Digital Green Transformation and Sustainable Performance: The Mediating Role of Green Process Innovation and Supply Chain Agility
Bibliographic record
Abstract
This study investigates how Digital Green Transformation Capability (DGTC) enables manufacturing firms to achieve superior Sustainable Performance (SP) through Green Process Innovation (GPI) and Sustainable Supply Chain Agility (SSCA). Building on Resource Orchestration Theory (ROT) and Socio Technical Systems (STS) theory, the research introduces a dual alignment orchestration framework that integrates digital and green transformation as interdependent capability systems. Using data from 516 manufacturing firms in Canada and the United States, the study employs PLS SEM and PROCESS Model 6 to test serial mediation effects. DGTC’s effect on SP is examined through sequential paths: DGTC, GPI, SSCA and SP. Moderating effects of Data Governance Quality (DGQ) and Institutional Pressure (IP) are also assessed. Results confirm that DGTC significantly enhances GPI and SSCA, which sequentially mediate its impact on SP. DGQ strengthens the DGTC and SSCA relationship, while IP amplifies the SSCA and SP path. The model demonstrates high predictive validity (GoF = 0.59; Q² = 0.34; PLSpredict RMSE < LM benchmarks). Cross country and sectoral analyses confirm robustness across manufacturing contexts. This study contributes by (1) introducing Green Process Innovation as a micro level mechanism linking DGTC to sustainable outcomes, (2) conceptualizing Dual Alignment Orchestration as a dynamic capability integrating digital and sustainability domains, and (3) developing the DGTC Strategic Response Matrix that translates theoretical insights into managerial strategy. The findings enrich the emerging discourse on Industry 5.0, where agility, innovation, and data governance jointly underpin sustainable competitiveness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".