The Role of Green Recruitment on Organizational Sustainability Performance: A Study within the Context of Green Human Resource Management
Bibliographic record
Abstract
In light of the increasing recognition among modern business communities regarding the importance of implementing environmentally sustainable practices, this study thoroughly examines the concept of green recruitment (GR) and its subsequent impact on organizational sustainability performance (OSP). Situated within the shift from conventional to contemporary organizational frameworks that prioritize capacity, this study emphasizes the crucial importance of integrating sustainability into recruitment processes. This alignment ensures that human resource practices are in line with both environmental and organizational goals. The primary purpose emerges as a thorough examination and identification of sixteen critical factors that intersect GR and OSP, using insights from both the current literature and expert viewpoints, so this fills a crucial gap in the existing research. This study utilizes an integrated ISM-DEMATEL strategy to systematically reveal the hierarchical and relational patterns that are inherent in the connections between GR and OSP variables. This technique allows for a thorough comprehension of how these variables interact with each other. The findings highlight several important variables, emphasizing the complex network of interdependencies among the elements studied. The suggested model in this research encapsulates its originality, as it not only sheds light on the interdependent interactions for policy- and decision-makers but also establishes a foundation for future research in this field.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".