{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T14:26:19Z","timestamp":1781792779388,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100018253","name":"Corvinus University of Budapest","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018253","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The rapid growth of FinTech services, particularly robo-advisors, has transformed how individuals engage with digital financial platforms. Understanding the behavioral drivers of technology acceptance in this context is critical for enhancing adoption and designing more effective user experiences. This study investigates whether user-level behavioral and transactional data can be leveraged to predict technology acceptance, operationalized through daily app usage. Grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the study uses behavioral proxies such as customer satisfaction, loyalty points, and lifetime value to reflect constructs like perceived usefulness, performance expectancy, and facilitating conditions. Using a real-world dataset of 7000 FinTech users sourced from Kaggle, we applied four machine learning algorithms, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to classify users into high and low acceptance categories. Results revealed that ensemble models, particularly XGBoost, outperformed linear classifiers, achieving moderate improvements in precision and recall for the high-acceptance class. However, overall predictive performance remained constrained by class imbalance and overlapping behavioral patterns. These findings suggest that while machine learning can reveal patterns linked to technology acceptance, predictive precision remains limited without richer temporal and psychographic features. The study contributes to the evolving discourse on FinTech adoption by offering a data-driven lens to complement intention-based models and inform adaptive engagement strategies.<\/jats:p>","DOI":"10.1007\/s42979-025-04214-8","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:28:07Z","timestamp":1753356487000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning-Based Analysis of Technology Acceptance in FinTech: A Behavioral Study Using Digital Wallet Data"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-1899","authenticated-orcid":false,"given":"Sayyed Khawar","family":"Abbas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzzammil","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yagya Nath","family":"Rimal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"4214_CR1","doi-asserted-by":"crossref","unstructured":"Barile D, Secundo G, Bussoli C, Exploring artificial intelligence robo-advisor in banking industry: a platform model. Manag Decis (2024).","DOI":"10.1108\/MD-08-2023-1324"},{"issue":"5","key":"4214_CR2","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1108\/JSM-05-2020-0162","volume":"35","author":"L Zhang","year":"2021","unstructured":"Zhang L, Pentina I, Fan Y. Who do you choose? comparing perceptions of human vs robo-advisor in the context of financial services. J Serv Mark. 2021;35(5):634\u201346.","journal-title":"J Serv Mark"},{"key":"4214_CR3","doi-asserted-by":"publisher","first-page":"000085","DOI":"10.1109\/CogInfoCom59411.2023.10397514","volume-title":"2023 14th IEEE international conference on cognitive infocommunications (CogInfoCom)","author":"SK Abbas","year":"2023","unstructured":"Abbas SK, K\u0151 A, Szab\u00f3 Z. B2B financial sector behavior concerning cognitive chatbots. personalized contextual chatbots in financial sector. In: 2023 14th IEEE international conference on cognitive infocommunications (CogInfoCom). IEEE; 2023. p. 000085\u201390."},{"issue":"1","key":"4214_CR4","doi-asserted-by":"publisher","first-page":"3641502","DOI":"10.1155\/2024\/3641502","volume":"2024","author":"L Theodorakopoulos","year":"2024","unstructured":"Theodorakopoulos L, Theodoropoulou A. Leveraging big data analytics for understanding consumer behavior in digital marketing: a systematic review. Hum Behav Emerg Technol. 2024;2024(1):3641502.","journal-title":"Hum Behav Emerg Technol"},{"key":"4214_CR5","first-page":"102680","volume-title":"Intention in information systems adoption and use: current state and research directions","author":"A Jeyaraj","year":"2023","unstructured":"Jeyaraj A, Dwivedi YK, Venkatesh V. Intention in information systems adoption and use: current state and research directions, vol. 73. Elsevier; 2023. p. 102680."