Team flexibility's relationship to staffing and performance in complex projects: An empirical analysis

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J. Eng. Technol. Manage. 24 (2007) 293–313 www.elsevier.com/locate/jengtecman

Team flexibility’s relationship to staffing and performance in complex projects: An empirical analysis Sara A. McComb a,*, Stephen G. Green b, W. Dale Compton c a

Isenberg School of Management, University of Massachusetts Amherst, Amherst, MA 01003, United States b Krannert Graduate School of Management, Purdue University, West Lafayette, IN 47907, United States c Industrial Engineering Department, Purdue University, West Lafayette, IN 47907, United States Available online 7 November 2007

Abstract We examine the role of flexibility in project team effectiveness. Specifically, we hypothesize that (1) it will mediate the relationship between staffing quality and effectiveness and (2) its relationship with team effectiveness will be moderated by project complexity, where more flexibility will be required when projects are complex. Hypotheses are tested using data collected from 60 cross-functional project teams. The results indicated that flexibility mediates the relationship between staffing quality and team performance (goal achievement and cohesion, but not project efficiency). Additionally, we find that two-dimensions of project complexity moderate the flexibility–performance relationship. Specifically, the more alternatives a team must consider, the stronger the negative relationship between flexibility and project efficiency is. The flexibility–cohesion relationship also was moderated, such that the relationship is more positive when the project is more ambiguous and more negative when the project team faces many alternatives. Implications for research and practice are discussed. # 2007 Elsevier B.V. All rights reserved. JEL classification: O32 (Management of Technological Innovation and R&D) Keywords: Cross-functional project team; Flexibility; Project complexity; Staffing quality

1. Introduction Flexibility is the ability to alter both behavior and structure as necessary to ensure survival in the face of uncertainty (Kaufman, 1985). It can allow management to confront and cope with * Corresponding author. Tel.: +1 413 545 5681; fax: +1 413 545 3858. E-mail address: [email protected] (S.A. McComb). 0923-4748/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jengtecman.2007.09.004

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current conditions or to anticipate future events (Evans, 1991). Thus, flexibility is seen as making entities capable of collectively assessing their behavior and structure and making any adjustments necessary to function effectively at the present time or into the future. Looking at cross-functional teams, which have the potential to exhibit high levels of flexibility, such flexibility can also make them capable of producing a variety of solutions when needed (Griffin, 1997). Thus, flexibility has been portrayed as an important factor in many aspects of organizational management and a potentially important aspect of cross-functional team performance. Yet, little research has focused on team flexibility. Thus, it deserves more attention than it has received to date in the research literature and is the focus of this study. We examine flexibility as a key aspect of cross-functional team processes. Moreover, we portray flexibility as a plausible link between team staffing and performance and examine the impact of project complexity on this relationship. When reviewing previous research, routinely we found flexibility operationalized as adaptability (e.g., Salas et al., 1993; Swezey and Salas, 1992) or as part of a generic team processes construct (e.g., Hirokawa et al., 2000). Herein, we flesh out the flexibility construct by building upon Evans’ (1991) strategic flexibility research. Evans introduced an integrative framework that identifies proactive and reactive elements of flexibility, where proactive elements are efforts in advanced preparation for a future change and reactive elements are adjustments required in response to a triggering episode. Previous team research supports this framework. Specifically, Gerwin (1993) and Wageman (1997) found that successful teams encouraged members to proactively improve their situation and reacted to changes in the task and/or work environment in a manner that kept teams moving toward their goal. Thus, flexibility may be the means by which team members function within a dynamic project environment. In this paper, team flexibility is our primary focus and we examine it from several angles. First, we assess the relationship between quality of team staffing and team flexibility. Second, we examine the effects of staffing quality and team flexibility on team effectiveness. Lastly, we determine if flexibility is more useful to a team in some circumstances than others by looking at the potential moderating effects of project complexity on the relationship between team flexibility and team effectiveness. Staffing quality and project complexity were selected as constructs of interest because they are two aspects of team life that have received considerable attention in research on team effectiveness (see below) and they are factors that can be directly affected by management practice. For example, managers can have control over the assignment of individuals to the team who bring requisite skills and complement the skills of others already assigned. Also, managers can be aware of project complexity and plan for it. Moreover, they can make efforts to reduce the complexity of a team’s project when appropriate by clarifying the project parameters and clearly communicating the project goal. Through this investigation of flexibility and its relationships with staffing quality and project complexity, our research seeks to contribute to the overall understanding of team processes in several ways. First, we revisit the concept of flexibility, ground it in Evans’ (1991) theoretical framework on strategic flexibility, and subsequently develop a measure of team flexibility. Second, we explore the relationship between team staffing and team flexibility, a little studied but significant question. In so doing, we look at staffing not in terms of functions, but in terms of quality of skills and knowledge. Furthermore, we introduce the idea that flexibility can serve as a potential link between team assignments and team performance. Third, in studying team flexibility effects on team effectiveness, we extend this question to include team cohesion. Although many authors argue for team cohesion as an aspect of team effectiveness (Hackman, 1983; Sundstrom et al., 1990), few have so studied it. Finally, we address the role of project

