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

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 902

Special Issue Editors

Dr. Stefanos Balaskas

E-Mail Website
Guest Editor
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

E-Mail Website
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

E-Mail Website
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1000 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 5349 KB  
Article
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 188
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 [...] Read more.
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  
Article
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 333
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 [...] Read more.
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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