A bibliometric study finds rapid growth in AI-driven green fintech research but little TOE-based theorizing, and proposes a bank-focused framework arguing that infrastructure, culture and regulation jointly unlock green investment benefits.

Mediating Pathways to Sustainable Investment: A TOE Framework for AI-Driven Green Fintech Adoption in Banking

Reem A. Abdalla, Lamya Abbas Hidaytalla, Gulnar Sadat Mulla · July 03, 2026 · Journal of risk and financial management

openalex theoretical n/a evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

Drawing on a bibliometric analysis of 79 Scopus papers (2020–2026), the paper develops a TOE-based conceptual framework proposing that technological readiness, sustainability culture, and regulatory support mediate how AI-driven green fintech adoption in banks yields improved sustainable investment outcomes.

Purpose: Despite growing research on green fintech and sustainable finance individually, no systematic theoretical framework explains how AI-driven green fintech solutions can be adopted in banking for sustainable investment purposes. This paper addresses this demonstrated gap by developing the first bibliometrically grounded, TOE-based conceptual framework for AI-driven green fintech adoption in banking. Design/Methodology/Approach: A two-phase approach is employed. First, a bibliometric analysis of 79 Scopus-indexed documents (2020–2026) using bibliometrix in R provides quantitative evidence of the research gap through keyword co-occurrence networks, thematic mapping, and trend topic analysis. Second, building on this evidence, a conceptual framework integrating the Technology–Organization–Environment (TOE) framework with three mediating constructs, technological readiness, sustainability culture, and regulatory support is developed and five theoretical propositions are derived. Findings: The bibliometric analysis reveals an annual growth rate of 78.4% in the field and confirms that the TOE framework has never occupied the motor themes quadrant of the green fintech literature. The proposed framework theorizes three mediated pathways through which technological, organizational, and environmental conditions translate into improved sustainable investment outcomes including enhanced ESG transparency, increased green investment allocation, and SDG alignment. Practical Implications: The framework provides bank executives with three actionable intervention points: technological infrastructure investment, sustainability culture embedding, and regulatory engagement and offers policymakers evidence-based guidance for designing supportive green fintech adoption frameworks. Originality/Value: This study presents a conceptual framework that is, to the authors’ knowledge, the first to combine TOE theory, AI-driven green fintech, a banking context, an explicit three-mediator architecture (technological readiness, sustainability culture, regulatory support), and sustainable investment outcomes as the dependent variable, grounded in reproducible bibliometric evidence. Existing studies address subsets of these dimensions; none integrates all six simultaneously.

Summary

Main Finding

The paper develops the first bibliometrically grounded, TOE-based conceptual framework explaining how AI-driven green fintech is adopted in banking to produce sustainable investment outcomes. Using a two-phase approach (bibliometric mapping + theory synthesis), it identifies a fast-growing but fragmented literature (79 Scopus documents, 2020–2026; annual growth 78.4%) and proposes a mediated TOE model where technological, organizational, and environmental conditions operate through three mediators — technological readiness, sustainability culture, and regulatory support — to deliver outcomes such as improved ESG transparency, greater green investment allocation, and alignment with the SDGs.

Key Points

Gap addressed: No prior systematic theoretical framework links AI-driven green fintech adoption in banking with sustainable investment outcomes using a TOE lens and bibliometric grounding.
Bibliometric evidence:
- Sample: 79 Scopus-indexed papers (2020–2026) analyzed with bibliometrix (R).
- Annual growth: 78.4%, indicating rapid expansion.
- Thematic finding: TOE-related concepts have not appeared in the motor-themes quadrant of the green fintech literature, signaling underutilization of TOE theory.
Conceptual contribution:
- Integrates the Technology–Organization–Environment (TOE) framework with three mediators:
  - Technological readiness (mediates the Technology context),
  - Sustainability culture (mediates the Organization context),
  - Regulatory support (mediates the Environment context).
- Specifies three mediated pathways from TOE contexts to sustainable investment outcomes and derives five theoretical propositions linking contexts, mediators, and outcomes.
Practical recommendations:
- For banks: invest in technological infrastructure, embed sustainability across culture/processes, and proactively engage with regulators to facilitate AI-driven green fintech adoption.
- For policymakers: design regulatory and incentive structures that strengthen regulatory support and lower adoption frictions.

Data & Methods

Data:
- 79 documents indexed in Scopus covering 2020–2026 on green fintech / sustainable finance themes.
Methods:
- Bibliometric analysis using bibliometrix (R):
  - Keyword co-occurrence networks,
  - Thematic mapping (motor/ niche/ emerging/ basic themes),
  - Trend topic analysis.
- Theory synthesis:
  - Built a conceptual framework grounded in bibliometric findings and the TOE framework,
  - Introduced three mediators and derived five theoretical propositions linking TOE elements to sustainable investment outcomes.
Key quantitative finding from bibliometrics: 78.4% annual growth; TOE not present among motor themes.

