Weakest Link Goal Orientations and Team Expertise: Implications for Team Performance

Abstract

Using prior theory and research, we argue that a team member with a low learning goal or a high avoid orientation is detrimental for the expertise–performance relationship in team tasks. Results from a study of 82 teams showed that, after controlling for goal orientation team composition, expertise improved team performance only when teams did not have a weak link team member. In contrast, when teams had this weak link teammate, expertise did not improve performance, and in some cases damaged it. Implications for theory and practice are discussed.

Keywords

goal orientation expertise weakest links

Expertise is a critical antecedent of performance in many jobs and a major selection criterion in most recruitment processes. In team settings, expertise is also a key input to task performance and can mean the difference between success and failure (Bunderson & Sutcliffe, 2003). Although it is expected that teams with more expertise should outperform teams with less expertise, this is not always the case (Gardner, Gino, & Staats, 2012). In fact, why teams often fail to fully utilize their expertise is a major organizational challenge that has been understudied in the literature (Hackman, 2011). Previous research has illuminated several process barriers to the effective utilization of expertise, including poor expertise identification (Bonner & Bolinger, 2013), poor communication and lack of collaborative planning (Woolley, Gerbasi, Chabris, Kosslyn, & Hackman, 2008), as well as contextual factors such as increased performance pressures (Gardner, 2012). However, there is a dearth of research investigating the moderating influence of team members’ characteristics and particularly motivational orientations on the expertise–performance link. Because motivational orientations govern how individuals typically utilize, maintain, and develop their skills and expertise, we argue that motivation orientations should affect how well expertise is utilized in teams.

In this study, we focus on the role of teammates’ goal orientations, defined as dispositions toward demonstrating or developing ability (Ford, Weissbein, Smith, & Gully, 1998), in underutilization of expertise. We argue that a single weak team member in terms of goal orientations will create negative dynamics for high expertise teams, and thereby undercut the benefits of expertise. Work is emerging on the impact of single difficult team members and the role they play in disrupting team work and performance (Felps, Mitchell, & Byington, 2006). We focus on such team members, hereby referred to as weakest links.

Team members with the weakest dispositional motivations can have disproportionate effect on team functioning given the nature of task interdependencies in teams. Additive models presume that each member is working on similar tasks, and contextual factors and interactions are such that the amount of member contributions are relatively uniform or the variation thereof is additive in nature (Kozlowski & Klein, 2000). In contrast, when working on nonadditive tasks, each team member might be performing a different set of functions and as such might be expected to coordinate and share information (i.e., have higher interdependence). In such cases, simple additive models might not be suitable to analyze team function. Nonadditive tasks are relatively more complex tasks, and the quality of a single member’s work can have a disproportionate effect on team output, thereby warranting the use of minimum or maximum team models (Kozlowski & Klein, 2000). Minimum or maximum models are also referred to as relative-contribution models of team composition (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014).

In minimum or maximum models, the situational factors or interactional processes are such that the highest or lowest value for a member determines the standing of the entire group. For instance, consider leadership skills of an executive team facing a crisis. In this case, it is not the average level of leadership in the group that determines the effectiveness of crisis management, but the maximum level of leadership. Even if most members do not show excellence in leadership, as long as there is one strong leader, the group as a whole can effectively navigate the crisis. Thus, in this case, the maximum level of leadership determines the collective outcome. Now consider the example of a pediatric cardiac intensive care unit, where there are strict rules for ensuring good sanitation practices to avoid fatal infections in pediatric patients. In this environment, having one noncompliant team member is sufficient to negatively affect the patient outcomes of the unit, even when on average other members follow the rules. One weak link is sufficient to cause fatal infections in the patients.

In line with relative contribution models, we focus on the goal orientation weakest link, rather than on the variance or average goal orientation of team members. Overall, we utilize a combination of the additive and the relative contribution models of team composition to test our hypotheses. Specifically, we model team-member expertise as a team resource that members add to the team, using the additive model. With respect to goal orientations, we focus on the effects of the weakest member relative to the rest of the team. We test our hypotheses using 82 teams who worked on a semester-long project.

Team Member Expertise

Experts are people who have more knowledge or skill than the average person (Bonner & Bolinger, 2013; Ericsson & Lehmann, 1996; Woolley et al., 2008). Researchers have drawn the distinction between general and specific expertise (Becker, 2009; Gardner, 2012). General expertise refers to abilities that should be useful in a wide range of contexts, tasks, or types of work. For instance, cognitive ability and college readiness tests, such as American College Testing (ACT), can be a measure of general expertise for various academic and professional tasks (Koenig, Frey, & Detterman, 2008; LePine, 2003; Woolley et al., 2008), or leadership, and creativity can reflect general expertise for various managerial jobs (Van Der Vegt, Bunderson, & Oosterhof, 2006). Specific expertise refers to task or organization-specific knowledge that is highly relevant in a given context but may not be helpful outside that context. Examples include domain-specific knowledge, such as principles of computer programming, task-specific proficiency (Bonner, Baumann, & Dalal, 2002), knowledge of and experience with a specific client (Gardner, 2012), or specialized technical skills (Van Der Vegt & Bunderson, 2005). In this article, we explore the role of both specific and general expertise.

Expertise improves team performance, yet teams do not often maximize their expertise (Bonner et al., 2002; Bunderson & Sutcliffe, 2003; Gardner, 2012). To better understand this organizational problem, researchers have focused on various behavioral processes, emergent states, and contextual factors that are thought to influence the relationship between expertise and team performance. Team processes which have been explored include the level of collaboration in a team (Woolley et al., 2008), how well a team exchanges knowledge (Friedrich, Vessey, Schuelke, Ruark, & Mumford, 2009; Stasser, Stewart, & Wittenbaum, 1995), the degree of relational conflict in teams (Martins, Schilpzand, Kirkman, Ivanaj, & Ivanaj, 2013), and the extent teams dedicate time to discussing expertise (Bonner & Sillito, 2011). Emergent states such as psychological safety (Martins et al., 2013) and collective identification (Van Der Vegt & Bunderson, 2005) have also been shown to influence the expertise–performance relationship. For example, Martins et al. (2013) found that when team psychological safety was low, less variance in type of expertise and more variance in level of expertise was better for team performance.

