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Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making

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A special issue of FinTech (ISSN 2674-1032).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 10795

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Special Issue Editors

Dr. Stefanos Balaskas

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Department of Management Science and Technology, School of Economics and Business, University of Patras, 26334 Patras, Greece
Interests: behavioral finance; technology acceptance models; fintech adoption; sustainable consumption; psychological modeling; cognitive models; trust in technology; human-computer interaction; neuromarketing; eye-tracking
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yannis C. Stamatiou

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Guest Editor

Department of Business Administration, University of Patras, 26504 Patras, Greece
Interests: cryptography; trust and privacy in digital systems; cybersecurity; algorithmic decision-making; risk modeling; fintech security and governance
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. George S. Androulakis

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Guest Editor

Department of Business Administration, University of Patras, 265 04 Rio, Greece
Interests: quantitative research in finance and education; behavioral modeling; optimization methods; neural network training; educational data analysis; burnout and engagement metrics

Special Issue Information

Dear Colleagues,

The global financial landscape is undergoing a profound transformation, driven not only by rapid technological innovation but also by how individuals cognitively, emotionally, and behaviorally engage with digital financial systems. This Special Issue aims to explore the psychological, behavioral, and cognitive mechanisms underlying FinTech adoption and resistance, with a special focus on trust, emotion, digital decision-making, and user modeling.

We welcome theoretical, empirical, and mixed-method contributions from a wide range of disciplines, including behavioral finance, psychology, information systems, HCI, and cognitive science. Topics may include (but are not limited to) the following: trust in AI-driven financial services, emotional and cognitive responses to robo-advisors, resistance to persuasive FinTech interfaces, and behavioral intention modeling in mobile banking, blockchain platforms, and crowdfunding systems.

Through this Special Issue, we aim to advance understanding of the human side of FinTech and offer actionable insights into the design, implementation, and regulation of financial technologies that align with user psychology, well-being, and trust.

We look forward to your contributions!

Dr. Stefanos Balaskas
Prof. Dr. Yannis C. Stamatiou
Prof. Dr. George S. Androulakis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. FinTech is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

behavioral FinTech
digital trust and perceived risk
cognitive biases in financial decision-making
emotional engagement in FinTech
technology acceptance and behavioral models
AI-driven personalization and persuasion
resistance to digital nudging
FinTech UX and HCI
financial psychology
robo-advising and user behavior

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Published Papers (9 papers)

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Research

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26 pages, 956 KB

Open AccessArticle

Women’s Reforms, Digital Payments, and Financial Inclusion in Saudi Arabia: Evidence from Global Findex 2014–2024

by Tifani Husna Siregar, Adnan Ameen Bakather and Emilios Galariotis

FinTech 2026, 5(2), 30; https://doi.org/10.3390/fintech5020030 - 7 Apr 2026

Viewed by 352

Abstract

Saudi Arabia experienced rapid convergence in women’s financial inclusion between 2014 and 2024, a period marked by the 2018–2019 reforms expanding women’s economic rights and the accelerated deployment of digital payment infrastructure. Using four waves of Global Findex microdata (2014, 2017, 2021, and 2024), this study estimates probability-weighted logit models with average marginal effects and decomposes gender gaps using nonlinear Kitagawa and Blinder–Oaxaca methods. Reform-era dynamics are examined by tracing changes in the gender gap across survey waves. The findings indicate that aggregate gender gaps in account ownership and digital payment usage narrowed substantially by 2024, with conditional gaps among employed adults no longer statistically significant, while sizable disparities persist among individuals outside the workforce. Decomposition results highlight increased female labor force participation as a key correlate of convergence, consistent with labor market integration playing a central role in women’s financial inclusion during the reform era. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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21 pages, 408 KB

