Why turnover matters in self-managing work teams: Learning, social integration, and task flexibility

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2010 36: 1168 originally published online 23 September 2009Journal of ManagementGerben S. van der Vegt, Stuart Bunderson and Ben Kuipers

Integration, and Task FlexibilityWhy Turnover Matters in Self-Managing Work Teams: Learning, Social

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Why Turnover Matters in Self-Managing Work Teams: Learning, Social Integration,

and Task Flexibility

Gerben S. van der VegtUniversity of Groningen

Stuart BundersonWashington University in St. Louis

Ben KuipersErasmus University

This study considers how turnover in self-managing work teams influences the team interaction processes that promote effective task accomplishment. Drawing from research on self-managing work teams and group process, the authors propose that team turnover affects performance in self-managing teams by affecting social integration, team learning behavior, and task flexibility. Hypotheses were tested in a sample of 47 self-managing work teams using longitudinal panel data and an objective measure of team performance. Results suggest that team turnover indeed decreases social integration, team learning behavior, and task flexibility in self-managing teams but that only task flexibility and team learning behavior mediate the negative relationship between team turnover and team effectiveness.

Keywords: self-managing work teams; team membership change; team learning, team flexi-bility; social integration

In an attempt to boost productivity and better engage frontline workers in continuous improvement efforts, an increasing number of organizations have adopted self-managing work teams. In a longitudinal study of Fortune 1000 firms, for example, Lawler, Mohrman,

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Corresponding author: Gerben S. van der Vegt, Department of HRM/OB, University of Groningen, Nettelbosje 2, Groningen, 9747 AE, The Netherlands

E-mail address: [email protected]

Journal of ManagementVol. 36 No. 5, September 2010 1168-1191

DOI: 10.1177/0149206309344117© The Author(s) 2010

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and Ledford (1995) found that the use of self-managing work teams increased substantially between 1987 and 1993 with as many as 68% of firms reporting self-managing team use in 1993 compared with only 27% in 1987. A broader study of team use in a sample of 12000 firms with 100 or more employees found that 35% reported using self-managing work teams in 1992 (see Devine, Clayton, Philips, Dunford, & Melner, 1999). And Reynolds (2006) concluded that the use of self-managing teams has remained fairly constant or even increased in recent years. Moreover, the use of self-managing work teams has been found to result in a number of important benefits, including increased worker satisfaction and engagement in team tasks (Cordery, Mueller, & Smith, 1991; Kirkman & Shapiro, 2001; Wall, Kemp, Jackson, & Clegg, 1986) and higher productivity and quality (Cohen & Ledford, 1994; Goodman, Devadas, & Hughson, 1988).

The defining characteristic of a self-managing work team (also referred to as an autono-mous or semiautonomous team) is that the team as a collective, rather than some external manager, has the authority to determine how member efforts will be organized, monitored, and managed to accomplish the team’s work (Hackman, 1995, pp. 511-512; Kirkman & Shapiro, 2001). In comparison with traditional groups, self-managing work teams therefore depend more heavily on invested members who engage in bottom-up processes of coordina-tion and self-organization (Cohen, Ledford, & Spreitzer, 1996; Druskat & Pescosolido, 2002). Consequently, creating an environment where these processes can take place is a critical requirement for self-managing work team effectiveness.

A key factor that may influence such an environment within a self-managing work team is the magnitude of turnover within the team. In today’s volatile economy, job changes are simply a fact of organizational life. In the United States, for example, more than 25% of all workers have been with their company less than 1 year and 33% of workers have been with their company less than 2 years (Rollag, Parise, & Cross, 2005). Moreover, internal restructur-ing initiatives, promotions and transfers, and temporary task force assignments all contribute to a constant state of membership flux in many if not most contemporary organizations (Arrow & McGrath, 1995; Dalton, 1997). As a result, many self-managing work teams must strive to foster collaborative, bottom-up interaction processes in an environment of frequent turnover—a difficult challenge given the documented disruptive effect of turnover in work teams (Shaw, Duffy, Johnson, & Lockhart, 2005).

This fact raises an important question: How does turnover in self-managing work teams influence the team interaction processes that are essential for effectiveness? Past research provides little guidance on how to answer this question. Although we know that team turn-over in general can compromise group efficiency and productivity (Argote, Insko, Yovetich, & Romero, 1995; Goodman & Leyden, 1991; Kacmar, Andrews, van Rooy, Steilberg, & Cerrone, 2006), we know very little about the specific group processes that might mediate these effects (Dineen & Noe, 2003: 7; Kacmar et al., 2006: 134). Our ability to understand and predict the group process and consequent group performance effects of team turnover in teams generally, let alone self-managing work teams, is therefore quite limited.

The study described here was designed to provide insight into this question. Drawing from past work on team self-management and team turnover, we propose that team turnover will disrupt key interaction processes in self-managing work teams. Moreover, we argue that these process disruptions will compromise team effectiveness by affecting a team’s capacity



1170 Journal of Management / September 2010

to self-regulate and self-coordinate. In terms of a specific model, we will suggest that the relationship between turnover and effectiveness in self-managing work teams will be medi-ated by three interaction processes deemed critical to the effectiveness of self-managing work teams in past research, namely, social integration, team learning behavior, and task flexibil-ity. We test this mediated model in a sample of self-managing work teams from a Volvo truck manufacturing plant in Sweden, using longitudinal panel data and an objective mea-sure of team effectiveness.

In pursuing this research agenda, the present article makes two key contributions. First, we provide deeper insight into the specific processes that mediate the oft-studied relationship between team turnover and team performance. Few studies have empirically examined these mediating processes. In a review of the literature on turnover in teams, for example, Dineen and Noe (2003: 7) argued that researchers should “look more closely at process issues and emergent states instead of focusing directly on performance outcomes.” Doing so should enable more accurate prediction and suggest ways to mitigate the negative consequences of team turnover. Second, this article extends our understanding of self-managing work teams by empirically examining key theoretical predictions about performance-enhancing processes and by explicitly examining the significance of team turnover for both team processes and performance.

