Communication in Transactive Memory Systems: A Review and Multidimensional Network Perspective

Abstract

The comprehensive review synthesizes 64 empirical studies on communication and transactive memory systems (TMS). The results reveal that (a) a TMS forms through communication about expertise; (b) as a TMS develops, communication to allocate information and coordinate retrieval increases, promoting information exchange; and (c) groups update their TMS through communicative learning. However, direct interpersonal communication is not necessary for TMS development or utilization. Nor do high-quality information-sharing processes always occur within developed TMS structures. For future research, we propose a multidimensional network approach to TMS that incorporates technologies, addresses member characteristics, considers multiple communication types, and situates groups in context.

Keywords

transactive memory group communication group cognition team cognition knowledge networks social networks team networks group learning

A transactive memory system (TMS) is a group-level knowledge sharing and memory system in which group members share responsibility for encoding, storing, and retrieving of information from different knowledge areas, and have a shared awareness about each member’s knowledge responsibilities (or “who knows what”; Wegner et al., 1985; Wegner, 1987). TMS was first proposed by Daniel Wegner in 1985 in response to traditional psychological notions of “group mind,” which neglected the communication and exchange processes that connect individual minds as a group. Since then, many researchers from different academic disciplines have explored the topic of communication and transactive memory systems across different types of groups (Hollingshead, 1998a; Kanawattanachai & Yoo, 2007; Peltokorpi & Hood, 2018).

A transactive memory system consists not only of shared memory structures and contents, but also the communication processes through which structures and contents are created, connected, and utilized. According to Wegner (1987, 1995), two components define a TMS: (1) an organized storage of knowledge contained in the individual memory systems of group members (TMS structure), and (2) a set of knowledge-relevant communication processes that connect the individual memories of group members: directory updating, information allocation, and retrieval coordination. Communication patterns in groups also shape TMS development through member specialization, coordination, and credibility (Lewis, 2003; Liang et al., 1995).

Despite the central role of communication in transactive memory, empirical research on this topic is fragmented across disciplines due to a broad range of communication constructs and measures employed across studies. The goal of this review article is to summarize what is known and identify opportunities for future research on group communication and TMS. We take a broad view of communication and define it as the set of interactive processes through which two or more individuals exchange messages (Tubbs, 2011). Based on our definition, communication in groups can occur in different forms (e.g., verbal, nonverbal), have different purposes (e.g., task-oriented, relational-oriented), occur in different contexts (e.g., professional, social), be transmitted through various channels (e.g., face-to-face, telephone, social media) and happen directly between some or all members or indirectly through documents or third parties.

This review is not the first to focus on TMS and communication. There are previous reviews of TMS research where communication was not a central focus (see Hollingshead et al., 2012; Lewis & Herndon, 2011; Ren & Argote, 2011). Most recently, Peltokorpi and Hood (2018) published a review of 20 theoretical papers and 34 empirical studies on this topic. Their summarized findings and critique were organized around four topic areas: communication frequency and quality, communication medium and group development, communication styles, and communication networks. Rather than replicate their main points and structure, we direct readers to Peltokorpi and Hood’s (2018) insightful review. Building on this work, we seek to advance and contextualize current research on communication and transactive memory through the analysis of an additional 64 empirical studies and discussion of key findings organized by two group stages: TMS development and TMS utilization. Moreover, we offer a multidimensional network approach to inform future theoretical and empirical research on this topic. We begin with a conceptual overview of the functions of communication in TMS gleaned from previous interdisciplinary research.

Functions of Communication in Transactive Memory

Existing research on transactive memory and communication tends to adopt a functional perspective (Hollingshead et al., 2005), emphasizing the functions or purposes of communication in creating or utilizing a group transactive memory system. Research characterized by a functional perspective generally adopts an input-process-output research approach and focuses on instrumental outcomes such as accuracy, effectiveness, or performance.

Wegner (1995) proposed that transactive memory systems consist of three core processes that connect group members’ individual memories: directory updating, information allocation, and retrieval coordination. Directory updating is learning about, storing, and revising perceptions about members’ relative expertise (Hollingshead, 1998b; Wegner, 1995). Information allocation is “the procedure whereby individual memories are fashioned into a differentiated group memory that is useful to the group” (Wegner, 1995, p. 332). New information entering the group is directed to and remembered by the person responsible for the corresponding knowledge area. Retrieval coordination refers to the process of locating and retrieving information in the group transactive memory system. What makes the memory system transactive are the “transactions” or communications among group members used to encode, store, and retrieve information from the members’ individual memory systems (Wegner, 1987). These knowledge-sharing transactions can change the memory structures in the system.

Hollingshead and Brandon (2003) described the potential benefits of communication in transactive memory systems based on an informal empirical review that intersected with the three core processes. They identified several functions of communication that cultivate the development and use of transactive memory systems. First, with respect to directory updating, communication helps move members from initial default notions of one another’s expertise based on surface-level attributes and characteristics, such as gender, educational level, or job title to more accurate representations of expertise informed by conversations and shared experiences. Second, communication enables members to describe and demonstrate their level of expertise (Bonner & Baumann, 2012; Laughlin & Ellis, 1986). It serves as the main conduit through which members can observe other members’ expertise in action (cf. Majchrzak et al., 2007). Moreover, communication from third parties can be used to update members’ directories (Moreland & Myaskovsky, 2000). For example, a positive team performance appraisal from a client might reinforce group members’ perceptions of expertise. Through communication, group members gradually build trust in one another’s expertise knowledge (credibility), develop a differentiated memory structure (specialization), and engage in orchestrated knowledge processing and exchange (coordination) (Lewis, 2003; Liang et al., 1995).

