ArticleFrontiers in artificial intelligence2026
Assessing the utility of advanced adoption models for AI-based financial services: insights into automated and hybrid robo-advisors.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
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4 authors.
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Abstract
Introduction: Financial robo-advisors (FRAs), based on artificial intelligence (AI), are changing the ways people make investment decisions by providing advisory services that are based on algorithms and have different degrees of automation. But little evidence exists on the appropriateness of existing technology adoption theories to explain investor adoption of such systems. The present study investigates the explanatory and predictive power of three established and distinct adoption models for AI-enabled FRAs. It further develops a context-specific integrated adoption framework and examines whether adoption mechanisms differ between fully autonomous and hybrid robo-advisory services. Methods: For data collection, a quantitative research design was employed on 397 Indian investors through purposive sampling. The study uses explanatory and predictive assessment, integrated factor analysis, structural equation modeling, and multigroup analysis. Results: The findings reveal that the SVAM outperforms the TPB and UTAUT-II in terms of explanatory and predictive power and underscores the significance of value perceptions and self-efficacy in AI-enabled financial decision-making. The integrated framework identified attitude, hedonic automatism, perceived value, self-efficacy, performance expectancy, effort expectancy, and normative control perception as significant antecedents of behavioural intention, which in turn drives usage behaviour. The results also indicate that the impact of adoption drivers differs across types of robo-advisors, with self-efficacy playing a more significant role in fully autonomous settings, and attitude, effort, and performance-related evaluations gaining more importance in hybrid environments. Discussion: This study contributes to the literature on AI adoption by integrating competing theoretical perspectives into a context-specific framework and by establishing the type of FRA as an important boundary condition for investor behaviour.
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