Theory and Hypotheses

Team Turnover and Team Processes

In an attempt to explicate the functioning and effectiveness of small groups, many researchers have turned to some version of a basic input–process–output (I-P-O) model (e.g., Hackman, 1987; McGrath, 1984; Steiner, 1972). In this model, group processes (e.g., com-munication, conflict) mediate the relationship between group inputs (e.g., composition, task, context) and group outputs (e.g., efficiency, productivity, innovation). A key assumption underlying I-P-O models of group effectiveness is that although establishing input–output relationships is an important first step in any research program, an articulation and under-standing of intervening mechanisms is critical if we are to truly understand, predict, and, ultimately, manage a given system (Anderson et al., 2006). In a recent review, Ilgen, Hollenbeck, Johnson, and Jundt (2005) noted that the I-P-O model has proven tremendously influential as an overarching framework for understanding and researching groups. They also note that, in fact, researchers adopting the I-P-O model have looked at other mediators besides those that we would typically classify as group processes, most notably, emergent cognitive or affective states (e.g., team cohesiveness, team efficacy). It is therefore important to keep in mind that, although groups researchers may use the I-P-O terminology, the term process should be understood in the sense described by Van de Ven (1992, p. 169), that is, as “a logic that explains a causal relationship between independent and dependent variables.” We might also use the term social mechanisms (Hedström & Swedberg, 1998) to capture what groups researchers often mean by the term process.

Team turnover, which we define as a type of membership change that involves the depar-ture and/or arrival of a formally designated member or members (see related discussion in




Arrow & McGrath, 1995; Levine & Choi, 2004), has occupied both an input role and an output role in past I-P-O models of group dynamics. Researchers who view team turnover as an output are interested in understanding how various input factors lead to interaction pro-cesses or emergent states within a group which then affect patterns of member turnover. So, for example, O’Reilly, Caldwell, and Barnett (1989) looked at how the demographic com-position of a group affected social integration which then affected member turnover (see also Sorensen, 2000; Wiersema & Bird, 1993). These studies recognize that team turnover may occur for reasons that are entirely unrelated to what may be going on in a group (e.g., down-sizing, retirements). Nevertheless, they seek to understand that portion of variance in team turnover that may be due to the structure, composition, and/or functioning of the group itself.

In contrast, research on team turnover as an input considers how turnover in groups affects group processes or emergent states in groups and how this translates into perfor-mance outcomes. Although recognizing that turnover may occur for reasons internal to the group, this approach positions turnover as an exogenous variable or as something which the group must accommodate regardless of why members may have left. This approach begins by acknowledging that turnover within a group can disrupt routines, change norms, alter composition, and introduce new ideas and ways of doing things, all of which have important implications for team effectiveness. Researchers adopting this version of the I-P-O model (including the present study) therefore seek to understand and document these effects.

Much of the empirical research examining team turnover as an input has focused on the relationship between turnover and various indicators of group performance such as produc-tivity, efficiency, or knowledge transfer without explicitly examining intervening processes or emergent states (Argote et al., 1995; Arrow & McGrath, 1993; Goodman & Leyden, 1991; Kane, Argote, & Levine, 2005; Levine & Choi, 2004; Virany, Tushman, & Romanelli, 1992; Ziller, Behringer, & Goodchilds, 1962). There are, however, a few exceptions. O’Connor, Gruenfeld, and McGrath (1993) found that turnover affected members’ experience of con-flict in laboratory groups but that conflict did not mediate a turnover-performance relation-ship. More recently, Kacmar et al. (2006), in an explicit attempt to address the lack of research examining process mediators of the team turnover-performance relationship, stud-ied turnover in fast food restaurants and found that efficiency (customer wait time) mediated the relationship between crew turnover and restaurant profitability. Although this finding is important, efficiency is often viewed as an output variable rather than as a process variable (Marks, Mathieu, & Zaccaro, 2001), which leaves the question of intervening group pro-cesses unaddressed.

Team Processes in Self-Managing Teams

In this study, we examine key processes/emergent states that mediate the relationship between team turnover and performance in self-managing work teams. In a recent review of the literature on self-managing work teams, Druskat and Pescosolido (2002) concluded that theories attempting to identify success factors in self-managing work teams have tended to converge on three categories of team process: (a) psychological ownership, (b) continuous learning, and (c) heedful interrelating.




At the core of psychological ownership is “the feeling of . . . being psychologically tied to an object” (Pierce, Kostova, & Dirks, 2001). Processes of psychological ownership are exemplified by the construct of social integration, defined as “the degree to which an indi-vidual is psychologically linked to others in a group” (O’Reilly et al., 1989: 22). Social integration is a key indicator of the groupiness of a group (Moreland & McMinn, 2004). It is evidenced by “attraction to the group, satisfaction with other members of the group, and social interaction among the group members” (O’Reilly et al., 1989: 22). Social integration is critical to effectiveness in self-managing teams because self-management requires a high degree of member engagement and investment in the team and its goals and processes.

Processes of continuous learning are those processes that promote self-evaluation and self-correction (Edmondson, 1999). Team learning processes are exemplified by the construct of “learning behavior,” defined as “an ongoing process of reflection and action characterized by asking questions, seeking feedback, experimenting, reflecting on results, and discussing errors or unexpected outcomes of actions” (Edmondson, 1999: 353; see also Drach-Zahavy & Somech, 2001; Van der Vegt & Bunderson, 2005). Research has suggested that these activi-ties can promote group effectiveness by facilitating adaptive and proactive responses to a group’s task environment (Edmondson, 1999; Gibson & Vermeulen, 2003; Van der Vegt & Bunderson, 2005). Such behaviors are critical in self-managing work teams in which learn-ing is the responsibility of the team rather than higher level managers and staff members.

Finally, heedful interrelating includes processes related to “flexible coordination” (Pearce & Ravlin, 1987; see also Campion Medsker, & Higgs, 1993), that is, member interactions that reflect awareness of each member’s task and how those tasks interrelate. These pro-cesses are exemplified by the construct of “task flexibility,” defined as the extent to which team members can and do substitute for one another in the performance of team tasks (Campion, Papper, & Medsker, 1996). A team is more flexible when team members are able to easily and seamlessly transition from one subtask to another and less flexible when mem-bers are not able to trade tasks. Although there are teams in which flexibility is an unrealistic or undesirable goal (e.g., teams composed of functional specialists or members with highly specialized knowledge), task flexibility can be a worthwhile goal in many self-managing work teams where members have similar training and/or perform tasks that do not require extensive training or education. In such teams, flexibility can lead to more adaptive, enrich-ing, collaborative, and higher quality work as members engage in task rotation, cross-training, and interpersonal helping.

In sum, effectiveness in self-managing work teams requires social integration, team learning behavior, and task flexibility. It follows that anything which affects these process variables in a self-managing work team should have a corresponding effect on team effec-tiveness. It is the central thesis of this article that team turnover is just such a factor. Specifically, we will argue that turnover in a self-managing team will interrupt established routines (Zellmer-Bruhn, 2003) and thereby disrupt these critical interaction processes. And these process disruptions can compromise team effectiveness by decreasing a self-managing team’s capacity to coordinate and regulate individual member efforts toward team goals. In other words, we will argue that the relationship between team turnover and effectiveness in self-managing work teams is mediated by social integration, team learning behavior, and task flexibility. We elaborate this basic proposition in the following sections.




