AI-related attitude segments in Hungary
How personal values influence the way we perceive the technology
DOI:
https://doi.org/10.14267/VEZTUD.2026.03.01Keywords:
artificial intelligence, attitude, segment, valueAbstract
The diffusion of AI-based technologies generates both new opportunities and challenges for society, which directly affects user attitudes. The aim of this study is to empirically test the applicability of the General Attitudes Towards AI Scale (GAAIS) in the Hungarian population and to identify attitude-based segments, along with their value-oriented profiles. Data collection took place in autumn 2024, resulting in a sample of n=415. The online questionnaire included demographic items and validated scales (GAAIS, PVQ21). The findings reveal three distinct attitude segments, which differ along individual value orientations. Among members of the Positive segment, the core values are Universality, Stimulation, and Hedonism, whereas in the Negative segment, Self Direction and Tradition emerge as the primary value dimensions. In the case of the Ambivalent segment, the dominant values are Power, Security, Conformity, and Tradition. These results provide a basis for further analyses and strategy development, while enriching the academic discourse on AI attitudes.
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Adebayo, O., Kumar, P., Patel, A., & Summers, J.D. (2024). Perception of automation, ai, & collaboration in manufacturing. In Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2A: 44th Computers and Information in Engineering Conference (CIE). August 25–28. V02AT02A049. ASME. https://doi.org/10.1115/DETC2024-143183
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Pearson.
Ajzen, I. (1988). Attitudes, Personality and Behavior. Open University Press. https://books.google.hu/books?id=zlMkAQAAIAAJ
Ajzen, I. (2001). Nature and Operation of Attitudes. Annual Review of Psychology, 52(1), 27–58. https://doi.org/10.1146/annurev.psych.52.1.27
Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed). Open University Press.
Allport, G.W. (1954). The Nature of Prejudice. Oxford.
Allport, G.W. (1961). Pattern and Growth in Personality. Holt, Rinehart and Winston. https://books.google.hu/books?id=sdt9AAAAMAAJ
Almaraz-López, C., Almaraz-Menéndez, F., & López-Esteban, C. (2023). Comparative study of the attitudes and perceptions of university students in business administration and management and in education toward artificial intelligence. Education Sciences, 13(6), 609. https://doi.org/10.3390/educsci1306060
Al-Shumrani, M.M. (2025). Factorial structure and measurement invariance of the general atitudes toward artificial intelligence scale for university students. Qubahan Academic Journal, 5(2), 34–48. https://doi.org/10.48161/qaj.v5n2a1672
Arachchi, H.A.D.M., & Samarasinghe, G.D. (2023). Impulse purchase intention in an AI-mediated retail environment: Extending the TAM with attitudes towards technology and innovativeness. Global Business Review. https://doi.org/10.1177/09721509231197721
Arthaud-Day, M. (2023). The role of values in corporate governance. In Research Handbook on Corporate Governance and Ethics (pp. 192–209). Edward Elgar Publishing.
Babiker, A., Alshakhsi, S., Supti, T.I., & Ali, R. (2024). Do Personality Traits Impact the Attitudes Towards Artificial Intelligence? In Proceedings of the 2024 11th IEEE International Conference on Behavioural and Social Computing (BESC). IEEE. https://doi.org/10.1109/BESC64747.2024.10780777
Baek, Y.M. (2010). An integrative model of ambivalence. Social Science Journal, 47(3), 609–629. https://doi.org/10.1016/j.soscij.2010.02.003
Balasa, K.A., Hiedie Dumagay, A., Alieto, E.O., & González Vallejo, R. (2025). Gender and Age Dynamics in Future Educators’ Attitudes toward AI Integration in Education: A Sample from State-managed Universities in Zamboanga Peninsula, Philippines. Seminars in Medical Writing and Education, 4, 668. https://doi.org/10.56294/mw2025668
