The role of target difficulty and career tournaments in retaining creative R&D employees
Bibliographic record
Abstract
Abstract We explore the turnover intentions of creative R&D employees and the role of performance management practices in shaping these considerations. Since the success of a firm's R&D efforts hinges on the innovative ideas of its employees, it is crucial to retain particularly creative individuals. At the same time, however, we argue that this is especially difficult because both the higher outside options of creative employees and their specific individual characteristics make them, on average, more likely to leave their company. Most importantly, we suggest that two widely studied performance management design choices (target difficulty and career tournaments) typically used to motivate effort may influence the loss of creative talent. Using survey data from our unique access to R&D employees of a large manufacturing firm and a complementary experiment among business students, we find evidence that creative employees are, on average, more likely to leave their firm. Consistent with creative employees possessing a stronger learning orientation, we also predict and find that this tendency to leave is mitigated by target difficulty (as difficult targets speak to creative individuals' learning orientation) and exacerbated by the intensity of career tournaments (as they reduce team cohesion and, ultimately, undermine learning opportunities).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".