ChatGPT As Management Tools For Organization Environment Analysis: Study From Indonesian Gen Y And Z
DOI:
https://doi.org/10.37385/msej.v6i4.8141Keywords:
Habit, Behavioral Intentions, Information Accuracy, ChatGpt, UTAUT2Abstract
ChatGPT has become a valuable tool for individuals, offering an interactive platform to complete tasks with precision and efficiency capabilities often beyond traditional tools. As AI adoption grows rapidly, especially in Indonesia where ChatGPT ranks among the top-used platforms, there is a growing need to understand what drives its continued use, particularly among young digital natives. This study aims to explore behavioral factors habit formation and perceived information accuracy that influence users' intention to adopt ChatGPT within educational and organizational environments. Drawing on the UTAUT2 framework, this research employs a quantitative method involving 129 Indonesian respondents from Generations Y and Z. Data were collected via structured questionnaires and analyzed using SmartPLS to examine the relationships among key variables. The study contributes to AI adoption literature by contextualizing behavioral technology acceptance within the Southeast Asian user base. Its novelty lies in examining ChatGPT use not only as a technological trend, but also as a cognitive and habitual practice. Managerially, the insights benefit AI developers, educators, and institutional leaders by helping them foster sustainable usage through accurate content delivery and reinforcement of positive user habits. The findings aim to provide insight into the factors driving ChatGPT adoption and broader adoption trends in decision-making within the management field.
References
Ahmad, S., Wasim, S., Irfan, S., Gogoi, S., Srivastava, A., & Farheen, Z. (2019). Qualitative v/s. Quantitative Research- A Summarized Review. Journal of Evidence Based Medicine and Healthcare, 6(43), 2828–2832. https://doi.org/10.18410/jebmh/2019/587
Ajayi, V. O. (n.d.). Primary Sources of Data and Secondary Sources of Data.
Ali Memon, M., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Huei Cham, T. (2020). Journal of Applied Structural Equation Modeling SAMPLE SIZE FOR SURVEY RESEARCH: REVIEW AND RECOMMENDATIONS. In Journal of Applied Structural Equation Modeling (Vol. 4, Issue 2).
Amora, J. T. (2021). Convergent validity assessment in PLS-SEM: A loadings-driven approach. In Data Analysis Perspectives Journal (Vol. 2, Issue 1).
Arisona, N., Rofianto, W., & Putriya, A. R. (n.d.). UTAUT 2 (UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY-2) AND TRUST INTEGRATION MODEL TOWARDS BEHAVIORAL INTENTION TO CONTINUE, WILLINGNESS TO RECOMMEND, AND LEVEL OF USE. http://sceco.ub.ro
Ceylan, G., Anderson, I. A., & Wood, W. (2023). Sharing of misinformation is habitual, not just lazy or biased. Proceedings of the National Academy of Sciences of the United States of America, 120(4). https://doi.org/10.1073/pnas.2216614120
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024a). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024b). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
Cohen, J. (n.d.). Statistical Power Analysis for the Behavioral Sciences Second Edition.
Dewi, Y. K., Hanifah, H., & Ping, T. A. (2024). Enhancing the Performance of Indonesian E-Business Start-Ups: The Influence of Innovation Capability and Dynamic Capability. International Journal of Academic Research in Progressive Education and Development, 13(4). https://doi.org/10.6007/IJARPED/v13-i4/23570
Druic?, E., B?icu?, C., Ianole-C?lin, R., & Fischer, R. (2021). Information or habit: What health policy makers should know about the drivers of self-medication among Romanians. International Journal of Environmental Research and Public Health, 18(2), 1–15. https://doi.org/10.3390/ijerph18020689
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. In Journal of Management Information Systems, Summer (Vol. 18).
Hair, J. F. ., Hult, G. T. M. ., Ringle, C. M. ., & Sarstedt, Marko. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Huang, W., Ong, W. C., Wong, M. K. F., Ng, E. Y. K., Koh, T., Chandramouli, C., Ng, C. T., Hummel, Y., Huang, F., Lam, C. S. P., & Tromp, J. (2024). Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence. BMC Health Services Research, 24(1). https://doi.org/10.1186/s12913-024-10861-z
Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/bjast/2015/14975
Kante, M., & Michel, B. (2023). Use of partial least squares structural equation modelling (PLS-SEM) in privacy and disclosure research on social network sites: A systematic review. In Computers in Human Behavior Reports (Vol. 10). Elsevier B.V. https://doi.org/10.1016/j.chbr.2023.100291
Khanthachai, N. (2014). Structural Equation Modeling with the Smart PLS. In Brazilian Journal of Marketing (Vol. 13, Issue 2). www.smartpls.de
Kim, T. W. (2023). Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review. In Journal of Educational Evaluation for Health Professions (Vol. 20). Korea Health Personnel Licensing Examination Institute. https://doi.org/10.3352/jeehp.2023.20.38
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. In Education and Information Technologies (Vol. 28, Issue 1). Springer US. https://doi.org/10.1007/s10639-022-11177-3
Li, C., & Li, Y. (2023). Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053939
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing. E Journal of the Association for Information Science and Technology, 1(1), 1–23.
