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When Can Teams Benefit from External Advice Ties?
The Asymmetric Influence of Spanners’ and Receivers’ Traits
Jinlong Huo
A dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science, M.Sc. specializing in: Management and Organization in the Brazilian School of Public and Business Administration of the Getúlio Vargas Foundation, Rio de Janeiro, Brazil.
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ABSTRACT
Leaning from external sources is a crucial but challenging task for improving team
performance. Using the role-based team composition approach, we investigate how traits of team members in different roles—spanners and receivers—influence a team’s ability to benefit from its external advice ties. We argue that the interplay between spanners’ learning goal orientation and receivers’ agreeableness affect the influence of teams’ external advice ties on team
performance. In 88 teams working over the course of 14 weeks, we find that team external advice ties influence team performance more positively when spanners’ learning goal orientation and receivers’ agreeableness are high. We discuss both theoretical and managerial implications of our findings.
Keywords: team external advice ties; boundary spanning; team role composition; agreeableness; learning goal orientation
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INTRODUCTION
Organizations increasingly rely on team-based and knowledge-oriented tasks and scholars have acknowledged that team performance is not merely an outcome of team internal processes but also heavily depends on team external functioning and learning (see Choi, 2002 and Marrone, 2010 for a review). Since Ancona and colleagues’ pioneer studies (Ancona, 1990; Ancona & Caldwell, 1988, 1992) that identified a variety of team external boundary spanning actions, such as ambassador activities (e.g. get resources and assess the political situation) and task
coordination activities (e.g. communicate and obtain feedback), researchers have considered the role of team external interactions in predicting outcomes including information sharing,
knowledge transfer, innovativeness, and performance (Tsai & Ghoshal, 1998; Hansen, 1999; Zellmer-Bruhn, 2003; Wong, 2004; Reagans & McEvily, 2003; Oh, Chung, & Labianca, 2004; Reagans, Zuckerman, & McEvily, 2004; Bresman, 2010; Haas, 2010; Bresman & Zellmer-Bruhn, 2013). Although external focus may harm team performance since such activities consume time and effort (Gibson & Dibble, 2013), team boundary spanning activities on average enable the generation of new knowledge and facilitate the effective transfer of non-codified and complex knowledge across distances, which ultimately enhances team effectiveness, as well as the organizational-level performance.
The boundary spanning literature has a rich history, focusing primarily on individuals who occupy the boundary spanning roles within their organizations and certain individual
characteristics, such as tenure and expertise (Adams, 1976; Tushman, 1977; Tushman & Katz, 1980; Tushman & Scanlan, 1981a, 1981b). However, such “role” consideration is largely missing
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in team boundary spanning studies, which conceptualize boundary spanning only as an aggregate team-level phenomenon (Marrone, Tesluk, & Carson, 2007 as one exception). Most research assumes, for example, that as long as the team has ten advices ties to access external knowledge, whether it is individual A or B who owns the ties is a trivial issue. However, person A and B may differ in many ways that can impact how the external knowledge gets transferred in a team.
In small groups and teams research, in contrast, individual differences are one of the central topics to study team composition and diversity (Williams & O’Reilly, 1998; Van Knippenberg & Schippers, 2007), and have long been identified as a key determinant of group dynamics (Arrow & McGrath, 1995). Scholars have applied theories of individual differences to the roles being performed within a team (Humphrey, Morgeson, & Mannor, 2009); with a role composition model, they argue that certain characteristics when held by role holders (team members) are more crucial for team performance than the same characteristics when held by non-role holders (see also Stewart, Fulmer, & Barrick, 2005; Pearsall & Ellis, 2006). They define strategic core inside the team as the roles that encounter more problems, have a greater exposure to the tasks, and are more central to the workflow (Humphrey et al. 2009). While this approach has several merits, and demonstrates that all roles are not created equally, it emphasizes the importance of strategically core roles alone and does not take the interplay between different roles into consideration.
In present study, we adopt and extend this role composition approach to study when teams can benefit from external advice ties. We separate team members with two types of roles, namely spanners (individuals who have external advice ties) and receivers (individuals who do not have
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external advice ties). This approach is meaningful as it captures the boundary spanning process in which spanners first get access to external knowledge through advice ties, then absorb and understand knowledge, and finally disseminate it to receivers in the team to improve team task achievement. There are two core contingencies in this process: the spanners’ motivation and ability to extract valuable information from external ties, and the receivers’ tendency to absorb the transferred knowledge. That is because unless the external knowledge is transferred and disseminated within the team, it cannot contribute to achievement of team tasks and eventually improve team performance.
We argue that spanners’ learning goal orientation and receivers’ agreeableness can jointly mitigate the difficulties in transferring knowledge, such as motivation, ability, and team cohesion (Szulanski, 1996; Argote, McEvily, & Reagans, 2003; Reagans et al. 2004), and thus enable the team to benefit from external knowledge. The spanners with high learning goal orientation are motivated to acquire new skills, master new situations and improve their competence, which gives them adequate abilities to grasp more knowledge (VandeWalle, 1997; Payne, Youngcourt, & Beaubien, 2007; Yao & Chang, 2017). On the other hand, receivers with high agreeableness, who are perceived as sympathetic and cooperative, can help the team cohesively utilize the external knowledge, and take actions toward the final task solution (LePine & Van Dyne, 2001; Stewart et al., 2005).
