. Mean rwg values of 0.70 or greater provide evidence of acceptable agreement among member responses on a scale (George 1990). The average rwg on the TMS scale for the learning task was 0.97, with 100% of the rwg values above 0.70. These results indicate that group member responses on the TMS scale were quite homogeneous and that aggregating members’ scores to the group level of analysis is statistically justified. A one-way ANOVA, with gender composition and prior expertise entered as covariates, shows that intact


groups had significantly higher mean scores on the TMS scale, compared with groups composed of reassigned members (M = 5547 versus M = 5385), F 1 96 = 406, p < 005. We also measured task recall in half of the sample (34 intact groups, and 32 reassigned groups); recall should be higher in groups with a TMS. The number of task steps recalled was significantly higher in intact groups, compared with reassigned groups (M = 2153 versus M = 1483), F 1 62 = 3303, p < 0001. The bivariate correlation between TMS and recall N = 66 is 0.28, p < 005. These results suggest that keeping groups intact gave groups full access to the TMS developed during training, while reassigning groups disabled any TMS that had developed. Thus, our manipulation for creating a TMS in some groups and not in others was successful. 5.3. Hypothesis Tests Figure 2 depicts the experimental design conditions and the results from ANOVAs and planned contrasts for all hypothesis tests. Hypothesis 1 predicted that relative to groups with no prior TMS, groups with a TMS would demonstrate higher performance on the learning task. Performance on the telephone assembly task was indeed higher in groups with full access to their training TMS, compared with groups whose training TMS had been disabled (M = 3222 versus M = 3119), and this difference is significant, F 1 96 = 380, p < 005. Thus, Hypothesis 1 is supported. Hypotheses 2 and 4 were tested together, using ANOVA and two planned contrasts. We predicted that groups that had developed and utilized a TMS on a previous task would perform better on a transfer task than groups with no prior TMS (Hypothesis 2), and that groups that experienced a disruption to an established TMS structure would perform worse on the transfer task than groups that had never developed a TMS (Hypothesis 4). ANOVA results show no significant differences among any of the performance means, F 1 94 = 035, p = 055, providing no support for Hypotheses 2 and 4. We discuss these findings in more detail below. Hypotheses 3 and 5 were tested together. We predicted that experience with two tasks would be more likely to produce an abstract, generalized understanding of the task domain when a group had a prior TMS (Hypothesis 3). If, however, a group experienced a disruption to an established TMS structure, a prior TMS was expected to interfere with learning and the development of abstract knowledge (Hypothesis 5). An ANOVA with planned contrasts shows that both Hypotheses 3 and 5 are supported, F 1 89 = 514, p < 005. Groups with a training TMS that remained intact demonstrated a better understanding of the underlying principles and strategies relevant to the task domain, compared with groups that had never developed a TMS (abstract knowledge score M = 341 versus M = 256), t89 = 213,


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Figure 2

Experimental Design and ANOVA Results

Training

Learning task (telephone)

Transfer task (stereo):

Knowledge task (stapler)

Week 1

Week 2

Week 3, Part 1

Week 3, Part 2

I Maintain TMS

22.73H2

I Maintain TMS 3.41H3

(0.28)

R

3.28

(0.32)

3.33

(0.25)

1.56H5

(0.31)

3.72

(0.36)

3.30

(0.29)

2.55

(0.41)

(1.00)

(I) Intact Maintain TMS

32.22H1 (0.37)

I R Disrupt TMS

23.28H4

(1.06) R Disrupt TMS

N = 100 groups I I 23.33

(1.07) R

(R) Reassigned 31.19H1 (0.37)

I

Disrupt TMS R Disrupt TMS

Sample mean/s.d.

