Fit for the future: Examining the impact of task-technology fit on bank employee intentions to use FinTech

Abstract

Background: FinTech revolutionizes banking by transforming traditional practices, making it crucial to understand how bank employees adopt these innovations for enhanced operational effectiveness.

Aim: This study examines the intention to use FinTech among employees, utilising an integrated task-technology fit (TTF) and technology acceptance model (TAM). Unlike previous studies, it examines the influence of TTF at three levels (underfit, overfit and ideal fit) on TAM and the intention to use FinTech from the perspectives of bank employees.

Setting: The research focuses on three major banks in Ethiopia, a developing economy with growing FinTech adoption in the banking sector.

Method: The study employed a quantitative research method, gathering data via a questionnaire survey of 213 bank employees in Ethiopia. The analysis was conducted using structural equation modelling via Smart-PLS software.

Results: The findings reveal that an ideal fit significantly and positively impacts perceived usefulness, ease of use and intention to adopt FinTech. Underfit negatively influences perceived usefulness, while overfit shows no significant impact on perceived usefulness, ease of use or adoption intention. In addition, perceived usefulness directly and significantly affects the intention to use FinTech.

Conclusion: FinTech adoption in banks is optimised when the technology is ideally suited to employees’ tasks. Excessive or insufficient task-technology alignment can hinder FinTech adoption in the emerging economy context.

Contribution: The study advances theoretical understanding by integrating TTF and TAM, introducing the concept of varying TTF levels. It also broadens the framework’s application to emerging economies, offering practical insights for optimising FinTech adoption in banking.