FinTech adoption and digital transformation are rapidly transforming the modern banking landscape. Using a hybrid analytical approach combining Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) techniques, a new study explores how banks’ technological, organizational, and environmental capacities interact with policy support to drive performance.
Published in SAGE Open and titled “Exploring the Mediating Role of FinTech Adoption and Digital Transformation in Enhancing Bank Performance: A TOE Framework Study Using a Hybrid SEM-ANN Approach”, the study offers quantitative evidence that both FinTech adoption and digital transformation act as critical mediators between internal capabilities and performance outcomes in the banking industry. The research is grounded in the Technology–Organization–Environment (TOE) framework, applying it to an Indian banking context that reflects the global digital shift underway in financial services.
FinTech adoption and digital transformation as drivers of bank performance
How do technological innovation and transformation interact to enhance bank performance in the digital era? To answer it, the authors surveyed 325 employees from public, private, and cooperative banks across Chennai, India, capturing responses on FinTech engagement, digital integration, and performance metrics.
Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) validation, the authors developed a causal model linking five key constructs, technology capability, organizational capability, government support, digital transformation, and FinTech adoption, to bank performance.
The analysis revealed a powerful pattern: FinTech adoption and digital transformation jointly mediate the relationship between a bank’s internal capacities and its overall performance. In other words, investing in digital infrastructure or innovation culture alone does not directly guarantee higher returns; rather, the benefits materialize when these investments translate into successful FinTech deployment and comprehensive digital transformation.
Government support emerged as the most significant external factor, directly influencing both transformation and performance. Policy initiatives such as India’s Unified Payments Interface (UPI), regulatory sandboxes, and data protection frameworks provide fertile ground for innovation, enabling banks to experiment and scale digital tools safely.
Understanding the mediating role of technology and transformation
The authors observed that technological capability, the ability to develop, deploy, and maintain digital systems, positively influences both transformation and adoption. However, it does not directly enhance performance unless mediated by these factors.
Organizational capability, including leadership vision, employee readiness, and process flexibility, also showed strong positive relationships with FinTech adoption and digital transformation but a weak direct effect on performance. This underscores that technology-driven culture and adaptive management are necessary to extract real value from innovation.
Quantitatively, both FinTech adoption and digital transformation had substantial direct effects on bank performance. The structural path coefficients, 0.298 and 0.294, respectively, demonstrate their nearly equal influence in driving outcomes such as profitability, efficiency, and customer engagement. The findings suggest that banks with strong digital ecosystems outperform those with fragmented or legacy systems, even when they share similar technological resources.
Notably, the mediation tests confirmed that both FinTech and digital transformation partially explain the effect of government support, technology capability, and organizational capability on performance. Among the two mediators, FinTech adoption emerged as the stronger channel, highlighting how the practical integration of financial technologies, such as mobile payments, AI-based risk scoring, blockchain transactions, and automated customer service, serves as the tangible manifestation of digital strategy.
Government support as a catalyst for transformation
The study introduces a compelling policy perspective: government support is both a direct and indirect determinant of banking performance. Through clear regulatory guidance, infrastructure investment, and innovation incentives, governments can shape the speed and scale of digital adoption in financial systems.
Empirically, government support showed the strongest total effect on performance (path coefficient 0.256) and significantly influenced both digital transformation (0.524) and FinTech adoption (0.534). This dual influence underscores that policy environments play a decisive role in shaping digital maturity in the financial sector.
In the Indian context, the success of the UPI framework and the growing prevalence of digital identification (Aadhaar) demonstrate how coordinated policy, regulation, and technology can foster innovation while maintaining consumer trust. The authors argue that these conditions are not unique to India, similar patterns are evident in other emerging economies where digital ecosystems depend on public-private collaboration.
This insight reframes digital transformation from a purely corporate initiative into a national digital infrastructure project. When governments build digital trust through stable regulation and data protection, they create external conditions that allow banks to take internal risks, experimenting with automation, data analytics, and new financial technologies without undermining customer confidence.
What the model reveals about the future of banking
The research extends the TOE framework into a predictive model for banking innovation, showing that technology, organization, and environment are interdependent and mutually reinforcing. In this model, internal digital capability provides the foundation, organizational readiness activates it, and environmental support sustains it.
The hybrid SEM-ANN approach allowed the authors to test not only linear relationships but also non-linear, layered effects. The ANN analysis confirmed the SEM findings and ranked government support and digital transformation as the two most influential predictors of performance. The model’s explanatory power was substantial, with R² values of 0.591 for performance, 0.559 for transformation, and 0.657 for FinTech adoption, indicating that these factors together explain over half the variance in bank outcomes.
Besides statistical validation, the study delivers a conceptual message: FinTech and transformation are not ends in themselves but continuous processes. Successful banks are those that treat innovation as an iterative cycle, constantly integrating new digital tools while retraining employees, modernizing systems, and aligning strategy with customer behavior.
The findings also caution against overreliance on technology without corresponding organizational reform. Digital transformation must be holistic, encompassing structural reorganization, process automation, and cultural adaptation. The absence of these changes limits the impact of FinTech, leading to inefficiencies rather than performance gains.
Strategic and policy implications
For bank leaders, the study recommends prioritizing long-term transformation over short-term digital adoption. Leaders should focus on building digital maturity frameworks that integrate FinTech solutions into core business strategy, not as isolated initiatives. Investing in cybersecurity, data governance, and workforce reskilling are essential steps to ensure sustainability.
For policymakers and regulators, the findings highlight the importance of supportive regulatory frameworks and innovation sandboxes that encourage responsible experimentation. Governments should balance innovation promotion with systemic risk management, ensuring data protection and consumer rights while incentivizing banks to pursue digital competitiveness.
For researchers, the study’s hybrid methodology provides a replicable model for exploring technology adoption in other industries. By combining SEM’s causal precision with ANN’s predictive power, it captures both the structure and the dynamics of digital transformation in complex systems.
The authors also call for extending this research across different national contexts to test the model’s generalizability, especially in regions where financial inclusion and digital literacy are still evolving.