Peaking Today, Taking It Easy Tomorrow: Daily Performance Dynamics of Working Long Hours
Bibliographic record
Abstract
ABSTRACT Underlying the “ideal worker” image that pervades many organizational cultures is the assumption that working longer hours equates to higher performance, despite recovery research that suggests that long work hours might actually impair future work performance. In an effort to reconcile these differences in how long work hours are thought to relate to job performance, we develop and test a conceptual model in which daily boosts in same‐day performance associated with working longer hours could be offset by lower next‐day performance. More specifically, we examine if working a longer day than usual reduces sleep, which has the potential to diminish physical (i.e., physical energy) and psychological (i.e., resilience) resources the next morning, consequently impairing next‐day work performance. In a 5‐day experience sampling study of 67 employee–coworker dyads (276 days), using sleep data from a wearable device (i.e., Fitbit) in combination with daily self‐report surveys and coworker performance ratings, results indicated that daily work hours were positively related to same‐day work performance. Our results further indicated that work hours were negatively related to next‐day work performance through reduced sleep duration and morning resilience, but not through diminished physical energy. Together, our findings indicate that although employees may experience same‐day performance gains related to working long hours, they also may pay a price the following day, as longer workdays prevent employees from recovering overnight.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".