The Influence of Ethical Human Resource Practices on Social Responsibility.
Bibliographic record
Abstract
The objective of the study is to examine the influence of human resource management (HRM) ethics, encompassing dimensions such as acquisition, development, and retention, on social responsibility and its various aspects. The research focused on Gurgaon Mobile Communications, probing whether the implementation of ethical HRM practices correlates with achieving Corporate Social Responsibility (CSR). The investigation was structured around two primary hypotheses, leading to the emergence of seven sub-hypotheses aimed at exploring the interplay between these variables. The research sample consisted of 169 employees selected from a pool of 300 within the company. Data collection primarily relied on a questionnaire designed by the researcher utilizing established metrics. Statistical methods such as arithmetic mean, standard deviation, coefficient of variation, relative importance, correlation coefficient, regression coefficient, as well as the utilization of statistical software such as SPSS V.26 and Smart PLS v.3.3 were employed. Analytical techniques such as t-tests, F-tests, and percentages were utilized for data analysis, adopting a descriptive analytical approach. One of the key findings of the study indicates a statistically significant relationship between HRM ethics and CSR across its economic, legal, moral, and voluntary dimensions, highlighting the impact of HRM practices on fostering social responsibility.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 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".