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Journal of Management Development

ISSN : 0262-1711

Article publication date: 1 March 1997

Looks at the use of the Team Management Index (TMI) as a team building intervention in a programme of organizational development (OD). Attempts to assess the instrument’s effectiveness using a variety of measures (including the taxonomy of De Meuse and Liebowitz, 1981). In so doing, raises wider methodological and epistemological issues as to the whole nature of data collection, validity and proof in measuring the effectiveness of OD interventions. Discovers that, according to the measurement criteria, set out by De Meuse and Liebowitz, the TMI can be considered to be an effective OD instrument. However, finds that these measurement techniques are “blunt” and, by their very nature, lacking in academic rigour. Argues, therefore, that all levels of data collection and evidence gathering can never constitute 100 per cent proof of a causal link between OD interventions and resultant changes in the organization. Concludes that what will be deemed to count as adequate evidence or proof of an intervention’s effectiveness ultimately will be a personal choice; that in concentrating on comparing before and after measures of a team’s effectiveness theorists have ignored the change process which is taking place as a team begins to become effective, and have treated teams at the end of a team building intervention as if they were finished products; and finally that research time should be devoted to studying the process of change which a team undergoes during its development (of which team building is just the beginning) in order to highlight the ways in which an organization could nurture, support and facilitate this process to ensure the effective development of its work teams.

• Effectiveness
• Organizational change
• Organizational development
• Team building

Rushmer, R.K. (1997), "How do we measure the effectiveness of team building? Is it good enough? Team Management Systems ‐ a case study", Journal of Management Development , Vol. 16 No. 2, pp. 93-110. https://doi.org/10.1108/02621719710164274

Copyright © 1997, MCB UP Limited

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Committee on the Science of Team Science; Board on Behavioral, Cognitive, and Sensory Sciences; Division of Behavioral and Social Sciences and Education; National Research Council; Cooke NJ, Hilton ML, editors. Enhancing the Effectiveness of Team Science. Washington (DC): National Academies Press (US); 2015 Jul 15.

Enhancing the Effectiveness of Team Science.

• Hardcopy Version at National Academies Press

3 Overview of the Research on Team Effectiveness

This chapter summarizes the research literature on team effectiveness, highlighting findings on the key features that create challenges for team science outlined in Chapter 1 . Based on its review of the literature (e.g., Marks, Mathieu, and Zaccaro, 2001 ; Kozlowski and Ilgen, 2006 ; Salas, Goodwin, and Burke, 2009 ), the committee defines team effectiveness as follows:

Team effectiveness, also referred to as team performance, is a team's capacity to achieve its goals and objectives. This capacity to achieve goals and objectives leads to improved outcomes for the team members (e.g., team member satisfaction and willingness to remain together) as well as outcomes produced or influenced by the team. In a science team or larger group, the outcomes include new research findings or methods and may also include translational applications of the research.

More than half a century of research on team effectiveness ( Kozlowski and Ilgen, 2006) provides a foundation for identifying team process factors that contribute to team effectiveness, as well as actions and interventions that can be used to shape the quality of those processes. As noted in Chapter 1 , this evidence base consists primarily of studies focusing on teams in contexts outside of science, such as the military, business, and health care. These teams share many of the seven features that can create challenges for team science introduced in Chapter 1 . For example, in corporations, top management teams and project teams are often composed of members from diverse corporate functions, and these teams seek to deeply integrate their diverse expertise in order to achieve business goals. Therefore, the committee believes the evidence on teams in other contexts can be translated and applied to improve the effectiveness of science teams and larger groups.

This chapter begins by presenting critical background information—highlighting key considerations for understanding team effectiveness and presenting theoretical models that conceptualize team processes as the primary mechanisms for promoting team effectiveness. The chapter then highlights those team process factors shown to influence team effectiveness ( Kozlowski and Bell, 2003 , 2013 ; Ilgen et al., 2005 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008) , based on well-established research (i.e., meta-analytic findings [see Box 3-1 ] or systematic streams of empirical research). Next, the discussion turns to interventions that can be used to improve team processes and thereby contribute to team effectiveness; these are discussed in greater detail in subsequent chapters. This is followed by a discussion of how this foundational knowledge can inform team science, a description of models of team science and effectiveness, and a discussion of areas in which further research is needed to address the challenges emerging from the seven features outlined in Chapter 1 .

What Is a Meta-Analysis. The foundation of scientific research is based on primary studies that collect data under a given set of conditions (i.e., experiments or field studies) and examine effects on, or relationships among, the observed variables of (more...)

• BACKGROUND: KEY CONSIDERATIONS AND THEORETICAL MODELS AND FRAMEWORKS

Key Considerations

One key consideration regarding team effectiveness is that it is inherently multilevel, composed of individual-, team-, and higher-level influences that unfold over time ( Kozlowski and Klein, 2000 ). This means that, at a minimum, three levels of the system need to be conceptually embraced to understand team effectiveness (i.e., within person over time, individuals within team, and between team or contextual effects; Kozlowski, 2012) . Broader systems that encompass the organization, multiple teams, or networks are obviously even more complex. Moreover, individual scientists may be part of multiple research projects spread across many unique teams and thus are “partially included” in their teams ( Allport, 1932) . As noted in Chapter 1 , a recent study suggests that scientists' level of participation (i.e., inclusion) in a team is related to team performance, with higher participation related to increased performance ( Cummings and Haas, 2012) .

A second critical consideration for understanding, managing, and improving team effectiveness is the degree of complexity of the workflow structure of the team task ( Steiner, 1972) . In simple structures, team members' individual contributions are pooled together or constructed in a fixed serial sequence. For example, in a multidisciplinary team, members trained in different disciplines combine their expertise in an additive way. Complex structures incorporate the integration of knowledge and tasks through collaboration and feedback links, making the quality of team member interaction more important to team effectiveness.

A final key consideration is the dynamic interactions and evolution of the team over time. According to Kozlowski and Klein (2000 , p. 55):

A phenomenon is emergent when it originates in the cognition, affect, behaviors, or other characteristics of individuals, is amplified by their interactions, and manifests as a higher-level, collective phenomenon.

In other words, emergent phenomena arise from interactions and exchange among individuals over time to yield team-level characteristics. Emergent phenomena unfold over time as part of the team development process. Time is also pertinent with respect to how teams themselves evolve. For example, Cash et al. (2003) reported on the evolution of a transdisciplinary group focused on developing improved varieties of wheat and corn. The authors reported that a strictly sequential approach—in which scientists first developed new crops in the laboratory or field and then later handed them over to native farmers—did not lead to widespread use of the new crops. However, when the native farmers were brought into the research at an earlier point in time, as valued participants and partners with the scientists, the group produced new crops that were widely used. Relatedly, teams have different time frames for interaction (i.e., their life cycle or longevity), and this too will alter the emergent dynamics (e.g., Kozlowski et al., 1999 ; Kozlowski and Klein, 2000 ; Marks, Mathieu, and Zaccaro, 2001 ).

Theoretical Models and Frameworks

Most of the research on team effectiveness has been substantially influenced by the input-process-output (IPO) heuristic posed by McGrath (1964) . Inputs comprise (a) the collection of individual differences across team members that determine team composition; (b) team design characteristics (e.g., information, resources); and (c) the nature of the problem that is the focus of the team's work activity. Processes comprise the means by which team members' cognition, motivation, affect, and behavior enable (or inhibit) members to combine their resources to meet task demands.

Although team processes are conceptually dynamic, researchers generally assess them at a single point in time. Hence, they are often represented in the research literature by static perceptions or emergent states ( Marks, Mathieu, and Zaccaro, 2001 ). More recently, team processes have been represented by dynamic or sequential patterns of communications ( Gorman, Amazeen, and Cooke, 2010 ) or actions ( Kozlowski, in press ). In this report, the committee uses the term “team processes” to refer to both dynamic team processes (e.g., communication patterns) and the emergent perceptual states that result from these processes (e.g., cohesion).

Contemporary theories of team effectiveness build on the IPO heuristic but are more explicit regarding its inherent dynamics. For example, Kozlowski et al. (1996 , 1999 ) and Marks, Mathieu, and Zaccaro (2001) emphasized the cyclical and episodic nature of the IPO linkages. Similarly, Ilgen et al. (2005) and Mathieu et al. (2008) are explicit about the feedback loop linking team outputs and subsequent inputs. Accordingly, various authors have urged more attention to team dynamics in research (e.g., Cronin, Weingart, and Todorova, 2011 ; Cooke et al., 2013) and advances in research design ( Kozlowski et al., 2013 ; Kozlowski, in press ) to better capture these dynamics and more clearly specify the relationships between variables. Moving from broad heuristics to more well-defined theoretical models would benefit the field.

In their monograph, Kozlowski and Ilgen (2006) adopted the dynamic IPO conceptualization and focused on those team processes with well-established, empirically supported contributions to team effectiveness. They then considered actions and interventions in three aspects of a team—composition, training, and leadership—that shape team processes and thus can be used to enhance team effectiveness (as shown in the shaded areas of Figure 3-1 ). Given the preponderance of literature that follows the IPO conceptualization, we emulate that approach in this chapter.

