Beyond the Buzzword: Why the Type of Demographic Diversity Determines Whether Your Teams Win or Lose
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Abstract: Practitioner and scholarly conversations about workforce diversity frequently collapse distinct demographic attributes—functional background, educational specialization, race, sex, age, and tenure—into a single, undifferentiated "diversity" category. This article draws on the meta-analysis by Bell, Villado, Lukasik, Belau, and Briggs (2011) and the broader team diversity literature to argue that such oversimplification is both theoretically unjustified and practically counterproductive. It translates Harrison and Klein's (2007) distinction among diversity as separation, variety, and disparity into guidance for leaders composing and managing work teams. Evidence-based organizational responses are presented, including task-aligned team staffing, psychological safety in diverse teams, accountability structures for inclusion, alignment of team design with performance criteria, and attention to team-level averages as well as differences. Research-based examples from high-technology product development, electronics, technology, neonatal intensive care, and private-sector employers illustrate what works and what remains uncertain. The article concludes with three pillars for long-term capability: purpose-driven inclusion, data-informed team composition, and continuous team development.
Few topics in management research have generated as much hope—and as much frustration—as workplace diversity. Over several decades, organizations have invested in diversity initiatives motivated by two promises: that diverse teams would outperform homogeneous ones, and that inclusion would advance fairness and opportunity. Yet the evidence on performance has been stubbornly mixed. Meta-analyses have reported effects ranging from small positives to small negatives to effectively zero, leaving practitioners unsure whether the diversity-performance link is real, illusory, or simply more complicated than headlines suggest (Horwitz & Horwitz, 2007; Webber & Donahue, 2001).
Part of the confusion stems from asking the wrong question. Rather than asking, "Is diversity good or bad for team performance?" leaders should ask, "Which kind of diversity, conceptualized how, matters for which outcomes in which context?" That reframing is the central contribution of Bell, Villado, Lukasik, Belau, and Briggs (2011), whose meta-analysis examined specific demographic diversity variables separately rather than lumping them together. Their findings were instructive: diversity in functional background had a small but reliable positive relationship with team performance (ρ = .11), especially for creativity and innovation and for design and cross-functional teams; race and sex diversity showed small negative relationships with performance in field settings; and age diversity was essentially unrelated to performance.
This article unpacks those findings for practitioners. It translates the Harrison and Klein (2007) diversity typology into organizational language, synthesizes evidence on when and how different kinds of diversity matter, and offers strategies for composing, leading, and developing teams that can realize diversity's potential. Throughout, it treats performance as one important lens among several; the ethical and legal case for fair treatment and equal opportunity does not depend on the size of any performance correlation.
The stakes are high. Workforces are more demographically diverse than ever, cross-functional and virtual teams are standard in knowledge work, and organizations face pressure from employees, investors, and regulators to show that diversity efforts produce results rather than rhetoric. Getting team diversity right is a strategic and human imperative.
The Team Diversity Landscape
Defining Diversity: Separation, Variety, and Disparity
The most important conceptual advance in the modern team diversity literature is Harrison and Klein's (2007) insight that the word "diversity" covers three different constructs. Understanding the distinctions helps leaders move from vague aspirations ("We need more diversity") to precise action ("We need more functional variety on our product teams").
Separation: Differences in position along a single continuum, such as attitudes or values. Maximum separation occurs when a team splits into two opposing camps. Separation is expected to trigger similarity-attraction and social categorization processes that can undermine cohesion and cooperation (Byrne, 1971; Tajfel & Turner, 1979).
Variety: Differences in kind or category, especially in knowledge, expertise, or experience. Maximum variety occurs when every member brings a distinct category of expertise. Variety is expected to expand a team's pool of information and perspectives, supporting creativity and decision quality (Cox & Blake, 1991; Williams & O'Reilly, 1998).
Disparity: Differences in the concentration of valued resources such as status, pay, or power. Maximum disparity occurs when one member holds most of the resource. Disparity can suppress voice and foster resentment (Harrison & Klein, 2007).
Bell et al. (2011) showed that how diversity is conceptualized shapes the observed relationship with performance. When diverse variables were grouped into broad categories, the aggregate picture was murky; when specific variables were examined separately and matched to appropriate conceptualizations, clearer patterns emerged.
