Biting off More Than You can Chew at Work: The Cultural Hard and Excessive Work (CHEW) Scale
Bibliographic record
Abstract
Existing research suggests that differences in working styles (e.g., working diligently, working excessive hours) are primarily due to individual traits, such as workaholism and work ethic. However, cultural-level values and beliefs about work also shape work patterns. This research advances a cultural perspective, arguing that variations in how hard and excessively individuals work can also be explained by the pressure of societal values of work. To capture the work ideals that individuals might feel are imposed on them by their culture, we introduce culturally imposed work ideals, and differentiate between the extent to which individuals perceive that their culture values (a) hard work (i.e., efficiency, quality, and wise use of time) and (b) excessive work (i.e., duration, quantity, and prioritizing work at all times). We develop and validate the Cultural Hard and Excessive Work (CHEW) Scale across six diverse Canadian and American samples (N = 1,902) of full-time employees, business undergraduates, and MBA students as well as alumni. Psychometric results support the reliability and validity of the two dimensions of CHEW. As expected, the cultural ideal of hard work predicts beneficial employee outcomes, such as lower cynicism and higher work engagement, above and beyond existing cultural-, organizational-, and individual-level predictors. Meanwhile, the cultural ideal of excessive work is consistently associated with detrimental consequences, including higher emotional exhaustion, lower job satisfaction and well-being, and impaired physical health. We discuss theoretical and practical implications, emphasizing the fundamental distinction between hard and excessive work ideals that individuals might hold based on their cultural milieu.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".