Understanding User and Investor Perspectives on Robo-Advisors Adoption in Fintech [védés előtt]

Kivonat, rövid leírás

The integration of Artificial Intelligence (AI) into financial services, particularly through AI-powered chatbots and Robo-advisors, is redefining customer interaction and service delivery in the FinTech sector. Despite their technological sophistication and growing global presence, user and investor adoption of Robo-advisors remains uneven, primarily due to persisting concerns around trust, data security, and privacy. This doctoral dissertation investigates the determinants influencing the acceptance and adoption of Robo-advisors, drawing on both user-centric and technology-driven perspectives. The research aims to fill a gap in existing literature by offering a comprehensive, theory-driven framework that explains how Robo-advisors can be successfully adopted in different sociotechnical environments—specifically within Pakistan and Hungary. The study employs a mixed-methods approach. The qualitative phase consists of semi-structured interviews with 34 FinTech professionals in Pakistan and 15 industry experts in Hungary. These interviews, analyzed using Grounded Theory and Social Representation Theory (SRT), identify key thematic concerns and solutions regarding trust, security, privacy, user experience, and institutional credibility. NVivo-based coding reveals six primary challenge domains: data-security anxieties, consent and privacy management, institutional trust gaps, user-experience design, technology integration, and regulatory oversight. Findings underscore that transparency in data handling, visible regulatory compliance, and intuitive user interfaces are crucial to building trust in AI-powered financial tools. To quantitatively validate these qualitative insights, the research proceeds with a Systematic Literature Review (SLR) and hypothesis-driven Structural Equation Modeling (SEM). The SLR analyzes 22 empirical studies, identifying relevant theoretical models such as the Technology Acceptance Model (TAM), DeLone & McLean Information Systems Success Model (ISSM), and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Constructs like perceived ease of use, perceived usefulness, system and information quality, perceived risk, customer experience, and trust were incorporated into four competing SEM models tested using SmartPLS and Adanco. The SEM analysis confirms that perceived ease of use, usefulness, and trust positively influence users’ attitudes toward Robo-advisors, which in turn affects their intention to adopt. Moreover, perceived information, system, and service quality impact customer experience, which mediates the relationship with adoption intention. Moderating effects of perceived risk and demographic variables (age, gender, digital literacy) are also found to be significant. The results reveal that while utilitarian value is foundational, emotional factors—especially trust and perceived security—are critical for mass acceptance, particularly in emerging markets. From a managerial standpoint, the findings offer actionable strategies for FinTech providers. In Pakistan, emphasis should be placed on education, transparency, and secure infrastructure to mitigate user fears about data misuse. In Hungary, where regulatory compliance and user interface sophistication are key, firms must invest in localized language interfaces, public regulatory disclosures, and responsive customer onboarding. The study also highlights the growing ethical implications of deploying Large Language Models (LLMs) in Robo-advisors, especially around algorithmic bias and privacy consent. This dissertation makes multiple theoretical and practical contributions. It advances the academic understanding of FinTech adoption by proposing an integrative adoption framework grounded in both user behavior theories and information systems models. Practically, it provides FinTech developers, regulators, and service providers with a roadmap to increase customer satisfaction, mitigate ethical risks, and improve adoption rates. Future research could explore longitudinal changes in user behavior, the role of human-AI collaboration in financial advice, and the cross-cultural transferability of Robo-advisor systems.