Leveraging green innovation and green ambidexterity for green competitive advantage: The mediating role of green resilient supply chain
Bibliographic record
Abstract
To mitigate global environmental impact, the textile industry must integrate environmental innovation and operational efficiency. This research delves into the influence of Green Innovation (GIV) and Green Ambidexterity (GAD) on the attainment of Green Competitive Advantage (GCG), with a specific focus on the crucial role played by Green Resilient Supply Chain (GRC) that prioritizes sustainability. The study employs a cross-sectional explanatory survey method, drawing data from 150 textile companies in Indonesia. To comprehend the dynamic relationships between the variables at hand, the study adopts the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings demonstrate that Green Ambidexterity and Green Innovation directly enhance Green Competitive Advantage while also indirectly contributing through the establishment of Green Resilient Supply Chain. These results affirm that sustainable practices and Green Innovation are pivotal components of business strategies that align with regulatory and social expectations and bolster firms' competitive positioning. The implications of this study offer valuable insights for stakeholders, enabling them to formulate strategies that incorporate sustainability aspects into their business operations to achieve optimal outcomes in a fiercely competitive market context.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".