In terms of contextual factors, researchers have found that task complexity raises the use of expertise (Bonner et al., 2002) and nonroutine task environments strengthen the positive link between diverse expertise and team performance (Hambrick, Cho, & Ming-Jer, 1996). Similarly, Gardner (2012) examined the effects of performance pressure on knowledge use and found that team performance decreased as performance pressure elevated, which was due to team members relying too much on general expertise (e.g., level of technical certification/degree) and not relying enough on domain-specific expertise (e.g., time spent with a particular client). Evidence also suggests that experience of working together as a group on related tasks helped in expertise recognition (Littlepage, Robison, & Reddington, 1997). Similarly, team members who were central in communication networks were better at recognizing other members’ expertise (Su, 2012). In addition, confident and task-related communication positively influenced expertise recognition in multicultural teams, and experts who espoused self-effacing cultural values (e.g., team members from Asian cultures) were underestimated in their expertise by members belonging to assertive cultures (Bazarova & Yuan, 2013; Yuan, Bazarova, Fulk, & Zhang, 2013). These studies shed light on a few elements that seem to influence how well expertise is utilized in teams. However, we know very little about how the dispositional composition of teams may help or hurt the utilization of team expertise. To address this problem, we focus on the weakest link teammate in terms of dispositional goal orientation as a possible boundary condition of the expertise–performance link in teams.

Team Members’ Goal Orientations

Goal orientations are fundamental inclinations, which govern how individuals utilize, maintain, and develop their skills and abilities in achievement situations (VandeWalle, 1997). Hence, goal orientations should be relevant in how team members utilize their expertise in achieving team goals. Although considerable ambiguity exists with regard to the stability of goal orientations (Payne, Youngcourt, & Beaubien, 2007), most studies conceptualize goal orientations as stable individual-level properties (see DeShon & Gillespie, 2005, for a review). Overall, research indicates that individuals have dispositional or trait goal orientations that predispose them to respond in stable ways across achievement situations. Nonetheless, individuals can also respond to various situational factors by assuming situational or state goal orientations (Button, Mathieu, & Zajac, 1996).

Various situational factors, such as group climate of learning, leader–member exchange, leadership styles, and the nature of team incentives have shown to induce a preference for a specific goal orientation (Bunderson & Sutcliffe, 2003; Chi & Huang, 2014; Dragoni, 2005). A meta-analysis showed that, while trait goal orientations influenced state goal orientations, traits were stronger predictors than states of distal consequences such as learning or job performance (Payne et al., 2007). Therefore, in this study, we focus on trait goal orientations.

Scholars have identified three fundamental types of goal orientations. First, a learning orientation reflects the desire to learn new things and develop mastery of new skills (VandeWalle, Brown, Cron, & Slocum, 1999). Second, a prove performance orientation reflects the desire to gain favorable opinion of others by demonstrating one’s existing levels of expertise in a task (Payne et al., 2007). Third, an avoid orientation reflects the need to avoid unfavorable evaluations and to evade negative attributions (DeShon & Gillespie, 2005; Payne et al., 2007). Some researchers have combined avoid orientation with prove performance orientation (e.g., (Bunderson & Sutcliffe, 2003; Button et al., 1996; LePine, 2005; Porter, 2005), however, recent work has provided ample empirical support for the conceptual distinction between avoid orientation and prove performance orientation (e.g., Hirst, Van Knippenberg, & Zhou, 2009; Payne et al., 2007; VandeWalle, Cron, & Slocum, 2001). We will treat them as three separate orientations to be consistent with this evidence. In terms of motivational underpinnings, Elliot (1999) argued that learning and prove performance goal orientations stem from a fundamental need to seek pleasure, positive outcomes, or success (i.e., approach motivation), whereas avoid goal orientation (AGO) stems from a need to evade pain, negative outcomes, or failure (i.e., avoid motivation). In other words, approach and avoid motivations are affective tendencies that lead to goal orientations.

There is a vast body of literature available on individual-level goal orientations spanning multiple disciplines. Although a detailed overview of this fecund area of research is beyond the scope of this article, several reviews are available to summarize how goal orientations influence individual-level employee behaviors (e.g., Button et al., 1996; Farr, Hofmann, & Ringenbach, 1993; Payne et al., 2007). Goal orientations have been shown to influence multiple individual-level employee outcomes such as job performance under various types of goals (Chen & Mathieu, 2008), training and learning outcomes (Brett & VandeWalle, 1999; Dierdorff, Surface, & Brown, 2010), and leadership effectiveness (Day & Sin, 2011).

Average goal orientations have also shown to be important compositional variables in teams (e.g., LePine, 2005; Porter, 2005). For example, average learning goal orientation (LGO) has been found to be positively associated with information exchange (Sanner, 2015), team efficacy (Dierdorff & Ellington, 2012), team identification (Dietz, van Knippenberg, Hirst, & Restubog, 2015), team commitment (Porter, 2005), learning behaviors, and team performance (Bunderson & Sutcliffe, 2003; Dragoni & Kuenzi, 2012; Huang, 2012). Yet, despite all the work on the average level of goal orientation in teams, we have little understanding of how weakest team members in terms of goal orientation may have an inordinate impact on team dynamics and performance.

Goal Orientation Weakest Links in Teams

Teams are often assigned tasks that are complex and require individuals to work together to be successful. Because complex tasks require higher task interdependence among team members (Saavedra, Earley, & Dyne, 1993), they are more conjunctive in nature (Harrison & Humphrey, 2010). Thus, the quality of work can be disproportionality influenced by the weakest teammate, or weakest link, in the team (Barrick, Stewart, Neubert, & Mount, 1998; Harrison & Humphrey, 2010). Yet, little research has explored weak links in terms of goal orientations. We chose to focus on the single weakest team member on goal orientations given that the weakest link is more damaging for team processes than the strongest link is helpful (Felps et al., 2006; Raver, Ehrhart, & Chadwick, 2012), according to the principle of bad is stronger than good (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001).

