Shared Leadership And Group Interaction Styles In ... - CiteSeerX

Proceedings of the 37th Hawaii International Conference on System Sciences - 2004

Shared Leadership And Group Interaction Styles In Problem-Solving Virtual Teams Pierre Balthazard1, David Waldman1, Jane Howell2, Leanne Atwater1 [email protected], [email protected], [email protected], [email protected] 1 Arizona State University West, School of Management, Phoenix, Arizona 2 University of Western Ontario, Richard Ivey School of Business, London, Ontario Abstract Despite their prevailing growth, little systematic evidence exists regarding the effectiveness of computermediated “virtual” teams (VTs), especially in relation to their traditional counterpart, face-to-face teams (FtFTs). A Partial Least Squares (PLS) analysis revealed that FtFTs were more likely to demonstrate higher levels of shared leadership and a constructive interaction style than were VTs. In turn, shared leadership and a constructive interaction style were shown to positively predict cohesion, whereas a defensive interaction style was shown to negatively predict cohesion. Shared leadership was also positively associated with a constructive interaction style and negatively associated with a defensive interaction style. Finally, task performance was shown to be a function of group cohesion.

1.

Introduction

The virtual organization, and its smaller version, the virtual team (VT) represent new and growing organization forms. VT members are geographically and often temporally distributed, and oftentimes, the members have different areas of expertise and may work in different functional areas [1,2]. VTs can potentially give organizations increased flexibility and responsiveness, permitting geographically dispersed experts to rapidly form a cohesive unit that can work on an urgent project. When finished, the team can be disbanded and members re-deployed to other projects; members may also serve on multiple virtual teams simultaneously. Although the VT has become an increasingly common work unit of many organizations, it remains an evolving and relatively unstudied organizational form. New organizational forms can present many managerial challenges such as ineffective team development, ambiguous roles for group members, lack of cohesion or teamwork, and performance problems [3,4]. The overall purpose of the present research is to define an assess-

ment protocol and test a model of the effectiveness of problem-solving VTs compared to their more traditional, face-to-face equivalent (FtFTs). As shown in Figure 1, the model follows a traditional inputprocess-output conceptualization of group effectiveness (e.g., [5]), highlighted by an initial focus on media type.

Team Size

Constructive Interactions

+ + + Media

Shared Leadership

+ + -

-

+

Cohesion

Task Performance

+

Defensive Interactions

Figure 1. Model of the hypothesized relationships between media, shared leadership, group interaction styles, cohesion, and task performance

2. Shared Leadership and Group Interaction Styles Much of the literature has questioned the effectiveness of VTs, as compared to their FtF counterparts (e.g., [6,7,8]). For example, Duarte and Snyder [1] argued that VTs may not be appropriate when issues are either highly emotional or ambiguous, or when the team is newly formed or short lived. We propose that to better understand the relative effectiveness of VTs versus FtFTs, it is necessary to understand potential mediating variables, including leadership and group interaction styles. One important characteristic underlying theoretical efforts to identify the key functional roles of team leaders is the assumption that the leader interacts directly with team members in the processes of team development and performance management

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(e.g., [9,10,11,12,13,14]). This underlying assumption also characterizes the literature on virtual teams, which often must provide these functions on their own in the absence of a formal leader (e.g., [9]). However, very few studies have examined how leader roles are duplicated, substituted, or eliminated given that the team may be widely dispersed in time and geography [9]. Studies have not measured the amount or type of leadership present in the VT (e.g., [15,16]). In fact, a review of studies of leadership in VTs found that of 12 studies addressing leadership, none measured leader style [17]. Further, as Shamir [18] suggests, leadership in VTs may be shared by team members as they interact with one another. Thus, leadership may not be the domain of just one assigned, elected, or emergent individual. Indeed, Bell & Kozlowski [9] purport that the challenge for VTs is to determine how leadership functions such as coaching, mentoring, performance management, and team development can be accomplished by distributing the functions to the team itself. In the current study, we felt that it was important to measure the degree to which leadership, specifically defined in transformational terms, is displayed within a team by its members. In a problem-solving context, transformational leadership (TFL) can be characterized by team members showing enthusiasm and confidence, promoting understanding and appreciation for differing views, and intellectually stimulating group members to reexamine critical assumptions and look at problems in new ways [19]. Research has demonstrated that within FtFTs, transformational leaders are likely to increase group performance because they are helpful at overcoming social loafing among group members [20,21], and because they infuse shared values and a sense of unified purpose or common identity [22]. Despite the apparent importance of TFL in both FtF and VT settings, little evidence exists suggesting which of these media is most likely to foster its emergence. However, work by Shamir and Ben-Ari [59] suggests that VT technologies may produce more distant or impersonal means of communication between leaders and potential followers. That is, because VTs lack the richness of social cues present in FtFTs (e.g., facial expression, tone of voice), and because they can inhibit communication due to the requirement to type responses, we believe that TFL displayed in VTs will be lower when compared to FtFTs. Individuals in a VT environment also may not be as able to utilize the impression management strategies so essential to the formation of TFL [23]. The result could be a reduction in the identification, trust-building, and emotional processes that characterize TFL within VTs as compared to FtF teams. Additionally, Bell & Kozlowski [9] suggest that the spatial distance between team members and the use of communications technology may impede