In essence, we propose the influence of teams’ external advice ties on team performance will be contingent on the spanners’ learning goal orientation and receivers’ agreeableness, such that teams’ external advice ties are more strongly associated with team performance when spanners’
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learning goal orientation and receivers’ agreeableness are high. Multi-source and lagged data collected from 88 teams over 14 weeks provide empirical support for our theorization. In the following sections, we first review existing theory and literature, and then build our hypotheses. After introducing the study, we present the results and discuss the implications for theory and practice. The theoretical model is presented in Figure 1.
--- Insert Figure 1 here ---
THEORETICAL BACKGROUND AND HYPOTHESES Team External Learning and Advice Ties
Studies on team external learning, boundary spanning, and knowledge transfer address
different research questions, such as the knowledge transfer between firms (Bresman, Birkinshaw, & Nobel, 1999; Argote & Ingram, 2000; Van Wijk, Jansen, & Lyles, 2008) and boundary
spanning activities aimed at getting political support from top management (Ancona & Caldwell, 1992; Oh et al. 2004). However, there is certain overlap among these research domains as the team is usually the focal point to conduct organizational tasks, and more importantly, team members are the individuals that learn and accumulate knowledge across team and
organizational boundaries from the external environment (Argote, 1999; Argote & Fahrenkopf, 2016). Indeed, Nonaka and colleagues point to teams as imperative to organizational learning process (Nonaka, 1994; Nonaka & Takeuchi, 1995). While a wide variety of activities may fall within the definition of boundary spanning, we will focus on the behaviors that are aimed at learning of knowledge that resides outside the team boundary and attribute team external advice
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ties as potential resources that members can learn from.
Teams tend to have poorer performance if they are isolated from their external environment (Ancona & Caldwell, 1992). When teams in knowledge-intensive work engage in more external learning through interpersonal networks, they can accumulate task-related information,
know-how, and feedback from experts. Subsequently, they can solve technical problems more effectively by comparing with others’ experiences (Orr, 1996), benefit from organization’s best practices (Szulanski, 1996), generate creative ideas through brainstorming (Sutton & Hargadon, 1996), acquire new work routines (Zellmer-Bruhn, 2003), and broadly enhance team
performance across different contexts (Bresman, 2010; Haas, 2010; Bresman & Zellmer-Bruhn, 2013). This is also paralleled with the intra-firm/inter-teams social network studies (Tsai & Ghoshal, 1998; Hansen, 1999; Reagans & McEvily, 2003; Reagans et al., 2004) that emphasize the relational underpinnings of successful knowledge transfer and performance. Therefore, as a baseline hypothesis, we propose
Hypothesis 1. The number of a team’s external advice ties is positively related to team
performance.
Nevertheless, such external learning is challenging as individuals may find it difficult to transfer complex knowledge across teams or organizational subunits. Challenges may come from the lack of motivation, ability, or team cohesion. Szulanski (1996) suggested that knowledge can be sticky (e.g. difficult to transfer) as a result of either an actor’s or a node’s lack of motivation or inability to absorb or retain the knowledge. Since learning is considerably complex and challenging, individuals may be less motivated to devote time and effort to it (Szulanski, 2000;
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Argote et al., 2003). For instance, Reinholt, Pedersen, and Foss (2011) found that without individuals’ autonomous motivation, employees fail to acquire existing knowledge inside their company. In addition, absorptive capacity, which is defined as one's ability to value, assimilate, and apply new knowledge, is a frequently cited explanation for effective knowledge transfer (Cohen & Levinthal, 1990). In line with this logic, Tsai (2001) found that only when an
organizational unit has high absorptive capacity, the unit can utilize its central network position to learn from other business units. Therefore, in order for the team to learn from the outside through their advice ties, they should deal with the motivation and ability challenges. However, whom does “they” refer to? Although it is possible, it is not realistic to assume that every team member has ties to learn from. Hence, the members who have not only the opportunity to learn from external ties but also motivation and ability to learn are likely to be more important for the overall learning of the team.
In addition, Reagans et al. (2004) found that the most productive teams are internally cohesive but have external networks full of structural holes or brokerage opportunities. They argued that when the group is cohesive, the individuals are willing to devote time and effort to assist others, thus easing the transfer of complex knowledge (see also Hansen, 1999; Reagans & McEvily, 2003; Tortoriello, Reagans & McEvily, 2012). In other words, if the team has external ties and is also internally cohesive, the learned external knowledge thus enables higher team performance.
A Role based Team Composition: Spanners and Receivers
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some team members act on behalf of their team), it is quite surprising that most studies have overlooked the role of specific individuals within the team (Marrone, 2010). The influence of member traits in teams has been addressed by two approaches used to study group composition (Humphrey et al., 2009). The first one is the individual attribute composition approach. This approach is used to create team input variables (e.g. demographic characteristics such as age and gender, task-related characteristics such as functional background, as well as psychological characteristics such as personality and goal orientation) that potentially influence team processes and outcomes. However, this approach discards the individual-level information by treating data from all of team members equally and aggregating them up to the team-level using several indices such as mean, standard deviation, minimum and maximum.
