Job Demands and Organizational Citizenship Behavior: The Roles of Organizational Commitment and Social Interaction
Bibliographic record
Abstract
Drawing from the Job Demands-Resources (JD-R) model and research on social exchange relationships, this study investigates the impact of three job demands (work overload, interpersonal conflict, and dissatisfaction with the organization’s current situation) on employees’ organizational citizenship behavior (OCB), the hitherto unexplored mediating role of organizational commitment in the link between job demands and organizational citizenship behavior (OCB), as well as how this mediating effect might be moderated by social interaction. Using a multi-source, two-wave research design, surveys were administered to 707 employees and their supervisors in a Mexican-based organization. The hypotheses were tested with hierarchical regression analysis. The results indicate a direct negative relationship between interpersonal conflict and OCB, and a mediating effect of organizational commitment for interpersonal conflict and dissatisfaction with the organization’s current situation. Further, social interaction moderates the mediating effect of organizational commitment for each of the three job demands such that the mediating effect is weaker at higher levels of social interaction. The study suggests that organizations aiming to instill OCB among their employees should match the immediate work context surrounding their task execution with an internal environment that promotes informal relationship building.
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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 | 0.002 | 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".