Unveiling the Pathways: Exploring Influential Factors Shaping the Intentions to Engage with ChatGPT DOI: https://doi.org/10.33093/ijomfa.2025.6.1.1
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Abstract
Artificial intelligence (AI), notably embodied in ChatGPT, has transformed various sectors, including education and content creation. Initially conceptualized in the 1950s, AI’s integration into education has facilitated personalized learning and dynamic evaluations. ChatGPT, an AI language model, exhibits advancements in natural language processing, aiding users in generating custom content. ChatGPT has rapidly gained popularity, reaching over 100 million monthly users. Its educational potential lies in providing tailored learning experiences and streamlining administrative tasks. However, there is a lack of research in the literature on factors influencing the use of ChatGPT among university students in Malaysia for educational purposes. Therefore, the objective of this review paper is to explore the factors that contributed to the intention to use ChatGPT which include academic content creation, information seeking, novelty, convenience, perceived usefulness and perceived ease of use. This study will use quantitative research methodology and a questionnaire will be created for the target respondents, the undergraduate students at Multimedia University at the Cyberjaya campus. A minimum of 109 responses will be collected for this study and the data will be processed for data analysis, using the SPSS software to analyze the collected data. The intention to ChatGPT among undergraduate students at MMU Cyberjaya was notably impacted by academic content creation, information seeking, convenience and perceived usefulness. In contrast, novelty and perceived ease of use did not exhibit significant influence. In summary, this study aims to provide valuable insights into the factors influencing the use of ChatGPT among university students in MMU Cyberjaya, offering significant implications for academics, researchers, policymakers, and AI developers. The outcome of this study holds significance for academics, researchers, policymakers and AI developers, contributing to their understanding of how individuals engage with and derive meaning from ChatGPT software in the education sector. The study emphasizes how difficult it is for schools to adapt to new technologies and how crucial it is to address a variety of issues in order to do so successfully. When integrating ChatGPT and other educational technologies, executives, education leaders, and other stakeholders should consider these factors. Policymakers could create policies addressing privacy and security issues, and educational institutions should build security, usability, and practicality into their digital plans.
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Abbasi, G. A., Sandran, T., Ganesan, Y., & Iranmanesh, M. (2022). Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA. Technology in Society, 68, 101937.
Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., & Sallam, M. (2023). Factors Influencing Attitudes of University Students towards ChatGPT and its Usage: A Multi-National Study Validating the TAME-ChatGPT
Abdul-Hm, N. A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A. P., & Nawaser, K. (2022). Understanding the determinants of e-wallet continuance usage intention in Malaysia. Quality & quantity, 56(5), 3413-3439.
Adapa, S., Fazal-e-Hasan, S. M., Makam, S. B., Azeem, M. M., & Mortimer, G. (2020). Examining the antecedents and consequences of perceived shopping value through smart retail technology. Journal of Retailing and Consumer Services, 52, 101901.
Ali, M., Wood-Harper, T., & Wood, B. (2023). Understanding the technical and social paradoxes of learning management systems usage in higher education: A sociotechnical perspective. Systems Research and Behavioral Science.
Alita, D., Putra, A. D., & Darwis, D. (2021). Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(3), 295. https://doi.org/10.22146/ijccs.65586
Arif, T. B., Munaf, U., & Ul-Haque, I. (2023). The future of medical education and research: Is ChatGPT a blessing or blight in disguise?. Medical education online, 28(1), 2181052.
Aver, B., Fosner, A., & Alfirevic, N. (2021). Higher education challenges: Developing skills to address contemporary economic and sustainability issues. Sustainability, 13(22), 12567.
Aydin, O., & Karaarslan, E. (2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare. Available at SSRN 4308687.
Best Private University in Malaysia. Multimedia University. (2023, March 15). https://www.mmu.edu.my/
Baidoo-Anu, D., & 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.
Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430.
Blumler, J. G. (1979). The role of theory in uses and gratifications studies. Communication research, 6(1), 9-36.
Blumler, J. G., & Katz, E. (1974). The Uses of Mass Communications: Current Perspectives on Gratifications Research. Sage Annual Reviews of Communication Research Volume III.
Chen, C., Lee, S., & Sundar, S. S. (2023). Interpassivity instead of interactivity? The uses and gratifications of automated features. Behaviour & Information Technology, 1-19.