Team Turnover and Social Integration

There is considerable research evidence to suggest that stability and continuity in inter-personal relationships are an important prerequisite for the development of social integration within a group and that team turnover, by extension, can have a negative effect on social integration. Relationship stability within a group allows for member familiarity, which smooths coordination and encourages positive, team-building behaviors (Gruenfeld, Mannix, Williams, & Neale, 1996; Rockett & Okhuysen, 2002). Stability and continuity in interper-sonal relationships also foster the development of a group identity which minimizes intra-group conflict and fosters team-oriented efforts (Brewer & Miller, 1984; Gaertner, Dovidio, & Bachman, 1996; O’Connor et al., 1993). And longevity in member relations facilitates trust among group members (Moreland & Levine, 2002).

Turnover within a self-managing work team complicates all of these integrating proc-esses. Assuming at least partial replacement, the departure of established members decreases familiarity (Goodman & Leyden, 1991), weakens feelings of a shared group identity, and introduces uncertainty in the place of patterns of increasing trust (Moreland & Levine, 2002). As a result, we would expect that teams experiencing turnover will be less socially integrated than teams with stable membership. This prediction is consistent with research by O’Reilly et al. (1989) and Smith et al. (1994) in which it was found that heterogeneity in team tenure—suggesting a history of team turnover within a team—was negatively related to group social integration. These arguments lead to the following hypothesis about the relationship between team turnover and social integration within a team:

Hypothesis 1: Turnover is negatively associated with social integration within self-managing work teams.

Team Turnover and Team Learning Behavior

The nature of the relationship between team turnover and team learning behavior is not obvious and compelling arguments can be developed for both positive and negative relation-ships. On one hand, turnover could promote team learning behavior within a self-managing work team by encouraging mindful reflection on the team’s functioning. Research by Katz (1982) suggests, for example, that teams develop routine patterns of working and interacting over time which decreases the felt need for communication and reflection. The departure of existing members should disrupt these routines and encourage team members to actively consider their work processes (Zellmer-Bruhn, 2003). Team turnover may also spur intrateam learning by introducing new team members who bring with them new ideas and different work approaches from their previous work settings. When new members offer these ideas and suggestions, dialogue and reflection about the best way to perform work may ensue, leading to the adoption of superior practices or the development of creative new syntheses. These arguments are consistent with research by Arrow and McGrath (1993) in which it was found that laboratory groups with an experimentally imposed new member and groups with member absences performed better on a task requiring reflection about the group’s internal




process. Also, research by Ziller et al. (1962) found that laboratory groups which either added or lost a member generated more new ideas than stable groups.

On the other hand, one of the more robust findings in research on team learning is that teams are more likely to engage in team learning behaviors when there is a climate of psy-chological safety within the group, that is, when group members feel that the group is a safe place for interpersonal risk taking (Edmondson, 1999). And this environment of safety and trust is simply more likely to be found in groups where there is stability in group member-ship such that members can come to know and trust one another (Moreland & Levine, 2002; O’Connor et al., 1993). When relationships are unstable or when members are unfamiliar with one another, there is greater uncertainty associated with admitting one’s ignorance, discussing failures, or experimenting with a new idea because one does not know what to expect from others (Gruenfeld et al., 1996). This evaluation apprehension may stifle learning behaviors by leading to greater inhibition and self-censoring.

One possible explanation for these conflicting predictions about the relationship between team turnover and learning behavior is that the relationship is actually inverse U-shaped, that is, positive at lower levels of team turnover but negative at higher levels. Small amounts of team turnover (e.g., involving one member) may spur learning behavior for the reasons outlined above without creating an uncertain or threatening environment for team members. In fact, the studies cited above in which team turnover was found to promote reflection and creativity were conducted using laboratory groups in which just one member was added to or removed from a group. It may be that at higher levels of team turnover, however, the uncertainty and discomfort associated with unfamiliar members will overwhelm the poten-tial positive effects of team turnover, resulting in a negative relationship between team turno-ver and performance. We therefore hypothesize the following:

Hypothesis 2: The relationship between team turnover and team learning behavior within self-managing work teams is inverse U-shaped; the relationship will be positive at small amounts of team turnover and will become negative at larger amounts of team turnover.

Team Turnover and Task Flexibility

Task flexibility is critical for successful self-management in teams because it allows for bottom-up (as opposed to top-down) coordination between and among team members in response to potentially shifting task demands. We expect that task flexibility will suffer when team membership is unstable (see Dineen & Noe, 2003). Stability in team membership allows time for members of a self-managing team to observe other members as they perform unfamiliar tasks, to ask questions about the performance of those tasks, and to eventually assist or even fill in for other members in performing those tasks. The extent to which mem-bers of a given group will be motivated to engage in these sorts of cross-task learning activities will, of course, vary across teams. But given a sample of self-managing work teams with the same task and incentive structures, we would expect to find that those teams with greater member stability will have greater task flexibility. Furthermore, even teams in which members begin with roughly the same background and training require time for




members to learn team-specific task and coordination routines before task rotation will be possible. Support for this expectation was found in Campion et al.’s (1996) study of 60 teams in a financial services organization in which they found a positive relationship between team permanence (i.e., membership stability) and task flexibility. It follows that higher levels of team turnover in self-managing teams should be negatively associated with task flexibility.

Hypothesis 3: Team turnover is negatively associated with task flexibility in self-managing work teams.

Process Mediators of the Team Turnover–Performance Relationship

The above hypotheses suggest that team turnover in self-managing work teams will result in lower levels of social integration, less flexibility in sharing tasks, and, at higher levels of team turnover, less engagement in learning behaviors. We next present arguments to suggest that these process consequences of team turnover will have important implications for team effectiveness in self-managing work teams. In other words, we will suggest that the relation-ship between team turnover and effectiveness in self-managing work teams will be generally negative and that this negative relationship can be explained by the effects of team turnover on social integration, team learning behaviors, and task flexibility.

Social integration. Past research has reported positive relationships between social inte-gration and task group effectiveness. Socially-integrated teams are expected to perform better because they are performing their work as a group rather than as a collection of individuals. Individual efforts, information, and knowledge are therefore more likely to be coordinated in the service of task accomplishment in a socially integrated team. A study by Smith et al. (1994) supports this expectation. They found that social integration was positively associ-ated with both return on investment and sales growth in a sample of 53 top management teams from technology-intensive firms. These results are consistent with research on group cohesiveness which suggests that members of cohesive groups work harder and longer to solve group problems (Berkowitz, 1954; Shaw & Shaw, 1962). As noted above, social inte-gration is particularly critical in self-managing work teams where member commitment and bottom-up coordination are critical success factors (Druskat & Pescosolido, 2002). We therefore expect that one way team turnover can influence effectiveness in self-managing work teams is through its effect on social integration. Stated formally,

Hypothesis 4: Social integration partially mediates a generally negative relationship between team turnover and effectiveness in self-managing work teams.

