Generative AI Reliance and Perceived Investment Decision Quality Among Saudi Arabian Retail Investors: The Sequential Roles of Perceived Reliability, Confidence, and AI Overreliance
Year: 2026
Type: article
Abstract: Generative AI is increasingly influencing how retail investors access financial information, evaluate investment alternatives, and make decisions. However, its behavioral implications remain insufficiently understood, particularly in rapidly developing digital financial markets. Against the backdrop of Saudi Arabia’s financial-sector digital transformation under Vision 2030, this study examines how Generative AI investment reliance relates to investment decision quality through perceived AI reliability, AI-Induced Investor Confidence, and AI investment overreliance. It also investigates the moderating role of Generative AI financial literacy. Drawing on Behavioral Finance Theory and Automation Bias, data collected from 497 Saudi Arabian retail investors with prior experience using Generative AI for investment-related activities were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings reveal contrasting relationships. Generative AI investment reliance is positively associated with perceived investment decision quality, while a negative sequential indirect relationship emerges through perceived AI reliability, investor confidence, and AI overreliance. Generative AI financial literacy weakens the relationship between perceived AI reliability and investor confidence. These findings highlight a potential GenAI confidence paradox, whereby reliance on AI may support investors’ perceived decision-making quality while also being associated with behavioral vulnerabilities arising from excessive confidence and dependence. The study emphasizes the importance of critical verification, calibrated AI reliance, and financial literacy. As the evidence is drawn exclusively from Saudi Arabian retail investors, its applicability to other financial markets requires further investigation.
DOI: 10.3390/jrfm19110908
Author: Emadaldeen Hassan Alomar
Institution: Jazan University
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $1,689
OpenAlex ID: W7225661693