Transactive encoding is the process of obtaining new information and storing it in the transactive memory system and involves information allocation. Interpersonal communication is one means by which members divide and assign responsibility for different knowledge areas such that no information passes through the group that is not assigned to one or more group members for storage (Wegner, 1987). Asking for or cueing members to access needed knowledge is a function of communication in retrieval coordination. Another function of communication is transactive retrieval in which multiple group members collaborate to locate and remember needed information.

Brandon and Hollingshead (2004) proposed that transactive memory systems vary in their accuracy (the degree to which group members’ perceptions about other members’ task-related expertise are accurate), sharedness (the degree to which member have a shared representation of the transactive memory system), and validation (the degree to group members accept responsibility in their areas of expertise). Thus, communication has an accuracy function: It can enhance or diminish the accuracy of members’ perceptions about relative expertise. Communication also has a sharedness function: The nature and manner in which group members communicate affect how integrated members’ perceptions makeup the transactive memory system. Communication also has a validation function: it is one method by which group members accept and demonstrate their responsibility for different knowledge areas.

Communication has generally been viewed as a positive influence on transactive memory systems, but it can also be detrimental and lead to errors (Hollingshead et al., 2010). Communication behaviors often serve as indicators of expertise. Speaking forcefully without hesitation, a higher frequency and longer duration of talking, and a larger proportion of group participation are positively associated with perceptions of expertise (Littlepage et al., 1995; Littlepage & Mueller, 1997; Pearsall & Ellis, 2006). However, those communication behaviors are not always indicative of true expertise. For example, individual members with expertise may hesitate to participate and display a lack of assertiveness when speaking with a higher status person. In certain situations, for example, experts may not want to let their expertise be known to their group, to avoid additional work assignments. Self-censorship may further exacerbate knowledge-sharing problems (Thomas-Hunt & Phillips, 2003). Communications from third parties who share opinions, experiences, and insights about team members may lead members to have inaccurate perceptions of other members’ expertise.

Search Strategy and Scope of Review

Building on previous reviews of transactive memory research (Hollingshead et al., 2012; Ren & Argote, 2011), we searched for and included published empirical studies that measured some aspect of communication in transactive memory systems regardless of discipline or research methodology. We conducted the search in four academic databases: Web of Science, SSRN, Scopus, and PsycInfo. We began by searching with the term transactive memory in the article title, abstract, or keyword list of published research papers, including journal articles, book chapters, and conference proceedings. Only empirical studies that examined both communication and transactive memory were included in the review. The search procedure resulted in a total of 64 empirical studies.

Coding Procedure

Four trained coders read the articles. The articles were assigned based on the alphabetical order of their authors. Nine of the 64 studies (14%) were randomly selected and coded by two coders. The coders achieved high agreement across the coded items (Cohen’s Kappa = 0.84–1.00). Coding conflicts and inconsistencies were discussed by the team until a consensus was reached. For each study, the coders identified the research design (i.e., field survey, lab experiment, interview, and ethnography; longitudinal or cross-sectional design), unit of analysis (i.e., groups, dyads, or individuals). Tasks performed by the study participants were annotated (e.g., decision-making, research and development or R&D).

Next, coders identified the dimensions of TMS and communication investigated in each study. For TMS dimensions, coders identified the TMS processes (i.e., directory updating, information allocation, and retrieval coordination) and/or other indicators of TMS (e.g., specialization, coordination, and credibility) reported in the findings.

Communication was coded and categorized by four general types (cf. Marlow et al., 2018): communication frequency, communication quality, communication structure, and other dimensions (e.g., channel, content, context, form). Communication frequency referred to the quantitative volume of communication. Communication quality referred to attributes of group communication, such as helpfulness, openness, elaboration, or valence. Communication structure referred to a group-level pattern of communication or communication relations such as communication network structure or distribution of speech turns. Other communication-related dimensions included: communication channel (e.g., face to face or mediated), content (e.g., task-oriented or relational-oriented) and context (e.g., formal or informal).

Coders then categorized the stage of group TMS into (1) development, (2) utilization, or (3) both. Research examining TMS development explored the effect of communication during the formation of group TMS. Studies of TMS utilization focused on groups with a developed TMS structure and investigated the role of communication in TMS functioning. For example, longitudinal studies of newly formed teams may examine both stages. Finally, coders annotated the findings of each study regarding communication and TMS. The detailed information for each study (including design, unit of analysis, tasks, measures of TMS and communication, and findings) can be accessed in Online Supplement A.

Key Findings

Although empirical research on transactive memory and communication began in the 1990s, it gained momentum around 2007 as 50 (78%) studies in our review were published between 2007 and 2017. Studies were most commonly published in management journals (17) followed by information systems (12), communication (10), and psychology (10). Cross-sectional (48) and quantitative (61) studies dominate the existing research. The research spans field teams and lab groups interacting across a diverse set of task contexts.