State of Practice: How Diversity Is Studied and Staffed
Despite the conceptual clarity of the separation-variety-disparity framework, practice has been slow to catch up. Many diversity reports track demographic representation without distinguishing what kind of diversity is being measured or how it is expected to matter (van Knippenberg & Schippers, 2007). Many staffing decisions default to simple heuristics—"more diversity is always better" or "hire for culture fit"—that ignore the nuance the evidence demands.
Measurement has also lagged theory. Harrison and Klein (2007) showed that each conceptualization implies a different measure: variety is best captured by indices such as Blau's index, separation by measures such as the standard deviation, and disparity by measures such as the coefficient of variation. Bell et al. (2011) noted that studies frequently used measures that did not match their theoretical rationale—for example, measuring tenure diversity as dispersion even when the underlying argument concerned variety of experience. Such mismatches can attenuate observed effects and may have led researchers and organizations to dismiss potentially useful forms of diversity prematurely.
Organizational and Individual Consequences of Team Diversity
Organizational Performance Impacts
The performance implications of team diversity depend on the specific variable and how it is conceptualized. Bell et al. (2011) offered some of the clearest benchmarks:
Functional background variety: A small positive relationship with team performance (ρ = .11), stronger for creativity and innovation outcomes and for design and cross-functional teams.
Educational background variety: Little relationship with general team performance but a positive relationship with creativity and innovation and with top management team performance.
Race and sex diversity: Small negative relationships with team performance in field studies, but not in laboratory studies.
Age diversity: Essentially unrelated to team performance, regardless of how it was conceptualized.
Team mean organizational tenure: A positive relationship with efficiency-oriented performance, suggesting that for routine tasks, accumulated experience can matter more than diversity.
A more recent registered-report meta-analysis by Wallrich et al. (2024), covering 615 reports and 2,638 effect sizes, found that the average relationships between demographic, job-related, and cognitive diversity and team performance were positive but very small (|r| < .1). Contrary to the authors' expectations, the relationships were not substantially shaped by team longevity or interdependence, and they concluded that promises of widespread performance gains may not be the strongest argument for diversity initiatives.
The practical message is that diversity is not a single force. A team whose members span different functions differs meaningfully from a team whose members differ in age or race, and these forms of diversity work through different mechanisms with different consequences. Treating them identically in staffing, training, or metrics invites confusion.
Individual and Team Member Impacts
The consequences of team diversity extend to members' daily experience. Social categorization theory suggests that visible differences can trigger in-group and out-group dynamics that affect trust, communication, and psychological safety (Tajfel & Turner, 1979). In a longitudinal study of student project teams, Harrison et al. (2002) found that the effects of surface-level diversity on team functioning weakened over time as members collaborated, while the effects of deeper differences in attitudes and values grew stronger.
Van Knippenberg et al. (2004) captured this duality in the categorization-elaboration model: diversity can trigger both social categorization, which tends to hurt performance, and information elaboration, in which members exchange, discuss, and integrate their distinct knowledge, which tends to help it. Which process dominates depends on leadership, team norms, task characteristics, and context. This helps explain why Bell et al. (2011) found negative race and sex diversity effects in field settings, where status, stereotypes, and organizational history are salient, but not in the laboratory.
For individuals, the management of diversity matters at least as much as its composition. Members who feel categorized as outsiders may experience stress, reduced voice, and lower engagement, while teams that build psychological safety and value diverse contributions create conditions for belonging and learning (Edmondson, 2019; Homan et al., 2007).