},{"issue":"10","key":"4214_CR6","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.3390\/en16104159","volume":"16","author":"J Sun","year":"2023","unstructured":"Sun J, et al. Prediction of toc content in organic-rich shale using machine learning algorithms: comparative study of random forest, support vector machine, and Xgboost. Energies. 2023;16(10):4159.","journal-title":"Energies"},{"issue":"219","key":"4214_CR7","first-page":"5","volume":"205","author":"FD Davis","year":"1989","unstructured":"Davis FD. Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Inf Seek Behav Technol Adopt. 1989;205(219):5.","journal-title":"Al-Suqri, MN, Al-Aufi, AS: Inf Seek Behav Technol Adopt"},{"issue":"3","key":"4214_CR8","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1108\/JEIM-09-2014-0088","volume":"28","author":"MD Williams","year":"2015","unstructured":"Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag. 2015;28(3):443\u201388.","journal-title":"J Enterp Inf Manag"},{"issue":"1","key":"4214_CR9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1108\/JEIM-07-2020-0277","volume":"36","author":"Y-M Cheng","year":"2023","unstructured":"Cheng Y-M. How can robo-advisors retain end-users? Identifying the formation of an integrated post-adoption model. J Enterp Inf Manag. 2023;36(1):91\u2013122.","journal-title":"J Enterp Inf Manag"},{"issue":"6","key":"4214_CR10","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1108\/IMDS-05-2024-0461","volume":"125","author":"Y Chen","year":"2025","unstructured":"Chen Y, Aw EC-X, Tan GW-H. Financial empowerment through robo-advisors: understanding the keys to trust and loyalty. Ind Manag Data Syst. 2025;125(6):2178\u2013205.","journal-title":"Ind Manag Data Syst"},{"issue":"3\u20134","key":"4214_CR11","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1080\/02642069.2022.2161528","volume":"43","author":"EC-X Aw","year":"2023","unstructured":"Aw EC-X, Zha T, Chuah SH-W. My new financial companion! non-linear understanding of robo-advisory service acceptance. Serv Ind J. 2023;43(3\u20134):185\u2013212.","journal-title":"Serv Ind J"},{"key":"4214_CR12","doi-asserted-by":"publisher","first-page":"103518","DOI":"10.1016\/j.erss.2024.103518","volume":"112","author":"A Sundaram","year":"2024","unstructured":"Sundaram A, Gon\u00e7alves J, Ghorbani A, Verma T. Network dynamics of solar PV adoption: reconsidering flat tax-credits and influencer seeding for inclusive renewable energy access in Albany county, New York. Energy Res Soc Sci. 2024;112:103518.","journal-title":"Energy Res Soc Sci"},{"key":"4214_CR13","doi-asserted-by":"publisher","first-page":"102540","DOI":"10.1016\/j.techsoc.2024.102540","volume":"77","author":"K-Y Lin","year":"2024","unstructured":"Lin K-Y, Huang TK. Shopping in the digital world: how augmented reality mobile applications trigger customer engagement. Technol Soc. 2024;77:102540.","journal-title":"Technol Soc"},{"key":"4214_CR14","doi-asserted-by":"publisher","first-page":"70434","DOI":"10.1109\/ACCESS.2024.3402092","volume":"12","author":"A Manzoor","year":"2024","unstructured":"Manzoor A, Qureshi MA, Kidney E, Longo L. A review on machine learning methods for customer churn prediction and recommendations for business practitioners. IEEE Access. 2024;12:70434\u201363.","journal-title":"IEEE Access"},{"key":"4214_CR15","first-page":"1","volume-title":"2025 international conference on intelligent systems and computational networks (ICISCN)","author":"V Selvalakshmi","year":"2025","unstructured":"Selvalakshmi V, Sree TMU, Saranya S, Devi AU, Basha MSA. Enhancing customer personality prediction using advanced machine learning techniques and data balancing strategies: a comprehensive approach to addressing imbalanced datasets in marketing analytics. In: 2025 international conference on intelligent systems and computational networks (ICISCN). IEEE; 2025. p. 1\u20137."},{"issue":"8","key":"4214_CR16","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1108\/MD-09-2019-1318","volume":"58","author":"S Singh","year":"2020","unstructured":"Singh S, Sahni MM, Kovid RK. What drives FinTech adoption? a multi-method evaluation using an adapted technology acceptance model. Manag Decis. 