The Informativeness of Relative Performance Information and Its Effect on Effort Allocation in a Multitask Environment
Bibliographic record
Abstract
ABSTRACT Prior research documents that providing relative performance information (RPI) motivates employees to increase effort; however, a potential downside of RPI is that it also motivates employees to distort their effort allocations between tasks such that it can be detrimental to overall firm performance. This study investigates via an experiment how the informativeness of RPI affects employees' effort allocations and performance in a multitask environment. We investigate the informativeness of two RPI design choices that are observed in practice: detail level and temporal aggregation. Regarding detail level, firms may provide each employee's performance ranking on tasks, which is less informative than providing the actual performance score of each employee. Regarding temporal aggregation, firms may provide RPI that is reset each period, which is less informative than RPI that is based on cumulative performance. We find RPI detail level and temporal aggregation interact to influence effort distortion. Specifically, we find that, compared to reset RPI, cumulative RPI leads to greater distortion of effort away from firm‐preferred allocations and that this effect is magnified when RPI provides actual performance scores rather than performance rankings. Finally, high levels of effort distortion hurt overall performance, thereby demonstrating the potentially detrimental effect of effort distortion on performance. Results of our study enhance our understanding of how firms can use their control over the design of RPI to enhance its usefulness in directing employees' effort in multitask environments by highlighting the role that informativeness of information can have on employee behavior.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".