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Record W4411348594 · doi:10.18280/ijsdp.200511

Collaborative and Reciprocal Influences Across Bank and Customer Synergistic Sustainability: Developing a Framework for Sustainable Business Viability

2025· article· en· W4411348594 on OpenAlexvenueno aff
Jugal Kishor Kushwaha, Abhijit Ghosh, Anjay Kumar Mishra, Om Prakash Giri

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessReciprocalProcess managementSustainable businessEcology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.302
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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