The majority (68%) of the reviewed studies applied self-reported survey measures to gauge group TMS and communication. The TMS scale developed by Lewis (2003), which evaluates group TMS based on specialization, coordination, and credibility, was the most commonly used measure of transactive memory. For communication, the majority of studies focused on frequency. About half of the studies investigated some qualitative aspects of communication, including perceived quality (Hewitt & Robterts, 2015; Liao et al., 2015), communication style (Yuan et al., 2013), and valence (Yuan et al., 2014).

Communication and TMS Development

The review revealed that group communication plays a critical but different role at the beginning than later in the group’s lifespan, consistent with earlier reviews (Hollingshead & Brandon, 2003; Peltokorpi & Hood, 2018). Hence, our key findings are organized into two parts: TMS development in newly formed groups and TMS utilization in established groups. Key findings were generally well supported. At the end of each section, we also summarize topics that produced inconsistent results.

Key finding 1: Exchange of expertise information facilitates TMS development

Expertise information is about group members’ relative knowledge in different knowledge domains (Brandon & Hollingshead, 2004). Frequent communication within groups, specifically when group members exchange expertise information and engage in face-to-face interactions, leads to greater TMS development (He et al., 2007; Ryan & O’Connor, 2013; Tang et al., 2015). Exchange of expertise information also occurs more frequently in the early stage of group interaction (Hollingshead, 1998b) and was shown to be particularly beneficial for TMS development (Pearsall et al., 2010). In contrast, barriers in communicating knowledge and expertise, such as differences in vocabulary, lexicon, and interests, can negatively impact TMS development in teams (Kotlarsky et al., 2015).

Our review was limited to articles that explicitly mentioned transactive memory and involved some sort of interpersonal communication between members. It is important to note studies that show patterns of individual remembering consistent with the development of a TMS in the absence of what would normally be considered communication. In these studies, information about member’s relative knowledge was provided by the experimenter (e.g., Baumann & Bonner, 2004; Hollingshead, 2000, 2001; Moreland & Myaskovsky, 2000); or inferred through stereotypes (e.g., Hollingshead & Fraidin, 2003; Yoon & Hollingshead, 2010).

Key finding 2: Groups can develop a TMS via various communication channels

The communication of expertise information does not have to occur via verbal or face-to-face communication. Overall, the use of multiple communication channels can be helpful for TMS formation (Jarvenpaa & Majchrzak, 2008). Although high quality face-to-face verbal communication seems to have a stronger effect on TMS development compared to other communication channels (Lazzara et al., 2015; Lewis, 2004), groups can form a TMS via computer-mediated communication (e.g., Oshri et al., 2008; Tang et al., 2015; Zhang et al., 2016), written feedback or note-taking (Moreland & Myaskovsky, 2000; Oertel & Antoni, 2015), and nonverbal exchange of expertise cues (Yoon & Hollingshead, 2010).

Compared to face-to-face communication, CMC may lead to a decrease in expertise inquiries and claims, justifications for answers, and transactive searches (Hollingshead, 1998a). However, on the other hand, CMC may encourage members in cross-cultural teams to speak more frequently, thus forming more accurate expertise recognition (Bazarova & Yuan, 2013). Frequent CMC with new members after member turnover can also facilitate the restoration of TMS (Argote et al., 2018). The specific effect of CMC is contingent upon teams’ geographical distribution (Su, 2012) and task (Tang et al., 2015). The perceived information accessibility of communication technologies may promote the perceived coordination, credibility, and specialization of others who share information on social media (Chung et al., 2015).

Key finding 3: High-quality communication is beneficial for TMS development

Several studies in the review examined how qualitative attributes of communication may affect the formation of TMS and measured communication quality in a variety of ways. Communication high in helpfulness, openness, timeliness, length or closeness is found to be positively associated with TMS development (Choi et al., 2010; Peltokorpi, 2004; Tang, 2015). Analytical and issue-oriented communication can facilitate group members form more accurate evaluations of others’ expertise, but communication confidence, dominance, or talkativeness does not appear to matter (Bazarova & Yuan, 2013; Yuan et al., 2013).

Inconsistent results on communication and TMS development

Although four studies investigated the relative utility of task versus relational communication in transactive memory development, the findings were inconclusive. Task-oriented communication seems to facilitate expertise recognition in TMS in virtual teams (Kanawattanachai & Yoo, 2007) and specialization in new product development teams (Akgun et al., 2006). However, a field study of Chinese employees found the opposite (Zhang et al., 2012). The effect of context may depend on the group task. Informal communication was a stronger predictor of TMS when the team task was explorative, whereas formal communication was a stronger predictor of TMS when the task was exploitative (Tang et al., 2015).

Another area of mixed results is the effect of communication structure on TMS. Five studies in our review investigated the effects of communication structure on TMS development by applying network measures. Communication network density is defined as the intensity of communication within a team. Whereas some researchers reported evidence suggesting a positive effect of communication network density on TMS (Manteli et al., 2014), others found that density had a direct negative effect on team TMS but was indirectly and positively related to TMS via network transitivity (Lee et al., 2014). Inconsistent findings also exist regarding member centrality and TMS. Member centrality in a team’s communication network captures the importance of the member in the team’s communication flow. Two studies showed that members’ degree centrality in teams’ communication network, which is the number of interactions they had with other team members, was positively related to their reported TMS with the team (Manteli et al., 2014; Su, 2012). However, the two studies disagreed on whether members’ between-centrality—which measures the extent to which a team member lies on the communication paths among other members—matters for team TMS development.

