No one is safe! But who’s more susceptible? Locus of control moderates pandemic perceptions’ effects on job insecurity and psychosocial factors amongst MENA hospitality frontliners: a PLS-SEM approach
Bibliographic record
Abstract
BACKGROUND: The research aimed to formulate and test a model concerning COVID-19 perceptions effects on job insecurity and a set of psychosocial factors comprising anxiety, depression, job burnout and job alienation in the Middle East and North African (hereafter, MENA) regional context. Also, the study attempted to examine whether locus of control can moderate these hypothesised linkages amongst customer service employees working in MENA hospitality organisations. METHODS: The study is based on a sample of 885 responses to an online survey and Partial Least Square Structural Equation Modelling (PLS-SEM). RESULTS: The main findings show the existence of a significant correlation between COVID perceptions and job insecurity and all psychosocial factors, i.e., more intense COVID-19 perceptions accompany higher levels of job insecurity, anxiety, depression, job burnout and job alienation. Furthermore, our results revealed that, in pandemic time, hospitality customer service employees with external locus of control are more likely to suffer higher alienation, anxiety and depression than those with internal locus of control. CONCLUSIONS: The research originality centres on the establishment that COVID-19 has a severe negative impact within the hospitality customer service labour force (in the MENA region). These effects were more profound for participants who claimed external locus of control than those with internal locus of control.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".