Reciprocity over time: Do employees respond more to kind or unkind controls?
Bibliographic record
Abstract
Abstract Reciprocity plays a critical role in the way employees respond to managerial control decisions. The current consensus is that employees punish managers for implementing unkind controls (negative reciprocity) more than they reward managers for implementing kind controls (positive reciprocity). We challenge this consensus. Prior research focuses on settings that emphasize employees' immediate reciprocal responses. However, in the workplace, employees often respond over long periods of time to sticky control decisions (e.g., budgets, pay, decision rights). Focusing on these long‐term settings, we predict and find that, while negative reciprocity is initially stronger than positive reciprocity, it also fades more over time than positive reciprocity. This differential fading is so pronounced in our setting that positive reciprocity is stronger overall in the long run. Thus, in long‐term settings, positive responses to kind controls may play a more important role than negative responses to unkind controls. Our results inform managerial decisions about the use of kind versus unkind controls and suggest potential long‐term benefits of pay disparity and other policies that treat employees differentially.
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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 | 0.034 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".