Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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ChatGPT's Rapid Adoption is an Enigma: A Cross-Cultural Analysis

  • Anjee Gorkhali (Sigmund Weis School of Business, Susquehanna University)
  • Received : 2023.11.20
  • Accepted : 2024.08.29
  • Published : 2024.12.30
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Abstract

In the past, individuals took years to adopt a technology meaningfully. However, the rapid increase in ChatGPT users shows a new trend where individuals actively participate in adopting new technology. I collected responses from 420 individuals in two countries and used PLS-SEM to evaluate the impact of UTAUT2 constructs on an individual's behavioral attitude on adopting ChatGPT and their actual intention to adopt it. This study finds that an individual's performance expectancy, satisfaction from using ChatGPT, and the influence of their social circle positively affect their attitude toward adopting it. However, their habit of using it, price value, facilitating conditions, and effort expectancy do not significantly impact their attitude to adopt it. The results from the cross-cultural analysis show that when it comes to using ChatGPT, individuals from different cultures demonstrate notable differences in attitudes resulting from their satisfaction and habit of using the technology.

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References

  1. Adeshola, I., and Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2023.2253858
  2. Alsaleh, D. A., Elliott, M. T., Fu, F. Q., and Thakur, R. (2019). Cross-cultural differences in the adoption of social media. Journal of Research in Interactive Marketing, 13(1), 119-140. https://doi.org/10.1108/JRIM-10-2017-0092
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior And Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  4. Bagchi, K., Cerveny, R., Hart, P., and Peterson, M. (2003). The influence of national culture in information technology product adoption. AMCIS 2003 Proceedings, 119.
  5. Banafaa, M., Shayea, I., Din, J., Azmi, M. H, Alashbi, A., Daradkeh, Y. I., and Alhammadi, A. (2023). 6G mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities. Alexandria Engineering Journal, 64, 245-274. https://doi.org/10.1016/j.aej.2022.08.017
  6. Baidoo-Anu, D., and Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
  7. Bin-Nashwan, S. A., Sadallah, M., and Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370
  8. Breuer, W., Riesener, M., and Salzmann, A. J. (2014). Risk aversion vs. individualism: what drives risk taking in household finance?. The European Journal of Finance, 20(5), 446-462. https://doi.org/10.1080/1351847X.2012.714792
  9. Chawla, D., and Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India–An empirical study. International Journal of Bank Marketing, 37(7), 1590-1618. https://doi.org/10.1108/IJBM-09-2018-0256
  10. Chueng Nainby, P., Fassi, D., and Xiao, D. (2015). Envisioning Collective Wishes with Local Community for Social Innovation towards Village Regeneration in Inner Mongolia. In Emerging Practices. Inquiry into the Developing (pp. 5-14). Tongji University Press.
  11. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral Dissertation), Massachusetts Institute of Technology.
  12. Fatima, T., Kashif, S., Kamran, M., and Awan, T.M. (2021). Examining factors influencing adoption of m-payment: extending UTAUT2 with perceived value. Int. J. Innov. Creat. Chang, 15, 276-299.
  13. Gansser, O. A., and Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. https://doi.org/10.1016/j.techsoc.2021.101535
  14. Gautam, V. (2023). Testing a unified model for understanding adoption of technology in classroom by academicians. Asia Pacific Journal of Information Systems, 33(1), 123-152. https://doi.org/10.14329/apjis.2023.33.1.123
  15. George, A. S., George, A. S. H., and Martin, A.S. G. (2023). ChatGPT and the future of work: A comprehensive analysis of A.I.'s impact on jobs and employment. Partners Universal International Innovation Journal, 1(3), 154-186. https://doi.org/10.5281/zenodo.8076921
  16. George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198-212. https://doi.org/10.1108/10662240410542634
  17. Glass, R., and Li, S. (2010). Social influence and instant messaging adoption. Journal of Computer Information Systems, 51(2), 24-30. https://doi.org/10.1080/08874417.2010.11645465
  18. Hair Jr, J. F., Sarstedt, M., Matthews, L. M., and Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review, 28(1), 63-76. https://doi.org/10.1108/EBR-09-2015-0094
  19. Han, Y., Nguyen, H. D. and Kim, T. H. (2024). 'Addressing user engagement in social media platforms with cultural differences based on Hofstede's dimensions', Asia Pacific Journal of Information Systems, 34(1), 191-208. https://doi.org/10.14329/apjis.2024.34.1.191
  20. Hofstede, G., and Bond, M. H. (1984). Hofstede's culture dimensions: An independent validation using Rokeach's value survey. Journal of Cross-Cultural Psychology, 15(4), 417-433. https://doi.org/10.1177/0022002184015004003
  21. Iqbal, M., and Pribadi, U. (2023). Why does effort expectancy not have a significant effect on the utilisation of e-reports in Sleman Regency?. Electronic Government, an International Journal, 19(5), 619-639. https://doi.org/10.1504/EG.2023.133113