Previous team research on goal orientations has mostly focused on average-level (e.g., LePine, 2005), and occasionally on variance in team members’ goal orientations (e.g., Pieterse, van Knippenberg, & van Ginkel, 2011). Models based on the average imply that it is the collective pool of a characteristic that matters and not how that characteristic is distributed among the team members (Barrick et al., 1998). In average models, weak and strong members are expected to balance out each other, and the bad is stronger than good principle is ignored. Models based on variance focus on the homogeneity of a characteristic and are relevant in contexts where fit, or lack thereof, among team members is central for team effectiveness (Barrick et al., 1998). Finally, team models based on the highest or the lowest member-trait are relevant in contexts where one person’s trait can have a disproportionate influence on the team. For example, in a sequential task—where one group member must act in a prescribed order, before another can act—the least competent person can become a bottleneck. Similarly, in an interdependent task—where members simultaneously interact to jointly diagnose, problem-solve, and collaborate—an uncooperative member can jeopardize the project. Models based on a minimum or maximum trait levels are suitable for exploring the bad is stronger than the good principle.

Extant research shows that the presence of a weak link, or a bad apple, in teams can negatively influence team outcomes. For example, research has identified traits such as disagreeableness, low conscientiousness (Bell, 2007), Machiavellianism, cognitive moral development, locus of control (Kish-Gephart, Harrison, & Treviño, 2010), and a lack of self-control (Marcus & Schuler, 2004) as disposition-based identifiers of problem employees at work. The behaviors of social loafing (Barrick et al., 1998; Bolin & Neuman, 2006; Kidwell & Bennett, 1993), withholding cooperation (Chatman & Barsade, 1995; Wu, Sun, Cai, & Jin, 2014), and spreading negative emotions (Barrick et al., 1998; Cole, Walter, & Bruch, 2008; Felps et al., 2006) have also been linked with problematic employees. The presence of such employees in teams is associated with destructive conflict (Barrick et al., 1998), lower cohesion (Barrick et al., 1998; Van Vianen & De Dreu, 2001), lower cooperation, poorer communication (Barrick et al., 1998; Raver et al., 2012; Wu et al., 2014), and weaker team performance (Halfhill, Nielsen, Sundstrom, & Weilbaecher, 2005; Neuman & Wright, 1999; Van Vianen & De Dreu, 2001). We seek to extend the work on weak links by focusing on individual team members with very low learning orientation or very high avoid orientation. We organize our arguments using primarily an information processing paradigm (Hinsz, Tindale, & Vollrath, 1997) for low learning orientation weak links, and a limited resource self-regulation paradigm (Vohs et al., 2008) for high avoid orientation weak links. Next, we highlight how these difficult teammates may hinder the effective use of expertise in teams.

Low learning orientation weak link

A team member who is particularly low on learning orientation is less motivated to explore new information or critically integrate information with the expertise existing in the team. As shown earlier, sufficient evidence exists in the literature to expect a negative relationship between low LGO and performance for both individuals and teams. Therefore, the performance deficiencies generated by a team member with a very low learning orientation are likely to affect how teams interact and share information. A low learning orientation member is less likely to seek feedback or input from others (Payne et al., 2007), thus precluding other team members from making corrections to one person’s weak performance. Accordingly, a team member very low on learning orientation may approach work in ways that are not conducive to the effective utilization of team expertise.

In addition to lower individual performance, a very low learning oriented member should also negatively affect the work of others. Specifically, low learning members may affect the way the group—as an information processor—attends, encodes, and processes information (Hinsz et al., 1997). In a review of team information processing literature, Hinsz et al. (1997) highlighted various reasons (e.g., social validation, time pressure, outcome focus) that compel teams to attend to and share common information more often than unique information. Research has shown that it is difficult for teams to recognize and fully utilize expertise (Baumann & Bonner, 2004; Bunderson, 2003; Gardner, 2012). This may result from the tendency of groups to seek common information, which deters exploring unique expertise of each member. For example, one important reason why teams fail to recognize and utilize their expertise is poor information integration (Woolley et al., 2008). In team research, LGO has shown to be associated with effective exchange of information (Gong, Kim, Lee, & Zhu, 2013; LePine, 2005), providing feedback to others (Payne et al., 2007), encouraging team discussion and diverse opinions (Park & DeShon, 2010), and fostering cooperation (Porter, 2005). Accordingly, a member low in learning orientation may fail to effectively engage with team members. More specifically, the lack of motivation to explore and consider diverse viewpoints that low learning individuals are likely to manifest may exacerbate the group phenomena of overlooking unique information. In turn, this may lead to problems with recognition and utilization of expertise.

Hinsz et al.’s (1997) review also highlights that groups do not always assemble the information available to their members into a more complex perspective; instead, they actually develop simpler, narrower perspectives. Although the reasons for such differences are poorly understood, one explanation may be that teams must resort to a perspective that requires the least amount of information processing. In particular, we argue that low learning orientation members are both less disposed and unmotivated to process the complex information available to them. In other words, the lowest learning orientation member can act as a bottleneck for processing complex information. Next, we explain the possible mechanism for this.

Low learning orientation members are likely to have a lower integrative complexity (Driver & Streufert, 1969; Suedfeld & Coren, 1992). Integrative complexity is a cognitive style that comprises differentiation and integration (Tetlock, Peterson, & Berry, 1993). Differentiation refers to the inclination to tolerate different points of view or view information from more than one perspective, whereas integration refers to the inclination to generate linkages between these different points of view or perspectives (Tetlock et al., 1993). Integrative complexity is conceptualized as a trait and is distinct from cognitive ability (Suedfeld & Coren, 1992).

Based on the two dimensions of differentiation and integration, individuals can be placed on a continuum ranging from simple to complex thinking. As illustrated in our review of the goal orientation literature, individuals with low learning orientation appear to share inclinations with individuals with low integrative complexity (i.e., simpler thinking). More specifically, both are less likely to value or encourage diverse opinions (Park & DeShon, 2010; Tetlock et al., 1993), and both are less inclined to be creative and innovative (Gong et al., 2013; Tadmor, Galinsky, & Maddux, 2012). Finally, individuals with low integrative complexity prefer less complex information environments, adopt less complex information searching strategies, and communicate and innovate less than individuals with high integrative complexity (Driver & Streufert, 1969). Similarly individuals low on learning orientation prefer less challenging situations (Dweck & Leggett, 1988; Elliott & Dweck, 1988), seek less feedback (Payne et al., 2007), and exchange less information (Gong et al., 2013).