The second, the role based composition approach investigates how the characteristics of a set of role holders impact team effectiveness. For instance, Pearsall and Ellis (2006) found that critical team members’ dispositional assertiveness (one aspect of extraversion personality traits), rather than non-critical members’ assertiveness, positively affected team performance. They defined the critical members as those who control vital information without which other members would not be able to perform their tasks. In a similar vein, Humphrey and colleagues (2009) defined the strategic core based on members’ importance of task and information flow, and found that strategic core role holders’ career experience was more strongly related to overall team performance than the career experience of non-core role holders. The social network research also verifies the value of role-based composition model. The notion that one's position (partially reflected by the role) in a network determines one's relative influence dates back to
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small-group communication experiments (Leavitt, 1951; Shaw, 1964). Brass (1985) reported that an individual’s position in the organization networks was strongly related to her/his workplace influence. However, although roles are a fundamental feature of teams and have been previously suggested as critical for accomplishing task and enhancing team effectiveness (Hackman, 1987; Mathieu, Maynard, Rapp, & Gilson, 2008), the role composition approach has not received adequate attention.
In our research context, we argue that the role composition approach more accurately captures the complexity of team external learning (Marrone, 2010) because such boundary spanning processes inherently involve two roles, spanner(s) and receiver(s). We refer to spanners as team members who have task-information related advice ties with people outside their team and receivers to team members who do not have such ties. We do not emphasize their
responsibilities as previous studies in defining boundary role persons (Adams, 1976), but focus on their resources that could potentially contribute to team performance.
While some spanners’ characteristics have been shown to impact team performance, our knowledge of these characteristics is far from complete. Additionally, previous research has not paid attention to the receivers’ characteristics. To illustrate, in the context of Humphrey et al. (2009)’s model, they compared work experience of strategic core role holders with that of non-strategic core role holders, i.e. the same characteristic for the two roles. However, when the task requires different inputs and involves different procedures or routines (Hackman, 1987; Mathieu et al., 2008), it is reasonable to argue that the factors that are critical for one role may not be important for another role.
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Such separation has been partially demonstrated in previous studies though not in the exact same sense. Bales and Slater (1955) found that the individual who was rated as having the “best ideas” within the team was not the same person who was rated as “most liked”. This observation led them to argue that each member leads activities oriented to different needs. The “idea” person focuses more on task activities and helps the team generate novel and useful ideas while the “most-liked” person focuses more on socioemotional activities to help the team to become internally integrated. One individual could not lead both activities because they require different types of personalities (Slater, 1955; Bales & Slater, 1955). Therefore, we argue different
characteristics matter for different roles, and a more nuanced approach is needed to study the joint impact of different role-characteristic combinations on team performance. Below we build rationale for the corresponding characteristics of both spanners and receivers that can alleviate the difficulties in the knowledge transfer process.
Spanners’ Learning Orientation and Receivers’ Agreeableness
As defined, only spanners have access to external knowledge through the team’s (in fact spanners’) advice ties, and hence the opportunity to learn from that knowledge. Accordingly, the characteristics associated with the spanners´ motivation and ability to learn are much more critical to achieve this goal than that of the other members of the team (Szulanski, 1996; Argote et al., 2003). A central characteristic that captures this motivation is learning goal orientation (LGO).
Dispositional goal orientation is a motivational concept that captures how individuals
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& Leggett, 1988; Elliot & Church, 1997). Goal orientations are relatively stable individual differences although they may be influenced by the environment (Button, Mathieu, & Zajac, 1996). Goal orientations have been commonly classified as one of two orientations: learning goal orientation is focused on the development of competence and task mastery; performance goal orientation is focused on outperforming others and demonstrating their ability (VandeWalle, 1997). A meta-analysis found that with a high LGO score, individuals usually achieve a better performance because they hold a belief that a person’s ability or intelligence is incremental (Dweck, 1986; Payne et al., 2007); in turn, these individuals are more likely to seek challenges and master new knowledge, put more effort into getting a thorough understanding of tasks, make more use of deep-level information processing, attribute failure to inadequate efforts, and persist in the tasks (Fisher & Ford, 1998; Dupeyrat & Mariné, 2005). This interest in the task itself acts as intrinsic motivation, leading to intensive task engagement and hence better performance. We argue that when spanners have high levels of LGO, it will benefit team performance as they will be more motivated to ask more questions, absorb more knowledge, and learn more carefully from their advice ties.
Existing research also suggests that individuals with high LGO may have higher ability to learn. Prior organizational absorptive capacity research suggests that learning is a cumulative process and the existing knowledge foundation is crucial for an individual or a firm to further acquire and utilize new knowledge (Cohen & Levinthal, 1990). Since individuals with high LGO accumulate new knowledge through their constant mastery and explorative activities, they have more experience to learn and thus develop a better understanding of the task. Indeed, Yao and
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Chang (2017) found that the core knowledge employees’ learning goal orientation was positively related to the firm-level absorptive capacity. Therefore, it is reasonable to argue that with a high LGO, spanners also have the ability to learn through their advice ties. In contrast, if the spanners have low LGO, they may either not want to spend time and efforts to learn external knowledge because of the low motivation, or not have foundation and capacity to acquire something new. Therefore, we argue that if the spanners have a high level of learning goal orientation (LGO), the team is in a better position to benefit from the external knowledge because LGO can mitigate not only the motivational but also the ability-related challenges for the learning.
Taken together, it is logical to expect that spanners with high LGO can handle the challenges of motivation and ability in transferring knowledge and boundary spanning processes. However, the combination of advice ties and LGO of spanners is only one part of the challenge that a team faces if members want to successfully benefit from external knowledge. Another obstacle that prevents teams from high levels of performance is the low cohesion that is induced by the team boundary spanning activities because external communication may signal identification with outsiders (Ancona, 1990; Keller, 2001). If team members, especially the receivers, have high levels of characteristics that can enhance team cohesion, it is yet another important factor that can influence the team’s likelihood of benefiting from external ties.
