Fogyasztói szegmensek a mesterséges intelligenciával kapcsolatos attitűd alapján
DOI:
https://doi.org/10.15170/MM.2026.60.02.02Kulcsszavak:
mesterséges intelligencia, attitűd, szegmensek, demográfiai jellemzőkAbsztrakt
A tanulmány célja
A mesterséges intelligencia alapjaiban alakítja át mindennapjaink különböző aspektusait, széleskörű társadalmi-gazdasági hatásai közvetlenül befolyásolják a technológiával szembeni egyéni attitűdöket és így a társadalom kollektív hozzáállását is. Az attitűd vizsgálata elsősorban a technológia elfogadásának elősegítése miatt indokolt, ez az MI-alapú innovációk sikerességének alappillére is. Jelen kutatás a MI-vel kapcsolatos lakossági hozzáállás feltárására, disztinkt attitűdalapú klaszterek felállítására és jellemzésére tesz kísérletet demográfiai tulajdonságok, személyiségjegyek és érdeklődési körök alapján.
Alkalmazott módszertan
Az adatfelvételre két validált skála integrálásával egy online kérdőív formájában került sor. Az attitűdök feltárására, az attitűdszegmensek meghatározására a General Attitudes Towards Artificial Intelligence Scale (GAAIS-) skálát alkalmaztuk, a domináns személyiségjegyeket pedig a Ten-Item Personality Inventory (TIPI) skála segítségével mértük. A kutatás célcsoportja a 18–65 év közötti Borsod-Abaúj-Zemplén vármegyei lakosság volt. A teljes mintát 520 fő alkotja, amely nem és életkor szerint reprezentálja az alapsokaságot.
Legfontosabb eredmények
Az elemzés során három klasztert azonosítottunk, ezek az MI-szkeptikusok, MI-semlegesek és MI-hívek. Demográfiai szempontból az életkor, életszínvonal és a gyermekek száma bizonyult szignifikáns klaszterjellemzőnek, a személyiségjegyeket vizsgálva az extraverzió és a nyitottság dimenziói emelkedtek ki, míg az érdeklődési kör esetén a technológiai irányultság azonosítható fő jellemvonásnak.
Gyakorlati javaslatok
Az eredmények többek között támogatják az MI-vezérelt társadalmi innovációk célzott kialakítását, az esetleges elutasításból, ellenállásból fakadó kockázatok minimalizálását. Javaslatot teszünk olyan egyedi elköteleződési kezdeményezések kidolgozására, amelyek egyszerre kezelik az MI-szkeptikusok aggályait és kihasználják az MI-hívek lelkesedését az MI-technológiák szélesebb körű elfogadásának elősegítése érdekében.
Hivatkozások
Ajitha, S. & Huxley, S. (2024), “Exploring public perception, awareness, and satisfaction with AI applications in Karnataka, India: The role of individual characteristics and media influence”, International Journal of System Assurance Engineering and Management. Scopus. https://doi.org/10.1007/s13198-024-02594-3
Babiker, A., Alshakhsi, S., Supti, T. I. & Ali, R. (2024), “Do Personality Traits Impact the Attitudes Towards Artificial Intelligence?”, in:2024 11th International Conference on Behavioural and Social Computing (BESC), 1–8. https://doi.org/10.1109/BESC64747.2024.10780777
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
Baruffaldi, S., Beuzekom, B. van, Dernis, H., Harhoff, D., Rao, N., Rosenfeld, D. & Squicciarini, M. (2020), Identifying and measuring developments in artificial intelligence: Making the impossible possible, (OECD Science, Technology and Industry Working Papers No. 2020/05. https://doi.org/10.1787/5f65ff7e-en
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
Bifkovics, B., Kisfürjesi, N., Hadadiné Jászay, M., Fehér, A. & Huszár, S. (2025), „MI által támogatott mentálhigiénés szolgáltatások elfogadása és megítélése”, Marketing & Menedzsment, 58(Különszám I. EMOK), 15–24. https://doi.org/10.15170/MM.2024.58.KSZ.01.02
Bochniarz, K. T., Czerwiński, S. K., Sawicki, A. & Atroszko, P. A. (2022), “Attitudes to AI among high school students: Understanding distrust towards humans will not help us understand distrust towards AI”, Personality and Individual Differences, 185, 111299. https://doi.org/10.1016/j.paid.2021.111299
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. Scopus. https://doi.org/10.1093/ser/mwae004
Cave, S., Coughlan, K. & Dihal, K. (2019),“Scary Robots”: Examining Public Responses to AI”, in: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 331–337. https://doi.org/10.1145/3306618.3314232
Chen, Y., Wu, Z., Wang, P., Xie, L., Yan, M., Jiang, M., Yang, Z., Zheng, J., Zhang, J. & Zhu, J. (2023), “Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study”, Journal of Medical Internet Research, 25(1), Article 1. Scopus. https://doi.org/10.2196/48249
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), Article 9. https://doi.org/10.1080/10447318.2022.2050543
Dabis, A. (2024), „Generatív oktatás: Mesterségesintelligencia-eszközökkel kapcsolatos attitűdök a Budapesti Corvinus Egyetem oktatói körében”, Új Pedagógiai Szemle, 74(5–6), 57–84.
