Modeling AI Adoption Intention in the Banking Sector Using the TOE Framework: The Mediating Role of Complexity

Sangita Basak; Rosli Mahmood; Noor Maimun Abdul Wahab; Nur Shazwani Rosli; Rasel Barua

doi:10.47405/mjssh.v11i6.4021

Sangita Basak Department of Management Studies, Comilla University, Cumilla-3506, Bangladesh;
Rosli Mahmood Putra Business School, UPM, 43400 Seri Kembangan, Selangor, Malaysia.
Noor Maimun Abdul Wahab Putra Business School, UPM, 43400 Seri Kembangan, Selangor, Malaysia.
Nur Shazwani Rosli Sunway Business School, Sunway University, Selangor, Malaysia.
Rasel Barua Al-Arafah Islami Bank PLC., Dhaka, Bangladesh.

DOI: https://doi.org/10.47405/mjssh.v11i6.4021

Keywords: AI adoption, T-O-E framework, Complexity, Banking Sector, Bangladesh

Abstract

The introduction of AI is one of the best changes any industry can adopt; the global banking sector is no exception, given that it brings both efficiency and highly personalised service, as well as systematic, data-driven decision-making. Empirical case studies of AI adoption in banking are limited, despite its strategic importance. Based on an extended Technology–Organisation–Environment (TOE) framework, this study investigates the factors influencing Artificial Intelligence adoption intention in Bangladesh's banking sector. In particular, relative advantage, top management support and competitive pressure are conceptualised as significant executives in the technology, organisational and environmental contexts, respectively, whereas complexity is added as a mediating mechanism to extend the TOE framework. The proposed model was tested on a cross-sectional survey database of 360 employees and managers from scheduled commercial banks using the Partial Least Squares Structural Equation Modelling (PLS-SEM). Increases in both competitive pressure and top management support were found to significantly positively influence AI adoption intention. On the contrary, relative advantage has little impact. Similarly, the complexity mediation effect is rejected, indicating that perceived technological difficulty does not serve as a significant mechanism through which TOE factors affect adoption intention in this context. This article adds to the target selection literature by conducting empirical research grounded in a recognized antecedent TOE framework that extends an in-use TOE model in the context of AI adoption in emerging-economy banking. The findings offer theoretical insights into the boundary conditions of complexity as a mediating construct and provide practical implications for banking executives and policymakers aiming to accelerate AI-driven transformation.

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