CoChat
Sign Up
Cited by 1,276Team Dynamics and PerformanceGender Diversity and InequalitySocial and Intergroup Psychology

The DOI matches its Crossref record; 224 of the 224 references it cites were found in a registry or an index.

What Differences Make a Difference?: The Promise and Reality of Diverse Teams in Organizations

Open access
Mannix, Elizabeth, Neale, Margaret A.·2005·Psychological Science in the Public Interest
DOI 10.1111/j.1529-1006.2005.00022.x
Open this paper in CoChat — free accountOpen PDF

Abstract

SUMMARY—As the workplace has become increasingly diverse, there has been a tension between the promise and the reality of diversity in team process and performance. The optimistic view holds that diversity will lead to an increase in the variety of perspectives and approaches brought to a problem and to opportunities for knowledge sharing, and hence lead to greater creativity and quality of team performance. However, the preponderance of the evidence favors a more pessimistic view: that diversity creates social divisions, which in turn create negative performance outcomes for the group. Why is the reality of diversity less than the promise? Answering this requires understanding a variety of factors, including how diversity is defined and categorized, and the moderating as well as mediating processes that affect the diversity–process–performance linkage. We start with a definition. The word diversity has been used to refer to so many types of differences among people that the most commonly used definition—“any attribute that another person may use to detect individual differences” (Williams & O'Reilly, 1998, p. 81)—while accurate, is also quite broad. As a result, various categorization schemes based on factors such as race or gender, or based on proportions such as the size of the minority, have been used to further refine the definition of diversity in teams. The choices researchers have made in using these categorization schemes, however, do lead to particular tradeoffs. Factor approaches, for example, allow an examination of multiple types of diversity and the interactions among them but ignore the sizes of factions and subgroups. Proportional approaches allow the consideration of minority-group size, and hence the study of issues such as tokenism, but also tend to focus on only one type of diversity and thereby overestimate its relevance relative to other types. The underlying effects of diversity, whichever way it is defined and categorized, have typically been understood through three primary theoretical perspectives: the similarity–attraction paradigm, self- and social categorization, and information processing. These approaches also have their biases. The predictions of similarity–attraction theory are straightforward: Similarity on attributes such as attitudes, values, and beliefs will facilitate interpersonal attraction and liking. Empirical research has supported that surface-level similarity tends to predict affiliation and attraction. The similarity–attraction paradigm was developed to understand dyadic relationships. Yet, individuals can express preferences for membership in particular groups even when they have had no prior social interaction with members of that group. This is primarily a cognitive process of categorization: Individuals are postulated to have a hierarchical structure of self-categorizations at the personal, group, and superordinate levels. Research has demonstrated that the specific categories on which we tend to focus in categorizing others—such as race, gender, values, or beliefs—are likely to be those that are the most distinctive or salient within the particular social context. The act of social categorization activates differential expectations for in-group and out-group members. This distinction creates the atmosphere for stereotyping, in which out-group members are judged more stereotypically than in-group members are. The self-categorization/social-identity and similarity–attraction approaches both tend to lead to the pessimistic view of diversity in teams. In these paradigms, individuals will be more attracted to similar others and will experience more cohesion and social integration in homogeneous groups. The information-processing approach, by contrast, offers a more optimistic view: that diversity creates an atmosphere for enhancing group performance. The information-processing approach argues that individuals in diverse groups have access to other individuals with different backgrounds, networks, information, and skills. This added information should improve the group outcome even though it might create coordination problems for the group. As we disentangle what researchers have learned from the last 50 years, we can conclude that surface-level social- category differences, such as those of race/ethnicity, gender, or age, tend to be more likely to have negative effects on the ability of groups to function effectively. By contrast, underlying differences, such as differences in functional background, education, or personality, are more often positively related to performance—for example by facilitating creativity or group problem solving—but only when the group process is carefully controlled. The majority of these effects have typically been explained in terms of potential mediators such as social integration, communication, and conflict. However, the actual evidence for the input–process–output linkage is not as strong as one might like. Clarifying the mixed effects of diversity in work groups will only be possible by carefully considering moderators such as context, by broadening our view to include new types of diversity such as emotions and networks, and by focusing more carefully on mediating mechanisms. As we delve into advice for organizational teams to enhance the assets of diversity and manage the liabilities, we focus on the benefits of “exploring” as opposed to “exploiting” types of tasks, of bridging diversity through values and goals, and of enhancing the power of the minority. Finally, we end with suggestions for how organizations can learn to create incentives for change within the firm.