VT leadership in terms of mentoring, coaching, and developmental functions. In short, we expect that: H1: Face-to-face teams will be more likely to demonstrate higher levels of shared TFL than virtual teams. As problem-solving groups perform their tasks, member roles become highly interdependent, and the need for well-orchestrated interactions, including reciprocal communication and feedback is essential [9,24]. One factor that has been shown to have a substantial effect on a team’s ability to perform is its interaction style [25,45]. Interaction style is best understood in terms of the communication patterns in which a group engages as it deals with the inherent conflicts of task orientation and maintenance of member relationships. Cooke and Szumal [45] showed that group interaction styles are either composed of constructive or defensive behaviors. The constructive interaction style is characterized by a balanced concern for personal and group outcomes, cooperation, creativity, free exchange of information, and respect for others’ perspectives. Conversely, defensive styles are likely to include both passive and aggressive behaviors. Passive behaviors place an emphasis on limited information sharing, lack of questioning, and lack of impartiality. Aggressive behaviors place an emphasis on personal agendas and ambitions being placed above concern for the group outcome. Previous research has provided empirical support for the distinction between constructive and defensive styles (e.g., [25,26,27]). An unanswered question is how does a predominant interaction style materialize in a group? For example, why does one group develop a predominant constructive style, while another group develops a predominant defensive style? Although a variety of personal variables of individual participants may be at play, we propose that the actual media type (i.e., face-to-face versus virtual) may have an important influence on the interaction style that materializes within a group. For example, we expect that a FtF context is more likely to engender the constructive interaction style because it allows more free exchange and expressions of nonverbal and paralinguistic messages that will help members develop respect for the perspectives of others in the group. In contrast, VTs, because they lack the richness of FtF communication, are less likely to foster empathy and concern, and they are more likely to produce information suppression, personal insults, or other hostile communication [28,29]. In addition, the VT medium is likely to reduce the thoroughness of discussion and produce less critical thinking [30].

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It follows that VTs are more likely than FtFTs to engage in a defensive style. Virtual interaction makes it easier to either consciously “free ride”, or unconsciously “socially loaf” [29]. This, coupled with a lack of emotional cues, may promote a tendency for VT members to sink into a passive mode. Moreover, the lack of personal inhibition in VTs, together with increased tendencies toward insults and profanity, could cause more aggressive behaviors. In sum, these arguments suggest that: H2: Face-to-face teams will be more likely to demonstrate a constructive interaction style than virtual teams. H3: Face-to-face teams will be less likely to demonstrate defensive interaction styles than virtual teams. Further, we expect that TFL will be associated with group interaction style. Teams with members who exhibit TFL behavior will develop an appropriate climate or tone by, for instance, fostering a free flow of ideas and show respect for differing opinions. They demonstrate values stressing a balance between personal concerns and achieving cooperative group outcomes. Members of teams who exhibit TFL also have a preference for action, rather than the passivity associated with a defensive group style [31]. In sum, we expect that:

higher levels of potency than transactional leadership. Based on this evidence, we propose the following: H6: The level of shared TFL within teams will positively predict their cohesion. We also propose a link between group interaction styles and cohesion. Logically, the cooperation, trust, and free exchange of ideas associated with a constructive interaction style should engender a desire on the part of team members to stick together. Conversely, the passivity or lack of interaction, coupled with the aggressive pursuit of personal agendas shown in defensive teams, should result in less cohesion. Indeed, Warkentin, Sayeed, and Hightower [36] provide evidence that, as compared to VTs, FtFTs ultimately obtain a higher degree of cohesion and have members who are more satisfied with the decision process followed by their respective teams because they suffer fewer communication problems. VTs are unable to duplicate the normal “give and take” of FtF discussions, and VT discussions may appear to lack focus because of multiple team members “talking” at once. The unfortunate result may be a less cohesive group. In sum: H7: A constructive interaction style will be positively associated with team cohesion.

