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Understanding Customers in AI-empowered Financial Advisory Systems and Services

An interdisciplinary study of Robo-advisors

Time: Tue 2023-05-30 10.00

Location: E2, Lindstedtsvägen 3, Stockholm

Video link:

Language: English

Subject area: Business Studies

Doctoral student: Hui Zhu , Fastighetsföretagande och finansiella system

Opponent: Docent Jakob Tholander, Stockholms universitet

Supervisor: Docent Inga-Lill Söderberg, Fastighetsföretagande och finansiella system; Professor Eva-Lotta Sallnäs, Medieteknik och interaktionsdesign, MID

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QC 20230508


AI-empowered financial advisory services, also known as robo-advisors, present both innovations and challenges as they replace human financial advisors, reshape customer service, and attract customers with different characteristics than their predecessors. Therefore, it is more important than ever for financial service providers to understand customers’ perception and experience of using robo-advisors on the service front line. However, our systematic literature review indicates that research on robo-advisors is scattered across different disciplines with a narrow focus on sharing knowledge across fields. Moreover, empirical studies on robo-advisors have primarily focused on customer acceptance and intentional behaviors, often based on data collected through surveys. Also, these studies have paid less attention to the role of robo-advisor design and the context in which customers interact with fully functional robo-advisors in real-life situations. To address these gaps, this thesis aims to synthesize interdisciplinary knowledge and identify gaps in the research on robo-advisors. It also aims to explore customers’ experience of using and interacting with robo-advisors and how the experience affects their perception and adoption of the service. By conducting a systematic literature review and a qualitative user study, this thesis finds that customers’ perceptions of robo-advisors often do not meet their expectations. Nontransparency and incomprehensible information about the system’s decision-making are significant barriers to customers’ adoption of robo-advisors. This thesis contributes to a deeper understanding of customers in AI-empowered financial advisory services by using theories and approaches across different disciplines. It also provides practical implications for practitioners in the robo-advisory service industry.