When You Wish Upon a Star: The Impact of High Performers on Exploratory Innovation
Bibliographic record
Abstract
Researchers have found that high-performers (or “stars”) play an important role in fostering firm innovation. But do stars necessarily promote exploratory innovation, which involves considerably high risks? This paper contributes to this stream of literature by studying this under-theorized question. We maintain that because stars derive many benefits from their high productivity, they are inclined to take a conservative approach to innovation to avoid jeopardizing their performance. As such we propose that the proportion of stars in an R&D team negatively influences the likelihood that the team develops an exploratory innovation. Applying multilevel modeling to a sample of 78,992 R&D teams in the U.S. high-technology sector, we find support for this negative relationship. This effect is attenuated by the proportion of innate risk-taking stars on the team as well as the degree to which a firm uses long-term incentives in its R&D department. We also investigate the firm-level strategic implications of these findings by examining the effect of the proportion of stars in a firm on firm-level exploratory innovation, and we discuss the contributions of these findings to the literature on star employees and firm exploratory search.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".