A dual-process contingency model of leadership, transactive memory systems and team performance

Journal of Business Research 96 (2019) 297–308

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Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres

A dual-process contingency model of leadership, transactive memory systems and team performance

T

⁎

Daniel G. Bachracha, , Ryan Mullinsb a b

Department of Management, Culverhouse College of Business, University of Alabama, United States of America Department of Marketing, College of Business, Clemson University, United States of America

A R T I C LE I N FO

A B S T R A C T

Keywords: Transactive memory systems Leadership Market dynamism Sales team performance

In complex markets, use of teams is becoming more prevalent to capitalize on shared knowledge and expertise across members – often called transactive memory systems (TMS). For organizations to execute and benefit from a transactive memory approach, it is critical to improve understanding of how leadership and external environments influence translation of TMS into improved performance. Drawing on leadership, transactive memory and contingency theories we examine internal and external factors to explain team performance via TMS. Using data from 79 sales teams in a Fortune 250 industrial goods and services firm, we find that transformational leadership has a stronger relationship with TMS in smaller teams and transactional leadership has a stronger relationship with TMS in less tenured teams. Finally, our results also indicate that the strength of the relationship between TMS and team performance depends on market dynamism. Implications of these results for theory and practice are discussed.

1. Introduction Progressively complex work in organizations and the consequent widespread adoption of teams has led to considerable focus on drivers of team performance (Kozlowski & Bell, 2003; Salas, Stagl, & Burke, 2004). This includes a proliferation of research on knowledge processes such as transactive memory systems (TMS; Chiang, Shih, & Hsu, 2014; Lewis, 2003). TMS, which is defined as the cooperative division of labor for learning, remembering and communicating relevant team knowledge (Hollingshead, 2001; Wegner, 1986), has recognized collective performance consequences across a range of contexts (Bachrach et al., 2018; Bachrach, Mullins, & Rapp, 2017; Faraj & Sproull, 2000; Lewis, 2004; Michinov, Olivier-Chiron, Rusch, & Chiron, 2008; Rau, 2005). While a number of process-related factors such as intimacy (Wegner, 1986), communication frequency (Lewis, 2004), prior learning (Lewis, Lange, & Gillis, 2005), familiarity (Lewis, 2004), and social network connectivity (Lee, Bachrach, & Lewis, 2014) have been associated with TMS, an intriguing question has emerged regarding the important antecedent role that leadership can play (Hammedi, van Riel, & Sasovova, 2013; Hood, Bachrach, & Lewis, 2014), which is our focus in the current study. For example, socio-cognitive processes underlie the team level information processing on which TMS depends (Ellis, 2006; Wegner, 1995). Hammedi et al. (2013) argued that transformational leadership ⁎

– defined as leadership that causes change in individuals and social systems (Bass, 1985; Bono & Judge, 2004) – can strengthen the coordinated social interactions and shared understanding (Day, Gronn, & Salas, 2004) necessary for TMS. Further, although evidence from the leadership domain suggests that transformational leadership (TFL) may generate more productive outcomes than transactional leadership (TAL) (Birasnav, 2014; Elenkov, 2002 – defined as leadership through rewards and incentives (e.g., Bass, Avolio, Jung, & Berson, 2003; Judge & Piccolo, 2004) – tangible inducements also may mechanically expedite the emergence of TMS (Hood et al., 2014). For example, Hood et al. (2014) argued that managers can encourage members to develop and share the “expertise maps that codify the informal domain differentiation characterized by TMS.” (2014, p. 11). However, the capacity of these dimensions of leadership to drive TMS likely depends on team characteristics that impact the fit between specific attributes of the approach and the team setting. Specifically, prior research has introduced team size as a critical boundary condition relating to the emergence of TMS (Palazzolo, Serb, She, Su, & Contractor, 2006; Ren, Carley, & Argote, 2006) and the role of transformational leadership in generating collaboration (Cha, Kim, Lee, & Bachrach, 2015). While TFL can drive collective focus and collaborative orientation critical to TMS, as physical and psychological distance increases with team size, its capacity to do so likely diminishes. Likewise, while TAL may provoke enthusiasm for developing the unique domains

Corresponding author. E-mail addresses: [email protected] (D.G. Bachrach), [email protected] (R. Mullins).

https://doi.org/10.1016/j.jbusres.2018.11.029 Received 19 April 2018; Received in revised form 16 November 2018; Accepted 17 November 2018 0148-2963/ Published by Elsevier Inc.

Journal of Business Research 96 (2019) 297–308

D.G. Bachrach, R. Mullins

2. Theoretical background and hypotheses

of expertise and information sharing foundational to TMS, because team tenure provides opportunities for these attributes to emerge implicitly (Hollingshead, 1998a) tenure may substitute for TAL as a driver of TMS in longer-tenured teams. Thus, the first goal of this study is to contribute by extending and deepening understanding of relationships between leadership and TMS, with a focus on contingencies impacting associations with both types of leadership. In conjunction with our focus on leadership antecedents of TMS, we also seek to enhance understanding of the relationship between TMS and team performance in an emerging performance context in the TMS domain. For example, in a sample of 54 sales teams, Bachrach et al. (2017) reported that TMS can strengthen relationships between both learning effort and service quality with salesperson performance. Yet, the role of TMS as a primary driver of team performance in customerfacing contexts remains unclear. Recent research has emphasized team knowledge management factors such as information exchange (Auh, Spyropoulou, Menguc, & Uslu, 2014), knowledge creation (Menguc, Auh, & Uslu, 2013) and team action processes (Rapp, Ahearne, Mathieu, & Rapp, 2010) as antecedents in sales settings, suggesting TMS may also play a key role in driving team performance in this domain. Furthermore, although TMS has recognized associations with team performance in general, little is known about factors that impact this relationship. For example, Ren and Argote (2011) urged that, “More research is needed…on understanding …factors that moderate the relationship between transactive memory systems and team performance” (p. 223). Specifically, it is widely established that the context and structure of work must ‘fit’ in order to achieve productive outcomes (Drazin & Van de Ven, 1985). Operational context also is critical for understanding the performance potential of constructs such as TMS, where the generation of productive outcomes depends on fit between the team's operational characteristics and its environment (Donaldson, 2001). Ren and Argote's (2011) call relating to conditions under which TMS may be most likely to lead to productive outcomes coincides with a growing focus on team performance in dynamic environments (Burke, Stagl, Salas, Pierce, & Kendal, 2006; Marques-Quinteiro, Curral, Passos, & Lewis, 2013; Rico, Sánchez-Manzanares, Gil, & Gibson, 2008). In an effort to address their call, we focus on a performance contingency with the potential to influence the fit between teams' operational characteristics and environment – market dynamism. Market dynamism is defined as the frequency and velocity of changes in customer preferences and competitive offerings (Davis, Eisenhardt, & Bingham, 2009; Dess & Beard, 1984). Our focus on market dynamisms is informed by research which suggests that TMS may play a role in how effectively teams adapt to dynamic work contexts (Marques-Quinteiro et al., 2013). TMS can help teams to more quickly locate and selectively leverage information and expertise in real-time in response to dynamic markets, and thus may play a more important role in team performance when these capacities are integral to team success. In testing the model in Fig. 1, we develop a framework to explain: 1) conditions impacting the relationships between distinct leadership approaches and TMS; and 2) conditions impacting the relationship between TMS and team performance and aim to make several contributions. First, building from leadership theory we extend the range of TMS antecedents to explain the role played by multiple leadership drivers (i.e., TFL; TAL). Given the relatively scant attention to TFL and TAL within the TMS domain, we seek to contribute with focus on TMS as a key mechanism translating TFL and TAL into team performance. Second, building from current framing in the TMS area we examine team characteristics (i.e., size; tenure) with potential to impact relationships between these forms of leadership and TMS. Finally, responding to calls from the literature we examine a theoretically derived moderator of the relationship between TMS and team performance (i.e., market dynamism). Thus, we provide contextual guidance – based on market dynamism – to help understand when TMS offers collective performance benefits.

