Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Verma, Balraj; Schulze, Mike; Goswami, Divya; Upreti, Kamal Title A name given to the resource Artificial intelligence attitudes and resistance to use robo-advisors: exploring investor reluctance toward cognitive financial systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Frontiers in Artificial Intelligence;Volume;8;Issue;;Article No.;1623534; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3389/frai.2025.1623534" target="_blank" rel="noreferrer noopener">https://doi.org/10.3389/frai.2025.1623534</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017896058?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017896058?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Verma B., Chitkara Business School, Chitkara University, Punjab, India; Schulze M., CBS International Business School, CBS University of Applied Sciences, Mainz, Germany; Goswami D., Chitkara Business School, Chitkara University, Punjab, India; Upreti K., Department of Computer Science, Christ University, Delhi, India Description An account of the resource Introduction: The study investigates resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, grounded in innovation resistance theory. The study examines the impact of functional barriers and psychological barriers on resistance to FRAs, while considering users attitudes towards Artificial Intelligence (AI) as a moderator. It further evaluate the influence of such resistance on users intentions to use and recommend FRAs. Methods: Utilizing purposive sampling data was collected from 409 investors and further analyzed using structural equation modelling. Results: The findings revealed that all barriers under study, expect value barrier, substantially derive resistance towards robo-advisors, with inertia being the strongest determinant. Further, this resistance impedes both the intention to use FRAs and to recommend them. Moderation analysis results finds that users attitude towards AI significantly weakens the influence of inertia, overconfidence bias and data privacy risk on resistance, with no such impact on other relationships. Discussion: Overall, the study enriches IRT in Fintech context and provides theoretical and practical insights to enhance FRAs adoption in emerging markets. Copyright 2025 Verma, Schulze, Goswami and Upreti. Subject The topic of the resource artificial intelligence; attitude toward AI; cognitive financial systems; FRA; resistance; robo-advisors Publisher An entity responsible for making the resource available Frontiers Media SA Relation A related resource ISSN: 26248212; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online