The Effect of Functional Diversity on Team Creativity: Behavioral and fNIRS Evidence
Bibliographic record
Abstract
Variation in functional expertise is an important decision that managers face when designing teams tasked with being creative. We develop theory that predicts that functional diversity has countervailing effects on team creativity through its positive (negative) effect on the uniqueness (usefulness) of the proposals generated. We conduct an experiment where functionally homogeneous or heterogeneous two-person teams are tasked with proposing a creative use for an unused university space. We measure the uniqueness, usefulness, and overall creativity of team proposals. We also use functional near-infrared spectroscopy (fNIRS) neuroimaging technology to investigate the underlying team cognitive processes. Measured outcomes and fNIRS data support our predictions. Combining conventional experiment and advanced neuroimaging techniques, our study informs theory and practice by providing evidence of how functional diversity affects team creativity. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the National Natural Science Foundation of China (NA) [Grant 72172132] and CPA Ontario’s Centre for Sustainability Reporting and Performance Management (NA). Supplemental Material: The online supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02157 .
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".