31.70/2.66

22.65H2,H4 (0.99) R Disrupt TMS 22.97/5.05

2.56H3,H5 (0.30)

3.00/1.20

Notes. Mean scores are shown for each condition. Standard errors are in parentheses. H1 comparison is significant, F 1 96 = 380, p < 005. H2 and H4 comparisons are not significant, F 1 94 = 035, p = 055. H3 and H5 comparisons are significant, F 1 89 = 514, p < 005, t89 = 213, p < 005, and t89 = −231, p < 001.

p < 005, supporting Hypothesis 3. Groups with a training TMS that later experienced a disruption to their existing TMS structure had significantly lower abstract knowledge scores than did groups that had never developed a TMS M = 156 versus M = 256), t89 = −231, p < 001. Thus, Hypothesis 5 is supported. Our results show that having developed a TMS not only affects performance on the task for which the TMS first developed, but also affects the development of abstract knowledge about the task domain, given experience with an additional task. Abstract knowledge facilitates mapping of the task principles to other similar tasks beyond those the group has already completed (Reeves and Weisberg 1994), suggesting that having developed a TMS can indeed facilitate learning and learning transfer. We also found evidence consistent with our prediction that a severe disruption to the TMS structure impedes abstract, conceptual learning about the task domain. We did not find evidence, however, of any learningtransfer effects after groups had experience with only one task (Hypotheses 2 and 4). Additional examination of the groups’ TMS structures revealed some explanations for these findings. When we examined elements of the TMS structures using members’ statements about “who is expert at what” for each task, we found that in more than 20% of the reassigned groups, members’ perceived expertise remained stable across tasks, while in nearly half of the intact groups, members’ perceived

expertise changed for the transfer task. Given these findings, we decided to create a variable that measures the extent to which perceived expertise remained stable across tasks and to reexamine Hypotheses 2 and 4, taking this new variable into account. 5.4. Expertise Stability Analysis Members’ consistent recognition of and agreement about location information in both tasks is evidence that members did indeed maintain specializations across the different task contexts. In our surveys, we had asked participants to identify which members had what expertise following the completion of the learning and transfer tasks. Two of the coauthors, blind to the conditions, independently coded individual responses into nine expertise categories: Mechanical/electrical, general assembly, small-parts assembly, large-parts assembly, assembly strategy, recall, general assistance, motivation, and parts management. Raters independently categorized each member’s response (Cohen’s kappa = 090, p < 001), discussed cases where there was disagreement, and came to a consensus about the appropriate expertise categorization. The consensus categorizations were used as the basis for the expertise stability scores. A measure of expertise stability was derived from the number of times members agreed about each member’s expertise, both within and between tasks. For example,


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if one member from the learning-task group indicated that a focal member was expert at small-parts assembly, and three people from the transfer task group indicated that the focal member was expert at small-parts assembly, then that focal member would receive a score of 3. Scores for each focal member range from 0 to 9, where a score of 9 indicates that three group members from the transfer task identified the focal member with the same expertise as did three members from the learning task. A group-level expertise stability score was computed as the sum of focal member scores, so the range of possible group-level scores is from 0 to 27. The distribution of the expertise stability scores was significantly skewed (skew = 063, SE = 024), so we transformed the scores (natural log). The distribution of the transformed scores does not differ significantly from normal (skew = −035, SE = 024). We computed a three-way interaction term from the group type during the learning task (intact versus reassigned), group type during the transfer task (intact versus reassigned), and the transformed expertise stability variable, and then performed a moderated regression analysis in four steps. Results of this regression analysis are reported in Table 1.

Results show a significant three-way interaction of group type during the learning task, group type during the transfer task, and expertise stability t87 = −199, p < 005 (B = −503 SE = 253). To more easily see the form of the interaction, we split the expertise stability scores into three groups for graphing purposes only: low (N = 28, M = 035, SE = 006); medium (N = 45 M = 173 SE = 006); and high (N = 24, M = 262, SE = 004). The two split points (creating three groups) were determined by relatively larger gaps between scores in the distribution. Post-hoc tests show that the differences among these means is significant, F 2 96 = 29884, p < 0001. The graphs, which are presented in Figure 3, reveal conditions under which an initial TMS had beneficial or detrimental effects on learning transfer. A prior (training) TMS had the most beneficial effect on learning transfer for members of groups who had been reassigned for the transfer task and who had maintained either a medium or high degree of expertise stability across tasks. The learning-transfer effects of a Figure 3