The mixed findings may suggest that the relation between group communication structure and TMS formation is dependent on other group processes. Argote et al. (2018) revealed that communication network centralization had a direct negative effect on TMS development and a weak positive effect on TMS through communication frequency. This relation was also moderated by member turnover: groups with a fully connected communication network developed a stronger TMS when membership was stable, whereas groups with centralized communication networks developed a stronger TMS when turnover occurred. In addition, the size of an individual’s information-sharing network may be negatively related to TMS development (Jarvenpaa & Majchrzak, 2008).

Communication and TMS Utilization

Key finding 4: Information exchange increases over time

Overall, a developed TMS leads to more frequent information exchange among team members (e.g., Borgatti & Cross, 2003; Cabeza Pullés et al., 2013; Smith-Jentsch et al., 2009). This effect is robust across the studies regardless of communication channels, group tasks, and levels of analysis. Compared to groups without a TMS, groups with a TMS have more frequent communication to allocate information (Vernham et al., 2014) and coordinate information retrieval (Hollingshead, 1998b; Mell et al., 2014; Vernham et al., 2014).

Information exchange in teams with a developed TMS can happen through digital knowledge repositories (Huang, Barbour, et al., 2013; Yuan et al., 2007). However, variation in TMS structure may result in different group communication patterns (Gupta, 2012; Gupta & Hollingshead, 2010). Gupta and Hollingshead (2010) showed that groups with a differentiated TMS (compared to teams with an integrated TMS) were more likely to communicate to update the expertise directory and coordinate information retrieval about unique information. In addition, the centralization of TMS structure (i.e., knowledge of expertise location is concentrated in one team member) was positively related to the frequency of retrieval coordination when information was distributed among team members (Mell et al., 2014).

Key finding 5: A developed TMS structure does not guarantee effective information exchange

A developed TMS structure does not promise well-functioning TMS processes or guarantee effective communication within teams. In a study of R&D teams, researchers (Huang & Huang, 2007) found that the higher level of information exchange associated with a developed TMS was not related to the effectiveness of information sharing. Yuan et al. (2007) also showed that individuals’ information exchange with colleagues does not always guarantee access to needed information. The effect of TMS on team outcomes is partially mediated by communication quality (Chen et al., 2013; Healey et al., 2009; Hsu et al., 2012). In a case study of the Columbia incident, Garner (2006) showed that despite the formal information-sharing network within NASA highly resembling the information-sharing pattern in a well developed TMS, the necessary information was not adequately shared due to insufficient information allocation and directory updating errors. Moreover, Palazzolo (2005) reported that although information retrieval among group members was reciprocal, knowledge exchange within a TMS closely matched team members’ expertise perceived by others rather than self-reported expertise.

Key finding 6: Information sharing in TMS increases with positive relationships and in a positive group climate

Members were more likely to exchange information with other members whom they communicated with frequently (Yuan, Fulk, et al., 2010) and perceived as accessible (Yuan, Carboni, et al., 2010). Team members having positive affect toward other members were more likely to engage in expertise-seeking within the team (Neff et al., 2014; Yuan et al., 2014). Otherwise, they were less likely to seek information despite being aware of others’ expertise (Yuan et al., 2014). In addition, relational communication moderates the relation between TMS and knowledge sharing quality (Huang, Liu, et al., 2013). Although a TMS can help teams perform better in decision-making following the loss of a member, its effect is reduced if the lost member is critical (Christian et al., 2014).

A negative group climate can reduce group information sharing and negatively impact TMS. Low awareness of mutual information needs, ambient noise, and frequent communication in high-stress environments can inhibit effective information sharing in teams with a TMS (Sarcevic et al., 2008). Under acute stress, teams are less likely to engage in communication that supports directory updating, information allocation, and retrieval (Ellis, 2006). Moreover, the strength of team members' communication with external parties (e.g., customers, suppliers) moderates the relation between TMSs and team performance: TMSs have a stronger positive effect on performance when external communication ties are strong, particularly in a dynamic environment (Heavey & Simsek, 2015). Intergroup communication quality partially mediates the effect of TMS developed among groups on the performance of these groups (Healey et al., 2009).

Key finding 7: The relative importance of verbal communication decreases over time

Verbal communication directly related to team tasks is not necessary for TMS functioning. Whereas discussions about members’ expertise occur frequently in the early stages of group development, the frequency of these discussions decline as groups develop TMSs (Hollingshead, 1998b; Rulke & Rau, 2000). The impact of explicit task-oriented verbal communication on TMS also wanes as groups interact (Jackson & Moreland, 2000; Kanawattanachai & Yoo, 2007; Yoo & Kanawattanachai, 2001). A number of studies have demonstrated that explicit verbal communication within teams with developed TMSs did not help knowledge retrieval (Hollingshead, 1998a; Littlepage et al., 2008) and, in some cases, even inhibited the retrieval process (Hollingshead, 1998a). In teams with a developed TMS, TMS processes rather than interpersonal communication were related to better team performance (Lewis et al., 2007; Moreland & Myaskovsky, 2000). In some cases, TMS mediated the effect of communication on group performance (Argote et al., 2018; Pearsall et al., 2010; Peltokorpi & Manka, 2008). Instead, group members may rely on nonverbal communication, such as eye contact, to coordinate their retrieval processes. Hollingshead (1998b) found that groups with a transactive memory system developed through face-to-face communication relied on paralingual and nonverbal communication in their knowledge retrieval.

