Application of Innovative Methods of Managing the Labor Potential in the Hotel and Restaurant Business Establishments
Bibliographic record
Abstract
Purpose: The purpose of the academic paper is to study the tendencies and practice of applying innovative methods of managing labor potential in the hotel and restaurant business in changing environmental conditions. Theoretical framework: Considering labor shortages in the hospitality industry and the necessity to attract employees after the pandemic, the EU hospitality and restaurant businesses have been changing their methods of managing the labor workforce. Design/methodology/approach: The research methodology is based on a systematic approach to studying trends and the practice of applying innovative methods of managing labor potential in the context of the external environment’s challenges. Based on the Eurostat panel data for 2020, linear regression models of the dependence between turnovers, employment, and labor productivity in the field of hospitality in EU countries were constructed. Secondary data from interviews with owners of the largest EU hotel chains were used to identify methods of managing labor potential after the pandemic. Findings: The results demonstrate the existence of challenges toward the EU hotel and restaurant chains related to the labor shortage in the conditions of spreading the pandemic, the set of skills and low-skilled migrants. In order to solve the problems outlined, network managers hire and quickly train employees without work experience from various social categories (young people, students, specialists from other sectors, migrants, etc.). Moreover, hotels’ and restaurants’ managers offer employees housing, additional wages, social insurance, bonus payments in order to attract the workforce. Research, Practical & Social implications: The research demonstrates a positive change in the employees’ working conditions in the hospitality industry in the EU states due to the problem of their shortage, which arose with spreading the pandemic and the labor force movement to other economy sectors. Originality/value: The practical value of the research lies in the possibility of using the knowledge about the identified trends of managing labor potential in the EU hospitality industry by small and micro enterprises 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".