Psychological predictors of financial technology adoption

The role of trust, attitude, and demographics in AI based financial ChatBots use

Bibliographic Data

ID21282993
AuthorsFalak Khan (0000-0003-4341-1072, National University of Computer and Emerging Sciences, corresponding author), Muhammad Hassaan (0000-0002-5652-8598, National University of Computer and Emerging Sciences), Neha Zainab (National University of Computer and Emerging Sciences), Aimel Hasan (National University of Computer and Emerging Sciences), Fatima Ajmal, Fahad Ajmal (National University of Computer and Emerging Sciences), Farah Jabeen Awan (National University of Computer and Emerging Sciences)
Year2026
Volume268
Pages107124
Publication date2026-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueActa Psychologica (JOURNAL)
Journal identifiersISSN: 0001-6918 • E-ISSN: 1873-6297
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.actpsy.2026.107124
PMID42259211
OpenAlexW7163882510
LanguageEN
References cited34

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

Demographics · Empirical research · Fintech · Investment (military) · Marital status · Socioeconomic status · Technology Acceptance Model · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · FinTech, Crowdfunding, Digital Finance

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