H4: Shared TFL will be positively associated with a constructive interaction style.

H8: A defensive interaction style will be negatively associated with team cohesion.

H5: Shared TFL will be negatively associated with a defensive interaction style.

4. Leadership and Performance

3. Leadership, Interaction Style, and Cohesion There is theoretical and empirical evidence that TFL is associated with the degree of cohesion in a group [32,33]. For example, as articulated by Waldman and Yammarino [34], transformational leaders have a desire to forge cohesion, and they do so by expressing confidence in the ability of the group to pursue common goals, thus enabling individuals within the group to experience a heightened sense of self-efficacy. Kozlowski et al. [12] stressed the developmental function of team leadership in terms of enacting a common orientation or coherence. Further, although not examining cohesion specifically, Sosik, Avolio and Kahai [35] examined the effects of TFL on group potency (the group’s belief that it can be effective) in a virtual group. They found that TFL was associated with

We further expect that shared TFL will have a direct positive effect on task performance in both FtFTs and VTs. TFL, which encourages individuals to contribute ideas and work toward group effectiveness, has indeed been shown to be advantageous to group outcomes [37]. However, to date, research has not examined the extent to which TFL is demonstrated in VTs compared to FtFTs, nor has it examined the degree to which the amount of such leadership shown in teams is relevant to group outcomes in both media. Nevertheless, based on the plethora of evidence regarding the effectiveness of TFL (e.g., [37]), we expect that: H9: The level of shared TFL within teams will positively predict their task performance. The combined effects of prior predictions suggest that cohesion may mediate the relationship between TFL and task performance. That is, the relationship between shared TFL and task performance might be

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largely dependent on the fact that TFL behavior influences cohesion as a mediating variable. Stated alternatively, shared TFL may ultimately affect task performance through its promotion of cohesiveness. Although we are not aware of prior research specifically testing the mediational effect of cohesion on the relationship between TFL and task performance, we nevertheless suggest that: H10: Team cohesion will mediate the relationship between shared TFL and task performance. Finally, based on the majority of prior research (cf., [5,38]), we expect to find a relationship between cohesion and team performance. The performanceenhancing effect of cohesion results largely from members’ commitment to the goals of the group. Further, Zaccaro, Gualtieri, and Minionis [11] obtained results suggesting that cohesion can improve team decision-making, especially when the team is under time pressure, as was the case in the present research. Accordingly, we posit that: H11: Team cohesion will positively predict task performance.

5. Method 5.1. Participants Media type, shared TFL, constructive and defensive interaction styles, cohesion, and task performance data were collected from 336 members of 88 teams who had completed the "Ethical Decision Challenge" [39], a structured problem-solving exercise used for management development and team building in classroom and corporate settings. Participants were MBA and senior undergraduate students in multiple sections of a Management Information Systems course that required a 12week long team project. Participants had a median age of 29 years; 50.7% were male; and 72.2% were Caucasian. They averaged 9.6 years of work experience, and 64.8% reported having held a supervisory position. There were no significant demographic differences between subjects in the two experimental conditions. The exercise, completed early in the team-building stage of the project, was performed for course credit. The median number of participants per team was four, with 24 three-member teams, 56 four-member teams, and 8 fivemember teams. Given that 67% of the participants reported knowing no members of their group well, groups could be best described as zero-history teams.

