Identification of Trust Determinants in LLM Technology Using the DEMATEL Method
Open accessAbstract
Purpose: The study identifies determinants of trust in Large Language Model (LLM) technology among office employees using the DEMATEL method.It addresses a gap in understanding factors like credibility, user experience, and data security, which are crucial for AI adoption in workplaces.Design/Methodology/Approach: The study employs a mixed-method approach, combining a literature review and empirical analysis using the DEMATEL method.The DEMATEL method was applied to develop causal-effect diagrams, identify prominence and relation indicators, and calculate the importance of individual factors.The methodology was chosen for its suitability in analyzing complex, interrelated variables.Findings: The research identified the most significant determinants of trust in LLM technology.Key findings highlight that perceived credibility and accuracy of responses, prior experience with AI, and awareness of productivity impacts are the most influential factors.Additionally, user education, intuitive user interfaces, and robust data security were identified as crucial for building trust.These factors underscore the importance of transparency, usability, and reliability in fostering employee confidence in LLM technology. Practical Implications: Organizations can enhance LLM adoption by ensuring credible outputs, providing training, and addressing data security. The results support trust-building strategies for sectors dependent on AI decision-making.Originality/Value: This study offers a novel application of the DEMATEL method to trust in LLMs, providing insights into workplace AI adoption and expanding understanding of trustbuilding mechanisms.
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User-centered studies empirically identified perceived credibility, neutrality, and bias minimization as key determinants of trust in LLMs.
“Additionally, usercentered studies like [39] empirically identified perceived credibility, neutrality, and bias minimization as key determinants of trust in LLMs.”
From Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making (2025) · cites this paper as [39]
The Discussion (section 4; page unavailable) identifies perceived credibility as the highest-weighted trust determinant (0.049). The Research Methodology (section 2) includes neutrality and bias minimization among the factors evaluated by 15 office employees, but the ranked top-ten results do not identify it as a key determinant. The claim therefore overstates that part of the empirical finding and leaves out the study’s office-employee setting. (LLM, conf=0.90)
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Research works printed but not linked: 18
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Cited by
Sorted by citations
Trustworthiness Evaluation of Large Language Models Using Multi-Criteria Decision Making
Aksoy, M., Adem, A., & Dağdeviren, M. (2025) · IEEE Access · doi:10.1109/access.2025.3612568 · Cited by 10 · see citing works
“Reference [39] also employed DEMATEL but focused on identifying criteria for user trust in LLMs, analyzing factors like credibility, data security, and user experience.”
- Authors
- Pawlowska-Nowak, M.
- Venue
- EUROPEAN RESEARCH STUDIES JOURNAL
- Publisher
- ISMA SYC INT
- Published
- 2024-05-01
- Volume
- XXVII
- Issue
- Special Issue A
- Pages
- 694-711
- ISSN
- 1108-2976 (ISSN-L)
- OpenAlex
- W4405494191
- Cited by
- 3
- References
- 0
- Citations by year
- 2025: 3
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- EUROPEAN RESEARCH STUDIES JOURNAL (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- workplace AI adoption, office workers, perceived credibility, data security, user experience, library instruction, user interface design, AI transparency, trust-building strategies, DEMATEL
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