№2 (31) / 2026 Full issue

Release Date 25.05.2026

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Samoilov Oleg

PhD student, Research assistant, Center for Socio-Cultural Studies

National Research University Higher School of Economics (Moscow)

osamoilov1@gmail.com

https://orcid.org/0009-0005-1509-7225

 

 

Tatarko Alexander

Doctor of Psychology, Professor, Department of Psychology, Faculty of Social Sciences, Chief Researcher, Center for Socio-Cultural Studies

National Research University Higher School of Economics (Moscow)

This email address is being protected from spambots. You need JavaScript enabled to view it.

https://orcid.org/0000-0001-7557-9107

 

SOCIO-PSYCHOLOGICAL FACTORS OF TRUST IN ARTIFICIAL INTELLIGENCE: STATE OF RESEARCH

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The article presents a theoretical review of literature from the last ten years devoted to the analysis of socio-psychological factors of trust in artificial intelligence. The widespread adoption of automated artificial intelligence systems, which is associated with expected economic growth, reduced resource costs, and the optimization of various work processes, often faces user distrust in new tools and a reluctance to transform traditional work processes. The combination of factors that reduce trust in artificial intelligence leads to low economic efficiency in the implementation of innovations, despite the wide range of technical possibilities. In addition to the importance of considering cognitive and affective factors of trust in artificial intelligence, socio-psychological aspects that determine the ethical and value acceptability of using automated AI systems are of particular significance. Theoretical analysis revealed that individualizing moral foundations are positively associated with trust in artificial intelligence in situations where the use of AI systems benefits society. Users who prioritize binding moral foundations demonstrate greater distrust of artificial intelligence and are less willing to delegate some tasks to automated assistants. The values of Openness to Change and Self-transcendence are more positively associated with trust in AI and digital innovation, excluding situations of high risk to life or social injustice. The values of Self-Enhancement are also positively associated with trust in AI tools, but mainly in situations where artificial intelligence simplifies the achievement of the user's goals or expands human capabilities to do so. There is a heterogeneous structure of relationships between Conservation values and trust in artificial intelligence, due to cultural characteristics. However, it should be noted that Conservation values are most often considered predictors of distrust in automated AI systems. The importance of considering value congruence between users and perceived AI profiles is discussed. For AI system developers, there is a need to pay special attention to the possibilities of adaptive personalized adjustment of the value profiles of generative models to users, which will lead to more effective human-machine interaction. The article highlights the areas of future research in this field as part of the development of a systemic model of trust in artificial intelligence.

 

Keywords: trust, artificial intelligence, socio-psychological factors, values, moral foundations

JEL: O33, D83, M15, A13, Z13

UDC: 159.9, 316.6

DOI: 10.52342/2587-7666VTE_2026_2_209_228

 

© O. Samoilov, A. Tatarko, 2026

© Institute of Economics of the Russian Academy of Sciences «Issues of Theoretical Economics», 2026

Article is an output of а research project HSE-BR-2025-52 implemented as part of the Basic Research Program at HSE University

 

REFERENCES

  1. Aghion P., Jones B.F., Jones C.I. (2017). Artificial Intelligence and Economic Growth // NBER Working Papers. No. 23928. DOI: 10.3386/w23928

  2. Anisha S.A., Sen A., Ahmad B. et al. (2025). Exploring Acceptance of Digital Health Technologies for Managing Non-Communicable Diseases Among Older Adults: A Systematic Scoping Review // Journal of Medical Systems. Vol. 49. Article 35. DOI: 10.1007/s10916-025-02166-3

  3. Awad E., Dsouza S., Kim R., Schulz J., Henrich J., Shariff A., Bonnefon J.F., Rahwan I. (2018). The Moral Machine experiment // Nature. Vol. 563. No. 7729. Pp. 59-64. DOI: 10.1038/s41586-018-0637-6

  4. Bai X., Yang L. (2025). Research on the influencing factors of generative artificial intelligence usage intent in post-secondary education: An empirical analysis based on the AIDUA extended model // Frontiers in Psychology. Vol. 16. Article 1644209. DOI: 10.3389/fpsyg.2025.1644209

  5. Bajpai S., Sameer A., Fatima R. (2025). Insights into Moral Reasoning of AI: A Comparative Study Between Humans and Large Language Models // Journal of Media Ethics. Pp. 1-15. DOI: 10.1080/23736992.2025.2553146

complete reference list

close list

  1. Banker S., Park J., Chan E.Y. (2023). The moral foundations of cryptocurrency: Evidence from Twitter and survey research // Frontiers in Psychology. Vol. 14. Article 1128575. DOI: 10.3389/fpsyg.2023.1128575

