Eindhoven University of Technology
MASTER
The validation of a measurement instrument for pro-diversity leadership
Engels, R.
Award date:
2016
Link to publication
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Eindhoven, June 2016
Identity number 0640036
in partial fulfilment of the requirements for the degree of
Master of Science in Innovation Management
Supervisors:
Dr. S. Rispens
Dr.ir. P.A.M. Kleingeld
The validation of a measurement instrument for pro-diversity leadership
by Rob Engels, Bsc
II TU/e. School of Industrial Engineering.
Series Master Theses Innovation Management
Subject headings: functional diversity, pro-diversity leadership, internal consistency, discriminant validity, criterion validity, team performance, task conflict, information elaboration, team identification.
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Abstract
In companies that are involved in innovation, functionally diverse teams are often used to do research and development or new product development tasks. However, the benefit of functional diversity is debated in current scientific literature. Given the right circumstances, functional diversity can be beneficial for team performance, although not much prior research exists on the subject of how to manage functional diversity. Therefore, a call was made for a type of leadership that strives to benefit from functional diversity. To fill this gap in scientific research, Rispens et al. (2012) introduced pro- diversity leadership (PDL) as a measurement tool to examine ways in which leaders can proactively, positively influence diversity’s effects on performance. This master’s thesis builds upon that study, using a student-gathered convenience sample of over 700 respondents in over 150 teams that are involved in innovative projects. Using this database, informing PDL and activating PDL were validated as reliable measures that are statistically distinct from two leadership styles, namely empowering leadership and temporal leadership. An analysis on the predictive validity of PDL suggests that even though functional diversity is negatively related to team performance, PDL mitigates these effects through improved elaboration of information, and through higher team identification of team members.
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Contents
Abstract ... III Contents ... IV
1. Introduction ... 1
2. Theoretical Background ... 3
Cross-functional teams ... 3
Team Leadership ... 9
3. Hypotheses ... 14
Internal Consistency ... 14
Discriminant Validity ... 15
Criterion Validity ... 15
4. Method ... 22
Analysis ... 22
Measures ... 23
Team aggregation ... 27
5. Results ... 30
Discriminant Validity ... 33
Criterion Validity ... 35
6. Discussion ... 46
Limitations and suggestions for future research ... 49
Conclusion ... 50
References ... 51
Appendix A: Empowering Leadership validity tests ... 59
Appendix B: Temporal Leadership validity tests ... 61
Appendix C: Informing PDL internal consistency tests ... 63
Appendix D: Activating PDL internal consistency tests ... 64
Appendix E: Team member questionnaire (excerpt) ... 65
Appendix F: Team leader questionnaire (excerpt) ... 68
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1. Introduction
For many companies, innovation is elementary for survival (Cefis & Marsili, 2003). Teams are often assigned to important work, and since innovation is regarded important, teams are often involved in it.
Therefore, in the context of innovation, team performance is an important research topic.
Innovation is usually bred in departments like Research & Development (R&D) or New Product Development (NPD). To innovate, firms often make use of cross-functional NPD teams (people with different functional expertise working towards a common goal) , however the effectiveness of cross- functional NPD teams is debated, as empirical evidence is mixed (Van Knippenberg, De Dreu, & Homan, 2004). Therefore, it is relevant to look at which factors determine success of cross-functional NPD teams.
The topic of cross-functional teams is extensively discussed in the scientific literature. Attention was especially paid to the link between diversity in teams and team performance. However, little attention has been paid to the influence of team leaders on team diversity (Guillaume , Dawson, Woods,
Sacramento & West, 2013). That is important, because research has shown that under the right circumstances, functionally diverse teams perform better than functionally uniform teams (Van Knippenberg & Schippers, 2007). These circumstances include high quality of team processes (Fay, Borrill, Amir, Haward, & West, 2006), high-tech industry setting (Joshi & Roh, 2009), and suitable leadership styles for the given situation (Hmieleski & Ensley, 2007). Therefore, it is important to gain more insight into how leaders can manage diversity to obtain beneficial results.
Rispens et al. (2012) coined pro-diversity leadership to fill this gap in scientific research, studying a leadership style that pro-actively seeks for ways to benefit from functional diversity in teams. The aim of this study is to validate the tool that measures team leaders’ pro-diversity leadership, and to test its internal consistency, discriminant validity and criterion validity. As for criterion validity, the most important issue is: does pro-diversity management have a positive relation to team performance? If so, which factors possibly mediate or moderate this relationship?
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First, it is required to establish that PDL is a reliable factor, i.e. that it is internally consistent. If that is the case, confirmatory factor analysis is used to verify whether PDL is a scale that measures a leadership style that is significantly different from other leadership styles. If that is also the case, the predictive validity of PDL will be explored using two conceptual models: one with a mediation effect (figure 1) and one with a moderation effect (figure 2).
Functional Diversity
Team Performance
Task Conflict Information Elaboration
Pro-diversity Leadership
Figure 1: Conceptual model with mediation effect
Functional Diversity
Team Performance Pro-diversity
Leadership
Team Identification
Figure 2: Conceptual model with moderation effect
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2. Theoretical Background
Cross-functional teams
Multidisciplinary teams, multifunctional teams, cross-functional teams and diverse teams are terms that are quite commonly found in the management literature. Regarding the success of cross-functional teams, many conclusions are shared among researchers, and many are disputed. In this section, I will distill the shared views, and discuss what could be of use in the current research.
Cross-functional teams and team performance
In a meta-analysis regarding the effects of team diversity on team outcomes, a positive relation between functional diversity and both the quality and quantity of team performance was found (Horwitz &
Horwitz, 2007). This relation was not found for bio-demographic diversity (such as age, sex and race).
Regarding functional diversity, the researchers could not confirm the hypotheses describing moderating effects of task complexity (due to a homogeneous sample), task interdependence and team size (no significant effect detected).
An influential study supporting the positive relation of diverse teams and team performance is by Van Knippenberg et al. (2004). They indicated ambiguity in the success of multidisciplinary teams. This idea originated from the categorization-elaboration model (CEM) (Van Knippenberg, De Dreu, & Homan, 2004). CEM (figure 3) describes a positive relation between diversity and performance through
elaboration of information. However, relational conflict, low cohesion and team identification diminish this relationship. Whereas elaboration of information between group members can lead to better communal understanding, hence improving team performance, it can also lead to intergroup bias through social categorization, which leads to less team identification. This leads to worse team performance. Van Knippenberg et al. (2004) implied that managers should foster elaboration, and prevent negative inter-group bias.
