Societal individualism–collectivism and uncertainty avoidance as cultural moderators of relationships between job resources and strain
Bibliographic record
Abstract
The job demands-resources model is a dominant theoretical framework that describes the influence of job demands and job resources on employee strain. Recent research has highlighted that the effects of job demands on strain vary across cultures, but similar work has not explored whether this is true for job resources. Given that societal characteristics can influence individuals' cognitive structures and, to a lesser extent, values in a culture, we address this gap in the literature and argue that individuals' strain in reaction to job resources may differ across cultures. Specifically, we theorize that the societal cultural dimensions of individualism-collectivism and uncertainty avoidance shape individual-level job resource-strain relationships, as they dictate which types of resources (i.e., individual vs. group preference-oriented and uncertainty-reducing vs. not) are more likely to be valued, used, or effective in combating strain within a culture. Results revealed that societal individualism-collectivism and uncertainty avoidance independently moderated the relationships between certain job resources (i.e., job control, participation in decision making, and clear goals and performance feedback) and strain (i.e., job satisfaction and turnover intentions). This study expands our understanding of the cross-cultural specificity versus generalizability of the job demands-resources model.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".