  22. Kapoor, S., Hughes, P. C., Baldwin, J. R., and Blue, J. (2003). The relationship of individualism–collectivism and self-construals to communication styles in India and the United States. International Journal of Intercultural Relations, 27(6), 683-700. https://doi.org/10.1016/j.ijintrel.2003.08.002
  23. Kline, T. J. (2005). Psychological Testing: A Practical Approach to Design and Evaluation. Sage publications.
  24. Koenig-Lewis, N., Marquet, M., Palmer, A., and Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554. https://doi.org/10.1080/02642069.2015.1043278
  25. Kohli, R., Devaraj, S., and Mahmood, M. A. (2004). Understanding determinants of online consumer satisfaction: A decision process perspective. Journal of Management Information Systems, 21(1), 115-136. https://doi.org/10.1080/07421222.2004.11045796
  26. Konstantis, K., Georgas, A., Faras, A., Georgas, K., and Tympas, A. (2023). Ethical considerations in working with ChatGPT on a questionnaire about the future of work with ChatGPT. A.I. and Ethics, 4(4), 1335-1344. https://doi.org/10.1007/s43681-023-00312-6
  27. Li, J., Lam, K. C. K., Karakowsky, L., and Qian, G. (2003). Firm resource and first mover advantages: A case of foreign direct investment (FDI) in China. International Business Review, 12(5), 625-645. https://doi.org/10.1016/S0969-5931(03)00079-9
  28. Lin, C. P., and Anol, B. (2008). Learning online social support: An investigation of network information technology based on UTAUT. CyberPsychology & Behavior, 11(3), 268-272. https://doi.org/10.1089/cpb.2007.0057
  29. Merhi, M., Hone, K., and Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151
  30. Notten, N., Peter, J., Kraaykamp, G., and Valkenburg, P. M. (2009). Research note: digital divide across borders—a cross-national study of adolescents' use of digital technologies. European Sociological Review, 25(5), 551-560. https://doi.org/10.1093/esr/jcn071
  31. Nunnally, J., Bernstein. I. H. (1994) Psychometric theory. McGraw Hill, New York.
  32. Özbilen, P. (2017). The impact of natural culture on new technology adoption by firms: A country level analysis. Int. J. Innov. Manag. Technol, 8(4), 299-305. https://doi.org/10.18178/ijimt.2017.8.4.745
  33. Peng, C., and Kim, Y. G. (2014). Application of the stimuli-organism-response (S.O.R.) framework to online shopping behavior. Journal of Internet Commerce, 13(3-4), 159-176. https://doi.org/10.1080/15332861.2014.944437
  34. Polites, G. L., and Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. M.I.S. Quarterly, 36(1), 21-42. https://doi.org/10.25300/MISQ/2012/36.1.02
  35. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems. 3, 121-154. https://doi.org/10.1016/j.iotcps.2023.04.003
  36. Saprikis, V., and Avlogiaris, G. (2023). Modeling users' acceptance of mobile social commerce: The case of 'Instagram checkout'. Electronic Commerce Research, 23(1), 511-540. https://doi.org/10.1007/s10660-021-09499-4
  37. Schrank, Z., and Running, K. (2018). Individualist and collectivist consumer motivations in local organic food markets. Journal of Consumer Culture, 18(1), 184-201. https://doi.org/10.1177/1469540516659127
  38. Sharif, A., and Raza, S. A. (2017). The influence of hedonic motivation, self-efficacy, trust and habit on adoption of internet banking: A case of developing country. International Journal of Electronic Customer Relationship Management, 11(1), 1-22. https://doi.org/10.1504/IJECRM.2017.086750
  39. Shuhaiber, A. (2018). The role of perceived control, enjoyment, cost, sustainability and trust on intention to use smart meters: An empirical study using SEM-PLS. In Trends and Advances in Information Systems and Technologies: Volume 26 (pp. 789-799). Springer International Publishing. https://doi.org/10.1007/978-3-319-77712-2_74
  40. Steers, R. M., Meyer, A. D., and Sanchez-Runde, C. J. (2008). National culture and the adoption of new technologies. Journal of World Business, 43(3), 255-260. https://doi.org/10.1016/j.jwb.2008.03.007
  41. Syed, H., and Malik, A. N. (2014). Comparative study of effect of culture on technology adoption in Pakistan and USA. The Business & Management Review, 5(1), 42.
  42. Tseng, T. H., Lin, S., Wang, Y. S., and Liu, H. X. (2022). Investigating teachers' adoption of MOOCs: the perspective of UTAUT2. Interactive Learning Environments, 30(4), 635-650. https://doi.org/10.1080/10494820.2019.1674888
  43. Veiga, J. F., Floyd, S., and Dechant, K. (2001). Towards modelling the effects of national culture on IT implementation and acceptance. Journal of Information technology, 16(3), 145-158. https://doi.org/10.1080/02683960110063654
  44. Venkatesh, V., Thong, J. Y., and Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. M.I.S. Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
  45. Zeb, A., Ullah, R., and Karim, R. (2024). Exploring the role of ChatGPT in higher education: Opportunities, challenges and ethical considerations. The International Journal of Information and Learning Technology, 41(1), 99-111. https://doi.org/10.1108/IJILT-04-2023-0046
  46. Zheng, H., and Gao, H. (2021). The research on influencing factors of autonomous vehicle usage behavior: Root analysis based on UTAUT2 model. Journal of Physics: Conference Series, 1910(1), 012057. I.O.P. Publishing, https://doi.org/10.1088/1742-6596/1910/1/012057