Human Resource Skill Adjustment in Service Sector: Predicting Dynamic Capability in Post COVID-19 Work Environment
Bibliographic record
Abstract
The havoc caused by the COVID-19 pandemic on hospitality businesses across the world affected the human resource skills of the industry to the extent that managers and industry experts are still finding difficult how best to upgrade the skills of their workforce and enhance their capability to withstand future disruptions. It is based on this problem that this research investigated the effect of human resource skill adjustment on the dynamic capability of hospitality businesses in sub-Saharan Africa post the COVID-19 work environment. The study employed cross-sectional survey design with a total population of two hundred and twenty participants drawn from sixty hospitality businesses in the south-eastern part of Nigeria. Formulated research hypotheses were analysed with linear regression. The results of the research demonstrated that human resource skill adjustment predicted the dynamic capability of hospitality businesses. The study concludes that human resource skill adjustment measured with upskilling and reskilling methodologies predicted the dynamic capability. The implication of the finding is that managers and operators of hospitality businesses should implement human resource skill adjustment in all the functional areas of their management to enable each section or department to attain its goals equally, and enhance the dynamic capability of the industry.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".