  2. Bhat S. et al. (2024). Value alignment and trust in human-robot interaction: Insights from simulation and user study // Discovering the frontiers of human-robot interaction: Insights and innovations in collaboration, communication, and control / R. Vinjamuri (ed.) – Cham: Springer Nature Switzerland. Pp. 39-63. DOI: 10.1007/978-3-031-66656-8_3

  3. Bigman Y.E., Gray K. (2018). People are averse to machines making moral decisions // Cognition. Vol. 181. Pp. 21-34. DOI: 10.1016/j.cognition.2018.08.003

  4. Cheung J.C., Ho S.S. (2025). The effectiveness of explainable AI on human factors in trust models // Scientific Reports. Vol. 15. Article 23337. DOI: 10.1038/s41598-025-04189-9

  5. Claudy M.C., Parkinson M., Aquino K. (2024). Why should innovators care about morality? Political ideology, moral foundations, and the acceptance of technological innovations // Technological Forecasting and Social Change. Vol. 203. Article 123384. DOI: 10.1016/j.techfore.2024.123384

  6. Dietvorst B.J., Simmons J.P., Massey C. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err // Journal of experimental psychology. General. Vol. 144. No. 1. Pp. 114-126. DOI: 10.1037/xge0000033

  7. Eubanks V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. — New York: St. Martin’s Press.

  8. Filippucci F. et al. (2024). The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges // Artificial Intelligence Papers. No. 15. OECD Publishing. Paris. DOI: 10.1787/8d900037-en.

  9. Gerke S., Minssen T., Cohen G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare // Artificial Intelligence in Healthcare / A. Bohr, K. Memarzadeh (eds.). – Elsevier. Pp. 295-336. DOI: 10.1016/B978-0-12-818438-7.00012-5

  10. Glikson E., Woolley A.W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research // Academy of Management Annals. Vol. 14. No. 2. Pp. 627–660. DOI: 10.5465/annals.2018.0057

  11. Gondauri D. (2025). The impact of artificial intelligence on gross domestic product: A global analysis // arXiv preprint. ArXiv:2505.11989. DOI: 10.48550/arXiv.2505.11989

  12. Graham J., Nosek B.A., Haidt J., Iyer R., Koleva S., Ditto P.H. (2011). Mapping the Moral Domain // Journal of Personality and Social Psychology. Vol. 101. No. 2. Pp. 366-385. DOI: 10.1037/a0021847

  13. Graham J., Haidt J., Koleva S., Motyl M., Iyer R., Wojcik S., Ditto P.H. (2013). Moral foundations theory: The pragmatic validity of moral pluralism // Advances in Experimental Social Psychology. Vol. 47. Pp. 55-130. DOI: 10.1016/B978-0-12-407236-7.00002-4

  14. Graham J., Haidt J., Motyl M., Meindl P., Iskiwitch C., Mooijman M. (2018). Moral Foundations Theory // Atlas of Moral Psychology / K. Gray, J. Graham (eds.). — New York, London: The Guilford Press. Pp. 211-222.

  15. Gursoy D., Della Corte V., del Gaudio G., Crisci A., Xu, Y. (2025). Factors influencing elderly adoption of artificial intelligence robots for aging-in-place: Motivators, barriers, and emotional impact // Work, Aging and Retirement. Article waaf016. DOI: 10.1093/workar/waaf016

  16. Hadar-Shoval D., Asraf K., Mizrachi Y., Haber, Y., Elyoseph Z. (2024). Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz’s Theory of Basic Values // JMIR Mental Health. Vol. 11. Article e55988. DOI: 10.2196/55988

  17. Hobeika J, Liew C.Y, Rajan M.E.S (2025). Are cryptocurrencies influenced by stereotypes and values? Evidence of the role of banker stereotypes and human values on cryptocurrency acceptance // International Journal of Bank Marketing. Vol. 43. No. 8. Pp. 1627–1661, DOI: 10.1108/IJBM-04-2024-0181

  18. Horowitz M.C., Kahn L. (2021). What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials // PLOS ONE. Vol. 16. No. 10. Article e0257732. DOI: 10.1371/journal.pone.0257732

  19. Jalan A., Matkovskyy R., Urquhart A., Yarovaya L. (2023). The role of interpersonal trust in cryptocurrency adoption // Journal of International Financial Markets, Institutions and Money. Vol. 83. Article 101715. DOI: 10.1016/j.intfin.2022.101715