Croes, E. A., & Antheunis, M. L. (2021). Can we be friends with Mitsuku? A longitudinal study on the process of relationship formation between humans and a social chatbot. Journal of Social and Personal Relationships, 38(1), 279-300.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
De Luna, I. R., Liebana-Cabanillas, F., Sanchez-Fernandez, J., & Munoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53, 103662.
Drew, C. (2023, September 29). Uses and gratifications theory: Examples and definition. Uses And Gratifications Theory: Examples And Definition. https://helpfulprofessor.com/uses-and-gratifications-theory-examples/
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 1-20.
Grassini, S. (2023). Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692.
Gihar, R. (2022). Research Paradigms: Qualitative, Quantitative and Mixed Method.
Guerra-Carrillo, B., Katovich, K., & Bunge, S. A. (2017). Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample. PloS one, 12(8), e0182276.
Hariri, W. (2023). Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing. arXiv preprint arXiv:2304.02017.
Harrer, S. (2023). Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90.
Hernandez, A. A., Padilla, J. R. C., & Montefalcon, M. D. L. (2023, October). Information Seeking Behavior in ChatGPT: The Case of Programming Students from a Developing Economy. In 2023 IEEE 13th International Conference on System Engineering and Technology (ICSET) (pp. 72-77). IEEE.
Hetler, A. (2023, November 17). What is chatgpt? everything you need to know. WhatIs.com. https://www.techtarget.com/whatis/definition/ChatGPT
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165.
Jishnu, D., Srinivasan, M., Dhanunjay, G. S., & Shamala, R. (2023). Unveiling student motivations: A study of ChatGPT usage in education. ShodhKosh: Journal of Visual and Performing Arts, 4(2), 65-73.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009). Handbook of metacognition in education. New York: Routledge.
Keiper, M. C., Fried, G., Lupinek, J., & Nordstrom, H. (2023). Artificial intelligence in sport management education: Playing the AI game with ChatGPT. Journal of Hospitality, Leisure, Sport & Tourism Education, 33, 100456.
Khatun, N. (2021). Applications of normality test in statistical analysis. Open Journal of Statistics, 11(01), 113.
Lewis, J. R. (2019). Comparison of Four TAM Item Formats: Effect of Response Option Labels and Order. Journal of Usability Studies, 14(4).
Limna, P., Kraiwanit, T., & Jangjarat, K. (2023). Adopting the Technology Acceptance Model (TAM) to Explore Online Purchase Intention via Facebook Live Streaming: Empirical Evidence from Bangkok, Thailand. ASEAN Journal of Management & Innovation,
Limna, P., Kraiwanit, T., Jangjarat, K., Klayklung, P., & Chocksathaporn, P. (2023). The Use of ChatGPT in the Digital Era: Perspectives on Chatbot Implementation. Journal of Applied Learning and Teaching, 6(1), 64-74. https://doi.org/10.37074/jalt.2023.6.1.32.
Limna, Pongsakorn, Tanpat Kraiwanit, Kris Jangjarat, Prapasiri Klayklung, and Piyawatjana Chocksathaporn. “The use of ChatGPT in the digital era: Perspectives on chatbot implementation.” Journal of Applied Learning and Teaching 6, no. 1 (2023).
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362.
Martina Kuvalja. (2023, November 29). The use of chatgpt for content creation: A student perspective. https://www.cambridgeassessment.org.uk/blogs/view/the-use-of-chatgpt-for-content-creation/
Mahajan, V. (2023, October 13). 100+ incredible chatgpt statistics & facts in 2024: Notta. notta. https://www.notta.ai/en/blog/chatgpt-statistics
Malodia, S., Islam, N., Kaur, P., & Dhir, A. (2021). Why do people use artificial intelligence (AI)-enabled voice assistants?. IEEE Transactions on Engineering Management.
McCormick, K. M. (2023, June 14). Content creation: Absolutely everything you ever need to know. WordStream by LocaliQ. https://www.wordstream.com/blog/ws/2023/06/13/content-creation
Menon, D. (2022). Updating ‘Stories’ on social media and its relationships to contextual age and narcissism: A tale of three platforms–WhatsApp, Instagram and Facebook. Heliyon, 8(5).
Malik, A., Dhir, A., & Nieminen, M. (2016). Uses and gratifications of digital photo sharing on Facebook. Telematics and Informatics, 33(1), 129-138.