Theoretical framework and review focus. SOURCE: Reproduced from Kozlowski and Ilgen (2006). Reprinted with permission.

• TEAM PROCESSES: THE UNDERPINNINGS OF TEAM EFFECTIVENESS

Team processes are the means by which team members marshal and coordinate their individual resources—cognitive, affective, and behavioral—to meet task demands necessary for collective goal accomplishment. When a team's cognitive, motivational, and behavioral resources are appropriately aligned with task demands, the team is effective. Thus, team processes are the primary leverage point for enhancing team effectiveness. The committee's review in this section examines team cognitive, motivational and affective, and behavioral processes, discussed below.

Cognitive Team Processes

Teams have been characterized as information processing systems ( Hinsz, Tindale, and Vollrath, 1997 ) such that their collective cognition drives task-relevant interactions. Here we discuss several cognitive and perceptual processes that are related to team effectiveness: team mental models and transactive memory, cognitive team interaction, team climate, and psychological safety.

Team Mental Models and Transactive Memory

Team mental models are conceptualized as shared understandings about “task requirements, procedures, and role responsibilities” that guide team performance ( Cannon-Bowers, Salas, and Converse, 1993 , p. 222). Whereas team mental models represent common understandings, transactive memory captures the distribution of unique knowledge across team members ( Wegner, Giuliano, and Hertel, 1985 ), especially their shared understanding of “who knows what” such that they can access and direct relevant knowledge ( Liang, Moreland, and Argote, 1995 ; Austin, 2003 ; Lewis, 2003 , 2004 ; Lewis, Lange, and Gillis, 2005 ; Lewis et al., 2007) . Meta-analytic findings indicate that both processes are positively related to team processes (ρ = .43) and team performance (i.e., effectiveness) (ρ = .38) ( DeChurch and Mesmer-Magnus, 2010) .

Studies of science teams and larger groups have also found that shared mental models enhance team effectiveness. To cite just a few examples, a study of research and development teams in India ( Misra, 2011) found that shared mental models were positively related to team creativity. A study focusing on larger groups of European scientists participating in interdisciplinary and transdisciplinary environmental research found that those groups whose members developed a shared understanding of the research goals were much more likely to succeed in synthesizing their perspectives to achieve those goals than those who did not develop shared understandings ( Defila, DiGiulio, and Scheuermann, 2006 ). In a recent qualitative study of the National Cancer Institute's Transdisciplinary Research on Energetics and Cancer Center, investigators and trainees reported that articulating concrete shared goals (through grant applications, for example) and investing time and effort in developing mutual understanding were essential to successfully carrying out their research projects ( Vogel et al., 2014) .

Both team mental models and transactive memory have the potential to be shaped in ways that enhance team effectiveness. For example, a number of studies demonstrate that mental models can be influenced by training, leadership, shared or common experiences, and contextual conditions ( Cannon-Bowers, 2007 ; see also Kozlowski and Bell, 2003 , 2013 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008 ; Mohammed, Ferzandi, and Hamilton, 2010 , for reviews). Similarly, transactive memory systems are formed through shared experiences in working together and training ( Bell et al., 2011 ; see also Blickensderfer, Cannon-Bowers, and Salas, 1997 ; Kozlowski and Bell, 2003 , 2013 ; Kozlowski and Ilgen, 2006 ; Mathieu et al., 2008 ; Mohammed, Ferzandi, and Hamilton, 2010 , for reviews). Accordingly, it is often recommended that training be designed to foster development of appropriate team mental models and transactive memory systems and that leaders shape early team developmental experiences to build shared mental models and transactive memory ( Kozlowski and Ilgen, 2006) .

Cognitive Team Interaction

Team mental models and transactive memory focus on cognitive structure or knowledge and how that knowledge is shared or distributed among team members. Although knowledge certainly contributes to team cognition, it is not equivalent to team-level cognitive processing. Teams often actively engage in cognitive processes, such as decision making, problem solving, situation assessment, planning, and knowledge sharing ( Brannick et al., 1995 ; Letsky et al., 2008) . The interdependence of team members necessitates cognitive interaction or coordination, often manifested through communication, the essential building block of team cognition ( Cooke et al., 2013) . These interactions facilitate information and knowledge sharing processes that are foundational to decision making, problem solving, and the other collaborative cognitive processes mentioned above ( Fiore et al., 2010a) .

The theory of interactive team cognition proposes that team interaction, often in the form of explicit communication, is at the heart of team cognition and in many cases accounts more than knowledge inputs for variance in team effectiveness ( Cooke et al., 2013) . In addition, unlike internalized knowledge states, team interaction in the form of communication is readily observable and can be examined over time, thus providing ready access to the temporal dynamics involved ( Cooke, Gorman, and Kiekel, 2008 ; Gorman, Amazeen, and Cooke, 2010 ).

Another approach to team cognition, focused more on the development of shared problem models, is the macrocognition in teams model ( Fiore et al., 2010b) . This model is based upon a multidisciplinary theoretical integration that captures the cognitive processes engaged when teams collaboratively solve novel and complex problems. It draws from theories of externalized cognition, team cognition, group communication and problem solving, and collaborative learning ( Fiore et al., 2010a) . It focuses on team processes supporting movement between internalization and externalization of cognition as teams build knowledge in service of problem solving. Recently the model has been examined in complex contexts such as problem solving for mission control, in which scientists and engineers were required to collaborate to understand and solve problems on the International Space Station (Fiore et al., 2014).

As with other interpersonal processes, interventions can improve cognitive interaction and ultimately team effectiveness. Training that exposes teams to different ways of interacting ( Gorman, Cooke, and Amazeen, 2010 ), as well as team composition changes ( Fouse et al., 2011 ; Gorman and Cooke, 2011) , have been found to lead to more adaptive and flexible teams. Similarly, training or professional development designed to support knowledge-building activities has been shown to enhance collaborative problem solving and decision making, leading to improved effectiveness ( Rentsch et al., 2010 , 2014 ). These and other professional development approaches are discussed in more detail in Chapter 5 .

Science teams and larger groups, like teams in general, are interdependent and require interaction to build new knowledge. They need to manage a range of technological and social factors to coordinate their tasks and goals effectively. Salazar et al. (2012) have proposed a model of team science, discussed later in this chapter, in which social integration processes support cognitive integration processes. These processes can help foster deep knowledge integration in science teams or larger groups.

Many of the features that create challenges for team science described in Chapter 1 introduce challenges to cognitive interaction, and, therefore, interventions that bolster cognitive interaction, such as professional development or training to expose teams to different ways of interacting, may be particularly helpful for science teams.

Team Climate

Climate represents shared perceptions about the strategic imperatives that guide the orientation and actions of team or group members ( Schneider and Reichers, 1983 ; Kozlowski and Hults, 1987) . It is always shaped by a particular team or organizational strategy. For example, if a team's goal is to innovate, then the team may have a climate of innovation ( Anderson and West, 1998) ; if the goal is to provide high-quality service, then the team may have a service climate ( Schneider, Wheeler, and Cox, 1992 ); if safety is critical for team or organizational success, then the team or the larger organization may have a safety climate ( Zohar, 2000) .

Climate has been studied for more than seven decades, and the relationship of climate to important work outcomes is well established (e.g., Carr et al., 2003 ; Zohar and Hofmann, 2012 ; Schneider and Barbera, 2013) .

Several types of interventions can shape team or group climate. For example, organizations communicate strategic imperatives through policies, practices, and procedures that define the mission, goals, and tasks for teams and larger groups within the organization ( James and Jones, 1974) . Team leaders shape climate through what they communicate to their teams from higher levels of management and what they emphasize to their team members ( Kozlowski and Doherty, 1989 ; Zohar, 2000 , 2002 ; Zohar and Luria, 2004 ; Schaubroeck et al., 2012) . And team members interact, share their interpretations, and develop shared understandings of what is important in their setting (Rentsch, 1990).

Psychological Safety

Psychological safety is a shared perception among team members indicative of an interpersonal climate that supports risk taking and learning ( Edmondson, 1999) . The research on psychological safety has been focused primarily on its role in promoting effective error management and learning behaviors in teams ( Bell and Kozlowski, 2011 ; Bell et al., 2011) . Learning from errors (i.e., to identify, reflect, and diagnose them and develop appropriate solutions) is particularly important in science as well as in other teams charged with innovation ( Edmondson and Nembhard, 2009) , and therefore, fostering psychological safety may be uniquely valuable for science teams and larger groups. Although research on this process has not yet been summarized in a published meta-analysis, support for its importance is provided by a systematic stream of theory and research (e.g., Edmondson, 1996 , 1999 , 2002 , 2003 ; Edmondson, Bohmer, and Pisano, 2001 ; Edmondson, Dillon, and Roloff, 2007 ).

