NONE 20251212070809648 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 1109-9526 2224-2899 Aivars Spilbergs Economics and Finance Department, BA School of Business and Finance, University of Latvia, Kr.Valdemara 161, Riga LV1013, LATVIA Integrating generative artificial intelligence (GenAI) in higher education provides opportunities and creates challenges. This study aims to determine and assess the key factors affecting the adoption of GenAI in social science studies, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) model. An expanded examination of prior research in the area has been conducted in the theoretical section. The empirical study is based on the analysis of survey results from social science students and researchers at universities in the Baltic Sea region. The PLS-SEM method was applied to investigate the relationships between the independent and endogenous variables in a theoretical model. Our findings show that habit, study values, and social norms have a notable impact on the adoption of GenAI. The study underlines the role of social norms as a moderating factor that influences the integration of GenAI in social science studies. This research facilitates a deeper understanding of how GenAI can be applied to enhance social science studies and research while addressing the challenges associated with its adoption. 12 12 2025 12 12 2025 2486 195 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23207.2025.22.195 https://wseas.com/journals/bae/2025/d945107-054(2025).pdf Bail, C. A., Can Generative AI improve social science? Proceedings of the National Academy of Sciences, Vol. 121, No.21, 2024, e2314021121. https://doi.org/10.1073/pnas.2314021121. Ma, D., Akram, H., & Hua, C., Artificial Intelligence in Higher Education: A CrossCultural Examination of Students’ Behavioral Intentions and Attitudes, The International Review of Research in Open and Distributed Learning, Vol. 25, No. 3, 2024, 134. https://doi.org/10.19173/irrodl.v25i3.7703. Southworth, J., Migliaccio, K. W., Glover, J., Glover, J., Reed, D. M., McCarty, C., Brendemuhl, J. H., & Thomas, A., Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy, Computers and Education: Artificial Intelligence, 4, 2023, 100127. https://doi.org/10.1016/j.caeai.2023.100127. Li, M. & Rohayati, M. I., A Bibliometric Analysis of Artificial Intelligence Applications in Global Higher Education, International Journal of Information System Modeling and Design, Vol. 16, No. 1, 2025, pp. 1-24. https://doi.org/10.4018/IJISMD.365913. Gulati, A., Saini, H., Singh, S., & Kumar, V. Enhancing learning potential: investigating marketing students’ behavioral intention to adopt ChatGPT, Marketing Education Review, Vol. 34, No. 3, 2024, 201. https://doi.org/10.1080/10528008.2023.230013 9. Batista, J., Mesquita, A., & Carnaz, G., Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review, Information, Vol. 15, No. 11, 2024, p. 676. https://doi.org/10.3390/info15110676. Chiu, T. K., Future research recommendations for transforming higher education with generative AI, Computers and Education: Artificial Intelligence, Vol. 6, 2024, 100197. https://doi.org/10.1016/j.caeai.2023.100197. Oliński M., Krukowski, K., & Sieciński, K., Bibliometric overview of ChatGPT: New perspectives in social sciences. Publications, Vol. 12, No. 1, 2024, p. 9. https://doi.org/10.3390/publications12010009. Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D.E., Thierry-Aguilera, R., and Gerardou, F., Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT, Education Sciences, Vol. 13, No. 9, 2023, p. 856. https://doi.org/10.3390/educsci13090856. Malik, F. S., & Terzidis, O., A hybrid framework for creating artificial intelligenceaugmented systematic literature reviews, Management Review Quarterly, 2025, pp. 1- 27. https://doi.org/10.1007/s11301-025-00522- 8. Schryen, G., Marrone, M., & Yang, J., Exploring the scope of generative AI in literature review development. Electronic Markets, Vol. 35, No. 1, 2205, pp. 1-26. https://doi.org/10.1007/s12525-025-00754-2. Perkins, M., & Roe, J., The use of Generative AI in qualitative analysis: Inductive thematic analysis with ChatGPT, Journal of Applied Learning and Teaching, Vol. 7, No. 1, 2024, pp. 390-395. https://doi.org/10.37074/jalt.2024.7.1.22. Williams, R. T., Paradigm shifts: exploring AI's influence on qualitative inquiry and analysis—frontiers in Research Metrics and Analytics, Vol. 9, 2024, 1331589. https://doi.org/10.3389/frma.2024.1331589. Andersen, J. P., Degn, L., Fishberg, R., Graversen, E. K., Horbach, S. P., Schmidt, E. K., ... & Sørensen, M. P., Generative