  20. Jobin A., Ienca M., Vayena E. (2019). The global landscape of AI ethics guidelines // Nature Machine Intelligence. Vol. 1. Pp. 389-399. DOI: 10.1038/s42256-019-0088-2

  21. Kai C., Ping W., Xiaomin J. (2026). AI anxiety and adoption intention in higher education based on an extended TAM-UTAUT and PLS-SEM analysis // Scientific Reports. Vol. 16. Article 3672. DOI: 10.1038/s41598-026-35823-9

  22. Kauttonen J., Rousi R., Alamäki A. (2025). Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis // Journal of Medical Internet Research. Vol. 27. Article e65567. DOI: 10.2196/65567

  23. Kazun A.D., Porshnev A.V. (2021) Who Believes in Conspiracy Theories? Factors Influencing Propensity for Conspiracy Thinking in Russia, Kazakhstan and Ukraine // Monitoring of Pub­lic Opinion: Economic and Social Changes. No. 6. Pp. 549-565. DOI: 10.14515/monitoring.2021.6.1889 (In Russ.).

  24. Lammert D., Liu M., Betz S., Lammert J., Pfeffer, J. (2026). Culturally-Aware Artificial Intelligence: Personal Values and Technology Acceptance among AI Researchers in China and Germany // EAI Endorsed Transactions on Internet of Things. Vol. 11. DOI: 10.4108/eetiot.10618

  25. Lep Ž., Trunk A., Babnik K. (2022). Value Orientations and Institutional Trust as Contributors to the Adoption of Online Services in Youth: A Cross-Country Comparison // Frontiers in Psychology. Vol. 13. Article 887587. DOI: 10.3389/fpsyg.2022.887587

  26. Li H., Wei X. (2025). Factors influencing older adults’ adoption of AI voice assistants: Extending the UTAUT model // Frontiers in Psychology. Vol. 16. Article 1618689. DOI: 10.3389/fpsyg.2025.1618689

  27. Longoni C., Bonezzi A., Morewedge C.K. (2019). Resistance to medical artificial intelligence // Journal of Consumer Research. Vol. 46. No. 4. Pp. 629-650. DOI: 10.1093/jcr/ucz013

  28. Lukichev P.M. (2024). Decision-making in the modern economy: artificial intelligence vs. behavioral economics // Issues of Innovation Economics. Vol. 14. No. 3. Pp. 649-666. DOI: 10.18334/vinec.14.3.121070 (In Russ.).

  29. Makarov M.Yu. (2020). The Impact of Artificial Intelligence on Productivity // Economics and Management. Vol. 26. No. 5. Pp. 479-486. DOI: 10.35854/1998-1627-2020-5-479-486 (In Russ.).

  30. Malle B.F., Magar S.T., Scheutz M. (2019). AI in the sky: How people morally evaluate human and machine decisions in a lethal strike dilemma // Robotics and Well-Being. Intelligent Systems, Control and Automation: Science and Engineering / A.M. Ferreira, S.J. Sequeira, S.G. Virk, M. Tokhi, E.E. Kadar (eds). Vol. 95. – Cham: Springer International Publishing. Pp. 111-133. DOI: 10.1007/978-3-030-12524-0_11

  31. Manakhova I.V., Makovskaya A.M. (2025). Human and artificial intelligence in the discourse of behavioral economics // Lomonosov Economics Journal. Vol. 60. No. 3. Pp. 3-19. DOI: 10.55959/MSU0130-0105-6-60-3-1 (In Russ.).

  32. Maninger T., Shank D.B. (2022). Perceptions of violations by artificial and human actors across moral foundations // Computers in Human Behavior Reports. Vol. 5. Article 100154. DOI: 10.1016/J.CHBR.2021.100154

  33. Massachusetts Institute of Technology. (2025). The GenAI divide: State of Business 2025. URL: ai_report_2025.pdf (access date: 26.02.2026)

  34. Mayer R.C., Davis J.H., Schoorman F.D. (1995). An integrative model of organizational trust // Academy of Management Review. Vol. 20. No. 3. Pp. 709-734. DOI: 10.5465/amr.1995.9508080335

  35. Mehrotra S., Jonker C.M., Tielman M.L. (2021). More Similar Values, More Trust? - The Effect of Value Similarity on Trust in Human-Agent Interaction. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery. — New York. Pp. 777–783. DOI: 10.1145/3461702.3462576

  36. Mirowska A., Arsenyan J. (2025). «It Feels Wrong»: Understanding Reactions to Artificial Intelligence as a Decision-Maker in Selection Through the Lens of Moral Foundations Theory // International Journal of Selection and Assessment. Vol. 34. Article e70039. DOI: 10.1111/ijsa.70039