Evidence-Based Organizational Responses
Table 1: Matching Diversity Strategies to Mechanisms and Outcomes
Response | Key Evidence | Key Practices | Illustrative Example |
Task-aligned team composition | Functional variety relates to innovation and design-team performance (Bell et al., 2011) | Audit needed expertise; match diversity type to task; avoid indiscriminate stacking | New product teams in high-technology firms (Ancona & Caldwell, 1992) |
Psychological safety in diverse teams | Safety enables information elaboration (Edmondson, 2019; van Knippenberg et al., 2004) | Model fallibility; frame work as learning; invite input across status lines | Google's Project Aristotle; neonatal intensive care teams |
Accountability structures for inclusion | Responsibility structures outperform training alone (Kalev et al., 2006) | Assign ownership; embed inclusion in decisions; voluntary skill-building | Large-scale study of U.S. private-sector employers |
Aligning design with performance criteria | Effects differ by outcome type (Bell et al., 2011) | Classify tasks; vary composition across the portfolio; evaluate on matching outcomes | Diversity, conflict, and performance in electronics work groups (Pelled et al., 1999) |
Attending to team averages | Team mean tenure relates to efficiency (Bell et al., 2011); mean ability matters (Bell, 2007) | Staff routine work for experience; separate levels from diversity in analytics | Team development over time (Harrison et al., 2002) |
Strategic Team Composition: Matching Diversity to Task
The most direct implication of Bell et al. (2011) is that team composition should be deliberate and task-aligned. Functional background variety has a reliable, if modest, positive relationship with performance, especially when tasks demand breadth of perspective, as in product development and innovation. For efficiency-oriented, routine work, the evidence points more toward accumulated experience.
Effective approaches for strategic team composition:
Audit functional representation: Before assembling a project team, identify the specializations the task requires and staff accordingly.
Match diversity type to performance criterion: For innovation-oriented outcomes, prioritize variety in functional and educational background; for efficiency-oriented outcomes, value experience and familiarity.
Use team type as a design heuristic: Design and cross-functional teams appear to benefit most from functional variety (Bell et al., 2011).
Avoid indiscriminate diversity stacking: Adding differences without a clear mechanism for how they will improve performance can add coordination costs without benefits.
A classic study of new product teams in high-technology companies by Ancona and Caldwell (1992) illustrates both the promise and the challenge of functional diversity. Functionally diverse teams communicated more with people outside the team, and this external communication was associated with more innovative products. At the same time, functional diversity had a direct negative association with some aspects of performance, apparently because it made internal coordination harder. The lesson for practitioners is that functional variety creates potential that must be actively managed: diverse teams need help integrating what they learn from their varied networks.
Cultivating Psychological Safety in Diverse Teams
Diverse teams face a paradox: the differences that expand a team's cognitive resources can also trigger categorization processes that impair communication and trust. A key bridge between potential and performance is psychological safety—a shared belief that the team is safe for interpersonal risk taking (Edmondson, 2019). For diverse teams, psychological safety supports information elaboration, without which the informational benefits of variety remain locked in individual members' heads (van Knippenberg et al., 2004).
Effective approaches for cultivating psychological safety:
Model fallibility at the top: Leaders who admit mistakes, ask questions, and acknowledge uncertainty signal that interpersonal risk taking is welcome.
Frame the work as a learning problem: When leaders present tasks as requiring everyone's input rather than as execution problems with known answers, members are more likely to share unique knowledge.
Respond productively to voice: Appreciation, follow-up questions, and explicit valuing of dissent reinforce safety norms.
Invite input across status lines: Explicitly solicit contributions from members with less power or seniority, so that disparity does not silence them.
Establish explicit team norms: Brief, early conversations about how the team will work together and handle disagreement help build a safety climate.
Google's Project Aristotle offers a well-known technology example. After studying many internal teams, Google's researchers concluded that how team members interacted mattered more than who was on the team, with psychological safety emerging as the most important factor among the team dynamics they identified (Duhigg, 2016). Healthcare research reinforces the point in settings with steep status differences. In a study of neonatal intensive care unit teams, Nembhard and Edmondson (2006) found that leader inclusiveness—words and deeds that invite and appreciate others' contributions—was associated with higher psychological safety and helped offset the tendency of lower-status professionals to feel less safe speaking up. Together, these examples show that inclusive team processes help translate compositional diversity into performance.
Accountability Structures and Targeted Inclusion Practices
Bell et al. (2011) found that the negative associations of race and sex diversity with performance appeared in field settings but not in laboratory studies. This pattern suggests that the negative effects are not inherent in demographic differences but arise from the social dynamics that differences can trigger in real organizations, where status hierarchies, stereotypes, and power are salient. Bell et al. accordingly suggested that organizations may gain more by investing in bridging race and sex differences in teams than age differences.
The evidence on how to do so favors structures over one-off training. Mandatory, compliance-oriented training has shown limited effects and can provoke resistance, while approaches that establish clear responsibility for inclusion and engage people in solving problems tend to fare better (Kalev et al., 2006).