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complexity in these processes. In doing so, we draw on Campbell’s (1988) theoretical treatment of task complexity to provide one of the few studies to empirically operationalize his concepts and to examine project complexity’s role in team performance. 2. Theoretical framework and research hypotheses 2.1. Flexibility and staffing quality Team composition is an often studied construct in the team literature (see for example, Cohen et al., 1996; Hoegl and Gemuenden, 2001; Jehn et al., 1999). In many instances, it is operationalized as functional diversity among team members. Yet, cross-functionality alone may be inadequate to allow for team members to react flexibly within a team. Shenhar (1998) found that successful project teams were comprised of qualified professionals able to identify technical tradeoffs and necessary changes throughout the project process. These tradeoffs and changes required the team to approach the project flexibly. Thus, one highly important aspect of team composition beyond cross-functionality appears to be the quality of the staff assigned. Staffing quality represents a necessary element in the team’s core capability and is, specifically, ‘‘excellence in technical and professional skills and knowledge base underlying’’ the team’s project (Leonard-Barton, 1992, p. 116). Thus, our focus is on the quality of the technical skills members bring to the team and if the collective team knowledge base represents a full complement of the technical skills necessary to complete the project successfully. Staffing quality may be the aspect of team composition that allows teams to exhibit flexible behavior. If a team is comprised of members who possess high quality technical skills, they will be adept at, for example, moving the project forward by identifying better ways to complete the project and resolving unexpected roadblocks faced by the team (Griffin, 1997). Indeed, properly staffed teams may adjust to fast-paced projects because they are able to quickly understand and devise many alternatives for moving forward (Eisenhardt and Tabrizi, 1995). When team members represent all the functional expertise necessary to complete the project, they have the potential to devise flexible approaches for moving forward across all aspects of the project. We think this potential is more likely to be realized when teams have members with high quality skills and a knowledge base that covers all technical aspects of project work; they have the means (and maybe the inclination) to be flexible in their approach to the project. Hence, for this research, we focus on staffing quality as the principal antecedent to team flexibility and hypothesize that: Hypothesis 1. Teams will be more flexible when staffing quality is higher. 2.2. Flexibility, staffing quality, and team effectiveness We now turn to flexibility’s role in helping us understand overall team functioning. We begin by examining the relationship between flexibility and team effectiveness. To assess team effectiveness in this research, we first focus on project performance. We evaluate project performance by measuring issues such as the extent to which teams achieve their technical objectives, meet their business goals, realize strategic value for the organization, stay within their original estimated costs, and remain on schedule (DeCotiis and Dyer, 1979; Green et al., 1992; Lewis et al., 2002; Souder et al., 1998; Thamhain and Wilemon, 1987). The second outcome of interest is team cohesion. Team cohesion is an assessment of how team members ‘‘perceive their [team] to be better than other [teams] with respect to the way the

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members get along together, the way they help each other out, and the way they stick together’’ (Seashore, 1954, p. 36). Cohesion is typically viewed as an independent variable. Herein, we view it as an outcome of interest because it reflects a desire for teams to work together in a manner that is conducive to future collaborations (Hackman, 1983). Companies typically move team members from one project to another throughout their careers, thus they will probably face the same cast of characters many times. For this reason, we regard cohesion as an important outcome of team interaction. As previously discussed, a number of authors have argued that successful teams encourage flexible team member behavior (e.g., Gerwin, 1993; Wageman, 1997). Empirical evidence also suggests that flexibility is positively related to project performance (e.g., Campion et al., 1993, 1996; Keller, 1986). In general, flexibility is believed to improve team performance because it allows the team to shift tasks between members as needed, thus sharing workload and allowing the best talents to be brought to bear on tasks (Campion et al., 1993; Sundstrom et al., 1990). Such flexibility also allows the team to handle increased information loads and to move resources from unproductive to productive uses (Ford and Randolph, 1992). Flexibility’s relationship to team cohesion is less straightforward. To our knowledge, no studies have directly tested the relationship between team flexibility and cohesion. Research on the antecedents of cohesion, however, suggests that a positive tie between flexibility and cohesion may exist. First, as noted above in studying flexibility effects on team performance, greater flexibility makes the team more adaptable in the face of task demands. Research on team or work group members’ adaptability has shown that higher levels of adaptability are related to higher levels of team cohesion (Kranjc-Cuk, 1968; Rapisarda, 2003; Tesluk and Mathieu, 1999). It follows that team flexibility may similarly affect team cohesion. Adaptability through flexibility allows the team to be better able to handle diverse points of view and to use diverse talents in more satisfying and effective ways, as noted above. Thus, flexibility allows the team members to feel more accepted, more willing to stick together for the current project, and more willing to work together on future projects, i.e., more cohesive. Second, it is well established that higher levels of conflict in a team are likely to reduce cohesion (Johnson and Johnson, 1994). Research on conflict in working relationships has found that greater flexibility in those relationships generally leads to lower levels of conflict (Balbanis, 1998; Baugh, 2005). This suggests another avenue for flexibility to promote greater cohesion; flexible teams can potentially reduce their conflict and thus enjoy higher levels of cohesion. Given the discussion above, we expect team flexibility to be related to levels of team effectiveness, both performance and cohesion. This reasoning also led us to consider flexibility’s role in possibly easing the tension between team composition and team effectiveness. Past research has identified positive (e.g., Cohen et al., 1996; Hoegl and Gemuenden, 2001), negative (e.g., Jehn et al., 1999), and non-significant (e.g., Jung and Sosik, 1999) relationships between team composition and team effectiveness. The mixed results suggest that team composition may not play a direct role in team performance achievements. As argued above (see Hypothesis 1), team composition, staffing quality in particular, is expected to facilitate the team’s ability to flexibly accomplish its work. Also, as noted above, that flexibility gives the team performance advantages. Moreover, Brown and Eisenhardt (1995) directly addressed this question and found that when teams were staffed with the appropriate complement of highly qualified team members, they performed more effectively by quickly understanding problems, improving design time, catching potential downstream issues early in the development process, etc. In other words, high quality team members allow the team to approach their project flexibly, which, in turn, allows the team to achieve effective results. Thus, there is reason to expect that team

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flexibility may mediate the relationship between staffing quality and team performance. Given the discussion above, we hypothesize that: Hypothesis 2a. Team flexibility will relate positively to team effectiveness. Hypothesis 2b. Team flexibility will mediate the relationship between staffing quality and team effectiveness. 2.3. Flexibility, project complexity, and team effectiveness Patterning our view of project complexity after Campbell’s (1988) information processing approach, we define complex projects as those that ‘‘have several, often conflicting elements to satisfy and which place substantial cognitive demands on the task-doer for comprehension and execution’’ (Campbell and Gingrich, 1986, p. 164). Campbell (1988) goes on to theorize that complexity and such information processing demands on team members increase when a project has (1) multiple approaches for completing the project that appear feasible, (2) multiple ways in which the project can/will be used, (3) conflicts among the approaches and uses that require tradeoffs, and (4) uncertainty associated with decisions relating to the approaches and uses (Campbell, 1988). Moreover, this view of complexity is consistent with reports by managers and technologists that product development delays are often the result of unclear definitions of product requirements and technological uncertainty in the project (Gupta and Wilemon, 1990), i.e., conditions that contribute to project complexity. Little research has specifically examined the role project complexity plays in team functioning. Two studies have reported on the effects of uncertainty, a critical dimension of project complexity as we have defined it. First, in his study of project teams, Shenhar (1998) found that when technological uncertainty was high, management needed to encourage higher levels of flexibility among team members in order to achieve the desired levels of performance. Likewise, Brown and Eisenhardt (1995) found that flexibility is important when uncertainty is present. For instance, under these conditions of uncertainty, flexibility will allow the team to adapt their approaches in order to make the tradeoffs among competing alternatives or experiment with alternative ways to approach a problem that could minimize uncertainty. While these results highlight the importance of flexibility under conditions of uncertainty, logic would suggest that teams facing multiple alternatives that must be considered would also benefit from flexibility. By exhibiting a willingness to entertain different approaches, search for new, creative ways to complete project tasks, among other flexible traits, the team members will have the basis for (1) identifying and addressing the multiple approaches for completing the project and (2) creating a project that meets the needs of the multiple potential end uses. Therefore, we expect flexibility to improve team effectiveness, especially under highly complex circumstances. Thus, we hypothesize that: Hypothesis 3. The positive relationship between team flexibility and effectiveness will be stronger when the project is more complex. 3. Research and measures 3.1. The setting and sample Cross-functional project teams from industry, government laboratories, and not-for-profit organizations comprise the research sample. The teams represent a variety of industries in both