Based on the above arguments, it appears that individuals with low LGO have lower integrative complexity. Driver and Streufert (1969) argued that individuals with high integrative complexity can effectively process more complex information inputs into more complex decision outputs. Accordingly, individuals low on learning orientation may bottleneck the processing of information in teams by either providing less complex informational inputs to team members, or by producing less complex outputs from information received from team members.

Overall, a team member with lowest LGO may effectively act as information processing bottleneck. This member might be less willing to expend effort to process complex information and, consequently, may deter teams from integrating complex information into decisions. The lower levels of integrative complexity may discourage such members to think in more complex ways even when information is provided by other group members. Consequently, in the presence of a very low learning orientation team member, the effects of other team members’ expertise on team performance are attenuated.

H1: The association between (a) general and (b) specific team expertise, and team performance will be moderated by the learning orientation weakest link, such that the less learning oriented the weakest link is, the less the team will benefit from the team’s expertise.

High avoid orientation weak link

Research has shown the proximal and distal outcomes of learning and avoid orientations to be similar in size but opposite in direction (Payne et al., 2007). Individuals high on avoid orientation are likely to reduce team performance for at least three reasons: (a) they are more likely to avoid information exchange; (b) when they do exchange information with others, they are more likely to focus on threats and negative contingencies, instead of opportunities; and (c) they can act as a confidence dampener in the team.

For example, a teammate with high avoid orientation may refrain from sharing relevant information or task concerns with the team, because of the risk of unfavorable attributions that such communication may involve (Gong et al., 2013; Pieterse, Van Knippenberg, & Van Dierendonck, 2013), thereby effectively impairing information exchange, albeit for different reasons to a member with low learning orientation. The tendency to avoid negative attributions in members with high avoid orientation is likely to exacerbate in team settings, as research has shown that people tend to become more self-conscious when working in teams (Hinsz et al., 1997). The effect of a person with high avoid orientation is particularly salient in team contexts because the situation may appear more evaluative when others are present during task performance (Rawsthorne & Elliot, 1999), and therefore may trigger increased anxiety for these individuals. Thus, the tendency of a high avoid orientation member to prefer safe approaches to the task and assume defensive rather than adaptive behaviors (Kozlowski & Ilgen, 2006; Pieterse et al., 2013; VandeWalle et al., 1999) is likely to be amplified in team contexts.

Having a weak link on avoid orientation may also attenuate the positive effects of other members’ expertise on team performance. In particular, when members with high avoid orientation participate in information sharing and decision making, they tend to do so in a manner that is taxing on team resources. The tendency to focus on risks rather than opportunities has implications for how the member with high avoid orientation and, by way of influence, the rest of the team, uses their regulatory resources (Vohs et al., 2008). Researchers have argued that individuals set and maintain progress on goals by making a series of cognitive, affective, and behavioral modifications through conscious and subconscious self-regulation processes (Lord, Diefendorff, Schmidt, & Hall, 2010; Vancouver & Day, 2005). Self-regulation processes depend on limited cognitive and attentional resources that can easily be depleted (Muraven & Baumeister, 2000). Strenuous use of self-regulation resources has been associated with underachievement on various tasks (Schmeichel, Vohs, & Baumeister, 2003). Individuals with high avoid orientation need and use more regulatory resources to accomplish their goals (Ferris et al., 2011). Specifically, when multiple ways of succeeding and failing exist, approach-oriented individuals (e.g., high learning or high prove performance) can focus on a single path to success, whereas individuals with high avoid orientation spread their resources thin by trying to preempt every path that may lead to negative outcomes. Thus, high avoid orientation is likely to cause depletion of self-regulatory resources and subsequent poor performance on the set goals.

Researchers have drawn parallels between an individual-level self-regulation processes and team-level transition and action processes (Lord et al., 2010). A high avoid orientation member is likely to increase the decision making burden in the team by urging others to consider and mitigate several negative contingences. This challenge may overtax team resources by increasing focus on contingency planning during transition phases and risk management during action phases. The resulting depletion of resources may decrease team performance.

A set of experiments (Vohs et al., 2008) showed that making decision is a taxing process, which reduces subsequent focus, physical stamina, persistence, and performance on arithmetic tasks. Thus, focusing extensively on threats may leave fewer resources to focus on available opportunities. Interestingly, the taxing effect on team resources by a high avoid orientation individual may not easily be countered by the presence of other approach-oriented members (i.e., high learning orientation and high prove performance orientation). Although approach-oriented members may attempt to redirect the team toward opportunities rather than threats, past research shows negative outcomes weigh more heavily emotionally than positive outcomes of equal value in decision making (Tversky & Kahneman, 1981). Consider an event planning team. A high learning orientation member in this team might focus on one promising course of action to maximize the success of the event. In contrast, a high avoid orientation member might be compelled to prepare for all possible contingencies that may jeopardize the project (e.g., arranging for the backup sound system, drafting a more detailed contract to deter vendor noncompliance, or conducting multiple venue inspections). Thus, unlike an approach-oriented member who just needs to identify and commit to one promising course of action, a high avoid orientation member may need to make many more decisions in the quest for searching and eliminating all possible sources of failure. Consequently, the increased focus on negative outcomes during decision making might drain the team emotionally.

Finally, past research has shown that team members high on avoid orientation are less extraverted, more neurotic, and have lower self-esteem and self-efficacy (Payne et al., 2007). It is plausible that members high on avoid orientation may stifle team confidence and act as a source of anxiety. Research on emotions has shown that strong emotions of one individual can be contagious (Hatfield, Cacioppo, & Rapson, 1994), and that negative emotions outweigh positive emotions in their effects on others (Baumeister et al., 2001). As a result of the decreased team confidence and increased anxiety, the regulatory resources of teams with a high avoid orientation member will be depleted more easily. Consequently, such teams will underperform, even when they possess sufficient expertise. We explore this association across general and specific expertise and hypothesize the following:

H2: The association between (a) general and (b) specific team expertise and team performance will be moderated by the avoid orientation weakest link, such that the more avoidant the weakest link is, the less the team will benefit from the team’s expertise.

