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DOI: 10.14254/1795-6889.2026.22-1.7
Submitted: 2025-05-21
Published: 2026-04-29

Negative relationship between cultural individualism and ChatGPT usage. Sociological analysis across 21 countries

Department of Social Research Methods and Techniques, Institute of Sociology, Faculty of Economics and Sociology, University of Lodz
ChatGPT cross-cultural studies generative artificial intelligence diffusion of innovation digital colonialism data colonialism

Abstract

This study investigates the explanatory potential of cultural dimensions on ChatGPT usage rates at the societal level across 21 countries. The research uses country-level regression models (n = 21) to examine the relationship between Hofstede’s cultural dimensions and ChatGPT usage reported in a late 2023 survey. Results reveal a significant negative relationship between one dimension, Individualism/Collectivism, and ChatGPT usage, with collectivist societies such as Kenya, Pakistan, and India exhibiting the highest usage rates. This suggests that generative artificial intelligence adoption in these cultures was driven by imitation rather than innovation. The paper also discusses the issues of digital and data colonialism in the Global South, where ChatGPT usage reflects both technological diffusion and economic exploitation. The study provides insights into the intersection of culture, generative artificial intelligence, and global power dynamics.

 

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References

  1. Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., … Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8 DOI: https://doi.org/10.1038/s41598-024-52549-8
  2. Achen, C. H. (1982). Interpreting and using regression. Sage Publications. DOI: https://doi.org/10.4135/9781412984560
  3. Adams, R. (2021). Can artificial intelligence be decolonized? Interdisciplinary Science Reviews, 46(1–2), 176–197. https://doi.org/10.1080/03080188.2020.1840225 DOI: https://doi.org/10.1080/03080188.2020.1840225
  4. Alsharhan, A., Al-Emran, M., & Shaalan, K. (2024). Chatbot Adoption: A Multiperspective Systematic Review and Future Research Agenda. IEEE Transactions on Engineering Management, 71, 10232–10244. https://doi.org/10.1109/TEM.2023.3298360 DOI: https://doi.org/10.1109/TEM.2023.3298360
  5. Babu, Md. A., Yusuf, K. Md., Eni, L. N., Jaman, S. Md. S., & Sharmin, Mst. R. (2024). ChatGPT and generation ‘Z’: A study on the usage rates of ChatGPT. Social Sciences & Humanities Open, 10, 101163. https://doi.org/10.1016/j.ssaho.2024.101163 DOI: https://doi.org/10.1016/j.ssaho.2024.101163
  6. Bao, H., Sun, M., & Teplitskiy, M. (2025). Where there’s a will there’s a way: ChatGPT is used more for science in countries where it is prohibited. Quantitative Science Studies, 1–23. https://doi.org/10.1162/qss_a_00368 DOI: https://doi.org/10.1162/qss_a_00368
  7. Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215–227. JSTOR. DOI: https://doi.org/10.1287/mnsc.15.5.215
  8. Bertomeu, J., Lin, Y., Liu, Y., & Ni, Z. (2025). The Impact of Generative AI on Information Processing: Evidence from the Ban of ChatGPT in Italy. Journal of Accounting and Economics, 101782. https://doi.org/10.1016/j.jacceco.2025.101782 DOI: https://doi.org/10.1016/j.jacceco.2025.101782
  9. Chatterji, A., Cunningham, T., Deming, D., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025). How People Use ChatGPT (No. W34255; p. w34255). National Bureau of Economic Research. https://doi.org/10.3386/w34255 DOI: https://doi.org/10.3386/w34255
  10. Cohen J., 1988. Statistical Power Analysis for the Behavioral Sciences. Psychology Press.
  11. Couldry, N., & Mejias, U. A. (2019). Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632 DOI: https://doi.org/10.1177/1527476418796632
  12. DeMaio, T. J. (1984). Social Desirability and Survey Measurement: A Review (pp. 257–282). Russell Sage Foundation. http://www.jstor.org/stable/10.7758/9781610447003.16
  13. Elliott, A. (2019). The culture of AI: Everyday life and the digital revolution. Routledge, Taylor & Francis Group. DOI: https://doi.org/10.4324/9781315387185
  14. Feher, K., & Katona, A. I. (2021). Fifteen shadows of socio-cultural AI: A systematic review and future perspectives. Futures, 132, 102817. https://doi.org/10.1016/j.futures.2021.102817 DOI: https://doi.org/10.1016/j.futures.2021.102817