Three-Way Interaction of Learning Task, Transfer Task, Expertise Stability

Intact for transfer task

DV = Transfer-task performance Step: Variable Controls: Prior expertise Gender composition (1 = female; 0 = male)

1 022 055 −385∗ 194

Predictors: Group type: Learning task Group type: Transfer task Expertise stability

2

3

4

022 058 −386 204

025 061 −383 209

041 060 −369 205

−037 106 023 105 −005 059

061 153 117 150 −011 105

117 153 090 149 −121 117

−194 217 058 121 −031 127

−237 214 395 207 172 161 −503∗ 253

Interactions: Group type: Learning task × Group type: Transfer task Group type: Learning task × Expertise stability Group type: Transfer task × Expertise stability Group type: Learning task × Group type: Transfer task × Expertise stability

Learning transfer (transfer-task performance)

Regression Results for Expertise Stability Analysis

24.3 23.9

23.4

24.1

22.7

23.0

21.3 21.0 Reassigned for learning task

19.0

Intact for learning task 17.0

15.0

Low

Med

High

Expertise stability Reassigned for transfer task

R2

006

006

007

011

R2

006

000

001

004∗

F

283

113

081

118

F

283

005

032

394∗

Notes. Group type during learning/transfer task is dummy coded, such that 1 = intact, 0 = reassigned. Expertise stability is transformed (ln). Unstandardized beta coefficients are shown; standard errors are in parentheses. ∗ p < 005.

25.0

24.3

24.1

Learning transfer (transfer-task performance)

Table 1

25.0

23.0

21.0

22.2

23.9

22.1 Reassigned for learning task

20.7

Intact for learning task

19.0

17.0

15.0 Low

Med

Expertise stability

High


training TMS are negative, however, for members who did not maintain expertise across tasks, and for group members who remained together but maintained only a moderate or low level of expertise stability across tasks. Moderate levels of expertise stability might be found in groups where only one or two members maintained their specializations, or where there was only weak consensus about location information. In all, the graphs suggest that the strongest impact of an initial TMS on learning transfer after experience with one task is for members who have been reassigned to new groups and who maintain their expertise specializations. We discuss this finding, along with the results supporting our prediction that TMS would impact the extent to which groups eventually develop an abstract, generalized understanding of the task domain, in more detail in the discussion.

6.

Discussion

The goal of this article was to explain how knowledge embedded with a group’s TMS creates sustained group performance. We presented a framework in which TMSs are conceptualized as learning systems that affect group learning and learning transfer across tasks. The framework describes three learning cycles experienced by a group performing several similar tasks. A TMS develops during Learning Cycle 1, as members learn about others’ knowledge, deepen their own expertise, and establish a TMS structure and set of processes for encoding, storing, and retrieving what members know. Evidence of learning during Learning Cycle 1 is higher performance on the same task for which the TMS developed. In Learning Cycle 2 of the framework, while performing that task, members’ learning continues. Learning by doing produces refinements in members’ understanding about others’ knowledge, creates an elaborated and contextualized understanding about the group and task, and establishes patterns for communicating and retrieving information. By building transferable knowledge structures and stable patterns for interacting, Learning Cycle 2 allows for learning transfer across functionally similar tasks and higher performance on a task other than the one for which the TMS was first developed. In Learning Cycle 3 of our framework, performing a second task produces additional learning by producing increasingly abstract knowledge about the principles underlying both tasks. Relying on a TMS structure and set of processes facilitates analogical encoding and collective induction, which further assist a group in recognizing underlying principles of the task domain. The effects of Learning Cycle 3 are evidenced by an abstract, strategic understanding of the task domain, which in turn facilitates learning transfer and performance on subsequent tasks in that domain. In an empirical study, we tested the major outcomes of the TMS-learning framework—namely, whether a TMS facilitates learning transfer, facilitates the development of abstract, generalized knowledge about the task

