Top management's green inclusive leadership and sustainable competitive advantage in manufacturing firms: The enabling role of internal CSR communication
Bibliographic record
Abstract
Achieving sustainable competitive advantage has become increasingly complex amid intense global environmental pressures. While leadership is widely recognized as an enabler of competitiveness, little is known about how top management's green inclusive leadership fosters sustainable competitive advantage through dynamic organizational capabilities such as green shared vision and green mindfulness. Drawing on the natural resource-based view, we theorize that top management's green inclusive leadership strengthens sustainable competitive advantage in manufacturing firms through these capabilities and that internal corporate social responsibility communication reinforces these effects. Data were collected in two waves from a purposive sample of 215 middle-level managers working in Pakistan's manufacturing sector and analyzed using PROCESS macro models in SPSS. We found that green inclusive leadership directly predicts sustainable competitive advantage, and that green mindfulness, but not green shared vision, mediates this relationship. We also identify a sequential pathway in which green inclusive leadership enhances green shared vision, which subsequently fosters green mindfulness, thereby improving sustainable competitive advantage. Crucially, this sequential mediation is stronger when internal corporate social responsibility communication is high, underscoring the role of communication as a strategic organizational capability. By integrating leadership, vision, mindfulness, and communication, this study advances the natural resource-based view and demonstrates how sustainability messages shared within organizations deepen employee engagement with leadership-driven environmental strategies. • Top management's green inclusive leadership positively predicts SCA. • Green shared vision fails to mediate the relationship between top management's green inclusive leadership and SCA. • Green mindfulness mediates the relationship between top management's green inclusive leadership and SCA. • Green shared vision and green mindfulness sequentially mediate top management's green inclusive leadership and SCA. • Internal CSR communication amplifies the impact of green inclusive leadership on SCA via sequential mediators.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".