A fundamental dispositional characteristic that influences such cohesion comes from the five-factor model of personality (Costa & McCrae, 1992), specifically agreeableness. The five-factor model is widely accepted as representing the higher-order structure of personality traits (Clark, 2007) and the composition of team members’ personality has been proven to
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influence several team processes and outcomes (Peeters, Van Tuijl, Rutte, & Reymen, 2006). Agreeable people are argued “fundamentally altruistic . . . sympathetic to others and eager to help them and believe that others will be equally helpful in return” (Costa & McCrae, 1992, page 16). Indeed, team members’ average agreeableness is positively related to members’ cooperative behavior (LePine & Van Dyne, 2001). Additionally, teams composed of highly agreeable people have been shown to perform better when cooperation was rewarded (Beersma et al., 2003) and agreeableness is positively related to the social roles and team cohesion (Stewart et. al., 2005). Hence, we argue that when receivers have high levels of agreeableness, they are likely to be more cooperative with spanners, and thereby enhance the team internal cohesiveness. As
indicated by Reagans et al. (2004), spanners in the environment of high internal cohesiveness are more willing to transfer the knowledge to receivers; such transfer of knowledge can help the team to fully assimilate and utilize external knowledge toward task execution. Without receivers’ agreeableness, spanners may simply hoard the knowledge and have little motivation to
disseminate it within the team (Von Hippel, 1976; Tushman & Scanlan, 1981b).
While we propose that spanners’ LGO and receivers’ agreeableness will be important for the team to benefit from external ties, we do not argue that the spanners’ agreeableness and receivers’ learning goal orientation are not important at all. It is quite plausible that the spanners’
agreeableness can enhance team cohesion and receivers’ learning goal orientation can contribute to team overall learning and performance. However, in line with the role theory, we believe when characteristics match with roles, their impact on outcomes is maximized. Since receivers do not have resources (advice ties) giving them access to learn from the external environment, their
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motivation and ability is less important than the team members who have such access (Argote et al., 2003). In a similar vein, since spanners spend a significant amount of time in seeking
external knowledge, they may not be able to expend adequate time and effort in integrating behaviors within the team as well as in coordinating task execution, as the receivers. Therefore, we believe it is the spanners’ learning goal orientation and receivers’ agreeableness that helps teams to learn from outside and perform better, highlighting that there is an asymmetric influence of members’ traits on team performance depending on who possesses them.
To sum up, first, we predict a positive relationship between the number of external advice ties and team performance, as our baseline hypothesis, and second, we argue this relationship is contingent on spanners’ learning goal orientation and receivers’ agreeableness simultaneously. Accordingly, we hypothesize:
Hypothesis 2. The relationship between a team’s advice ties and team performance is
influenced by the boundary spanners’ learning goal orientation, and the receivers’
agreeableness. Specifically, we predict the following two sub-hypotheses,
Hypothesis 2a. When boundary spanners’ learning goal orientation is high, the
relationship between advice ties and team performance is more positive when receivers’ agreeableness is high than when it is low.
Hypothesis 2b. When receivers’ agreeableness is high, the relationship between advice ties and team performance is more positive when boundary spanners’ learning goal
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METHODS Participants, Task, and Procedure
We collected longitudinal data from 351senior bachelor students enrolled into a business administration program. As part of their corporate strategy course, students played a business strategy game as an elaborate simulation experience, in which participants ran a virtual company and competed with other teams in a cyber industry environment. Participants determined the best strategy for their “firm” in order to run a profitable business and outperform other teams taking part in the simulation. To this end, teams took decisions in areas such as operations, finance, marketing, sales, and human resources. The simulation format helped participants emerge into realistic experiential learning environment. Additionally, companies’ performance in the industry determined 50 percent of the participants’ grade, which provided them further incentive to perform well. The game thus created a realistic, engaging and challenging representation of business environment for team members, and aimed at inducing an environment similar to real companies (Chen, Katila, McDonald, & Eisenhardt, 2010). This game has been used by other research as well (Mathieu & Rapp, 2009; Boies, Lvina, & Martens, 2011). The students were randomly assigned into four-member teams and three teams had two or three members due to drop outs. Preliminary analyses revealed no significant effects of team size on group
performance, so it was not incorporated into further analyses.
In addition to the simulation game, students also completed two online surveys at lagged time points over a period of 14 weeks. Before the simulation game started, participants filled in the first online questionnaire that contained individual-level measures (e.g. personal
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characteristics). After the game ends, participants filled in the second online questionnaire to assess individuals’ advice ties. The overall response rate was 94 percent for two waves of data collection. We assured students that their survey answers would remain confidential through anonymization procedures.
Measures
We first describe the detailed items of each construct at the individual level; then we discuss how we created the team-level variables that were used in data analyses. Because the team score for advice ties, learning goal orientation, and agreeableness simple reflected the team inputs rather than a shared emergent construct, the empirical justification for aggregation was not necessary (Chan, 1998).
Advice Ties. Participants completed an ego-network generator about their task advice
networks. Specifically, individuals were asked to write the first name and last initial of people whom they approached to attain advice with regards to a task problem during the simulation. Participants could list up to 10 contacts. At the individual level, the number of ties varies from 0 to 7, with an average 1.35 (S.D. = 1.45). People who did not have ties were coded as receivers and those who had ties as spanners.
