Activating Organizational Green Activism Via Environmental Human Resource Initiatives: The Mediating and Moderating Role of Employees' Green Innovative Behaviour and Responsible Green Leadership
Bibliographic record
Abstract
ABSTRACT A growing body of research highlights the positive impact of environmental human resource initiatives (EHRIs) on employees' green innovative behaviour (EGIB) within the workplace. Nevertheless, the literature has largely ignored the broader impact of EHRIs on employees' outside‐work organizational green activism (OGA) their involvement in environmental crusades, support for environmental groups, and partaking in political activities aimed at environmental protection. This study addresses this gap by hypothesizing that EHRIs not only directly foster EGIB and OGA but also influence OGA through the mediating role of EGIB. Furthermore, we propose that responsible green leadership (RGL) acts as a moderator in the EHRIs‐EGIB relationship. Using a multisource, time‐lagged survey design, the researchers collected data from 348 top‐notch respondents from 50 organizations in Ghana, the data were analyzed with PLS‐SEM in Mplus (version 8.8), supporting the proposed hypotheses. The results demonstrate that EHRIs positively impact both EGIB and OGA. Additionally, EGIB was found to significantly enhance OGA, partially mediating the relationship between EHRIs and OGA. Moreover, RGL was shown to significantly moderate the EHRIs‐EGIB link, emphasizing the leadership role in fostering green behaviour. These findings underscore the strategic importance of integrating EHRIs and RGL into HR initiatives to inspire both workplace innovation and broader environmental advocacy, offering actionable insights for managers committed to sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".