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the manufacturing and the service sector, including aerospace, automotive, and insurance, among others. Each project team for study was selected by a high-level manager who was requested to select projects that were currently ongoing, involved more than two functional areas, and focused on significant tasks for their organizations. Managers were not restricted in the type of project they chose and projects spanned a diverse set of tasks including product development, process improvement, information technology, and human resource management. Once project teams were identified, a liaison was also identified to work with our research team to identify team members and team leaders, manage the distribution and collection of questionnaires, and help us follow-up with those respondents who did not respond by the due date. The response rate for this sample was 72.1% useable questionnaires. To be included in the sample, responses from a team leader and a minimum of one-third of the team members were required. Following this criterion, 60 teams are included in the sample (N = 360 team members and N = 69 team leaders). An average of 71% of team members per team (M = 6.0 team members per team) represent the teams included. The average size of the teams was 7.71 (S.D. = 9.13) full-time members representing 5.98 (S.D. = 3.21) different functional areas and 3.08 (S.D. = 1.04) organizational levels. Thirty-nine teams were described as being co-located (all team members physically located at the same site), 9 teams had members located in the same city, but at different sites, and 12 teams had team members located in different cities or countries. 3.2. Measures Where practical, data for the predictors and criterion were collected from different sources to minimize same source bias. Specifically, team members provided the data for the independent, mediator, and moderator variables and team leaders reported on the team’s performance. Unless otherwise specified, all measures were reported on five-point Likert scales. A team-level analysis was conducted as it was the theoretical level of interest. Our measurement design aligned with our theoretical level of interest (Klein et al., 1994). More specifically, to ensure aggregation was appropriate, questions were written with the team as the referent (Chan, 1998). Furthermore, between-project variability (George and Bettenhausen, 1990; Hays, 1994) and rwg (James et al., 1984, 1993) were examined and are reported for each variable. Once we were certain that aggregation was appropriate, we averaged the respondents’ ratings when multiple respondents existed. Table 1 presents the descriptive statistics, reliabilities, and correlations for all measures. 3.2.1. Dependent variables To assess project performance, we drew on prior research that identifies project performance indicators (DeCotiis and Dyer, 1979; Green et al., 1992; Lewis et al., 2002; Souder et al., 1998; Thamhain and Wilemon, 1987). Based on this research, we developed a twelve-item measure of project performance. Additionally, Seashore’s (1954) cohesiveness scale, with minor modifications to the text, was used to assess team cohesion. All dependent variable questionnaire items can be found in Appendix A. Team leaders were asked to rate each project’s effectiveness and each team’s level of cohesion on five-point Likert scales. We conducted principle factor analysis with both varimax and oblimin rotation, where the number of factors selected was based on a combination of four criterion: eigenvalue(s) greater than 1.0, scree plots, a theoretical understanding of the construct being measured, and high factor loadings (Hair et al., 1998). A three-factor solution was identified using both rotation methods. Goal achievement, a six-item

Table 1 Correlations and internal consistency estimates S.D.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

3.38 4.07 4.24 4.05 3.79 3.64 2.53 0.77 0.15 7.71 0.65 0.15 0.49 3.07 3.80 25.0

0.91 0.57 0.70 0.54 0.38 0.40 0.48 0.43 0.36 9.13 0.48 0.36 0.23 1.40 1.24 21.7

0.86 0.52 0.33 0.23 0.17 0.05 0.38 0.23 0.19 0.27 0.06 0.16 0.04 0.09 0.17 0.07

0.86 0.56 0.24 0.27 0.03 0.26 0.13 0.09 0.26 0.08 0.27 0.01 0.01 0.13 0.13

0.96 0.24 0.32 0.09 0.39 0.16 0.04 0.34 0.06 0.12 0.04 0.04 0.10 0.19

0.83 0.56 0.05 0.60 0.19 0.19 0.14 0.17 0.20 0.25 0.21 0.07 0.10

0.85 0.19 0.40 0.10 0.22 0.04 0.13 0.01 0.24 0.04 0.14 0.07

0.77 0.13 0.18 0.18 0.15 0.27 0.10 0.02 0.22 0.09 0.10

0.64 0.03 0.05 0.12 0.24 0.21 0.09 0.01 0.03 0.03

0.76 0.05 0.07 0.01 0.20 0.23 0.01 0.18

0.14 0.11 0.05 0.27 0.22 0.08 0.20

0.12 0.11 0.20 0.20 0.27 0.12

0.57 0.14 0.06 0.20 0.02

0.14 0.08 0.02 0.13

0.11 0.13 0.06

0.10 0.12

15

0.04

Note: N = 60. r > 0.25 are significant at p < 0.05; r > 0.33 are significant at p < 0.01; r > 0.40 are significant at p < 0.001. Estimates of internal consistency are italicized on the diagonal where applicable.

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1. Team efficiency 2. Goal achievement 3. Team cohesion 4. Staffing quality 5. Flexibility 6. Project multiplicity 7. Project ambiguity 8. Sector 1 9. Sector 2 10. Team size 11. Co-location 1 12. Co-location 2 13. Functional diversity 14. Organization levels 15. Project stage 16. Together (in mo.)