2.1. Transactive memory systems (TMS) TMS theory provides that members of collectives can function as external memory aids to one another (Wegner, 1986). TMS allows members to encode, store and retrieve information (Lewis & Herndon, 2011), providing members with insight into who knows what and who is best at what within a team. Members of collectives with a functioning TMS maintain two types of meta-memories. These relate to the kinds of knowledge and information maintained by each member, and to the location of these disparate domains within the team. Expertise and location knowledge are encoded, stored, and retrieved through on-going transactions (Wegner, 1995). A consequence is that TMS enhances the speed of information search, facilitating efficient application of knowledge and more fluid adaptation to performance contingencies. The system is described as transactive because it depends on intermember interaction and communication. This fosters deeper, more functional, specialized collective knowledge, providing teams with efficient access to more task-critical information (e.g., Austin, 2003; Moreland, 1999). 2.2. Contingency theory However, these relationships may be subject to contingency factors impacting the fit between the processes and structures within teams with a functioning TMS and the team's operating environment. For example, research suggests that relationships between TMS and team performance may depend on communication medium (Griffith, Sawyer, & Neale, 2003; Hollingshead, 1998b), which can impact the effectiveness with which members are able to locate, verify and retrieve expertise (Lewis, 2004; Yuan, Carboni, & Ehrlich, 2010). Although there is a relatively broad class of contingency theories (Drazin & Van de Ven, 1985), ranging from focus on leadership (Fiedler, 1981) to organizational structure (Lawrence & Lorsch, 1967), a key underlying premise is that there is no best way to lead, organize or make decisions (Beersma et al., 2003). The most productive outcomes emerge when approaches to leading or decision-making coincide with internal and/or external contingencies impacting fit between the approach and environmental constraints. We leverage the notion of fit to explain the role played by internal (i.e., size; tenure) and external (i.e., market dynamism) contingencies relating to the emergence and consequences of TMS. 2.3. Transformational leadership and TMS In light of its potential to enhance team outcomes, one of our primary goals is to expand insight into discrete leadership drivers of TMS. TFL has broadly recognized benefits for team performance (Schaubroeck, Lam, & Cha, 2007), group processes (Wang & Howell, 2010) and team collaboration (Kahai, Sosik, & Avolio, 2003). Through idealized influence, inspirational motivation, intellectual stimulation and individualized consideration transformational leaders motivate followers to work collaboratively, beyond their immediate self-interests (Bass, 1985; Judge & Piccolo, 2004). Collaborative culture is a recognized consequence of TFL (e.g., Hammedi et al., 2013; Peltokorpi & Hasu, 2016), supporting this behavior as a driver of TMS, which depends on coordinated interactions for the development and maintenance of collective knowledge. For example, transformational leaders can create changes in individuals and social systems, influencing members to transcend personal interests (Howell & Higgins, 1990) and focus on working together to establish and maintain collective understanding of who knows what and who depends on whom to achieve team goals. Transformational leaders also encourage followers to focus on collective outcomes (Bass, 1985), promoting and sustaining a shared vision and understanding that contributes to more effective teamwork (Day et al., 2004). 298

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Time 2

Time 1

Team Size

Shaping Team Values Transformational Leadership

Market Dynamism

–

+

+ Transactive Memory System

+ Transactional Leadership

Team Performance

– Sales Team Response

Clarifying Expectations

Team Tenure

Manager Response Archival Records

Fig. 1. Proposed conceptual framework.

knowledge relating to who knows what and who depends on whom within a team. Contingency theory suggests that mechanical differences (e.g., smaller teams are better able to communicate and coordinate) impact the fit between TFL and TMS. In larger teams, the capacity of TFL to facilitate the coordination and communication that underlie TMS is likely to be more limited. Coordination and communication also are likely to be more difficult due to members' physical and psychological distance (Reagans & McEvily, 2003). These difficulties increase the probability that members will have poorer understanding of the member-expertise associations that undergird TMS (Moreland, 1999; Palazzolo et al., 2006). Further, although TFL can motivate followers to work cooperatively, the coordination that TFL can generate also is likely to be more diffuse in larger teams, making it difficult for followers to discover and integrate members' uniquely-held knowledge (Moreland, 1999; Ren et al., 2006). Because transformational leaders' capacity to facilitate integrated, collaborative interactions and communication is likely to be more limited in larger teams, we expect that team size weakens the strength of the relationship between TFL and TMS, leading to the following:

For example, Zhang, Cao, & Tjosvold, 2011reported that TFL encourages members to approach conflict cooperatively, promoting intrateam coordination. Again, this focus on collective outcomes and cooperative understanding is core to the functioning of a TMS, which depends on members explicitly specializing in unique domains of collective work. Finally, TMS also requires teams to work collectively to develop and coordinate specialized knowledge and expertise; and TFL emphasizes the role of individual contributions to collective goals (Burke et al., 2006). Because transformational leaders can motivate focus on team outcomes, inspire cooperative effort toward collective goals, and motivate followers to transcend standard performance expectations, we predict the following: Hypothesis 1a. Transformational leadership is positively associated with TMS.

2.4. Team size as a moderator of transformational leadership However, building from current insight from the TMS literature, the strength of the relationship between TFL and TMS is likely to be impacted by a dilution of leaders' influence and capacity to effectively inspire coordinated interactions. Specifically, team size has been introduced as a critical boundary condition relating to the emergence of TMS (Palazzolo et al., 2006; Ren et al., 2006). The culture of collaboration inspired by TFL provides teams with opportunities to develop insight into who is best at what within the team. This is essential for the development of a TMS. We argue below that the capacity of TFL to catalyze the coordinated interactions and communication necessary for TMS is likely to be diminished in larger teams. Research has explored TMS in teams (and other collectives) across a range of different sizes. For example, while Wegner's original research focused on relational dyads (Wegner, 1986), research also has examined TMS in larger groups of thirty or more (Ren et al., 2006). More specifically, team size has been a recurring point of focus because it has implications for the conditions necessary for the mechanical emergence of TMS. For example, Michinov and Michinov (2009) reported that smaller groups may be able to coordinate more effectively, while Palazzolo et al. (2006) reported that smaller groups also tend to communicate more effectively. Thus, team size may impact the emergence of meta-

Hypothesis 1b. Team size moderates the relationship between transformational leadership and TMS, such that the relationship weakens as team size increases.