Bardi, A., & Schwartz, S.H. (2003). Values and Behavior: Strength and Structure of Relations. Personality and Social Psychology Bulletin, 29(10), 1207–1220. https://doi.org/10.1177/0146167203254602
Barnes, A.J., Zhang, Y., & Valenzuela, A. (2024). AI and culture: Culturally dependent responses to AI systems. Current Opinion in Psychology, 58, 101838. https://doi.org/10.1016/j.copsyc.2024.101838
Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Soares Mantere, E., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. https://doi.org/10.1016/j.tele.2023.102013
Borwein, S., Magistro, B., Loewen, P., Bonikowski, B., & Lee-Whiting, B. (2024). The gender gap in attitudes toward workplace technological change. Socio-Economic Review, 22(3), 993–1017. https://doi.org/10.1093/ser/mwae004
Brink, A., Benyayer, L.D., & Kupp, M. (2024). Decision- making in organizations: Should managers use AI? Journal of Business Strategy, 45(4), 267–274. https://doi.org/10.1108/JBS-04-2023-0068
Burgin, C.J., Chun, C.A., Horton, L. E., Barrantes-Vidal, N., & Kwapil, T. R. (2015). Splitting of Associative Threads: The Expression of Schizotypal Ambivalence in Daily Life. Journal of Psychopathology and Behavioral Assessment, 37(2), 349-357. https://doi.org/10.1007/s10862-014-9457-7
Centeno-Martín, H., Toledano-Buendía, S., & Ardèvol- Abreu, A. (2023). Who interacts with communicative AI and what attitudes are displayed toward it? Sociodemographic, personality, and futurephobia variables. Profesional de La Informacion, 32(5), Article 5. https://doi.org/10.3145/epi.2023.sep.02
Chaiklin, H. (2011). Attitudes, Behavior, and Social Practice. The Journal of Sociology & Social Welfare, 38(1), 31-54. https://doi.org/10.15453/0191-5096.3583
Chen, H., Chan-Olmsted, S., Kim, J., & Mayor Sanabria, I. (2022). Consumers’ perception on artificial intelligence applications in marketing communication. Qualitative Market Research, 25(1), 125–142. https://doi.org/10.1108/QMR-03-2021-0040
Dabic, M., Potocan, V., Pavicic, J., & Nedelko, Z. (2019). Personal values as predictors of managers’ innovativeness – From theory to practice. In 2019 IEEE Technology and Engineering Management Conference, TEMSCON 2019. IEEE. https://doi.org/10.1109/TEMSCON.2019.8813725
Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
De Freitas, J., Agarwal, S., Schmitt, B., & Haslam, N. (2023). Psychological factors underlying attitudes toward AI tools. Nature Human Behaviour, 7(11), 1845– 1854. https://doi.org/10.1038/s41562-023-01734-2
De Oliveira Santini, F., Ladeira, W.J., & Sampaio, C.H. (2018). Tourists’ perceived value and destination revisit intentions: The moderating effect of domain‐specific innovativeness. International Journal of Tourism Research, 20(3), 277–285. https://doi.org/10.1002/jtr.2178
Doroszewicz, S. (2014). The method of classification of consumer attitude accessibility in relation to inherent product features. Polish Journal of Natural Sciences, 29(3), 211-223. http://www.uwm.edu.pl/polish-journal/sites/default/files/issues/articles/doroszewicz_2014.pdf
Dreezens, E., Martijn, C., Tenbült, P., Kok, G., & De Vries, N.K. (2005). Food and the relation between values and attitude characteristics. Appetite, 45(1), 40–46. https://doi.org/10.1016/j.appet.2005.03.005
Edwards, A.L. (1957). Techniques of Attitude Scale Construction. Appleton-Century-Crofts. https://books.google.hu/books?id=6WV9AAAAMAAJ
Edwards, J.D., & Ostrom, T.M. (1971). Cognitive structure of neutral attitudes. Journal of Experimental Social Psychology, 7(1), 36–47. https://doi.org/10.1016/0022-1031(71)90053-9
Elizarov, E., Benish-Weisman, M., & Ziv, Y. (2025). Fostering Academic Performance in 5-Year-Olds: The Role of Self-Direction Values, Presented Self-Esteem, and Positive Self-Perception. Early Education and Development, 36(7), 1599-1617. https://doi.org/10.1080/10409289.2025.2454727
Erciyas, Ş.K., Ekrem, E.C., & Edis, E.K. (2024). Relationship Between Individual Innovativeness Levels and Attitudes Toward Artificial Intelligence Among Nursing and Midwifery Students. Computers Informatics Nursing, 42(11), 802-808. https://doi.org/10.1097/CIN.0000000000001170