Key finding 8: Collective learning processes increase over time through communication

Whereas formal knowledge storage and retrieval (i.e., documentation, note-taking) have a more significant effect on TMS in the early stages of group development, learning behaviors, such as co-construction, reflexivity, and dialogical practices benefit TMS in later stages of group development (Gabelica et al., 2016; Jarvenpaa & Majchrzak, 2008; Oertel & Antoni, 2015). When utilizing a TMS, group members engage in collective learning by reflecting on and evaluating collaboration processes and performance (Hollingshead, 1998b; Rulke & Rau, 2000; Vernham et al., 2014). Group TMS structures (i.e., expertise directories, information repositories) are constantly updated and maintained (Oertel & Antoni, 2015).

Inconsistent findings on communication and TMS utilization

Research has generated inconsistent findings regarding whether communication in teams with a TMS becomes more centralized over time. Whereas some studies found that groups with a developed TMS displayed more decentralized communication structure by having more equal communication and more speech turns (Prichard & Ashleigh, 2007), others showed that multiple team members tended to retrieve information from one member, resulting in a centralized communication network (Palazzolo, 2005).

At the individual level, members may play different roles in information gathering and dissemination in a TMS, occupying various positions in communication networks. Boundary spanners may occupy central positions (Manteli et al., 2014) and connect the group with outside information sources (Whelan & Teigland, 2013). In trauma resuscitation teams, Sarcevic et al. (2008) observed that top information seekers also acted as top information providers, implying that information seekers became secondary information providers, responding to inquiries and relaying information acquired from primary sources.

Discussion

Toward A Multidimensional Network Perspective

Overall, our review demonstrates that group communication is an integral part of TMS, playing a critical but distinct role in the system’s formation, maintenance, and functioning. In addition to key findings, our review uncovered inconsistent findings and areas that deserve future research attention. Most studies in our review were quantitative, cross-sectional, and adopted a functional view of communication. More research that involves in-depth qualitative analyses (cf. Garner, 2006) and represents other methodological perspectives is clearly needed.

Observations and Future Research Directions

In our review, we identified six topics worthy of future research on communication and TMS: (1) the role of technology; 2) member roles and characteristics; (3) non-task and nonverbal communication (i.e., facial expression, emotional/relational interaction); (4) communication and TMS structures, (5) group context, and (6) TMS errors. In their review, Peltokorpi and Hood (2018, p. 18) identified five additional and more specific future research directions on this topic: (1) combined effects of communication frequency and quality over time; (2) the impact of frequency of communication on expert inferences; (3) the effects of task, relationship and process conflict on TMS-related communication; (4) the impact of language and cultural differences on TMS-related communication; and (5) the impact of network size on TMS-related communication. In the following sections, we discuss each of the six topics identified above in more detail and then advocate for a multidimensional network perspective that can address many of these topics.

Technology

Research has shown that individuals in modern society search for information online rather than through people, suggesting that technologies serve as external memory repositories in daily life (Sparrow et al., 2011). Others have shown that attributes of technologies applied by virtual teams shape how teams form shared mental models (Maynard & Gilson, 2014). However, despite the prevalence of technology embedded in today’s group work, only a handful of research we reviewed has included some examination of information technology use (e.g., organizational knowledge repositories, social media) in TMS (e.g., Kanawattanachai & Yoo, 2007; Lewis, 2004; Yuan et al., 2007). This stream of research has demonstrated that virtual teams can develop TMS through mediated communication (Kanawattanachai & Yoo, 2007), and technologies can alter how members share information among themselves. For instance, Yuan et al. (2007) found that heavy use of organizational digital knowledge repositories may suppress information sharing quality among members. Their findings indicate that technologies generate important implications for group communication, shared cognition, and performance and call for a more nuanced investigation of technologies’ impact on information sharing, TMS formation, and TMS functioning in today’s teams.

Human teams are also collaborating with intelligent technologies to perform tasks traditionally considered human work (Yan, Kim, et al., 2019). Recent research suggests that the use of robots or intelligent personal assistants (i.e., Amazon Alexa) in group tasks may disrupt group communication processes, hurting the development of shared cognition (Burke et al., 2004; Yan, Figge, et al., 2019) and task performance (Shaikh & Cruz, 2019). Therefore, research on how teams of humans interact and develop TMS with intelligent machines constitutes a promising research area.

Member Roles and Characteristics

Transactive memory is about diversity in member expertise. However, whereas existing research implicitly or explicitly addresses diversity in member expertise, the majority of studies in our review did not examine team diversity based on attributes such as demographics, motivation, cognitive styles, roles and status that are likely to shape group processes (van Knippenberg & Schippers, 2007). In two studies, researchers examined whether member-turnover would influence the relation between group communication and TMS (Argote et al., 2018; Christian et al., 2014), both revealing that the role criticality of the lost member may be a moderating factor. These findings suggest that the different types of diversity in TMS and how they together impact communication is a particularly worthwhile topic of study.