Effective approaches include:
Assign clear responsibility: Designate people and groups accountable for inclusion outcomes, rather than relying on training alone.
Focus on skills, not blame: Teach specific behaviors, such as structured interviewing, inclusive meeting facilitation, and bias interruption in decisions.
Make participation voluntary where possible: Voluntary programs tend to attract motivated learners and reduce backlash.
Embed inclusion in workflow: Build inclusive practices into hiring rubrics, promotion criteria, and meeting routines rather than isolated workshops.
Prioritize the dynamics that matter: Focus attention on the categorization dynamics most associated with negative outcomes in field settings.
A large study by Kalev et al. (2006) illustrates why structure matters. Analyzing decades of data from several hundred U.S. private-sector establishments, the researchers found that programs establishing responsibility for diversity—such as affirmative action plans, diversity committees, and dedicated diversity staff—were followed by the largest increases in the share of women and Black employees in management. Mentoring and networking programs produced more modest gains, while diversity training and diversity evaluations for managers showed little effect on their own. The lesson for team-level diversity is similar: inclusion improves when someone owns it and when it is built into how decisions are made.
Aligning Team Design with Performance Criteria
One of the most important and frequently overlooked findings in Bell et al. (2011) is that the relationship between diversity and performance depends on the type of performance being measured. Functional background variety was more strongly related to creativity and innovation than to efficiency, while team mean organizational tenure was related to efficiency. The optimal team composition therefore depends on what the organization is trying to achieve.
Effective approaches include:
Classify tasks before composing teams: Use a framework such as Devine's (2002) team classification to distinguish innovation-oriented from efficiency-oriented work.
Vary composition across the portfolio: Apply different composition strategies to different kinds of teams rather than a single template.
Track the right outcomes: Evaluate innovation teams on innovation metrics and efficiency teams on efficiency metrics.
Communicate the rationale: Members who understand why the team was composed as it was, and what it is expected to achieve, are better positioned to integrate their perspectives (van Ginkel & van Knippenberg, 2008).
Research in electronics companies shows why mechanisms matter. Pelled et al. (1999) studied work groups in three large electronics firms and found that functional background diversity was associated with more task conflict—disagreement about the work itself—which in turn was associated with better performance on cognitive tasks. Diversity in race and tenure, by contrast, was associated with more emotional conflict, which did not improve performance. The study illustrates why organizations should match diversity strategies to the outcomes they seek and manage different forms of diversity in different ways.
Leveraging Team Levels: The Power of the Mean
An underappreciated finding from Bell et al. (2011) is that for some demographic variables, the team average, rather than diversity, is the stronger predictor of performance. Team mean organizational tenure was related to efficiency-oriented performance. This echoes evidence that team mean cognitive ability predicts team performance better than variation in ability (Bell, 2007; Devine & Philips, 2001).
The practical implication is not to confuse levels of an attribute with diversity on it. A team in which everyone has long tenure differs from a team that spans a wide range of tenure: the former concentrates accumulated knowledge; the latter brings breadth of experience. Both can matter, for different outcomes.
Effective approaches include:
Staff efficiency-oriented teams for experience: For routine, convergent work, prioritize members with relevant organizational and team experience.
Separate levels from diversity in analytics: Include team averages as controls when analyzing diversity effects, to avoid attributing to diversity what reflects levels.
Balance experience with renewal: The attraction-selection-attrition framework warns that organizations tend to become homogeneous over time (Schneider, 1987); periodically adding new members brings fresh perspectives while retaining an experienced core.
Time also changes how diversity operates. In their longitudinal study, Harrison et al. (2002) found that as team members spent more time collaborating, the effects of surface-level demographic differences on team functioning diminished, while differences in deeper attitudes and values became more consequential. Organizations can use this insight by giving new, diverse teams structured opportunities to collaborate early, helping members move beyond surface categorizations toward a shared understanding of one another's knowledge and values.
Building Long-Term Team Diversity Capability
Purpose-Driven Inclusion: From Compliance to Belonging
The long-term sustainability of diversity efforts depends on whether they are experienced as genuine or performative. Homan et al. (2007) found that informationally diverse teams performed better when members held beliefs that valued diversity, because such beliefs encouraged information elaboration. Diversity strategies are most durable when embedded in organizational purpose.