Research on psychological safety has focused on the role of team leaders in coaching, reducing power differentials, and fostering inclusion to facilitate psychological safety, so that team members feel comfortable discussing and learning from errors and developing innovative solutions (e.g., Edmondson, Bohmer, and Pisano, 2001 ; Edmondson, 2003 ; Nembhard and Edmondson, 2006) . Hall et al. (2012a) proposed that creating an environment of psychological safety is critical to lay the groundwork for effective transdisciplinary collaboration. Thus, the research base suggests that appropriate team leadership is a promising way to promote psychological safety, learning, and innovation in science teams and larger groups.

Motivational and Affective Team Processes

Key factors that capture motivational team processes—team cohesion, team efficacy, and team conflict—have well-established relations with team effectiveness.

Team Cohesion

Team cohesion—defined by Festinger (1950 , p. 274) to be “the resultant of all the forces acting on the members to remain in the group”—is among the most frequently studied team processes. It is multidimensional, with facets focused on task commitment, social relations, and group pride, although this latter facet has received far less research attention ( Beal et al., 2003) . Our primary focus is on team task and social cohesion because that is where most of the supporting research is centered.

There have been multiple meta-analyses of team cohesion, with two of the more recent ones ( Gully, Devine, and Whitney, 1995 ; Beal et al., 2003) being the most thorough and rigorous. Both papers concluded that team cohesion is positively related to team effectiveness and that the relationship is moderated by task interdependence such that the cohesion-effectiveness relationship is stronger when team members are more interdependent. For example, Gully et al. (1995) reported that the corrected effect size (ρ) for cohesion and performance was .20 when interdependence was low, but .46 when task interdependence was high. Because high task interdependence is one of the features that creates challenges for team science, fostering cohesion may be particularly valuable for enhancing effectiveness in science teams and larger groups.

Remarkably, although team cohesion has been studied for more than 60 years, very little of the research has focused on antecedents to its development or interventions to foster it. Theory suggests that team composition factors (e.g., personality, demographics; see Chapter 4 ) and developmental efforts by team leaders (e.g., Kozlowski et al., 1996 , 2009 ) are likely to play an important role in its formation and maintenance.

Team Efficacy

At the individual level, research has established the important contribution of self-efficacy perceptions to goal accomplishment ( Stajkovic and Luthans, 1998) . Generalized to the team or organizational level, similar, shared perceptions are referred to as team efficacy ( Bandura, 1977) . Team efficacy influences the difficulty of goals a team sets or accepts, effort directed toward goal accomplishment, and persistence in the face of difficulties and challenges. The contribution of team efficacy to team performance is well established (ρ = .41) ( Gully et al., 2002) , across a wide variety of team types and work settings ( Kozlowski and Ilgen, 2006) . As with team cohesion, Gully et al. (2002) reported that team efficacy is more strongly related to team performance when team members are more interdependent (ρ = .09 when interdependence is low, and ρ = .47 when interdependence is high).

Antecedents of team efficacy have not received a great deal of research attention. However, findings about self-efficacy antecedents at the individual level can be extrapolated to the team level. These antecedents include individual differences in goal orientation (i.e., learning, performance, and avoidance orientation; Dweck, 1986 ; VandeWalle, 1997) and experiences such as enactive mastery, vicarious observation, and verbal persuasion ( Bandura, 1977) . To develop team efficacy, leaders may consider goal orientation characteristics when selecting team members, but these characteristics can also be primed (i.e., encouraged) by leaders. Similarly, leaders can create mastery experiences, provide opportunities for team members to observe others succeeding, and persuade a team that it is efficacious (see Kozlowski and Ilgen, 2006 , for a review).

Team Conflict

Team or group conflict is a multidimensional construct with facets of relationship, task, and process conflict:

Relationship conflicts involve disagreements among group members about interpersonal issues, such as personality differences or differences in norms and values. Task conflicts entail disagreements among group members about the content and outcomes of the task being performed, whereas process conflicts are disagreements among group members about the logistics of task accomplishment, such as the delegation of tasks and responsibilities ( de Wit, Greer, and Jehn, 2012 , p. 360).

Although conflict is generally viewed as divisive, early work in this area concluded that although relationship and process conflict were negative factors for team performance, task conflict could be helpful for information sharing and problem solving provided it did not spill over to prompt relationship conflict (e.g., Jehn, 1995 , 1997 ). However, a meta-analysis by De Dreu and Weingart (2003) found that relationship and task conflict were both negatively related to team performance. A more recent meta-analysis ( de Wit, Greer, and Jehn, 2012 ) has shown that the relationships are more nuanced. For example, all three types of conflict had deleterious associations with a variety of group factors including trust, satisfaction, organizational citizenship, and commitment. In addition, relationship and process conflict had negative associations with cohesion and team performance, although the task conflict association with these factors was nil. Thus, this more recent meta-analysis suggests that task conflict may not be a negative factor under some circumstances, but the issue is complex.

Group composition that yields demographic diversity and group faultlines or fractures is associated with team conflict ( Thatcher and Patel, 2011) . Because diverse membership is one of the features that creates challenges for team science introduced in Chapter 1 , science teams and groups can anticipate the potential for conflict. Many scholars suggest that teams and groups should be prepared to manage conflict when it manifests as a destructive and counterproductive force. Two conflict management strategies can be distinguished ( Marks, Mathieu, and Zaccaro, 2001 )—reactive (i.e., working through disagreements via problem solving, compromise, and flexibility) or preemptive (i.e., anticipating and guiding conflict in advance via cooperative norms, charters, or other structures to shape conflict processes) ( Kozlowski and Bell, 2013 ).

Team Behavioral Processes

Ultimately, team members have to act to combine their intellectual resources and effort. Researchers have sought to measure the combined behaviors of the team members, or team behavioral processes, in several ways, including by looking at team process competencies and team self-regulation.

Team Process Competencies

One line of research in this area focuses on the underpinnings of good teamwork based on individual competencies (i.e., knowledge and skill) relevant to working well with others. For example, Stevens and Campion (1994) developed a typology of individual teamwork competencies with two primary dimensions (interpersonal knowledge and self-management knowledge) that are each assessed with a set of more specific subdimensions. Based on this typology, they also developed an assessment tool, although empirical evaluations of this tool have yielded somewhat mixed results ( Stevens and Campion, 1999) .

Others have focused on behavioral processes at the team level. Integrating many years of effort, Marks, Mathieu, and Zaccaro (2001) developed a taxonomy of team behavioral processes focusing on three temporal phases: (1) transition, which involves preparation (e.g., mission, goals, strategy) before task engagement and reflection (e.g., diagnosis, improvement) after; (2) action, which involves active task engagement (e.g., monitoring progress, coordination); and (3) interpersonal processes (e.g., conflict management, motivation), which are viewed as always important.

A recent analysis by LePine and colleagues (2008) extended the Marks, Mathieu, and Zaccaro (2001) taxonomy to a hierarchical model that conceptualized the discrete behavioral processes as first-order factors loading onto second-order transition, action, and interpersonal factors, which are then loaded onto a third-order, overarching team process factor. Their meta-analytic confirmatory factor analysis found that the first- and second-order processes were positively related to team performance (mostly in the range of ρ = .25 to in excess of .30.).

Team Self-Regulation

For teams focused on reasonably well-specified goals, team processes and performance can be related to the team's motivation and self-regulation, similar to models of the relationship between motivation and performance at the individual level. Feelings of individual and team self-efficacy, discussed above ( Gully et al., 2002) , are jointly part of a multilevel dynamic motivational system of team self-regulation. Team self-regulation affects how team members allocate their resources to perform tasks and adapt as necessary to accomplish goals ( DeShon et al., 2004 ; Chen, Thomas, and Wallace, 2005 ; Chen et al., 2009) . In addition, there is meta-analytic support for the efficacy of group goals for group performance ( O'Leary-Kelly, Martocchio, and Frink, 1994 ; Kleingeld, van Mierlo, and Arends, 2011 ).

Finally, there is meta-analytic support ( Pritchard et al., 2008) for the effectiveness of an intervention designed to increase team regulation by measuring performance and providing structured feedback—the Productivity Measurement and Enhancement System (ProMES; Pritchard et al., 1988) . On average and relative to baseline, productivity under ProMES increased 1.16 standard deviations.

Measuring Team Processes

To assess team processes and intervene to improve them, team processes must be measured. Team process factors such as making a contribution to the team's work, keeping the team on track, and appropriately interacting with teammates have traditionally been measured through self or peer reports of team members ( Loughry, Ohland, and Moore, 2007 ; Ohland et al., 2012) .

Instruments relying on behavioral observation scales and ratings of trained judges have also been used to measure processes associated with collaborative problem solving and conflict resolution as well as self-management processes such as planning and task coordination ( Taggar and Brown, 2001) . Brannick et al. (1995) evaluated judges' ratings of processes of assertiveness, decision making/mission analysis, adaptability/flexibility, situation awareness, leadership, and communication. The ratings were found to be psychometrically sound and with reasonable discriminant validity, though the importance of task context was also noted: that is, process needs to be assessed in relation to the ongoing task. “Team dimensional training” was developed to measure a set of core team processes of action teams (e.g., Smith-Jentsch et al., 1998) and has since been validated in numerous settings (e.g., Smith-Jentsch et al., 2008) . Another approach that provides for context is the use of checklists of specific processes that are targeted for observation ( Fowlkes et al., 1994) .