Non-Task and Nonverbal Communication

Most research in our review focused on verbal and task-oriented communication. Following the functional perspective, research on group communication and TMS focuses on task-related interactions and neglects the functions of social-relational communication in transactive memory (Hollingshead et al., 2005). Nevertheless, our review shows that members who like one another are more likely to exchange knowledge, thereby benefiting the TMS (Neff et al., 2014; Yuan et al., 2014). This finding demonstrates the significance of social-relational and emotional communication in transactive memory.

Furthermore, several studies revealed a decrease in the importance of explicit verbal communication as a TMS develops (Hollingshead, 1998a; Littlepage et al., 2008; Yoo & Kawanattanachai, 2001). Instead, group members may rely on tacit, nonverbal cues (e.g., eye contact) to form TMS and coordinate their information exchange (Hollingshead, 1998a; Yoon & Hollingshead, 2010). Yet much remains unknown about the processes of nonverbal communication in TMS. Today, new tools (e.g., smart watch, eye-tracking software) allow researchers to track and analyze the subtle nonverbal and paralinguistic signals (e.g., tone, gesture, attention) in daily social interactions (Pentland, 2010) and have great potential to produce new insights regarding group communication and TMS.

Communication Network and TMS Structure

Our review also revealed that research on communication and TMS tended to focus more on quantity or quality of communication than its structural patterns. There are inconsistent findings among the few studies that investigated communication network structure and TMS (e.g., Lee et al., 2014; Palazzolo, 2005; Prichard & Ashleigh, 2007). How group communication network structure, such as centralization and density, shape TMS formation, and function, is a continuing question for future research.

In addition to communication network structure, the study of communication and transactive memory structure represents another research opportunity. The handful of research examining variations in TMS structure across groups such as the degree of differentiation and integration (Gupta & Hollingshead, 2010; Gupta, 2012; Lewis et al., 2007; Mell et al., 2014) revealed significant differences in communication patterns and group performance under distinctive TMS structures. TMS structure within groups is dynamic and adaptive: specialization may develop based on members’ social capital rather than knowledge expertise (Whelan & Teigland, 2013). TMS structure is likely to adapt to aspects of the group context, such as the task and the distribution of member knowledge (Lewis et al., 2007; Sarcevic et al., 2008). Together, these studies support the necessity to further examine structural variation and its relation with group communication dynamics.

Group Context

The group context plays an important but understudied role in understanding how communication processes and TMS. Most reviewed research investigated task-performing teams in an organizational or a laboratory setting. Family, friendship, educational, and voluntary groups whose primary purpose is not task performance remain understudied. Moreover, teams situated in organizations are constantly shaped by organizational policies, their relative position in the organization, and their collaboration with other organizational teams (Seibold et al., 2014; Yan, Kim, et al., 2019). Yet only four studies examined the relations among aspects of the group context, communication, and TMS. The studies revealed that environmental factors and external communication greatly influenced TMS functioning (Ellis, 2006; Sarcevic et al., 2008) as well as the relation between TMS and group performance (Healey et al., 2009; Heavey & Simsek, 2015).

TMS Errors

Whereas most research supports the positive impacts of communication on transactive memory and group performance (Lewis & Herndon, 2011; Ren & Argote, 2011), evidence in our review showed that TMSs can malfunction and lead to suboptimal group outcomes if information about a task is not fully disclosed even when group members are interdependent (Huang, Liu, et al., 2013; Garner, 2006). Several factors discussed above (i.e., member relationships and emotions, TMS structure, and group context/external communication) may play a role in TMS functioning (e.g., Mell et al., 2014; Heavey & Simsek, 2015; Yuan et al., 2014). For instance, environmental uncertainty may determine which type of TMS structure is developed in teams and whether that structure is beneficial for information sharing and task coordination—a fast-changing environment faced by first responding teams may require teams to form flexible TMS structures in which member responsibilities are constantly negotiated in real time through communication (Majchrzak et al., 2007; Sarcevic et al., 2008).

A Multidimensional Network Perspective of TMS

Building from social network theory, we argue that a multidimensional network perspective (Contractor et al., 2011; Monge & Contractor, 2003; Yan, Kim, et al., 2019) can help researchers address these understudied topics theoretically and methodologically. A social network consists of a set of actors (nodes) connected by their relations (ties or edges) (Wasserman & Faust, 1994). From the network perspective, a TMS is a group-level network with individual memories as nodes and the TMS/communication processes as ties (Monge & Contractor, 2003). Multidimensional networks, as opposed to unidimensional networks with a single type of nodes and ties, consist of multiple categories of nodes (e.g., people, computers) as well as the various relations among them (e.g., friendship, task communication; Contractor et al., 2011; Monge & Contractor, 2003). The nodes can also include diverse attributes such as gender, race, tenure, expertise or organizational roles.

