Trusting the Machine: How Consumer Trust in Artificial Intelligence Shapes Future Adoption Intentions

Amirhossein Pezeshgi; Mohsen Valiei Abarghoei; Masoud Naeimi; Qazale Family

doi:10.31181/msa31202640

Authors

Amirhossein Pezeshgi Price College of Business, University of Oklahoma, USA Author https://orcid.org/0009-0002-4241-8766
Mohsen Valiei Abarghoei Management and Business Department, Electronic Branch, Islamic Azad University, Iran Author https://orcid.org/0009-0001-1810-0144
Masoud Naeimi Management and Business Department, South Tehran Branch, Islamic Azad University, Iran Author https://orcid.org/0009-0009-6995-8050
Qazale Family Faculty of Business, University of Hamburg, Germany Author https://orcid.org/0009-0008-3871-8217

DOI:

https://doi.org/10.31181/msa31202640

Keywords:

Artificial Intelligence, Consumer Trust, Artificial Intelligence Adoption, Human- Artificial Intelligence Interaction, Artificial Intelligence Literacy

Abstract

Although the use of artificial intelligence (AI) by consumers is growing, one of the most important factors influencing whether or not they incorporate AI into their daily lives is trust. This study examines the relationship between trust and the intentions to adopt AI-driven products in the future as well as their current use. Based on the Technology Acceptance Model (TAM) and related frameworks, we argue that perceived knowledge about AI helps to explain how trust translates into behavioral intentions and that trust functions as a fundamental mechanism influencing openness toward AI technologies. We also investigate whether this relationship is moderated by education and age. Regression results from a survey of 205 consumers provide strong support for H1: future adoption of AI is significantly predicted by trust in AI. H2 is supported by mediation analyses, which show that the trust-adoption relationship is partially mediated by perceived knowledge. Although older and less educated people descriptively reported lower trust and adoption intentions, H3, which proposed moderation by age and education, was not statistically supported. Separate trust-based segments with various behavioral patterns were also identified through cluster analysis. By highlighting trust and user knowledge as key factors in AI adoption contexts, these findings go beyond the Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT). They also recommend that companies looking to increase AI acceptance should give top priority to improving transparency, lowering uncertainty, and informing customers about AI capabilities.

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References

Mehta, C., & Pradhan, P. (2024). A Multidimensional Approach using TAM, UTAUT and TOE to determine social media marketing adoption among MSMEs. Economic Sciences, 20(2), 352-369. https://doi.org/10.69889/ktkc2a59

Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540

Pezeshgi, A., Naeimi, M., & Family, Q. (2025). Buying on Impulse in the Age of AI: Mechanisms, Evidence, and Moral Dilemmas. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5402344

Choung, H., David, P., & Ross, A. (2023). Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human-Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543

Choi, S., Jang, Y., & Kim, H. (2022). Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools. International Journal of Human-Computer Interaction, 39, 1–13. https://doi.org/10.1080/10447318.2022.2049145

Ferrario, A., & Loi, M. (2022). How Explainability Contributes to Trust in AI. ACM International Conference Proceeding Series, 1457–1466. https://doi.org/10.1145/3531146.3533202

Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260–278. https://doi.org/10.1080/12460125.2020.1819094

Hassan, N., Abdelraouf, M., & El-Shihy, D. (2025). The moderating role of personalized recommendations in the trust–satisfaction–loyalty relationship: an empirical study of AI-driven e-commerce. Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00476-z

FALLAHI, K., & SHABESTAR, M. S. Örgütsel Davranış Araştırmaları Dergisi. EVALUATION, 2528, 9705.

Fallahi, K., & Shabestar, M. S. (2018). Evaluation of The Effectiveness of USING PERSONALIZED ADVERTISING ON FACEBOOK, 3(2), 1-13.

Gabriel, I. (2020). Artificial Intelligence, Values, and Alignment. Minds and Machines, 30(3), 411–437. https://doi.org/10.1007/s11023-020-09539-2

Liang, W., Tadesse, G. A., Ho, D., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022). Advances, challenges and opportunities in creating data for trustworthy AI. Nature Machine Intelligence, 4(8), 669-677. https://doi.org/10.1038/s42256-022-00516-1

Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J. (2024). Adopting AI: how familiarity breeds both trust and contempt. AI and Society, 39(4), 1721–1735. https://doi.org/10.1007/s00146-023-01666-5

Ferrario, A., Loi, M., & Viganò, E. (2020). In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions. Philosophy and Technology, 33(3), 523–539. https://doi.org/10.1007/s13347-019-00378-3

AlHogail, A. (2018). Improving IoT Technology Adoption through Improving Consumer Trust. Technologies, 6(3). https://doi.org/10.3390/technologies6030064

Vrančić, A., Zadravec, H., & Orehovački, T. (2024). The Role of Smart Homes in Providing Care for Older Adults: A Systematic Literature Review from 2010 to 2023. In Smart Cities (Vol. 7, Issue 4, pp. 1502–1550). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/smartcities7040062

Lundegård, I., Arvanitis, L., Hamza, K., Schenk, L., Wojcik, A., & Haglund, K. (2022). Facts and values in students’ reasoning about gene technology in the frame of risk–a thick comprehension. Environmental Education Research, 28(9), 1283–1296. https://doi.org/10.1080/13504622.2022.2031900

Shin, D., Rasul, A., & Fotiadis, A. (2022). Why am I seeing this? Deconstructing algorithm literacy through the lens of users. Internet Research, 32(4), 1214–1234. https://doi.org/10.1108/INTR-02-2021-0087