Since we are interested in the team level analysis, we summed all members’ ties at the team level. With only few exceptions, some spanners in one team had common advice ties (i.e. with same person). Excluding the overlap ties did not influence our results; however, it was possible that different individuals got or interpreted information differently even when from the same tie contact. Therefore, we decided to retain the overlap in ties for those teams. Only one team did
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not have ties, which was excluded from analysis, leaving a total of 88 teams.
Learning Goal Orientation. We assessed learning goal orientation using the scale validated
by VandeWalle (1997). Participants provided answers to five questions on the 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). Sample items include “I am
willing to select a challenging work assignment that I can learn from”. We took the mean for
each individual and the Cronbach’s Alpha was 0.82. Then for each team, we calculated the mean of spanners’ LGO and receivers’ LGO and used these average scores in our analyses.
Agreeableness. We measured this personality trait using the HEXACO-60 inventory with
the ten associated items (Ashton & Lee, 2009). Participants used a 5-point scale ranging from strongly disagree (1) to strongly agree (5) to assess their agreeableness. A sample of the items is
“I rarely hold a grudge, even against people who have badly wronged me”. The Cronbach’s
Alpha was 0.77 and we took the average across the items as individual’s agreeableness. Then for each team, we calculated the mean across the spanners for spanners’ agreeableness and across the receivers for receivers’ agreeableness, and used these average scores in our analyses
Although almost all teams had outside advice ties, the number of spanners in a team varied from 1 to 4, which consequentially meant that the number of receivers changed from 3 to 0. Taking an example of four-member team that has two spanners and two receivers; the spanners’ learning goal orientation was based on the average of the two spanners while the receivers’ agreeableness was based on the average of two receivers. There were 13 teams in which all members had advice ties, which meant that there were no “pure receivers” in those teams. Since excluding these teams did not influence our results, as shown in Model 7 in Table 4 (Robustness
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Check), we decided to use all members’ average learning goal orientation and agreeableness as spanners’ and receivers’ traits for those teams.
Team Performance. The team performance scores were produced at the end of the
simulation by means of a mix of two sets of objective performance measures: (i) the extent to which they met the previously set five performance criteria by investors (i.e., return on equity, earnings-per-share, stock price, credit rating, image rating) and (ii) the extent to which they achieved better performance in the industry based on these five criteria. The scores ranged from 49 to 110. Further detailed information can be obtained from Mathieu and Rapp (2009).
Control Variables. We controlled for the number of spanners for each team, as well as the
gender diversity using Blau’s Index. When assessing the robustness of our model, we also controlled for individuals’ another motivational dimension, performance goal orientation (Cronbach’s Alpha = 0.81) adopted from VandeWalle (1997), to rule out its influence on team performance. The separation of avoid and approach dimensions for performance goal orientation did not influence our results. In addition, we also measured individuals’ openness to experience (Cronbach’s Alpha = 0.74) with ten items since their openness traits may also influence whether they are willing to seek new experiences and knowledge. As scholars found that boundary spanner’s team identification influences their efforts and activities (Richter, West, van Dick, & Dawson, 2006), we also measured their collective team identification (Cronbach’s Alpha = 0.85) with four items based on Van Der Vegt, Emans, & Van De Vliert (2000). For performance goal orientation, openness to experience, and team identification, we calculated the mean of spanners’ and receivers’ corresponding traits and used these average scores in our analyses. Since these
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variables may also contribute team performance and influence whether teams can successfully benefit from the external knowledge from advice ties, ruling out these alternative explanations can give more confidence regarding the robustness of our model. As will be highlighted later, these variables did not change the results.
RESULTS
Table 1 displays the descriptive statistics and Pearson correlation coefficients. It is
interesting to notice that the positive correlation between a team’s advice ties and performance was not significant, which indicates that having ties only may not necessarily lead to a better team performance and the importance of considering the roles and characteristics of team
members. We used the ordinary least squares (OLS) linear regression to test Hypotheses 1 and 2. In addition, we conducted two post-hoc comparisons to test H2a and H2b.
--- Insert Table 1 here ---
Our first baseline hypothesis predicted that the relationship between the number of team outside advice ties and team performance would be positively related. As evident in Table 2 (Model 1), the coefficient was positive but only marginally significant (β = 1.12, p = .056), indicating the importance of boundary conditions. To test the second hypothesis, we analyzed a three-way interaction with the team’s advice ties, spanners’ learning goal orientation, and receivers’ agreeableness on team’s performance as the outcome variable. The variables were mean-centered before entering the interaction terms to avoid multicollinearity. As seen in Table 2, Models 3 to 5 tested the two-way interactions only and none of them were significant. However,
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the coefficient of three-way interaction term (Model 6) was positive and significant (β = 8.54, p
= .001).
--- Insert Table 2 and Table 3 here
---
We used the conditional process analysis (Hayes, 2013) to estimate the simple slopes and its 95% bias-corrected bootstrap confidence interval (Table 3) on 5000 generated samples, allowing us to avoid the normality assumption and get robust results. In support of H2a, in the condition of spanners’ high learning goal orientation, simple slope analysis revealed that when receivers’ agreeableness was high, the relationship between advice ties and team performance was positive (β = 2.50), in contrast to the non-significant relation when receivers’ agreeableness is low (β =
-0.21). We also found support for H2b such that when receivers’ agreeableness was high, the
relationship between advice ties and team performance was positive when spanners’ LGO is high but not significant when spanners’ LGO was low (β = -1.02). The interaction effect was shown in Figure 2.
