Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance
In recent years, the rapid strides made by artificial intelligence (AI) in the financial services industry have not only reshaped its landscape but have also brought along disruptive innovations. Prominent among them are the robo-advisors (RAs), which have brought in catalytic changes in efficiency,...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188825001406 |
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| author | Sindhu Singh Bhargavi Karamcheti |
| author_facet | Sindhu Singh Bhargavi Karamcheti |
| author_sort | Sindhu Singh |
| collection | DOAJ |
| description | In recent years, the rapid strides made by artificial intelligence (AI) in the financial services industry have not only reshaped its landscape but have also brought along disruptive innovations. Prominent among them are the robo-advisors (RAs), which have brought in catalytic changes in efficiency, scalability, and personalization in the fintech space. Despite these inherent strengths, the penetration of these algorithm-driven platforms has continued to be suboptimal. The main hindrance to their adoption has been concerns over trust, perceived risks, and lack of human interaction. Against this backdrop, the current study presents an integrative framework of dual-factor theory and the benefit-risk model to identify and focus on the interplay between the push-pull factors, both positive (enablers) and negative (inhibitors), that determine the perceived benefit and perceived risk of RAs, resulting in the willingness and objection to RA usage. By deploying the PLS-SEM analysis on a diverse Indian sample, the study identified and empirically validated perceived anthropomorphism, social influence, and trust as the three distinct enablers that lead to the perceived benefit of employing RAs. The study further highlighted the pivotal role of poor interaction quality and lack of awareness in driving the perceived risk of RAs. By incorporating robo-design features, customer-centric and service factors as antecedents, this study not only contributes to the theoretical discourse on RA adoption research but also provides actionable insights for financial institutions, RA platform developers, and policymakers to improve increased market penetration of these AI-driven financial services in India's rapidly evolving fintech landscape. |
| format | Article |
| id | doaj-art-ff510ba8eae240658d470c3d0f8465e2 |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-ff510ba8eae240658d470c3d0f8465e22025-08-20T03:45:34ZengElsevierSustainable Futures2666-18882025-06-01910057010.1016/j.sftr.2025.100570Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptanceSindhu Singh0Bhargavi Karamcheti1Corresponding author.; K J Somaiya Institute of Management, Somaiya Vidyavihar University, Vidyavihar, Mumbai-77, IndiaK J Somaiya Institute of Management, Somaiya Vidyavihar University, Vidyavihar, Mumbai-77, IndiaIn recent years, the rapid strides made by artificial intelligence (AI) in the financial services industry have not only reshaped its landscape but have also brought along disruptive innovations. Prominent among them are the robo-advisors (RAs), which have brought in catalytic changes in efficiency, scalability, and personalization in the fintech space. Despite these inherent strengths, the penetration of these algorithm-driven platforms has continued to be suboptimal. The main hindrance to their adoption has been concerns over trust, perceived risks, and lack of human interaction. Against this backdrop, the current study presents an integrative framework of dual-factor theory and the benefit-risk model to identify and focus on the interplay between the push-pull factors, both positive (enablers) and negative (inhibitors), that determine the perceived benefit and perceived risk of RAs, resulting in the willingness and objection to RA usage. By deploying the PLS-SEM analysis on a diverse Indian sample, the study identified and empirically validated perceived anthropomorphism, social influence, and trust as the three distinct enablers that lead to the perceived benefit of employing RAs. The study further highlighted the pivotal role of poor interaction quality and lack of awareness in driving the perceived risk of RAs. By incorporating robo-design features, customer-centric and service factors as antecedents, this study not only contributes to the theoretical discourse on RA adoption research but also provides actionable insights for financial institutions, RA platform developers, and policymakers to improve increased market penetration of these AI-driven financial services in India's rapidly evolving fintech landscape.http://www.sciencedirect.com/science/article/pii/S2666188825001406Robo-advisorPerceived benefitAIPerceived riskFinancial literacyTrust |
| spellingShingle | Sindhu Singh Bhargavi Karamcheti Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance Sustainable Futures Robo-advisor Perceived benefit AI Perceived risk Financial literacy Trust |
| title | Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance |
| title_full | Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance |
| title_fullStr | Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance |
| title_full_unstemmed | Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance |
| title_short | Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance |
| title_sort | robo advisor enablers and inhibitors a dual factor framework and a benefit risk model integration for understanding customer acceptance |
| topic | Robo-advisor Perceived benefit AI Perceived risk Financial literacy Trust |
| url | http://www.sciencedirect.com/science/article/pii/S2666188825001406 |
| work_keys_str_mv | AT sindhusingh roboadvisorenablersandinhibitorsadualfactorframeworkandabenefitriskmodelintegrationforunderstandingcustomeracceptance AT bhargavikaramcheti roboadvisorenablersandinhibitorsadualfactorframeworkandabenefitriskmodelintegrationforunderstandingcustomeracceptance |