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Figure 3: The CEM model (van Knippenberg et al., 2004)
Mello and Delise (2015) found that when there is difference in cognitive styles within a team, this relates to poor group cohesion, which relates negatively to team performance. Cognitive styles are different ways in which an individual gathers, processes, and organizes information; for instance rational (relying on logic and data) and intuitive (comfortable with gut reaction). Conflict management moderates the relation between cognitive diversity and group cohesion, suggesting an important role for the team leader. Without such management in place, frustration can take place between team members of different cognitive types (Mello & Delise, 2015).
A recent meta-analysis called for attention to be directed towards effects of team diversity over time (Srikanth, Harvey, & Peterson, 2016). They argued the “double edged sword” of access to diverse
information on one hand, and social categorization on the other, to hold truth, but that temporal aspects of the situation are overlooked. They suggested, combining previous studies, that ineffective group processes and conflict occur in the medium term, whereas they can be overcome in the longer term.
Furthermore, the upside of having access to diverse information would according to Srikanth et al.
(2016), only in the longer term lead to improved team performance.
5 Pro-diversity beliefs
Van Dick et al. (2008) contributed to the CEM model (Van Knippenberg, De Dreu, & Homan, 2004) that the difference in the levels of performance in diverse teams comes from the level of pro-diversity beliefs in a team. In general terms: the more a team believes in the success of diverse teams, the more
information elaboration takes place and the more willingness there is to stay in a team. This results in high team performance. Homan et al. (2010) supported this view by finding that the more group
members value diversity, the more likely they are to perceive diversity in terms of individual differences, and the less likely they are to create subgroups to deal with this diversity.
A contextual factor that contributes to team performance in diverse teams is called “people-oriented workgroup environments”, which are environments where there is an emphasis on common goals and shared commitment, rather than stimulating values such as competition, autonomy and achievement (Jehn & Bezrukova, 2004). They can improve the performance outcomes of groups because emphasizing collectivity and group work can actually facilitate the alignment of actions in the group. It could be the case that group members are more concerned with their group success rather than their own social status. However, this effect might also come to exist because of the bonus structure that rewards group performance in the context of Jehn & Bezrukova (2004).
People oriented workgroup environments and pro-diversity beliefs are two factors that, according to previous research, enhance group performance (Van Dick, Van Knippenberg, Hägele, Guillaume, &
Brodbeck, 2008) (Jehn & Bezrukova, 2004). Both topics are relevant for team leaders as they appear easily manipulatable, although unfortunately they were not researched from that angle.
Interpersonal congruence is a measure that describes the degree to which “group members see others in the group as others see themselves” (Polzer, Milton, & Swann Jr., 2002). In a longitudinal study of 83 demographically diverse work groups, it was found that interpersonal congruence moderates the
relationship between group diversity and group effectiveness, such that when there is high interpersonal congruence, diverse group are more effective than non-diverse groups. On the other hand, when there is low interpersonal congruence, diverse teams are less effective than non-diverse groups.
6 Conflict and elaboration
Another subject that is mentioned in scientific literature is conflict. Conflict is distinguished in task conflict, relationship conflict and process conflict. Task conflict is in a situation when group members disagree about task issues, including goals, procedures and the appropriate choice for action (Pelled et al., 1999). Relationship or emotional conflict is a condition when people have personal issues
characterized by negative emotions such as frustration and anger (Pelled et al., 1999). Process conflict is defined as an awareness of controversies about aspects of how to proceed to fulfill tasks (Jehn &
Mannix, 2001). In an integrative model of relationships between diversity, conflict and performance in teams, Pelled et al. (1999) found that functional background diversity drives task conflict, and not so much drive emotional conflict, which would be the case with demographic diversity. However, they also found a positive relationship between task conflict and cognitive task performance. They explain this effect by saying that “Such conflict […] fosters a deeper understanding of task issues and an exchange of information that facilitates problem solving, decision making, and the generation of ideas.” (Pelled, Eisenhardt, & Xin, 1999, p. 22) A direct connection between diversity and performance could however not be made, possibly due to a small sample size.
The view that task conflict potentially leads to increased group performance through elaboration was shared by Jehn et al. (2008). Information elaboration is defined as “the exchange of information and perspectives, individual-level processing of the information and perspectives, feeding back the results of this individual-level processing into the group, and discussion and integration of their implications.”
(Hinsz, Tindale, & Vollrath, 1997). Jehn et al. (2008) showed that different educational backgrounds is associated with increased task-conflict. On the other hand, value congruence decreases both
relationship and task conflict.
As shown, elaboration of information to share information within a group of different minded people drives team performance. The concept of reflexivity, which is described as “the extent to which teams reflect upon and modify their functioning” (Schippers, Hartog, Koopman, & Wienk, 2003, p. 779), can also enhance team performance. In their research, Schippers et al. (2003) found reflexivity to be a mediator between team diversity and team satisfaction, commitment and performance. However, the
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types of diversity that were considered in this research are gender, age, educational level and tenure.
Therefore, they provided no information on functional diversity.
Cross-functional teams and innovation or NPD
Next, a closer look was taken at innovation and NPD in relation to multidisciplinary teams. The link between multidisciplinarity and innovation was made by Fay et al. (2006). They found in their study that innovation quality (rather than quantity) is improved in multidisciplinary teams, if the quality of team processes is high. In their study, the quality of team processes was measured by using a reflexivity measure (West, 1996) and part of the Team Climate Inventory (Anderson & West, 1998).
In a study of 93 R&D and NPD teams among 4 different firms, Keller (2001) found that cross-functional teams in NPD can deliver better quality, schedule and budget performance, although none of these effects are direct. Instead, this happens through better external communication. With multiple
disciplines in a team, you are better connected to external networks of areas of expertise. Furthermore, negative effects were found on group cohesion through increased job stress, which is similar to the double edged-sword that was depicted in the CEM model (van Knippenberg et al., 2004), where social categorization is mentioned as a negative affecter of the relation between diversity and information elaboration.
A meta-analysis of the importance of context in work team diversity showed small yet significant direct effects of contextual factors such as industry, occupation and team on performance outcomes (Joshi &
Roh, 2009). One of their more relevant conclusions was that task-oriented diversity was positively related to performance. This effect is slightly larger in the high-tech industry. The longer the teams operate, the larger this effect becomes.
Conclusion
Although the effect of multi-disciplinarity is debated upon in scientific literature, some general conclusions can be drawn. In general, it can be stated that diversity fosters performance, if the right circumstances are in place. Some of these circumstances were identified in this chapter. The first one is pro-diversity beliefs. Across multiple studies, it was found that when team members value diversity, they
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are more likely to embrace diversity obtaining beneficial team performance from it. Closely related, creating interpersonal congruence or emphasis on group identity has also shown to lead to positive performance outcomes.