2.5. Transactional leadership and TMS Compared to TFL, TAL depends less on transcendent collective processes and focuses, rather, on explicit links between rewards/punishments and behavior. A great deal of research supports this tenet of management theory; followers are significantly more likely to engage in behaviors which are rewarded and avoid behaviors which are punished (Judge & Piccolo, 2004). In light of the fact that TMS has a recognized, consistent impact on team performance, the importance of coordinated, integrated work effort is likely to be salient to team leaders. Teams can develop a shared directory of expertise associations in a number of ways, including past performance records (Moreland & Myaskovsky, 2000), perceptions and expectations (Hollingshead & Fraidin, 2003) or joint training experiences (Liang, Moreland, & Argote, 1995). However, 299

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Hood and colleagues argued “the structure and transactive processes underlying TMS may be encouraged, practiced … and rewarded …. Managers may reward employees for the development and sharing of explicit member expertise maps.” (Hood et al., 2014: p. 11). Consistent with this argument, we expect transactional leaders to seek to motivate the structures and processes underlying TMS by rewarding behaviors that contribute to its emergence; these include behaviors such as developing and sharing specialized knowledge and expertise associated with team tasks, coordinating work efforts and accepting suggestions from other team members. It is broadly recognized that TAL can have a strong impact on critical workplace behavior. Thus, we expect that TAL is likely to have a positive effect on the behaviors that drive the structures and processes critical to TMS, and as a consequence should have a positive relationship with TMS, and the following:

Hypothesis 2b. Team tenure moderates the relationship between transactional leadership and TMS, such that the relationship weakens as tenure increases.

2.7. Relationship between TMS, market dynamism and team performance Finally, as noted above, TMS facilitates efficient encoding, storage and retrieval of critical task information and expertise, enhances the speed of information search, and enables fluid adaptation to performance contingencies (Austin, 2003; Lewis, 2003; Lewis & Herndon, 2011; Moreland, 1999). Teams with a functioning TMS have access to deeper and more functional collective knowledge, as well as higher quality information (Lewis, 2004; Moreland, 1999). Sales teams in particular are uniquely positioned to benefit from the improved knowledge and information management available with a functioning TMS. For example, salespeople have access to valuable market intelligence which can improve sales team outcomes when this information is collectively shared and disseminated among team members (Auh et al., 2014). Thus, when task-critical knowledge is stored and accessed via transactive memory, teams should benefit from faster knowledge retrieval and a broader collective knowledge base to leverage during task activities. In light of these attributes, coupled with the consistent relationship between TMS and team performance reported in the literature (Bachrach et al., 2018; Faraj & Sproull, 2000; Lewis, 2004; Rau, 2005), we expect TMS is positively related to team performance in the current study.

Hypothesis 2a. Transactional leadership is positively associated with TMS.

2.6. Team tenure as a moderator of transactional leadership However, several leadership theories (i.e., House, 1996) have identified experience or tenure as a critical factor impacting leadership effectiveness. We expect the operant relationships that drive the emergence of TMS via TAL are likely to be less impactful as team tenure (i.e., the length of time members have worked together) (Schippers, Den Hartog, Koopman, & Wienk, 2003) increases. A critical hurdle to a functioning TMS is insight into what other members know and reliance on others for their unique domains of expertise, skill, and task knowledge. We argue above that TAL can contribute to the emergence of TMS. As managers encourage and reward employees for developing and maintaining unique domains of responsibility, developing and sharing their expertise maps – which catalogue the domain differentiation that defines a TMS – and for working with one another in coordinated ways that coincide with these maps (Hood et al., 2014) TMS should increase. However, TMS also can develop organically as members implicitly divide the cognitive labor for learning, remembering, and communicating task-relevant information (Wegner, 1986). In this way, expertise maps develop as members gain experience with one another, learn about one another's expertise, divide the labor for learning, remembering, and communicating workrelated information (Hollingshead, 1998a; Lewis, 2003). For example, as Lewis, Belliveau, Herndon, and Keller (2007) noted: “groups develop an implicit structure for dividing responsibility for information based on members' shared understanding of one another's expertise. (p. 160). This implicit structure emerges as members gain experience with one another, come to understand one another's areas of expertise and ultimately begin to rely on one another for knowledge and skills in discrete aspects of collective activities. Thus, members' experience with one another can provide a foundation for implicit development of the processes and structures that define the TMS. Although TAL can catalyze rewards-driven motivation for members to develop and share their expertise and coordinate their efforts (Hood et al., 2014), experience working together may function as a TAL substitute (Podsakoff, MacKenzie, & Bommer, 1996). Specifically, in teams with less tenure, where knowledge of members' expertise has only emerged superficially and reliance on other members for their unique skill domains has yet to develop momentum, leaders may encourage this process by offering incentives for doing so (Hood et al., 2014). However, as team tenure increases, insight into members' expertise domains has had more of an opportunity to solidify and reliance on others' expertise has had more of an opportunity to develop inertia. As this awareness and these patterns of interaction cohere, the influence of contingent incentives to develop and share unique domains of expertise is likely to be weaker, and thus the fit between TAL and TMS is likely to be lower at higher levels of team tenure, leading to the following:

Hypothesis 3a. TMS is positively associated with team performance.

2.8. Market dynamism as a moderator of the TMS-performance relationship However, although TMS has widely recognized team performance benefits, little is known about contingency factors that moderate this relationship. Below, we explain the role of market dynamism. Our focus on market dynamism is informed by theory and evidence that TMS can play a role in how effectively teams are able to respond adaptively (Marques-Quinteiro et al., 2013). TMS increases decision making speed and the number of options teams are able to consider, which is critical in dynamic markets. As an external contingency, market dynamism increases information processing hurdles and complexity (Dess & Beard, 1984; Tushman, 1979). Dynamic markets are unpredictable, characterized by rapid change and uncertainty (Miller, Ogilvie, & Glick, 2006). As a consequence, effectiveness depends on sophisticated information search and processing routines (Eisenhardt, 1989), and consideration of a broad range of alternatives (Judge & Miller, 1991). The capacity of teams to achieve quality performance outcomes becomes more difficult as a consequence of dynamism and complexities in information processing. Although intrateam dynamism in the form of membership change (Anderson & Lewis, 2014; Lewis et al., 2007) for example, can impact collective learning, extra-team dynamism may enhance the fit between the structures and processes in teams with a functioning TMS and the environment. When market dynamism is lower, information processing hurdles also are likely to be lower and decision effectiveness depends less on integrated and coordinated knowledge and expertise. Decision-making and task execution are more predictable because the operating environment is more predictable, and thus the benefits of a TMS for achieving team performance are likely to be lower. Importantly, cultivating and establishing a TMS consumes scarce resources. For example, developing and maintaining collective awareness of distributed knowledge and expertise can engender connection and synchronization costs or “communication overhead” (MacMillan, Entin, & Serfaty, 2004). Communication overhead reflects the time spent in communication 300

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Table 1 means, standard deviations, and bivariate correlations among study variables. Variables 1. Transformational leadership 2. Transactional leadership 3. Transactive memory system 4. Team size (members) 5. Team tenure (years) 6. Market dynamisma 7. Competitive reward climatea 8. Industry experience (years) 9. Gender diversity (%) 10. Team performance (% to quota) Average variance extracted Composite reliability

