Pages: 143-152 |
DOI: 10.37724/RSU.2026.3.79.015 |
EDN: — |
UDK: 316.6:004.8
AbstractThe ongoing intervention of artificial intelligence into professional activity actualizes the problem of psychological determinants of strategies for its use by various professionals. In the psychological model of the activity of the negotiation process leader within the ergatic system “Human — AI — Human,” the leader is considered as the appointed head of a subgroup who achieves performance through team integration and coordination of work with the help of AI tools. The key component of the model is the typology of artificial intelligence strategies (ignoring, passive trusting, or actively analytical); achievement motivation, internal locus of control, tolerance for ambiguity, and professional experience (implemented according to the leader’s age); all these are regarded as major predictors in choosing suitable strategies. The aim of the study was to empirically test the influence of personal characteristics and professional experience on the choice of strategy for using artificial intelligence in a sample of 108 leaders of the negotiation process (mean age 34,2 years, SD = 9,0). The research hypotheses were as follows: higher indicators of achievement motivation, internal locus of control, and tolerance for ambiguity are associated with the choice of an analytical strategy for employing AI. Senior leaders in negotiation processes are more likely to embrace an analytical strategy. Psychodiagnostic techniques were used to measure these characteristics; the strategy was recorded on a three-level scale with parallel self-assessment and supervisor assessment; statistical processing included correlation and regression analysis, the chi-square test, ANOVA, and the non-parametric Kruskal — Wallis test. Artificial intelligence technologies (ChatGPT and GPT-4o) were used at the stages of statistical data processing and translation of materials into English; all results were verified. The concept, text, and conclusions were formed exclusively by the author. The study showed that personal characteristics (achievement motivation, locus of control, tolerance for ambiguity) are not significant predictors of the choice of a strategy for using AI, whereas the age of negotiation process leaders reveals a significant non-monotonic association with the strategy choice: professionals over 30 show complete rejection of the passive-trusting strategy. This effect is interpreted, within the activity-based approach, as a manifestation of their professional experience, which constitutes the central hypothesis for further research. Prospects for the study include direct empirical verification of the role of professional experience through measurement of negotiation tenure and the number of negotiations conducted, as well as the study of organizational-activity factors and structured functional instructions that determine the choice of a strategy.
none
References
1. Бажин Е. Ф., Голынкина Е. А., Эткинд А. М. Метод исследования уровня субъективного кон-троля // Психологический журнал. — 1984. — Т. 5, № 3. — С. 152–162.
2. Бодров В. А. Психология профессиональной деятельности: теоретические и прикладные проблемы. — М. : Ин-т психологии РАН, 2006. — 623 с.
3. Забабурин Д. Р. Черты Большой пятерки как предикторы выбора переговорных стилей на примере HR-специалистов // Психологические исследования. — 2025. — Т. 18, № 104. — С. 1–11. — DOI: 10.54359/ps. v18i104.2015.
4. Иванова Е. М. Психологическая системная профессиография. — М. : ПЕР СЭ, 2003. — 208 с.
5. Климов Е. А., Носкова О. Г., Солнцева Г. Н. Психология труда, инженерная психология и эр-гономика : учеб. — 2-е изд., перераб. и доп. — М. : Юрайт, 2023. — 661 с.
6. Корнилова Т. В. Новый опросник толерантности-интолерантности к неопределенности // Психологический журнал. — 2010. — Т. 31, № 1. — С. 74–86.
7. Кочнева А. Ю. Стратегии использования искусственного интеллекта в профессиональной деятельности // Психология. Историко-критические обзоры и современные исследования. — 2026. — Т. 15, № 3А. — С. 181–188. — DOI: 10.34670/AR.2026.95.29.017.
8. Леонтьев А. Н. Деятельность. Сознание. Личность. — М. : Политиздат, 1975. — 304 с.
9. Реан А. А. Психология личности: социализация, поведение, общение. — СПб. : Прайм-Еврознак, 2004. — 416 с.
10. Сорокоумова Е. А., Кочнева А. Ю. Модель деятельности лидера переговорного процесса в эргатической системе «Человек — Искусственный интеллект — Человек» // Человеческий капитал. — 2026. — № 6. — DOI: 10.25629/HC.2026.06.08.