Prove performance orientation weakest link

The effects for prove performance orientation on team processes and outcome are more equivocal compared with those of learning and avoid orientation. On one hand, prove performance orientation has been shown to positively influence team information exchange (Gong et al., 2013) and team planning (Mehta, Feild, Armenakis, & Mehta, 2009), which should translate into more effective identification and utilization of expertise. However, other researchers found that prove performance orientation had negative effects on team learning (Dierdorff & Ellington, 2012), team creativity (Hirst et al., 2009), and interpersonal, transition, and action processes (LePine, 2005), thus potentially hindering the use of expertise. Moreover, although a meta-analysis on goal orientations found that prove performance orientation was weakly related to state anxiety (Payne et al., 2007), it was not significantly related to most of the outcomes studied including task performance, job performance, or learning. Given these conflicting results, we chose to treat the effects of a particularly high or particularly low prove performance member on the use of expertise as research questions.

RQ1: Does the presence of a particularly low prove performance goal orientation team member moderate the relationship between expertise and performance?

RQ2: Does the presence of a particularly high prove performance goal orientation team member moderate the relationship between expertise and performance?

Method

Participants

The study participants were 432 undergraduate business students enrolled in a junior-level principles of management course across two semesters. The sample was 77% Caucasian, 63% male, and average age was 22.3 years (SD = 3.8). The course was taught by the same instructor and had an identical course design and grading scheme in both semesters. The instructor of the course was not involved in the research and was not privy to the hypotheses of this study.

Nature of Task

Students were randomly assigned to teams (N = 82) of five to six individuals to work on a semester-long project as part of the course requirements. The focus of the project was to learn and explain how to successfully manage change in organizations. Each team was expected to (a) research a real-world example of an organization that altered its own structure or made significant changes within its market or industry, (b) describe the organization before and after the changes, and (c) analyze how successful the implementation of change was using various business models and outcomes. The project was a rigorous group task demanding learning, critical thinking, idea generation, and communication. Effective project delivery required division of responsibilities as well as collaboration and integration of efforts to formulate a successful report. Thus, the nature of the task demanded considerable interdependence among group members. Therefore, it is less likely that simple pooling of each member’s work (additive tasks) or having the most competent person on the team (disjunctive task) could result in successful project delivery. In contrast, each member was expected to perform at a minimally acceptable level for the team to deliver an effective report (conjunctive task).

Measures

Expertise variables

We operationalized expertise in two ways. First as a measure of general expertise, we used the ACT. The scores on ACT have shown to be a good indicator of cognitive ability (Koenig et al., 2008), and cognitive ability has been used as a measure of general expertise in past research (Woolley et al., 2008). Moreover, because ACT scores capture students’ college readiness for academic work, these scores should be relevant for students’ performance on a class project. ACT scores were collected at the beginning of the semester.

In addition, we used student’s knowledge of key course concepts as a measure of specific expertise for the class project, given that the project required the use of class concepts in the analysis. Midterm exam scores were used for measuring specific expertise. Because we treat expertise as a type of input that individual members add to the team, we do not expect members’ expertise scores to coalesce and therefore do not report aggregations statistics (Chan, 1998).

Goal orientations

Dispositional goal orientations were measured 2 weeks after the midterm with 13 items developed and validated specifically for academic contexts (VandeWalle et al., 2001). Four items measured learning orientation (α = .83), five items measured avoid orientation (α = .68), and four items measured prove performance orientation (α = .76). Item response scales were 7-point Likert-type ranging from 7 (strongly agree) to 1 (strongly disagree). Confirmatory factor analysis showed the intended three-factor solution fit to the data reasonably well, χ² = 203.72, df = 62, comparative fit index (CFI) = .91, root mean square error approximation (RMSEA) = .07, as per the guidelines (MacCallum, Browne, & Sugawara, 1996). Moreover, this model had a significantly better fit than a two-factor solution with prove and avoid indicators loading on a single performance-orientation factor (χ² = 534.29, df = 64, CFI = .70, RMSEA = .13; Δχ² = 330.57, p < .001), or a one-factor solution (χ² = 829.17, df = 65, Δχ² = 625.45, p < .001).

Because our theory called for assessing a team’s weakest link, we retained the following team-level variables: minimum LGO, maximum AGO, and minimum and maximum prove performance orientation in the team. Thus, we modeled the weak links as continuous variables (i.e., we did not dummy code teams as either having or lacking a weak link). However, consistent with the standard in the field for interpreting interaction graphs, we will refer to teams as either having or lacking a weak link based on whether their minimum LGO member and maximum avoid orientation member, respectively, are one standard deviation above or below the mean in the sample’s minimum learning and maximum avoidance scores.

Team performance

The team project was evaluated by the instructor 1 week before the end of the semester on the basis of the use of evidence, explanation of the issues faced by the company, the objective and the impact of the proposed change, the incorporation of management concepts, and the quality of writing. The maximum possible score on the project was 100. A subset of projects was rated by another trained rater, and the interrater reliability was .85.

Covariates

Average learning, avoid performance, and prove performance orientations were used as covariates in our models. We also used standard deviation of goal orientations, maximum AGO and minimum LGOs in a team, in additional analyses as covariates. Because we operationalized goal orientations as dispositions, we do not expect teammates’ scores to coalesce, and therefore, do not report any aggregation statistics (Chan, 1998).

Results

All analyses and results are based on team-level data and basic statistics and correlations are reported in Table 1. We used hierarchical regression to test the hypotheses. To facilitate the interpretation of interaction graphs, all predictor variables were mean-centered and the outcome variables were standardized (Cohen, Cohen, West, & Aiken, 2003). For all models, control variables (i.e., average learning, avoid, and prove performance orientations of a team) were entered in Step 1. We controlled for the average orientations in the team to clearly highlight the unique effects of the weakest link on the utilization of expertise. In doing so, we capture the effects of the weakest link irrespective of whether the weakest link was member of a team with high or low average goal orientations. In addition, we reran all our analyses, first with both means and standard deviations of goal orientations controlled for, and second with means, standard deviations, and the strong links (i.e., maximum LGO and minimum AGO in each team) controlled for. This was to further parcel out the effects of weak links, by controlling for the diversity in goal orientations and the presence of strong links in a team. Our results for weak goal orientations hold irrespective of how the rest of the team was composed in terms of goal orientations.