Mean 5.35 5.61 5.35 6.81 4.51 4.69 3.37 11.32 2.94 100.16

Team-level SD 1.02 0.81 0.54 2.09 2.32 1.18 1.11 3.55 6.48 2.61

Employee-level SD 1.62 1.17 1.03 – – – – 8.07 – –

1

2

(0.97) 0.78⁎ 0.69⁎ −0.17 −0.16 −0.03 −0.14 −0.10 0.06 0.14 0.85 0.97

3 ⁎

0.75 (0.88) 0.71⁎ −0.13 −0.10 −0.12 −0.14 0.01 0.12 0.15 0.73 0.89

4

5

6

7

8

9

10

– 0.18 −0.15 0.12 0.06 0.24⁎ −0.08 – –

– 0.06 0.06 0.40⁎ −0.04 −0.06 – –

(0.85) −0.29⁎ 0.13 −0.31⁎ −0.04 0.54 0.85

(0.77) −0.01 0.06 −0.09 0.67 0.79

– −0.08 0.05 – –

– −0.15 – –

– – –

⁎

0.55 0.50⁎ (0.92) −0.11 −0.08 −0.05 −0.15 −0.02 0.16 0.20⁎ 0.72 0.88

Note: N = 538 for individual-level variables. N = 79 for team-level variables. Cronbach's alphas are reported on the diagonal. Individual-level correlations are above the diagonal. Team-level correlations are below the diagonal. ⁎ p < .05. a Manager responses.

offerings that team members routinely access for assistance in the execution of their roles. For example, teams sell and support a wide range of products and services linked to several different mechanical systems. In order to be successful, teams need broad and deep knowledge of these offerings not only to make initial sales, but also to provide continuing on-site customer support (e.g., efficiency checks; repairs; installations) and make effective cross-sell offers. Given these complexities, members often seek out “experts” on particular systems to assist with repair/installation needs, support questions, or provide general guidance on unfamiliar features. Neither the firm nor managers classify these experts formally. Crucial here, members understand that certain others carry the knowledge needed to help them in their own roles. In this way, members operate interdependently to provide knowledge, support, and assistance to one another to fulfill role responsibilities. Before data collection, we conducted in-depth interviews with executives, managers, and front-line salespeople to ensure our materials were appropriate for the firm's context. Results from this evaluation supported our focus in this setting. Our data collection involved two-time periods over a 4-month span. Time 1 included the employee- and manager-level survey administration. Reminders to complete the survey were sent at 2 and 3 weeks following the initial distribution (1 Month). Three months following the survey, we collected archival team performance data (i.e., percentage of quota) over a three-month period (Months 2–4). We distributed surveys to 752 salespeople in the eastern United States and their 89 team managers. We received responses from 602 salespeople (80%) and 87 managers (98%). After removing incomplete and unmatched surveys, and cases lost to attrition, we arrived at a final matched sample of 538 salespeople nested within 79 sales teams (~6.81 members per team, SD = 2.09). Additional analyses showed that the incomplete sample did not differ significantly from the final sample on any of the variables included in our model. We found no significant differences between early and late responders, and the final sample was an average age of 27.2 years (SD = 4.69) with 7.43 years of sales experience (SD = 3.72).

with team members at the expense of productive work. While critical, communication overhead also has the potential to engender a cognitive and communication load that diminishes teams' capacity to complete core tasks. Resources diverted toward TMS may be misallocated because the decision speed and breadth afforded by TMS are less relevant in placid markets (Modi & Mishra, 2011). In contrast, when market dynamism is higher, fluid decision making, integration, and coordination of disparate knowledge and expertise is critical because decision makers are unlikely to either possess or have ready access to all of the necessary information to make effective decisions. TMS allows teams to more quickly locate expertise embedded within the team, enabling generation of creative solutions in real time in response to market changes (Gino, Argote, Miron-Spektor, & Todorova, 2010). TMS also facilitates team learning (Lewis et al., 2005), which is critical as dynamic market conditions require teams to establish new approaches to generating productive outcomes, leading to the following: Hypothesis 3b. Market dynamism moderates the relationship between TMS and team performance such that the relationship is stronger when market dynamism is higher. As a body, the model we develop in Hypotheses 1–3 lead us to predict a conditional indirect relationship between leadership, TMS, and team performance. Specifically, we expect that both TFL and TAL have strong, indirect positive relationships with team performance through TMS in teams operating in dynamic market contexts, leading to the following: Hypothesis 4. Market dynamism moderates the strength of the mediated relationships between (a) transformational and (b) transactional leadership with team performance via TMS, such that the mediated relationships are stronger under high market dynamism.

3. Methods 3.1. Participants and data collection We tested our model using lagged, multisource data that included employee and supervisor sources, and objective archival performance outcomes from a Fortune 250 industrial goods and services supplier. Employees in this firm are organized in teams to sell and service geographic territories to meet formally defined team quotas. This context is particularly suited to test our model because the firm's goods and services are highly technical, with new innovations introduced multiple times a year, making the extensive knowledge domain unsuitable for compartmentalization by any given individual. Rather, teams collectively have knowledge “housed” within members for particular

3.2. Measures Table 1 provides the means, standard deviations, correlations, as well as measurement validity statistics. All variables were measured using established scales. Based on referent-shift logic (Chan, 1998), we used the team and the manager as referents for the items in our employee surveys, and aggregated responses to the team level. We assessed within-team agreement, rwg(j), group-level effect size, ICC(1), and interrater reliability, ICC(2) to determine whether aggregation was justified (Chen & Bliese, 2002). Although some items had rwg(j) values 301

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3.2.3. Team performance We operationalized performance using an objective measure of teams' archived quarterly sales totals relative to an established target referred to as percentage of quota (Mathieu, Maynard, Rapp, & Gilson, 2008). Percentage of quota represents a conservative performance measure because it controls potential contaminating factors such as territory size, team ability and previous sales (Churchill Jr, Ford, Hartley, & Walker Jr., 1985). Because it is a visible and collective team goal, percentage of quota has been used previously as a performance outcome of team level processes (e.g., team efficacy; Rapp, Bachrach, Rapp, & Mullins, 2014) and behavior (e.g., team helping behavior; Ahearne, MacKenzie, Podsakoff, Mathieu, & Lam, 2010). Team sales quotas were established by an outside consultant based on a number of factors including territory size and customer density which helps control extraneous factors.

below the 0.70 threshold, we followed Chen, Mathieu, and Bliese (2005) and retained all available cases for analysis. We abided by a threshold of ICC(1) greater than 0.05 as evidence for group-level effects, while ICC(2) values greater than 0.50 served as evidence for reliable group means (Bliese, 2000).

