Workforce's crisis-induced career shock, career preferences, job insecurity, layoff and perceived employability: examining variations based on gender, education level and ethnic origin
Notice bibliographique
Résumé
Purpose The COVID-19 crisis caused a high level of job insecurity, layoff and low employment opportunities. It generated a worldwide shock, which might have a long-lasting effect on individuals' careers. Changes might occur in terms of individuals’ career choices, objectives, perception of career success and preferences in terms of industries and work arrangements. This study aims to examine crisis-induced career shock among the workforce, which might be translated into changes or doubts related to career choices, objectives and perception of career success, and to analyze individuals' preferences in terms of industries and work arrangements. It focuses mainly on investigating variations among the workforce, based on gender, education level and ethnic origin. These variations are also examined regarding job insecurity, layoff and perceived employability. Design/methodology/approach Frequency counts, percentages, mean ranking, independent t -test and analysis of variance (ANOVA) were used for a sample of 317 workers in the United Arab Emirates (UAE). Three research questions were developed and examined, which are: (1) is there a variation in the workforce, based on gender, education level and ethnic origin concerning crisis-induced career shock (specifically changes related to career choices, objectives and career success? (2) Is there a variation among the workforce related to career preference per industry and work arrangement? If yes, is there a difference in the workforce-based gender, education level and ethnic origin? And (3) is there a variation in the workforce, based on gender, education level and ethnic origin concerning job insecurity, layoff and perceived employability? Findings The findings revealed that career shock was significantly higher among pre-university respondents (specifically, doubts about career choices and perceived career success). As for career preferences per industry, e-business, media and marketing had significant values for all respondents, with e-business as the top-rated choice except for Emiratis who rated it as their third choice. Education was the choice of both men and women. The choices related to other industries (e.g. Healthcare, information, communication technology, etc.) and work arrangements (telework) varied significantly based on gender, education and ethnic origin. Men seem to worry more than women about losing their job as well as Emiratis compared to expatriates, and university-level respondents compared to pre-university. Practical implications This study contributes to highlighting variations related to career shock and career preferences per industry among the workforce based on gender, education level and ethnic origin. This can help organizations in these industries to have a portrait of the situation in the employment market to be able to develop relevant interventions. This research provides insights for managers and HRM practitioners. Originality/value This study contributes to expanding research on career and career shocks in a context of a crisis. It responded to authors who called for more research about career shocks, as well as their implication for specific target groups, by examining variations based on gender, education level and ethnic origin.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».