Who’s more vulnerable? A Generational Investigation of COVID-19 Perceptions effect on Organisational Citizenship Behaviours: Job Insecurity, Burnout and Job Satisfaction as Mediators
Bibliographic record
Abstract
Abstract This paper is an empirical investigation that examines a path model linking COVID-19 perceptions to organisational citizenship behaviour (OCBs) via three mediators: job insecurity, burnout, and job satisfaction. The research examines the path model invariance spanning Generations X, Y, and Z. Three countries in the Middle East and North Africa (MENA) were the focus of the study. The data was collected from a sample of employees in service companies. We used a Partial Least Square Structural Equation Modelling (PLS-SEM) to analyse the data. Our findings reveal that COVID-19 positively predicts job insecurity, which positively impacts burnout levels. Burnout negatively predicts job satisfaction. The findings established that job satisfaction positively predicts OCBs. The mediation analysis determined that job insecurity, burnout and job satisfaction convey the indirect effects of COVID-19 perceptions onto OCBs. However, our multi-group analysis revealed that the indirect effects of COVID-19 on OCBs were only valid amongst younger generations, i.e., Generation Y and Generation Z. Finally, our hypothesised model is not invariant across Generations X, Y and Z. Specifically, younger generations are substantially more vulnerable to the indirect effects of COVID-19 perceptions on their engagement in OCBs than Generation X whose job satisfaction blocks the effects of COVID-19 perceptions on OCBs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".