How and when top management green commitment facilitates employees green behavior: a multilevel moderated mediation model
Bibliographic record
Abstract
Purpose The purpose of this paper is to argue that green hope (GH) and green organizational identification (GOI) play critical roles in transforming top management green commitment (TMGC) into desired employees task-related green behavior (TRGB) and voluntary workplace green behavior (VWGB) based on positive psychology. Design/methodology/approach The authors test the multilevel moderated mediation model by analyzing data collected from 491 hospitality employees and their direct supervisors in 103 teams. At Time 1, the authors conducted a survey of 905 team members to provide demographic information and evaluate TMGC, as well as their own GOI. At Time 2, the authors sent a follow-up questionnaire to employees who participated Time 1, asking them to evaluate their GH in the workplace. At Time 3, the authors sent questionnaires to the leaders of the respondents of T2 survey and invited them to evaluate TRGB and VWGB in the workplace. Findings The results show that TMGC facilitates two types of employees’ behaviors toward both TRGB and VWGB by enhancing hospitality employees’ GH. As a team-level variable, GOI has a positive moderating effect on the association between TMGC and GH. The authors discuss the theoretical implications as well as practical implications for managers seeking to promote sustainability in their hospitality industry. Originality/value This is one of the first empirical studies to investigate the mediating effects of a positive psychology variable, namely, GH – and the moderating effects of GOI on the relationship between TMGC and employee green behavior (EGB).
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".