Understanding ChatGPT Adoption among Higher Education Students in Punjab, India: An Application of UTAUT2 Model

Sukhjit Singh; Pooja Singh; Vismaad Kaur

doi:10.24310/ijtei.111.2025.20219

Authors

Sukhjit Singh Chitkara University, India https://orcid.org/0009-0008-6915-3423
Pooja Singh Chitkara University, India https://orcid.org/0009-0004-6355-3208
Vismaad Kaur Chitkara University, India https://orcid.org/0000-0002-0112-4502

DOI:

https://doi.org/10.24310/ijtei.111.2025.20219

Keywords:

ChatGPT, Acceptance and Use, UTAUT2, PLS-SEM, Higher Education

Abstract

This study examines Indian Higher Education students' behavioural intention to use ChatGPT in their learning. Unified Theory of Acceptance, and Use of Technology 2 (UTAUT2) model is used to investigate the impact of the eight UTAUT2 factors on the students' behavioural intention towards using ChatGPT. A pilot study on 100 students was done to check the reliability and validity of the instrument based on the UTAUT2 model. Using a quantitative research approach, data was gathered from 362 students of Punjab (A North region State), India (313 students’ data was included in final analysis) using purposive sampling technique. The study's findings revealed that PE (Performance Expectancy), SI (Social Influence), HM (Hedonic Motivation), Hb (Habit), FC (Facilitating Conditions) had significant positive influence on BI (Behavioural Intention) whereas EE (Effort Expectancy) had not significantly influenced BI. On ChatGPT use, H and BI had a positive influence, but FC did not significantly influence ChatGPT use. 67% of the respondents gave priority to learning AI tools in school. In terms of practical implications, this study adds to the current literature on ChatGPT or AI tools in higher education, being useful to education scholars. Also, this study highlights the validation of UTAUT2 model to use ChatGPT among HEI students in Punjab, India. The findings of this study could facilitate discussions among educators working for policies related to the use of AI tools, specifically ChatGPT in India.

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Author Biographies

Sukhjit Singh, Chitkara University,

Sukhjit Singh, Chitkara University, Departament of Education

Pooja Singh, Chitkara University,

Pooja Singh, Chitkara University, Departament of Education

Vismaad Kaur, Chitkara University,

Vismaad Kaur, Chitkara University, Departament of Education

References

Abbas, J. (2018). Exploring the relationships among system quality, knowledge quality, and user satisfaction in the success of knowledge management systems: An empirical study. Journal of Information & Knowledge Management, 17(1), 1–21. https://doi.org/10.1142/S0219649218500079

Abu-Taieh, E. M., AlHadid, I., Abu-Tayeh, S., Masa’deh, R., Alkhawaldeh, R. S., Khwaldeh, S., & Alrowwad, A. (2022). Continued intention to use of M-banking in Jordan by integrating UTAUT, TPB, TAM, and service quality with ML. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), Article 120. https://doi.org/10.3390/joitmc8030120

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

Aldosari, S. A. (2020). The future of higher education in light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145. https://doi.org/10.5430/ijhe.v9n3p145

Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004

Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-covid era and its impact on faculty members’ occupational well-being and teaching self efficacy: A path analysis using the UTAUT 2 model. Applied Artificial Intelligence, 37(1), Article 2175110 https://doi.org/10.1080/08839514.2023.2175110

Ali, M., Raza, S. A., Qazi, W., & Puah, C. (2018). Assessing E-learning system in higher education institutes. Interactive Technology and Smart Education, 15(1), 59-78. https://doi.org/10.1108/itse-02-2017-0012

AlKursheh, T. (2024). Higher tertiary education perspectives: Evaluating the electronic assessment techniques of the blackboard platform for fairness and reliability. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 144-165. https://doi.org/10.24310/ijtei.101.2024.17813

Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, Article 101293. https://doi.org/10.1016/j.techsoc.2020.101293

Althunibat, A. (2015). Determining the factors influencing students’ intention to use M-learning in Jordan higher education. Computers in Human Behavior, 52, 65-71. https://doi.org/10.1016/j.chb.2015.05.046

Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437. https://doi.org/10.1016/j.acalib.2021.102437

Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659-673. https://doi.org/10.1007/s10209-019-00685-8

Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: Application of UTAUT2 model. BMC Medical Education, 20(1). https://doi.org/10.1186/s12909-020-02302-2

