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Understanding the Acceptance of Robo-Advisors: Towards a Hierarchical Model Integrated Product Features and User Perceptions

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Human Aspects of IT for the Aged Population. Technology Design and Acceptance (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12786))

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Abstract

Robo-advisors have recently become increasingly accessible and gaining interest among consumers. However, there still exist problems in acceptance of robo-advisors among consumers. In this study, we have identified influential factors of robo-advisor acceptance, conducting an online survey involving 207 participants to examine their relationship with use intention. A hierarchical model of robo-advisor acceptance was built that integrated product features, user perceptions, and use intention. The model showed that the competence and expected earnings of a robo-advisor contribute to users’ perception of its usefulness. Furthermore, the customization and competence of a robo-advisor decreases users’ perceived risk. Additionally, better designed account management and more authority for users increase perceived control over a robo-advisor. Moreover, although usefulness was regarded as the most important factor, only perceived risk (negatively) and perceived control (positively) were significantly associated with the intention to use robo-advisors. Based on the acceptance model, implications for the robo-advisor design were discussed.

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Authors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Beijing, People’s Republic of China

    Man Wu & Qin Gao

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  1. Man Wu
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  2. Qin Gao
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Correspondence to Qin Gao .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Qin Gao

  2. Chongqing University, Chongqing, China

    Jia Zhou

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Wu, M., Gao, Q. (2021). Understanding the Acceptance of Robo-Advisors: Towards a Hierarchical Model Integrated Product Features and User Perceptions. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. Technology Design and Acceptance. HCII 2021. Lecture Notes in Computer Science(), vol 12786. Springer, Cham. https://doi.org/10.1007/978-3-030-78108-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-78108-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78107-1

  • Online ISBN: 978-3-030-78108-8

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