Understanding sustainable outcomes in the digital age: The vital role of digital leadership in leveraging the impact of green innovation
Bibliographic record
Abstract
The increasing complexities posed by green organizational challenges and technological advancements require a thorough grasp of the vital capabilities needed to maintain the firm’s sustainable performance. This study aims to explore the associations of green technological innovation and management innovation to sustainable performance and estimate to what extent digital leadership moderates these associations. The resource-based view (RBV) and socio-technical system theory (STS) are leveraged to conceptualize the research model. By adopting a survey-based approach on data gathered from 419 manufacturing and service firms, the PLS-SEM is the statistical approach conducted to test the validity of predictions The outcomes demonstrated that green technological innovation, green management innovation, and digital leadership are positively linked to sustainable performance. Furthermore, higher digital leadership strengthens the linkage of green technology and management innovation to sustainable performance. The outcomes made a powerful contribution to the current literature on green innovations and digital transformation, and substantial recommendations were inferred for organizations aiming to achieve environmental, social, and economic performance.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".