Work hours, work intensity, satisfactions and psychological well‐being among hotel managers in China
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship of work intensity and of work hours on potential antecedents and work and well‐being consequences. Design/methodology/approach Data are collected from 309 male and female managers working in 3‐, 4‐ and 5‐star hotels in Beijing, China using anonymously completed questionnaires with a 90 percent response rate. Findings The 15‐item measure of work intensity is found to have high internal consistency reliability. Work intensity is significantly correlated with work hours, but modestly. Gender, age and organizational level predict work intensity but not work hours; males, younger hotel managers and hotel managers at higher organizational levels indicate greater work intensity. Hierarchical regression analyses, controlling for personal demographic and work situation characteristics, show that work intensity but not work hours is a more consistent and significant predictor of work outcomes (e.g. work engagement) and psychological well‐being (e.g. exhaustion, work‐family conflict). Somewhat surprisingly, neither work intensity nor work hours have significant relationships with important work outcomes (job satisfaction, career satisfaction, intent to quit). The interaction of work intensity and work hours is not a significant predictor of work or well‐being outcomes. Interestingly, work intensity is positively related to work engagement and negatively related to indicators or psychological well‐being. Originality/value These findings are only partially consistent with previous conclusions suggesting the possible role played by cultural values and level of economic development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".