Evidence map›Paper›PMID 41855175›Full record

ArticlePloS one2026

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

Tomasz Szopiński, Michał Buszko, Małgorzata Porada-Rochoń

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tomasz SzopińskiSchool of Business, VIZJA University, Warsaw, Poland.
Michał BuszkoDepartment of Financial Management, Institute of Economics and Finance, Faculty of Economic Sciences and Management, Nicolaus Copernicus University, Toruń, Poland.ORCID https://orcid.org/0000-0002-1743-1902
Małgorzata Porada-RochońInstitute of Economics and Finance, Faculty of Economics, Finance and Management, University of Szczecin, Szczecin, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceConsumer BehaviorDecision MakingAdultAttitudeFemaleHumansMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID41855175
PMCPMC13001959

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.