THE ESG EDGE: INNOVATING THE VALUE CHAIN FOR SUSTAINABLE BANKING IN INDIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.018 | 0.000 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it