3.2.1. Employee responses We used scales adapted from MacKenzie, Podsakoff, and Rich (2001) to capture members' perception of their manager's transformational leadership (α = 0.97) (7 items) and transactional leadership behaviors (α = 0.88) (3 items). Example items include “When leading our team, my manager leads by example,” and “When leading our team, my manager always gives positive feedback when a team member performs well.” While TFL is often modeled as a higher order construct, several studies find equally good fit and greater parsimony with single factor model using selected scale items from each dimension (e.g., Barling, Loughlin, & Kelloway, 2002; Boichuk et al., 2014; Bono & Judge, 2003). In line with these studies, we measured TFL by drawing 4 items across the subdimensions of core TFL and 1 item each from the other 3 dimensions. As is typically found, these items were highly interrelated, with interitem correlations between 0.81 and 0.88 before aggregation. Exploratory factor analysis also revealed that the items formed a single factor that explained 85% of the variance among the 7 items. Thus, we combined the selected scale items to form a single TFL factor. For TAL, we initially drew two items for each of the two dimensions. However, a reverse scored item exhibited poor loading and was removed from the analysis. With the remaining 3 items, we still found strong evidence for a single factor scale with high interitem correlations (0.78–0.85) and a single factor explaining 73% of the variance among the items. Thus, we combined the remaining 3 items to form a single TAL factor. To operationalize team level measures, TFL and TAL were indexed as the average rating across members of each team. Both constructs exhibited good consistency (median rwg(j) = 0.77 and 0.76), strong evidence of group effects (ICC(1) = 0.34, (F = 4.64, p = .000) and 0.19, (F = 2.60, p = .000)) and good reliability (ICC(2) = 0.78 and 0.62). We measured TMS using 13 items adapted from Lewis (2003) resulting in strong reliability (α = 0.92). Sample items included “Different team members are responsible for expertise in different areas,” and “My team has a lot of faith in each team member's ‘expertise’.” To ensure the validity of TMS as a second-order factor, consistent with how the construct is typically measured, we conducted a confirmatory factor analysis to support the use of the three related first-order factors (e.g., specialization, credibility, coordination) as indicators of TMS (Lewis, 2003). Results demonstrated strong fit (χ2(62) = 282.25, p < .01; RMSEA = 0.07, CFI = 0.96; SRMR = 0.05), providing support for TMS as a second-order factor. We operationalized TMS at the team-level by indexing average ratings across team members. This approach showed high consistency (median rwg(j) = 0.72) and good evidence of group effects and reliability (ICC(1) = 0.13; (F = 2.06, p = .000); ICC(2) = 0.52), supporting aggregation to the team level.

3.2.4. Control variables Previous research reveals that team-based factors such as experience (Rapp et al., 2014), reward structure (Beersma et al., 2003) and demographics (Horwitz & Horwitz, 2007) are likely to play a role in explaining team-based constructs. Thus, we covaried additional teamlevel variables – average team industry experience, competitive reward climate (Yilmaz & Hunt, 2001), and gender diversity (i.e., average of the coded gender variable across the team where 0 = male; 1 = female) to help rule out alternative explanations for our findings. We adopted a summary index model to aggregate these variables (Chan, 1998) using the average value derived from each team, which aligns with previous research (Chen & Bliese, 2002). Adopting a conservative approach, we included previous team performance as an additional covariate in our analysis, and we also covary TFL with TAL, given their positive association. 3.3. Analysis We conducted a single-level confirmatory factor analysis to examine whether our latent measures captured distinct constructs. Following recommendations by Hu and Bentler (1991), we used a combination of fit index thresholds to provide evidence of model fit. Model fit results indicated the latent measurement model fit the data well (χ2(224) = 762.45, p < .01; RMSEA = 0.06, CFI = 0.96; SRMR = 0.05). All indicators loaded significantly on their respective constructs. Prior to discussing the results, it is important to acknowledge that while some variables were formed through aggregation (i.e., leadership behaviors, TMS), these variables were collected from team members' reports. Thus, there was potential for common method variance (CMV) to inflate variable associations. We took several steps to detect and mitigate potential CMV. We followed Podsakoff, MacKenzie, Lee, and Podsakoff's (2003) guidance for ex ante considerations by using separate sources (salespeople, sales managers, archival data) for the predictor and criterion variables. In addition, the survey was designed to ensure concise measurement, randomly ordered items, and that participants understood their anonymous status. Regarding ex post considerations our results indicate significant interaction effects. This undermines the plausibility of implicit theories of CMV a driver of our results (Siemsen, Roth, & Oliveira, 2010).

3.2.2. Manager responses We also drew responses from team managers to capture market dynamism, measured using five items from previous research (e.g., Jayachandran, Sharma, Kaufman, & Raman, 2005; Rapp, Trainor, & Agnihotri, 2010). This scale also demonstrated strong reliability (α = 0.85) with sample items such as “In our district, customer preferences change frequently.” Following previous research (De Dreu, 2007), team tenure was assessed by asking each team manager how long the majority of current team members had been on the team. We operationalized team size based on the firm's organizational hierarchy, which was used to link team member and manager responses. As an additional check, we asked managers to confirm team size reported in the hierarchy (De Dreu, 2007).

4. Results 4.1. Model specification testing Because our model encompassed multiple dependent variables, including latent interactions, we used covariance-based structural equation modeling (SEM). This approach provides a robust option to test theoretical questions such as those present in the model we describe. We mean-centered all focal model variables to reduce multicollinearity 302

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related to TMS (β = −0.25, p < .01); supporting Hypothesis 1b. To illustrate this interaction (Fig. 2a), as well as those that follow, we adopted Cohen, Cohen, West, and Aiken's (2013) approach, using simple slopes analysis to plot this effect.

Table 2 Structural equation modeling results. Main effects model

Full effects model

β

SE

β

SE

DV: Transactive memory system Transformational leadership Transactional leadership

0.45⁎⁎ 0.34⁎⁎

(0.12) (0.12)

0.47⁎⁎ 0.41⁎⁎

(0.09) (0.09)

Covariates Team gender diversity Team industry experience Team size Team tenure

0.09 −0.07 −0.10 0.12

(0.08) (0.08) (0.08) (0.08)

0.03 −0.06 −0.05 0.09

(0.04) (0.04) (0.04) (0.06)

–

–

−0.25⁎⁎

(0.08)

–

–

−0.23⁎⁎

(0.08)

0.40⁎⁎

(0.18)

0.44⁎

(0.26)

0.27⁎⁎ −0.07 −0.10 −0.18 0.13 −0.13 −0.04 −0.14

(0.11) (0.17) (0.18) (0.11) (0.11) (0.11) (0.14) (0.12)

0.14 −0.08 −0.03 −0.08 0.10 −0.10 −0.04 −0.12

(0.10) (0.11) (0.12) (0.07) (0.07) (0.07) (0.12) (0.11)

–

–

0.54⁎⁎

(0.16)

Interactions Transformational leadership × team size Transactional leadership × team tenure DV: Team performance Transactive memory system Covariates Previous performance Transformational leadership Transactional leadership Team gender diversity Team industry experience Team tenure Competitive reward climate Market dynamism Interactions Transactive memory system × market dynamism Δdf −2Log-likelihood −2LL change AIC BIC ⁎⁎ ⁎

