EXPLORING USER PERSPECTIVES ON THE APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL TECHNOLOGY

Abstract

This study investigates the factors influencing the adoption of fintech AI among 397 government college students from middle-class families in Haryana, India. Structural equation modeling was employed to study the relationships between the adoption of fintech AI and its predictors, namely Perceived Usefulness, Perceived Privacy and Security, Perceived Ease of Use, and Satisfaction of Fintech AI. This study utilized a cross-sectional research design. In this study, a non-probability convenience sampling method was utilized. The researchers opted for a sample of 397 government college students, consisting of 200 males and 197 females, hailing from middle-class families in Haryana, India. This sampling approach was selected because of its simplicity and ease of access, as it enabled the researchers to effortlessly connect with participants without resorting to random selection. The participants were drawn from various subject streams to guarantee a diverse representation of the student body. Data collection occurred between November 2022 and January 2023. The findings suggest that as perceived usefulness, ease of use, and satisfaction with fintech AI increase, the likelihood of adoption also increases. Conversely, as perceived Privacy and Security increase, the likelihood of adoption decreases. It is important to note that the strength of these relationships varies, with the strongest positive relationship observed between Adoption and Satisfaction of Fintech AI, while the relationships with Perceived Usefulness, Perceived Privacy and Security, and Perceived Ease of Use are weaker. These insights provide valuable information for organizations and policymakers seeking to promote the adoption of fintech AI among college students. Strategies aimed at addressing Privacy and Security concerns, enhancing user satisfaction, and improving perceived usefulness and ease of use may prove effective in increasing the adoption of fintech AI within this demographic.

Keywords

• structural equation modeling
• perceived usefulness
• perceived privacy and security
• perceived ease of use
• satisfaction of fintech ai
• adoption of fintech ai