Becker, J., Cheah, J., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2022). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346. https://doi.org/10.1108/ijchm-04-2022-0474

Binyamin, S. S., & Zafar, B. A. (2021). Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis. Health Informatics Journal, 27(1), Article 1460458220976737. https://doi.org/10.1177/1460458220976737

Bozorgkhou, N. (2015). An internet shopping user adoption model using an integrated TTF and UTAUT: Evidence from Iranian consumers. Management Science Letters, 5(2), 199-204. https://doi.org/10.5267/j.msl.2014.12.017

Cabrera-Sánchez, J., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics, 58, Article 101529. https://doi.org/10.1016/j.tele.2020.101529

Camilleri, M. A. (2024). Evaluating service quality and performance of higher education institutions: A systematic review and a post-COVID-19 outlook. Journal of Marketing for Higher Education, 34(1), 1–24. https://doi.org/10.1080/08841241.2023.2165965

Chang, L., Wang, Y., Liu, J., Feng, Y., & Zhang, X. (2023). Study on factors influencing college students’ digital academic reading behavior. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1007247

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443-3463. https://doi.org/10.1007/s10639-020-10159-7

Cheah, J., Amaro, S., & Roldán, J. L. (2023). Multigroup analysis of more than two groups in PLS-SEM: A review, illustration, and recommendations. Journal of Business Research, 156, Article 113539. https://doi.org/10.1016/j.jbusres.2022.113539

Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. (2023). ChatGPT goes to law school. 71 Journal of Legal Education, 387. https://doi.org/10.2139/ssrn.4335905

Dečman, M. (2015). Modeling the acceptance of E-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272-281. https://doi.org/10.1016/j.chb.2015.03.022

Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The banality of evil or the best of both worlds? Finance Research Letters, 51, 103362. https://doi.org/10.1016/j.frl.2023.103362

Duong, D., Nguyen, T., & Nguyen, A. (2023). The impact of ChatGPT on education: A study on the use of AI tools in academic settings. Journal of Educational Technology Development and Exchange, 16(2), 1-15. https://doi.org/10.18785/jetde.1602.01

Eke, D. O. (2023). ChatGPT and the rise of AI in creative writing: Implications for authors and publishers. Publishing Research Quarterly, 39(2), 203-217. https://doi.org/10.1007/s12109-023-09912-3

Fadzil, F. (2018). A study on factors affecting the behavioral intention to use mobile apps in Malaysia. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3090753

Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies. Interactive Technology and Smart Education, 14(4), 329-348. https://doi.org/10.1108/itse-06-2016-0015

Fornell, C., & Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, 18(1), 39-50.

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, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495

Gansser, O. A., & 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, Article 101535. https://doi.org/10.1016/j.techsoc.2021.101535

Guggemos, J., & Seufert, S. (2021). Teaching with and teaching about technology- Evidence for professional development of in-service teachers. Computers in Human Behavior, 115, Article 106613. https://doi.org/10.1016/j.chb.2020.106613

Guggemos, J., Seufert, S., & Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864-1883. https://doi.org/10.1111/bjet.13006

Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190

Habibi, A., Yaakob, M. F., & Sofwan, M. (2022). Student use of digital libraries during COVID-19: Structural equation modelling in Indonesian and Malaysian contexts. The Electronic Library, 40(4), 472-485. https://doi.org/10.1108/el-12-2021-0212

Hair, J. F., Hult, G. T., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). An introduction to structural equation modeling. In Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion: Business (pp. 1-29). Springer. https://doi.org/10.1007/978-3-030-80519-7_1

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203

Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). 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

Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761. https://doi.org/10.1016/j.compedu.2019.103761

Huang, K. (2023). Artificial intelligence in education: A review of current developments and future prospects. Educational Technology & Society, 26(1), 105-117. https://doi.org/10.1007/s11423-023-10012-8

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1–8. https://doi.org/10.1016/j.im.2010.09.001

Kono, S., & Sato, M. (2022). The potentials of partial least squares structural equation modeling (PLS-SEM) in leisure research. Journal of Leisure Research, 54(3), 309-329. https://doi.org/10.1080/00222216.2022.2066492

Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24(2), 1793-1817. https://doi.org/10.1007/s10639-018-09858-z

Kwateng, K. O., Atiemo, K. A., & Appiah, C. (2018). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151. https://doi.org/10.1108/JEIM-03-2018-005