Researchers have measured cognitive processes somewhat differently, relying typically on indirect knowledge elicitation methods such as card sorting to identify team mental models ( Mohammed, Klimoski, and Rentsch, 2000 ) and assess their accuracy (e.g., Smith-Jentsch et al., 2009) . In addition, concept maps corresponding to team member mental models have been developed by instructing participants to directly create them (e.g., Marks, Zaccaro, and Mathieu, 2000 ; Mathieu et al., 2000) or by indirectly creating them through similarity ratings of pairs of concepts analyzed using graphical techniques such as Pathfinder ( Schvaneveldt, 1990) . Transactive memory systems focusing on team members' knowledge of what each member knows have been measured both via self-assessment ( Lewis, 2003) and via communications coding ( Hollingshead, 1998 ; Ellis, 2006) . Cooke et al. (2000) reviewed different measurement approaches for measuring team mental models (including process tracing and conceptual methods), pointing out challenges related to knowledge similarity for heterogeneous team members and methods of aggregation.

Recent work in this area has focused on developing measures that are unobtrusive to the teamwork and can capture its complex dynamics (e.g., videorecording, team work simulations, and sociometric badges; Kozlowski, in press ). Communication data, for example, can be captured with relatively little interference and provide a continuous record of team interaction ( Cooke, Gorman, and Kiekel, 2008 ; Cooke and Gorman, 2009) . This research has identified changes in patterns of simple communication flow (who talks to whom) that are associated with changes in the state of the team (such as loss of situation awareness or conflict). These continuous methods provide a rich view of team process, not captured by static snapshots in time.

• INTERVENTIONS THAT SHAPE TEAM PROCESSES AND EFFECTIVENESS

Table 3-1 identifies actions and interventions that have been found to influence team processes related to three aspects of a team—its composition, professional development, and leadership. This section and the associated three chapters that follow provide detail on each of these three aspects.

Team Processes Related to Team Effectiveness: Interventions and Support.

Team Composition: Individual Inputs to Shape Team Processes

Team composition results from the process of assembling a combination of team members with the expertise, knowledge, and skills necessary for accomplishing team goals and tasks. At the individual level, the logic of staffing is based on selecting individuals with knowledge, skills, abilities, and other characteristics that fit job requirements. At the team level, staffing is more complex because one is composing a combination of members who must collaborate well, not merely matching each person to a well-defined job ( Klimoski and Jones, 1995) . Chapter 4 takes a detailed look at how team composition and assembly are related to team processes and effectiveness.

Professional Development to Shape Team Processes

Once a team has been assembled, its effectiveness can be facilitated by formal professional development programs (in the research literature, these are referred to as training programs). Although much of the research on team training has focused on programs developed for military teams ( Swezey and Salas, 1992 ; Cannon-Bowers and Salas, 1998 ), these teams face many of the same process challenges as science teams and groups, resulting from features, such as high diversity of membership, geographic distribution, and deep knowledge integration. Further evidence supporting training as an intervention to facilitate positive team processes is reviewed in Chapter 5 , along with discussion of educational programs dedicated to preparing individuals for future participation in team science.

Leadership to Shape Team Processes

Research has shown the influence of leadership on team and organizational effectiveness. Most of this research, however, focuses on the leader, rather than the team, and measures the effectiveness of the leader based on individual perceptions rather than measuring team effectiveness. The leadership literature is also rich with theories of leadership, some of which seem particularly relevant for science teams and larger groups. There is also promising new work on the concept of shared leadership by all team members. Moreover, recent meta-analytic findings provide support for the positive relationship between shared leadership and team effectiveness (42 samples, ρ = .34; Wang, Waldman, and Zhang, 2014 ), suggesting that it may be a useful concept for science teams. Team science leadership is discussed further in Chapter 6 .

• CONNECTING THE LITERATURE TO TEAM SCIENCE

New Models of Team Science

Researchers have developed and begun to study models of team science and effectiveness. Moving beyond traditional models of group development, such as Tuckman's (1965) phases of storming, norming, forming, and performing, these models incorporate elements specific to science teams and larger groups, such as deep knowledge in interdisciplinary teams, to meet scientific and societal goals. They provide different windows into team science and serve different purposes with respect to team science practice and policy. For instance, Hall et al. (2012b) proposed a model that serves as a heuristic for considering the broad research process. The model delineates four dynamic and recursive phases: development, conceptualization, implementation, and translation (see Box 3-2 ). Key team and group processes from the literature on teams and organizations are then linked to each of four phases. One of the unique contributions of this model is to highlight the breadth of collaborative and intellectual work that can be done in the early stages of developing a team science research project. Currently, such work in the development phase is often carried out hastily because of resource constraints. This part of the model helps to highlight the need for planning, institutional support, and funding specifically for the development phase. Overall, the model emphasizes key team and larger group processes that may, across the four phases, increase the comprehensiveness and sophistication of the science and effectiveness of the collaboration.

Two Models of Team Science. In the first model, Hall et al. (2012b) proposed that transdisciplinary team science includes four phases: development, conceptualization, implementation, and translation: In the development phase, the primary goal is to define (more...)

In contrast, Salazar et al. (2012) presented a model that specifically focuses on enhancing a team's integrative capacity through the interplay of social, psychological, and cognitive processes (see Box 3-2 ). Hadorn and Pohl (2007) presented a model of the transdisciplinary research process that discusses elements of both research and integration processes. The three phases of the model include (1) problem identification and structuring, (2) problem analysis, and (3) bringing results to fruition. This model is specifically designed for incorporating the community perspective (i.e., via “real-world actors”) and includes strategies linked to these phases. It draws heavily on a European perspective of transdisciplinarity, science policy, and sustainability research. Reid et al. (2009) and Cash et al. (2003) also discussed models of engaging and integrating knowledge from community stakeholders for sustainability. For instance, Cash et al. (2003) identified key mechanisms for information exchange, transfer, and flow that facilitate communication, translation, and mediation across boundaries in transdisciplinary team science projects.

Existing models of team science have primarily focused on specific aspects of research and knowledge integration processes, but work has recently begun on a team science systems map project that would provide a broader, holistic understanding of the system of factors involved in the context, processes, and outcomes of team science ( Hall et al., 2014 a). Such a map would aid in identifying possible leverage points for interventions to maximize effectiveness, as well as areas where further research is needed.

Features That Create Challenges for Team Science and Team Processes

Most of the key features that create challenges for science teams and larger groups have direct impacts on team processes:

• As noted by Hall et al. (2012b) and Salazar et al. (2012) , science teams or larger groups with high diversity of membership (feature #1) face challenges particularly in the area of team process. Communication across scientific disciplines or university boundaries, for instance, may prove difficult.
• Deep knowledge integration (feature #2) is required to achieve the objectives of interdisciplinary or transdisciplinary team science projects, yet also points to team process as a central mechanism for effectiveness. Strategies and interventions to foster positive team processes (described more fully in Chapters 4 , 5 , and 6 ) are critical for effective collaboration within science teams and larger groups that have diverse membership and seek to foster deep knowledge integration.
• The research on how team process influences effectiveness described in this chapter has primarily been based on relatively small teams of 10 or less, as few researchers have attempted to conduct empirical team research on larger groups (feature #3). As noted in Chapter 1 , most science teams include 10 or fewer members, suggesting that the findings in this chapter are relevant to science teams. Although it is unclear whether the findings scale to larger groups, the committee assumes that increasing size poses a challenge to group processes and ultimately group effectiveness.
• Large science groups composed of subteams that may be misaligned with other subteams (feature #4), as well as teams or groups of any size with permeable boundaries (feature #5), may also be less cohesive than other teams or groups. When team or group membership changes to meet the changing goals of different phases of a transdisciplinary research project, leaders need to make renewed efforts to develop shared understandings of the project goals and individual roles ( Hall et al., 2012b) . Such efforts, along with other leadership strategies described in Chapter 6 , can help to address these features.
• Geographic dispersion (feature #6) limits face-to-face interaction and development of transactive memory and thereby places a toll on cognitive interaction in a team or group. Some ways to address this particular challenge are described in Chapter 7 .
• High task interdependence (feature #7) is often exaggerated in science teams or groups because of the complex demands of scientific research that may involve sharing highly sophisticated technology or carrying out tasks with experts from a different discipline. Increasing task interdependence creates increasing demand for such team processes as shared mental models (shared understanding of research goals and member roles) and transactive memory (knowledge of each team members' expertise relevant to the research goals).

The seven features create challenges through the processes in which science teams engage. The features of diversity, large size, permeable boundaries, and geographic dispersion push team or group members apart, impacting cohesion and conflict and generally challenging cognitive interaction. On the other hand, features such as the need for deep knowledge integration in interdisciplinary and transdisciplinary team or groups and high task interdependence demand enhanced team processes. Thus these features demand high-quality team processes while also posing barriers that thwart them, creating a team process tension.