M

299

300

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measure, assessed the ability of the team to meet its technical objectives and business goals, such as providing the firm with an implementable technical solution and a product or process with commercial value (a = 0.86). Project efficiency, a six-item measure, assessed the ability of the team to meet its budget and schedule goals (a = 0.86). Team cohesion, a three-item measure, assessed how well the team gets along (a = 0.96). Higher scores indicate better performance or greater cohesion. 3.2.2. Independent, mediator, and moderator variables The team members reported on team flexibility, staffing quality, and project complexity. The questionnaire items for these measures were developed for this research project. Thus, we conducted exploratory factor analysis to ensure our measures had acceptable psychometric properties for hypothesis testing. The results for both varimax and oblimin rotation suggest a four factor structure. We report the oblimin results herein. Four eigenvalues were greater than one. In addition, an inspection of the scree plot suggested that a break at four factors was reasonable. Therefore, a four factor solution was examined. Following the guidelines of Hair et al. (1998), a four factor solution appeared appropriate with items loading acceptably on four factors (see Table 2). Moreover, a four factor solution well represented the a priori, theoretical constructs that guided the development of these four measures and demonstrated acceptable levels of internal consistency (see below). Finally, an examination of the intercorrelations between measures based on these four factors suggested that, while the constructs are related, they still demonstrate reasonable separation (the absolute value mean intercorrelation is 0.36). Therefore, four variables were retained for hypothesis testing as shown in Table 2 and described in the following paragraphs. Flexibility requires that teams be adaptable to their working environment (Kaufman, 1985). As such, flexibility encompasses multiple characteristics of team members, including having the ability to handle a variety of tasks (Modrick, 1986; Swezey and Salas, 1992) and being highly imaginative in thinking about new or better ways to complete their tasks (Evans, 1991). Six items drawing on both proactive (e.g., ‘‘. . .team frequently experiments. . .’’) and reactive (e.g., ‘‘team members adjust their approach(es) to overcome obstacles) behaviors were devised to tap this construct (a = 0.85). Factor analysis supported a single factor for flexibility (see Table 2). Between-project variability on the team members’ ratings of flexibility was significant (F = 2.03, p < 0.0001) with a median and mean rwg of 0.94 and 0.85, respectively. Higher scores indicate more flexible teams. Staffing quality identifies if teams are staffed with individuals possessing high quality technical skills and, as a whole, have a full complement of the technical skills required to complete the project (Leonard-Barton, 1992). This three-item measure has an internal consistency of 0.83, significant between-project variability on the team members’ ratings of staffing quality (F = 3.77, p < 0.0001), and a median and mean rwg of 0.90 and 0.82, respectively. Better staffing quality is depicted by higher scores. The set of questionnaire items created to assess project complexity drew on Campbell’s (1988) theoretical framework. Twelve items were developed that had the project as the referent of interest. Three items were dropped during the principle factor analysis because of their low factor loadings. Project multiplicity assesses the number of ways in which a project can be accomplished and the number of different outcomes the project must satisfy (a = 0.77). Betweenproject variability on team member ratings of project multiplicity was significant (F = 1.56, p = 0.009) with a median and mean rwg of 0.86 and 0.80, respectively. Project ambiguity assesses the conflict among alternatives and the uncertainty associated with various approaches or

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Table 2 Team member exploratory factor analysis results Project Flexibility Staffing Project quality multiplicity ambiguity Flexibility (a = 0.85) Team members adapt their working style to complement the team Team members adjust their approach(es) to overcome obstacles Team members change the way they perform a task when necessary Team members easily handle a variety of tasks The team frequently experiments with alternative ways we might accomplish our work The team is highly imaginative in thinking about new or better ways to complete our task(s) Project multiplicity (a = 0.77) The goal(s) for the project can be met with different product(s)/process(es) having different attributes Attributes can be modified from those identified at the beginning of the project and still achieve the same final goal(s) The project has firmly established final goal(s) that can be met by following different approaches There is only one viable approach to this project (R) The project allows for multiple approaches to meet its goal(s)

0.42 0.60 0.64

0.01 0.20 0.09

0.20 0.07 0.05

0.01 0.01 0.02

0.81 0.78

0.14 0.07

0.02 0.07

0.01 0.01

0.84

0.07

0.01

0.03

0.01

0.61

0.04

0.27

0.02

0.64

0.06

0.16

0.05

0.51

0.06

0.30

0.07 0.11

0.71 0.70

0.01 0.02

0.01 0.02

0.02

0.62

0.11

0.04 0.04

0.93 0.73

0.05 0.03

0.05

0.16

0.03

0.73

0.06

0.23

0.11

0.35

0.02

0.03

0.02

0.44

0.02

0.07

0.14

0.59

4.16

2.37

1.14

1.07

Staffing quality (a = 0.82) The appropriate organizational members are assigned to the 0.05 team to ensure that necessary skills and abilities are available The functional managers assign high quality employees to this team 0.04 Team members are competent in their individual skills and abilities 0.08 Project ambiguity (a = 0.64) The final goal(s) of this project are not precise and allow for a wide variety of interpretation The project/process attributes needed to meet the final goal(s) for the project are incompatible Reaching consensus regarding the priorities to be assigned the various product attributes leads to delays in reaching the final goal(s) The team is uncertain about the best approach to attain project goals Variance explained Primary factor loadings are shown in bold.

outcomes. The internal consistency of the project ambiguity measure is 0.64, somewhat lower than the recommended level of 0.70 (George, 1990). We continue to use project ambiguity in the remaining analyses, but we interpret the results with caution. Between-project variability on team member ratings of ambiguity was significant (F = 2.08, p < 0.0001) with a median and mean rwg of 0.90 and 0.77, respectively. For both measures, higher scores indicate greater complexity. 3.2.3. Control variables We were concerned that several factors relating to cross-functional project teams may influence the relationships of interest. Thus, we examined company sector (two dummy variables

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based on SIC codes representing: manufacturing; transportation, communication, electric, gas, and sanitary services; services), team size (number of full-time members on the team), colocation (two dummy variables representing three possible situations: same site; same city, but different sites; different cities or countries), functional diversity (calculated using Blau’s (1977) heterogeity index and the team members’ self-reported functional area of expertise), number of organization levels assigned to the team, project stage (1 = beginning; 3 = half-way; 5 = end), and how long the team had worked together (months), as potential control variables prior to hypothesis testing. As can be seen in Table 1, organization levels, project stage, and how long the team had worked together were not significantly correlated with any of the four dependent variables (flexibility, efficiency, business goal achievement, or cohesion). The other four potential control variables were correlated with at least one dependent variable. Thus, we retain sector, team size, co-location, and functional diversity as controls during hypothesis testing. We dropped the other potential control variables from consideration to conserve power. 4. Results We tested Hypotheses 1 and 2 using hierarchical regression analysis (see Table 3). In all models, team size, co-location, and functional diversity were first entered as control variables. The first hypothesis predicted that staffing quality would be positively associated with flexibility. As can be seen in model 1 in Table 3, staffing quality is positively related to flexibility ( p < 0.0001) as predicted, supporting Hypothesis 1. Hypothesis 2a predicted a positive relationship between flexibility and team performance. As can be seen in the model 2 results for all three performance criteria, this hypothesis was supported for goal achievement and team cohesion (see Table 3). While the team efficiency model was not Table 3 Tests of main effect and mediation hypotheses DV