Team learning behaviors. Research from several past studies has suggested that team learning behaviors are positively associated with team performance outcomes in task groups (Edmondson, 1999; Gibson & Vermeulen, 2003; Schippers, Den Hartog, Koopman, & Wienk, 2003; Van der Vegt & Bunderson, 2005). Teams that engage in learning behaviors are more likely to learn from their mistakes, integrate new information about their environment,




benefit from member experience, effectively use member diversity in information and per-spective, and better coordinate their efforts—all of which should lead to higher task perfor-mance. And these activities are especially critical in self-managing work teams where learning takes place within the group. A second means by which team turnover can influence effec-tiveness in self-managing work teams is by influencing levels of learning behavior within the team.

Recall, however, that Hypothesis 2 posited a curvilinear relationship between team turnover and team learning behavior. That is, we expected small amounts of team turnover to have a positive effect on team learning behavior whereas larger amounts of team turnover would have a negative effect. If the relationship between team turnover and team effective-ness were fully explained by team learning behavior, this hypothesis would imply that the team turnover–effectiveness relationship in self-managing work teams would also be curvi-linear. But, because we have acknowledged that other processes will also mediate the team turnover-performance relationship, processes which we expect to exhibit monotonic rela-tionships with team turnover, we expect that the relationship between team turnover and performance in self-managing work teams will be generally negative. And at higher levels of team turnover, this negative relationship can be partially explained by a team’s decreased engagement in team learning behaviors. Stated formally,

Hypothesis 5: Team learning behavior partially mediates a generally negative relationship between team turnover and effectiveness in self-managing work teams.

Task flexibility. Past research has also suggested that task flexibility can be associated with higher task group effectiveness. When group members can rotate tasks, the best prac-tices learned or developed by each member in performing particular work are more likely to be shared because group members can observe and learn from the task performance of other group members. Furthermore, these group members can more readily fill in for one another or provide task assistance when unusual situations arise and can work together to solve complicated task-related problems. And as noted earlier, these bottom-up task coordination behaviors are particularly critical for self-managing work teams. Consistent with this expec-tation, Campion et al. (1993) found a positive and significant relationship between task flexibility and supervisor ratings of team effectiveness in a sample of 79 work teams. The following hypothesis therefore seems reasonable:

Hypothesis 6: Task flexibility partially mediates a generally negative relationship between team turnover and effectiveness in self-managing work teams.

Method

Sample and Data

We tested these hypotheses using data obtained from self-managing production teams in a large truck manufacturing plant of the Volvo Company in Sweden. This manufacturing




facility builds between 45,000 and 55,000 truck cabs annually, mostly for the European market, from steel plate to completely fitted cabs. The teams included in this study were operating in the “press and detail” and “assembly” areas of the manufacturing facility where they were responsible for completing production tasks of similar complexity and workflow interdependence with efficiency and high quality. Volvo has been a pioneer in using prin-ciples of self-managing teams in its manufacturing facilities and has been doing so with documented success for several decades (see Berggren, 1993; Engström, Blomquist, & Holmström, 2004). The teams in this facility were organized according to these established principles such that each team had the autonomy to decide how to organize their own efforts in order to maintain high standards of quality and productivity (for a detailed description of these teams, see Kuipers, De Witte, & Van der Zwaan, 2004).

This sample of teams had several notable strengths given the purposes of this research. First, these are ongoing self-managing production teams with a history of intensive, work-related interaction. Second, these teams all come from the same organization, which mini-mizes concerns about potential contextual confounds that arise when teams come from different organizations. Third, meaningful, objective measures of performance were avail-able for these teams (described below). And fourth, there was variation in team turnover across these teams.

We collected data from these production teams using a cross-lagged panel design (Bateman & Strasser, 1984; Rogosa, 1980). In this research design, measures are obtained at two (or more) points in time such that a researcher can consider the effects of an indepen-dent variable (or variables) at Time 1 (t1) on a dependent variable (or variables) at Time 2 (t2) while controlling for the dependent variable at t1. This approach allows us to examine whether team turnover helps to explain that portion of variance in team process or perfor-mance that is not accounted for by prior or baseline levels of each process or performance dependent variable. By controlling for process or performance at an earlier point in time, we are essentially examining the relationship between team turnover and changes in process and performance between t1 and t2 (Finkel, 1995). Although a significant coefficient for team turnover or process in such an analysis does not prove causation, it is strongly suggestive (Williams & Podsakoff, 1989). As a result, researchers studying team turnover (e.g., Dineen & Noe, 2003) have recommended this design (while lamenting that few studies of team turnover have actually adopted it).

Data were collected at two points in time separated by 1 year. We selected a 1-year period in this study under the assumption that 1 year would be long enough for team turnover to have observable effects on team processes and performance (see Keck & Tushman, 1993). Data were collected on site by the third author with the cooperation and assistance of our orga-nizational partners. Data were collected by means of a team member survey, a supervisor survey, and by consulting organizational archives. Team members and supervisors completed surveys during one of their daily team meetings. Individual responses were anonymous.

Usable team member responses were received from 55 teams at t1 and 61 teams at t2. Usable supervisor responses were received from 52 supervisors at t1 and 56 supervisors at t2. Mean intrateam response rates were 88% at t1 (SD .15) and 93% at t2 (SD .17). Our final sample consisted of 47 teams for which the necessary t1 and t2 team turnover, process, and performance data were available. This sample size is comparable with sample sizes




reported in other studies of team turnover (e.g., Campion et al., 1996; Shaw et al., 2005). Average team size was 10 (SD 2.7). Average team tenure was 2.8 years (SD 2.3). In terms of sample characteristics, 81% of the sample was male, average age was 33.6 years (SD 6.8), and 11% of respondents had completed only a secondary education (with 61% completing a lower level of vocational education and 28% completing an intermediate level of vocational education). None of these sample characteristics was significantly related to team turnover.

Measures

The team member and supervisor surveys consisted of Likert-type scale items with a 5-point response scale ranging from 1 strongly agree, to 5 strongly disagree. Survey items were randomly ordered to alleviate bias. All items were adapted from existing scales. However, based on the results of a pilot study and after extensive discussions with employ-ees, the wording of some items (particularly those measuring social integration and team learning behavior) were changed to increase their interpretability and relevance for the teams under study. We conducted a validation study with 94 masters of business administration (MBA) students to examine the convergence of these revised items with published, validated scales. Those results are described below.

