Green Human Resource Management and Brand Citizenship Behavior in the Hotel Industry: Mediation of Organizational Pride and Individual Green Values as a Moderator
Bibliographic record
Abstract
In recent years, there has been growing awareness of the need for sustainability in the hospitality industry. The hotel industry, in particular, has been identified as a significant contributor to environmental degradation. To address this issue, hotel managers have begun to adopt green human resource management (GHRM) practices to promote sustainable behavior among employees. This research paper explores the relationship between GHRM practices, brand citizenship behavior (BCBs), organizational pride, and individual green values in the hotel industry. The study examines how GHRM practices influence BCB through the mediation of organizational pride and the moderation of individual green values. A survey was conducted with 328 employees from five-star hotels and the obtained data were analyzed using PLS-SEM. The results indicate that GHRM practices positively affect BCB and that this relationship is partially mediated by organizational pride. Furthermore, individual green values were found to moderate the relationship between GHRM practices and BCB, indicating that employees with stronger green values are more likely to exhibit BCB. These findings contribute to the literature on GHRM and BCB and offer insights for hotel managers on how to enhance their sustainability efforts through effective GHRM practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".