Fedotova, V.A. (2016). Values and Attitudes towards Innovation among Generations of Russians. Social Psychology and Society, 7(2), 82–92. https://doi.org/10.17759/sps.2016070206
Festinger, L. (1962). Cognitive Dissonance. Scientific American, 207(4), 93–106. http://www.jstor.org/stable/24936719
Fousiani, K., Michelakis, G., Minnigh, P.A., & De Jonge, K.M.M. (2024). Competitive organizational climate and artificial intelligence (AI) acceptance: The moderating role of leaders’ power construal. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1359164
Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126. https://doi.org/10.1016/j.ijer.2024.102381
Gherhes, V., & Obrad, C. (2018). Technical and humanities students’ perspectives on the development and sustainability of artificial intelligence (AI). Sustainability (Switzerland), 10(9), 3066. Scopus. https://doi.org/10.3390/su10093066
Gnambs, T., Stein, J.P., Zinn, S., Griese, F., & Appel, M. (2025). Attitudes, experiences, and usage intentions of artificial intelligence: A population study in Germany. Telematics and Informatics, 98. https://doi.org/10.1016/j.tele.2025.102265
Graham, C., & Stough, R. (2025). Consumer perceptions of AI chatbots on Twitter (X) and Reddit: An analysis of social media sentiment and interactive marketing strategies. Journal of Research in Interactive Marketing, 19(7), 1096–1124. https://doi.org/10.1108/JRIM-05-2024-0237
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
Grassini, S., & Ree, A.S. (2023). Hope or Doom AI-ttitude? Examining the Impact of Gender, Age, and Cultural Differences on the Envisioned Future Impact of Artificial Intelligence on Humankind. In ACM International Conference Proceeding Series. ACM. https://doi.org/10.1145/3605655.3605669
Guarana, C.L., & Hernandez, M. (2015). Building sense out of situational complexity: The role of ambivalence in creating functional leadership processes. Organizational Psychology Review, 5(1), 50-73. https://doi.org/10.1177/2041386614543345
Guarana, C.L., & Hernandez, M. (2016). Identified ambivalence: When cognitive conflicts can help individuals overcome cognitive traps. Journal of Applied Psychology, 101(7), 1013–1029. Scopus. https://doi.org/10.1037/apl0000105
Gülırmak Güler, K., & Şen Atasayar, B. (2025). The relationship between nursing students’ attitudes toward artificial intelligence and their creative personality traits. International Nursing Review, 72(1), e70008. https://doi.org/10.1111/inr.70008
Gutman, J. (1982). A Means-End Chain Model Based on Consumer Categorization Processes. Journal of Marketing, 46(2), 60–72. https://doi.org/10.1177/002224298204600207
Haddock, G., & Maio, G.R. (2008). Attitudes: Content, Structure and Function. In M. Hewstone, W. Stroebe, & K. Jonas, Introduction to social psychology: A European perspective (4th ed), (pp. 112-133). BPS Blackwell.
Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: Development of the attitudes toward AI in defense (AAID) scale. Frontiers in Psychology, 14, 1164810. https://doi.org/10.3389/fpsyg.2023.1164810
Hamlin, R. (2016). Functional or constructive attitudes: Which type drives consumers’ evaluation of meat products? Meat Science, 117, 97–107. https://doi.org/10.1016/j.meatsci.2016.02.038
Hohnsbehn, J.M., Urschler, D.F., & Schneider, I.K. (2022). Torn but balanced: Trait ambivalence is negatively related to confirmation. Personality and Individual Differences, 196, 111736. https://doi.org/10.1016/j.paid.2022.111736
Homer, P.M., & Kahle, L.R. (1988). A structural equation test of the value-attitude-behavior hierarchy. Journal of Personality and Social Psychology, 54(4), 638–646. https://doi.org/10.1037/0022-3514.54.4.638
Honkanen, P., & Verplanken, B. (2004). Understanding Attitudes Towards Genetically Modified Food: The Role of Values and Attitude Strength. Journal of Consumer Policy, 27(4), 401–420. https://doi.org/10.1007/s10603-004-2524-9