  15. Filipek, K. (2024). Theorising Artificial Intelligence as Practice. In D. Brzeziński, K. Filipek, K. Piwowar, & M. Winiarska-Brodowska, Algorithms, Artificial Intelligence and Beyond (1st ed., pp. 39–54). Routledge. https://doi.org/10.4324/9781032646930-4 DOI: https://doi.org/10.4324/9781032646930-4
  16. Gillespie, N., Lockey, S., Ward, T., Macdade, A., & Hassed, G. (2025). Trust, attitudes and use of artificial intelligence: A global study 2025. The University of Melbourne. https://doi.org/10.26188/28822919
  17. Goodrich, K., & de Mooij, M. (2014). How ‘social’ are social media? A cross-cultural comparison of online and offline purchase decision influences. Journal of Marketing Communications, 20(1–2), 103–116. https://doi.org/10.1080/13527266.2013.797773 DOI: https://doi.org/10.1080/13527266.2013.797773
  18. Gray Widder, D., West, S., & Whittaker, M. (2023). Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4543807 DOI: https://doi.org/10.2139/ssrn.4543807
  19. Gude, V. (2023). Factors Influencing ChatGpt Adoption for Product Research and Information Retrieval. Journal of Computer Information Systems, 1–10. https://doi.org/10.1080/08874417.2023.2280918 DOI: https://doi.org/10.1080/08874417.2023.2280918
  20. Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions, and organizations across nations (2. ed. [Nachdr.]). Sage.
  21. Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1014 DOI: https://doi.org/10.9707/2307-0919.1014
  22. Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base—Analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
  23. Irani, L. (2016). The hidden faces of automation. XRDS: Crossroads, The ACM Magazine for Students, 23(2), 34–37. https://doi.org/10.1145/3014390 DOI: https://doi.org/10.1145/3014390
  24. Jo, H. (2023). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption. Information Development, 02666669231202764. https://doi.org/10.1177/02666669231202764 DOI: https://doi.org/10.1177/02666669231202764
  25. Kotkin, J. (2020). The coming of neo-feudalism: A warning to the global middle class (First paperback edition). Encounter Books.
  26. Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. https://doi.org/10.1016/j.jwb.2012.06.003 DOI: https://doi.org/10.1016/j.jwb.2012.06.003
  27. Lees, N. (2021). The Brandt Line after forty years: The more North–South relations change, the more they stay the same? Review of International Studies, 47(1), 85–106. https://doi.org/10.1017/S026021052000039X DOI: https://doi.org/10.1017/S026021052000039X
  28. Liu, Y., & Wang, H. (2024). Who on Earth Is Using Generative AI ? Washington, DC: World Bank. https://doi.org/10.1596/1813-9450-10870 DOI: https://doi.org/10.1596/1813-9450-10870
  29. Liu, Z. (2021). Sociological perspectives on artificial intelligence: A typological reading. Sociology Compass, 15(3), e12851. https://doi.org/10.1111/soc4.12851 DOI: https://doi.org/10.1111/soc4.12851
  30. Loewen, P. J., Lee-Whiting, B., Arai, M., Bergeron, T., Galipeau, T., Gazendam, I., Needham, H., Slinger, L., & Yusypovych, S. (2024). Global Public Opinion on Artificial Intelligence. Schwartz Reisman Institute for Technology and Society and the Policy, Elections and Representation Lab at the Munk School of Global Affairs and Public Policy at the University of Toronto. https://srinstitute.utoronto.ca/public-opinion-ai
  31. Magnusson, P., Wilson, R. T., Zdravkovic, S., Xin Zhou, J., & Westjohn, S. A. (2008). Breaking through the cultural clutter: A comparative assessment of multiple cultural and institutional frameworks. International Marketing Review, 25(2), 183–201. https://doi.org/10.1108/02651330810866272 DOI: https://doi.org/10.1108/02651330810866272
  32. Mann, M., & Daly, A. (2019). (Big) Data and the North- in -South: Australia’s Informational Imperialism and Digital Colonialism. Television & New Media, 20(4), 379–395. https://doi.org/10.1177/1527476418806091 DOI: https://doi.org/10.1177/1527476418806091
  33. Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., & Clark, J. (2024). The AI Index 2024 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. Retrieved from https://hai.stanford.edu/assets/files/hai_ai-index-report-2024-smaller2.pdf
  34. Minkov, M., & Kaasa, A. (2022). Do dimensions of culture exist objectively? A validation of the revised Minkov-Hofstede model of culture with World Values Survey items and scores for 102 countries. Journal of International Management, 28(4), 100971. https://doi.org/10.1016/j.intman.2022.100971 DOI: https://doi.org/10.1016/j.intman.2022.100971
  35. Perrigo, B. (2023, January 18). Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer. TIME. https://time.com/6247678/openai-chatgpt-kenya-workers/