• SUMMARY AND CONCLUSION

Based on its review of the robust research on teams in contexts outside of science and the emerging research on team science, the committee concludes that team processes (such as shared understanding of goals and team member roles, team cohesion, and conflict) are related to effectiveness in science teams and larger groups, and that these processes can be influenced. The committee assumes that research-based actions and interventions developed to positively influence these processes and thereby increase effectiveness in contexts outside of science can be extended and translated to similarly increase the effectiveness of science teams and larger groups. Actions and interventions targeting team composition, team leadership, and team professional development are discussed further in the following chapters.

CONCLUSION. A strong body of research conducted over several decades has demonstrated that team processes (e.g., shared understanding of team goals and member roles, conflict) are related to team effectiveness. Actions and interventions that foster positive team processes offer the most promising route to enhance team effectiveness; they target three aspects of a team: team composition (assembling the right individuals), team professional development, and team leadership .

• Cite this Page Committee on the Science of Team Science; Board on Behavioral, Cognitive, and Sensory Sciences; Division of Behavioral and Social Sciences and Education; National Research Council; Cooke NJ, Hilton ML, editors. Enhancing the Effectiveness of Team Science. Washington (DC): National Academies Press (US); 2015 Jul 15. 3, Overview of the Research on Team Effectiveness.
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Stop Wasting Money on Team Building

• Carlos Valdes-Dapena

There are better ways to learn collaboration.

Many companies, when they decide to invest in team building, decide to do offsite events like bowling nights or ropes courses. Some spend thousands on special events, hoping to improve collaboration. These efforts often fail, so Mars Inc. took a different approach. HR leaders decided to interview and survey 125 teams. They used this data to develop a team-building approach that focused on the work itself. In an intensive two-day workshop, they asked a pilot group to discuss two questions: Why is their collaboration essential to achieving their business results? And second: What work, which specific tasks, would require collaboration to deliver those results? The results from these discussions were so good that Mars rolled out this approach to the whole company.

Most corporate team building is a waste of time and money. I say this based on my 25+ years of research and practice in the field of team effectiveness. Seventeen of those years were with Mars Inc., a family-owned $35 billion global business with a commitment to collaboration.

• CV Carlos Valdes-Dapena is the CEO of Corporate Collaboration Resources and the author of Lessons from Mars: How One Global Company Cracked the Code on High Performance Collaboration and Teamwork .

Partner Center

ORIGINAL RESEARCH article

Shared leadership and team effectiveness: an investigation of whether and when in engineering design teams.

• 1 School of Business, Macau University of Science and Technology, Macau, China
• 2 Lero – The Irish Software Research Centre, School of Engineering, National University of Ireland, Galway, Ireland

Shared leadership is lauded to be a performance-enhancing approach with applications in many management domains. It is conceptualized as a dynamic team process as it evolves over time. However, it is surprising to find that there are no studies that have examined its temporally relevant boundary conditions for the effectiveness of the team. Contributing to an advanced understanding of the mechanism of shared leadership in engineering design teams, this research aims to investigate whether shared leadership is positively related to team effectiveness and when shared leadership is more likely to be effective. Using a field sample of 119 individuals in 26 engineering design teams from China and the technique of social network analysis, we found that, consistent with cognate studies, shared leadership is positively related to team effectiveness when measured in terms of team task performance and team viability. Moreover, by integrating the project life cycle as a moderator, this study is among the first to investigate the temporal factors, for the effectiveness of shared leadership. The result shows that the stage of the project life cycle moderates the positive shared leadership-team effectiveness relationship, such that this association is stronger at the early phase than at the later phase of the project. Overall, these findings offer insightful thoughts to scholars in the field of shared leadership and bring practical suggestions for project managers in business who seek to implement best practice in organizations toward high team effectiveness.

Introduction

In recent years, leadership researchers have emphasized a team-level phenomenon, where leadership is carried out by the team as a whole, rather than exclusively by those at the top or by those in formal leadership positions ( Carson et al., 2007 ; Pearce et al., 2014 ). As such, the notion of shared leadership has gained more traction in the extant literature. By definition, shared leadership is described as “a dynamic, interactive influence process among individuals in groups for which the objective is to lead one another to the achievement of group or organizational goals or both” ( Pearce and Conger, 2003 , p. 1). As Acar (2010) noted, shared leadership represents a fundamental shift away from the notion of a single, appointed leader, to the idea that team members mutually influence each other and collectively share leadership roles, responsibilities and functions. Recent empirical work has provided evidence for the important role of shared leadership in groups ( Nielsen and Daniels, 2012 ; Nicolaides et al., 2014 ; Sousa and Van Dierendonck, 2016 ; Sun et al., 2016 ). More interestingly, some studies have even found that shared leadership is more influential than convectional vertical leadership for team effectiveness ( Pearce and Sims, 2002 ; Ensley et al., 2006 ). However, our understanding of whether shared leadership is positively related to team effectiveness and when shared leadership is more likely to be effective is still limited in at least three fundamental ways.

First, in recent years, researchers and practitioners have advocated the benefits of shared leadership as a way to promote team effectiveness. For example, Ramthun and Matkin (2012) stated that shared leadership is often advantageous, since members are more likely to follow the person having the best knowledge and skills than depending solely on the vertical influence process of traditional leadership. Indeed, many other empirical studies have also demonstrated that teams with shared leadership yield higher team effectiveness ( Pearce and Sims, 2002 ; Wang et al., 2014 ; Serban and Roberts, 2016 ). However, we must caution that this is not always the case. Fausing et al. (2013) and Mehra et al. (2006) failed to find support for this significant and positive relationship, and Boies et al. (2011) even found that shared leadership exerts a negative influence on team effectiveness. Such inconsistent findings point to the need for more empirical evidence. Therefore, in order to enrich our understanding of the value of shared leadership, the first purpose of our study is to explicitly examine the shared leadership – team effectiveness relationship. In this study, we define team effectiveness as the extent to which teams meet the expectations of organizations ( Essens et al., 2009 ). This viewpoint encourages us to think about team effectiveness from a multidimensional perspective. Consequently, we follow Aube and Rousseau (2005) , Balkundi and Harrison (2006) , and Mathieu et al. (2008) , who consider team effectiveness from two distinct aspects: team task performance and team viability. Team task performance refers to how well the group meets (or even exceeds) work expectations while team viability is the potential of teams to retain its members and to function effectively over time ( Balkundi and Harrison, 2006 ).

Second, in order to gain a more fine-grained understanding of the impacts of shared leadership, unanswered questions must be addressed. More specifically, there is a clear need to investigate the temporally relevant moderators for its effectiveness. Researchers have emphasized that shared leadership is a dynamic, emergent, time-varying construct ( Avolio et al., 2009 ) that is affected by the environment of a team ( Carson et al., 2007 ; Wu et al., 2020 ) and task characteristics ( Serban and Roberts, 2016 ; Hans and Gupta, 2018 ). Therefore, continuous changes in the inputs, processes and outputs of different phases of the project life cycle could influence the emergence of shared leadership in teams ( Wu and Cormican, 2016 ) as well as its relationship with team effectiveness. However, the potential moderating impact of the project life cycle for the effectiveness of shared leadership is not well theoretically developed nor rigorously empirically tested. This important unaddressed gap needs further attention so as to provide insights into the boundary conditions regarding when shared leadership is more or less influential to team effectiveness. Consequently, the second research goal is to focus on the dynamic nature of shared leadership and investigate the moderating effect of the project life cycle in the relationship between shared leadership and team effectiveness.

Third, although there is growing interest in the shared leadership domain, studies concentrating on project teams are still limited and under-developed ( Scott-Young et al., 2019 ). Shared leadership theory has been widely spread and applied across a range of team types, e.g., top management teams ( Singh et al., 2019 ), entrepreneurial teams ( Zhou, 2016 ), consulting teams ( Carson et al., 2007 ), and change management teams ( Pearce and Sims, 2002 ). However, there is a dearth of investigations relating to project teams. While the current workplace is becoming increasingly project-centric ( Scott-Young et al., 2019 ), there remain very few studies focusing on shared leadership theory in the project management context. In order to extend the external validity of the shared leadership construct in project settings, this study examines the effectiveness of shared leadership in project-based engineering design teams. Moreover, as project teams uniquely have definitive start and end times based on the duration of the tasks ( Farh et al., 2010 ), it is well suited to help explain when shared leadership is more likely to be effective in teams.

Taken together, this research seeks to enrich our understanding of the mechanisms of shared leadership and investigates whether and when shared leadership is positively related to team effectiveness in engineering design teams. To do this, we used the social network approach to measure the construct of shared leadership by calculating network density and creating binary matrices as well as sociograms. Team effectiveness was measured using nine items consisting of two separate, theoretically derived subscales: team task performance and team viability . Moreover, an internal consistency analysis and confirmatory factor analysis was performed to assess the reliability and validity of our measurement model. We then conducted a two-way moderated hierarchical regression analysis ( Carson et al., 2007 ; Erkutlu, 2012 ; Fausing et al., 2013 ) in this study so as to test hypotheses proposed. By doing so, our study makes several significant contributions: (1) it extends a line of research and explicitly examines the relationship between shared leadership and team effectiveness; (2) it builds on the dynamic nature of shared leadership and is among the first to investigate an important temporal moderator, the project life cycle, for the effectiveness of shared leadership; (3) it adds to the academic debate by extending the external validity of shared leadership theory in engineering design teams; (4) it brings insightful thoughts to the field of project management by providing practical suggestions for project managers in business who seek to implement best practice in their organizations.