Artificial Intelligence (GenAI) in the research process: A survey of researchers’ practices and perceptions. Technology in Society, Vol. 81, 2025, 102813. https://doi.org/10.1016/j.techsoc.2025.102813. McDonald, N., Johri, A., Ali, A., & Collier, A. H., Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines, Computers in Human Behavior: Artificial Humans, 2025, 100121. https://doi.org/10.1016/j.chbah.2025.100121. Rudolph, J., Ismail, F., Tan, S., & Seah, P., Don't believe the hype. AI myths and the need for a critical approach in higher education, Journal of Applied Learning and Teaching, Vol. 8, No. 1, 2025, pp. 6–27. https://doi.org/10.37074/jalt.2025.8.1.1. Jain, R., & Jain, A., Generative AI in writing research papers: a new type of algorithmic bias and uncertainty in scholarly work, In Intelligent Systems Conference, 2024, pp. 656– 669. https://doi.org/10.1007/978-3-031-66329- 1_42. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, Vol. 13, No. 3, 1989, pp. 319–340. https://doi.org/10.2307/249008. Aguilos, V., & Fuchs, K., Using an extended Technology Acceptance Model (eTAM) to determine university students' behavioral intentions of ChatGPT: An empirical study from Thailand. QWERTY-Interdisciplinary Journal of Technology, Culture and Education, Vol. 19, No.2, 2024, pp. 102–123. https://doi.org/10.30557/QW000088. Albayati, H., Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study, Computers and Education: Artificial Intelligence, Vol. 6, 2024, 100203. https://doi.org/10.1016/j.caeai.2024.100203. Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A., Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective, Heliyon, Vol. 10, No.11, 2024, e31887. https://doi.org/10.1016/j.heliyon.2024.e31887. Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M., Determinants of ChatGPT Adoption Intention in Higher Education: Expanding on TAM with the Mediating Roles of Trust and Risk. Information, Vol. 16, No. 2, 2025, p. 82. https://doi.org/10.3390/info16020082. Kleine, A. K., Schaffernak, I., & Lermer, E., Exploring predictors of AI chatbot usage intensity among students: Within-and betweenperson relationships based on the technology acceptance model, Computers in Human Behavior: Artificial Humans, Vol. 3, 2025, 100113. https://doi.org/10.1016/j.chbah.2024.100113. Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B., Determinants of Intention to Use ChatGPT for Educational Purposes: Findings From PLS-SEM and fsQCA, International Journal of HumanComputer Interaction, Vol. 40, No. 7, 2024, pp. 4501–4520. https://doi.org/10.1080/10447318.2023.222649 5. Venkatesh, V., Morris, M., Davis, G., & Davis, F., User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, Vol. 27, No.3, 2003, pp. 425–478. https://doi.org/10.2307/30036540. Acosta-Enríquez, B. G., Farroñan, E.V.R., Zapata, L.I.V., García, F.S.M., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J.C.S., Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory, Heliyon, Vol. 10, 2024, e38315. https://doi.org/10.1016/j.heliyon.2024.e38315. Bhat, M. A., Tiwari, C. K., Bhaskar, P., & Khan, S. T., Examining ChatGPT adoption among educators in higher educational institutions using the extended UTAUT model, Journal of Information, Communication and Ethics in Society, Vol. 22, No. 3, 2024, pp. 331–353. https://doi.org/10.1108/JICES-03- 2024-0033. Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Andrade-Abarca, P. S., The impact of pedagogical beliefs on adopting generative AI in higher education: predictive model from UTAUT2, Frontiers in Artificial Intelligence, Vol. 7, 2024, 1497705. https://doi.org/10.3389/frai.2024.1497705. Camilleri, M. A., Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework, Technological Forecasting and Social Change, Vol. 201, 2024, 123247. https://doi.org/10.1016/j.techfore.2024.123247. Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., Karacapilidis, N., Determinants of Humanities and Social Sciences Students' Intentions to Use Artificial Intelligence Applications for Academic Purposes. Information, Vol. 15, 2024, 314. https://doi.org/10.3390/info15060314. Grassini, S., Aasen, M. L., & Møgelvang, A., Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model. Applied Artificial Intelligence, Vol. 38, No. 1, 2024, 2371168. https://doi.org/10.1080/08839514.2024.237116 8. Nguyen, H. H., Le Pham, N., Nguyen, T. T., & Mai, C. M., Determinants of the Intention to Use ChatGPT in the Work of University Lecturers, Educational Process: International Journal, Vol. 14, 