Ethical Leadership Style in Moderating the Influence of Green Organizational Culture on Green Innovative Behavior: SMEs Cases
Bibliographic record
Abstract
This study aims to test: 1) the influence of green organizational culture on innovative green behavior, 2) the role of ethical leadership in moderating the influence of green organizational culture on innovative green behavior. This research was conducted with a survey and involved all Batik Small & Medium Enterprises (SMEs) employees in Berbah subdistrict, Sleman, Yogyakarta, Indonesia. The number of employees is 115 people and work in 12 SMEs. The researcher used mixed methods (quantitative and qualitative) to obtain factual data. Interviews and questionary were data carried out. Researchers used the qualitative method by conducting in-person interviews of SMEs owners and workers. Instrument tests use confirmatory factor analysis and reliability tests. The research instruments are examined using confirmatory factor analysis and reliability tests. The hypothesis testing is done by simple regression and moderation regression. Hypothesis test results show that green organizational culture positively affects innovative behavior, and ethical leadership moderates the influence of green organizational culture on innovative green behavior. The results of this study have theoretical implications and managerial implications that are beneficial to leaders in improving innovative environmentally friendly behaviors. Given the lack of analysis of the role of ethical leadership in increasing the influence of green organizational culture on innovative behavior that is environmentally friendly, further research is needed in the future.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".