Team turnover. We measured team turnover as the number of members who left a team between t1 and t2. Furthermore, as Arrow and McGrath (1995: 396) noted, “[M]agnitude of team turnover must be considered in relation to group size and relative proportion of mem-bers involved . . . [T]he bigger the proportion of the group involved, the stronger the effects.” (p. 396). We therefore divided the number of departures between t1 and t2 by group size at t1 to arrive at our measure of team turnover for this study. This measure is consistent with existing measures of team turnover (e.g., Kacmar et al., 2006; Shaw et al., 2005). All mem-bership changes that took place in this sample of teams during the study period were volun-tary changes.

Social integration. Past research has measured social integration using items that assess three underlying dimensions: team cohesiveness, behavioral integration, and an individual’s personal satisfaction with the team (see O’Reilly et al., 1989; Shaw, 1981; Smith et al., 1994; Van der Vegt, 2002). Given limitations in the items that we could include in our survey, we developed a three-item measure of social integration to assess these three dimensions of social integration. Specifically, we measured social integration using the following three items: “The members of this team form a cohesive unit” (team cohesiveness), “The members of this team really work together as a team” (behavioral integration), and “I like to be a member of this team” (personal satisfaction with the team). Cronbach’s alpha for this three-item measure was .80 at t1 and .79 at t2. To verify that this reduced scale remains a valid measure of the underlying construct, we compared our three-item scale with the six-item scale in Van der Vegt (2002) using data from our validation study among 94 MBA students. We found that a one-factor model provided strong fit, F2(27) 87.90, p .001, comparative




fit index (CFI) .92, standardized root mean square residual (SRMR) .06, goodness-of-fit index (GFI) .89, normed fit index (NFI) .89, and that a two-factor solution in which these two scales loaded on separate factors did not reduce the chi-square over a one-factor model ('F2 1.03, p .31). Moreover, the Pearson correlation between the two social integration measures was .76 (p � .001). These results support our scale as a valid measure of the one-dimensional construct measured with the six-item scale in Van der Vegt (2002).

Team learning behavior. Team learning behavior was measured from team member responses to four items that were adapted from the work of Drach-Zahavy and Somech (2001) and Van der Vegt and Bunderson (2005) with input from our organizational partners. The four items were “The team acts upon errors/mistakes,” “Team members support initia-tives for improvement from others,” “Team members take the initiative to improve things,” and “The team often reflects upon its own functioning.” Cronbach’s alpha for this four-item scale was .72 at t1 and .74 at t2. As with social integration, we compared our revised items with the team learning behavior scale in Van der Vegt and Bunderson (2005) using data from our validation study. We found that a two-factor solution in which these two scales loaded on separate factors actually increased the chi-square over a one-factor model (two-factor solu-tion, F2[13] 39.1, p � .001, CFI .92, SRMR .06, GFI .88, NFI .88; one-factor solu-tion, F2[14] 19.1, p � .16, CFI .96, SRMR .06, GFI .94, NFI .88). Moreover, the Pearson correlation between the two learning measures was .78 (p � .001). This pattern of results confirms that these two scales assess the same underlying construct.1

Task flexibility. Task flexibility was measured using a three-item scale adapted from the scale developed by Campion et al., (1993). The items were revised slightly based on pilot study results and discussions with employees. The three items were “All team members fill in for one another if necessary,” “Team members are able to perform all different tasks in the team,” and “Team members regularly shift tasks.” Cronbach’s alpha for this three-item scale was .77 at t1 and .80 at t2.

Confirmatory factor analysis. We conducted confirmatory factor analyses to assess the convergent and discriminant validity of the social integration, team learning behavior, and task flexibility scales. Parameter estimates were computed using the LISREL 8 computer package, with the maximum likelihood method. The expected three-factor model resulted in a satisfactory fit of the measurement model to the data at t1: F2(32) 222.79, p � .001, SRMR .05, GFI .91, CFI .90, NFI .89. For the t2 data also, results showed a satisfac-tory fit, F2(32) 247.43, p � .001, SRMR .06, GFI .90, CFI .89, NFI .88). The load-ing of each item on its corresponding construct was significant at the .001 level or better at both t1 and t2, indicating convergent validity. These three-factor models fit the data signifi-cantly better than all possible alternative one- or two-factor models (p � .001), supporting the discriminant validity of the task flexibility, learning behavior, and social integration scales.

Interrater agreement and reliability. Team-level measures of social integration, team learning behavior, and task flexibility were computed by averaging responses from all team members. To verify that aggregation was justified, we evaluated interrater agreement by




computing an rwg score for each team on each of these three variables for both t1 and t2 responses using a “small skew” distribution (James, Demaree, & Wolf, 1984: 93-94). Results suggested strong interrater agreement within teams with median rwg scores of .93 (t1) and .90 (t2) for social integration, .95 (t1) and .94 (t2) for team learning behavior, and .94 (t1) and .94 (t2) for task flexibility. The lowest rwg score for any of these t1 or t2 scales across all teams was .63.

We also evaluated the extent to which social integration, team learning behavior, and task flexibility were, in fact, meaningful team-level measures in this sample of teams by comput-ing ICC(1) and ICC(2) scores (ICC intraclass correlation) for each of these three variables at t1 and t2 (see Bliese, 2000; Dineen & Noe, 2003). ICC(1) scores were very strong with scores of .59 (t1) and .63 (t2) for social integration, .48 (t1) and .52 (t2) for team learning behavior, and .59 (t1) and .75 (t2) for task flexibility. ICC(2) scores were also strong for social integration (.74 at t1 and .77 at t2), team learning behavior (.65 at t1 and .68 at t2), and task flexibility (.73 at t1 and .86 at t2). These results provide very strong evidence that these process measures converge within teams while varying across teams and therefore provide valid and useful insight into key process differences across the teams in this sample.

Team effectiveness. Team effectiveness was measured as the percentage of a team’s assigned daily work that left the team without unacceptable defects the first time through. In this context, zero-defect percentage is a direct measure of the extent to which a team effec-tively accomplishes the goals it was designed to accomplish, namely, to perform high-quality work on a given number of vehicles each day. To calculate a team effectiveness measure for this study, we averaged zero-defect measures for 4 consecutive weeks that coin-cided with the period in which survey data were being collected. A shorter period than 4 weeks (e.g., 1 week) would have resulted in effectiveness data that might be biased by short-term fluctuations in effectiveness (e.g., production stops due to maintenance, staffing problems due to illness, etc.). A much longer period (e.g., 1 year) would have resulted in effectiveness scores that do not reflect the changes in effectiveness resulting from interteam differ-ences regarding our process measures at the time of our study. Moreover, the choice of a 4-week period was practical because the planning and feedback systems at Volvo are based on a 4-week period. So, we computed one 4-week measure of team effectiveness from t1 weekly quality measures and a second 4-week measure of team effectiveness from t2 weekly quality measures.

