Human Resource Management and Institutional Resilience during the COVID-19 Pandemic—A Case Study from the Westfjords of Iceland
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".