Flexibility Efficiency 1

a

Controls Sector 1 0.19 Sector 2 0.20 Team size 0.10 Co-location 1 0.23+ Co-location 2 0.05 Functional diversity 0.08 IV Flexibility Staffing quality

0.58***

F-value R2

5.02*** 0.40

Standardized estimates are reported. a Model. * p < 0.05. ** p < 0.01. *** p < 0.001. + p < 0.10.

2a

a

3a

Goal achievement a

4a

a

2b

a

3b

a

Cohesion 4b

a

2ca

3ca

4ca

0.25 0.22 0.09 0.07 0.30* 0.32* 0.02 0.02 0.18 0.17 0.07 0.05

0.23 0.21 0.15 0.19 0.22 0.29 0.26 0.08 0.16 0.12 0.16 0.01 0.03 0.01 0.32* 0.30* 0.33* 0.31* 0.37** 0.41** 0.40** 0.01 0.04 0.11 0.06 0.12 0.20 0.17 0.17 0.32* 0.32* 0.32* 0.24+ 0.23 0.22 0.05 0.03 0.03 0.02 0.11 0.09 0.08

0.13

0.02 0.19

0.25+

1.83+ n.s. 0.20 0.20

3.09* 0.22

0.20 n.s. 0.18

0.19 0.11

0.28*

0.22+ 2.16* 0.23

2.09* 0.25

3.03** 0.29

0.31**

0.16 0.23

3.25** 0.30

2.98** 0.32

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significant (model 2a), when the team reported having greater flexibility, goal achievement was more likely (model 2b, p = 0.06) and the team was more cohesive (model 2c, p = 0.04). Thus, Hypothesis 2a is supported for two of the three performance measures examined. Hypothesis 2b predicted that flexibility would mediate the relationship between staffing quality and team performance. In testing this hypothesis, we followed procedures recommended by Baron and Kenny (1986) and MacKinnon et al. (2002) for testing mediation effects. As noted above, it has already been demonstrated that staffing quality does predict team flexibility (model 1). Similarly, for goal achievement and team cohesion, team flexibility was found to predict performance (models 2b and 2c). Thus, mediation may be occurring for these two performance variables. Therefore, two more models were examined. First, we regressed performance on staffing quality and found that both goal achievement (model 3b) and cohesion (model 3c) were significantly related to staffing quality. In the final models, performance was regressed on staffing quality controlling for levels of flexibility in the team. These results support Hypothesis 2b for both goal achievement (model 4b) and cohesion (model 4c). Specifically, staffing quality is no longer significantly related to goal achievement or cohesion when levels of team flexibility are taken into account. The reader also might note that team flexibility is no longer significant in the mediated models either. This underscores the strong relationship between staffing quality and flexibility (r = 0.56). To further test for mediation, we ran two additional analyses. First, we conducted a joint test of the effects of the independent and mediator variables to estimate the size of the indirect effect of the independent variable on the dependent variable, as one limitation of the Baron and Kenny (1986) approach is that it does not test for joint effects (MacKinnon et al., 2002). To test the joint effects of the independent and mediator variables, we followed the guidance of MacKinnon and colleagues, who identified (and developed) a number of methods for determining the statistical significance of intervening variables and ran simulations to ascertain those most appropriate for identifying mediation. The two methods they recommend test the significance of the product of coefficients, a and b, where a represents the relationship between the independent and mediator variables and b represents the relationship between the mediator and dependent variables adjusted for the effect of the independent variable. Specifically, they are the distribution of products and the distribution of ab/sab, where sab is the standard error of the intervening variable effect ab. These two Table 4 Additional tests of mediation hypotheses Parameter estimate

S.E.

Z

Distribution of products

Distribution of ab/sab

Goal achievement t 0.24 a 0.42 b 0.28 t0 0.12

0.14 0.08 0.23 0.17

1.67 4.99 1.20 0.70

5.99

1.17

Cohesion t a b t0

0.16 0.08 0.27 0.20

2.52 4.99 1.04 0.48

5.20

1.02

2.18

0.97

0.41 0.42 0.28 0.29

Critical value at 0.05 significance

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methods were found to be superior to others in that they ‘‘have the greatest power when both a and b are non-zero and the most accurate Type I error rates when both a and b are zero’’ (MacKinnon et al., 2002, p. 98). Our results for these two tests are presented in Table 4. These results show that the joint effects of the independent and mediator variables are significant, indicating that flexibility is a mediator of the relationship between staffing quality and both goal achievement and cohesion. Second, we used Olkin and Finn’s (1995) approach for determining the effect of a third variable on the association of two others to ascertain the size of the indirect effect of the independent variable on the dependent variable, as the Baron and Kenny (1986) approach does not include this test either. For goal achievement, the Z-score = 1.70 ( p = 0.04) and for cohesion, the Z-score = 2.10 ( p = 0.02). The significant results indicate that we have an intervening variable effect. Together, these results indicate that we do have mediation when goal achievement and cohesion are the dependent variables. In Hypothesis 3, we predicted that project complexity would moderate the relationship between flexibility and team performance in such a way that flexibility would help teams to overcome any complexity they encounter. To test for moderation, we ran two models (see Table 5). In model 1, we regressed the performance variables on the full set of predictors including controls, staffing quality, flexibility, and the two project complexity main effects Table 5 Tests of moderation hypotheses DV

Efficiency 1

a

Goal achievement 2

a

1

a

2

a

Controls Sector 1 Sector 2 Team size Co-location 1 Co-location 2 Functional diversity

0.24 0.01 0.34** 0.09 0.17 0.06

0.20 0.01 0.29* 0.04 0.13 0.05

0.22 0.14 0.33** 0.16 0.39* 0.02

0.21 0.12 0.31* 0.13 0.37* 0.02

IV Flexibility Staffing quality

0.05 0.05

1.74 0.03

0.21 0.01

0.43 0.02

Moderators Multiplicity Ambiguity

0.02 0.46**

2.50* 1.20

0.20 0.15

0.55 0.97

Interactions Flex  Mult Flex  Amb F-value R2 DR 2

3.65* 0.75 2.30* 0.32

Standardized estimates are reported. a Model. * p < 0.05. ** p < 0.01. *** p < 0.001. + p < 0.10.