  37. Morselli D., Spini D., Devos T. (2012). Human Values and Trust in Institutions across Countries: A Multilevel Test of Schwartz’s Hypothesis of Structural Equivalence // Survey Research Methods. Vol. 6. No. 1. Pp. 49-60. DOI: 10.18148/srm/2012.v6i1.5090

  38. Nagy S., Hajdú N. (2021). Consumer acceptance of the use of artificial intelligence in online shopping: Evidence from Hungary // Amfiteatru Economic. Vol. 23. No. 56. Pp. 155-173. DOI:10.24818/EA/2021/56/155

  39. Nunes J.L. et al. (2024). Are large language models moral hypocrites? A study based on moral foundations // Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. Vol. 7. No. 1. Pp. 1074-1087. DOI: 10.48550/arXiv.2405.11100

  40. Panari C., Lorenzi G., Mariani M.G. (2021). The Predictive Factors of New Technology Adoption, Workers’ Well-Being and Absenteeism: The Case of a Public Maritime Company in Venice // International Journal of Environmental Research and Public Health. Vol. 8. No. 23. Article 12358. DOI: 10.3390/ijerph182312358

  41. Petrunin Y.Y., Nuralieva N.Z. (2025) Trust in Generative Artificial Intelligence as a Mirror of Institutional Trust // Public Administration: E-journal. Vol. 113. Pp. 22-30. DOI: 10.55959/MSU2070-1381-113-2025-22-30 (In Russ.).

  42. Purcell Z.A., Bonnefon J.F. (2023). Humans feel too special for machines to score their morals // PNAS nexus. Vol. 2. No. 6. Article pgad179. DOI: 10.1093/pnasnexus/pgad179

  43. Robinette P. et al. (2016). Overtrust of robots in emergency evacuation scenarios // 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). — Christchurch, New Zealand. Pp. 101-108. DOI: 10.1109/HRI.2016.7451740.

  44. Ruelas-Villavicencio A.L., Contreras-Yáñez I., Gómez-Ruiz R.P., Zagaglia Del Valle M.C., Malagón-Liceaga A., Pascual-Ramos V. (2025). Digital health literacy is linked to attitudes regarding the ethical aspects of digital health among patients with dermatologic comorbidities // PLOS ONE. Vol. 20. No. 9. DOI: 10.1371/journal.pone.0330916

  45. Sakamoto Y., Uchida T., Ishiguro H. (2025). Value-based large language model agent simulation for mutual evaluation of trust and interpersonal closeness // Scientific Reports. Vol. 15. Article 41653. DOI: 10.1038/s41598-025-25531-1

  46. Salatino A., Prével A., Caspar E. et al. (2025). Influence of AI behavior on human moral decisions, agency, and responsibility // Scientific Reports. Vol. 15. Article 12329. DOI: 10.1038/s41598-025-95587-6

  47. Schwartz S.H. (1992). Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries // Advances in Experimental Social Psychology. Vol. 25. No. 1. Pp. 1-65. DOI: 10.1016/S0065-2601(08)60281-6

  48. Schwartz S.H., Cieciuch J., Vecchione M., Davidov E., Fischer R., Beierlein C., Ramos A., Verkasalo M., Lönnqvist J.-E., Demirutku K., Dirilen-Gumus O., Konty M. (2012). Refining the theory of basic individual values // Journal of Personality and Social Psychology. Vol. 103. No. 4. Pp. 663-688. DOI: 10.1037/a0029393

  49. Segerer R. (2025). Cultural value alignment in large language models: A prompt-based analysis of Schwartz values in Gemini, ChatGPT, and DeepSeek // arXiv preprint. ArXiv:2505.17112. DOI: 10.48550/arXiv.2505.17112

  50. Shahzad M.F., Xu S., Lim W.M. et al. (2024). Cryptocurrency awareness, acceptance, and adoption: The role of trust as a cornerstone // Humanities and Social Sciences Communications. Vol. 11. Article 4. DOI: 10.1057/s41599-023-02528-7

  51. Shank D.B., DeSanti A. (2018). Attributions of morality and mind to artificial intelligence after real-world moral violations // Computers in Human Behavior. Vol. 86. Pp. 401-411. DOI: 10.1016/J.CHB.2018.05.014

  52. Shank D.B., Gott A. (2020). Exposed by AIs! People personally witness artificial intelligence exposing personal information and exposing people to undesirable content // International Journal of Human–Computer Interaction. Vol. 36. No. 17. Pp. 1636-1645. DOI: 10.1080/10447318.2020.1768674

