Employees Aren’t Factory Slaves: Factors Determining Work Demand and Implications for HRM Practices
Bibliographic record
Abstract
Although a plethora of studies on factors determining work demand have been investigated in the West, the Western findings cannot be directly applied to another cultural context and there is still rather constraint studies in collectivist cultural nations. Drawing on the conservation of resources theory and Hofstede’s cultural framework, the present study aims to fill a lacuna by identifying factors determining work demand in a collectivist cultural context. Anchored in ontological and epistemological assumptions, the study employed hypothetic-deductive approach with a survey strategy. Data were garnered from randomly selected 569 employees working in the banking sector with the aid of a self-administrated questionnaire. The results disclose that males work longer hours and experience greater work demand than females. The study further reveals the predictors of work demand: working hours had shown the largest impact, followed by tenure, gender, income, formal work-life policies and supervisory status. The present study questioned the worthiness of the equal policies for both men and women in the workplace and emphasised the needs for gender-based HR policies. On balance, the study pushes back the frontiers of work-family literature and becomes a springboard to future scholarly works.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".