How trustworthy AI fosters adoption and user advocacy in financial advisory services

Abstract

In many countries, and particularly in developing economies, the availability of professional financial advisors remains far below what is needed. This shortage leaves large numbers of people without structured support for saving, investment, or retirement planning. Those who do seek out human advisors often report strong trust in their expertise, but they represent only a small portion of society. Recent advances in artificial intelligence have opened a different pathway. AI Financial Advisors are not positioned to replace professionals outright but to act as complementary tools that extend affordable and timely guidance to wider groups. Their acceptance, however, depends on whether people regard such systems as ethical and therefore trustworthy. To investigate this question, the study develops a model that brings together the Stimulus–Organism–Behavior–Consequence (SOBC) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). SOBC is used to trace the process of decision-making: perceptions of ethical design—such as equity and sustainability, privacy and safety, transparency and accountability, shape trust in AI, which then lead to intentions and advocacy. UTAUT complements this by highlighting the role of familiar technology drivers, including effort expectancy, performance expectancy, social influence, and the extent to which users perceive anthropomorphism and personalization in AI systems. Survey data were gathered from 420 employed adults in Thailand who actively use mobile banking applications that incorporate AI-enabled financial advisory features. Results from structural equation modeling confirm that trust in AI acts as the central mechanism. It strongly predicts intention to use and directly influences both word-of-mouth advocacy and customer loyalty. Ethical dimensions that emphasize equity and sustainability, transparency and accountability, privacy and safety strengthen trust. From the technology side, performance expectancy, social influence, and anthropomorphism all had significant effects, while effort expectancy and personalization did not. Overall, the findings show that AI Financial Advisory Services should be understood not only as technological artifacts but as strategic mechanisms for expanding access to financial planning in ways that support inclusion and sustainability. The proposed model connects business strategy with sustainable development and AI governance, offering direction for both policymakers and practitioners in designing advisory systems that are effective in practice and trusted by society.