--- Insert Figure 2 here --- Robustness Checks
We also conducted several extra analyses to check the robustness of our model and found similar results, as can be seen in Table 4. In Model 7, we excluded teams in which all members were spanners (thus no pure receivers). However, the coefficient of three-way interaction term was still positive and significant. From Model 8 to 11, we sequentially added more control variables to rule out potential alternative explanations, such as spanners' agreeableness, receivers'
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learning goal orientation, individuals’ (spanners' and receivers’) performance goal orientation, personality trait of openness to experience, as well as their team identification. As shown in Table 4, the coefficient became even stronger. In Model 12 and 13, we tested two alternative three-way interactions. Model 12 is the interaction among a team’s advice ties, boundary spanners’ agreeableness, and receivers’ learning goal orientation on team’s performance, i.e. switched the relevant characteristics for spanners and receivers. Consistent with the argument that for different roles different characteristics matter, we did not find significant results in Model 12. While Model 13 is the interaction among a team’s advice ties, teams’ average agreeableness, and teams’ average learning goal orientation on team’s performance; in such case, we did not take the role into consideration but used the attribute composition approach. We found that the role composition approach had more power to explain the variance (compare Model 11 and 13 with the same control variables), indicating the importance of role consideration. Therefore, the results are highly consistent and robust.
--- Insert Table 4 here ---
DISCUSSION
We adopted a role-based composition approach to study how much a team gains from having access to external knowledge and under what conditions the team can successfully utilize the external knowledge to improve team performance, by focusing on the characteristics held by spanners and receivers. As shown by our results, a team’s advice ties, spanners’ learning orientation, and receivers’ agreeableness interactively enhance team performance, highlighting
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their asymmetric influence. The model and findings have important implications for theory and practice.
Theoretical Contribution
In a recent review of team external learning and boundary spanning literature, Marrone (2010) called for a bottom-up view examining “how” and “by whom” effective boundary spanning processes can be carried out in a team. This study contributes to the team boundary spanning literature by demonstrating how team members’ characteristics relate to the boundary spanning effectiveness. We proposed that some traits will be important when possessed by spanners, while other traits will be important when possessed by receivers, in predicting when teams integrate imported information into their solutions. We demonstrated the impact of different traits on team performance is asymmetric depending on who possesses them. Although the past studies investigating trait effects in teamwork have almost always assumed that
regardless of who owns them (i.e. traits are uniformly effective), we challenged this assumption by developing our argument for asymmetric impact of traits on teamwork and showing empirical support for it. While the attribute composition approach has provided valuable insights, we develop a better understanding of the micro-underpinnings of team external learning and
boundary spanning activities by adopting a role approach and considering relevant characteristics associated with each role. From the view of team role-based composition, we thus advance our understanding of how teams benefit from external knowledge and improve performance.
Given that motivation, ability, and team cohesion have been recognized as challenges for knowledge transfer (Szulanski, 1996; Argote et al., 2003; Reagans et al., 2004), we argue that these
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challenges can be partially mitigated by having team members who have high learning goal orientation or agreeableness based on their roles. Therefore, we illustrate an example of how team design, as a building block, can potentially improve group learning (Argote, 1999). The combination of some psychological factors and social network allow us to shed light on how teams are able to more effectively utilize information and thus benefit from the teams’ external knowledge.
We also contribute to the team composition research. As Humphrey and his colleagues (2009) claimed, a significant implication of the role-based composition model is to allow researchers to identify the differential impact of various team roles on team effectiveness, rather the individual attribute composition model that largely ignores the roles on the tasks being performed in a team. We not only borrowed from, but also extended, this approach by studying the joint impact of two role-characteristic combinations on team performance. A clear pattern that emerged from the current research indicates that different roles should be accompanied with different individual characteristics. Although the fundamental logic was proposed quite a long time ago (Slater, 1955; Bales & Slater, 1955), many studies using the individual attribute composition approach
overlooked its importance on team processes and outcomes. We believe a reinvestigation of individual attributes with their corresponding roles can broaden the field of small group and team research, and help us go a step further in understanding the complexity of teamwork that is embedded in the external environment.
Limitations and Suggestions for Future Research
27
Although a student sample granted us a high response rate that is difficult to reach in an organizational setting and yielded comparable teams that allows us to control for unobserved team-level heterogeneity (e.g., task characteristics), the generalizability of our findings could be limited. As a recent meta-analysis on the link between diversity and performance found no reliable differences between lab and field settings (Van Dijk, Van Engen, & Van Knippenberg, 2012), we expect that a similar pattern will emerge in real organizations as well. Future research should study the theorized links in organizational settings.
Second, although we randomly assigned members to teams within the class and thus
potentially randomly determined the tie numbers, which helps us to reduce the noise in the study, the observational nature of our study prevents us from making causal inferences. Additional randomized experiments of the advice network relationships and team role-characteristic composition will enable the making of stronger causal inferences.