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domain, and whether a TMS interferes with learning and transfer under certain conditions. The findings support our contention that having developed a TMS influences the degree to which groups develop an abstract understanding of the task domain after experience with several tasks, but the findings do not confirm that a TMS affects learning transfer after experience with a single task. Further investigation revealed that the degree to which members maintained expertise across tasks influenced learning transfer after one task, especially for groups whose members had previously developed a TMS in another group. 6.1. Implications for Theory Our study’s findings have several theoretical implications. First, the effects of a TMS appear to extend beyond the task for which the TMS first developed. Previous group TMS research has focused on the performance benefits of a TMS for the same task for which the TMS first developed. Our research shows that TMSs have broader benefits because they affect members’ ability to develop a collective, abstract understanding of the task domain. The development of abstract knowledge is critical to groups’ leveraging and transferring what they learned on previous tasks. Our findings suggest that TMSs help groups develop such knowledge. Second, the results of our study show that having developed a prior TMS has advantages for individual group members. Members who were reassigned to new groups and maintained their expertise specializations appeared to benefit from having developed a TMS in the past, even though they were no longer working with the same teammates. These findings are similar to those from Baumann’s (2001) study, which suggested that maintaining a cognitive division of labor helps members leverage a prior TMS developed in another group. However, our findings extend those to a more complex case in which the task as well as group members change. We speculate that members who maintained their specializations enabled their new groups to quickly establish a division of cognitive labor, and that those members could draw on TMS processes and patterns of interaction that they used for previous tasks. These findings highlight some individual-level advantages to developing a TMS that past research on group TMSs had not revealed. Although we expected to find evidence of TMS-learning transfer after one task, our findings are consistent with individual-level learning research that suggests that one problem may not be sufficient for understanding the general principles underlying the task, and that experience with two functionally similar problems is needed to induce a generalized understanding of the problem domain (e.g., Gentner et al. 2003). Considering our findings in light of the individual-level learning research leads us to speculate that the initial learning that occurs

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with TMS development may be focused on superficial features of the task, rather than on its underlying structure. While this learning appears to be sufficient for producing enhanced performance on the same task for which the TMS developed, this task-specific learning has limited application. Developing a TMS can eventually foster more abstract learning, but a critical trigger for this learning appears to be additional experience in the task domain. The fact that a TMS nevertheless helped individual members perform in new groups after experience with one task raises the possibility that the shared knowledge structures necessary for developing an abstract, generalized understanding of the domain may be slower to develop, or more difficult to retrieve, than are transferable structures at the individual level, until other analogous tasks are encountered by the group. Finally, our study highlights the need for integrating learning theories at multiple levels of analysis. Individual-level learning research offers rich and detailed explanations of the cognitive processes and knowledge structures involved in learning and learning transfer (see Singley and Anderson 1989 for a review). Individual-level theories are not sufficient, however, for explaining the complexities introduced by member interactions and the effects of these interactions on individual and collective learning. In contrast, grouplearning research has focused more on internal group processes and outcomes of learning (cf. Argote 1999) and less on explaining the cognitive structures involved in learning. Our TMS-learning framework integrates these perspectives, using TMS theory as the basis for explaining how, and through what mechanisms, group learning might occur. TMS theory offers an explanation for the interplay between individual (unshared knowledge) and group (shared knowledge), as well as of the cognitive structures and processes relevant to learning. 6.2. Limitations While our experimental design and laboratory setting allowed us to hold constant many potential confounding effects, our tasks and the expertise required to complete them do not completely capture the complexity of many organizational workgroups. Participants in our experiment may have had weak motivation to learn and perform the tasks because the tasks may have seemed childish or because we provided no additional incentives for high performance. Thus, while our speculations about why groups failed to transfer TMS learning after one task are plausible, it is also possible that weak motivation impacted our findings about learning transfer after one task, and consequently, our lack of support for Hypotheses 2 and 4. Additionally, it is important to note that some of the effects we found, even where statistically significant, are small. Small but significant effects in the TMS manipulation check and in the test for initial TMS learning