Control variables. We controlled for several variables to explicitly consider and eliminate alternative explanations for observed effects. First, we controlled for average team tenure and team tenure heterogeneity because these two variables have been commonly used in past research as indirect measures of team turnover/stability within teams (Ancona & Caldwell, 1992; Katz, 1982). We measured team tenure from team member responses and team tenure heterogeneity as the coefficient of variation (standard deviation divided by the mean) in team tenure scores for members of a given team.

We controlled for changes in member experience to explicitly consider the alternative explanation that any performance effects due to team turnover result from experience losses rather than process losses (see Shaw et al., 2005). We measured changes in member experience




as the average organizational tenure of team members at t2 minus the average organizational tenure of team members at t1. We also controlled for changes in team size because the effects of team turnover on team processes and performance may be different for teams that grew rather than shrunk (Arrow & McGrath, 1995). We measured changes in team size as team size at t2 divided by team size at t1.

Finally, we controlled for past levels of each process and performance dependent variable in all regression models as required by a cross-lagged panel design. And because process can be affected by prior performance levels (Lindsley, Brass, & Thomas, 1995), we left prior performance levels in all regression models (not just in models where performance was the dependent variable).

Analyses

Hypotheses were tested using cross-lagged hierarchical multiple regression analysis as noted above (Bateman & Strasser, 1984; Rogosa, 1980; Williams & Podsakoff, 1989). Prior levels of process and performance (the lagged dependent variable and prior performance) were entered in the first block. Control variables (team tenure, team tenure heterogeneity, experience change, and team size change) were entered in a second block. Process mediators were entered in a third block (for performance only). And, finally, team turnover (and in the case of team learning behavior, team turnover squared) was entered in a fourth block. The change in R2 was noted and reported for each block. We report only the beta coefficients from the final block of the regression model.

We examined mediation using the procedures recommended by Baron and Kenny (1986). That is, we looked for significant relationships between team turnover and each of our three mediating variables (social integration, team learning behavior, and task flexibility) and between team turnover and performance without the mediating variables included in the model. We then added the mediators to the performance model and looked for significant relationships between each mediator and performance and a smaller and statistically nonsig-nificant relationship between team turnover and performance. This pattern of results is consistent with our expectation that the effect of team turnover on performance is accounted for by its effect on the three mediating processes.

Results

Descriptive Statistics

Descriptive statistics and correlations among all study variables are presented in Table 1. As we see in Table 1, mean team turnover was around .40 per year with considerable varia-tion across teams in the magnitude of team turnover. Each of the t1 process and performance measures was significantly correlated with the corresponding measure at t2, suggesting sta-bility in these measures across time, although these autocorrelations were lower for task flexibility and social integration (.55 and .50 vs. .74 and .78 for learning behavior and



Tabl

e 1

Mea

ns, S

tand

ard

Dev

iati

ons,

and

Cor

rela

tion

s fo

r A

ll St

udy

Var

iabl

es (N

4

7)

Var

iabl

e M

ean

SD

1 2

3 4

5 6

7 8

9 10

11

12

1.

Team

tenu

re(t

1)

2.72

2.

10

2

. Te

am te

nure

het

erog

enei

ty(t

1)

1.58

2.

01

.88*

**

3.

Cha

nge

in e

xper

ienc

e (t1 �

t2)

.36

2.96

�.

11

�.19

4.

Cha

nge

in te

am s

ize (t

1 �

t2)

�.01

.1

1 .2

0 .2

8 �.

01

5.

Team

turn

over

(t1 �

t2)

.40

.46

�.28

�.

17

�.28

�.

28

6

. So

cial

Int

egra

tion (t

1)

4.15

.4

6 .1

1 .0

3 �.

21

.05

�.28

7

. L

earn

ing

beha

vior

(t1)

3.

64

.36

.36*

* .2

6 .0

6 .2

0 �.

34*

.60*

**

8

. Ta

sk f

lexi

bilit

y (t1)

4.

07

.44

.30*

.2

1 �.

17

.17

�.27

.6

6***

.6

2***

9

. Pe

rfor

man

ce(t

1)

97.0

7 3.

01

.24

.26

.05

.14

�.23

.1

0 .3

5*

.19

10

. So

cial

inte

grat

ion (t

2)

3.93

.4

8 .2

3 .1

6 �.

01

.07

�.46

**

.50*

**

.42*

* .3

2*

.44*

*

11

. L

earn

ing

beha

vior

(t2)

3.

61

.39

.29

.24

.07

�.04

�.

46**

.5

3***

.7

4***

.5

3***

.4

0**

.66*

**

12

. Ta

sk f

lexi

bilit

y (t2)

3.

89

.54

.09

.09

.12

.15

�.50

***

.42*

* .3

5*

.55*

**

.42*

* .5

3***

.5

8***

13

. Pe

rfor

man

ce(t

2)

95.3

0 3.

72

.36*

* .3

3*

.13

.24

�.55

***

.29*

.5

8***

.4

9***

.7

8**

.51*

**

.67*

**

.66*

**

*p �

.05.

**p

� .0

1. *

**p �

.001

.

1182




performance, respectively). Finally, the Pearson zero-order correlations between t2 process and performance measures were significant and, for the most part, in the directions antici-pated by the above hypotheses. Interestingly, the correlations between the process and perfor-mance measures were stronger at t2 than at t1. A possible reason for this change in relations over time is that in the 6 months preceding t1, the facility changed the lay-out of the assem-bly stations and the work methods used. Teams had to become familiar with these new work practices, which may have attenuated the concurrent relationships between team processes and performance at t1. However, because these changes took place before the actual study was conducted, it is unlikely that they have affected the relationships between team turnover at t1 and subsequent changes in team processes and performance between t1 and t2.

Hypothesis Testing

Table 2 presents the results of our cross-lagged hierarchical multiple regression analyses. All regressions met the major model assumptions. That is, no serious violations were found in the plots of standardized residuals as compared with the predicted values, in the normal probability plots of standardized residuals, and in the independence of error terms. Moreover, variance inflation factors for all models were all well below 4, suggesting that multicol-linearity was not a problem in our analyses (Miles & Shevlin 2001).