Mazhar, S. A. (2021). Methods of Data Collection: A Fundamental Tool of Research. Journal of Integrated Community Health, 10(01), 6–10. https://doi.org/10.24321/2319.9113.202101
Ofosu-Ampong, K., Asmah, A., Kani, J. A., & Bibi, D. (2023). Determinants of digital technologies adoption in government census data operations. Digital Transformation and Society, 2(3), 293–315. https://doi.org/10.1108/DTS-11-2022-0056
Palacios-Marqués, D., Correia, M. B., Dematos, N. S., García, M., Sebastián, B., Ramón, J., Guede, S., & Antonovica, A. (n.d.). Application and extension of the UTAUTT model for determining behavioral intention factors in use of the artificial intelligence virtual assistants.
Perceptions of accuracy in online news during the COVID-19 pandemic. (n.d.).
Pillai, R. (2023). Students ’ adoption of AI-based. https://doi.org/10.1108/ITP-02-2021-0152
Pratiwi, I. K. (2024). Behavioral intention to adopt Islamic banking digital services: A modified UTAUT2 approach. Journal of Enterprise and Development (JED), 6(1).
Raman, A., & Thannimalai, R. (n.d.). Factors Impacting the Behavioural Intention to Use E-learning at Higher Education amid the Covid-19 Pandemic: UTAUT2 Model. 26(3), 82–93.
Rasoolimanesh, S. M. (n.d.-a). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. https://www.scriptwarp.com,
Rasoolimanesh, S. M. (n.d.-b). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. https://www.scriptwarp.com,
Rathje, S., Bavel, J. Van, & Linden, S. van der. (2022). Accuracy and Social Incentives Shape Belief in (Mis)Information. https://doi.org/10.21203/rs.3.rs-1293101/v1
Risitano, M., Sorrentino, A., & Quintano, M. (2017). Critical Success Factors in Strategic Brand Management in Luxury Fashion Markets: The Case of Isaia. In Advancing Insights on Brand Management. InTech. https://doi.org/10.5772/intechopen.69735
Safitri, D., Faruk Sofyan, J., Angga Negoro, D., & Kusmayadi, A. (n.d.). Analisis Behavioral Intention Mobile Banking dengan Model UTAUT2. Dimas Angga Negoro, Andri Kusmayadi INNOVATIVE: Journal Of Social Science Research, 4, 571–587.
Shukla, S. (2020). CONCEPT OF POPULATION AND SAMPLE. https://www.researchgate.net/publication/346426707
Sui, Y., & Zhang Citation, B. (2021). Determinants of the Perceived Credibility of Rebuttals Concerning Health Misinformation. Int. J. Environ. Res. Public Health, 18, 1345. https://doi.org/10.3390/ijerph
Suo, W.-J., Goi, C.-L., Goi, M.-T., & Sim, A. K. S. (2021). Factors Influencing Behavioural Intention to Adopt the QR-Code Payment. International Journal of Asian Business and Information Management, 13(2), 1–22. https://doi.org/10.4018/ijabim.20220701.oa8
Swanson, R. A., & Holton III, E. F. (n.d.). Research in Organizations: Foundations and Methods of Inquiry.
Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1). https://doi.org/10.3390/bdcc7010035
The Role and Impact of Online Learning Platforms in Higher Education. (2024). Adult and Higher Education, 6(5). https://doi.org/10.23977/aduhe.2024.060501
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. In Source: MIS Quarterly (Vol. 27, Issue 3).
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412
Widyanto, H. A., Kusumawardani, K. A., & Septyawanda, A. (2020). ENCOURAGING BEHAVIORAL INTENTION TO USE MOBILE PAYMENT: AN EXTENSION OF UTAUT2. Jurnal Muara Ilmu Ekonomi Dan Bisnis, 4(1), 87. https://doi.org/10.24912/jmieb.v4i1.7584
Yahaya, A. A., Habu, J., Sani, A., & Haruna, U. (2024). Examining the Potential Misuse of Artificial Intelligence in Education. March.
Zhang, J., Li, W., & Li, S. (n.d.). ChatGPT: Analyzing Intelligent Chatbots Based on Big Language Models.