5.2. Task

The "Ethical Decision Challenge" requires participants to rank 10 biomedical and behavioral research practices—all of which involve human subjects— in terms of their relative permissibility and acceptability [39]. It provides participants with an opportunity to practice their skills in both ethical analysis and group decision-making. Solutions to the "Ethical Decision Challenge" are developed first on an individual basis and then as a group. Individual and team solutions are then compared to experts’ solution. Comparisons between individual solutions and the experts’ solution indicate how well participants have solved the problem. Comparisons between participants’ individual scores and their team’s score indicate whether they were able to achieve synergy by fully using and building on their collective knowledge and skills (see [40]). In other words, the team's performance should be better than any individual performer if group synergy is achieved. Although task complexity has not received much attention with respect to VTs, it has critical implications for the structure, processes, and leadership of virtual teams. Based on Van de Ven, Delbecq, and Koenig [41], as well as Thompson’s topology [60], we would characterize the workflow processes of the Ethical Decision Challenge as intensive. That is, it represents an interdependent arrangement where team members must diagnose, problem solve, and/or collaborate simultaneously as a team to accomplish their task. Such tasks are typically quite challenging, with the need for greater levels of synchronous collaboration and information sharing among team members [42].

5.3. Technology Participants in 42 teams completed a paper version of the exercise with a FtF discussion, while participants in 46 teams completed a Web version of the exercise with a computer-mediated textual discussion. The assignment of teams to the virtual or FtF condition was random. In both scenarios, participants completed the "Challenge" during a regularly scheduled 90-minute class meeting. FtF participants were given paper booklets of the exercise and directed to the problem statement. Computermediated participants were provided with the URL of the home page for the Web version of the exercise and directed to the same problem statement. Each participant was then given 10 minutes to read the situation and “Challenge”, followed by an additional 10 minutes to rank the items (e.g., permissibility and acceptability of 10 behaviors). Those in FtFTs indicated their ranking on a provided answer form, whereas those in computer-mediated teams (i.e., VTs)

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submitted their personal solution via an interactive Web form. Based on earlier research and extensive pretesting, teams were given 35 minutes to discuss the problem and provide the best possible consensus ranking of the items —a ranking with which all team members could "live with." This amount of time allowed all FtFTs and VTs to complete the task without excessive time pressure and without generating participant fatigue or disinterest. The literature on VTs identifies three key dimensions to characterize their “virtualness:” relative permanence of the team, team dispersion, and technological enablement. Although synthetic and atypically short in duration, our textbased virtual teams appear to be consistent with the published theoretical boundaries of “virtualness” and provide a consistent protocol for comparison with FtFTs. More importantly, text-based communication represents a clear baseline condition for VTs, especially in contrast to others that might use a variety of bandwidth-intensive technologies. Upon achieving a consensus solution, a team representative either registered the ranking with the facilitator (FtFTs) or submitted a Web form (VTs). Lastly, each team member independently completed three questionnaires: (1) the Group Styles Inventory™ questionnaire assessing the team’s interaction style [43], (2) a TFL questionnaire where each team member assessed the leadership exhibited by each of the other members in his or her team, and (3) a quantitative measurement of group cohesion. All questionnaires were completed before receiving feedback on the "experts' rank" or the quality of their own (and team’s) solution.

5.4. Measures As justified below, the level of analysis in the present study is the group. We included team size as a control variable in all analyses. 5.4.1. Shared transformational leadership. To measure the level of shared TFL in the team, participants assessed each team member by rating eight behavioral statements taken from a short form of the Multifactor Leadership Questionnaire (MLQ) [44]. Specifically, items were chosen that were judged by the researchers to be potentially relevant to a 35minute team problem-solving task. These items generally tapped charismatic, inspirational, and intellectual stimulation leadership behaviors [44]. Items were answered on a five-point scale ranging from not at all (1) to a very great extent (5). The ratings were then summed and an average shared TFL score was computed for each team.

5.4.2. Group interaction styles. Group interaction styles were measured using 24 items drawn from the Group Styles Inventory™ [45]. These measures focus on the ways in which members of a group interact with one another and approach their task during a problem-solving session. Specifically, a constructive interaction style was measured by four subscales composed of three items each. Similarly, a defensive interaction style was measured by four subscales also comprised of three items each. Participants indicated the extent to which each item described the interaction style of their team using a five-point response scale ranging from not at all (1) to a very great extent (5). Responses to the items for the constructive and defensive group interaction subscales were averaged, and then an average score was computed for each team on the two scales. 5.4.3. Group cohesion. Group cohesion was measured by asking participants to rate five items that dealt with group atmosphere and satisfaction with the group [46,47]. Responses were rated on a five-point scale ranging from 1 = strongly disagree to 5 = strongly agree. The items for group cohesion were summed and averaged for each team. 5.4.4. Task performance. We assessed task performance by measuring “performance” and “synergy.” Our performance measure has been referred to as the “conventional scoring algorithm” as it calculates the difference between group consensus versus average individual performance on problem-solving tasks like ours (see [40], p.321). Our “synergy” measure calculates the difference between the group consensus versus the score of the best member in the team. Previous research has shown that in many cases this synergistic “gain” is negative, indicating a failure to fully use the expertise of the group and a loss due to group process [40].