Purpose-driven inclusion connects diversity to the organization's mission in specific, credible ways. For a healthcare organization, this might mean linking team diversity to serving a diverse patient population; for a technology firm, connecting functional variety to building products for varied users. When members understand why diversity matters for their work, they are more likely to engage in the collaborative behaviors that make it productive.
Organizations that treat inclusion as a strategic capability invest in inclusive infrastructure: structured hiring, inclusive meeting practices, mentoring, and leadership development that prepares diverse talent for senior roles. These investments create a virtuous cycle in which diverse talent is attracted, retained, and developed, strengthening the organization's ability to staff diverse teams well.
Data-Informed Team Composition
People analytics offers new opportunities to move team composition from intuition to evidence. Organizations that analyze team composition, processes, and outcomes can identify which combinations of skills, backgrounds, and experiences are associated with high performance in specific contexts.
The Bell et al. (2011) findings are a caution about analytical precision. Aggregating different kinds of diversity into a single index can produce misleading null results, and organizations that track "diversity" as one number risk the same trap. The evidence calls for disaggregation: track functional diversity separately from demographic diversity, distinguish variety from separation and disparity, and match each metric to the relevant outcome. Following Harrison and Klein (2007), the choice of measure should follow from the theoretical mechanism, not convenience. Analytics should also be governed carefully, with attention to privacy, consent, and the risk of reinforcing bias in staffing decisions.
Continuous Learning and Team Development Systems
Building long-term diversity capability requires a commitment to continuous learning at both team and organizational levels. At the team level, after-action reviews, facilitated feedback, and periodic team health checks can help diverse teams surface and address tensions before they harden into dysfunction.
At the organizational level, continuous learning means treating diversity strategy as a set of hypotheses to be tested rather than a policy to be enforced. Effective organizations pilot different approaches to team composition and inclusion, measure outcomes, and iterate.
Kozlowski et al. (1999) proposed that teams develop through progressive phases, from formation through the development of individual task proficiency to integrated team functioning. For diverse teams, development time is especially important because categorization challenges tend to be most acute early and to ease as members develop shared understanding (Harrison et al., 2002). Organizations that rush diverse teams into high-stakes performance without adequate development time may set them up to struggle—and then wrongly blame diversity for the result.
Conclusion
The team diversity literature has traveled from early enthusiasm through frustrating null findings to the more nuanced understanding that Bell et al. (2011) and their contemporaries have built. The central lesson is simple: diversity is not one thing. Functional background variety, race and sex diversity, and age diversity relate to team performance in different ways, through different mechanisms. Treating them identically—in research, in practice, or in conversation—guarantees confusion.
For practitioners, several takeaways follow:
Staff for functional variety when innovation matters: Cross-functional teams with members from different specializations tend to perform better on creativity and innovation, especially in design and product development, when their integration is actively managed.
Staff for experience when efficiency matters: For routine execution, the team's average experience can matter more than its diversity.
Invest in managing demographic diversity: The small negative field effects of race and sex diversity appear to reflect social dynamics that leaders can address through psychological safety, inclusive leadership, and accountability structures.
Match metrics to mechanisms: Measure variety with variety indices and dispersion with dispersion measures, and measure team averages separately from diversity.
Embed inclusion in systems, not slogans: Ownership, inclusive routines, and data-informed team design turn compositional diversity into performance advantage.
The promise of diversity is real but conditional. It depends on which differences, how they are configured, what the team is trying to achieve, and how the team is led. Organizations that grasp these contingencies—and build systems to act on them—will be best positioned to realize that promise.
Research Infographic
References
Ancona, D. G., & Caldwell, D. F. (1992). Demography and design: Predictors of new product team performance. Organization Science, 3(3), 321–341.
Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: A meta-analysis. Journal of Applied Psychology, 92(3), 595–615.
Bell, S. T., Villado, A. J., Lukasik, M. A., Belau, L., & Briggs, A. L. (2011). Getting specific about demographic diversity variable and team performance relationships: A meta-analysis. Journal of Management, 37(3), 709–743.
Byrne, D. (1971). The attraction paradigm. Academic Press.
