This research investigates the socio-demographic determinants of the use and acceptance of artificial intelligence, by comparatively analyzing the context of personal financial decisions and organizational financial decisions. In this sense, the purpose of the study is to identify how the profile of the respondents influences their openness to algorithmic solutions in the two distinct spheres of their lives. The methodology used is based on machine learning, namely on decision trees, and the profiles of the respondents are segmented according to age, level of education and professional experience. The main results indicate a significant divergence between the two settings analyzed. Thus, in the sphere of personal finance, professional experience is the main factor, while in the organizational environment, age becomes the central variable. The study’s conclusions highlight the contextual nature of AI acceptance in financial decisions. While in private life respondents exhibit proactive behavior, based on the accumulation of skills and the need to optimize financial resources, in the workplace context, compliance with artificial intelligence is mediated differently. However, it should be emphasized that the model explains only a marginal share of the variance in financial decision-making, more exactly 3.4% for individual decisions and almost 0 for organizational financial decisions. Therefore, the paper offers important and valuable insights for managers but also for Fintech solution developers, suggesting the need to adapt the implementation strategies for algorithm-based tools in accordance with the conditions of use and the user profile.
- Open Access
- Article
Modeling Financial AI Adoption: A Comparative Decision Tree Analysis of Demographic Profiles in Personal and Organizational Contexts
Author Information
Received: 28 Jan 2026 | Revised: 04 Mar 2026 | Accepted: 19 Mar 2026 | Published: 20 Mar 2026
Abstract
Keywords
AI adoption | financial decisions | demographic profiles | decision tree
References
- 1.
De La Rosa, W.; Bechler, C.J. Unveiling the Adverse Effects of Artificial Intelligence on Financial Decisions via the AI-IMPACT Model. Curr. Opin. Psychol. 2024, 58, 101843. https://doi.org/10.1016/j.copsyc.2024.101843.
- 2.
Gunturu, S.R.; Josyula, H.P.; Landge, S.R.; et al. Assessing the Role of AI in Developing Knowledge-Based Financial Decisions. In Proceedings of the 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 15-16 March 2024; pp 458–463.
- 3.
Zha, R.K.S.S.; Mitra, D. Empowering Financial Decisions with AI: Optimizing Strategies for Financial Planning and Wealth Management. Drishtikon Manag. J. 2024, 15, 19.
- 4.
Bag, S.; Routray, S.; Saikouk, T.; et al. Generative AI-Powered Innovation on Data-Driven Financial Decision-Making in Operations and Supply Chain Management: A Moderated Mediation Analysis. Inf. Syst. Front. 2025. https://doi.org/10.1007/s10796-025-10662-7.
- 5.
Scholtz, S. Sacrifice Is a Step beyond Convenience: A Review of Convenience Sampling in Psychological Research in Africa. SA J. Ind. Psychol. 2021, 47, a1837. https://doi.org/10.4102/sajip.v47i0.1837.
- 6.
Parker, C.; Scott, S.; Geddes, A. Snowball Sampling. In SAGE Research Methods Foundations; SAGE Publications: London, UK, 2019.
- 7.
Maillard, G. Hold-out and Aggregated Hold-Out. Ph.D. Thesis, Université Paris-Saclay, Gif-sur-Yvette, France, 2020.
- 8.
Méndez-Suárez, M.; Monfort, A.; Hervas-Oliver, J. Are You Adopting Artificial Intelligence Products? Social-Demographic Factors to Explain Customer Acceptance. Eur. Res. Manag. Bus. Econ. 2023, 29, 100223. https://doi.org/10.1016/j.iedeen.2023.100223.
- 9.
Alasmari, A.; Alruwaili, R.; Alotaibi, R.; et al. Demographic Influences on Trust in Artificial Intelligence across Cognitive Domains: A Statistical Perspective. PLoS ONE 2025, 20, e0331003. https://doi.org/10.1371/journal.pone.0331003.
- 10.
Baidoo, J.; Adzifome, N.S. Demographic Factors as Predictors of AI Use: A Focus on Postgraduate Teachers. Curr. Perspect. Educ. Res. 2025, 8, 22–42.
- 11.
Medeiros, W. Understanding Global Attitudes towards Artificial Intelligence in the Workplace. Bachelor’s Thesis, Bryant University, Smithfield, RI, USA, 2024.
- 12.
Vasiliu, V.; Yavetz, G. Who’s Afraid of AI? Socio-Technological Perspectives on Artificial Intelligence in the Workforce: An Israeli Case Study. ASLIB J. Inf. Manag. 2026, 78, 49–64. https://doi.org/10.1108/AJIM-12-2023-0562.
- 13.
Soomro, S.; Fan, M.; Sohu, J.M.; et al. AI Adoption: A Bridge or a Barrier? The Moderating Role of Organizational Support in the Path toward Employee Well-Being. Kybernetes 2026, 55, 1059–1085. https://doi.org/10.1108/K-07-2024-1889.
- 14.
Fülöp, M.T.; Ionescu, C.A.; Măgdaș, N.; et al. Digital Transformation of the Accounting Profession at the Intersection of Artificial Intelligence and Ethics. Economics 2025, 19, 20250155.
- 15.
Daniilidou, A.; Antoniadou, N. Uncovering Hidden Profiles: A Latent Profile and Class Analysis of Personality and Demographics in Workplace AI Acceptance. Int. J. Hum. Comput. Interact. 2025. https://doi.org/10.1080/10447318.2025.2560520.
- 16.
Soluk, J.; Miroshnychenko, I.; Nambisan, S. AI Adoption in Family Firms: A Mixed‐Methods Study on the Paradoxical Roles of Passive and Active Family Involvement. J. Prod. Innov. Manag. 2026, 43, 125–159. https://doi.org/10.1111/jpim.12789.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com