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241.
Nikolopoulou, K. (2022, June 20). What is non-probability sampling?: Types & examples. Scribbr. https://www.scribbr.com/methodology/non-probability-sampling/
Oravec, J. A. (2019). Artificial intelligence, automation, and social welfare: Some ethical and historical perspectives on technological overstatement and hyperbole. Ethics and Social Welfare, 13(1), 18-32.
Park, Y. S., Konge, L., & Artino Jr, A. R. (2020). The positivism paradigm of research. Academic medicine, 95(5), 690-694.
Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2023). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People.
Prastiawan, D. I., Aisjah, S., & Rofiaty, R. (2021). The Effect of Perceived Usefulness, Perceived Ease of Use, and Social Influence on the Use of Mobile Banking Through the Mediation of Attitude Toward Use. Asia Pacific Management and Business Application, 9(3), 243-260. https://apmba.ub.ac.id/index.php/apmba/article/view/420.
Rahman, A., & Muktadir, M. G. (2021). SPSS: An imperative quantitative data analysis tool for social science research. International Journal of Research and Innovation in Social Science, 5(10), 300-302.
Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J. P., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study.
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of Applied Learning and Teaching, 6(1).
Rawas, S. (2023). ChatGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 1-14.
Martínez, S. J. R., Ordóñez-Camacho, X. G., Guillén-Gamez, F. D., & Agapito, . B. (2020). Attitudes Toward Technology Among Distance Education Students: Validation of an Explanatory Model. Online Learning, 24(2), 59-75.
Sallam, M. (2023, March). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In Healthcare (Vol. 11, No. 6, p. 887). MDPI.
Sankey, M. D., & Marshall, S. J. (2023). Perspective chapter: The Learning Management System of 2028 and how we start planning for this now. In Higher Education-Reflections From the Field. IntechOpen.
Shaengchart, Y., Bhumpenpein, N., Kongnakorn, K., Khwannu, P., Tiwtakul, A., & Detmee, S. (2023). Factors Influencing the Acceptance of ChatGPT Usage Among Higher Education Students in Bangkok, Thailand. Advance Knowledge for Executives, 2(4), 1-14.
Sharma, S. (2019). Descriptive statistics and factorial design. Australian Journal of Crop Science, 13(05), 2019.
Sürücü, L., & Maslakci, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694-2726.
Shewale, R. (2024, January 12). CHATGPT statistics - user demographics (January 2024). demandsage. https://www.demandsage.com/chatgpt-statistics/
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
Tubachi, P. (2018, December). INFORMATION SEEKING BEHAVIOR: AN OVERVIEW. Research Gate. https://www.researchgate.net/publication/330521546_INFORMATION_SEEKING_BEHAVIOR_AN_OVERVIEW
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2023). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education.
University of Cambridge (2023, April 2023). ChatGPT (We need to talk). https://www.cam.ac.uk/stories/ChatGPT-and-education
Van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226.
Vrbka, J., & Rowland, Z. (2020). Using artificial intelligence in company management. In Digital Age: Chances, Challenges and Future 7 (pp. 422-429). Springer International Publishing.
Wardana, A. A., Saputro, E. P., Wahyuddin, M., & Abas, N. I. (2022, June). The effect of convenience, perceived ease of use, and perceived usefulness on intention to use e-wallet. In International Conference on Economics and Business Studies (ICOEBS 2022) (pp. 386-395). Atlantis Press.
Warsono, H., Yuwono, T., & Putranti, I. (2023). Analyzing technology acceptance model for collaborative governance in public administration: Empirical evidence of digital governance and perceived ease of use. International Journal of Data and Network Science, 7(1), 41-48.
Woodeson, K. (2022). The Role of Student Satisfaction between TAM and Intentions to Use English Online Dictionary: A Conceptual Review. Advance Knowledge for Executives 1(2), 1-9. https://ssrn.com/abstract=4248152.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student Attitudes towards Chat GPT: A Technology Acceptance Model Survey. International Educational Review, 1(1), 57-83.
Yifan, W., Mengmeng, Y., & Omar, M. K. (2023). ‘A Friend or A Foe’Determining Factors Contributed to the Use of ChatGPT among University Students. International Journal of Academic Research in Progressive Education and Development, 12(2).
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025.