Collaborative and Reciprocal Influences Across Bank and Customer Synergistic Sustainability: Developing a Framework for Sustainable Business Viability
Bibliographic record
Abstract
Sustainability in banks is now widely recognized as a relationship that grows from a dialogue between banks and customers, especially in developing countries.This paper offers a construct in framing toward mutual accountability in sustainability and managing ESG risks.Backed by stakeholder theory, systems thinking, and corporate social responsibility, the framework presents a six-level ESG co-creation model based on prosocial values of trust, transparency, and mutual goals.Scoping review methodology was used for this framework and guided by the framework of Arksey and O'Malley, extended by Levac et al., and presented in compliance with the PRISMA-ScR guidelines.We conducted an online search within Scopus, Web of Science, Google Scholar, and JSTOR for articles between 2010 and 2025 on bank-customer sustainability interactions.Fifty studies that met the inclusion/exclusion criteria were reviewed using a structured data extraction.Inductive thematic analysis was performed with validated inter-coder reliability.The study reveals internal and external drivers of sustainability for banks and customers with digital innovation and co-creation as the main enablers.This framework provides actionable recommendations for banks, policymakers, and sustainability champions to create ethical, resilient, and inclusive financial ecosystems.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".