Huang, Y., Jiang, S., & Gong, Z. (2025). Validity and Reliability of the Chinese Version of General Attitudes towards Artificial Intelligence Scale. International Journal of Human-Computer Interaction, 41(20), 12884-12894. https://doi.org/10.1080/10447318.2025.2465868
Jahan, S. (2019). Human Development and Universalism: From Ideas to Policies. Journal of Human Development and Capabilities, 20(2), 233–250. https://doi.org/10.1080/19452829.2019.1574726
Kasinidou, M., Kleanthoys, S., & Otterbacher, J. (2025). Cypriot teachers’ digital skills and attitudes towards AI. Discover Education, 4(1). https://doi.org/10.1007/s44217-024-00390-6
Katz, D. (1960). The Functional Approach to the Study of Attitudes. The Public Opinion Quarterly, 24(2), 163– 204. https://doi.org/10.1086/266945
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human–Computer Interaction, 40(2), 497- 514. https://doi.org/10.1080/10447318.2022.2151730
Kim, S. (2025). Perceptions of discriminatory decisions of artificial intelligence: Unpacking the role of individual characteristics. International Journal of Human Computer Studies, 194, 103387. https://doi.org/10.1016/j.ijhcs.2024.103387
Kovacevic, A., & Demic, E. (2024). The Impact of Gender, Seniority, Knowledge, and Interest on Attitudes to Artificial Intelligence. IEEE Access, 12, 129765–129775. https://doi.org/10.1109/ACCESS.2024.3454801
Kozak, J., & Fel, S. (2024). The Relationship between Religiosity Level and Emotional Responses to Artificial Intelligence in University Students. Religions, 15(3), 331. https://doi.org/10.3390/rel15030331
Kumar, J., Rani, M., Rani, G., & Rani, V. (2024). Human- machine dialogues unveiled: An in-depth exploration of individual attitudes and adoption patterns toward AI-powered ChatGPT systems. Digital Policy, Regulation and Governance, 26(4), 435–449. https://doi.org/10.1108/DPRG-11-2023-0167
Kwang, N.A., Ang, R.P., Ooi, L.B., Shin, W.S., Oei, T.P.S., & Leng, V. (2005). Do adaptors and innovators subscribe to opposing values? Creativity Research Journal, 17(2–3), 273–281. https://doi.org/10.1207/s15326934crj1702&3_12
Laupichler, M.C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical studentsf AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24(1), 401. https://doi.org/10.1186/s12909-024-05400-7
Lebedeva, N.M. (2009). Values and attitudes to innovations: Intercultural differences. Psikhologicheskii Zhurnal, 30(6), 81.92.
Liang, Y., Lee, S.H., & Workman, J.E. (2020). Implementation of Artificial Intelligence in Fashion: Are Consumers Ready? Clothing and Textiles Research Journal, 38(1), 3-18. https://doi.org/10.1177/0887302X19873437
Lichtenstein, S., & Higgs, M. (2021). Strategy through Personal Values: A behavioural approach. Springer Nature. https://doi.org/10.1007/978-3-030-88269-3
Ma, D., Akram, H., & Chen, I.H. (2024). Artificial Intelligence in Higher Education: A Cross-Cultural Examination of Studentsf Behavioral Intentions and Attitudes. International Review of Research in Open and Distributed Learning, 25(3), 134-157. https://doi.org/10.19173/irrodl.v25i3.7703
Madinga, N., Aspeling, D., & Dlamini, S. (2024). Understanding sustainable fashion consumption among millennials in South Africa. Young Consumers, 26(7), 38.54. https://doi.org/10.1108/YC-02-2024-1999
Maier, S.B., Jussupow, E., & Heinzl, A. (2020). Good, bad, or both? Measurement of physicianfs ambivalent attitudes towards AI. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. Research Papers. ECIS. https://aisel.aisnet.org/ecis2019_rp/115
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46.60. https://doi.org/10.1016/j.futures.2017.03.006
Martin, B.A.S., Jin, H.S., Wang, D., Nguyen, H., Zhan, K., & Wang, Y.X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108.111. https://doi.org/10.1016/j.jhtm.2020.06.004
McGuire, W.J. (1985). The nature of attitude and attitude change. In G. Lindzey & E. Aronson (Eds.), Handbook of Social Psychology (pp. 223.349). Random House.