– 2362.83 – 2462.83 2423.65

4.2.2. Transactional leadership, team tenure, and TMS In support of Hypothesis 2a, we predict and find a significant, positive association between TAL and TMS (β = 0.41, p < .01). In Hypothesis 2b we predict that team tenure weakens this relationship, and find that the interaction between TAL and team tenure has a significant negative effect on TMS (β = −0.23, p < .01). Fig. 2b illustrates this effect. Together, we find support for both H2 a and b. 4.2.3. TMS, market dynamism, and team performance Hypothesis 3a posited that TMS is positively associated with team performance. We find support for this link (β = 0.40, p < .05). In Hypothesis 3b, we propose that the relationship between TMS and team performance is strengthened in dynamic markets. In support of Hypothesis 3b, we find that the interaction between market dynamism and TMS has a significant and positive effect on team performance (β = 0.54, p < .01). We illustrate this significant influence in Fig. 2c. To fully test Hypothesis 4 we followed Preacher, Rucker, and Hayes (2007), and implemented the model indirect syntax in Mplus. For Hypotheses 4a and b, we focused on the conditional indirect effect of market dynamism on the indirect relationship between leadership and team performance. We found that the indirect relationship between both forms of leadership and team performance is stronger under high market dynamism, providing support for both Hypotheses 4a and b. Specifically, the unstandardized indirect relationship between TFL and team performance as mediated by TMS is positive for high market dynamism (B = 2.37, p < .01; 95% CI [0.80, 3.94]), and negative but insignificant at low market dynamism (B = −0.28, p > .10; 95% CI [−1.53, 0.97]). The effect of the difference between the two conditions was 2.65 with a 95% CI of [0.82, 4.47]. Similarly, the unstandardized indirect relationship between TAL and team performance through TMS is positive under high market dynamism (B = 1.69, p < .01; 95% CI [0.55 2.82]) while having a negative, but not significant relationship under low market dynamism (B = −0.20, p > .10; 95% CI [−1.09, 0.69]). The effect of the difference between the two conditions was 1.88 with a 95% CI of [0.57, 3.20].

3 2332.04 30.79⁎⁎ 2438.05 2396.52

p < .01. p < .05.

and facilitate interpretation of interaction effects. We tested two successive models to allow nested model fit comparisons. In Table 2, we provide these comparisons using log-likelihoods, as well as the Akaike's information criterion (AIC) and Bayesian information criterion (BIC) indexes. We first fit a main-effects only model that included all controls and main effects. This model demonstrated strong fit (χ2(82) = 99.95, p > .05; RMSEA = 0.05, CFI = 0.95; SRMR = 0.07). The final model was built from the main effects model by including the proposed interactions. To accommodate the latent interaction in our model (TMS x Market Dynamism) we applied the latent moderated structural equations method (Klein & Moosbrugger, 2000) using MPlus software. Standard fit indices are not available when employing the numerical integration procedure with this approach, so we used a scaled loglikelihood difference test to compare the fit between models 2 and 3. Nested model comparisons showed significant improvement in fit (Δχ2(3) = 30.79, p < .01), providing strong support for the proposed model.

4.3. Additional robustness checks As an additional robustness check, we tested an alternative model specifying market dynamism to interact with the antecedents of TMS. We specified an alternative model where market dynamism moderates the relationships between TFL, TAL, and TMS. Results indicated that the alternative moderator specification did not significantly improve model fit as compared to the main effects model (Δχ2(3) = 4.25, p > .10, AIC = 2464.19, BIC = 2422.65). However, while the interactions did not achieve significance, we did find that the interaction between TFL and market dynamism was positive (β = 0.20, SE = 0.12) and the interaction between TAL and market dynamism was negative (β = −0.22, SE = 0.14), suggesting that future research should examine for potential differential contingencies for leadership approaches and TMS. Given the relative difference in effect sizes, we also examined whether one of the two leadership styles had a greater effect on TMS. We tested differences between these leadership effects by constraining both effects to be equal and compared the fit of the constrained model with that of the unconstrained model using chi-square values. Results showed that the difference between the constrained and unconstrained models was not significant (Δχ2(1) = 2.84, p > .10), providing little evidence of differential effects between TFL and TAL on TMS. However, this finding substantiates the importance of each leadership approach to enacting TMS in the sales team context.

4.2. Hypothesis testing 4.2.1. Transformational leadership, team size, and TMS Table 2 presents the SEM coefficient estimates for our hypothesized model. Hypothesis 1a posits a positive association between TFL and TMS. As can be seen in the full effects model, we find support for this link as TFL is significantly, positively associated with TMS (β = 0.47, p < .01). Building on this relationship, Hypothesis 1b predicts that team size weakens the link between TFL and TMS. Results show that the interaction between TFL and team size was significantly, negatively 303

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c

High Team Size Average Team Size Low Team Size

7

Team Performance (% to Quota)

Transative Memory System

a

6

5

4

102

High Market Dynamism Average Market Dynamism Low Market Dynamism

101 100 99 98

Low Transformational Leadership

High Transformational Leadership

Low Transactional Memory System

High Transactional Memory System

b Transative Memory System

7

High Team Tenure Average Team Tenure Low Team Tenure

6

5

4 Low Transactional Leadership

High Transactional Leadership

Fig. 2. a: The effect of team size on the relationship between transformational leadership and transactive memory system. b: The effect of team tenure on the relationship between transactional leadership and transactive memory system. c: The effect of market dynamism on the relationship between transactive memory system and team performance.

Foundational leadership theory also suggests – although little empirical research has explored the speculation – that there may be an augmentation effect (an interaction) between transformational and transactional leadership (Bass & Avolio, 1993). We explored the possibility of an interaction between TAL and TFL, and found a positive, but not significant effect (b = 0.07, p > .10). While we did not find support for the augmentation effect in current study, the positive effect does provide some evidence that in larger samples of teams, with greater power, it might be possible to detect this type of augmentation effect. Together, these results provide additional empirical support for our proposed dual process leadership framework.

antecedents of TMS, we found that both TFL (Hypothesis 1a) and TAL (Hypothesis 2b) are positively associated with TMS. Here, with the aim of expanding insight into antecedents of TMS - an established driver of team performance - we sought to broaden the range of performancecritical consequences of leadership with a focus on TMS. These findings compliment previous leadership research which has focused on the influence of a range of dimensions of leadership on patterns of follower behavior, from identification and engagement to job self-efficacy, trust and communication (e.g., Boies, Fiset, & Gill, 2015; Hannah, Schaubroeck, & Peng, 2016; Hoffman, Bynum, Piccolo, & Sutton, 2011; Ng, 2017). Further, our results simultaneously extend the breadth of important leadership consequences and TMS antecedents, while also expanding the functional levers available to managers to encourage development of TMS. It will be important for future research to continue to expand the range of leadership antecedents in this domain to provide managers with a larger toolset with the potential to drive TMS. Further, in recognition that leadership effectiveness is likely contingent on attributes of the team context, we introduced team size (Hypothesis 1b) and team tenure (Hypothesis 2b) as moderators of the relationships between TFL, TAL, and TMS respectively. Although we found significant main effects for both forms of leadership, these effects must be interpreted in light of significant higher order effects suggestive of the importance of various aspects of team context as important boundary conditions. Consistent with the contingency frame we adopt, what emerges is that TFL is more highly associated with TMS in smaller teams, while TAL is more highly associated with TMS in teams with lower levels of tenure. This suggests that leaders seeking to drive TMS should strongly consider the potential role played by various team attributes in the effectiveness of these leadership approaches. It will be important for future research to explore a broader range of moderators of the leadership-TMS relationship. Seeking to ground our research within the broader TMS domain, we also examined the relationship between TMS and sales team performance (Hypothesis 3a). Consistent with TMS theory and empirical