The first set of analyses examined the relationship between team turnover and the three process variables: social integration, team learning behavior, and task flexibility. Adding the first block of variables to the regression equations revealed significant and positive coeffi-cients for all of the lagged dependent process variables, and significant and positive coeffi-cients for prior performance in the case of social integration (E .34, p � .05) as well as task flexibility (E .29, p � .05). Of the control variables entered in the second block, only change in team size was significantly and negatively related to learning behavior. In the final block of the analysis, team turnover (and team turnover squared in the case of learning behavior) was added to the regression equations. Significant and negative relationships were found between team turnover and social integration (E �.32, p � .05, 'R2 .06), learning behavior (E �.41, p � .01, 'R2 .10), and task flexibility (E �.37, p � .01, 'R2 .09). The relationships between team turnover squared and learning behavior were not significant. Together, these results are consistent with Hypothesis 1 (social integration) and Hypothesis 3 (task flexibility), but not Hypothesis 2 (team learning behavior squared; we did, however, observe a negative linear relationship between team turnover and team learning behavior).

In the second set of analyses, we tested the direct relationship between team turnover and team effectiveness as well as the hypothesized mediating role of the three process variables. After controlling for prior performance, team tenure, team tenure heterogeneity, experience change, and team size change, team turnover was found to be significantly and negatively related to team effectiveness (E �.36, p � .001, 'R2 .09). When the three process media-tors were added to the model, however, the coefficient for team turnover no longer reached significance (E �.17, NS) whereas significant coefficients were found for both learning behavior (E .33, p � .01) and task flexibility (E .22, p � .05). The coefficient for social integration was not significant (E �.17, NS). Together, the three process mediators



1184

Tabl

e 2

Team

Tur

nove

r, T

eam

Pro

cess

es, a

nd T

eam

Per

form

ance

: R

egre

ssio

n R

esul

ts (N

4

7)

So

cial

L

earn

ing

Task

In

tegr

atio

n (t2)

B

ehav

ior (t

2)

Flex

ibili

ty(t

2)

Perf

orm

ance

(t2)

Pe

rfor

man

ce(t

2)

Inde

pend

ent V

aria

bles

E�

VIF

E�

VIF

E�

VIF

E�

VIF

E�

VIF

Prio

r le

vels

of

proc

ess

and

perf

orm

ance

So

cial

inte

grat

ion (t

1)

.38*

* 1.

29

—

—

—

—

Lea

rnin

g be

havi

or(t

1)

—

.6

7***

1.

45

—

—

—

Ta

sk f

lexi

bilit

y (t1)

—

—

.5

0***

1.

33

—

—

Perf

orm

ance

(t1)

.3

4*

1.19

.1

1 1.

28

.29*

1.

19

.69*

**

1.17

.5

7***

1.

38 'R

2 .4

0

.56

.4

0

.60

.6

0 C

ontr

ol v

aria

bles

Te

nure

(t1)

.0

1 2.

11

�.25

2.

29

�.37

2.

20

.09

1.90

.1

6 2.

49

Tenu

re h

eter

ogen

eity

(t1)

.0

3 1.

78

.27

1.76

.2

1 1.

73

.02

1.72

�.

08

2.16

E

xper

ienc

e ch

ange

�.

03

1.35

�.

07

1.22

.0

9 1.

28

.01

1.17

.0

1 1.

23

Team

siz

e ch

ange

�.

13

1.32

�.

37**

* 1.

30

�.11

1.

30

�.01

1.

30

.08

1.46

'R

2 .0

2

.05

.0

6

.06

.0

6 Pr

oces

s m

edia

tors

So

cial

inte

grat

ion (t

2)

—

—

—

—

�.17

2.

98

Lea

rnin

g be

havi

or(t

2)

—

—

—

—

.33*

* 2.

14

Task

fle

xibi

lity (t

2)

—

—

—

—

.22*

2.

42 'R

2 —

—

—

—

.1

9 Te

am tu

rnov

er e

ffec

ts

Team

turn

over

(t1 �

t2)

�.32

* 1.

79

�.41

**

1.74

�.

37**

1.

70

�.36

***

1.60

�.

17

2.14

Te

am tu

rnov

er(t

1 �

t2)2

—

.0

2 1.

45

—

—

—

'R

2 .0

6 .1

0 .0

9 .0

9 .0

1Fi

nal R

2 .4

8 .7

1 .5

4 .7

5 .8

6F

(tot

al m

odel

) 5.

01**

* 11

.85*

**

6.64

***

15.5

9***

24

.69*

**

Not

e: S

tand

ardi

zed

regr

essi

on c

oeff

icie

nts

(Es)

fro

m th

e fi

nal s

tep

of th

e an

alys

es a

re r

epor

ted.

VIF

v

aria

nce

infl

atio

n fa

ctor

.† p �

.10.

*p �

.05.

**p

� .0

1. *

**p �

.001

.




explained an additional 19% of the variance in performance. These results, together with those above, suggest that learning behavior and task flexibility fully mediate the effects of team turnover on performance as suggested in Hypotheses 5 and 6.

Discussion

Taken together, the findings of this study support two key conclusions about the relation-ship between team turnover, process, and performance in self-managing work teams. First, team turnover has a negative effect on the performance of self-managing work teams. This finding confirms and extends research conducted in other types of teams (Argote et al., 1995; Goodman & Leyden, 1991; Kacmar et al., 2006). Second, and more important, this negative effect of team turnover on performance in self-managing teams is due in large part to the disruptive effect of team turnover on the key interaction processes that enable success-ful self-management, namely, on team learning behavior and task flexibility.

Theoretical Implications

The present study advances our understanding of self-managing work teams by examin-ing how team turnover affects specific interaction processes deemed critical to effective self-management in teams, namely, social integration, team learning behavior, and task flex-ibility (Druskat & Pescosolido, 2002). Our results confirm that team learning behavior and task flexibility do indeed promote effectiveness in self-managing work teams. Social inte-gration was not significantly associated with team performance when included in a regres-sion model with the other process variables, suggesting that social integration may not be a critical performance requirement in self-managing teams. Moreover, we extend past research on self-managing work team effectiveness by suggesting and demonstrating that team turn-over can disrupt processes of team learning and task flexibility and thereby compromise team effectiveness. These results therefore enrich our understanding of group processes and effec-tiveness in self-managing work teams by elaborating an important antecedent condition.