2020;58(8):1675\u201397.","journal-title":"Manag Decis"},{"key":"4214_CR17","doi-asserted-by":"publisher","first-page":"112014","DOI":"10.1016\/j.rser.2021.112014","volume":"157","author":"F Barjak","year":"2022","unstructured":"Barjak F, Lindeque J, Koch J, Soland M. Segmenting household electricity customers with quantitative and qualitative approaches. Renew Sustain Energy Rev. 2022;157:112014.","journal-title":"Renew Sustain Energy Rev"},{"issue":"1","key":"4214_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10660-021-09527-3","volume":"24","author":"T Roh","year":"2024","unstructured":"Roh T, Yang YS, Xiao S, Park BI. What makes consumers trust and adopt fintech? an empirical investigation in China. Electron Commer Res. 2024;24(1):3\u201335.","journal-title":"Electron Commer Res"},{"issue":"6","key":"4214_CR19","doi-asserted-by":"publisher","first-page":"7367","DOI":"10.1007\/s13369-022-06560-8","volume":"47","author":"T Kavzoglu","year":"2022","unstructured":"Kavzoglu T, Teke A. Predictive performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arab J Sci Eng. 2022;47(6):7367\u201385.","journal-title":"Arab J Sci Eng"},{"issue":"7","key":"4214_CR20","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1016\/j.spinee.2021.02.007","volume":"21","author":"Z DeVries","year":"2021","unstructured":"DeVries Z, et al. Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability. Spine J. 2021;21(7):1135\u201342.","journal-title":"Spine J"},{"key":"4214_CR21","doi-asserted-by":"publisher","first-page":"130265","DOI":"10.1016\/j.chemosphere.2021.130265","volume":"276","author":"S Singha","year":"2021","unstructured":"Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S. Prediction of groundwater quality using efficient machine learning technique. Chemosphere. 2021;276:130265.","journal-title":"Chemosphere"},{"key":"4214_CR22","doi-asserted-by":"publisher","first-page":"120303","DOI":"10.1016\/j.eswa.2023.120303","volume":"227","author":"A Kumaravel","year":"2023","unstructured":"Kumaravel A, Vijayan T. Comparing cost sensitive classifiers by the false-positive to false-negative ratio in diagnostic studies. Expert Syst Appl. 2023;227:120303.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4214_CR23","doi-asserted-by":"publisher","first-page":"2361321","DOI":"10.1080\/23311975.2024.2361321","volume":"11","author":"N Ali","year":"2024","unstructured":"Ali N, Shabn OS. Customer lifetime value (CLV) insights for strategic marketing success and its impact on organizational financial performance. Cogent Bus Manag. 2024;11(1):2361321.","journal-title":"Cogent Bus Manag"},{"key":"4214_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3554642","author":"G Zemin","year":"2025","unstructured":"Zemin G, et al. MIRRIFT: Multimodal image rotation and resolution invariant feature transformation. IEEE Trans Geosci Remote Sens. 2025. https:\/\/doi.org\/10.1109\/TGRS.2025.3554642.","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"4214_CR25","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1080\/23750472.2022.2072372","volume":"29","author":"D Won","year":"2024","unstructured":"Won D, Lee C. What influences season ticket holders\u2019 satisfaction and renewal intention? the role of season ticket service quality. Manag Sport Leisure. 2024;29(4):572\u201390.","journal-title":"Manag Sport Leisure"},{"issue":"1","key":"4214_CR26","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s41512-024-00177-1","volume":"8","author":"L Barre\u00f1ada","year":"2024","unstructured":"Barre\u00f1ada L, Dhiman P, Timmerman D, Boulesteix A-L, Van Calster B. Understanding overfitting in random forest for probability estimation: a visualization and simulation study. Diagn Progn Res. 2024;8(1):14.","journal-title":"Diagn Progn Res"},{"issue":"1","key":"4214_CR27","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1108\/JSM-04-2024-0156","volume":"39","author":"F Ehsani","year":"2025","unstructured":"Ehsani F, Hosseini M. Customer churn analysis using feature optimization methods and tree-based classifiers. J Serv Mark. 2025;39(1):20\u201335.","journal-title":"J Serv Mark"},{"key":"4214_CR28","doi-asserted-by":"publisher","DOI":"10.3727\/152599525X17367484906390","author":"J Fry","year":"2024","unstructured":"Fry J, Fuller-Love N, Owen R. VIP\/Hospitality event packages: using online reviews to improve the ticket purchase journey map. Event Manag. 