Viewing a group TMS from a multidimensional network perspective allows researchers to theorize TMS as a social system containing both human and technology nodes with multiple attributes and simultaneously occurring communication processes (e.g., verbal and nonverbal communication, social-relational and task-oriented communication). The nodes may differ from one another in terms of group members’ expertise and roles, allowing for the conceptualization and analysis of how members’ individual characteristics influence group communication and TMS structure. External communication can be studied as network ties between members and their embedding organization. The multidimensional network perspective can provide a theoretical basis for the advancement of research on communication and TMS and a set of methodological tools. The tenets of the perspective and suggested research questions are summarized in Table 1.

Table 1.

Summary of Tenets and Research Questions for a Multidimensional Network Perspective of Transactive Memory Systems.

Tenets	Potential research questions
Technologies are key components of TMS	• Can teams develop a TMS with technologies (e.g., digital knowledge repositories, artificial intelligence)? If yes, how? • How does malfunction of technologies (e.g., algorithm glitches, temporary loss of Internet connection) in teams affect communication and TMS utilization?
Member roles and characteristics matter	• How does organizational hierarchy/roles impact communication and TMS formation/functioning in field teams? What if leadership roles conflict with member expertise?
Multiple types of communication co-exist	• What are the roles of different types of communication networks (e.g., formal reporting network, friendship network, the emotional status between members) in TMS formation and function? • How do group members leverage nonverbal cues to coordinate retrieval in groups? How is the use of nonverbal cues related to verbal communication?
Communication and TMS structures vary	• How does communication network centralization affect the formation and function of TMS? Does the content of the communication network (e.g., task-related information, gossip, rumor) moderate the effects of network centralization on team TMS? If yes, how? • What are the different forms of TMS structure that may develop in teams? Will they lead to different patterns of group communication? In what ways?
Group context is vital	• How does the network position of a group/specific group members in its embedding organizational network shape its internal communication and TMS formation/function?
TMS can malfunction	• Under what conditions are TMSs more likely to lead to inefficient information sharing?

Technology as a Component of TMS

Based on the multidimensional network perspective, technologies can be viewed as an intrinsic component of group TMS, rather than a tool used by teams (Contractor et al., 2011; Yan, Kim, et al., 2019). The perspective is thus a useful theoretical tool to study teams when use of digital knowledge repositories are common in today’s organizations (Yuan et al., 2007), people are used to relying on computers for information (Sparrow et al., 2011), and technologies are becoming more intelligent (Yan, Figge, et al., 2019). Incorporating technology as a key component of TMS can expand TMS theory and research by promoting research on the consequence of technologies on team interaction, shared cognition, and performance (Fiore & Wiltshire, 2016). Some key questions to be investigated include: can teams develop a TMS with technologies (e.g., digital knowledge repositories, intelligent assistants)? If yes, how? How are TMSs incorporating technologies different from TMS with only people? How does malfunction of technologies (e.g., algorithm glitches, temporary loss of Internet connections) in teams affect team communication and TMS utilization?

Methodologically, social network analysis provides a set of concepts and analytical tools to analyze a TMS as a network consisting of both people and technology. With two-mode network analysis (i.e., analysis of networks with two types of nodes; Opsahl, 2020; Snijders & Bosker, 2012; Wang et al., 2014), researchers can map TMS network structures including human members and technology as nodes and their interactions as ties. The team-level communication structure with the technologies and its longitudinal evolutions can be investigated. Researchers can study the overall density, clustering, and centralization of TMS as a network of both human and non-human components, or scrutinize the structural position (e.g., centrality, structural equivalence) of specific members or technology to understand the impact of technologies on group interaction dynamics.

Member Roles and Characteristics

The multidimensional network perspective of TMS can complement the existing approaches and help incorporate member characteristics into TMS theory and empirical investigation. In a multidimensional network, nodes can differ in their attributes. For example, they can represent humans or artifacts, have different motivations and cognitive styles, and play different team roles. This enables researchers to address a set of questions that are missing in existing research. For instance, since it is common for organizational teams to assign team leaders or members with different ranks in organizational hierarchies, it is critical to understand to what extent the leaders or the diversity of member status may promote or interfere with team communication, TMS development, and TMS function. Some interesting questions include—do teams develop TMS structures based on team leadership roles, expertise, or both? And how? What if the team leader overlaps in expertise with another member, but is not considered as an expert in the area by the team? These questions tap into the complexity generated by member diversity in addition to heterogeneity in member expertise.

Social network analysis can quantitatively examine the impact of node attributes (e.g., tenure, experience, expertise) on tie formation in the networks. For example, researchers can study if members are more likely to seek information from an expert or a team leader. The attributes can also be analyzed at the dyadic or network level. One can study the extent to which people allocate information to peers with whom they share similar attributes (e.g., gender, motivation) or with those who have different attributes. These tools thus allow researchers of TMS to study the impact of member characteristics on group communication dynamics and TMS processes (i.e., directory updating, retrieval coordination, and information allocation).

Multiple Types of Communication

An advantage of the multidimensional network perspective is it acknowledges the coexistence of multiple types of relations among nodes and allows examination of these relations simultaneously. For example, scholars can investigate how social relationships, emotional status and nonverbal communication among members are associated with explicit task-oriented communications and with one another. Social network analysis enables researchers to compare the similarity of different networks, thus probing the potential influence of one network on the other. For instance, one can study to what extent the structure of a group conflict network (with ties as prior conflict happening between members) mimics the information-sharing network applying the Quadratic Assignment Procedure (QAP) (Krackhardt, 1988). A significant negative association would suggest that member conflicts are adversely related to information sharing, generating implications for group TMS processes.