Open AccessArticle

Institutional Trust, Risk-Taking, and FinTech Adoption: Evidence from an Emerging Economy

by Zsuzsanna Deák and Ádám Béla Horváth

FinTech 2026, 5(2), 27; https://doi.org/10.3390/fintech5020027 - 1 Apr 2026

Viewed by 406

Abstract

This paper explores the relationship between risk-taking attitudes, different dimensions of trust, and the adoption of financial technology (FinTech) in an emerging Central European economy. Based on survey data collected via LimeSurvey (October to December 2025) in Hungary, multivariate linear regression models were estimated to explore the relationship between FinTech usage, individual risk-taking propensity, and four dimensions of trust, while controlling for socioeconomic variables. The results indicate that higher institutional trust in independent financial actors facilitates FinTech adoption. However, higher institutional trust in domestic financial and governmental actors has an inhibiting effect. When trust dimensions are added to the model, the positive association with general risk-taking propensity becomes statistically marginal, indicating that trust-related factors account for a substantial share of the observed variation. Further tests regarding the possible direction of this causation confirm that FinTech use is also linked to increased trust in independent financial actors. This study adds to the FinTech literature by demonstrating that usage is related not only to generalized trust and individual risk propensity but also to differentiated institutional trust attitudes. The findings highlight that institutional background is an important determinant of digital financial behavior in emerging economies. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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26 pages, 2375 KB

Open AccessArticle

Hybrid Machine Learning–Econometric Framework for Financial Distress Scoring: Evidence from German Manufacturing Firms

by Karim Farag, Loubna Ali and Mohamed A. Hamada

FinTech 2026, 5(1), 17; https://doi.org/10.3390/fintech5010017 - 10 Feb 2026

Viewed by 838

Abstract

Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly contributes to addressing these issues by providing data-driven analytical tools that improve the assessment and monitoring of firms’ financial position. However, in the literature, we have not found any paper that uses machine learning (ML) algorithms to assess the financial distress of German manufacturing firms, highlighting methodological and sectoral gaps that need to be bridged. Therefore, this study aims to develop an econometric and ML-based financial distress scoring model for German manufacturing firms by estimating contemporaneous Altman Z-scores that provide better insights into the financial distress determinants, enabling better financial management. The econometric findings revealed that the regression model has an adjusted R-squared value of 86%, confirming that the selected firm-specific and macroeconomic factors play a substantial role in explaining financial distress. The findings recommend that German manufacturing businesses retain more earnings rather than distributing them as dividends, while reducing their debt in capital structures to enhance financial stability. Moreover, the ML results found that Gradient Boosting and Random Forest have the highest accuracy scores among the ML methods, suggesting that these models provide strong capability for assessing financial distress and supporting more effective financial risk management, allowing firms to effectively respond to the threats of a dynamic environment and thereby better support the growth of the German and European economies. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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21 pages, 823 KB

Open AccessArticle

Unraveling User Switching Dynamics in P2P Mobile Payments: Investigating Satisfaction and Trust in a Duopoly Market

by Claudel Mombeuil and Sadrac Jean Pierre

FinTech 2026, 5(1), 7; https://doi.org/10.3390/fintech5010007 - 8 Jan 2026

Viewed by 727

Abstract

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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23 pages, 443 KB

Open AccessArticle

Knowledge or Confidence? Exploring the Interplay of Financial Literacy, Digital Financial Behavior, and Self-Assessment in the FinTech Era

by Szilvia Módosné Szalai, Szonja Jenei and Erzsébet Németh

FinTech 2025, 4(4), 75; https://doi.org/10.3390/fintech4040075 - 16 Dec 2025

Cited by 1 | Viewed by 1438

Abstract

Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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20 pages, 631 KB

Open AccessArticle

Determinants of Consumer Trust in Green FinTech Platforms

by Regina Veckalne

FinTech 2025, 4(4), 72; https://doi.org/10.3390/fintech4040072 - 11 Dec 2025

Viewed by 1241

Abstract

The rapid growth of financial technology (FinTech) has created new opportunities to promote environmentally responsible consumption. Yet, little is known about the factors that shape consumer trust in green FinTech platforms, which is crucial for their adoption and long-term impact. This study develops and tests a partial least squares structural equation model (PLS-SEM) integrating sustainability and technology determinants of trust. Survey data from 240 consumers were analyzed. Results show that green transparency, platform security and privacy, and ease of use significantly enhance perceived credibility, while social influence and perceived environmental responsibility increase green perceived value. In turn, perceived credibility reduces perceived risk and promotes trust. Trust is also strengthened by environmental responsibility, green perceived value, and platform innovativeness, but weakened by perceived risk. All hypothesized relationships were statistically significant. The findings highlight the importance of credible sustainability communication, high level security, and social endorsement in building trust for green FinTech services. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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26 pages, 5349 KB