Theory and Hypotheses

Shared leadership theory.

Leadership scholars have realized the importance of shared leadership and worked to understand how to conceptualize it, measure it, and to assess what impacts it brings to teams. Table 1 presents details of relevant prior empirical studies. As illustrated, conceptually, shared leadership is a team-centric phenomenon ( Ensley et al., 2006 ; Serban and Roberts, 2016 ) whereby team members engage in “leadership roles and responsibilities on behalf of the team” ( Robert and You, 2018 , p. 503), and “accepts their colleagues’ leadership” ( Aubé et al., 2017 , p. 199). Furthermore, shared leadership is not a static process; it is defined as an emergent, dynamic phenomenon that unfolds over time ( Avolio et al., 2009 ; Drescher et al., 2014 ; Wang et al., 2014 ). According to Carson et al. (2007) , shared leadership is considered in terms of a continuum ranging from low to high, which implies that shared leadership is not a rigid either-or category, but occurs in every group at various levels ( Liu et al., 2014 ).

Table 1. Definitions, measures, and impacts of shared leadership.

While progress has been made relating to the definitions of shared leadership, many empirical studies have centered on what impacts shared leadership brings. As shown in Table 1 , the positive relationship between shared leadership and team performance has received much attention ( Sivasubramaniam et al., 2002 ; Ensley et al., 2006 ; Mehra et al., 2006 ; Carson et al., 2007 ; Drescher et al., 2014 ). Additionally, shared leadership is also demonstrated to be positively related to team functioning ( Bergman et al., 2012 ), team proactive behavior ( Erkutlu, 2012 ), team and individual learning ( Liu et al., 2014 ), team member’ diversity and emotional conflict ( Acar, 2010 ), team members’ trust, autonomy and satisfaction ( Robert and You, 2018 ). These findings are encouraging and suggest the need for more sophisticated designs on the notion of shared leadership. Accordingly, this study extends a line of research to further examine its relationship with team effectiveness and goes beyond simple relationships to investigate when shared leadership plays a stronger or weaker role in the effectiveness of teams. The relevant research hypotheses are proposed below.

Shared Leadership and Team Effectiveness

Based on the work of Aube and Rousseau (2005) , Balkundi and Harrison (2006) , and Mathieu et al. (2008) , team effectiveness is considered in terms of two distinct aspects: team task performance (how well the group meets (or even exceeds) work expectations) and team viability (the potential of teams to retain its members and to function effectively over time). This assessment conforms to the classic work of Barrick et al. (1998) , who suggested that a comprehensive assessment of team effectiveness should capture both current team effectiveness (i.e., present task performance) and future team effectiveness (i.e., capability to continue working together). Therefore, this research adopts a broad perspective to team effectiveness and explores the relationship between shared leadership and team effectiveness.

First of all, this study expects that shared leadership is positively associated with team task performance. As suggested by Day et al. (2004) , shared leadership advances the social capital of the team via the utilization of team resources such as the knowledge and capability of group members, which subsequently fosters team task performance. Katz and Kahn (1978) also proposed that when group members offer leadership to others and to the mission or purpose of their group, they bring more personal and organizational resources to the task, share more information, and they experience greater commitment. Further, when group members are influenced by their fellows, team functioning is improved as high levels of respect and trust are evidenced among group members. Collectively, teams exhibiting these characteristics, can also exhibit greater levels of performance ( Day et al., 2004 ). This premise aligns with many empirical studies (see Table 1 ). For instance, Carson et al. (2007) , in a study of 59 consulting teams, found that shared leadership is positively associated with team performance as rated by clients. Ensley et al. (2006) , in a study of 66 top management teams, demonstrated that shared leadership is a more significant predictor than vertical leadership of new venture performance when considered in terms of revenue and employee growth. Furthermore, Drescher et al. (2014) , in a longitudinal examination of 142 teams who engaged in a strategic simulation game, also demonstrated support for the positive influence of shared leadership on team task performance. Taken these together, this study proposes:

Hypothesis 1a: Shared leadership is positively related to team task performance in engineering design teams.

Shared leadership, as an important intangible resource available to teams ( Carson et al., 2007 ), fosters not only team task performance, but also team viability. As Wood and Fields (2007) suggested, shared leadership exerts a series of positive impacts on team members’ job perceptions: it brings low levels of role overload, role conflict, role ambiguity and job stress, as well as high levels of job satisfaction. Similarly, Bergman et al. (2012) also demonstrated that teams with shared leadership experience less conflict, greater consensus, and higher intragroup trust and cohesion. This may foster team viability as members in shared leadership teams experience increased interdependence, more collaboration, and they sense greater levels of satisfaction. Additionally, when there is effective coordination and collaboration among team members fulfilling leadership responsibilities, it is easier for them to identify the potential causes of conflicts and propose potential solutions. It thus reduces the amount of conflict and promotes team consensus and trust ( Balkundi and Harrison, 2006 ). As a consequence, team viability, which retains members and maintains good team functioning over time, could be enhanced. This research therefore posits:

Hypothesis 1b: Shared leadership is positively related to team viability in engineering design teams.

Taken these two hypotheses (hypothesis 1a and 1b) together, this study expects that shared leadership will foster team effectiveness by enhancing team task performance and team viability. As Wang et al. (2014) suggested, shared leadership nurtures a collective identity among members of the team and strengthens the level of engagement with and commitment to the group, which in turn enhances team effectiveness. Moreover, Mathieu et al. (2015) mentioned that shared leadership fosters social inclusion and enhances team cohesion, which can, subsequently, facilitate team effectiveness. In light of this, this research suggests:

Hypothesis 1c: Shared leadership is positively related to team effectiveness in engineering design teams.

The Moderating Role of the Project Life Cycle

Notwithstanding the fact that research on the relationship between shared leadership and team effectiveness brings valuable insights into the understanding of shared leadership in teams, there is an important omission in prior studies regarding its temporal moderating roles on such a relationship ( Carson et al., 2007 ; D’Innocenzo et al., 2014 ; Wang et al., 2014 ). In an attempt to open the black box, this study seeks to examine a potential moderator of shared leadership, namely the project life cycle, and expects that the positive association between shared leadership and team effectiveness will be stronger at the early phase than the later phase of the project. This is because the focal concern of the early stage is toward planning and strategy generation ( Chang et al., 2003 ; Farh et al., 2010 ), where project team members are more willing to engage in mutual leadership as they become proactively involved in constructive communication and decision-making ( Wu and Cormican, 2016 ). It thus allows individuals to bring more resources to the task, share more information, and to experience higher levels of commitment ( Bergman et al., 2012 ). Collectively, these consequences would result in greater team effectiveness ( Day et al., 2004 ; D’Innocenzo et al., 2014 ). Furthermore, as time and resources are less constrained at the early stage ( Farh et al., 2010 ), members are able to take initiative to develop their own leadership abilities as well as to facilitate the leadership skills of others, which subsequently fosters the effectiveness of project teams ( Ensley et al., 2006 ; Serban and Roberts, 2016 ). However, when the project advances into the later stage, resources are dedicated to execute project plans ( Farh et al., 2010 ). This leads to a change in the leadership distribution from many team members to a few individuals, who assume the responsibility of integrating resources, controlling the development of the project to meet deadlines and keeping costs within budget ( Wu and Cormican, 2016 ). Teams may no longer afford to spend too much time cultivating a positive team environment to promote shared leadership ( Carson et al., 2007 ). As such, any potential of shared leadership for enhancing team effectiveness would be more difficult to realize in the later stage of the project life cycle. Therefore, this research expects that:

Hypothesis 2 : The stage of the project life cycle moderates the positive association between shared leadership and team effectiveness, such that this relationship will be stronger at the early phase than at the later phase of the project in engineering design teams.

Methodology

Research setting and sample.