  53. Shen H. et al. (2025). ValueCompass: A framework for measuring contextual value alignment between human and LLMs / Proceedings of the 9th Widening NLP Workshop. Pp. 75-86.   DOI: 10.48550/arXiv.2409.09586

  54. Stanciu A., Partsch M., Lechner C.M. (2024). Basic human values and the adoption of cryptocurrency // Frontiers in Psychology. Vol. 15. Article 1395674. DOI: 10.3389/fpsyg.2024.1395674

  55. Stein J.P., Messingschlager T., Gnambs T. et al. (2024). Attitudes towards AI: measurement and associations with personality // Scientific Reports. Vol. 14. Article 2909.  DOI: 10.1038/s41598-024-53335-2

  56. Sullivan Y., de Bourmont M., Dunaway M. (2022). Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems // Annals of Operations Research. Vol. 308. Pp. 525-548. DOI: 10.1007/s10479-020-03702-9

  57. Tang L., Ferronato P., Bashir M. (2023). Do Users’ Values Influence Trust in Automation? // Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science / H. Zaynidinov, M. Singh, U.S. Tiwary, D. Singh (eds). – Cham: Springer. Vol. 13741. DOI: 10.1007/978-3-031-27199-1_30

  58. Tatarko A.N. (2017). The relationship of basic human values and voting behavior // Social Psychology and Society. Vol. 8. No. 1. Pp. 17-37. DOI: 10.17759/sps.2017080102 (In Russ.).

  59. Ulfert A.S., Antoni C.H., Ellwart T. (2022). The role of agent autonomy in using decision support systems at work // Computers in Human Behavior. Vol. 126. Article 106987. DOI:10.1016/j.chb.2021.106987

  60. Vinokurov F.N., Sadovskaya E.D. (2023). Whom We Trust More: AI-driven vs. Human-driven Economic Decision-Making // Experimental Psychology. Vol. 16. No. 2. Pp. 87-100. DOI: 10.17759/exppsy.2023160206 (In Russ.).

  61. Vuori N., Burkhard B., Pitkäranta L. (2025). It’s Amazing–But Terrifying!: Unveiling the Combined Effect of Emotional and Cognitive Trust on Organizational Member’Behaviours, AI Performance, and Adoption // Journal of Management Studies. Vol. 63. No. 2. Pp. 473-514. DOI: 10.1111/joms.13177

  62. Wen Y., Wang J., Chen X. (2025). Trust and AI weight: Human-AI collaboration in organizational management decision-making // Frontiers in Organizational Psychology. Vol. 3. Article 1419403. DOI: 10.3389/forgp.2025.1419403

  63. Witte E.H., Stanciu A., Boehnke K. (2020). A New Empirical Approach to Intercultural Comparisons of Value Preferences Based on Schwartz’s Theory // Frontiers in Psychology. Vol. 11. Article 1723. DOI: 10.3389/fpsyg.2020.01723

  64. Xiong Y. et al. (2023). More trust or more risk? User acceptance of artificial intelligence virtual assistant // Human Factors and Ergonomics In Manufacturing. Vol. 34. No. 3. Pp. 190-205. DOI: 10.1002/hfm.21020

  65. You Y., Hu Y., Yang W., Cao S. (2022). Research on the Influence Path of Online Consumers’ Purchase Decision Based on Commitment and Trust Theory // Frontiers in Psychology. Vol. 13. Article 916465. DOI: 10.3389/fpsyg.2022.916465

  66. Zhang Q., Lee M.L., Carter S. (2022). You complete me: Human-ai teams and complementary expertise // CHI Conference on Human Factors in Computing Systems. Article 114. Pp. 1-28. DOI: 10.1145/3491102.3517791

  67. Zhang Q., Wang F., Liao G., Li M. (2025). How Does AI Trust Foster Innovative Performance Under Paternalistic Leadership? The Roles of AI Crafting and Leader’s AI Opportunity Perception // Behavioral Sciences. Vol. 15. No. 8. Article 1064. DOI: 10.3390/bs15081064

 

  

Manuscript submission date: 09.03.2026

Manuscript acceptance date: 09.04.2026

 

For citation:  Samoilov O., Tatarko A. Socio-Psychological Factors of Trust in Artificial Intelligence: State of Research // Voprosy teoreticheskoy ekonomiki. 2026. No. 2. Pp. 209–228. DOI:  10.52342/2587- 7666VTE_2026_2_209_228.