One way of explaining how this happens is through conflict and information elaboration. In diverse groups, task conflict more frequently occurs than in non-diverse groups. Task conflict fosters elaboration among group members: people from different disciplines explain more thoroughly what they are doing, creating communal understanding. When this is the case, different disciplines lead to having more knowledge in your team which, given that the information is processed and shared by the group, leads to higher team performance.
Finally, we saw that the effects of cross-functional teams is especially high in innovative, creative or high- tech industries. So some understanding of the success of multidisciplinary teams has been achieved.
However, so far we know little about team leaders’ or managers’ abilities to influence these processes.
Therefore, the next chapter looks into the state of art of cross-functional teams in relationship to leadership.
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Team Leadership
The subject of team leadership was also vastly discussed in the scientific literature. Research areas include management or leadership styles such as temporal leadership, shared leadership, empowering leadership, and the newly introduced pro-diversity leadership. In this chapter I have looked into the present knowledge of the subject of leadership, with a special interest in how leadership drives
performance in diverse teams. Finally, I have concluded on what we already know about the influence of leadership on diverse teams: how can you manage a diverse team so that you profit from the advantages it offers, whilst evading the negative consequences of diversity?
Leadership styles in relation to diverse teams
In general, team leaders can provide enabling support to cross-functional teams. Functional diversity of cross-functional teams increases the amount and variety of available information (McDonough III, 2000).
It is the team leader’s task to make sure the information is shared to benefit from the variety of information.
Team leadership potentially has a large influence on team processes and team results. Zaccaro et al.
(2002) made this general claim, and extended it by showing the influences of functional leadership.
Functional leadership is described as that it is “the leader’s job to do or get done, whatever functions are not being handled adequately in terms of group needs.” (Zaccaro, Rittman, & Marks, 2002). They showed through literature research that functional leadership improves team effectiveness through cognitive, affective, motivational and coordination processes.
Directive leadership is defined as providing a framework for decision making and action to team members (Somech, 2006). In a study on leadership impact on functionally heterogeneous teams it was found that directive leadership improves in-role performance of a heterogeneous team through
improved team reflection (Somech, 2006). Unfortunately, the study did not go beyond the description of directive leadership to describe which behavior of a team leader is desired. The researcher concluded that team reflection improves in-role performance. When heterogeneity is present, leaders play an
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important role by making sure reflection takes place, improving team performance even more than in homogenous teams (Somech, 2006).
In top-management teams, contextual factors concerning team diversity were studied (Hmieleski &
Ensley, 2007). They found that in a dynamic industry environment, directive leadership moderates the positive relation between functional team heterogeneity and new venture performance, such that the relationship is stronger when leadership is more directive. Empowering leadership shows a contrary effect. Empowering leadership is leadership where the leader shares power with the employee,
providing decision-making autonomy, expressing confidence in the employee’s capabilities and removing hindrances to performance (Zhang & Bartol, 2010). In a stable industry environment, it was found that the effects were exactly opposite: empowering leadership was beneficial towards performance, whereas directive leadership was not. In the study by Hmieleski and Ensley (2007), no general claims could be made on a direct relationship between top-management team heterogeneity and new venture
performance: this was completely depending on the contextual factors of leadership style and dynamism of the industry. For NPD, it could generally be stated that it takes place in a dynamic environment, meaning a directive leadership style might be beneficial to profit from team functional diversity, whereas empowering leadership might less be so.
Shared leadership is a collective social influence process shared by team members and aimed toward the achievement of team goals (Hoch, Pearce, & Welzel, 2010). Shared leadership is defined as “an emergent team property that results from the distribution of leadership influence across multiple team members. It represents a condition of mutual influence embedded in the interactions among team members that can significantly improve team and organizational performance” (Carson, Tesluk, & Marrone, 2007, p. 1218).
Hoch et al. (2010) found shared leadership to have a positive relation with team performance. This effect is moderated by age diversity and coordination. When age diversity and coordination are low, shared leadership has a very large positive influence on the team performance, whereas when age diversity and coordination are high, shared leadership seems to have little impact. Even though shared leadership is tested in contexts of different types of variety than that of the focal research, the conclusion that for
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diverse teams there will be a relatively small influence of shared leadership is likely to apply. When appointing a leader “from within” when working in a functionally diverse team, it is likely the advantages of the different areas of expertise will not fulfill its potential, as a non-member leader should be able to distance him/herself. In that way, the leader is likely to be more open to the ideas of the members from different backgrounds.
The concept of shared temporal cognition (STC) describes to what extent team members have a common perspective about temporal approaches and behaviors (Gevers, van Eerde, & Rutte, 2004). Research showed that STC has a positive relationship with team satisfaction, whereas temporal conflict has a negative relationship with team satisfaction (Standifer, Raes, Peus, Passos, & Santos, 2015). Temporal leadership is aimed at benefitting from this.
Temporal leadership is leadership that stresses on temporal aspects of teamwork (like setting deadlines, having regular meetings, and etcetera), and that is aimed at creating common temporal norms in a team.
It is shown to have a direct, positive influence on team performance (Mohammed & Nadkarni, 2011).
Moreover, the researchers showed a moderating effect of temporal leadership between pacing style diversity and team performance.
Call for pro-diversity leadership
Knippenberg et al. (2013) proposed that the positive relationship between diversity and performance is enhanced by diversity mindsets in the team. More specifically, the more team members know about diversity and information elaboration, the more conductive the diversity mindset will be. The influence of diversity mindsets on the relationship between diversity and performance increases, according to the authors, the more shared the diversity mindsets are, and the more aware team members are of this sharedness. Finally, they proposed an important role for the team leader in this process, stating that the level of diversity mindset, the sharedness thereof and the awareness of the sharedness, is positively influenced by team leadership “Advocating an understanding of diversity as an informational resource, stimulating experience with elaboration of these informational resources, and engendering team reflexivity” (Van Knippenberg, Van Ginkel, & Homan, 2013, p. 189).
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Gong et al. (2013) found that team information exchange moderates the relation between team performance approach and learning goals and individual and team creativity. Furthermore, this
moderating effect was found to be larger for situations where there is high trust in the team leader. As we saw before, team information exchange can be stimulated by a team leader (e.g. McDonough III, 2000; Somech, 2006) to benefit from the information at hand. In functionally diverse teams, there is more variety in the available information, making it even more beneficial to share information. Next, it is even more important for a team leader to raise awareness among team members of the value of the available variety of the information, and of the necessity to share it.