Table 1.

Mean Values, Standard Deviations, and Intercorrelations of Study Variables.

	M	SD	1	2	3	4	5	6	7	8	9	10
1. General expertise	24.24	2.25
2. Specific expertise	71.38	6.22	0.10
3. Minimum LGO	3.49	0.91	−0.09	0.04	(.83)
4. Maximum AGO	5.55	0.72	0.36	0.01	−0.35	(.68)
5. Minimum PGO	3.06	0.91	−0.17	0.21	0.23	−0.05	(.76)
6. Maximum PGO	5.58	0.77	0.16	0.21	−0.12	0.39	0.07	(.76)
7. Mean LGO	4.82	0.55	−0.14	0.16	0.79	−0.39	0.20	−0.07	(.83)
8. Mean AGO	4.44	0.51	0.36	−0.04	−0.29	0.79	0.10	0.40	−0.47	(.68)
9. Mean PGO	4.40	0.54	0.17	0.29	0.09	0.22	0.64	0.62	0.15	0.34	(.76)
10. Team performance	82.54	8.79	−0.18	0.20	0.27	−0.19	0.08	−0.11	0.26	−0.24	0.01	(.85)

Note. n = 82 teams. All correlations greater or equal to .22 are significant at p < .05. Reliability estimates shown in parentheses are Cronbach’s alpha value for all variables except team performance, where interrater reliability is reported instead. LGO = learning goal orientation; AGO = avoid goal orientation; PGO = prove performance goal orientation.

Finally, we also ran our models without any covariates. Our results remain virtually unchanged across all these analyses. Hence, for simplicity, we report results with controls for mean only. In Steps 2 and 3, we entered the main effects of expertise and the weakest link, respectively. Finally, in Step 4, we entered the interaction of expertise and the weakest link. The model results are reported in Table 2. Interaction terms were further interpreted through simple slope analysis (Aiken & West, 1991).

Table 2.

Expertise and Goal Orientations as Predictors of Team Performance: Parameter Estimates.

Variable	Step 1	Step 2	Step 3	Step 4
H1a
Mean LGO (control)	.26*	.24*	.10	.13
Main effects
General expertise		−.15	−.15	−.09
LGO (minimum)			.18	.17
Interaction effect
General expertise × LGO (minimum)				.24*
R²	.07	.09	.10	.15
Model F change	5.74*	1.82	1.02	4.88*
ΔR²		.02	.01	.05*
H1b
Mean LGO (control)	.26*	.23*	.07	.01
Main effects
Specific expertise		.16	.18	.12
LGO (minimum)			.21	.26
Interaction effect
Specific expertise × LGO (minimum)				.25*
R²	.07	.09	.11	.16
Model F change	5.74*	2.21	1.42	5.16*
ΔR²		.03	.02	.06*
H2a
Mean AGO (control)	−.24*	−.20	−.20	−.20
Main effects
General expertise		−.11	−.11	−.06
AGO (maximum)			.01	−.05
Interaction effect
General expertise × AGO (maximum)				−.30**
R²	.06	.07	.07	.15
Model F change	4.70*	.88	.00	7.55**
ΔR²		.01	.00	.08**
H2b
Mean AGO (control)	−.24*	−.23*	−.20	−.17
Main effects
Specific expertise		.19	.19	.17
AGO (maximum)			−.04	−.04
Interaction effect
Specific expertise × AGO (maximum)				−.13
R²	.06	.09	.09	.11
Model F change	4.70*	3.10	0.04	1.31
ΔR²		.03	.00	.02

Note. Entries are standardized regression coefficients rounded to two decimals. n = 82 teams. LGO = learning goal orientation; AGO = avoid goal orientation.

p < .05. **p < .01.

H1a stated that teams with weak links on LGO will benefit less from general expertise. Regression results showed that this model explained a significant amount of variance in team performance and the interaction was significant (ΔR² = .05, p < .05). We plotted the slopes of expertise at high and low levels (i.e., ± 1 standard deviation) of the weakest learning orientation member. The dashed line represents teams whose lowest scoring teammate on learning orientation was extremely weak compared with the whole sample; these are teams with a weak link. The solid line represents teams whose lowest scoring teammate on learning orientation was not very weak as compared with the sample; these are teams without a weak link. As predicted, simple slope analysis showed that teams with a particularly low minimum LGO utilized their general expertise less effectively, b = −.18, t(78) = −2.58, p < .05, than teams with higher minimum LGO, b = .09, t(78) = 1.08, p = .28; see Figure 1. In fact, general expertise appears to harm team performance in the presence of a member with a very low learning orientation, supporting our weak link theoretical rationale.

Figure 1.

Team performance on general expertise by minimum LGO.

H1b predicted that minimum LGO will also moderate the relationship between specific team expertise and team performance. This model and the predicted interaction were also significant (ΔR² = .06, p < .05); the interaction is depicted in Figure 2. As predicted, teams benefited more from their specific expertise (i.e., had a positive and significant simple slope for expertise) when they did not have a weak link on LGO, b = .06, t(78) = 2.69, p < .01 versus b= −.02, t(78) = −.68, p = .50. Thus, H1a and H1b were supported.

Figure 2.

Team performance on specific expertise by minimum learning orientation.

