The Influence of Workforce Diversity on Organizational Performance in the UAE Hospitality Sector: The Moderating Role of HR Practices
Bibliographic record
Abstract
The aim of this paper is to investigate the impact of workforce diversity on organizational performance (OP) from the perspective of resource-based view (RBV) theory in the context of the hospitality sector in the United Arab Emirates (UAE). We suggest training and performance appraisal as moderators. Sequential regression analysis was applied, including information from directors, unit managers, supervisors, low-level workers, and customers, as well as financial outcomes from 167 organizations in the UAE (683 full-time employees) in the hospitality sector, including hotels, restaurants, theme parks, travel agents, recreational centers, and museums. This analysis assisted in supporting the hypotheses. The findings confirm that there is a positive relationship between diversity and OP. Additionally, the emphasis on training and performance appraisal will strengthen this relationship and lead to higher organizational performance. This improvement is expected to increase customer satisfaction and sales growth. Researchers have emphasized the necessity of conducting a comprehensive investigation to fully understand the impact of diversity on OP. In this regard, we propose that training and performance appraisal serve as potential tools to enhance OP through diversity.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".