5. Discussion In response to growing reliance on teams to overcome the challenges of expanding employee knowledge requirements, managers need insight into conditions under which different forms of leadership are most useful for leveraging TMS to enhance team performance. With this focus, building from a contingency theory frame, we develop and test a model of leadership, TMS, and team performance. We first aimed to broaden the range of TMS antecedents to include multiple dimensions of leadership behavior. Further, we approached this question with the recognition that the impact of different leader behaviors on TMS likely depends on team characteristics that influence the fit of these approaches with the characteristics of the team. Toward this goal, we tested relationships between TMS with both TFL and TAL, and the moderating role of team size and team tenure in these relationships, respectively. Continuing with the implications of contingency theory in this context, and building from TMS research reporting benefits of TMS for team adaptivity (Marques-Quinteiro et al., 2013) we also examined the moderating role of market dynamism in the TMS – team performance relationship in the sales team context. The contingent indirect effects model we tested advances theoretical understanding of relationships between leadership, TMS, and sales team performance. Consistent with our focus on expanding leadership 304

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and intra-team coordination critical to development of TMS. This focus also begs the question of the role of followership in the relationship between these dimensions of leadership and TMS (UhlBien, Riggio, Lowe, & Carsten, 2014), which may be of particular relevance given the interdependencies inherent to the processes and structure that define TMS. For example, while Uhl-Bien and Pillai (2007) referred to followership as deference to leadership, DeRue and Ashford (2010) characterized followership as granting some form of leadership identity to another while simultaneously adopting followership identity for oneself. What these depictions share is the explicit deference of one (or multiple) members of a collective to another (or multiple) other members. TMS depends on differentiation of domain expertise, and reliance on other (central) members for expertise in nonoverlapping domains, who have control over a specific domain of information/knowledge. Thus, fundamentally, in a functioning TMS members defer to other members for expertise (or leadership) in domains for which they do not have responsibility – and thus adopt a followership role in the context of TMS. Although no research in the TMS domain has sought to explain the key role played by followership in the development, and effective leveraging of TMS, it will be important for future research to build out, and systematically incorporate followership theory into the TMS conceptual frame. Our focus on the TMS-team performance link also provides a muchneeded contribution to the literature on sales teams. Over the years, empirical studies focused on sales team performance have been relatively scarce despite repeated calls for research in this area (e.g., Weitz & Bradford, 1999), and the growing use of teams in the sales context (Jones, Dixon, Chonko, & Cannon, 2005). However, guided by the increasing importance of information management in sales roles (Verbeke, Dietz, & Verwaal, 2011), we find that sales teams that actively catalogue, archive and systematically access knowledge and information embedded in their teams generate better collective sales outcomes. This distinction is important for two reasons. First, while previous research has shown that information sharing and knowledge creation are drivers of sales team performance (Auh et al., 2014; Menguc et al., 2013), our focus on TMS illustrates the importance of a differentiated knowledge storage system for enabling improved sales team performance. Second, recent research has highlighted the effort associated with knowledge sharing, noting that it can actually place a significant resource burden on sales managers or expert peers within the sales team (Hall, Mullins, Syam, & Boichuk, 2017). TMS should help avoid these kinds of “sharing burdens” by providing an efficient means of knowledge storage and access across team members, ultimately leading to greater balance in knowledge sharing responsibilities. Building from contingency theory, we also find that market dynamism can significantly impact the relationship between TMS and sales team performance. However, theory development and a fuller understanding of the TMS-performance relationship depends on continued focus on factors with potential to impact this relationship. For example, Lewis and Herndon (2011) categorized tasks to reflect “three elemental processes” (p. 1258), labeled “produce,” “choose,” and “execute” tasks. Lewis and Herndon (2011) argued that the performance benefits of TMS are likely strongest in teams “…for which performance depends on access to diverse knowledge, …a division of the cognitive labor for the task, …efficient coordination of members' activities, and new learning that occurs during task processing” (p. 1259). Thus, another area for future research will be a further focus on proximal moderators, such as task type, as well as distal factors with potential to impact the potency of TMS; this focus also informs potential differences relating to the performance implications of TMS for different kinds of performance outcomes (Dai, Du, Byun, & Zhu, 2017).

evidence from the literature, and while controlling teams' previous performance, we found that TMS is significantly positively associated with a conservative, objective measure of sales performance reflecting teams' archived quarterly sales totals relative to established sales targets. This result speaks directly to the importance of TMS as a team performance accelerator in the sales context, which is a relatively new setting for TMS research (e.g., Bachrach et al., 2017). Further, continuing from the contingency frame we describe, and in an effort to address calls from the literature bearing on moderators of the TMS – team performance relationship (Ren & Argote, 2011), consistent with the expectation that information-processing hurdles are steeper when market dynamism is higher (Dess & Beard, 1984), we found that the relationship between TMS and team performance is significantly stronger when market dynamism is higher (Hypothesis 3b). This suggests greater fit between the processes and structures present in sales teams operating a TMS with the operating environment when market dynamism is high. It may be that when market dynamism is lower that the time, energy, and effort expended in communication overhead relating to development and maintenance of a TMS (MacMillan et al., 2004) represents a resource misallocation (Modi & Mishra, 2011). Although not significant, the relationships we uncover suggest that TMS may be less useful in sales contexts when market dynamism is low. Finally, consistent with the framing we develop in support of H1–3, we also found that market dynamism moderates the strength of the mediated relationships between TAL (Hypothesis 4a) and TFL (Hypothesis 4b) with sales team performance through TMS, such that the mediated relationships are stronger under high market dynamism. 5.1. Implications for theory and future research Literally decades of leadership research speaks to the critical role of leaders for team functioning (Morgeson, DeRue, & Karam, 2010). This is significant since, although much is known about processes that contribute to the emergence of TMS over time (e.g., Lewis et al., 2005), relatively little is known of the role played by leadership in this process. The current results suggest that TFL and TAL may both play an indirect role in sales teams' performance through TMS. Attention to TFL and TAL is also an important point of focus for sales team leadership research in light of the fact that previous studies have focused primarily on leader empowering behaviors (see Ahearne et al., 2010; Menguc et al., 2013; Rapp, Ahearne, et al., 2010). This pattern of relationships not only reifies the critical role played by leader behavior for achieving critical team outcomes in sales contexts, but also points to important directions for future research in both the leadership and TMS domains. It will be important for future research to continue to examine ways that leaders can contribute to the emergence of TMS and thus indirectly to important team performance outcomes. From this perspective, the collective performance value of leaders materializes less as a consequence of their impact on performance directly, and more as a consequence of their impact on their teams' ability to deliver performance. Leaders' capability to hasten development of TMS is of critical importance given increasing dynamism in team membership (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014), which can diminish the impact of collective learning (Anderson & Lewis, 2014; Lewis et al., 2007). In light of consistent emphasis in leadership research on understanding different leadership styles (Antonakis, Avolio, & Sivasubramaniam, 2002), insight into relationships with TMS represents an opportunity for further theoretical development; here, we extend points of intersection linking TMS and leadership theory with a focus on both TFL and TAL. For example, research exploring ethical leadership (Kacmar, Bachrach, Harris, & Zivnuska, 2011), which is promotion of normatively appropriate conduct among followers, indicates ethical leadership may help drive interpersonal behaviors with potential to hasten insight into who knows,

5.2. Implications for practice The current results offer several material implications for managers. First, we find that the strength of the relationship between TFL and TMS 305