2.44** 0.38 0.06*

Cohesion 1a

2a

0.26 0.05 0.40** 0.21 0.21 0.09

0.31+ 0.12 0.34** 0.11 0.17 0.08

0.09 0.04 0.02 0.37**

1.70+ 0.30 n.s.

1.95+ 3.44** 2.82+ 2.88*

1.09 0.78 2.03* 0.29

0.17 0.03

3.24** 0.40

3.96*** 0.50 0.10***

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(project multiplicity and project ambiguity). In model 2, we added the two flexibility–project complexity interaction terms to the previous model. Prior to creating the interaction terms, the main effects constructs were centered following the guidance of Cohen et al. (2003). As can be seen in Table 5, moderation was found for two of the three performance variables. The change in R2 between models 1 and 2 is significant for efficiency and team cohesion indicating that the interaction of flexibility and dimensions of project complexity explain significant variance in team performance. These results hold when a Bonferroni-type adjustment is made to the p-value cutoff criteria, as we demonstrate via two alternative methods for

Fig. 1. Graphs interpreting interaction terms. (a) Cohesion, flexibility, and project ambiguity. (b) Efficiency, flexibility, and project multiplicity. (c) Cohesion, flexibility, and project multiplicity.

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determining if the set of results for our three dependent variables is significant. Using Maxwell and Delaney (2004) method, our cutoff of 0.05 is adjusted to 0.05/3 = 0.017, which is greater than the p-values of both the efficiency and cohesion models. Following Tabachnick and Fidell’s (1996) approach, we assign 0.01 for each dependent variable since it is the largest significant pvalue (for efficiency) and calculate alpha for the set of dependent variables as 1  (1  0.01)3 = 0.03, which is acceptable since it is below the typical cutoff of 0.05. Thus, both approaches indicate that our results are significant as a set. Examination of the interaction terms found significant moderation of flexibility effects for both project multiplicity and for project ambiguity. The form of the significant moderated relationships are shown in Fig. 1 where high and low levels of project multiplicity or ambiguity were calculated as one standard deviation above and below the mean, respectively. As can be seen in Fig. 1a, project ambiguity moderates the relationship between flexibility and team cohesion in the predicted direction ( p = 0.006). Flexibility is associated with more cohesion when project ambiguity is higher. Project multiplicity was found to moderate the flexibility– efficiency relationship ( p = 0.02) and the flexibility–cohesion relationship ( p = 0.05). These interactions, however, are not as predicted (see Fig. 1b and c). When we take into account levels of project multiplicity, flexibility is negatively related to efficiency and cohesion, and these relationships are more pronounced when teams have multiple alternatives to consider. Thus, Hypothesis 3 receives partial support and has contra-hypothesis findings. In interpreting these findings, we took one final precaution to ensure that multicollinearity between flexibility and staffing quality (r = 0.56***) was not affecting our regression findings. When both variables are included in the regression models used for hypothesis testing, the largest variance inflation factor found was 1.72 and the largest condition index was 2.29, both of which are well below the commonly used criterion of 10 and 30, respectively (Freund and Littell, 2000). Thus, mulitcollinearity does not appear to be a factor in these hypotheses tests. 5. Discussion Scholars have suggested (e.g., Gerwin, 1993; Wageman, 1997; Shenhar, 1998) that flexibility is an important, but understudied, aspect of team effectiveness. To our knowledge, ours is the first study to focus on the role of team flexibility specifically and our findings confirm its potential importance in understanding project team behavior. Through this project we contribute to the literature on team behavior in several ways. First, we introduce a scale to measure flexibility that is grounded in the literature about strategic flexibility. Second, we identified team flexibility as a mediating link between staffing quality and several aspects of team performance. Finally, we further extended our understanding of flexibility’s role within teams by providing evidence that flexibility effects on team performance can vary as levels of project complexity vary. In addition to our contributions relating to team flexibility, we also introduce staffing quality as an important aspect of team composition worthy of further research and examine project complexity as a multi-dimensional construct. 5.1. Flexibility as a mediator Researchers have suggested that staffing quality is a critical aspect of team composition. For example, having a team that is too small (Gupta and Wilemon, 1990), has too many inexperienced team members (Gupta and Wilemon, 1990), and/or has members with the wrong capabilities (Thamhain and Wilemon, 1987) can act as barriers to good project performance. Our

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study confirms these past findings. Staffing quality was related to higher levels of efficiency, greater goal achievement, and more team cohesion. Our results suggest that staffing quality is a construct worthy of further consideration and its relationship to flexibility may help to uncover the often conflicting results between team composition and performance (e.g., Cohen et al., 1996; Hoegl and Gemuenden, 2001; Jehn et al., 1999; Jung and Sosik, 1999). Thus, we extend the findings relating staffing quality to team performance by examining the mediating effects of flexibility on these relationships. Specifically, the positive relationships staffing quality has with goal achievement and cohesion are no longer significant when levels of team flexibility are controlled for in the model. This finding, in conjunction with the positive relationship between staffing quality and team flexibility, indicate a mediated relationship as predicted in our hypotheses. While the mediation effects are interesting, one should not overlook the implications of the staffing quality’s relationship to flexibility as a finding. Staffing often focuses on matching knowledge, skills and abilities (see for example Hitt et al., 1993) and/or personality (see for example Reilly et al., 2002) to the task demands with an emphasis on the technical requirements of the task, e.g., engineering or marketing skills. Our findings suggest that staffing can affect what one might term meta-skills, i.e., the ability to be flexible in the face of project and task challenges. Such staffing effects could be beneficial to project management in a variety of ways and deserves closer examination in future research. Better measurement focused specifically on how staffing criteria can affect flexibility could be useful and one might even consider how project leaders could assess staffing quality as they consider the flexibility requirements of projects. Regrettably, flexibility did not translate staffing quality into the team being more efficient in managing project resources. Staffing quality was related to efficiency, but flexibility was not. This result may be due to the way in which efficiency is operationalized in this study. Efficiency in managing project resources is often fostered by the control of processes more than by flexibility in processes. In other words, teams that adhere to their schedules and budgets may not be allowed (or may not allow themselves) the luxury of considering alternative solutions or identifying more appropriate approaches to the project because giving each alternative its due consideration requires time and resources. The team’s focus on time, and the costs associated with using additional time, may cause them to follow the first plausible solution suggested without fully considering the range of options available. Indeed, evidence suggests that if the team spends too much time examining alternatives their efficiency will suffer (Patrashkova and McComb, 2004). The troubling extension of this logic is that a team may be efficient, but it may not uncover the most effective solution in its rush to stay efficient. We would like to thank one of our anonymous reviewers for identifying this critical point. 5.2. Project complexity as a moderator To our knowledge, ours is the first study to examine the moderating effects of project complexity on the relationship between flexibility and team performance. To test this hypothesis, we introduced a multi-dimensional measure of project complexity grounded in Campbell’s (1988) theoretical depiction of complexity from an information processing perspective. The team experienced two dimensions of project complexity as demonstrated by our factor analysis and regression results: multiplicity (multiple feasible approaches and multiple uses) and ambiguity (conflict and uncertainty relating to approaches/uses). The factor analysis results have face validity and parallel the distinction between complexity and ambiguity articulated in other research (e.g., Pich et al., 2002), where complexity refers to the number of different actions and

