Crisis-induced career shock and entrepreneurial intention among employees: what is the role of layoff, job insecurity and perceived employability during COVID-19?
Bibliographic record
Abstract
Purpose Crises significantly affect the “people” dimension of the triple bottom line, disrupting careers through economic consequences, reducing organizational trust and altering career choices. Entrepreneurial careers may emerge as an alternative to secure income and career control. Crises can generate career shocks, prompting transitions from traditional employment to entrepreneurship. This study aims to investigate how crises influence career transitions, particularly entrepreneurial intentions, focusing on the effects of layoffs, job insecurity and perceived employability during COVID-19. It explores both direct and indirect impacts of these factors through career shock, contributing to career and entrepreneurship research. Design/methodology/approach Cross-sectional data were collected in the United Arab Emirates (UAE) using snowball sampling during the COVID-19 health crisis. The final sample consisted of 211 working individuals. An online questionnaire was distributed to participants. The study hypotheses were tested using Partial Least Squares (PLS) analysis conducted with SmartPLS 3.0. Findings The obtained results showed that the layoff of others, job insecurity and low perceived employability are significantly associated with career shock. And that career shock mediates the relationship between these variables and entrepreneurial intention in times of crisis, except for job insecurity. Practical implications This research provides insights for employees, managers, organizations and policymakers. It is necessary to carefully address employee expectations and experiences to identify career decisions resulting from career shocks and determine the needed interventions and support. Originality/value Very few studies examined the relationship between career shock and entrepreneurial intention. This cannot help human resources management practitioners understand how career shock can trigger the transition from paid employment to an entrepreneurial career. This study broadens the scope of research on human resource management, entrepreneurship and career by examining the direct effects of layoff, job insecurity and perceived employability on career shock as well as on entrepreneurial intention, in addition to their direct effects on the latter through career shock during a crisis.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".