Safizadeh, M., Yazdanparast, A., & Felix, R. (2026). Taking Pride in Vegan Consumption: A Construal Level Theory Account of Ad Message Appeal and Future Self Connectedness. Psychology & Marketing. 1–26. https://doi.org/10.1002/mar.70107

Karimi, M., & Damirchi, F. (2025). Re-Evaluating the Functionalist Approach of Urban Management towards Sustainable Architecture and Urban Layout with Emphasis on the Role of Digital Technologies. Journal of Modern Technology, 2(2), 327-345. https://doi.org/10.71426/jmt.v2.i2.pp327-345

Karizaki, M. S., Gnesdilow, D., Puntambekar, S., & Passonneau, R. J. (2024, July). How Well Can You Articulate that Idea? Insights from Automated Formative Assessment. In International Conference on Artificial Intelligence in Education (pp. 225-233). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-9_16

Mora-López, J. P., Lopez-Lopez, D., & Rivera-Hernaez, O. (2025). Unveiling the Generative AI boom: what hype metrics reveal for digital business and E-commerce. Electronic Commerce Research. https://doi.org/10.1007/s10660-025-09984-0

Pezeshgi, A., & Malhotra, P. (2025). Transforming Marketing Research with Generative AI: Opportunities, Limitations, and Ethical Implications. https://doi.org/https://dx.doi.org/10.2139/ssrn.5386443

Büchi, M., Festic, N., Just, N., & Latzer, M. (2021). Digital Inequalities In Online Privacy Protection: Effects Of Age, education and gender. In Handbook of Digital Inequality (pp. 296–310). Edward Elgar Publishing Ltd. https://doi.org/10.4337/9781788116572.00029

Nasiri, S., Shahabi, S., Shafiesabet, A., Talebbeidokhti, M., & Behineh, E. A. (2026). Cybersecurity in Action: Unraveling the Effects of Individual, Social, and Organizational Determinants. Tehnički glasnik, 20(2),1-10, https://doi.org/10.31803/tg-20240627004731

Roshdieh, N. (2024). The Effect of Monetary Policy Uncertainty on Stock Market Uncertainty with NARDL Approach. Research Journal of Finance and Accounting, 15(10), 1-9.

Gudarzi Farahani, Y., Mirarab Baygi, S. A., Abbasi Nahoji, M., & Roshdieh, N. (2026). Presenting the early warning model of financial systemic risk in Iran's financial market using the LSTM model. International Journal of Finance & Managerial Accounting, 11(42), 29-38. https://doi.org/10.30495/ijfma.2024.77586.2115

Tazehkanda, S. A., Wanga, M. C. (2024). Leveraging XGBoost to Reduce Failure, Withdrawal, and Dropout Rates in Undergraduate Level Mathematics/Statistics Education. In book: Modern Management based on Big Data V. https://doi.org/10.3233/FAIA240276

Tazehkand, S. A. (2024). Enhancing student graduation rates by mitigating failure, dropout, and withdrawal in introduction to statistical courses using statistical and machine learning. Graduate Thesis and Dissertation 2023-2024. 329. https://stars.library.ucf.edu/etd2023/329

Heidari, S., Zarei, M., Rad, S. S., Sanaei, F., Hajian, E., & Boti, M. (2025). Integrating Green Supply Chain Management and Total Quality Management: A SEM-ANN Analysis of Performance Enhancement in SMEs. Computer and Decision Making: An International Journal, 2, 558-569. https://doi.org/10.59543/comdem.v2i.13683

Bevilacqua, C., Hamdy, N., & Sohrabi, P. (2025). Linking Land Uses and Ecosystem Services Through a Bipartite Spatial Network: A Framework for Urban CO2 Mitigation. Sustainability, 17(22), 10113. https://doi.org/10.3390/su172210113

Bevilacqua, C., Vitiello, G., Sebillo, M.M.L., Provenzano, V., Sohrabi, P., Hamdy, N., Trapani, F. and Pizzimenti, P. (2025). A Multidisciplinary approach to plan ECOsystem SErvices for cities in Transition. In Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter (pp. 1-1). https://doi.org/10.1145/3750069.3757877

Hassani, A., Mohajer, S., Darvishan, S., Shafiesabet, A., & Tashakkori, A. (2025). The Impact of Financial Literacy on Financial Behavior and Financial Resilience with the Mediating Role of Financial Self-Efficacy. International journal of industrial engineering and operational research, 7(2), 38-55. https://doi.org/10.22034/ijieor.v7i2.146

Khorsand, M-S., Tashakkori, A., Talebzadeh, H., Zarei, E., Arani, G. G., & Dadashi, N. (2026). Prediction of the Influential Factors on Stock Price Reaction Delay: An Integrated approach based on Structural Equation Modelling and Artificial Neural Network. Tehnicki vjesnik - Technical Gazette, 33(4), 1-9. https://doi.org/10.17559/TV-20250916002995

Khorsandi, H., Kazemi, B., Zeynali, S., Mohsenibeigzadeh, M., Zarei, P., & Mirshekari, S. (2025). The Impact of Social Media Marketing on Digital Service Adoption in Educational Institutions: Exploring the Mediating Role of Brand Equity, Trust, and Word-Of-Mouth Advertising. Tehnički glasnik, 19(3), 396-403. https://doi.org/10.31803/tg-20240331030139

Talebzadeh, S., Behineh, E. A., Masoomifard, M., Taheri, M., & Nasab, S. S. (2025). Performance evaluation and importance–performance analysis of universities based on the BSC-AHP in fuzzy environment. Cadernos de Educação Tecnologia e Sociedade, 18(2), 487-503. https://doi.org/10.14571/brajets.v18.n2.487-503