Additionally, since we do not know the content of the communication, we make some assumptions about the nature of the flow of information. Future studies can tackle this more directly, focusing on the content of the information flow from the team’s external environment, to spanners, and then to receivers. Furthermore, we recognize that some other factors may bound the theory outlined in this article. Due to our focus on team external learning and knowledge transfer, we studied only one type boundary spanning. Other activities, such as mobilizing resources and gaining legitimacy (Ancona & Caldwell, 1992), deserve more close study as different external activities are triggered by differing motivations and thus may require different role-characteristics combinations. More research is needed to determine what role-based
28
characteristics may influence different boundary spanning activities and to understand how the team can productively interact with external environment and utilize it to maximize team performance.
Managerial Implications
It is almost axiomatic to mention that a team is only one part of the whole organization and organizational teams are often required to directly interact with intra- or extra- organizational actors for resources, support, and information. We suggest that such activities should be accomplished by the appropriate roles accompanied by specific traits, i.e. the right
role-characteristic combination. Both spanners and receivers are crucial for teams to benefit maximally from their external resources. And, it is important to ensure not only that individuals who develop external relationships possess learning goal orientation but also that members on the team who learn from the boundary spanners are inclined to cooperate with the boundary spanners to absorb imported knowledge. This understanding can help organizations to better utilize all the available member resources and to succeed in the competitive global market. Conclusion
External environment constitutes a key variable that shapes team outcomes. We adopted a role-based team composition approach to study when the team can benefit from the external knowledge through their advice ties and perform better. We found that teams benefit more from their access to external knowledge when spanners and receivers have certain characteristics. Both roles are important, and when they exist with role-appropriate characteristics, they will be most beneficial to the team’s ability to utilize external knowledge.
29
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TABLE 1: Descriptive Statistics
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
Gender Diversity (1)
Team Performance (2) -0.03
Team's Advice Ties (3) 0.20^ 0.17
Spanners' Learning Orientation (4) 0.06 0.20^ -0.09
Receivers' Learning Orientation (5) -0.03 0.14 -0.12 0.22*
Spanners' Agreeableness (6) -0.11 -0.01 0.17 -0.34** -0.03
Receivers' Agreeableness (7) 0.17 -0.12 0.13 -0.09 -0.16 -0.05
Spanners' Performance Orientation (8) -0.03 0.10 0.09 0.04 0.01 0.77** -0.02
Receivers' Performance Orientation (9) 0.09 -0.09 0.11 -0.03 0.13 0.02 0.81** 0.08
Teams' Learning Orientation (10) -0.10 -0.02 0.22* -0.26* -0.09 0.20^ 0.04 0.05 0.04
Teams' Performance Orientation (11) 0.07 0.21* -0.03 0.78** 0.70** -0.18^ -0.16 0.11 0.04 -0.17
Spanners' Openness to Experience (12) -0.15 -0.06 0.19^ 0.23* 0.01 -0.16 0.16 0.04 0.16 -0.11 0.14
Receivers' Openness to Experience (13) -0.02 -0.03 -0.02 0.02 0.24* 0.05 0.03 0.01 0.19^ -0.09 0.10 0.00
Spanners' Team Identification (14) -0.16 0.09 -0.06 0.07 -0.01 0.04 0.12 0.10 0.10 -0.02 0.00 0.18^ -0.03
Receivers' Team Identification (15) 0.15 0.11 -0.11 0.00 0.10 -0.10 0.01 -0.06 0.05 -0.01 0.04 -0.02 0.12 0.21^
Mean 0.30 95.72 5.33 5.45 5.34 3.60 3.54 4.12 4.01 3.07 5.39 2.88 3.05 3.55 3.49
Standard Deviation 0.19 11.38 2.84 0.48 0.56 0.69 0.92 0.51 0.69 0.30 0.38 0.42 0.49 0.38 0.42
Minimum 0.00 49.00 0.00 4.00 3.80 2.00 1.25 2.13 2.00 2.13 4.40 1.90 1.80 2.50 2.40
Maximum 0.50 110.00 14.00 7.00 6.40 5.25 5.38 5.32 5.26 3.55 6.10 4.20 4.00 4.40 4.60
35
TABLE 2: Results of Hypotheses Hypotheses (Model 1 to Model 6)
Team Performance Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Gender Diversity -4.32 -5.38 -5.38 -5.03 -5.37 -4.95
(6.73) (6.71) (6.75) (6.73) (6.75) (6.39)
