Psychological predictors of financial technology adoption: The role of trust, attitude, and demographics in AI based financial ChatBots use

As Large Language Models (LLMs) are increasingly shaping decision-making across various sectors, including finance, understanding the psychological and demographic factors influencing their adoption is both timely and critical. This study examines the role of perceived trust in the adoption intention of financial robo-advisors for solving investment problems. It also tests the moderating role of gender in shaping the behavioral intention, which remained underexplored in the prior literature. By exploring other demographic and socioeconomic factors like age, marital status, and gender, the study analyzes surveys of 364 participants in a developing country context. Results show that education significantly enhances the intention to adopt LLM-based financial services, while women exhibit higher adoption intention as compared to their counterparts, thereby highlighting a gender trust gap. These findings offer important implications for policymakers and FinTech developers aiming to foster inclusive, trust-based engagement with AI-driven financial tools. By contextualizing LLM adoption within the socio-technical realities of developing economies, this study contributes to the literature on technology acceptance and digital trust, offering a theoretical and empirical basis for future interdisciplinary research in AI adoption behavior.