2025, e2025053. https://doi.org/10.22521/edupij.2025.14.53. Shuhaiber, A., Kuhail, M. A., & Salman, S., ChatGPT in higher education: A Student's perspective, Computers in Human Behavior Reports, 17, 2025, 100565. https://doi.org/10.1016/j.chbr.2024.100565. Strzelecki, A. ChatGPT in higher education: Investigating bachelor and master students’ expectations towards AI tool, Education and Information Technologies, Vol. 30, No. 8, 2025, pp.10231-10255. https://doi.org/10.1007/s10639-024-13222-9. Wu, Q., Tian, J., & Liu, Z., Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness, Current Psychology, 2025, pp. 1–14. https://doi.org/10.1007/s12144-024-07193-w. Venkatesh, V., Thong, J., & Xu, X., Unified theory of acceptance and use of technology: A synthesis and the road ahead, Journal of the Association for Information Systems, Vol. 17, No. 5, 2016, pp. 328–376. https://doi.org/10.17705/1jais.00428. Tang, X., Yuan, Z., & Qu, S., Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model. Journal of Computer Assisted Learning, Vol. 41, No.1, 2025, e13105. https://doi.org/10.1111/jcal.13105. Korchak, A., Al Murshidi, G., Getman, A., Raouf, N., Arshe, M., Al Meheiri, N., Shulgina, G., & Costley, J., The role of social influence in generative artificial intelligence: ChatGPT adoption intentions among undergraduate and graduate students, Innovations in Education, 2025, pp.1-15. https://doi.org/10.1080/14703297.2025.249694 2. Ajzen, I., The theory of planned behaviour: Reactions and reflections, Psychology & health, Vol. 26, No.9, 2011, pp. 1113-1127. https://doi.org/10.1080/08870446.2011.613995 Bittle, K., & El-Gayar, O., Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda, Information, Vol. 16, No. 4, 2025, p. 296. https://doi.org/10.3390/info16040296. Zhao, L., Rahman, M. H., Yeoh, W., Wang, S., & Ooi, K. B., Examining factors influencing university students' adoption of generative artificial intelligence: a cross-country study, Studies in Higher Education, 2024, pp. 1–23. https://doi.org/10.1080/03075079.2024.242778 6. Faraon, M., Rönkkö, K., Milrad, M., & Tsui, E., International perspectives on artificial intelligence in higher education: An explorative study of students’ intention to use ChatGPT across the Nordic countries and the USA, Education and Information Technologies, 2025, pp. 1-46. https://doi.org/10.1007/s10639-025-13492-x. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., Ray, S., An Introduction to Structural Equation Modeling. In: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion: Business, Springer, Cham, 2021, pp. 1-29. https://doi.org/10.1007/978-3-030- 80519-7_1. Kim, C., Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach, Electronics, Vol. 14, No. 3, 2025, p. 530. https://doi.org/10.3390/electronics14030530. Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M., Progress in partial least squares structural equation modeling use in marketing research in the last decade, Psychology & Marketing, Vol. 39, No. 5, 2022, pp. 1035–1064. https://doi.org/10.1002/mar.21640. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M., When to use and how to report the results of PLS-SEM, European Business Review, Vol. 31, No. 1, 2019, pp. 2-24. https://doi.org/10.1108/EBR-11-2018-0203. Henseler, J., Ringle, C. M., & Sarstedt, M., A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, Vol. 43, No.1, 2015, pp.115-135. https://doi.org/10.1007/s11747- 014-0403-8. Reiter, L., Jörling, M., Fuchs, C., & Böhm, R., Student (Mis) Use of Generative AI Tools for University-Related Tasks, International Journal of Human–Computer Interaction, 2025, pp. 1-14. https://doi.org/10.1080/10447318.2025.246208 3. Aure, P. A. H., & Cuenca, O., Fostering socialemotional learning through human-centered use of generative AI in business research education: an insider case study, Journal of Research in Innovative Teaching & Learning, Vol. 17, No.2, 2024, p. 168. https://doi.org/10.1108/jrit-03-2024-0076. Licht, K. de F., Generative Artificial Intelligence in Higher Education: Why the "Banning Approach" to Student Use is Sometimes Morally Justified. Philosophy & Technology, Vol. 37, No. 3, 2024, p. 113. https://doi.org/10.1007/s13347-024-00799-9. Chan, C. K. Y., & Hu, W., Students' voices on generative AI: perceptions, benefits, and challenges in higher education, International Journal of Educational Technology in Higher Education, Vol. 20, No.1, 2023, p. 43. https://doi.org/10.1186/s41239-023-00411-8. Saúde, S., Barros, J. P., & Almeida, I., Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions, Social Sciences, Vol. 13, No. 8, 2024, p. 410. https://doi.org/10.3390/socsci13080410.