Studies reported a positive relation between diversity in teams and intrateam conflicts (eg. Jehn et al.
(2008), Pelled, Eisenhardt and Xin (1999)). However, conflict was shown to deliver positive outcomes in previous chapter, and even dissatisfaction is not always negative as shown by Zhou and George (2001), as long as leadership responds accordingly. They found a significant increase in creativity in dissatisfied workers, when there is high coworker support, and when employees have a high continuance
commitment. Here, the importance of both leadership (continuance commitment) and communication with team members (coworker support) is stressed.
Finally, team goal commitment is shown to positively influence team performance, in innovative tasks (Hoegl & Parboteeah, 2006). Furthermore, team goal commitment is raised by participative decision making. This could be interpreted as another call for information sharing among the team.
Guillaume et al. (2013) stated that there has been little cross-fertilization between the literature on diversity and on the management of diversity. This is a bridge I tried to gap in my research, by
establishing the effects of functional diversity on work outcomes on one hand, and the role of the team leader’s behavior on the other hand.
Furthermore, the issue is raised that there seems to be “too little development of theoretical
frameworks that are more widely applied in the study of diversity” (Van Knippenberg & Schippers, 2007, p. 533). Furthermore, they stated that theoretical models are too vaguely described to be able to reach
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predictable conclusions. Hence, I concluded that building a well-described and grounded measure is a good development.
Above, some arguments were given that call for a type of leadership that exploits the advantages and prevents the disadvantages of working in functionally diverse teams. Today, this is becoming increasingly important as diverse teams play an increasingly important role in NPD and innovation processes.
Therefore, Rispens et al. (2012) proposed pro-diversity leadership, which is expected to utilize the functional diversity in teams.
The encouraging of pro-diversity beliefs is the main goal of pro-diversity leadership. Leaders can do that for example, by “explaining how task performance can benefit from the diverse perspectives and
information present in the team; by communicating their belief in the value of diversity; or by acting as an example” (Rispens, Homan, & Dijkstra, 2012).
In this chapter I have shown some leadership styles’ premises, and their relation with diverse teams.
Furthermore, I have shown the need for a leadership style that specifically deals with diversity. Finally, I showed the definition of PDL , which I propose will fill the previously mentioned need. In the next chapter, I will propose a study setup that will test the measurement instrument for pro-diversity leadership, and propose a research model that tests relationships of PDL.
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3. Hypotheses
As has been argued in the previous chapters, it makes sense to be looking into the creation of a measurement tool of pro-diversity leadership.
By narrowing the research down to NPD teams, a clear scope is set, and a setting is chosen which has already been proven to be beneficial to functionally diverse teams (Hambrick, Cho, & Chen, 1996).
Because of its potential beneficial influence on team performance in functionally diverse teams, Rispens et al. (2012) proposed the measurement tool of Pro-diversity leadership (PDL). This tool was recently created and has only been empirically tested for a few relationships. Therefore, in this study, I will test the measurement tool of PDL so that it can be used in future research. I will test it in three ways: is the PDL tool internally consistent? Does the tool show discriminant validity, so that it is different from other existing measurement tools of leadership behaviors? And does PDL show criterion validity, i.e., does it have predictive value? This chapter will show how the research was done and what the hypotheses are, the next chapter will introduce the sample and measures.
Internal Consistency
To test for internal consistency is relatively straightforward. Internal consistency poses the question: do the measurement’s item sub scores all measure the same construct? This will be done through the measurement of Cronbach’s Alpha. Cronbach’s Alpha (α) is a statistic that is calculated by correlating items pairwise, ranging from negative infinity to 1, although it is mostly between 0 and 1. An α below 0.5 is commonly referred to as unacceptable, between 0.5 and 0.7 is questionable, and above 0.7 acceptable (Hair J. F., 2006). However, Cronbach’s Alphas of over 0.95 are not necessarily desired, as the items might be too closely related to add value to one another, or even be entirely redundant (Steiner, 2003).
Hypothesis 1: All the items of the PDL construct will contribute to the measure such that they are specific enough, and non-redundant.
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Discriminant Validity
The second test for PDL is Discriminant validity: does it say something different than existing constructs?
This will be done by confirmatory factor analysis, comparing it with the other leadership styles that were tested in the questionnaire: temporal leadership and empowering leadership. Substantial correlation between the different leadership measures means they share predictive power over dependent variables (Hair & Black, 2009). To a large extent, this is not desired, since we want to test a new measure that is supposed to moderate the link between team diversity and performance more than other types of leadership. If the measures share predictive power, this distinction cannot be made.
Hypothesis 2: The PDL measure will be statistically distinct from the other leadership styles that are included in the survey; temporal leadership and empowering leadership.
Criterion Validity
In this test setup, where a single measurement in time is used, we speak of a Concurrent Validity test (Rispens, Homan, & Dijkstra, 2012). That is because we cannot keep track of temporal changes, which would be required to be able to actually predict future results. Instead, we can look for correlations, where we cannot make definite statements about causality. To achieve causal relationships, a second measurement with the same participants will be required in the future. However, based on previous research and existing theories, I expect certain causal relationships to be in place, even though they cannot be proven using the data in this research.
Two research models (figures 4 & 5) will be explained, describing the hypothesized correlations. The mediation model describes Functional Diversity to be positively related to Team Performance, mediated by task conflict and information elaboration. Furthermore, it shows that PDL affects task conflict
(negatively), and information elaboration (positively).
16 Functional
Diversity
Team Performance
Task Conflict Information Elaboration
Pro-diversity Leadership
Figure 4: Mediation model
The moderation model (figure 5) describes functional diversity to influence team performance positively, moderated by PDL and Team Identification, which both positively influence the diversity – performance link. Furthermore, PDL positively affects team identification.
Functional Diversity
Team Performance Pro-diversity
Leadership
Team Identification
Figure 5: Moderation model
As a high level of PDL entails that leaders point out the advantages of having diverse members in a team, and encourages cooperation, I expect to see less conflict of all types, including task conflict. A high level of PDL also contains that leaders make team members eager to use the different views that are
represented in the team, which is why I expect a positive relation between PDL and information elaboration. Due to reduction of conflict, and raised awareness of the value of the different team members, I also expect an increased team identification caused by PDL. Furthermore, Guillaume et al.
(2015) propose in their literature study which leadership behaviors would be beneficial to a work climate
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that enhances information elaboration and uncertainty reduction. Among these behaviors, they mention
“Advocate for diversity as an informational resource” and “Stimulate information-elaboration”
(Guillaume, Dawson, Otaye-Ebede, Woods, & West, 2015, p. 18).