Communication/TMS Structure

The multidimensional network perspective can advance research on group communication networks, TMS structures and their relations, and resolve inconsistencies in existing research (e.g., Manteli et al., 2014; Palazzolo, 2005; Prichard & Ashleigh, 2007). Social network theories offer conceptual and analytical tools (e.g., density, clustering, centralization, small-worldness) to study patterns of social interactions. For instance, researchers can theorize TMS structures as network patterns: a differentiated TMS structure can be represented as a network with nodes holding specialized memories and the disparity in nodes’ memory predicts the ties (or TMS processes) among the nodes; whereas with an integrated TMS the homophily of knowledge may be associated with a stronger TMS. A differentiated TMS structure may be related to more centralized information seeking if knowledge is needed from one of the knowledge areas specialized by the group members, but an integrated TMS may promote cohesion, enhancing the density of social-relational interactions in groups. These propositions are interesting hypotheses to be tested by future research.

In addition to social network analysis that allows an examination of group-level communication and TMS structures, development in group communication technologies (e.g., online collaboration systems, chatting software) makes it possible to experimentally manipulate group structure and content of communication network by directing who can talk to whom and restricting what can be discussed (e.g., Argote et al., 2018). Future research can investigate how various communication network configurations are related to TMS development and function in different task conditions.

Group Context

From the network perspective, the group context can be represented as the larger social network in which groups are embedded (Katz et al., 2004). Thus, a group’s exchange with other groups and their embedding organizations can be investigated as the set of ties external to group boundaries. Leveraging social network theories and analysis, TMS researchers can investigate how the network position of a group within the organizational network shapes its internal communication and TMS formation/function. Future research can also study the role of boundary spanners, or group members occupying structural holes in the broader organizational network (Burt, 2004) in group TMS. Other features of the group context such as stress level, time pressure, or resource level could be represented as individual attributes in the network.

TMS Errors

Taken together, the multidimensional network perspective can help researchers illuminate the conditions for ineffective TMS functioning. The theoretical and methodological apparatuses facilitate the formulation of more nuanced research questions and empirical analysis at multiple levels—from group members to the embedding context. By studying how member characteristics interact with communication structure and group context to shape the function of a developed TMS, researchers can gain insights about the factors that intertwine to impact TMS’s effectiveness. Variation in TMS structures, as discussed above, may also affect multiple types of group interactions, which in turn influences the effectiveness of team TMS in various contexts.

Conclusion

Our comprehensive review reveals a dynamic relation between communication and transactive memory systems that changes over the group’s lifespan. A multidimensional network perspective has the potential to advance TMS theory and research. It provides theoretical and methodological tools that allow researchers to incorporate technologies as a key part of TMS, address various member characteristics and roles, examine multiple types of communication and relations simultaneously, and investigate the impact of group context. Future studies should apply a variety of qualitative and quantitative research methods, from ethnography to in-depth interviews to social network analysis; and utilize new data sources (e.g., social behavior tracking) to enhance our understanding of the dynamic communication processes involved in the creation and utilization of a group’s memory system. In addition, research from other theoretical approaches such as cognitive, evolutionary, feminist, interpretive, indigenous, linguistic, and psychodynamic (to name a few) would be of great value for broadening and deepening our understanding of communication and the creation, meaning, practices and impacts of collective memory on groups, their members and their embedding context (cf. Poole & Hollingshead, 2005).

Supplemental Material

sj-pdf-1-sgr-10.1177_1046496420967764 – Supplemental material for Communication in Transactive Memory Systems: A Review and Multidimensional Network Perspective

Supplemental material, sj-pdf-1-sgr-10.1177_1046496420967764 for Communication in Transactive Memory Systems: A Review and Multidimensional Network Perspective by Bei Yan, Andrea B. Hollingshead, Kristen S. Alexander, Ignacio Cruz and Sonia Jawaid Shaikh in Small Group Research

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Bei Yan

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Bei Yan is an Assistant Professor at the School of Business, Stevens Institute of Technology, USA. She studies the influence, learning and coordination processes in technology-supported groups, ranging from teams interacting with artificial intelligence to large online crowds.

Andrea B. Hollingshead is a Professor of Communication, Psychology, and Management and Organizations at the University of Southern California, USA. Her research examines the relations between communication, technology, and group processes, and the topic of transactive memory.

Kristen S. Alexander is a research assistant at the Annenberg School for Communication and Journalism, University of Southern California, USA. Her research examines group communication processes and collective knowledge. Her interests include collaboration, human-computer interaction, expertise, and decision-making in teams and organizations.

Ignacio Cruz is a PhD Candidate at the Annenberg School for Communication and Journalism, University of Southern California, USA. His research examines the adoption and evaluation of artificial intelligent (AI) technologies within the workplace.

Sonia Jawaid Shaikh, PhD, is the George Gerbner Postdoctoral Fellow at the Annenberg School for Communication, University of Pennsylvania, USA. Her research program examines individual and group behaviors and decision-making within the context of AI technology and resource abundance and scarcity. She also studies research methods.

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