H2a stated that a weak link on avoid orientation (using a maximum score within the team) will interact with general expertise and reduce the effect of general expertise on performance. This interaction is significant (ΔR² = .08, p < .01) and is illustrated in Figure 3. The dashed line represents teams whose highest scoring teammate on avoid orientation was extremely weak (i.e., one standard deviation above the mean of all weakest members across all teams) compared with the whole sample; these are teams with a weak link. The solid line represents teams whose highest scoring teammate on avoid orientation was not very weak compared with the sample; these are teams without a weak link. As predicted, teams benefited less from expertise when they had a member who was particularly high on AGO, b = −.16, t(78) = −2.45, p < .05 versus b = .10, t(78) = 1.38, p = .17. We made a similar prediction for specific team expertise (H2b) but the interaction was not significant (p = .26). Thus, H2a was supported, but H2b was not. We also tested the effects of team members with very low or very high prove performance orientation and found no evidence that they influence the expertise–performance relationship. Models based on minimum prove performance orientation explained little variance in team performance (R² = .03, p = .60 for general expertise; R² = .06, p = .29 for specific expertise). Models based on maximum prove performance orientation also fit poorly (R² = .08, p = .19 for general expertise; R² = .09, p = .14 for specific expertise). The interaction with expertise was nonsignificant in all cases.

Figure 3.

Team performance on general expertise by maximum avoid orientation.

Discussion

This study found that team members with a very low learning orientation or a very high avoid orientation acted as weak links in their teams and hindered the effective use of team expertise. After controlling for the effects of average goal orientations, low learning orientation weak links moderated the effects of both general and specific expertise on performance, whereas high avoid orientation weak links moderated the effects of general expertise. This evidence supports the conclusion that even one team member with a poor goal orientation can reduce overall team effectiveness, irrespective of team composition. A focus on average goal orientations in teams may thus be insufficient to fully understand the influence of weak motivational orientations on team performance. Taken together, our findings add to the body of research that outlines under what conditions expertise promotes or hinders group performance. Our results clearly support the contingent view of expertise and underscore the need to examine dispositional moderators of expertise–performance link in future research.

The results have important theoretical implications for team expertise research. This study furthers our understanding of factors that govern the translation of team expertise into team performance. In the past, researchers have focused on many contextual factors (e.g., time pressure to perform, team identity) that influence the expertise–performance link (e.g., Gardner, 2012; Van Der Vegt & Bunderson, 2005). However, less attention has been paid to how deep-level member attributes may influence the utilization of expertise in teams. Deep-level team attributes are notable factors that have shown to influence various team processes and outcomes (e.g., Bell, 2007; LePine, 2003; Somech & Drach-Zahavy, 2013). In this study, we draw attention to one such deep-level composition variable, member goal orientations, to examine the expertise–performance relationship in teams. The effects of poor goal orientations on team performance suggest that team members who may otherwise be able and motivated to work, could nonetheless hinder team performance when they have a disposition to be apprehensive of negative judgments by others, or lack a desire to develop new skills or abilities. Thus, our findings may be one explanation for the failure of teams composed of talented and motivated individuals.

In team research, we have ample evidence for the positive influence of average LGO and negative influence of the average AGO. However, the effects for average prove performance orientation have been less consistent, and range from positive (Mehta et al., 2009) to neutral (Pieterse et al., 2013) to negative (Dierdorff & Ellington, 2012). Our results shows that a member with a low or high prove performance orientation was not consequential for team outcomes in a team task requiring idea generation and problem solving. In this context, only low learning and high avoid orientations acted as weak links. However, in competitive tasks where success is measured in terms of being the fastest or the most efficient team, a member with a weak prove performance orientation might also be harmful for the team.

Our findings further the research on problematic members in teams. In teams, the bad apple model proposed by Felps et al. (2006) categorized difficult teammates as those who have poor affect, withhold effort, or show interpersonal deviance. Similarly, a team with a highly disagreeable or an unconscientious member has worse performance than teams without these problem members (Bell, 2007). Our results extend this literature by showing that goal orientations represent another important way to identify difficult teammates. It may be that teammates low on learning orientation or high on avoidance are similar to withholders of effort. However, conscientiousness is only moderately correlated with learning (ρ = .32) and weakly correlated with avoid (ρ = −.18) orientations (i.e., Payne et al., 2007). Thus, it is less likely that weak goal orientation compels members to consciously withhold effort or engage in disruptive behaviors.

Alternatively, the problem may have to do more with the direction of effort, rather than the intensity of effort from such team members. An important distinction between high avoid orientation and high approach orientation (i.e., high learning or high prove performance) individuals is that the former invest their efforts mainly in identifying and mitigating threats, whereas the latter invest their efforts mainly in identifying and pursuing opportunities (Ferris et al., 2011). Team members with low learning orientation also have lower need for achievement (r = .48, Payne et al., 2007), and may influence the team to set lower performance targets. Finally, the pattern of relationships with emotional stability (r_LGO = .18; r_AGO = −.37), general self-efficacy (r_LGO = .71; r_AGO = −.61), and self-esteem (r_LGO = .38; r_AGO = −.39) reported in past meta-analysis (Payne et al., 2007) suggests that team members low on learning or high on avoid may generate anxiety in the team and sap team confidence.

Interestingly, in our study, the slopes of general expertise on team performance were negative when a weak link was present, echoing results by Woolley et al. (2008) that showed expertise alone was not sufficient for, and could actually decrease, team performance. In the case of avoid orientation weak links, this may have occurred because teams with greater general expertise can identify a greater variety of potential risks and threats to the success of the team. Paradoxically, while at face value, this may appear to be an advantage for high expertise teams, the greater focus on threats may in fact deplete the limited regulatory resources of a team to an extent that substantially impairs team performance. We can only speculate why this effect occurred just in teams with high general, not specific expertise. Perhaps teams with high task-specific expertise were able to also identify more task-specific rather than general threats, which may have made the overuse of regulatory resources more worthwhile. In the case of learning orientation weak links, the increased complexity of information in teams with greater general expertise may have more quickly exceeded the characteristic integrative complexity of the low learning individual, accentuating his or her hindering effects on information exchange and integration.

