The Diversity Heuristic: How Team Demographic Composition Influences Judgments of Team Creativity
Bibliographic record
Abstract
Despite mixed evidence for the relationship between demographic diversity and creativity, we propose that observers hold a lay belief that demographic diversity increases creativity and apply this lay belief in judgments about teams and their creative work. Across eight preregistered studies (n = 5,530), we find that observers judge teams diverse in terms of race and gender to be more creative than teams homogeneous in terms of race and gender, including in incentive-compatible predictions made about real teams competing in a creativity challenge. We also find that products attributed to demographically diverse teams are evaluated as more creative compared with identical products attributed to demographically homogenous teams. Mediation analyses provide evidence consistent with the notion that people perceive demographic diversity (i.e., social category differences) to be correlated with cognitive diversity (i.e., difference of perspectives), and this belief contributes to attributions of greater creativity to diverse teams and the ideas they generate. We can also turn off the perceived association between demographic diversity and creativity by directly manipulating people’s perceptions of team cognitive diversity. Furthermore, we find evidence of a curvilinear relationship between the proportion of racial minorities or women in a group and judgments of the group’s creativity. Together, our results suggest that the popular uptake of the belief that diversity boosts creativity may impact how creativity is identified in organizational contexts. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This research was supported by funds from the School of Industrial and Labor Relations, Cornell University, and Harvard Business School, Harvard University, including a grant from the Cornell Center for Advanced Human Resource Studies. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4862 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".