Feedback‐Driven Time Segmenting: The Effect of Feedback Frequency on Employee Behavior*
Bibliographic record
Abstract
ABSTRACT How employees mentally break up or segment time likely influences key performance behaviors. Thus, it is important to understand how features of the work environment influence the mental time segments employees create. Consistent with my predictions, I provide evidence across four experiments that feedback systems can alter the way employees segment their work time—a process that I refer to as feedback‐driven time segmenting. Consistent with the theory of feedback‐driven time segmenting, the experiments demonstrate that more (less) frequent feedback leads employees to create smaller (larger) mental time segments. Furthermore, the results indicate that employees who create smaller mental time segments are less likely to find efficiencies at work, suggesting an unintended cost of increasing feedback frequency. However, I also find that employees with smaller mental time segments work with higher levels of effort intensity. Together, these experiments provide evidence that the economically meaningless time segments employees create can significantly influence their behavior. Consequently, firms and future researchers should carefully consider how features of the work environment influence the mental time segments employees create.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".