(Hypothesis 1) might point to a weakness in our training manipulation, or to a weakness in the specific training we provided for participants in this experiment. We do note, however, that the differences among the abstract knowledge means are relatively large, providing strong evidence of TMS learning (Hypothesis 3) and learning interference (Hypothesis 5) in groups with experience with two tasks. 6.3. Future Research Directions This study points to a number of interesting directions for future research. First, our findings about the conditions under which learning transfer is most and least likely to occur highlight the need to study the predictors of, and processes associated with, TMS learning across tasks and across time. For example, future research might examine likely predictors of learning and learning transfer, such as prior task performance, TMS quality, and social or attitudinal variables such as conflict and social integration. Such research might best be conducted in the field, where social processes are most likely to develop and impact group functioning. Other research might directly test the theoretical mechanisms that we propose for learning and learning transfer, including interactive cueing, recognition of functional similarity across tasks, analogical encoding, and collective induction. Protocols used in past laboratory research on analogical encoding (e.g., Loewenstein et al. 1999), collective induction (see Laughlin 1999), and communication and TMSs (e.g., Hollingshead 1998) could be modified to examine the mechanisms and boundaries for TMS learning and learning transfer. A second avenue for future research is to examine the role of expertise in TMSs, group learning, and performance. We found evidence that learning transfer in the short term depends in part on members maintaining their expertise. We did not find evidence in our data to suggest why group members chose to maintain or not maintain their specializations, however. Research that explores what might cause members to reorganize their cognitive division of labor, the necessary content and distribution of members’ expertise, and when maintaining or reorganizing the cognitive division of labor is beneficial to learning and future performance would enhance our understanding of the conditions under which embedded group knowledge can be leveraged. We opted to control for variations in individuals’ prior experience and to study tasks on which individuals’ knowledge was unlikely to vary within a group. As tasks increase in complexity, however, the extent and composition of members’ knowledge may become important factors that influence TMS learning and learning transfer. Future research that investigates the level, distribution, and variation in members’ expertise is needed to more fully understand both the effects of individual and collective learning associated with TMSs, and the functioning of TMSs in organizational settings.


6.4. Implications for Practice A key implication from our study is that there are long-term advantages to fostering and managing TMSs. Our findings also suggest that some of these advantages may be delayed, as groups appear to need experience with at least two tasks before developing the kind of abstract knowledge that facilitates learning transfer across tasks. Leveraging prior knowledge gained on a single task, in contrast, is likely to require explicit effort on the part of groups and organizations. Organizations can facilitate learning transfer in the short term by keeping membership stable across tasks that a group performs, and by encouraging stability in the expertise and roles that members have within these groups (Moreland and Argote 2003). Groups will be better able to transfer learning gained on a single task if members explicitly compare tasks and deliberately consider the underlying principles common to both. It may be worthwhile for groups to formally debrief their projects, focusing not only on elements of success and failure, but also on the nature of their tasks and the learning that developed. Explicit consideration of task strategies, the fundamental principles of the task, and how members’ knowledge links to the execution of these strategies can help groups leverage knowledge embedded with the TMS they developed. Acknowledgments

The authors are grateful to Maura Belliveau, George Huber, Janet Dukerich, and Jeffrey Loewenstein for their insightful comments on earlier drafts of this manuscript. Special thanks to Linda Argote, Senior Editor Terri Griffith, and three anonymous reviewers for their challenging comments and thoughtful suggestions, all of which were instrumental to the development of this paper in the review process.

Endnotes 1

Shared location information has been referred to in the TMS literature as a directory of member-expertise associations (e.g., Wegner 1995), an index or indexing system (e.g., Lewis 2003, 2004), and a shared awareness of who knows what (e.g., Moreland 1999). 2 Prior to conducting our study, we obtained approval from our institution’s Human Subjects Committee, who considered the risks posed by our debriefing plan to be extremely low, considering the experiment and the experimental manipulations.

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Transactive Memory Systems, Learning, and Learning Transfer

Transactive Memory Systems, Learning, and Learning Transfer

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