Open AccessArticle

Smart Forest Modeling Behavioral for a Greener Future: An AI Text-by-Voice Blockchain Approach with Citizen Involvement in Sustainable Forestry Functionality

by Dimitrios Varveris, Vasiliki Basdekidou, Chrysanthi Basdekidou and Panteleimon Xofis

FinTech 2025, 4(3), 47; https://doi.org/10.3390/fintech4030047 - 1 Sep 2025

Viewed by 1644

Abstract

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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15 pages, 1001 KB

Open AccessArticle

Do Fintech Firms Excel in Risk Assessment for U.S. 30-Year Conforming Residential Mortgages?

by Zilong Liu and Hongyan Liang

FinTech 2025, 4(3), 42; https://doi.org/10.3390/fintech4030042 - 14 Aug 2025

Viewed by 1932

Abstract

This study examines whether fintech lenders outperform traditional banks and non-fintech non-banks in risk assessment for U.S. 30-year fixed-rate conforming mortgages. Analyzing Fannie Mae and Freddie Mac loans from Q1 2012 to Q1 2020 using ROC/AUC and risk-pricing regressions, we find fintech lenders have lower predictive accuracy and pricing misalignment, charging higher rates to borrowers who remain current and lower rates to those who default or prepay. These results indicate that conforming mortgage regulations and rapid loan sales to government-sponsored enterprises (GSEs) diminish fintech firms’ incentives for enhanced borrower screening, thus reducing their risk assessment effectiveness. Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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Review

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35 pages, 1352 KB

Open AccessReview

Trust as Predictor and Mechanism in Green FinTech Adoption: A Systematic Review and Meta-Analysis

by Stefanos Balaskas

FinTech 2026, 5(1), 22; https://doi.org/10.3390/fintech5010022 - 5 Mar 2026

Cited by 1 | Viewed by 731

Abstract

Green FinTech involves facilitating sustainable payments, banking, and investment; nevertheless, it is subject to consumer trust and perceptions of ‘green’ value. The literature on this topic is fragmented, with information systems literature typically considering trust as a broad acceptance construct, while sustainable literature considers it as a risk of ‘greenwashing’ without integrating credibility into adoption models. This systematic review aggregates 15 empirical studies and addresses five research questions. RQ1 examines the theoretical models applied to examine trust in green/sustainable FinTech adoption. RQ2 examines the conceptualization and measurement of trust across different contexts, distinguishing institutional/provider trust, platform/tech trust, and sustainability claim credibility trust. RQ3 examines the function of trust within behavioral models (predictor, mediator, moderator). RQ4 examines methodological characteristics and quality indicators (research design, sampling frame, reliability, and bias). RQ5 examines the direct relationship between trust and adoption intention using meta-analysis. The systematic review follows a set of PRISMA guidelines, where we searched Scopus and Web of Science (2015–2026) and applied an RQ-based coding scheme to peer-reviewed articles. Measures of trust varied significantly (unidimensional, integrity–competence–benevolence, and technology-specific scales), limiting cross-study comparability. Using random effects, we found a significant positive relationship between trust and intention (pooled standardized direct path coefficient β = 0.27, 95% CI [0.14, 0.41]) with considerable heterogeneity (I² = 88%) and a wide prediction interval including near-zero effects. Literature essentially endorses trust as a significant yet context-dependent construct, emphasizing the necessity for measurement standardization, a more distinct differentiation between sustainability trust and general platform trust, regular reporting of reliability and bias assessments, and focused evaluations of boundary conditions (e.g., environmental skepticism, regulatory framework, and FinTech type). Full article

(This article belongs to the Special Issue Modeling Behavioral and Cognitive Drivers of FinTech Adoption: Trust, Emotion and Digital Decision-Making)

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