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likely to be profitably deployed within the context of team training (Moreland & Myaskovsky, 2000), a regularly occurring implementation within sales forces, where the effects of leadership are likely to be magnified as sales team members both receive performance feedback and have repeated opportunities to learn about one another's knowledge and skills. Another important practical consideration centers around members' role as domain experts within the TMS. Functionally, TMS enhances members' centrality in the team's expertise network, and as a consequence their instrumental control in the functioning of the TMS. However, salespeople may not wish to take ownership control over a domain of expertise for various reasons such as a perceived time commitment, lack of knowledge, or the fear of losing a competitive advantage over other sales team members. Thus, it will be essential for sales managers to match sales team members' perceptions of control and their desire for control in this knowledge/information exchange system (Mullins, Bachrach, Rapp, Grewal, & Beitelspacher, 2015). Establishing control congruence may help sales managers to both hasten the emergence of TMS, and also to magnify its performance potential.

depends on team size and that the relationship between TAL and TMS depends on team tenure. Managers can leverage this insight to approach active generation of TMS within their work teams with an explicit focus on team characteristics that have the potential to impact the viability of particular leadership approaches. While TFL can lead to cooperative interactions, enhancing coordination and communication necessary for TMS, this is likely to be more difficult in larger teams due in part to members' physical and psychological distance (Reagans & McEvily, 2003). Thus, expenditure of the time, energy, and effort to develop TMS in larger sales teams via TFL may represent a misallocation of resources. Likewise, although Hood et al. (2014) argued that TAL may encourage teams to develop and share member expertise maps crucial to development of TMS, the utility of this approach is likely to depend on team tenure. In light of the negative interaction we observe, managers leveraging a TAL approach to drive TMS also may risk a misallocation of scare resources in teams with longer tenure. While both forms of leadership we examine have the potential to facilitate the processes and structures underlying TMS, it is critical that managers be aware of - and account for - team attributes likely to influence the strength of these relationships. In specific terms, sales leaders should develop distinct leadership strategies that explicitly incorporate team tenure, team size, and market dynamism. When sales teams are smaller, transformational leadership is likely to be a more effective approach to generating TMS, which will benefit sales team performance when market dynamism is higher. Sales managers can diagnose market dynamism with a focus on the frequency with which customers look for new products, for example, modify their product preferences, and the consistency of new customers' productrelated needs with those of current customers. When market dynamism is higher, sales managers are likely to benefit from investing scarce resources in transformational leadership. This approach is likely to be less effective in larger sales teams. However, TMS theory and research has progressed from a focus on the nature and consequences of this complex knowledge sharing system within relational dyads to emergent speculation relating to firm-level outcomes (Heavey & Simsek, 2015). Thus, TMS functions in collectives across a wide spectrum of sizes. Although we find less utility for transformational leadership within larger teams, sales managers could effectively leverage transformational approaches within sub-sets of their sales teams – even to the level of the dyad – in dynamic markets. Transformational sales leaders also could consider deploying smaller teams in an effort to generate benefits from TMS. We also find that transactional approaches are more effective in generating sales performance for lower tenured teams. Thus, in dynamic markets sales managers leading newer teams also are likely to generate measurable sales returns with an explicit transactional focus. This approach should be tempered, however, as teams gain more experience. Investment of scarce resources in developing TMS through transactional approaches should be undertaken in conjunction with a specific focus on market conditions. Sales managers may be less likely to generate tangible returns on these investments when information processing hurdles are lower, and decision effectiveness depends less on integrated and coordinated knowledge and expertise (e.g., under lower dynamism). When decision-making and task execution are more predictable, the synchronization costs of generating TMS – or “communication overhead” (MacMillan et al., 2004) may generate inefficiencies. Resources diverted toward TMS may be misallocated because the decision speed and breadth afforded by TMS are less relevant in placid markets (Modi & Mishra, 2011). For salespeople organized within a team structure and sales managers who encourage knowledge sharing and integration within their sales teams, our study offers practical ways to leverage the inherent knowledge of each sales team member for sales team performance. The current results suggest that, depending on team characteristics, multiple forms of leadership have the potential to drive the emergence of TMS. TMS research suggests that these leadership behaviors also are

5.3. Study limitations The conclusions we draw should be contextualized against the limitations of our design. First, although we adopted a lagged design coinciding with the serial nature of our model, we did not collect longitudinal data allowing us to evaluate changes over time or to substantiate causal inferences. Thus, the best we can conclude is that the current results provide support for our conclusions; that team attributes impact the strength of the relationships between discrete forms of leadership with TMS, and that market dynamism impacts the strength of the relationship between TMS and team performance. It will be important for future longitudinal and experimental research to explore relationships between leadership and TMS to substantiate the inferences we draw. Second, although we measured objective performance following the study survey, it is possible participants were aware of their team's performance at the time of the survey, impacting their ratings. For example, Staw (1975) (and others – e.g., Bachrach, Bendoly, & Podsakoff, 2001) have reported that correlations between evaluations of team processes and performance may be artificially inflated as a consequence of the attributions evaluators make to explain team performance. Although we control prior team performance, it is possible raters' awareness of their team's performance may have accounted for variation in ratings of TMS. 6. Conclusion The contingent indirect effects model of leadership and TMS we report was intended to accomplish several goals; develop theory extending the breadth of antecedents associated with TMS to include a range of leadership behaviors, provide insight into boundary conditions of these relationships, and advance what we know of contextual moderators impacting the TMS-sales team performance relationship. We extend leadership theory in the TMS area and have also begun to shed some light on the interrelationships between leadership, TMS, and sales team performance. We offer a framework to help understand conditions under which different forms of leadership may be effective for leveraging TMS to enhance sales team performance. Practical and theoretical development will require continued focus in this important area of research. Dr. Daniel (Dan) Bachrach (PhD Indiana University) is a Professor of Management and the Robert C. and Rosa P. Morrow Faculty Excellence Fellow at the University of Alabama's Culverhouse College of Commerce. He is the coauthor/coeditor of 9 books and more than 50 articles published in numerous prestigious journals including Journal of Applied Psychology, Strategic Management Journal, Journal of Operations Management, Production and Operations Management, Journal of Management, Organizational Behavior and Human Decision Processes, 306

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D.G. Bachrach, R. Mullins

Organization Science, Decision Sciences, and Personnel Psychology. He was awarded the 2017–2018 National Alumni Association Outstanding Commitment to Teaching Award, which is the University of Alabama's highest honor for excellence in teaching, and sits on the editorial boards of the Journal of Applied Psychology and Organizational Behavior and Human Decision Processes. Dr. Ryan Mullins (PhD University of Houston) is an associate professor of Marketing at Clemson University. Dr. Mullins is working on research projects related to sales effectiveness, branding, team selling, customer relationship management, and sales leadership. Ryan's work has appeared in the Journal of Marketing, Journal of the Academy of Marketing Science, Journal of Applied Psychology, Industrial Marketing Management, and the Journal of Personal Selling and Sales Management. Dr. Mullins also serves the field through his associations with marketing journals across domains. Ryan actively serves on the editorial review boards at the Journal of Service Research and the Journal of Personal Selling and Sales Management. In addition, he serves as an ad-hoc reviewer at several marketing journals and is a guest co-editor for a special issue on selling teams at Industrial Marketing Management.

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