The Reality of Applying Human Resources Diversity Management and Its Impact on Workers’ (Performance, Cooperation, Commitment and Loyalty): A Field Study on the Jordanian Food Industry Companies
Bibliographic record
Abstract
Globalization, political instability, poverty, forced immigration and many other factors created a state of diversity in the composition of the world population, and accordingly diversity transferred into organizations, causing a big and unavoidable challenge to business organizations. Diverse human resources can be a source of success if they are well managed strategically, and could be the opposite if they were ignored, and don’t have the needed inclusiveness.This study aimed first to find out the degree to which the Jordanian food industry companies are engaged in Human Resources Diversity Management (DM) in terms of (equity and justice in applying human resources strategies), empowerment and religious freedom. And second to examine the influence of (DM) strategies on workers (performance, cooperation, commitment and loyalty), 150 of non-Jordanian workers at 5 companies operating at King Abdulla the second industrial city were surveyed, depending on the workers responses it was found that the 5 companies do engage in (DM) strategies, except for staffing which is restricted by the Jordanian Labor Law, the results of multiple regression showed that all DM strategies have an influence on workers performance.Empowerment has only an influence on workers cooperation, and religious freedom affects workers commitment and loyalty.The researcher recommended that more involvement in organizational activities, equity, justice and good treatment, will enhance workers cooperation, commitment and loyalty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".