2024. https:\/\/doi.org\/10.3727\/152599525X17367484906390.","journal-title":"Event Manag"},{"key":"4214_CR29","doi-asserted-by":"publisher","first-page":"107056","DOI":"10.1016\/j.sab.2024.107056","volume":"221","author":"C Duan","year":"2024","unstructured":"Duan C, et al. A combination of XGBoost and neural network in LIBS spectrum processing for precise determination of critical elements in 620 iron ore samples of various origins. Spectrochim Acta, Part B. 2024;221:107056.","journal-title":"Spectrochim Acta, Part B"},{"key":"4214_CR30","doi-asserted-by":"publisher","first-page":"13686","DOI":"10.1109\/ACCESS.2025.3531662","volume":"13","author":"M Altalhan","year":"2025","unstructured":"Altalhan M, Algarni A, Alouane MT-H. Imbalanced data problem in machine learning: a review. IEEE Access. 2025;13:13686\u201399.","journal-title":"IEEE Access"},{"issue":"3","key":"4214_CR31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Commun ACM. 2021;64(3):107\u201315.","journal-title":"Commun ACM"},{"key":"4214_CR32","doi-asserted-by":"publisher","DOI":"10.1080\/1528008X.2025.2460202","author":"Y Pan","year":"2025","unstructured":"Pan Y, Chen H. Securing customer loyalty in the highly competitive chinese hospitality market: an examination of the influence of sustainability, service quality, and brand equity. J Qual Assur Hosp Tour. 2025. https:\/\/doi.org\/10.1080\/1528008X.2025.2460202.","journal-title":"J Qual Assur Hosp Tour"},{"issue":"6","key":"4214_CR33","doi-asserted-by":"publisher","first-page":"131","DOI":"10.12700\/APH.22.6.2025.6.9","volume":"22","author":"SK Abbas","year":"2025","unstructured":"Abbas SK, Szab\u00f3 Z, K\u0151 A. Robo-advisors in fintech-challenges and solutions. Acta Polytech Hung. 2025;22(6):131\u201351.","journal-title":"Acta Polytech Hung"},{"issue":"11","key":"4214_CR34","first-page":"1011","volume":"20","author":"SK Abbas","year":"2024","unstructured":"Abbas SK. AI meets finance: the rise of AI-powered Robo-advisors. J Electr Syst. 2024;20(11):1011\u20136.","journal-title":"J Electr Syst"},{"issue":"7","key":"4214_CR35","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1108\/IMDS-08-2018-0368","volume":"119","author":"D Belanche","year":"2019","unstructured":"Belanche D, Casal\u00f3 LV, Flavi\u00e1n C. Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Ind Manag Data Syst. 2019;119(7):1411\u201330.","journal-title":"Ind Manag Data Syst"},{"key":"4214_CR36","doi-asserted-by":"publisher","first-page":"101260","DOI":"10.1016\/j.frl.2019.08.008","volume":"34","author":"M J\u00fcnger","year":"2020","unstructured":"J\u00fcnger M, Mietzner M. Banking goes digital: the adoption of FinTech services by German households. Financ Res Lett. 2020;34:101260.","journal-title":"Financ Res Lett"},{"issue":"1","key":"4214_CR37","first-page":"1","volume":"10","author":"P Mantello","year":"2023","unstructured":"Mantello P, Ho M-T, Nguyen M-H, Vuong Q-H. Machines that feel: behavioral determinants of attitude towards affect recognition technology\u2014upgrading technology acceptance theory with the mindsponge model. Hum Soc Sci Commun. 2023;10(1):1\u201316.","journal-title":"Hum Soc Sci Commun"},{"issue":"3","key":"4214_CR38","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s00187-022-00343-w","volume":"33","author":"\u00c1 Szukits","year":"2022","unstructured":"Szukits \u00c1. The illusion of data-driven decision making\u2013The mediating effect of digital orientation and controllers\u2019 added value in explaining organizational implications of advanced analytics. J Manag Control. 2022;33(3):403\u201346.","journal-title":"J Manag Control"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04214-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04214-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04214-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T21:32:54Z","timestamp":1757280774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04214-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["4214"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04214-8","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]},"assertion":[{"value":"17 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"674"}}