Altogether, I expect PDL to moderate the relationship between diversity and team performance.
Hypothesis 3a: PDL moderates the positive relationship between functional diversity and team performance such, that when PDL is high, there will be a positive relation between diversity and team performance; whereas when PDL is low, the relationship between functional diversity and team performance is weaker or even negative.
I expect that when team leaders demonstrate high levels of PDL, this will lower intrateam conflict, which includes task conflict. Research suggested that when team members believe in the benefits of diversity, they are less likely to break down in to subgroups (Homan, Greer, Jehn, & Koning, 2010). Furthermore, high levels of PDL indicate that a team leader expresses the value of other viewpoints than your own, which likely makes you more susceptible to the views of others. This could lead to less discussion on unimportant subjects, which is believed to diminish the chances of conflict escalation (Rispens, 2012).
Hypothesis 3b: PDL has a direct, negative relationship with intrateam task conflict.
Previous research suggests that the more a team believes in the benefits of diversity, the more
information elaboration takes place (Van Dick, Van Knippenberg, Hägele, Guillaume, & Brodbeck, 2008).
High levels of PDL encompasses the stimulation of pro-diversity beliefs. This leads to believe that PDL positively influences information elaboration. Furthermore, some of the items that measure PDL (like
“Our team leader tries to make the members of this new product development team eager to use the different views”), are very closely related to sharing information and making use of the shared information. Therefore:
Hypothesis 3c: PDL has a direct, positive relation with information elaboration.
Guillaume et al. (2014) propose in their theoretical review that team leadership plays a crucial role in creating a climate for inclusion, which is defined as a climate in which dissimilar employees feel not only
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valued, but also respected and empowered (Guillaume et al., 2014). Inclusion, which shows similarity to team identification, is likely affected by a team leader who stimulates the use of different views in a team. This behavior makes sure your opinion is heard and respected, even when there is no other team member from your functional background that initially shares similar views.
Hypothesis 3d: PDL has a direct, positive relation with team identification.
In accordance with Joshi and Roh’s findings (2009), I expect to find a positive relation between functional diversity and team performance. In their meta-analysis, Joshi and Roh find a small but significant effect size, which increases when introducing the contextual factor of industry type. They find that the effect size is larger for high-tech than for manufacturing and service settings. The direct effect of functional diversity was previously found in a meta-analysis by Horwitz and Horwitz (2007). Furthermore it is suggested that non-routine tasks are more suitable to diverse teams than routine tasks (Hambrick, Cho,
& Chen, 1996). New product development is non-routine by its nature, and has more in common with high-tech than with manufacturing and service (routine) settings. Given the beneficial setting for diverse teams (NPD), and given that two meta-analyses have previously confirmed the positive relation between functional diversity and team performance, I state:
Hypothesis 4: Functional diversity will have a positive relation with team performance.
Like Jehn et al. (1997), I expect a positive relation between functional diversity and task conflict.
Relationship and process conflict are commonly linked to demographic, racial or age diversity rather than functional diversity (Jehn et al., 2007; Pelled et al., 1999). Because of no clear connection to the type of diversity that is referred to in this research, relationship and process conflict are left out of the model.
In a meta-analysis it was found that relationship conflict and process conflict are generally negatively related to team performance, whereas task conflict is found to have varying relations with group performance (De Wit, Greer, & Jehn, 2012). Both Amason (1996) and Jehn (1995) claim a positive effect of task conflict on team performance, the main benefits being an increased understanding of the task at hand (Amason, 1996), a more critical evaluation of each other’s ideas (Jehn, 1995), and the sharing and
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elaborating of information (Van Knippenberg, De Dreu, & Homan, 2004). Task conflict was positively related to team performance in a recent research studying the effects of different types of conflict on performance (Chun & Choi, 2014). The authors argue nonetheless that open communication should be treated carefully, since it can benefit (through task conflict) and harm (through relationship conflict and general discomfort) group outcomes at the same time. Furthermore, in a recent meta-analysis task- conflict was also found to be positively related to team performance, together with team affective outcomes (Dechurch & Mesmer-Magnus, 2013).
On the other hand, task conflict showed to have a negative relationship with decision making, because of group members holding on to their initial preferences, regardless of whether they are in the interest of the firm or not (De Wit, Jehn, & Scheepers, 2013). In support of the negative view of task conflict, Puck and Pregernig (2014) studied 268 students who were placed in groups in a quasi-experimental laboratory setting. They found that task conflict both has a negative relation with performance and with
cooperation, with cooperation relating positively to performance. This finding contradicts other sources that found a positive relation between task conflict and performance. This might have occurred due to the short-term nature of the task (two tasks of 20 minutes each), where the benefits of task conflict do not yet arise. The interdependent nature of the tasks may have evoked task-conflict, which could hardly be of benefit within 20 minutes.
Todorova, Bear and Weingart (2013) show in their research that task conflict is associated with positive outcomes such as job satisfaction, because it likely to improves information acquisition and positive action emotions. However, that is only the case for mild task conflict: intense task conflict shows
negative relations with positive action emotions and job satisfaction. This suggests an inverted U-shaped relation between task conflict and job satisfaction. This research was an individual level field study among 232 employees of a long-term health organization, in which two questionnaires were taken two months apart. Given this time span, it allows for both positive and negative effects of task conflict to occur, and might therefore be more reliable than the research by Puck and Pregernig (2014) in which groups were created to perform two 20-minute tasks.
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A similar inverted U-shaped relation was also found between task conflict and team innovation (Xie, Wang, & Luan, 2014). According to their research among 337 college students in 59 teams in a four- month project, this relation was moderated by knowledge integration capacity, which is defined as “the team’s ability to effectively combine, capitalize, and utilize the resource pool” (Xie, Wang, & Luan, 2014, p. 240).
The current state of the art is varying all the way from negative, inverted U-shaped to positive relations between task conflict and (varying types of) team performance. I conclude that a positive, linear relationship between task conflict and team performance is most likely for the teams in this context.
Since no task conflict does not evoke discussion or creating a shared understanding of the problem at hand (Amason, 1996), to some extent task conflict is likely desired. Even though too much task conflict might create friction in a group, leading to less cooperation (Puck & Pregernig, 2014), the numerous sources indicating a linear relation cannot be ignored. Furthermore, due to the innovative nature of the projects our respondents are involved in, critical evaluation of each other’s ideas (Jehn, 1995) and the sharing of and elaborating on information (Van Knippenberg, De Dreu, & Homan, 2004) could prove to be important affecters of team performance, supporting the positive view in the current research setting.