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states to be considered and ambiguity refers to unawareness regarding states and causal relationships among them. We also found differential effects on performance for multiplicity and ambiguity. Taken as a whole, these findings suggest that our measure of project complexity may be useful in future research exploring the important question of project complexity. Our regression findings provide evidence that complexity moderates the relationship between flexibility and team performance for the dependent variables efficiency and cohesion. This moderating relationship, however, is dependent upon the type of complexity faced by the teams. Thus, as suggested by Thompson (1967), different strategies are needed to make decisions under varying contextual conditions. In the case of our results, flexibility is the strategy and the two dimensions of project complexity are the contextual conditions. Under ambiguous circumstances, flexibility helps the team become more cohesive, supporting our hypothesis. When facing multiple goal paths to consider, higher levels of flexibility diminished the teams’ abilities to meet their budget and schedule targets and diminished their cohesion, contrary to our hypothesis. Social judgment theory may help explain these results (Brehmer, 1976; McGrath, 1984). Social judgment theory argues that systematic differences among humans’ judgments about their circumstances can translate into conflict among the participating individuals (Balke et al., 1973). To the extent that flexibility allows for all perspectives to be considered (O’Connor et al., 2003), higher levels of flexibility within teams should prompt them to be provisional in their approaches to projects and to consider many team members’ perspectives in deciding how to move a project forward. In the case of ambiguous projects, the team members’ judgments should be less entrenched because they face high levels of uncertainty (Brehmer, 1976). Under these conditions, greater flexibility within the team may help resolve the uncertainty and achieve a common understanding of the project. Toward this end, as teams discuss their various judgments in an effort to clarify ambiguity in a provisional (flexible) manner, team members may feel more accepted and supported within the team and more attracted to it, i.e., more cohesive. Alternatively, when teams face a multiplicity of alternatives, team members may have clear and differing preferences. In this case, teams are not concerned with ambiguous path alternatives, rather a path choice must be resolved. With higher levels of team flexibility, the team is likely to embody many views of the best path. Under these conditions, conflict among the team members regarding the best path may shift from being work-related conflict to being relationship conflict. When this shift occurs, cohesion among team members is likely to suffer (Jehn, 1995; Jehn and Mannix, 2001; Labianca et al., 1998). Increased relationship conflict will interfere with the team’s ability to cooperate, intensify grudges and feuds, and increase intrateam politics (Baron, 1991), and, in doing so, it will damage team cohesion. Moreover, resolving these conflicts will distract the team from is ultimate goal, thereby increasing the time and cost (a.k.a. the efficiency) of the project. Efficiency also may be impacted by a willingness to continually entertain new alternative solutions as suggested by Gupta and Wilemon’s (1990) anecdotal evidence. The role of cohesion in this study also raises some interesting questions in project management theory. Cohesion here, as judged by the team leader, was sensitive both to team characteristics (i.e., flexibility) and to the context of the project (i.e., its complexity). Thus, team cohesion appears to be intertwined with project processes in several ways. As we know from the larger group literature on cohesion, however, it can have complex effects on performance and other group dynamics. In this study, it was positively related to other performance criteria. One can imagine circumstances where higher levels of cohesion could have negative effects on a team. Highly cohesive teams could suffer from group think or have trouble when it comes time to end a project and disband the team. This criterion has received little attention in the project management literature and we would like to see future research correct that oversight.

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6. Limitations, future research and implications 6.1. Limitations and opportunities for future research Like all studies, ours has limitations that highlight areas for future research. First, our measures of flexibility, staffing quality and project complexity were developed for this research project because, to our knowledge, no scales for these important constructs were available at the time of data collection. With the exception of project ambiguity, the scales demonstrated very good measurement properties and our results further support their validity. Nevertheless, further measurement development and research is in order. For example, other aspects of team composition such as interpersonal skills, communication effectiveness, and leadership may also be important in understanding how teams can be more flexible and, in turn, achieve high performance. With respect to the results where project ambiguity is included in the model, they need to be interpreted with caution given the marginal internal consistency of the measure. Nevertheless, the influence of project ambiguity on team cohesion found in this research suggests that a revised measure should be used in future work to confirm the results of this study and explore this interesting construct. Second, we limited ourselves to studying the ways in which staffing quality and project complexity impact flexibility in team functioning. Future research is needed to identify and study other key constructs that are related to flexibility. Third, we examined team effectiveness from the leader’s perspective. Other sources of this information may include the team members or their superiors. An examination of the relative similarities and differences between raters of team effectiveness might prove fruitful. In addition to these points regarding our constructs, some limitations exist regarding our study design. First, our study data is provided via a survey and thus open to common method bias. Also, some relationships may have introduced response-response bias. The strong relationship between staffing quality and team flexibility suffers from this limitation and the mediation findings with these two variables should be viewed with that in mind. Nevertheless, the key relationships that were explored between team processes and team effectiveness were not affected by response– response bias and can be viewed with greater confidence. The study being a cross-sectional design also prohibits the establishment of causality in the relationships among variables. Longitudinal data collection would alleviate this limitation. Longitudinal data collection would also help to address the potential limitation of same-source bias. Staffing quality, flexibility, and project complexity could be assessed by the team members or other sources at different points in time. Thus, future efforts may want to consider longitudinal data collection. Second, our study design allowed for teams completing a wide array of projects. Future researchers may want to replicate our study with more homogeneous task types to ascertain if our findings generalize to the teams assigned to these tasks. Finally, we requested access to teams in the middle of their projects. Thus, our performance assessments are the team leaders’ estimates for the projects at the time of study. Future endeavors may want to include only teams that have recently completed their projects. 6.2. Practical implications Technology management professionals can benefit in several ways from our research findings. Our results confirm the anecdotal evidence that staffing quality is an important construct. Hence,