Cox, T. H., & Blake, S. (1991). Managing cultural diversity: Implications for organizational competitiveness. Academy of Management Executive, 5(3), 45–56.
Devine, D. J. (2002). A review and integration of classification systems relevant to teams in organizations. Group Dynamics: Theory, Research, and Practice, 6(4), 291–310.
Devine, D. J., & Philips, J. L. (2001). Do smarter teams do better: A meta-analysis of cognitive ability and team performance. Small Group Research, 32(5), 507–532.
Duhigg, C. (2016, February 25). What Google learned from its quest to build the perfect team. The New York Times Magazine.
Edmondson, A. C. (2019). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Wiley.
Harrison, D. A., & Klein, K. J. (2007). What's the difference? Diversity constructs as separation, variety, or disparity in organizations. Academy of Management Review, 32(4), 1199–1228.
Harrison, D. A., Price, K. H., Gavin, J. H., & Florey, A. T. (2002). Time, teams, and task performance: Changing effects of surface- and deep-level diversity on group functioning. Academy of Management Journal, 45(5), 1029–1045.
Homan, A. C., van Knippenberg, D., Van Kleef, G. A., & De Dreu, C. K. W. (2007). Bridging faultlines by valuing diversity: Diversity beliefs, information elaboration, and performance in diverse work groups. Journal of Applied Psychology, 92(5), 1189–1199.
Horwitz, S. K., & Horwitz, I. B. (2007). The effects of team diversity on team outcomes: A meta-analytic review of team demography. Journal of Management, 33(6), 987–1015.
Kalev, A., Dobbin, F., & Kelly, E. (2006). Best practices or best guesses? Assessing the efficacy of corporate affirmative action and diversity policies. American Sociological Review, 71(4), 589–617.
Kozlowski, S. W. J., Gully, S. M., Nason, E. R., & Smith, E. M. (1999). Developing adaptive teams: A theory of compilation and performance across levels and time. In D. R. Ilgen & E. D. Pulakos (Eds.), The changing nature of performance: Implications for staffing, motivation, and development (pp. 240–292). Jossey-Bass.
Nembhard, I. M., & Edmondson, A. C. (2006). Making it safe: The effects of leader inclusiveness and professional status on psychological safety and improvement efforts in health care teams. Journal of Organizational Behavior, 27(7), 941–966.
Pelled, L. H., Eisenhardt, K. M., & Xin, K. R. (1999). Exploring the black box: An analysis of work group diversity, conflict, and performance. Administrative Science Quarterly, 44(1), 1–28.
Schneider, B. (1987). The people make the place. Personnel Psychology, 40(3), 437–453.
Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Brooks/Cole.
van Ginkel, W. P., & van Knippenberg, D. (2008). Group information elaboration and group decision making: The role of shared task representations. Organizational Behavior and Human Decision Processes, 105(1), 82–97.
van Knippenberg, D., De Dreu, C. K. W., & Homan, A. C. (2004). Work group diversity and group performance: An integrative model and research agenda. Journal of Applied Psychology, 89(6), 1008–1022.
van Knippenberg, D., & Schippers, M. C. (2007). Work group diversity. Annual Review of Psychology, 58, 515–541.
Wallrich, L., Opara, V., Wesołowska, M., Barnoth, D., & Yousefi, S. (2024). The relationship between team diversity and team performance: Reconciling promise and reality through a comprehensive meta-analysis registered report. Journal of Business and Psychology, 39(6), 1303–1354.
Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job-related diversity on work group cohesion and performance: A meta-analysis. Journal of Management, 27(2), 141–162.
Williams, K. Y., & O'Reilly, C. A., III. (1998). Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20, 77–140.
Jonathan H. Westover, PhD, Chief Research Officer (Nexus Institute for Work and AI); Co-Founder & Chief Workforce and Learning Officer (Future State University); Founder & CEO (Human Capital Innovations); Professor of Organizational Leadership & Change (UVU). Read Jonathan Westover's executive profile here.
Suggested Citation: Westover, J. H. (2026). Beyond the Buzzword: Why the Type of Demographic Diversity Determines Whether Your Teams Win or Lose. Human Capital Leadership Review, 39(4). doi.org/10.70175/hclreview.2020.39.4.6