Danó, G. & Kovács, S. (2025), „A Mesterséges Intelligencia gyakorlati alkalmazásának lehetőségei a marketingkutatásban”, Marketing & Menedzsment, 58(Különszám I. EMOK), 25–34. https://doi.org/10.15170/MM.2024.58.KSZ.01.03
Davis, F. D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology”, MIS Quarterly, 13(3), Article 3. https://doi.org/10.2307/249008
European Commission. Directorate General for Communications Networks, Content and Technology & TNS Opinion & Social. (2017), Attitudes towards the impact of digitisation and automation on daily life: Report. Publications Office, 2017. https://data.europa.eu/doi/10.2759/835661
Gerencsér, J. (2025), „Átalakuló pedagógus kompetenciák, szerepek, feladatok és attitűdök az oktatásban a mesterséges intelligencia tükrében”, Különleges Bánásmód – Interdiszciplináris folyóirat, 11(2), 43–54. https://doi.org/10.18458/KB.2025.2.43
Gillespie, N., Lockey, S. & Curtis, C. (2021), Trust in artificial Intelligence: A five country study, The University of Queensland and KPMG, 2021. https://doi.org/10.14264/e34bfa3
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. Scopus. https://doi.org/10.1016/j.tele.2025.102265
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. Scopus. ACM International Conference Proceeding Series, 2023. https://doi.org/10.1145/3605655.3605669
Ha, L. T. & Thanh, T. T. (2022), “Effects of digital public services on trades in green goods: Does institutional quality matter?”,Journal of Innovation & Knowledge, 7(1), Article 1. https://doi.org/10.1016/j.jik.2022.100168
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
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
Horváth, E. (2024), „MI-csoda szépség!”,Információs Társadalom, 24(1), 95. https://doi.org/10.22503/inftars.XXIV.2024.1.5
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), Article 2. https://doi.org/10.1080/10447318.2022.2151730
Kelly, S., Kaye, S.-A., White, K. M. & Oviedo-Trespalacios, O. (2023), “Clearing the way for participatory data stewardship in artificial intelligence development: A mixed methods approach”, Ergonomics, 66(11), 1782–1799. Scopus. https://doi.org/10.1080/00140139.2023.2289864
Kieslich, K., Lünich, M. & Marcinkowski, F. (2021), “The Threats of Artificial Intelligence Scale (TAI): Development, Measurement and Test Over Three Application Domains”, International Journal of Social Robotics, 13(7), Article 7. https://doi.org/10.1007/s12369-020-00734-w
Kim, S. (2025), “Perceptions of discriminatory decisions of artificial intelligence: Unpacking the role of individual characteristics”, International Journal of Human Computer Studies, 194. Scopus. https://doi.org/10.1016/j.ijhcs.2024.103387
Kim, S.-W. & Lee, Y. (2024), “Investigation into the Influence of Socio-Cultural Factors on Attitudes toward Artificial Intelligence”, Education and Information Technologies, 29(8), Article 8. Scopus. https://doi.org/10.1007/s10639-023-12172-y
Kiss, C., Harmat, V. & Milassin, A. (2022), „A robotizáció térnyerésével kapcsolatos attitűdök Magyarországon = Attitudes towards the rise of robotization in Hungary”, Vezetéstudomány / Budapest Management Review, 2–13. https://doi.org/10.14267/VEZTUD.2022.08-09.01
Kovačević, A. & Demić, 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
Lancelot Miltgen, C., Popovič, A. & Oliveira, T. (2013), “Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context”, Decision Support Systems, 56, 103–114. https://doi.org/10.1016/j.dss.2013.05.010
Leung, X. Y. & Wen, H. (2020), “Chatbot usage in restaurant takeout orders: A comparison study of three ordering methods”, Journal of Hospitality and Tourism Management, 45, 377–386. https://doi.org/10.1016/j.jhtm.2020.09.004
Liang, Y. & Lee, S. A. (2017), “Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling”, International Journal of Social Robotics, 9(3), Article 3. https://doi.org/10.1007/s12369-017-0401-3
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
Neudert, L.-M., Knuutila, A. & Howard, P., N. (2020), Global attitudes towards AI, machine learning & automated decision making (Tech. Rep.), Oxford Internet Institute, 2020. https://perma.cc/6PB6-X56B
Nyagadza, B. (2022), “Sustainable digital transformation for ambidextrous digital firms: Systematic literature review, meta-analysis and agenda for future research directions”, Sustainable Technology and Entrepreneurship, 1(3), Article 3. https://doi.org/10.1016/j.stae.2022.100020
