IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0344901.html

Acceptance of AI-based tools in consumer financial decision-making: An application of the extended technology acceptance model

Author

Listed:
  • Tomasz Szopiński
  • Michał Buszko
  • Małgorzata Porada-Rochoń

Abstract

The development of artificial intelligence has led to an increase in scientific papers on its applications in financial services. Some of them focus on consumers, particularly presenting the aspects of automated servicing through robo-advisory systems. Nonetheless, relatively rarely does the research relate to the aspects of supporting consumers with AI-driven solutions for their own financial decision-making, which we find as a research gap. Our investigation focuses on finding how and to what extent AI-based tools empower consumers, providing support to their personal financial decisions. Our goal is to identify the determinants of acceptance of AI-based tools supporting financial decision-making, including perceptions of ease of use, usefulness, attitudes, and intentions to use. We base our work on the TAM model, for which we developed a dedicated scale to measure the individual items. The investigation is a pilot study that tests the survey on a representative sample of society. We gathered data through the CAWI survey research on a group of 371 respondents from three Polish universities. Our research shows that AI-based tools supporting financial decisions are primarily perceived through their usefulness and the potential to improve financial literacy. Furthermore, the PLS-SEM modelling confirms positive relationships between perceived ease of use (PEOU), perceived usefulness (PU), attitudes (ATT), behavioral intentions (BI), and actual use (AU) of AI-based tools in making financial decisions by consumers, except for the impact of PU on BI. We observed the strongest relation between PU and ATT towards AI-based tools, and between BI and AU. Our findings demonstrate that PU influences BI only indirectly through the construct of attitude (ATT). Such a phenomenon of a fully mediated relationship deviates from the traditional TAM assumptions. Our study confirms the coherence of the survey and the validity of the extended theoretical model of the acceptance and use of AI-based tools in consumers’ financial decision-making.

Suggested Citation

  • Tomasz Szopiński & Michał Buszko & Małgorzata Porada-Rochoń, 2026. "Acceptance of AI-based tools in consumer financial decision-making: An application of the extended technology acceptance model," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-24, March.
  • Handle: RePEc:plo:pone00:0344901
    DOI: 10.1371/journal.pone.0344901
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0344901
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0344901&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0344901?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ko, Hyungjin & Lee, Jaewook, 2024. "Can ChatGPT improve investment decisions? From a portfolio management perspective," Finance Research Letters, Elsevier, vol. 64(C).
    2. Abraham,Facundo & Schmukler,Sergio L. & Tessada,Jose, 2019. "Robo-Advisors : Investing through Machines," Research and Policy Briefs 134881, The World Bank.
    3. Chiu, Ya-Ling & Gao, Xuechen & Liu, Hung-Chun & Zhai, Qiong, 2025. "Financial literacy of ChatGPT: Evidence through financial news," Finance Research Letters, Elsevier, vol. 78(C).
    4. Schneider, Constantin J. & Yilmaz, Yahya, 2025. "Stock portfolio selection based on risk appetite: Evidence from ChatGPT," Finance Research Letters, Elsevier, vol. 82(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chon, Sora & Kim, Jaehoon & Kim, Jaeho, 2025. "Multifaceted variability in LLM-driven stock recommendations," Finance Research Letters, Elsevier, vol. 86(PG).
    2. Jang, Junkyu, 2025. "Selective news selection model for explainable stock prediction via cross-attention integration," Finance Research Letters, Elsevier, vol. 85(PD).
    3. Schneider, Constantin J. & Yilmaz, Yahya, 2025. "Stock portfolio selection based on risk appetite: Evidence from ChatGPT," Finance Research Letters, Elsevier, vol. 82(C).
    4. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 2020. "Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    5. Xuewen Han & Neng Wang & Shangkun Che & Hongyang Yang & Kunpeng Zhang & Sean Xin Xu, 2024. "Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research," Papers 2411.04788, arXiv.org.
    6. Chiu, Ya-Ling & Gao, Xuechen & Liu, Hung-Chun & Zhai, Qiong, 2025. "Financial literacy of ChatGPT: Evidence through financial news," Finance Research Letters, Elsevier, vol. 78(C).
    7. Chacon, Alvaro & Kausel, Edgar E. & Reyes, Tomas & Trautmann, Stefan, 2025. "Preventing algorithm aversion: People are willing to use algorithms with a learning label," Journal of Business Research, Elsevier, vol. 187(C).
    8. Liyuan Chen & Shuoling Liu & Jiangpeng Yan & Xiaoyu Wang & Henglin Liu & Chuang Li & Kecheng Jiao & Jixuan Ying & Yang Veronica Liu & Qiang Yang & Xiu Li, 2025. "Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges," Papers 2507.18577, arXiv.org, revised Dec 2025.
    9. Wang, Qishu, 2025. "Generative AI-assisted evaluation of ESG practices and information delays in ESG ratings," Finance Research Letters, Elsevier, vol. 74(C).
    10. Zhisheng Chen, 2025. "Revolutionizing finance with conversational AI: a focus on ChatGPT implementation and challenges," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-11, December.
    11. Dominik M. Piehlmaier, 2022. "Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    12. Jeong, Woojin & Park, Seongwan & Lee, Seungyun & Son, Bumho & Lee, Jaewook & Ko, Hyungjin, 2024. "Influence and predictive power of sentiment: Evidence from the lithium market," Finance Research Letters, Elsevier, vol. 68(C).
    13. Li, Degang & Bo, Lili, 2025. "The impact of health concerns on household financial asset allocation in the context of population aging," Finance Research Letters, Elsevier, vol. 85(PB).
    14. Gregory, Gadzinski & Vito, Liuzzi, 2024. "ChatGPT: A canary in the coal mine or a parrot in the echo chamber? Detecting fraud with LLM: The case of FTX," Finance Research Letters, Elsevier, vol. 70(C).
    15. Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2025. "Do AI incidents and hazards matter for AI-themed cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 74(C).
    16. Wang, Keyun & Xu, Fengmin & Wang, Shihao & Li, Benchu, 2024. "Data analysis technology and inequality in capital costs," Economics Letters, Elsevier, vol. 237(C).
    17. Lars Hornuf & David J. Streich & Niklas Töllich, 2025. "Making GenAI Smarter: Evidence from a Portfolio Allocation Experiment," CESifo Working Paper Series 11862, CESifo.
    18. Guido Abate & Pierpaolo Ferrari & Bianca Pisino, 2026. "Robo-Advisors: Artificial Intelligence-Driven Services for Retail Investors’ Asset Allocation," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 15(1), pages 1-3.
    19. Sharbek Nermin, 2022. "How Traditional Financial Institutions have adapted to Artificial Intelligence, Machine Learning and FinTech?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 16(1), pages 837-848, August.
    20. Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024. "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers 2406.05972, arXiv.org, revised Oct 2024.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0344901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.