Number of Spanners -1.73 -0.92 -0.92 -0.46 -1.03 0.12
(1.88) (1.94) (1.95) (2.01) (1.98) (1.94)
Team's Advice Ties (AT) 1.12^ 1.06^ 1.05^ 0.88 1.10^ 0.96
(0.58) (0.58) (0.58) (0.61) (0.59) (0.59)
Spanners' Learning Orientation (LGO) 5.01^ 5.04^ 5.44* 4.85^ 3.90
(2.61) (2.64) (2.66) (2.66) (2.58)
Receivers' Agreeableness (AG) 1.51 1.47 0.50 1.61 3.11
(2.93) (2.97) (3.15) (2.96) (3.10)
AT # Spanners' LGO 0.10 0.78
(0.93) (0.92)
AT # Recivers' AG -1.06 -0.75
(1.20) (1.17)
Spanners' LGO # Recivers' AG -2.35 -0.09
(6.69) (6.66)
AT # Spanners' LGO # Recivers' AG 8.54**
(Main Hypotheses) (2.50) Constant 101.43** 99.67** 99.69** 98.36** 99.94** 96.27** (5.23) (5.30) (5.34) (5.52) (5.39) (5.31) Observations 88 88 88 88 88 88 R-squared 0.05 0.09 0.09 0.10 0.09 0.22 F-Statistics 1.34 1.61 1.33 1.47 1.35 2.42*
Standard errors in parentheses ** p<0.01, * p<0.05, ^ p<0.1
36
TABLE 3: Simple Slope Analysis
Spanners' Learning Goal Orientation Receivers' Agreeableness Effect of Ties on Performance 95% Bootstrap Confidence Interval
High (5.94; 1 S.D. above) High (3.47; 1 S.D. above) 2.50 [0.2458, 4.7446]
High (5.94; 1 S.D. above) Low (2.64; 1 S.D. below) -0.21 [-1.8894, 1.4665]
Low (4.97; 1 S.D. below) High (3.47; 1 S.D. above) -1.02 [-3.0856, 1.0413]
37
TABLE 4: Robustness Checks
Roboustness Check (Model 7 to 13)
Team Performance Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Model 13
Gender Diversity -4.00 -4.99 -3.25 -4.78 -5.10 -6.69 -10.95
(7.34) (6.40) (6.27) (6.41) (6.56) (7.17) (6.92)
Number of Spanners 0.03 0.24 -0.24 -0.02 0.27 -1.64 -0.88
(2.48) (1.93) (1.90) (1.95) (1.96) (2.13) (1.93)
Team's Advice Ties (AT) 0.96 1.06^ 1.19* 1.09^ 0.92 1.27^ 1.16^
(0.76) (0.59) (0.59) (0.60) (0.61) (0.66) (0.61)
Spanners' Learning Orientation (LGO) 3.63 2.77 1.69 2.22 1.45 3.50
(3.14) (2.73) (2.70) (2.77) (2.89) (3.18)
Receivers' Agreeableness (AG) 2.95 4.07 3.51 3.95 4.29 2.14
(3.41) (3.16) (3.17) (3.21) (3.23) (3.22)
AT # Spanners' LGO 0.78 0.28 0.41 0.47 0.26
(1.41) (0.97) (0.94) (0.95) (0.96)
AT # Recivers' AG -0.44 -0.70 -0.47 -0.42 -0.59
(1.47) (1.17) (1.14) (1.15) (1.16)
Spanners' LGO # Recivers' AG -3.23 1.32 -2.72 -3.93 -3.87
(7.80) (6.77) (6.84) (6.93) (6.95)
AT # Spanners' LGO # Recivers' AG 8.93* 8.95** 10.73** 11.15** 10.18**
(Main Hypotheses) (3.64) (2.50) (2.56) (2.60) (2.68)
Spanners' Agreeableness -0.62 0.86 0.18 0.44 0.66
(3.06) (3.09) (3.14) (3.24) (3.69)
Receivers' Learning Orientation 3.72 4.19^ 4.19^ 4.27^ 4.02
(2.31) (2.27) (2.34) (2.44) (2.54)
Spanners' Performance Orientation 5.22* 5.51* 4.35^ 0.89 -0.23 -0.23
(2.45) (2.47) (2.60) (2.52) (2.42) (2.42)
Receivers' Performance Orientation -3.14 -2.32 -1.77 -1.28 -1.25 -1.25
(1.95) (2.06) (2.12) (2.33) (1.97) (1.97)
Spanners' Openness to Experience -3.50 -4.06 -2.90 -2.31 -2.31
(3.05) (3.08) (3.32) (3.03) (3.03)
Receivers' Openness to Experience -1.51 -1.84 -2.49 -1.50 -1.50
(2.57) (2.59) (2.84) (2.68) (2.68)
Spanners' Team Identification 4.43 6.82^ 7.21* 7.21*
(3.18) (3.45) (3.22) (3.22)
Receivers' Team Identification -0.23 -1.41 -0.13 -0.13
(2.54) (2.78) (2.62) (2.62)
AT # Recivers' LGO -0.70
(0.82)
AT # Spanners' AG -1.53
(1.27)
Recivers' LGO # Spanners' AG -1.81
(7.39)
AT # Recivers' LGO # Spanners' AG -2.83
(2.71)
Teams' Learning Orientation (LGO) 5.99
(3.83)
Teams' Agreeableness (AG) 2.21
(4.90)
AT # Teams' LGO -1.29
(1.20)
AT # Teams' AG -2.77^
(1.63)
Teams' LGO # Teams' AG -21.22^
(12.59)
AT # Teams' LGO # Teams' AG 6.88
(4.35) Constant 95.61** 95.90** 87.47** 97.49** 85.29** 97.83** 89.67** (6.20) (5.32) (13.22) (15.99) (20.08) (22.21) (20.82) Observations 75 88 88 88 88 88 88 R-squared 0.17 0.24 0.30 0.32 0.34 0.23 0.26 F-Statistics 1.49 2.24* 2.48** 2.25* 2.11* 1.2 1.70^
Standard errors in parentheses ** p<0.01, * p<0.05, ^ p<0.1
38
FIGURE 1: Theoretical Model
Team Performance Team’s external Advice Ties Spanners’ Learning Goal Orientation Receivers’ Agreeableness
39
FIGURE 2: Interaction Effect on Team Performance
Note: In the three-way interaction graph above, the number of a team’s advice ties, spanners’ learning goal orientation (LGO), and receivers’ agreeableness (AG) were centered at mean level to avoid multicollinearity. Low indicates one standard deviation below the mean and high indicates one standard deviation above the mean.