A survey-based design was conducted in this study. The sample comprised 26 project-based engineering design teams working in the construction industry in China. As suggested by Carson et al. (2007) , shared leadership is effective for teams composed of knowledge-based employees, because people having high levels of expertise and skills seek autonomy in how they apply their specialties, and thus desire more opportunities to shape and participate in the leadership functions for their groups. Engineering design teams comprising knowledge workers have the potential to leverage the expertise of a diverse group of members by pooling their talent and knowledge. This kind of team is likely to nourish the emergence or development of shared leadership. This perspective thus adds to the academic debate on the relationship between shared leadership and team effectiveness and extends the external validity of shared leadership theory into engineering design teams. Moreover, we chose a Chinese sample due to the fact that the conceptualization and operationalization of shared leadership is predominantly developed in the Western countries (see Table 1 ) and it remains uncertain whether its theoretical models hold up in Chinese cultural settings. Furthermore, scholars, like Whetten (2009) , have called for more attention to be paid to explaining cultural context effects. Therefore, to plug this gap, this study seeks to extend the validity of the shared leadership construct to a Chinese context, whereby its organizational culture differs from Western countries. Specifically, according to Hofstede et al. (2005) , the power distance and collectivism in China are rated stronger than in Western cultures. Initially, a pilot test was conducted with 16 employees from three engineering design teams. Based on feedback provided, minor modifications to the survey items were made. Next, 146 members from 34 engineering design teams were invited to participate in this study. Of the 146 participants who received the questionnaire, 127 returned it, yielding an 87% response rate. Teams with less than three members were eliminated from the sample. It resulted in a sample of 119 employees working in 26 project teams. The average team size of the sample is 5.26. The specific participant demographics are outlined in the Table 2 .

Table 2. Sample characteristics.

Shared Leadership

This research study adopted a social network approach to assess the nature of shared leadership. The social network technique is an intrinsically relational method that advocates a natural theoretical and analytical method to modeling the patterns of the relationships among interconnected individuals ( D’Innocenzo et al., 2014 ). This study used the most common index of social network analysis, network density, to explicitly measure the extent to which team members are perceived to be involved in the sharing of leadership ( Wang et al., 2014 ). This popular measurement was employed in many empirical studies of shared leadership ( Carson et al., 2007 ; Lee et al., 2015 ; Chiu et al., 2016 ; Serban and Roberts, 2016 ). Following Carson et al. (2007) , this study assessed the level of shared leadership by requiring every team member to rate each of his/her peers on the following question: “To what degree does your team rely on a particular individual for leadership?” A five-point Likert scale was used to measure the level of perceived leadership, where 1, represents “not at all,” and 5, “to a very great extent.” Network density was then calculated by summing all of the responses from group members divided by the total number of possible relations among group members ( Carson et al., 2007 ; Mathieu et al., 2015 ). The values of density ranged from 0 to 1, where higher values indicate higher degrees of shared leadership within a team. Furthermore, as shared leadership is a team-level phenomenon, agreement among the respondents’ ratings of group members was also measured thus proving appropriate interrater reliability [mean r wg = 0.75, ICC(1) = 0.44, ICC(2) = 0.77].

To visually represent the density of shared leadership, this study developed leadership sociograms for each sample team similar to Carson et al. (2007) and Pastor and Mayo (2002) . To do this, binary matrices were created, which were then used to quantify the degree of leadership influence for each team and to represent the presence or absence of leadership relations between pairs of team members. More specifically, the raw leadership ratings collected from each participant were aggregated and included in g × g squared matrices. These data were then dichotomized, where values of 4 (to a great extent) or 5 (to a very great extent) are considered as 1, and values of 3 and less are given a value of 0. The second step was to create leadership sociograms based on these binary matrices. Figure 1 shows the leadership sociograms in our study. Specifically, it illustrates three examples with low, middle and high levels of density of shared leadership networks. Among all of our sample data (26 engineering design teams), 0.52 is the lowest score, 0.66 is the medium score, and 0.75 is the highest score of network density. The nodes symbolize team members and the arrows represent leadership relations. One arrow points from team member (A) to member (B), indicating that B is perceived as a source of leadership by A. In this vein, two-headed arrows imply that two members perceive each other as a source of leadership.

Figure 1. Leadership sociograms in this study. Low degree of shared leadership Medium degree of shared leadership (Network density = 0.52) (Network density = 0.66). High degree of shared leadership (Network density = 0.75).

Team Effectiveness

Team effectiveness was measured by team participants (including team leaders and members) via nine items consisting of two separate, theoretically derived subscales: team task performance and team viability using a five point Likert scale ranging from 1 “strongly disagree” to 5 “strongly agree.” Team task performance was assessed using five items derived from Sousa and Van Dierendonck (2016) and Suprapto et al. (2018) . It measures the degree to which the project meets its goals, quality, schedule, budget, and overall level of customer satisfaction. Team viability was measured using four items derived from Aube and Rousseau (2005) . These include the extent of a team’s capacity to solve problems, the ability to integrate new members, the ability to adapt to changes, as well as the ability to continue to work together in the future. In order to test for the discriminant validity, a confirmatory factor analysis (CFA) was performed. This yielded a good fit to the data (X 2 27 = 33.90, CFI = 0.99, GCI = 0.94, AGFI = 0.09, RMSEA = 0.05). These CFA results demonstrate the support for the hypothesized structure to measure team effectiveness. This study further examined the correlation between these two subscales to check the convergent validity of this measurement model. The finding provides evidence that these two subscales are highly correlated with each other ( r = 0.92, p < 0.001). Given the strong support of the hypothesized measurement model, this study aggregated these two subscales to the group level and then averaged the scores to generate a single variable to represent team effectiveness (Cronbach α = 0.95). To justify whether this aggregation is appropriate, this research used the interrater agreement statistic, r wg ( James et al., 1993 ). The mean r wg value of 0.82 was much larger than the conventional cut-off value of 0.70 ( James et al., 1993 ), which implies that on average, there is a high degree of agreement among different raters with a group. Furthermore, the intraclass correlation coefficient, ICC (1) and the reliability of the group-level mean, ICC (2) were also calculated to test between-group variance and within-group agreement ( Bliese, 2000 ). The results showed that the ICC (1) value of 0.73 suggested that team membership accounted for significant variance and the ICC (2) value of 0.92 demonstrated that the group-level means were reliable.

Project Life Cycle

Led by the research of Farh et al. (2010) , the phase of the project life cycle was measured from the percentage of the project work completed at the time of the survey, as reported by project managers. In the sample of our study, the mean project completion rate across 26 teams was 56%. This research checked journal guidelines and similar papers (see Farh et al., 2010 ) and used a mean split, where teams with a percentage of project completion equal to and below 56% were classified as being at an early phase and teams above 56% were classified as being at a later phase . Accordingly, there are 14 project teams in the early phase subgroup with the percentage of project completion ranging from 5% to 56%, and 12 in the later phase subgroup with 57–100% project completion. Figure 2 graphically illustrates the distribution of network density of shared leadership in the early phase vs. later phase.

Figure 2. The distribution of network density of shared leadership in the early phase vs. later phase.

Control Variables

Several control variables were included in the study. First is team size, as it has been proposed to be negatively related to the emergence of shared leadership ( Cox et al., 2003 ) and negatively to customer ratings and team self-ratings of team effectiveness ( Pearce and Sims, 2002 ). The second control variable is team tenure (the length of time an individual has worked on a specific team). It was included as it reflects the experience of group members working together which may influence team effectiveness ( Marrone et al., 2007 ) and shared leadership because team longevity affects mutual familiarity, trust and interaction among team members ( Cox et al., 2003 ). Third is team members’ educational levels, since the team member’s diversity has been demonstrated to moderate the relationship between shared leadership and team outcomes ( Hoch, 2014 ). Therefore, team members’ educational levels were controlled, together with team size, team tenure for the analysis of this present research.

Table 3 presents the means, standard deviations and zero-order correlations of all the constructs. As illustrated, shared leadership is positively and significantly correlated to team task performance ( r = 0.52, p < 0.01), team viability ( r = 0.43, p < 0.05) as well as team effectiveness ( r = 0.50, p < 0.05), which provides preliminary evidence to support hypothesis 1a, 1b, and 1c. Figure 3 , a three-panel correlation plot, visually depicts the relationship between shared leadership and team task performance, team viability as well as team effectiveness.

Table 3. Descriptive statistics and correlations.

Figure 3. The three-panel correlation plot.

To further test the relationship between shared leadership and team effectiveness, as well as the moderating role of the project life cycle in such relationships, this research employed a two-way moderated hierarchical regression analysis ( Carson et al., 2007 ; Erkutlu, 2012 ; Fausing et al., 2013 ). Led by the procedure delineated in Cohen et al. (2014) , in the regression model, the control variables, team size, team tenure and educational diversity were entered in the first step for this research; shared leadership as an independent variable was entered in the second step; the interaction terms (predictor variable, shared leadership and moderator variable, project life cycle) was entered in the third step. In order to avoid multicollinearity problems, the standardized scores were utilized in the regression analysis ( Aiken et al., 1991 ). Table 4 depicts the results of the moderated regression analyses.

Table 4. Results of regression analysis for team effectiveness.

As can be seen in step 1 in Table 4 , the control variables were not significantly associated with team effectiveness. In step 2, we find that there is a significant positive relationship between shared leadership and team effectiveness (β = 0.53, p < 0.05), supporting hypothesis 1c (shared leadership is positively related to team effectiveness in engineering design teams). Moreover, the result of step 3 shows that the interaction between shared leadership and the project life cycle is significantly related to team effectiveness (β = −0.47, p < 0.05). We then graphically plotted the relationship between shared leadership and team effectiveness as moderated by the project life cycle ( Figure 4 ) as recommended by Aiken et al. (1991) . We see that a positive relationship is stronger in the early stage, when compared to the later phase of the project life cycle. Therefore, hypothesis 2 (the stage of project life cycle moderates the positive association between shared leadership and team effectiveness, such that this relationship will be stronger at the early phase than at the later phase of the project in engineering design teams) was fully supported in this study.