  36. R Core Team. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
  37. Radhakrishnan, J., & Chattopadhyay, M. (2020). Determinants and Barriers of Artificial Intelligence Adoption – A Literature Review. In S. K. Sharma, Y. K. Dwivedi, B. Metri, & N. P. Rana (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (Vol. 617, pp. 89–99). Springer International Publishing. https://doi.org/10.1007/978-3-030-64849-7_9 DOI: https://doi.org/10.1007/978-3-030-64849-7_9
  38. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.
  39. Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society, 75, 102372. https://doi.org/10.1016/j.techsoc.2023.102372 DOI: https://doi.org/10.1016/j.techsoc.2023.102372
  40. Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 205395171773810. https://doi.org/10.1177/2053951717738104 DOI: https://doi.org/10.1177/2053951717738104
  41. Sebastian, G. (2023). Privacy and Data Protection in ChatGPT and Other AI Chatbots: Strategies for Securing User Information. International Journal of Security and Privacy in Pervasive Computing, 15(1), 1–14. https://doi.org/10.4018/IJSPPC.325475 DOI: https://doi.org/10.4018/IJSPPC.325475
  42. Shin, D., Chotiyaputta, V., & Zaid, B. (2022). The effects of cultural dimensions on algorithmic news: How do cultural value orientations affect how people perceive algorithms? Computers in Human Behavior, 126(August 2021), 107007. https://doi.org/10.1016/j.chb.2021.107007 DOI: https://doi.org/10.1016/j.chb.2021.107007
  43. Skjuve, M., Haugstveit, I. M., Følstad, A., & Brandtzaeg, P. B. (2019). Help! Is my chatbot falling into the uncanny valley? An empirical study of user experience in human-chatbot interaction. Human Technology, 30–54. https://doi.org/10.17011/ht/urn.201902201607 DOI: https://doi.org/10.17011/ht/urn.201902201607
  44. Sohail, M., Wang, F., Archer, N., Wang, W., & Yuan, Y. (2024). Lower than expected but still willing to use: User acceptance toward current intelligent conversational agents. Information & Management, 61(8), 104033. https://doi.org/10.1016/j.im.2024.104033 DOI: https://doi.org/10.1016/j.im.2024.104033
  45. Søndergaard, M. (1994). Research Note: Hofstede’s Consequences: A Study of Reviews, Citations and Replications. Organization Studies, 15(3), 447–456. https://doi.org/10.1177/017084069401500307 DOI: https://doi.org/10.1177/017084069401500307
  46. The Culture Factor. (2023). Country comparison tool. https://www.theculturefactor.com/country-comparison-tool
  47. Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese Higher Education: Adoption, Perception, and Influence Among Graduate Students—an Integrated Analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, 1268549. https://doi.org/10.3389/fpsyg.2024.1268549 DOI: https://doi.org/10.3389/fpsyg.2024.1268549
  48. Tubadji, A., Denney, T., & Webber, D. J. (2021). Cultural relativity in consumers’ rates of adoption of artificial intelligence. Economic Inquiry, 59(3), 1234–1251. https://doi.org/10.1111/ecin.12978 DOI: https://doi.org/10.1111/ecin.12978
  49. United Nations, Department of Economic and Social Affairs, Population Division. (2024, November 8). Population by Single Age—Both Sexes. World Population Prospects. https://web.archive.org/web/20241108093430/https://population.un.org/wpp/Download/Standard/Population/
  50. United Nations Development Programme. (2025) Human Development Report. https://hdr.undp.org/content/human-development-report-2025.
  51. Varoufakis, Y. (2024). Technofeudalism: What killed capitalism. Melville House.
  52. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
  53. World Bank. (2024). World Development Report 2024: The Middle-Income Trap. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2078-6 DOI: https://doi.org/10.1596/978-1-4648-2078-6
  54. World Bank Open Data. (2024). Individuals using the Internet (% of the population). https://data.worldbank.org/indicator/IT.NET.USER.ZS
  55. Youssef, E., Medhat, M., Abdellatif, S., & Al Malek, M. (2024). Examining the effect of ChatGPT usage on students’ academic learning and achievement: A survey-based study in Ajman, UAE. Computers and Education: Artificial Intelligence, 7, 100316. https://doi.org/10.1016/j.caeai.2024.100316 DOI: https://doi.org/10.1016/j.caeai.2024.100316

How to Cite

Żulicki, R. (2026). Negative relationship between cultural individualism and ChatGPT usage. Sociological analysis across 21 countries. Human Technology, 22(1), 139–157. https://doi.org/10.14254/1795-6889.2026.22-1.7