Orhan, A., Aydın Yıldız, T. & Çınar Yağcı, Ş. (2024), “Assessing EFL learners’ 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. Scopus. https://doi.org/10.1080/15391523.2024.2437744
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, Y., Froese, F., Liu, N., Hu, Y. & Ye, M. (2022), “The adoption of artificial intelligence in employee recruitment: The influence of contextual factors”, The International Journal of Human Resource Management, 33(6), Article 6. https://doi.org/10.1080/09585192.2021.1879206
Park, J., Woo, S. E. & Kim, J. (2024), “Attitudes towards artificial intelligence at work: Scale development and validation”, Journal of Occupational and Organizational Psychology, 97(3), Article 3. https://doi.org/10.1111/joop.12502
Priya, P. K., Rudra, K., Sai Kandula, D., Teja, C. & Venkata Koushik Reddy, K. (2023), “An innovative analysis of AI-powered automation techniques for business management”, in: V. H. C. De Albuquerque, P. Raj, & S. P. Yadav (eds.), Toward Artificial General Intelligence, De Gruyter, 2023, 269–286. https://doi.org/10.1515/9783111323749-013
Rahman, M. M., Babiker, A. & Ali, R. (2025), Motivation, Concerns, and Attitudes Towards AI: Differences by Gender, Age, and Culture, 15439 LNCS, 375–391. Scopus. https://doi.org/10.1007/978-981-96-0573-6_28
Rawashdeh, A., Bakhit, M. & Abaalkhail, L. (2023), “Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation”, International Journal of Data and Network Science, 7(1), 25–34. https://doi.org/10.5267/j.ijdns.2022.12.010
Rózsa, S., Bandi, S., Hartung, I., Török, I. A., Varga, J. É., Somlai, E. H., Herold, R. & Kállai, J. (2025), “General Attitudes towards Artificial Intelligence Scale (GAAIS): Hungarian adaptation and links to personality traits”, Frontiers in Psychology, 16, 1703750. https://doi.org/10.3389/fpsyg.2025.1703750
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, 2024. Scopus. https://doi.org/10.1155/2024/6992824
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(13), Article 13. https://doi.org/10.1080/10447318.2022.2085400
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 1. https://doi.org/10.1007/s44202-022-00020-y
Singh, N., Pandey, A., Tikku, A. P., Verma, P. & Singh, B. P. (2023), “Attitude, perception and barriers of dental professionals towards artificial intelligence”, Journal of Oral Biology and Craniofacial Research, 13(5), Article 5. https://doi.org/10.1016/j.jobcr.2023.06.006
Singla, A., Sukharevsky, A., Yee, L., Chui, M. & Hall, B. (2025). The state of AI: How organizations are rewiring to capture value, McKinsey, 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/
Somosi, Z. & Hajdú, N. (2023), „Mesterséges intelligencia etikai dilemmái: Ellenszenv felmérés és következmények”, Marketing & Menedzsment, 57(Különszám EMOK 3), 65–74. https://doi.org/10.15170/MM.2023.57.KSZ.03.07
Stefkovics, Á., Batiz, K., Orbán, F., Tariska, A. & Pavalacs, A. (2024), „A mesterséges intelligenciával kapcsolatos társadalmi attitűdök Magyarországon, 2023 őszén”, socio.hu, 14(3), 90–116. https://doi.org/10.18030/socio.hu.2024.3.90
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
Thormundsson, B. (2024), Artificial intelligence (AI) market size worldwide in 2021 with a forecast until 2030, Statista, 2024. https://www.statista.com/statistics/1365145/artificial-intelligence-market-size/
Wang, Y.-Y. & Wang, Y.-S. (2022), “Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior”, Interactive Learning Environments, 30(4), Article 4. https://doi.org/10.1080/10494820.2019.1674887
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
Windasari, N. A., Kusumawati, N., Larasati, N. & Amelia, R. P. (2022), “Digital-only banking experience: Insights from gen Y and gen Z”, Journal of Innovation & Knowledge, 7(2), Article 2. https://doi.org/10.1016/j.jik.2022.100170
Winkler, R., Hobert, S., Salovaara, A., Söllner, M. & Leimeister, J. M. (2020), “Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent”, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376781
Yildiz, T. (2023), “Measurement of Attitude in Language Learning with AI (MALL:AI)”, Participatory Educational Research, 10(4), Article 4. https://doi.org/10.17275/per.23.62.10.4
Zhang, B., & Dafoe, A. (2019), “Artificial Intelligence: American Attitudes and Trends”, SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3312874
Downloads
Megjelent
Hogyan kell idézni
Folyóirat szám
Rovat
License
Copyright (c) 2026 Marketing & Menedzsment

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.