Hypothesis 5a: Functional Diversity is positively related to Team Performance, mediated by Task Conflict.
Functional Diversity will be positively related to Task Conflict, which in turn will be positively related to Team Performance.
Hoever et al. (2012) found information elaboration to be moderating the relationship between diversity and team creativity. This makes sense, because when different perspectives are present in a team, consciously sharing and explaining the information makes sure that different viewpoints are taken into account when creating new ideas. Furthermore, creativity and performance are often linked (Amabile, 1996; Woodman, Sawyer & Griffin, 1993).
The categorization-elaboration model (Van Knippenberg, De Dreu, & Homan, 2004) establishes improved performance by means of elaboration of task-relevant information, especially in relation to creativity and
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innovation. This view is supported by Kearney and Gebert (2009), who also found a positive relation between elaboration of task-relevant information and team performance. Furthermore, studies show a direct positive effect of task conflict and cognitive task performance (e.g. Pelled et al., 1997), where information sharing or elaboration is mentioned as an explanation for the relation. Therefore, I propose:
Hypothesis 5b: Functional Diversity will have a positive relation with Team Performance, mediated by Information Elaboration. Functional Diversity will be positively related to Information Elaboration, which in turn will be positively related to Team Performance.
In the current research model, team identification is expected to moderate the positive relationship between functional diversity and team performance. It was found in a study of 283 participants that when team identification is high among team members, this positively influences diversity’s effect on performance because the natural tendency to stereotype other team members (from different functional backgrounds than your own) may be transcended when team identification is high (Van der Vegt & Bunderson, 2005). On the other hand, when team identification is low, biases and stereotypes are more likely to occur, increasing the tendency to overlook and reject information coming from different perspectives than their own. Hence, performance is likely to decrease (Van der Vegt &
Bunderson, 2005). Similar results were found to by Hu and Liden (2015), who found direct and indirect effects of Team Identification on Team Performance in a study of 67 project teams that were measured three times (to prove causality). Another positive effect of team identification that was found was the reduction of the link between task conflict and relationship conflict (Schaeffner, et al., 2015), of which the latter is found to diminish team performance (Dechurch & Mesmer-Magnus, 2013). Hence:
Hypothesis 6: Team identification moderates the positive relationship between functional diversity and team performance such that when team identification is high, the positive relationship between
functional diversity and team performance is stronger. When team identification is low, the relationship between functional diversity and team performance is weaker, possibly resulting in a negative
relationship.
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4. Method
Analysis
SampleIn the years 2013, 2014 and 2015, students enrolled in a master’s course gathered a convenience sample from companies that were involved in NPD or innovation, be it radical or incremental. The students used a questionnaire containing scales on 32 theoretical constructs about team characteristics, leadership characteristics, team processes, satisfaction and several performance outcomes. Furthermore, several demographics were asked. There was a separate questionnaire for team leaders, containing similar topics and more extensive performance measures. Altogether, 701 team members and 182 team leaders (partially) completed a questionnaire, spread across more than 100 different companies.
Team selection
When dealing with the subject of diversity, missing data are potentially harmful to one’s research. That is, because to get an accurate image of diversity, one needs all members of a group to have the complete picture. Since our data set was pretty large, we could afford quite strict selection criteria to allow teams into the research.
The first selection criterion was that every team member completed the questionnaire. This was tested by comparing the team leader’s team size variable to the number of questionnaires that we received from the respective team. If at least 100% (for some teams there were more questionnaires than members mentioned by the team leader) of the questionnaires were available, the team passed the first selection criterion. This was decided because not only diversity measures are affected by missing data, potentially all measures can be affected. Selecting in this way automatically entails that all teams without team leader data are dropped. After applying this selection criterion, 83 out of the 174 original teams remained.
In their study on the impact of nonresponse, Allen et al. (2007) found through computer simulations that within group response rate influences observed correlations such that the correlations weaken when response rates drop. In their meta-analysis the selecting criteria for group retention are mentioned.
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Many papers adapted the “at least 3 respondents” rule, (e.g. Klein et al., 2001; Lichtenstein et al., 1997;
West & Schwenk, 1996), I have chosen to adapt this rule as a second selection criterion, because in
“teams” of just two members, there are only two possibilities of how functional backgrounds are divided: either team members are from the same functional background (scoring a Blau’s index score of 0), or from a different background (scoring 0,5). This dichotomous choice does likely not obtain the reliable results we are looking for. Based on this criterion, 1 team was dropped, leaving 82 teams.
Just like with the “at least 3 respondents” rule, a minimum response or completion rate is also
mentioned. Many of the studies from the Allen et al. (2007) meta-analysis stated minima of 50%, 66% or 75% response rate, and did not clearly argue why. In my case, a trade-off needed to be made between including enough teams to obtain sufficient statistical power, and to be selective enough to obtain accuracy. The Allen et al. (2007) study reveals that at a 75% response rate, results are still acceptable, and in my case this criterion only excluded 1 team. Setting the minimum at 80% would eliminate another 6 teams, which is a relatively high sacrifice for a small improvement in accuracy. That is why I set the minimum completion rate (regarding Functional Background) in a team at 75%. This step eliminated 1 team, leaving a final sample of 81 teams.
Measures
In this section I will show the measures that have been used to test the hypotheses that were posited in previous chapter. Except for pro-diversity leadership, all measures are thoroughly grounded in scientific literature. Therefore, I sufficed with the verification of Cronbach’s α to confirm the reliability of the measure.
Functional Diversity
To be able to determine the functional diversity in a team, we first need to define functional diversity as it is used in this study. In the questionnaire that is used in this study, as part of the personal details, the question “What is your functional background?” was asked. Respondents were asked to choose between 9 categories, as listed in table Table 1: Functional backgrounds. Although it limits the possibilities to pick an exact description of a person’s background, which could influence the potential number of
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backgrounds that are present in a team, this method is preferable over an open question that allows for an endless amount of varieties of backgrounds, as this would result into the necessity of judging on a case-by-case basis how many different backgrounds there actually are in a group.
Table 1: Functional backgrounds
1 Software Engineering 2 Mechanical Engineering 3 Electrical Engineering 4 Systems Engineering 5 Manufacturing 6 Logistics
7 Sales / Marketing 8 Finance
9 Other (please specify)
Furthermore, a formula is required that objectively quantifies the degree of diversity of a team. Simply counting the amount of backgrounds is not desirable, as there would likely be a significant relation between diversity and team size. Therefore, Kearney and Gebert (2009) suggested using Blau’s index of heterogeneity (Blau, 1977). The formula of this index is 1−∑pi2, which would read out as 1 minus the sum of squared proportions of each of the functional backgrounds that are present in a group. This index varies from 0 (when all members are from the same background) to a theoretical 1 (when an infinite amount of people would have an infinite amount of backgrounds). This leads to a scoring that is not directly influenced by team size, and where the proportion of team members that belong to different categories is taken into account. For instance, a team in which 5 people are in category A and 5 in category B is considered more diverse than a team in which 9 people belong to category A and 1 in category B, even though both teams consist of 10 people belonging to 2 categories.