Overview

Integrity signals

Registry

Crossref · 10.1111/j.1529-1006.2005.00022.x

Cites

224 of 224 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 33

  1. Affirmative Action: A strong prejudice, 1995, The Economist, 335, 69
  2. Argote L., 2000, Groups at work: Advances in theory and research
  3. Blau P., 1977, Inequality and heterogeneity
  4. Cameron K., 1988, Paradox and transformation, 1
  5. 10.2307/256391 · DOI · Paper page
  6. Guillon M., 1983, Cahiers de Psychologie Cognitive, 3, 65
  7. 10.2307/258823 · DOI
  8. 10.1016/S0149-2063_03_00080-1 · DOI
  9. Jackson S., 1995, Team decision making effectiveness in organizations, 204
  10. Jackson S., 1995, Diversity in workteams
  11. Jackson S., 1992, Research in Organizational Behavior, 15, 45
  12. Johnston W.B., 1987, Work force 2000
  13. Kanter R., 1977, Men and women of the organization
  14. Kramer R., 1991, Research in organizational behavior, 13, 191
  15. 10.1037/0022-3514.71.2.315 · DOI
  16. 10.2307/259377 · DOI
  17. 10.2307/259204 · DOI
  18. Lovaglia M., 2004, Theories of small groups: An interdisciplinary perspective
  19. Mannix E., 2004, Research on managing groups and teams: Temporal issues, 6, 11
  20. McGrath J., 1995, Diversity in workteams, 47
  21. Meyerson D., 2000, Harvard Business Review, 78, 1
  22. Mohrman S., 1995, Designing team-based organizations: New forms for knowledge work
  23. Moscovici S., 1985, Handbook of social psychology, 2, 347
  24. Mueller P., 2004, The New Yorker
  25. O'Reilly C.A., Flatt S. (1989). Executive team demography, organizational innovation, and firm performance . Unpublished manuscript, Stanford University.
  26. Peterson R., 2003, Understanding people in the dynamic organization
  27. Pettigrew T., 1982, Prejudice
  28. 10.2307/30040667 · DOI
  29. SmithW.TushmanM.(in press). Managing strategic contradictions: A top management team model for simultaneously exploring and exploiting. Organization Science .
  30. Tajfel H., 1986, Psychology and intergroup relations, 7
  31. Turner J., 1985, Advances in group processes, 2, 77
  32. Tushman M., 1985, Research in Organizational Behavior, 171
  33. Williams K., 1998, Research in organizational behavior, 21, 77

Cited by

Sorted by citations

See all 1,276

Related

See all

Metadata

Authors
Mannix, E., & Neale, M. A.
Affiliations
  • Cornell University (US)
  • Stanford University (US)
Venue
Psychological Science in the Public Interest
Publisher
SAGE Publications
Published
2005-10
Volume
6
Issue
2
Pages
31-55
ISSN
  • 1539-6053 (ISSN-L)
  • 1529-1006
DOI
10.1111/j.1529-1006.2005.00022.x
OpenAlex
W2132440291
Semantic Scholar
34e05cfdd1143ba3a1a13e45efc0e92b3c00a96b
Cited by
1,276
References
224
Citations by year
2026: 36 · 2025: 53 · 2024: 74 · 2023: 68 · 2022: 53 · 2021: 90 · 2020: 68 · 2019: 69 · 2018: 57 · 2017: 73 · 2016: 102 · 2015: 81 · 2014: 71 · 2013: 69 · 2012: 69
FWCI
40.21
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Gothic.net (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
  • Social Sciences › Gender Studies › Gender Diversity and Inequality
  • Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
Keywords
team diversity, team performance, similarity attraction, self-categorization, social categorization, information processing, tokenism, team creativity, group conflict, workplace diversity, knowledge sharing, group decision making, group size

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review