Additionally, our findings provide support for a process losses theory of membership change in self-managing work teams, an important complement to the social capital losses theory proposed by Dess and Shaw (2001). Dess and Shaw have argued that commonly-used human capital and cost-based explanations cannot fully account for the negative effects of personnel departures on organizational performance and that we must also consider “social capital losses” resulting from departures, that is, decreases in the quality and efficiency of network interactions. This social capital losses theory of personnel turnover was supported in a study of restaurants in which it was found that changes in network structure due to member departures compromised organizational performance after controlling for human capital losses, that is, the loss of high-performing employees (Shaw et al., 2005). The present article complements this past work by supporting a “process losses theory” of team turnover in self-managing work teams, a theory in which the negative effect of team turnover on per-formance is a function of decreases in the quality and efficiency of key interaction processes




within the team, over and above the effect of changes in the aggregate experience of group members (i.e., human capital losses or gains). This approach extends the social capital losses theory to the domain of self-managing work teams and to the question of group processes.

The research presented here also offers several refinements to the way we think about learning behavior in teams. For example, past research has often focused on the advantages of team turnover for team learning, suggesting that team turnover can signal an opportunity for self-reflection in teams, can shake up existing practices, and can introduce new ideas and insights (Arrow & McGrath, 1993; Katz, 1982; Ziller et al., 1962). In this article, we acknowledged these potential benefits but suggested that the disruptive effect of high levels of team turnover could also compromise team learning efforts by creating an environment of uncertainty and reduced psychological safety (Edmondson, 1999; Moreland & Levine, 2002; O’Connor et al., 1993). We therefore explicitly considered the possibility that the relationship between team turnover and team learning behavior would be quadratic—positive at lower levels of team turnover but negative at higher levels. This quadratic hypothesis was not supported. Instead, we found a negative linear relationship between team turnover and learning behavior in this sample of self-managing teams. This result suggests that in the case of self-managing work teams, any amount of team turnover may create an uncertain inter-personal environment in which team members are uncomfortable taking the risks necessary to engage in learning behaviors. Some degree of membership stability therefore seems impor-tant to learning behaviors in self-managing work teams.

These results also confirm that team learning behavior is positively associated with team performance, even after controlling for the effects of past performance, tenure, tenure het-erogeneity, experience changes, changes in team size, and the other group process measures (social integration and task flexibility). This finding provides an important validation of past research in which a positive relationship between learning behavior and team perfor-mance was observed using subjective performance ratings (Edmondson, 1999; Gibson & Vermeulen, 2003; Schippers et al., 2003; Van der Vegt & Bunderson, 2005). Although the use of subjective performance ratings is certainly defensible in research on teams, it is important to establish that the same performance relationships hold when performance measures that truly matter to an organization are used. This study confirms a relationship between team learning behaviors and team performance using an objective measure of performance quality.

Finally, the research design adopted in this study addresses many of the criticisms that have been leveled at past research on team turnover, offering greater assurance that the observed findings are, in fact, robust. Specifically, most research on team turnover in teams has been conducted in laboratory teams (with minimal history, artificial tasks, low stakes) by rotating a member out of the team and then noting the consequences for decision quality or performance. Although this approach has the benefits of experimental control, it raises questions about how team turnover will affect group outcomes in real organizational teams with varying levels of team turnover and using objective and strategically important mea-sures of group performance. The design of this study allowed us to speak directly to these questions. Furthermore, the cross-lagged panel design adopted in this study allowed us to control for prior levels of process and performance in these teams in order to better isolate the effects of team turnover in a given time period for group process and performance




following that period. These design characteristics give us greater confidence that the effects observed here are robust effects.

Limitations and Future Research Directions

Although this study had several notable strengths (e.g., a field setting with self-managing team experience, objective performance measures, longitudinal data), there were also some accompanying limitations in terms of measurement. Specifically, we used condensed mea-sures of social integration and team learning behavior rather than complete scales from previously-validated research. To provide some assurance that these revised scales were valid measures of the underlying constructs, we conducted a validation study in a separate sample of MBA students. Results from that analysis strongly supported the validity of our measures. Nevertheless, future research using more complete scales would provide additional confidence in these results.

In addition, this study was conducted using a sample of self-managing work teams in a manufacturing setting. Although this sample is certainly representative of the type of work that many teams perform, it is possible that the results observed here would not generalize to teams that must constantly change their routines and structures in response to a changing stream of nonroutine inputs, and therefore, might respond very differently to changes in team membership. One might even argue that team turnover would be critical in such teams because it offers a key source of new ideas and perspectives. On the other hand, teams facing more complex, nonroutine task environments might need even greater stability to consis-tently apply a more complex set of decision-making routines in making sense of that envi-ronment. In other words, the moderating role of task complexity and variability on the relationship between team turnover and team outcomes (process or performance) is still largely unknown and, consequently, it is premature to assume that the results observed here would be obtained in teams engaged in different tasks.

Finally, our focus in this article has been on the disruptive effects of team turnover rather than on the potentially divergent effects of member arrivals. In most standing self-managing work teams, including the teams in our sample, team turnover and member additions are generally highly correlated. As a result, it is difficult if not impossible in such settings to disentangle the independent effects of one versus the other. In settings where departures and arrivals are loosely coupled, however, it may be useful to examine the independent effects of these distinct change events (see Arrow & McGrath, 1995). If the goal is to isolate the independent process effects of additions and subtractions, samples in which teams experi-ence just one or just the other type of membership change are needed. This may require follow-up research in controlled laboratory settings.

Managerial Implications

Despite these limitations, the results of this study have important implications for practic-ing managers involved in designing or overseeing self-managing work teams. Specifically,




this study confirms that team turnover can have detrimental consequences for effectiveness in self-managing work teams and, just as importantly, helps us to understand why this is the case. These results imply that organizations adopting self-managing work teams should commit to maintaining stable team membership where possible and should carefully con-sider the effect of other, perhaps well-intentioned, human resource policies and practices on the membership of established teams (e.g., restructuring initiatives, temporary team assign-ments). Moreover, if organization-initiated team membership changes are necessary or if changes are initiated by members themselves, the results of this study can be helpful in predicting how turnover will affect the key interaction processes that are necessary for team effectiveness. This knowledge may, in turn, help managers to craft targeted remedies for minimizing the negative process consequences of team turnover—in the same way that knowing how patients tend to react to a particular drug or surgical procedure improves our ability to dampen the negative consequences of that drug or procedure and therefore hasten recovery. In the case of self-managing teams experiencing turnover, this may mean targeted interventions to improve intrateam trust (e.g., off-site team-building) or member awareness of each member’s task (e.g., cross-training).

Note

1. As a test of discriminant validity, we compared this one-factor team learning behavior scale with the one-factor social integration scale described above. A two-factor solution provided a significantly better fit over a one-factor solution with 'F2(1) 121.27, p .001, suggesting that survey respondents clearly differentiated between these theoretically distinct items.

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Why turnover matters in self-managing work teams: Learning, social integration, and task flexibility

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