THE ESG EDGE: INNOVATING THE VALUE CHAIN FOR SUSTAINABLE BANKING IN INDIA
Bibliographic record
Abstract
The transition from Banking 4.0 to Banking 5.0 necessitates integrating sustainability and innovation throughout the banking value chain. This study proposes and validates a framework to evaluate these integrations' impact on Economic, ESG (Environmental, Social, and Governance), and Sustainability metrics in the Indian banking sector. The study uses rigorous statistical analysis to validate the framework using data from 325 banking experts from ten major banks as well as secondary sources like sustainability reports. The framework includes Primary Activities (customer-centric solutions, risk assessment, digital transformation, stakeholder engagement, and continuous monitoring) and Support Activities (governance, human capital development, data analytics, risk management, and operations efficiency). The findings demonstrate strong validity and reliability across dimensions, as indicated by goodness-of-fit indices (RMSEA, CFI, IFI). Value chain integration innovation improves overall sustainability results and has a favorable impact on economic and ESG performances. The analysis indicates consistent positive connections between value chain integration, ESG performance, and economic outcomes, with high explanatory power (R-squared 0.616 to 0.953). This research provides guidance and ideas for banks navigating sustainable development, bridging the gap between Banking 4.0 and 5.0. It emphasizes the importance of banks embracing technology and sustainability in tandem in order to achieve long-term resilience and societal impact in Banking 5.0. Keywords: Banking 5.0, Indian Banking System, Innovation, Sustainability, Value Chain
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".