Mengi, A., Singh, R.P., Mengi, N., Kalgotra, S., & Singh, A. (2024). A questionnaire study regarding knowledge, attitude and usage of artificial intelligence and machine learning by the orthodontic fraternity of Northern India. Journal of Oral Biology and Craniofacial Research, 14(5), 500-506. https://doi.org/10.1016/j.jobcr.2024.06.004
Montag, C., Ali, R., & Davis, K.L. (2024). Affective neuroscience theory and attitudes towards artificial intelligence. AI and Society, 40, 167.174. https://doi.org/10.1007/s00146-023-01841-8
Moravec, V., Hynek, N., Skare, M., Gavurova, B., & Kubak, M. (2024). Human or machine? The perception of artificial intelligence in journalism, its socio-economic conditions, and technological developments toward the digital future. Technological Forecasting and Social Change, 200, 123162. https://doi.org/10.1016/j.techfore.2023.123162
Mousa, A.H., Maria, N.T., Almuntashiri, F.S., Alsaywid, B.S., & Lytras, M.D. (2023). The potential of artificial intelligence in healthcare: Perceptions of healthcare practitioners and current adoption. In Digital Transformation in Healthcare in Post-COVID-19 Times (pp. 27.41). Academic Press. https://doi.org/10.1016/B978-0-323-98353-2.00001-0
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology.Organisation.Environment (TOE) Framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090
Nazirova, Z., & Simonovits, B. (2024). Values, Attitudes and the Behaviour Paradigm: A Systematic Literature Review. Journal of Human Values, 30(2), 214.239. https://doi.org/10.1177/09716858241236902
Neudert, L.M., Knuutila, A., & Howard, P.N. (2020). Global attitudes towards AI, machine learning & automated decision making (Tech. Rep.). Oxford Internet Institute. https://perma.cc/6PB6-X56B
Nikoli., J., & Zlatanovi., D. (2020). Critical Systems Perspective of Strategic Decision Making: The Role of Values and Context. In Z. Nedelko & M. Brzozowski (Eds.), Advances in Human Resources Management and Organizational Development (pp. 55.80). IGI Global. https://doi.org/10.4018/978-1-7998-1013-1.ch004
Orhan, A., Ayd.n Y.ld.z, T., & C.nar Ya.c., .. (2024). Assessing EFL learnersf attitudes on Generative Artificial Intelligence: Development and validation of Generative Artificial Intelligence attitude scale for EFL learners (GenAIAS). Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2024.2437744
Osgood, C.E., Suci, G.J., & Tannenbaum, P.H. (1957). The Measurement of Meaning. University of Illinois Press. https://books.google.hu/books?id=qk5qAAAAMAAJ
Ozbey, F., & Yasa, Y. (2025). The relationships of personality traits on perceptions and attitudes of dentistry students towards AI. BMC Medical Education, 25(1), 26. https://doi.org/10.1186/s12909-024-06630-5
Pan, W., Xie, T., Wang, Z., & Ma, L. (2022). Digital economy: An innovation driver for total factor productivity. Journal of Business Research, 139, 303.311. https://doi.org/10.1016/j.jbusres.2021.09.061
Park, J., & Woo, S.E. (2022). Who Likes Artificial Intelligence? Personality Predictors of Attitudes toward Artificial Intelligence. Journal of Psychology: Interdisciplinary and Applied, 156(1), 68-94. https://doi.org/10.1080/00223980.2021.2012109
Park, J., Woo, S.E., & Kim, J. (2024). Attitudes towards artificial intelligence at work: Scale development and Organizational Psychology, 97(3), 920-951. https://doi.org/10.1111/joop.12502
Reffien, M.A.M., Selamat, E.M., Sobri, H.N.M., Hanan, M.F.M., Abas, M.I., Ishak, M.F.M., Azit, N.A., Abidin, N.D.I.Z., Hassim, N.H.N., Ahmad, N., Rusli, S.A.S.S., Nor, S.F.S., & Ismail, A. (2021). Physicians’ attitude towards artificial intelligence in medicine, their expectations and concerns: an online mobile survey. Malaysian Journal of Public Health Medicine, 21(1), 181–189. https://doi.org/10.37268/MJPHM/VOL.21/NO.1/ART.742
Rosenberg, M.J., & Hovland, C.I. (1960). Cognitive, affective, and behavioral components of attitudes. In Attitude Organization and Change: An Analysis of Consistency among Attitude Components (pp. 1–14). Yale University Press.