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managers need to heed the staffing process to ensure that high quality, professional team members who can work together flexibly are assigned to projects. For those team members not possessing the desired skill set, we recommend training them prior to any team assignments. In addition to the staffing implications, our results suggest that flexibility within a project team is not universally a positive characteristic. When projects embody high levels of project multiplicity, management must be careful of how team flexibility affects team performance. Our findings suggest that teams might be helped by reducing the number of project alternatives to be considered by the team. When that is not possible, highly flexible teams may need help managing conflicts that might arise. Finally, these findings suggest that management might benefit from more systematic assessment of project complexity when staffing and managing the project. Such complexity appears to come in different forms and to potentially have different effects on the team. Better understanding the extent and types of complexity teams face could aid project management practices. In conclusion, our research provides first steps toward a better understanding of a number of potentially important project management issues: team staffing, the role flexibility has in team functioning, and how complexity may affect the team. Taken together, these constructs have only received limited consideration in literature on project teams. It is our hope that the measures developed in this research and the findings presented here will promote increased attention to these issues in the future as we seek to better understand project team behavior. Acknowledgments This research is based on work supported by the Transformations to Quality Organizations program of the National Science Foundation, Grant no. SBR-9529904. The authors gratefully acknowledge Carolyn Y. Woo for her helpful contributions to this research. The authors would also like to thank Ralitza Patrashkova and Tom Brashear-Alejandro for their help in preparing this manuscript and the three anonymous reviewers who provided insightful comments and suggestions regarding our research. Appendix A. Dependent variable items Goal achievement (a = 0.86): 1. 2. 3. 4. 5. 6.

This This This This This This

team team team team team team

will will will will will will

be able to overcome all technical hurdles. meet all of its technical objectives. provide a technical solution that can be implemented. meet all of its business goals. provide its expected commercial value to the firm. complete its objectives in time to achieve its strategic value.

Project efficiency (a = 0.86): 1. 2. 3. 4.

This project is more costly than expected. (R) Estimated project costs have been adjusted multiple times. (R) Actual project costs are within original estimated costs. This project is on time in terms of projected schedule.

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5. Progress on this project is too slow. (R) 6. The project schedule is repeatedly adjusted. (R) Team cohesion (a = 0.96): 1. This team gets along better than most teams in this firm. 2. This team sticks together better than most teams in this firm. 3. Team members help each other better than most teams in this firm. References Balbanis, G., 1998. Antecedents of cooperation, conflict, and relationship longevity in an international trade intermediary’s supply chain. Journal of Global Marketing 12, 25–46. Balke, W.M., Hammond, K.R., Meyer, G.D., 1973. An alternate approach to labor-management relations. Administrative Science Quarterly 18, 311–327. Baron, R.A., 1991. Positive effects of conflict: a cognitive perspective. Employee Responsibilities and Rights Journal 4, 25–36. Baron, R.A., Kenny, D., 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 1173–1182. Baugh, F.G., 2005. The influence of interpersonal flexibility on work team conflicts over time. Unpublished Doctoral Dissertation, Dissertation Abstracts International: Section B: The Sciences and Engineering 65, 3754. Blau, P.M., 1977. Inequality and Heterogeneity. The Free Press, New York. Brehmer, B., 1976. Social judgment theory and the analysis of interpersonal conflict. Psychological Bulletin 83, 985– 1003. Brown, S.L., Eisenhardt, K.M., 1995. Product development: past research, present findings, and future directions. Academy of Management Review 20, 343–378. Campbell, D.J., 1988. Task complexity: a review and analysis. Academy of Management Review 13, 40–52. Campbell, D.J., Gingrich, K.F., 1986. The interactive effects of task complexity and participation on task performance. Organizational Behavior and Human Decision Processes 38, 162–180. Campion, M.A., Medsker, G.J., Higgs, A.C., 1993. Relations between work group characteristics and effectiveness: Implications for designing effective work groups. Personnel Psychology 46, 823–850. Campion, M.A., Papper, E.M., Medsker, G.J., 1996. Relations between work team characteristics and effectiveness: a replication and extension. Personnel Psychology 49, 429–452. Chan, D., 1998. Functional relations among constructs in the same content domain at different levels of analysis: a typology of composition models. Journal of Applied Psychology 83, 234–246. Cohen, J., Cohen, P., West, S.G., Aiken, L.S., 2003. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed. Lawrence Erlbaum, Mahwah, NJ. Cohen, S.G., Ledford Jr., G.E., Spreitzer, G.M., 1996. A predictive model of self-managing work team effectiveness. Human Relations 49, 643–676. DeCotiis, T.A., Dyer, L., 1979. Defining and measuring project performance. Research Management 23, 17–22. Eisenhardt, K.M., Tabrizi, B.N., 1995. Accelerating adaptive processes: product innovation in the global computer industry. Administrative Science Quarterly 40, 84. Evans, J.S., 1991. Strategic flexibility for high technology manoeuvers: a conceptual framework. Journal of Management Studies 28, 69–89. Ford, R.C., Randolph, W.A., 1992. Cross-functional structures: a review and integration of matrix organization and project management. Journal of Management 18, 267–294. Freund, R.J., Littell, R.C., 2000. SAS System for Regression. SAS Institute Inc., Cary, NC. George, J.M., 1990. Personality, affect, and behavior in groups. Journal of Applied Psychology 75, 107–116. George, J.M., Bettenhausen, K.L., 1990. Understanding prosocial behavior, sales performance, and turnover: a grouplevel analysis in a service context. Journal of Applied Psychology 75, 698–709. Gerwin, D., 1993. Manufacturing flexibility: a strategic perspective. Management Science 39, 395–410. Green, S.G., Welsh, M.A., Dehler, G.E., 1992. A longitudinal investigation of the selection, management, and performance of technological innovation projects: a 6 year, multivariate study. Decision and Management Science Program. National Science Foundation, Grant SES-8519415.

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