Duality in Diversity: How Intrapersonal and Interpersonal Cultural Heterogeneity Relate to Firm Performance
Bibliographic record
Abstract
How does cultural heterogeneity in an organization relate to its underlying capacity for execution and innovation? Cultural diversity is commonly thought to present a tradeoff between task coordination and creative problem solving, with diversity arising primarily through cultural differences between individuals. In contrast, we propose that diversity can also exist within persons when individuals hold multiple cultural beliefs about the organization. We refer to these different forms as interpersonal and intrapersonal cultural heterogeneity. We argue that the former tends to undermine coordination and portends worsening firm profitability, while the latter facilitates creativity and supports greater patenting success and more positive market valuations. To evaluate these propositions, we use computational linguistics to identify cultural content in employee reviews of nearly 500 publicly traded firms on a leading company review website and then develop novel, time-varying measures of cultural heterogeneity. Our empirical results lend support for our two core propositions, suggesting the need to rethink the performance tradeoffs of cultural heterogeneity: it may be possible to reap the creativity benefits of higher intrapersonal heterogeneity and, at the same time, the efficiency benefits of lower interpersonal heterogeneity.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".