Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model

Abstract

Introduction/Main Objectives: This study investigates the determinants of willingness to use financial robo-advisory services. The study aims to identify the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumers’ acceptance of financial robo-advisory services. Background Problem: Fintech and AI-based applications have opened up new prospects for financial management, but studies into the adoption and implementation of robo-advisors are limited and scant. Novelty: The study offers novel insights by exploring the direct and indirect effects of perceived value and risk on consumer deci­sions around adopting robo-advisory services. The study also identifies other major drivers of robo-advisory service adoption and formulates a comprehensive model. Research Methods: A quantitative method using a deductive approach was applied, with PLS-SEM performed on a sample of 285 respondents from Bangladesh. The sample was gathered using a purposive sampling method. Findings/Results: Findings revealed that while relative advantage and perceived innovativeness positively affected perceived value and adoption intention, complexity negatively impacted perceived value and adoption intention. The findings also highlighted that attitude had a negative effect on perceived risk and intention to adopt robo-advisory services. The mediating impact of perceived value and risk in predicting the relationship between relative advantage, attitude and behavioral intention to adopt robo-advisory services was also identified. Moreover, the study revealed that perceived financial knowledge moderated the relationship between perceived value and behavioral intention. Conclusion: This study contributes to the existing body of literature by showing the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumer acceptance of robo-advisory services. The study provides meaningful insights for financial institutions, and policymakers seeking to make robo-advisory services more reliable and acceptable to consumers through innovative service design and positioning.

Prediction of robo-advisory acceptance in banking services using tree-based algorithms

“…The literature identifies factors that significantly influence the acceptance of robo-advisory among consumers. Very often they refer to technological and operational aspects of robo-advisory: level of automation, transparency, and control [17], comprehensibility [18], perceived usefulness [19], privacy [20], perceived innovativeness [21], data security [22], ease of interaction and work efficiency [23][24][25][26]. Demographic and socio-economic factors are also important determinants of the acceptance of robo-advisory.…”