Alternatively, the negative slopes may reflect the problematic interpersonal dynamics in teams with greater general expertise and a very weak link on goal orientations. A growing body of research shows that teams composed of all-star (or high) performers tend to experience dysfunctional and counterproductive behaviors (Groysberg, Polzer, & Elfenbein, 2011; Overbeck, Correll, & Park, 2005), such as lower levels of cooperation, lack of information sharing, and poor joint decision making (Bendersky & Hays, 2012; Tiedens & Fragale, 2003). This often results from too many individuals feeling entitled to give direction and two few willing to follow others (Overbeck et al., 2005). The identities and egos of experts are often intertwined with their expertise (Polzer, Caruso, & Brief, 2008; Polzer, Milton, & Swarm, 2002), which can escalate task-related conflict into relationship conflict. Low learning and high avoid orientation weak links can contribute to problems in high-expert teams by initiating and/or exacerbating dysfunctional behaviors including less information sharing, lower collaboration, and more focus on emotionally taxing negative outcomes. We observe more pronounced negative effects in teams with greater general, as opposed to specific expertise, potentially because we measured general expertise with a general intelligence proxy (i.e., ACT scores). In expertise research, intelligence is often used as a measure of general expertise (e.g., Woolley et al., 2008). Team members with high ACT scores may regard themselves as smarter than others in a variety of contexts and a wide array of tasks. This attitude may be associated with some of the dysfunctionalities seen in the members of all-star teams (i.e., hypercompetitiveness, overconfidence, too many trying to lead, not enough willing to follow). In contrast, team members with higher course content scores (i.e., specific expertise) may not have regarded themselves to be experts in a variety of tasks, thereby reducing overcompetition and showing more willingness to listen open-mindedly. It is important to note that there was a weak positive correlation between ACT and exam score, which makes our explanation of specific and general expertise fostering different team dynamics more plausible. The presence (and absence) of all-star teams’ dysfunctionalities may also explain why the overall effects of expertise, whereas nonsignificant, are nonetheless negative for general and positive for specific expertise.

Limitations and Directions for Future Research

Our study has some limitations. One limitation of the present research is use of student teams. However, these students were engaged in a semester-long project, which was a significant proportion of their grade. Unlike laboratory studies, these students had a common membership in a class and identified with each other as a cohort. As a result, our participants were more likely than laboratory studies to identity with their teams and experience the realistic performance pressures. Thus, our task had a fair degree of psychological realism.

An important boundary condition for our findings is the nature of the task. Our findings are especially relevant for interdependent or conjunctive tasks, but may be less pronounced in additive tasks (where team outcome is the sum of individual contributions), which are less interdependent. Nevertheless, as modern work has become increasingly interdependent (Humphrey, Nahrgang, & Morgeson, 2007), the nature of many team tasks in the modern organization is likely to be more conjunctive than disjunctive or additive, thus lending a good degree of generalizability to our findings.

Our study did not directly test the mediational mechanisms implied in our hypotheses development. In particular, while the parallels between integrative complexity and learning orientation are compelling, neither our study nor any previous studies have tested a link between these constructs. Future studies can shed more light on this link, which may explain both the effects of weak and strong learning orientation members on performance. Perhaps the weak links interfere with the transition, action, and interpersonal processes (Marks, Mathieu, & Zaccaro, 2001) that are necessary for the effective use of expertise. Other team processes, including expertise coordination (Faraj & Sproull, 2000) and team reflection (West, 1996), may also suffer from having a member with weak goal orientations. In addition, team states such as team cohesion and team affect could also suffer from the presence of such team members. Future research should explore not only the mechanisms that link weak link teammates on goal orientations to team performance, but also potential strategies or conditions that might help managers minimize their effects. It may be that strong leaders or unique team structures can alleviate the negative effects of weak links.

Practical Implications

Unlike team members low on agreeableness or conscientiousness, team members with poor goal orientations may be less easily identified as bad apples. Team members with poor goal orientations may otherwise be hardworking and capable individuals; for example, a meta-analysis has shown that goal orientations do not correlate with intelligence and are also distinct from the big-five personality traits (Payne et al., 2007). Thus, performance problems due to poor goal orientations may be harder to diagnose, unless employee assessment measures specifically include goal orientation items. Our study suggests that when selecting team members for team problem-solving tasks, managers should use selection procedures that can identify team members’ goal orientations. This should help identify potential weak links.

However, in many situations, it might be neither practical nor ethical to eliminate members with poor goal orientations. We suggest that in situations where there is a member with a weak goal orientation, managers should not rely heavily on self-managed teams and take a leadership role in designing interventions to enhance communication, knowledge sharing, feedback, and participation. For instance, past research has indicated that extrinsic factors such as encouraging learning climate can attenuate the negative influence of dispositional goal orientations on team outcomes (Hirst et al., 2009). Managers can also coach members with weak goal orientations to overcome their limitations, as indicated in previous research (Brett & VandeWalle, 1999).

Similarly, having a process in place where every team member must seek and share information during weekly update meetings, might nudge members with low LGO to share information. Members with high avoid orientation can be encouraged to focus on a positive course of action. Devil’s advocacy is often used to instill cross-examination in a team. However, with a high avoid orientation member, teams may need to do exactly the opposite (i.e., curb criticism and reduce too much focus on negative contingencies). Thus, managers can appoint high avoid orientation members as promoters of a cause to focus on positives in a situation, and use goal setting to encourage delivery on the agreed-upon course of action. Targeted focus on weak links may strengthen the team as a whole. This targeted strategy would be more effective than just focusing on average team disposition.

Conclusion

Goal orientations are important explanatory variables for the proper utilization of expertise in teams. In particular, goal orientation weak-links can reduce the effectiveness of team expertise by introducing poor information processing mechanisms and depleting limited self-regulatory resources in teams. Such problems are likely to increase with rising team expertise. Developing further understanding of the mechanisms through which weak links in terms of goal orientations have an inordinate impact on teams, and how to remediate the negative impact of such team members, may thus be a fecund avenue of research both for theoretical and practical reasons.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Maria Hamdani

Author Biographies

Sorin Valcea is assistant professor of management at Cleveland State University, the United States. His research concerns recruitment, ego development, teamwork, leadership, and organizational citizenship behaviors.

Maria Hamdani is associate professor of management at the University of Akron, the United States. Her main areas of research include recruitment, teamwork, emotions, and leadership.

Bret Bradley is associate professor of management and international business at the University of Oklahoma, the United States. His research intersects the areas of small group dynamics, team performance, leadership, and ethics.

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