25 Task Conflict
The items for Task Conflict used in this study originated from the study by Jehn (1995), and was measured with three items. A sample item is “How often are there disagreements between team members?”, and all items are answered on a 1-5 point Likert scale, where 1 stands for (almost) never and 5 for often.Cronbach’s α of task conflict is 0.82.
Information Elaboration
The scale used for measuring information elaboration was retrieved from the study by Kearney and Gebert (2009). The three items include “The members of this team carefully consider all perspectives in an effort to generate optimal solutions.”, and are answered on a 5 point Likert scale: a 1 indicating strongly disagree and a 5 indicating strongly agree. Cronbach’s α of information elaboration is 0.750.
Team Identification
Originally coined as “collective team identification”, this measure is defined as: “the emotional
significance that members of a given group attach to their membership in that group” (Van der Vegt &
Bunderson, 2005). The four items include “I feel emotionally attached to this team.”, and are answered on a 5 point Likert scale. A “1” represents strong disagreement and a “5” represents strong agreement.
Cronbach’s α of team identification is 0.81.
General Team Performance
As a measure for general team performance, we used a three item measure that displays the team member’s self-perceived performance of his/her team. The items were previously developed by Rispens et al. (2007). The three items, that are answered on a 7 point Likert scale, include: “This team performs well at work”. Cronbach’s α of general team performance is 0.81.
Pro-diversity Leadership
Pro-diversity leadership is measured using 6 items from Rispens et al. (2012). Items include “Our team leader explains clearly why various functional areas are needed for new product development” and are answered on a 7 point Likert scale, ranging from strongly disagree (1) to strongly agree (7). Pro-diversity leadership is a relatively new concept which has been analyzed thoroughly in terms of validity, reliability and consistency. This will be shown in the next chapter, under hypotheses 1 and 2.
26 Empowering Leadership
Empowering leadership is measured using 7 items from Ahearne et al. (2005). An example from these 5 point Likert scale items is: “Our team leader allows us to make important decisions.” A 1 represents strong disagreement, a 5 represents strong agreement. Empowering leadership will be used to compare PDL against, to see if PDL is statistically distinct from existing leadership styles.
Temporal Leadership
Temporal leadership was measured using 7 items from Mohammed & Nadkarni (2011). Items include
“Our team leader prioritizes tasks and allocates time to each task.”, and are answered on a 5 point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5). Just like empowering leadership, temporal leadership will be used to compare PDL against, to see if PDL is statistically distinct from existing leadership styles.
Control variables
To test the predictive validity of the previously mentioned measures, some control variables were introduced. According to Srikanth et al. (2016), the integration of diverse information only happens on the long term in a project, whereas the negative effects of social categorization processes and
coordination failure happen much sooner. In accordance with this view, better performance for diverse teams might be expected the longer a project is running. That is why I included project duration as a control variable. With project duration, I refer to the time a project has been running up to the time of questioning, in months.
Another control variable to be used is group size. According to Goodman et al. (1986) and Jehn and Bezrukova (2004), group size is of great importance for group processes and outcomes. For example, even though group 1 with 5 people from category A and 5 people from category B, and group 2 with 1 person from category A and 1 person from category B have the same index of heterogeneity, completely different group dynamics are likely to occur.
As a third control variable, task interdependence has been added. Previous research has indicated an influence of interdependence on measures like conflict and information elaboration (Jehn, 1995), which in its turn can affect performance. Task interdependence was measured using three items (Sharma &
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Yetton, 2007) on a five-point scale with a “1” indicating strong disagreement, and a “5” indicating strong agreement. An exemplary item is “The tasks I perform in this project require frequent coordination with the efforts of other team members”. Cronbach’s α of task interdependence was 0.62.
Team aggregation
Diversity is a team characteristic by definition. The other measures in this study are also to a more or lesser extent related to the team. Above all, to be able to make comparisons between diversity and other measures, they should all be regarded at the same level, which is why I performed tests to see if the measures that are used in this study may be aggregated to the team level.
The level of within-team agreement and between-team agreement should be tested statistically (Klein &
Kozlowski, 2000). Table 2 shows the mean square between teams and the mean square within teams with their respective F-values, and the significance, indicating whether or not the difference between within-team agreement and between-team agreement is significant. In this case, I wanted the
differences within teams to be smaller than between teams, as this indicates that team mates are more likely to resemble each other than members of different teams, meaning we could aggregate to team level. All measures show a significant (<0,01) difference between the two, meaning the first indicator suffices.
Next, interrater reliability (IRR) and interrater agreement (IRA) needed to be verified, which can most efficiently be done with interclass correlation measures (ICCs) (LeBreton & Senter, 2008). ICC(1) provides the reliability of individual’s ratings whereas ICC(2) indicates reliability of mean ratings. Within the context of multilevel modeling, ICC(1) is usually used to indicate the extent to which individual ratings attribute to group membership (Bliese, 2000). ICC(1) is calculated with the formula: 𝑀𝑆𝐵 − 𝑀𝑆𝑊
𝑀𝑆𝐵 +(𝑘−1)𝑀𝑆𝑊, where 𝑀𝑆𝐵 stands for Mean Squares Between groups, 𝑀𝑆𝑊 is for Mean Squares Within groups, and “k”
is the average group size. For ICC(1), a value of 0.01 can be called a “small” effect, 0.10 can be called a
“medium” effect and 0.25 can be called a “large” effect (LeBreton & Senter, 2008). Judging from ICC(1), all measures pass the test: Informative Pro-Diversity Leadership and Task Interdependence have medium
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to large values, whereas all others score above the “large” mark. LeBreton & Senter (2008) give a minimum cut-off value of 0.05, which is exceeded by all measures in table 2.
ICC(2) is calculated with the formula 𝑀𝑆𝐵 − 𝑀𝑆𝑊
𝑀𝑆𝐵 . It is often stated to have a cut-off of at least 0.70 (LeBreton & Senter, 2008). However, it is difficult to achieve high values for ICC(2) when group sizes are relatively small, as larger groups likely reduce the mean square within value. In this sample, the average group size is 5.05, which is not very large as the bare minimum is set at 3. Because of the negative influence of group size, combined with the very convincing results of the ICC(1) test and the significance of all F-values, I decided to approve all measures for aggregation.