Figure 4. The moderating effect of the project life cycle on the relationship between shared leadership and team effectiveness.

By integrating concepts from shared leadership, team effectiveness and project management literature, the current research sheds light on our understanding of whether and when shared leadership is positively related to team effectiveness. More specifically, this research advances prior work by demonstrating that there is a positive relationship between shared leadership and team effectiveness in Chinese engineering design teams. Furthermore, we also demonstrated that the stage of the project life cycle moderates the relationship between shared leadership and team effectiveness; where the positive association is stronger at the early phase than at the later phase of project life cycle. These findings provide significant theoretical contributions as well as practical implications.

Theoretical Contribution

First of all, by joining a handful of researchers in the field of shared leadership ( Liu et al., 2014 ; Chiu et al., 2016 ; Serban and Roberts, 2016 ), this study further confirms that shared leadership plays a significant role in building effective team outcomes. Specifically, this research linked shared leadership with team task performance [defined in terms of how well the group meets (or even exceeds) expectations regarding its assigned tasks]. Shared leadership has been consistently shown to be critical for improving team performance in practice and in the extant literature ( Ensley et al., 2006 ; Carson et al., 2007 ; D’Innocenzo et al., 2014 ; Hoch, 2014 ; Wang et al., 2014 ; Chiu et al., 2016 ; Fransen et al., 2018 ). Although these studies have advocated the benefits of shared leadership on team performance, there is still some disagreement and controversy surrounding it ( Mehra et al., 2006 ; Boies et al., 2011 ; Hmieleski et al., 2012 ). This current study therefore extends this line of research by demonstrating that the positive association between shared leadership and team task performance holds up in engineering design teams, thus supporting cogent work in the field of shared leadership. Moreover, the results of the current study also suggest that shared leadership is positively associated with team viability (considered in terms of the potential of teams to retain its members and to keep good team functioning over time). This finding is consistent with previous studies that suggested that shared leadership fosters team functioning and team member satisfaction. For example, Bergman et al. (2012) suggested that teams with shared leadership experience less conflict, greater consensus, and higher intragroup trust and cohesion than teams without shared leadership. Wood and Fields (2007) proposed that shared leadership exerts positive impacts on the job satisfaction of team members as shared leadership inherently advocates greater empowerment and autonomy. Therefore, as demonstrated in the current study, members of teams who share leadership, experience increased interdependence, higher levels of collaboration, and a greater sense of satisfaction. Furthermore, the ability to retain team members and to maintain positive team functioning over time is enhanced.

Another important theoretical contribution is that this study provides interesting insights into an important boundary condition of shared leadership effects. Specifically, this study investigated and demonstrated that phases of the project life cycle moderate the shared leadership-team effectiveness relationship; such relationship is stronger at the early phase than the later phase. The result of this investigation is consistent with the theory on the dynamic nature of shared leadership. As Avolio et al. (2009) noted, shared leadership is not a static, but a transferable and quite a fluid process, wherein roles and relations among individuals merge, co-evolve, and change throughout the entire life cycle of the project. Moreover, this result also supports the proposition proposed by Ford and Sullivan (2004) who asserted that creative ideas and strategies generated at the early stage of the team cycle are more likely to be valued and integrated into effective outcomes. Our findings extend this theory by identifying shared leadership as a potential source to encourage novel ideas. Specifically, at the early stage of the project life cycle where the focus is on planning and strategy generation, team members proactively participate in constructive communication and decision-making process. It thus provides a positive environment to nourish shared leadership. Such high-levels of leadership shared by individuals helps to generate more novel ideas, which could sequentially be valued and incorporated into effective results. Therefore, by integrating the project life cycle as a moderator, this study demonstrated how the temporal factor influence the shared leadership-team effectiveness association.

Practical Lmplications

This research brings several significant practical implications to project management practitioners. Most notably, our findings confirm the positive relationship between shared leadership and team effectiveness in engineering design teams. It indicates that shared leadership can be a useful way to improve project team outcomes. This suggests that project managers seeking to foster high-levels of effectiveness should be supportive of sharing leadership within their groups and take steps to encourage group members to share leadership roles and responsibilities and provide them with adequate opportunities to interact with each other. Moreover, this study demonstrated that the association between shared leadership and team effectiveness is stronger at the early phase of the project life cycle. This emphasizes the need for managers to support shared leadership forms particularly at the early phase of the project in order to leverage benefits and maximize team effectiveness. Moreover, this research provides a benchmark with social network technique to help managers to assess their leadership development programs, in order to determine the extent to which they are reinforcing the notion of leadership as a collective process.

Limitation and Future Research

As is the case for any research, there are some limitations related to this current study which are worthy of being acknowledged. First of all, since the measurements for the variables used in the study were taken from the same source, there could be common source bias influencing the relationship between shared leadership and team effectiveness. However, this research assessed team effectiveness by measuring the entire team’s behavior and outcomes, while shared leadership measured the behavior of individual members and was analyzed by a social network method. As such, the common source bias was mitigated to some extent because of this measurement distinction. In addition, the sample of this experimental study consisted of 26 teams for both the early and later phase of the project life cycle. Replications of current research and future studies are encouraged to increase the sample size so as to achieve greater statistical power.

Second, while the definition of team effectiveness (measured in terms of team task performance and team viability) is multidimensional in nature, it does not take every possible aspect into consideration, e.g., happiness of the team members. In other words, the predictors used in this research are not an exhaustive list. There can be other consequences of shared leadership that have not been accounted for. This study thus encourages more studies to examine additional predictors of shared leadership, especially predictors from a multilevel perspective. For example, more consequences at the firm and organizational level should be examined, e.g., firm competitive advantage, organizational effectiveness and creativity. Furthermore, since our research focused only on engineering design teams, it limits the generalizability of the results. Therefore, future studies can make a valuable contribution by examining the relationship between shared leadership and its outcomes from a wide variety of contexts.

Third, an important premise of this investigation, regarding when shared leadership influences team effectiveness across the project life cycle, is the dynamic nature of shared leadership. Its emergence is likely to be influenced by team environments (i.e., cross-functional communication and coordination, and active participation in the decision-making process); as well as task characteristics (i.e., creative tasks). Unfortunately, the design of the current study did not directly examine these factors that could simulate the occurrence and development of shared leadership. It thus would be a promising research direction for future studies. Moreover, since shared leadership is a dynamic and emergent process, research with a longitudinal design that captures multiple iterations and cyclic feedback loops of shared leadership, to understand how it changes or evolves throughout stages of the project team life cycle, is another fruitful avenue for future studies.

Fourth, this study is among the first to explore the moderating role of the project life cycle in the relationship between shared leadership and team effectiveness. We thus encourage future research to provide a more complete understanding of the boundary conditions of shared leadership effectiveness, particularly for project-related moderators. Examples like project complexity, project uncertainty, and project creativity are worthy of attention in future studies. Moreover, the potential temporal indicators should also be examined considering shared leadership is a dynamic process in nature. This would serve as another promising direction for future research.

Fifth, shared leadership, as a new leadership pattern that has been demonstrated to facilitate team effectiveness in the engineering design teams. However, we do not advocate that shared leadership is a panacea for all organizational woes. There may be many circumstances where shared leadership is not suitable e.g., non-knowledge teams. Furthermore, Pearce (2004) suggested that shared leadership is a more complex and time-consuming process than traditional vertical leadership. In light of this, research concerning when and for whom shared leadership is inappropriate should be another interesting avenue and thus worthy of further attention.

Contribution

The current study was designed to produce novel theoretical and empirical insights regarding whether shared leadership is positively related to team effectiveness and when shared leadership is more likely to be effective. By demonstrating a positive association between shared leadership and team effectiveness in engineering design teams, this study adds to a growing literature extolling the value of shared leadership. Another important contribution of the present research is that it is among the first to investigate a temporally relevant moderator, the project life cycle, for the effectiveness of shared leadership. The authors hope that the insightful findings gained through this effect will spur future studies aimed at understanding the dynamics of shared leadership in project teams and further explore temporal factors for its effectiveness.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Graduate Research Committee (GRC), National University of Ireland, Galway. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

QW was responsible for conducting analysis and writing the first draft. KC contributed to the structure and content and revised all versions of the manuscript. QW and KC both participated in idea development.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : shared leadership, team effectiveness, project life cycle, social network analysis, engineering design teams

Citation: Wu Q and Cormican K (2021) Shared Leadership and Team Effectiveness: An Investigation of Whether and When in Engineering Design Teams. Front. Psychol. 11:569198. doi: 10.3389/fpsyg.2020.569198

Received: 03 June 2020; Accepted: 24 December 2020; Published: 18 January 2021.

Reviewed by:

Copyright © 2021 Wu and Cormican. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qiong Wu, [email protected] ; Kathryn Cormican, [email protected]

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