Rothman, N.B., Pratt, M.G., Rees, L., & Vogus, T.J. (2017). Understanding the dual nature of ambivalence: Why and when ambivalence leads to good and bad outcomes. Academy of Management Annals, 11(1), 33–72. https://doi.org/10.5465/annals.2014.0066
Salem, G.M.M., El-Gazar, H.E., Mahdy, A.Y., Alharbi, T.A.F., & Zoromba, M.A. (2024). Nursing Students’ Personality Traits and Their Attitude toward Artificial Intelligence: A Multicenter Cross-Sectional Study. Journal of Nursing Management. https://doi.org/10.1155/2024/6992824
Sánchez, A.F., López-González, J., & López-Ros, S.R. (2025). Intergenerational differences on the cultural imagery of AI in the storytelling and iconicity of animated films for children and young people. Revista Latina de Comunicacion Social, 1(83), 1-28. https://doi.org/10.4185/rlcs-2025-2303
Santos, Z.M.B., Cadano, K.J., Gyawali, Y.P., Alieto, E.O., & Clorion, F.D. (2024). Navigating Between Conditions and Convictions: Investigating the Influence of Socio-geographical Factors on Interest and Attitudes Toward Artificial Intelligence Among Secondary School Teachers. In Motahhir, S., & Bossoufi, B. (Eds.), Digital Technologies and Applications. ICDTA 2024. Lecture Notes in Networks and Systems, vol 11011101 LNNS (pp. 168–177). Springer. https://doi.org/10.1007/978-3-031-68675-7_17
Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
Schepman, A., & Rodway, P. (2023). 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, 39(8), 1-18. https://doi.org/10.1080/10447318.2022.2085400
Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102537
Schneider, I.K., Novin, S., van Harreveld, F., & Genschow, O. (2021). Benefits of being ambivalent: The relationship between trait ambivalence and attribution biases. British Journal of Social Psychology, 60(2), 5705-586. https://doi.org/10.1111/bjso.12417
Schwartz, S.H., & Bilsky, W. (1987). Toward a universal psychological structure of human values. Journal of Personality and Social Psychology, 53(3), 550–562. https://doi.org/10.1037/0022-3514.53.3.550
Schwartz, S.H. (1994). Are There Universal Aspects in the Structure and Contents of Human Values? Journal of Social Issues, 50(4), 19–45. https://doi.org/10.1111/j.1540-4560.1994.tb01196.x
Schwartz, S.H. (2003). A proposal for measuring value orientations across nations. In Questionnaire Package of ESS (pp. 259–290). ESS. https://www.researchgate.net/publication/312444842_A_proposal_for_measuring_value_orientations_across_nations
Schwartz, S.H. (2012). An Overview of the Schwartz Theory of Basic Values. Online Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1116
Schwartz, S.H. (2021). A Repository of Schwartz Value Scales with Instructions and an Introduction. Online Readings in Psychology and Culture, 2(2), Article 2. https://doi.org/10.9707/2307-0919.1173
Sindermann, C., Yang, H., Elhai, J.D., Yang, S., Quan, L., Li, M., & Montag, C. (2022). Acceptance and Fear of Artificial Intelligence: Associations with personality in a German and a Chinese sample. Discover Psychology, 2(1), Article 8. https://doi.org/10.1007/s44202-022-00020-y
Singla, A., Sukharevsky, A., Yee, L., Chui, M., & Hall, B. (2025, March 12). The state of AI: How organizations are rewiring to capture value. McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/
Stapels, J.G., & Eyssel, F. (2021). Let’s not be indifferent about robots: Neutral ratings on bipolar measures mask ambivalence in attitudes towards robots. PLoS ONE, 16(1), Article 1. https://doi.org/10.1371/journal.pone.0244697
Stefański, D., & Jach, Ł. (2022). What do people think about technological enhancements of human beings? An introductory study using the Technological Enhancements Questionnaire in the context of values, the scientistic worldview, and the accepted versions of humanism. Current Issues in Personality Psychology, 10(1), 71–84. https://doi.org/10.5114/cipp.2021.110061
Stehn, M., & Robert Wilson, F. (2012). Ambivalence: The tension between ‘yes’ and ‘no’. Journal of Creativity in Mental Health, 7(1), 83–94. https://doi.org/10.1080/15401383.2012.657587
Stieglitz, S., Möllmann, N.R.J., Mirbabaie, M., Hofeditz, L., & Ross, B. (2023). Recommendations for managing AI-driven change processes: When expectations meet reality. International Journal of Management Practice, 16(4), 407–433. https://doi.org/10.1504/IJMP.2023.132074
