Human Resource Management and Institutional Resilience during the COVID-19 Pandemic—A Case Study from the Westfjords of Iceland
Notice bibliographique
Résumé
Human resource management (HRM) is challenging in times of crisis, more so than when there is a stable business environment. Consequently, the overall aim of the study is to identify the preparedness, transition process, learning, and growth that businesses in the Westfjords region experienced because of the COVID-19 pandemic. In total, 42 semi-structured interviews were conducted with various members of the society, such as health authorities, healthcare workers, staff of a university center, social workers, and business owners, to gain as broad of an understanding of the local impacts as possible, as well as the coping strategies that emerging or were employed. The model employed for the analysis is an organizational resilience and organizational coping strategies model, which considers both the pre- and post-crisis situation. The core components of this model—anticipate and plan, manage and survive, and learn and grow—were the themes that were used in the thematic analysis of the interviews presented in the results. The findings of the study suggest that the preparedness aspect of the model employed, namely anticipate and plan, was negligible, as institutions were neither very ready for disruption prior to the crisis, nor had plans in place to deal with such a situation. Despite the lack of pre-crisis anticipation and planning mechanisms, examples of how institutions managed and coped during the pandemic were evident in the data. Also, during the crisis, some institutions managed to not just learn and grow, but, through adaptation to the situation, they were able to thrive. The findings also suggest both positive and negative aspects to HRM in public and private institutions. The implications of the study are theoretical in cases of alteration to the analytical model employed, practical in the case of coping mechanisms and practical solutions suggested, and have policy relevance, as the study emphasizes the importance of integrating flexible approaches to national mandates, thus enabling local conditions to be taken into account.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».