Implications for AI Economics

Research agenda:
- Empirical testing: the framework specifies testable mediated relationships — suitable for structural equation modeling, mediation analysis, or causal inference approaches (e.g., panel regressions with bank-level adoption measures, difference-in-differences exploiting regulatory or technology shocks, IV strategies).
- Measurement guidance: operationalize mediators (technological readiness: AI infrastructure, data quality; sustainability culture: governance, incentives; regulatory support: clarity, incentives) and outcomes (ESG reporting transparency, share of green assets, SDG-aligned investments).
- Broader economic questions: how AI-driven information processing in banks affects green capital allocation, pricing of climate risk, and reduction of information asymmetries in sustainable finance markets.
Policy relevance:
- Identifies leverage points where policy can accelerate adoption with desirable social returns (e.g., standards for ESG data, support for bank digital infrastructure, regulatory sandboxes).
Practical economics for banks:
- Investment prioritization: quantify returns to technological readiness and culture-change initiatives when assessing green fintech projects.
- Cost–benefit framing: frame adoption not only as IT upgrades but as drivers of market differentiation through improved ESG transparency and SDG alignment.
Originality/value for AI economics:
- Presents a reproducible, bibliometrically justified theoretical structure linking AI-driven fintech adoption to sustainable investment outcomes — a useful scaffold for empirical models, calibration of structural models, and policy simulations in the economics of AI and sustainable finance.

Assessment

Paper Typetheoretical Evidence Strengthn/a — The paper is conceptual and bibliometric: it maps the literature and proposes a TOE-based framework but does not provide causal identification or empirical tests of the proposed pathways. Methods Rigormedium — Bibliometric methods (keyword co-occurrence, thematic mapping, trend analysis) are appropriate and reproducible using bibliometrix/R, and the conceptual development is logically grounded in TOE and the mapped literature; however the empirical basis is limited to 79 Scopus-indexed documents, the analysis is descriptive (no causal tests), and the framework is not validated with primary data or robustness checks. SampleA bibliometric dataset of 79 Scopus-indexed documents from 2020–2026 analyzed using bibliometrix in R (keyword co-occurrence networks, thematic mapping, trend topic analysis). Themesadoption governance innovation GeneralizabilityScopus-only coverage excludes non-indexed, local-language, practitioner, and grey literature which may bias topic representation, Short and recent time window (2020–2026) captures an emerging field but may miss earlier relevant work or lagging publications, Bibliometric patterns indicate research attention but cannot establish real-world adoption dynamics or causal relationships, Framework is conceptual and developed for banking; it is untested empirically and may not generalize to non-bank financial institutions or other sectors, Heterogeneity in AI technologies and green fintech solutions is not empirically disaggregated, limiting applicability to specific tools or contexts, Possible geographic bias if the underlying literature is concentrated in particular regions or regulatory regimes

Claims (11)

Claim	Direction	Outcome	Confidence & Evidence	Details
A bibliometric analysis of 79 Scopus-indexed documents (2020–2026) using bibliometrix in R was conducted. Research Productivity	null_result	number of Scopus-indexed documents analyzed (bibliometric sample)	Reading fidelity high Study strength medium	n=79 0.12
The bibliometric analysis reveals an annual growth rate of 78.4% in the field. Research Productivity	positive	annual publication growth rate in the green fintech / AI-driven green fintech literature	Reading fidelity high Study strength medium	n=79 78.4% annual growth rate 0.12
The TOE framework has never occupied the motor themes quadrant of the green fintech literature. Research Productivity	negative	presence/positioning of TOE framework in motor themes quadrant of thematic map	Reading fidelity medium Study strength medium	n=79 0.07
This paper develops the first bibliometrically grounded, TOE-based conceptual framework for AI-driven green fintech adoption in banking. Adoption Rate	positive	existence of a TOE-based, bibliometrically grounded conceptual framework for AI-driven green fintech adoption in banking	Reading fidelity medium Study strength speculative	not reported 0.01
The proposed framework theorizes three mediated pathways through which technological, organizational, and environmental conditions translate into improved sustainable investment outcomes including enhanced ESG transparency, increased green investment allocation, and SDG alignment. Decision Quality	positive	sustainable investment outcomes (ESG transparency, green investment allocation, SDG alignment)	Reading fidelity high Study strength speculative	not reported 0.02
The framework integrates the Technology–Organization–Environment (TOE) framework with three mediating constructs: technological readiness, sustainability culture, and regulatory support. Adoption Rate	positive	inclusion of the three mediating constructs in the conceptual framework	Reading fidelity high Study strength speculative	not reported 0.02
Five theoretical propositions are derived. Research Productivity	null_result	number of theoretical propositions derived	Reading fidelity high Study strength speculative	5 propositions 0.02
The framework provides bank executives with three actionable intervention points: technological infrastructure investment, sustainability culture embedding, and regulatory engagement. Organizational Efficiency	positive	identification of three intervention points for bank executives	Reading fidelity high Study strength speculative	not reported 0.02
The framework offers policymakers evidence-based guidance for designing supportive green fintech adoption frameworks. Governance And Regulation	positive	availability of evidence-based policy guidance for green fintech adoption	Reading fidelity medium Study strength speculative	not reported 0.01
This study is, to the authors' knowledge, the first to combine TOE theory, AI-driven green fintech, a banking context, an explicit three-mediator architecture (technological readiness, sustainability culture, regulatory support), and sustainable investment outcomes as the dependent variable, grounded in reproducible bibliometric evidence. Innovation Output	positive	novelty/uniqueness of the study's combination of six elements	Reading fidelity medium Study strength speculative	not reported 0.01
Existing studies address subsets of these dimensions; none integrates all six simultaneously. Research Productivity	negative	coverage of the six-element combination in existing literature	Reading fidelity medium Study strength speculative	not reported 0.01