War vs pandemic—investigating the type of extreme context as a moderator of extreme-context perception effects on work alienation and its organisational outcomes
Bibliographic record
Abstract
With many organisations operating in challenging circumstances such as military conflicts and pandemics, a growing body of research has investigated the effects of extreme-context perceptions on job attitudes and organisational outcomes. While prior investigations focused on specific extreme contexts like warzones or pandemics, research has yet to examine whether different extreme contexts would have different intensities regarding shaping job attitudes and organisational outcomes. This study takes a unique approach to address this gap in the existing literature by exploring whether the type of extreme context (war vs pandemic) moderates extreme-context-perception effects on work alienation and subsequent job attitudes and organisational outcomes. Specifically, this study investigates the consequences of differentiated extreme contexts by using 668 valid responses from two samples: Sample 1 (N = 449), collected via snowball sampling during the Syrian civil war (2016-2019), and Sample 2 (N = 219), recruited using systematic random sampling from the Middle East during COVID-19 in 2021. Employing t-tests, Sample 1 in the wartime crisis reported higher levels of extreme-context perception, yet not quite as much worsened job satisfaction, but they had lower levels of alienation and job insecurity than those in health-crisis contexts. Using PLS-SEM, we found extreme-context perception heightened alienation, leading to increased job insecurity, decreased job satisfaction, and lower organisational citizenship behaviour (OCB) outcomes. Interestingly, some of these relationships were moderated by the extreme context type. Specifically, while no significant differences were found between the two contexts regarding the relationship between extreme context perception and alienation and between alienation and job insecurity, we found that extreme-context-driven alienation is more likely to damage job satisfaction and OCB in war zones than in pandemic contexts. These findings contribute to important matters of both theory and practice. They specifically expand existing theory by illustrating differentiated consequences of distinct types of extreme contexts to organisational and employee-related concerns. They also demonstrate the need to develop customised organisational strategies as well as policy responses to mitigate and/or capitalise on the effects of differentiated extreme contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".