11. An Early and Unequal Decline: Life Course Trajectories of Cognitive Aging in the United States / Y. C. Yang, C. E. Walsh, K. Shartle [et al.] // Journal of Aging and Health. — 2024. — Vol. 36, N 3-4. — Pp. 230–245. — DOI: 10.1177/08982643231184593.
12. Artificial intelligence in psychology / Gado S., Kempen R., Lingelbach K., Bipp T. // Psychology Learning & Teaching. — 2022. — Vol. 21, N 1. — Pp. 37–56. — DOI: 10.1177/14757257211037149.
13. Assessing the attitude towards artificial intelligence: introduction of the attitudes towards artificial intelligence scale (ATAIS) / C. Sindermann, P. Sha, M. Zhou [et al.] // KI — Künstliche Intelligenz. — 2021. — Vol. 35. — Pp. 109–118. — DOI: 10.1007/s13218-020-00689-0.
14. Calluso C., Devetag M. G. The impact of technology acceptance and personality traits on the willingness to use AI-assisted hiring practices // International Journal of Organizational Analysis. — 2025. — Vol. 33, N 5. — Pp. 1368–1385. — DOI: 10.1108/IJOA-06-2024-4562.
15. Cummins T., Jensen K. Friend or foe? Artificial intelligence (AI) and negotiation // International Journal of Commerce and Contracting. — 2024. — Vol. 8, N 1-2. — Pp. 35–43. — DOI: 10.1177/20555636241256852.
16. Dietvorst B. J., Simmons J. P., Massey C. Algorithm aversion: People erroneously avoid algorithms after seeing them err // Journal of Experimental Psychology: General. — 2015. — Vol. 144, N 1. — Pp. 114–126. — DOI: 10.1037/xge0000033.
17. Lee J. D., See K. A. Trust in automation // Human Factors. — 2004. — Vol. 46, N 1. — Pp. 50–80. — DOI: 10.1518/hfes.46.1.50_30392.
18. Logg J. M., Minson J. A., Moore D. A. Algorithm appreciation // Organizational Behavior and Human Decision Processes. — 2019. — Vol. 151. — Pp. 90–103. — DOI: 10.1016/j.obhdp.2018.12.005.
19. Marakas G. M., Yi M. Y., Johnson R. D. The multilevel and multifaceted character of computer self-efficacy: toward clarification of the construct and an integrative framework for research // Information Systems Research. — 1998. — Vol. 9, N 2. — Pp. 126–163. — DOI: 10.1287/isre.9.2.126.
20. McLain D. L. The MSTAT-I: A new measure of an individual’s tolerance for ambiguity // Educational and Psychological Measurement. — 1993. — Vol. 53, N 1. — Pp. 183–189. —DOI: 10.1177/0013164493053001020.
21. Mosier K. L., Skitka L. J. Human decision makers and automated decision aids // Automation and Human Performance / eds. R. Parasuraman, M. Mouloua. — Mahwah : Erlbaum, 1996. — Pp. 201–220.
22. Parasuraman R., Riley V. Humans and automation: Use, misuse, disuse, abuse // Human Factors. — 1997. — Vol. 39, N 2. — Pp. 230–253. — DOI: 10.1518/001872097778543886.
23. Schepman A., Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale // Computers in Human Behavior Reports. — 2020. — Vol. 1. — Art. 100014. — DOI: 10.1016/j.chbr.2020.100014.
24. Schepman A., Rodway P. The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust // International Journal of Human-Computer Interaction. — 2023. — Vol. 39, N 13. — Pp. 2724–2741. — DOI: 10.1080/10447318.2022.2085400.
25. Sondern D., Hertel G. Employment Negotiations With an Algorithm? How AI as Negotiation Counterpart Would Affect Negotiators’ Trust and Subjective Value Expectations // Conflict Resolution Quarterly. — 2025. — Vol. 43, N 1. — Pp. 5–13. — DOI: 10.1002/crq.21472.
26. The technology acceptance model and adopter type analysis in the context of artificial intelligence /F. Ibrahim, J.-C. Münscher, M. Daseking, N.-T. Telle // Frontiers in Artificial Intelligence. — 2025. — Vol. 7. —Pp. 1–14. — DOI: 10.3389/frai.2024.1496518.
27. User acceptance of information technology / V. Venkatesh, M. G. Morris, G. B. Davis, F. D. Davis //MIS Quarterly. — 2003. — Vol. 27, N 3. — Pp. 425–478. — DOI: 10.2307/30036540.