Unleashing green innovation: navigating the path with green inclusive leadership, green knowledge management and internal CSR communication
Bibliographic record
Abstract
Purpose Drawing upon the natural resource-based view (NRBV), organizational learning (OL) and contingency theories, this paper aims to develop and test a theoretical framework that examines the impact of green inclusive leadership on green innovation in business-to-business (B2B) context. This framework further examines the simple and serial mediation of green knowledge acquisition and sharing and the moderation of internal corporate social responsibility(CSR) communication. Design/methodology/approach Using survey questionnaires, authors collected multiwave data from 215 middle managers from different manufacturing and production organizations operating in Pakistan. The hypotheses were inspected using the PROCESS macro. Findings According to the findings, green inclusive leadership and green innovation are positively associated, and green knowledge acquisition and green knowledge sharing are efficient serial mediators of this relationship. Furthermore, the results suggest that internal CSR communication moderates the serial mediation such that the indirect relationship between green inclusive leadership and green innovation was stronger at high levels of internal CSR communication rather than at lower levels. Practical implications This research offers implications for manufacturing industry leaders and policymakers. Green inclusive leadership nurtures green knowledge dynamics, making it vital for achieving United Nations’ Sustainable Development Goals and promoting ecological stewardship. Investing in green knowledge processes and transparent internal CSR communication can enhance sustainable innovation and align with broader sustainability goals in organizations predominantly operating under the B2B model. Originality/value By merging NRBV, OL and contingency theories and drawing links across different genres of literature, this study provides unique insight into leadership, knowledge management, corporate communication, sustainability and CSR and innovation in the B2B sector.
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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 | 0.011 |
| 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 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".