5.5. Level of Analysis The justification for the aggregation of the items to the group level is provided by tests based on the multiple-item estimator rwg for scales with moderately skewed distributions [61]. A median rwg value of .7 is considered sufficient agreement within the group on any given measure [62]. The median rwg(j) estimate across the 88 groups is .76 for the cohesion measure, .86 for the shared leadership measure, .84 for the constructive measure, and .72 for the defensive measure, indicating that there is sufficient agreement between members' reports, and that the amount of within-team variance is moderately small relative to the expected amount of variance in responses. Fur-

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thermore, results of a series of one-way analyses of variance and Bartlett’s test for homogeneity of variance within groups support the proportional consistency of variance among the responses of members within the same group, as compared to the responses of members across groups [48]. Thus, team members’ individual assessments of group phenomena were aggregated to the group level of analysis.

synergy). As postulated by Hypothesis 6, shared TFL positively predicted cohesion (for performance and for synergy). Consistent with Hypotheses 7 and 8, a constructive interaction style positively predicted cohesion (for performance and for synergy), and a defensive interaction style negatively predicted cohesion (for performance and for synergy). Table 1. PLS analysis of the hypothesized relationships

5.6. Data Analyses The hypotheses were tested using a structural equation modeling procedure called Partial Least Squares (PLS) [49]. PLS is recommended for predictive research models where the focus is on theory development and testing, and it is suitable for use with smaller samples [50]. The path coefficients in a PLS model are standardized regression coefficients. The loadings of items on the constructs can be interpreted in a similar way as factor loadings in more traditional factor analysis. PLS avoids the problems of inadmissible solutions and factor indeterminacy common to covariance fitting approaches (such as LISREL or EQS). However, its main disadvantage is that parameter estimates are sub-optimal when the sample size is small, or when the numbers of indicators per latent variable is small. Our sample size of 88 teams exceeded the minimum suggested requirements (see [50]). In PLS, constructs that are measured with more than one observed variable (indicator) represent either underlying factors of (i.e., reflective construct), or indices produced by, the observed variables (i.e., formative construct). In the present study, we modeled indicators of TFL, constructive and defensive group interaction styles, and cohesion reflectively because we expected each of these constructs to be homogeneous. Our expectation was supported by the high levels of variance shared by the indicators of the constructs in question with their respective constructs, and by the high values of internal consistency, as discussed in the section below.

6. Results The results supported Hypotheses 1 and 2: the FtF media type was positively related to TFL (for performance and for synergy) and to a constructive interaction style (for performance and for synergy). However, Hypothesis 3, which stated that FtF teams would be less likely to demonstrate defensive interaction styles than VTs, was not supported. Hypotheses 4 and 5 were supported: shared TFL was positively related to a constructive interaction style (for performance and for synergy) and negatively related to a defensive interaction style (for performance and for

Hypotheses H1: Media →leadership H2: Media →constructive interaction

Performance Synergy Path Path t t coefficient coefficient (df = 87) (df = 87) *** 0.4 0.4 4.24*** 4.16 ** 0.19 2.27** 0.19 2.16

H3: Media →defensive behaviors

0.07

-0.78

0.07

-0.73

H4: Leadership →constructive interaction

0.57

0.57

8.07***

H5: Leadership →defensive interaction

-0.43

7.18 -4.56***

-0.43

-4.74***

H6: Leadership →cohesion

0.13

1.75*

0.12

1.78*

H7: Constructive interaction →cohesion

0.42

4.71***

0.42

5.12***

H8: Defensive interaction →cohesion

-0.46

-4.71***

-0.46

-4.69***

H9: Leadership →task performance

0.01

0.32

-0.12

-0.52

H11: Cohesion →task performance

0.21

2.02**

0.26

1.88*

Control: Team size →task performance

0.08

0.30

-0.04

-0.01

***

*p