Table 2: Aggregation measures
Measure Mean
square between
Mean square within
F ICC1 ICC2 Sig.
Informing Pro-Diversity Leadership 2,67 1,05 2,55 0,23 0,61 <0,01 Activating Pro Diversity Leadership 2,62 0,80 3,26 0,31 0,69 <0,01
Task Conflict 1,51 0,50 3,02 0,29 0,67 <0,01
Information Elaboration 0,96 0,35 2,74 0,26 0,63 <0,01
Team Identification 1,24 0,44 2,80 0,26 0,64 <0,01
General Team Performance 1,95 0,55 3,56 0,34 0,72 <0,01
Task Interdependence 0,94 0,50 1,88 0,15 0,47 <0,01
Internal Consistency
Internal consistency was judged through the measurement of Cronbach’s Alpha. In this study, it would be wise to strive for relatively high Cronbach’s Alpha values, since this will make a stronger claim regarding the internal consistency of the PDL measure, which as a goal has priority over setting a measure with a wide scope. However, notion should be given to situations where Cronbach’s Alphas exceed 0.95, possibly due to redundancy (Steiner, 2003).
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Cronbach’s Alpha is influenced by the test length, to be more precise: more items tend to lead to a higher Alpha (Tavakol & Dennick, 2011). Therefore, in my study, relatively high Alpha values can be expected, as PDL is measured as a 6 item measure.
Furthermore, the possibility of PDL existing of sub-constructs was examined. Possible subdivisions were made theoretically, after which confirmatory factor analysis is used to determine which model structure fits best.
Discriminant Validity
To test for discriminant validity, confirmatory factor analysis will be done. All items of the three leadership styles (temporal leadership, empowering leadership and PDL) have been placed in varying LISREL models, such that all possible combinations of factors are made. Next, based on comparisons of several goodness of fit measures, the models have been judged. To assess the model fit, several
measures must be used, including the χ2 and degrees of freedom goodness-of-fit statistic, one absolute fit index (such as the Goodness of Fit Index, GFI), one incremental fit index (such as the Comparative Fit Index, CFI) and a badness-of-fit indicator (like the Root Mean Square Error of Approximation, RMSEA) (Hair & Black, 2009). RMSEA values of <0.08 are generally regarded acceptable, whereas CFI and GFI have a lenient cut-off value of >0.90, and a stricter cut-off value of 0.95 (Hair & Black, 2009). The cut-off values for χ2 and degrees of freedom goodness-of-fit statistic depends on the amount of degrees of freedom, and can therefore not be stated generally.
Criterion Validity
To test for criterion validity, two research models (figures 4 & 5) are tested using hierarchical regression.
Both models will have Team Performance as dependent variable, and independent variables will be functional diversity, task conflict, information elaboration, PDL, team identification and the control variables.
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5. Results
In this chapter, I will describe the steps towards reaching a factor model that both fits with the data, and is theoretically grounded. A balance between a good model fit and explanation power of the model is required. The focal construct of Pro Diversity Leadership (PDL) will carefully be examined whether or not it consists of multiple sub-constructs: which model fits best with both theory and data. For empowering leadership and temporal leadership, the focus is on reestablishing what the original research on the respective constructs showed: that the constructs are valid in content, and that they are reliable, as indicated by Cronbach’s Alpha. After the factor models are established, the discriminant validity of PDL will be assessed: is it statistically different from the constructs of empowering leadership and temporal leadership?
After discussing the examination of the PDL construct, I will show the analysis on the proximal and distal effects of PDL, which are formulated in hypotheses 3 through 6.
Empowering leadership
In its original research, Empowering Leadership (EL) was measured using four multi-item subscales (Ahearne, Mathieu, & Rapp, 2005). In the Ahearne et al. (2005) study, the underlying dimension of
“empowering behaviors” was found, with a Cronbach’s alpha of 0.88, depicting a high internal consistency. Theoretically, this is justified: Ahearne et al. (2005) establish a theoretical framework in which the four subscales of comparable behavior styles are so closely related, that it is theoretically sound to summate the scale of EL. Therefore, an internal consistency test suffices to determine whether the seven EL items contribute to the factor of Empowering Leadership. The reliability coefficient, which is measured by Cronbach’s Alpha is computed in SPSS. An α of 0.82 was found, which is well above the generally considered lower limit of 0.70 (Hair & Black, 2009). Furthermore, α does not increase when deleting items, and the item-to-total correlations all exceed the 0.50 base mark (Hair & Black, 2009).
Temporal Leadership
For Temporal Leadership, it is difficult to make a theoretically grounded subdivision of factors based on the seven items in the questionnaire. Judging from the questions, it is hard to separate them in
categories, so up to this point I expect a one-factor model to fit the data, as found by Gevers et al.
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(2004). Just like for EL, it was previously established that TL conforms to conceptual definition, and a factor analysis in SPSS undoubtedly shows unidimensionality (see appendix A). The reliability coefficient, calculated with SPSS, gives α = 0.854, which is a good score compared to the cut-off value of 0.70 (Hair &
Black, 2009). Just as with EL, α does not increase upon deletion of items, and the item-to-total correlations exceed 0.50.
Pro-diversity leadership
Although Pro Diversity Leadership was previously coined by Rispens et al. (2012), it was not thoroughly established with data, which is one of the goals of my research. Therefore, I approach a factor model of PDL with an open-minded view, and carefully consider the options through confirmatory factor analysis.
Table 3: PDL questionnaire items
PDL01 Our team leader clarifies the value of the various functional areas that are present in this team to the team members
PDL02 Our team leader explains clearly why various functional areas are needed for new product development
PDL03 Our team leader tries to convince the team members of this new product development team that various functional areas are useful for the project
PDL04 Our team leader tries to make the members of this new product development team eager to use the different views
PDL05 Our team leader gives the team members tools to handle functional diversity in this team
PDL06 Our team leader encourages collaboration among team members from all the functional areas that are present in the team
Looking at the six items that tap into PDL related issues (see table 3), two subdivisions of items seem possible. Firstly, PDL01 and PDL02 together might form a factor called “Informing on pro-diversity benefits” and PDL03 through PDL06 could be “Encouraging pro-diversity habits”. Another possible division could be PDL01 through PDL03 which would be “Informing PDL” where PDL04 through PDL06 would be “Activating PDL”. Naturally, the third alternative is that all items form a single factor, namely PDL.
