The effect of COVID-19 on human resource management practices and organizational sustainability
Bibliographic record
Abstract
The current study tries to identify the impact of COVID-19 on human resource management practices, business processes, and organizational sustainability. Furthermore, it identified the impact of sustainable HRM practices on hypermarkets' sustainability. The outbreaks of COVID-19 have considerably impacted organizations and businesses all over the world. Most companies were not ready to face such force majeure. Moreover, like everywhere else in the world COVID-19 has significantly affected HRM functions and organizational sustainability in the organizations in Gulf Cooperation Council (GCC) countries. The research is based on a survey of 363 HR practitioners working in hypermarkets in the GCC countries to examine the impact of the pandemic COVID-19 on organizational sustainability. The findings reveal the negative impact of COVID-19 on human resource management practices, business processes, and organizational sustainability. Furthermore, they identify the positive impact of sustainable HRM practices and effective business processes on hypermarkets' sustainability. Finally, the results show that effective business processes and sustainable HRM practices annihilate the negative effect of COVID-19 on organizational sustainability in hypermarkets operating in the GCC countries. This study is unique since it is conducted during the pandemic period and analyses the negative impact of Covid-19 on organizations in the GCC countries. Moreover, it suggests solutions to minimize the negative effect of COVID-19 on organizational sustainability.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".