Sung, B., Im, H., & Duong, V.C. (2023). Task type’s effect on attitudes towards voice assistants. International Journal of Consumer Studies, 47(5), 1772–1790. https://doi.org/10.1111/ijcs.12946
Sytsma, J., Muldoon, R., & Nichols, S. (2021). The meta- wisdom of crowds. Synthese, 199(3–4), 11051– 11074. https://doi.org/10.1007/s11229-021-03279-1
Tuncer, G.Z., & Tuncer, M. (2024). Investigation of nurses’ general attitudes toward artificial intelligence and their perceptions of ChatGPT usage and influencing factors. Digital Health, 10. https://doi.org/10.1177/20552076241277025
Valle, N.N., Kilat, R.V., Lim, J., General, E., Dela Cruz, J., Colina, S.J., Batican, I., & Valle, L. (2024). Modeling learners’ behavioral intention toward using artificial intelligence in education. Social Sciences and Humanities Open, 10. https://doi.org/10.1016/j.ssaho.2024.101167
van Harreveld, F., Nohlen, H.U., & Schneider, I.K. (2015). The ABC of Ambivalence: Affective, Behavioral, and Cognitive Consequences of Attitudinal Conflict. In Advances in Experimental Social Psychology (Vol. 52), (pp. 285–324). Elsevier. https://doi.org/10.1016/bs.aesp.2015.01.002
van Harreveld, F., Rutjens, B.T., Schneider, I.K., Nohlen, H.U., & Keskinis, K. (2014). In doubt and disorderly: Ambivalence promotes compensatory perceptions of order. Journal of Experimental Psychology: General, 143(4), 1666–1676. https://doi.org/10.1037/a0036099
Vasiljeva, T., Kreituss, I., & Lulle, I. (2021). Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries. Journal of Risk and Financial Management, 14(8), 339. https://doi.org/10.3390/jrfm14080339
Vaughan-Johnston, T.I., Fowlie, D.I., Wallace, L.E., Susmann, M.W., & Fabrigar, L.R. (2025). The preference for attitude neutrality. Journal of Experimental Psychology: General, 154(4), 1038–1062. https://doi.org/10.1037/xge0001703
Vinichenko, M.V., Rybakova, M.V., Ńhulanova, O.L., & Makushkin, S.A. (2020). The social environment change under the influence of artificial intelligence the views of orthodox clergy and parishioners. European Journal of Science and Theology, 16(5), 57-68. https://www.ejst.tuiasi.ro/Files/84/5_Vinichenko%20et%20al.pdf
Westaby, J.D. (2005). Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior. Organizational Behavior and Human Decision Processes, 98(2), 97–120. https://doi.org/10.1016/j.obhdp.2005.07.003
Xu, Y., Shieh, C.H., Van Esch, P., & Ling, I.L. (2020). AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention. Australasian Marketing Journal, 28(4), 189–199. https://doi.org/10.1016/j.ausmj.2020.03.005
Yildiz, T. (2023). Measurement of Attitude in Language Learning with AI (MALL:AI). Participatory Educational Research, 10(4), 111-126. https://doi.org/10.17275/per.23.62.10.4
Yi-No Kang, E., Chen, D.R., & Chen, Y.Y. (2023). Associations between literacy and attitudes toward artificial intelligence–assisted medical consultations: The mediating role of perceived distrust and efficiency of artificial intelligence. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107529
You, Z., Guo, M., Wainaina, G., Qi, K., & Guo, H. (2024). AI Literacy and Academic Performance of Chinese University Students: Mediating Role of Learning Attitude. In EKI ‚24: Proceedings of the 2nd International Conference on Educational Knowledge and Informatization (pp. 495–503). ACM. https://doi.org/10.1145/3691720.3691805
Zarafshani, K., Gorgievski, M.J., & Moradi, K. (2008). Identifying value hierarchies among indigenous women entrepreneurs in agriculture: A case of Iran. International Journal of Business and Globalisation, 2(2), 173–182. https://doi.org/10.1504/IJBG.2008.016625
Zhao, T., Cui, J., Hu, J., Dai, Y., & Zhou, Y. (2022). Is Artificial Intelligence Customer Service Satisfactory? Insights Based on Microblog Data and User Interviews. Cyberpsychology, Behavior, and Social Networking, 25(2), 110–117. https://doi.org/10.1089/cyber.2021.0155
Zibenberg, A., Greenspan, I., Katz-Gerro, T., & Handy, F. (2018). Environmental Behavior Among Russian Youth: The Role of Self-direction and Environmental Concern. Environmental Management, 62(2), 295–304. https://doi.org/10.1007/s00267-018-1032-7
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