Lim, W. M., Kumar, S., & Ali, F. (2022). Understanding Generation Z: A review and future research agenda. Journal of Business Research, 142, 1-16. https://doi.org/10.1016/j.jbusres.2021.12.038

Lim, W. M., Kumar, S., & Ali, F. (2023). Exploring the potential of ChatGPT in higher education: A preliminary study. Education and Information Technologies, 28(3), 2501-2520. https://doi.org/10.1007/s10639-023-11021-2

Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74. https://doi.org/10.1504/ijmlo.2022.119966

Lund, B. D., & Wang, T. (2023). ChatGPT in academia: Opportunities and challenges. Journal of Academic Librarianship, 49(1), Article 102123. https://doi.org/10.1016/j.acalib.2022.102123

Magno, F., Cassia, F., & Ringle, C. M. (2024). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal, 36(5), 1242-1251 https://doi.org/10.1108/tqm-06-2022-0197

Marto, A., Gonçalves, A., Martins, J., & Bessa, M. (2019). Applying UTAUT model for an acceptance study alluding the use of augmented reality in archaeological sites. In A. Tremeau, G. M. Farinella, & J. Braz (Eds.). Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 111-120). https://doi.org/10.5220/0007364101110120

Mogavi, O., Smith, J., & Lee, A. (2024). Balancing AI assistance and cognitive development in education. Computers & Education, 182, Article 104484. https://doi.org/10.1016/j.compedu.2022.104484

Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-Neural network modelling approach. Sustainability, 14(19), Article 12726. https://doi.org/10.3390/su141912726

Moorthy, K., Chun T'ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2018). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128-144. https://doi.org/10.1080/10572317.2018.1463049

Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran. Computers & Education, 61, 158-167. https://doi.org/10.1016/j.compedu.2012.09.016

Mustafa, S., Zhang, W., Shehzad, M. U., Anwar, A., & Rubakula, G. (2022). Does Health Consciousness Matter to Adopt New Technology? An integrated model of UTAUT2 with SEM-fsQCA approach. Frontiers in Psychology, 13, Article 836194. https://doi.org/10.3389/fpsyg.2022.836194

Narayan, J., & Naidu, S. (2024). A new contextual and comprehensive application of the UTAUT2 model post-COVID-19 pandemic in higher education. Higher Education Quarterly, 78(1), 47–77. https://doi.org/10.1111/hequ.12441

Niloy, M. I., Rahman, M. M., & Islam, M. S. (2024). Factors influencing students' adoption of AI-based educational tools: A structural equation modeling approach. Computers & Education, 182, Article 104484. https://doi.org/10.1016/j.compedu.2022.104484

Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93. https://doi.org/10.1177/10776958221149577

Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A., & Bañeres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in higher education. Computers & Education, 182, Article 104468. https://doi.org/10.1016/j.compedu.2022.104468

Ragheb, M. A., Tantawi, P., Farouk, N., & Hatata, A. (2022). Investigating the acceptance of applying chat-bot (Artificial intelligence) technology among higher education students in Egypt. International Journal of Higher Education Management, 8(2), 1-13. https://doi.org/10.24052/ijhem/v08n02/art-1

Ramayah, T. J. F. H., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smart PLS 3.0: An updated guide and practical guide to statistical analysis (2nd edition). Pearson

Ramu, P., Osman, M., Abdul Mutalib, N. A., Aljaberi, M. A., Lee, K., Lin, C., & Hamat, R. A. (2023). Validity and reliability of a questionnaire on the knowledge, attitudes, perceptions, and practices toward food poisoning among Malaysian secondary school students: A pilot study. Healthcare, 11(6), Article 853. https://doi.org/10.3390/healthcare11060853

Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020a). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183-208. https://doi.org/10.1177/0735633120960421

Raza, S. A., Qazi, W., Umer, B., & Khan, K. A. (2020b). Influence of social networking sites on life satisfaction among university students: A mediating role of social benefit and social overload. Health Education, 120(2), 141-164. https://doi.org/10.1108/he-07-2019-0034

Raza, S. A., Qazi, Z., Qazi, W., & Ahmed, M. (2021). E-learning in higher education during COVID-19: Evidence from blackboard learning system. Journal of Applied Research in Higher Education, 14(4), 1603-1622. https://doi.org/10.1108/jarhe-02-2021-0054

Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617-1643. https://doi.org/10.1080/09585192.2017.1416655

Salifu, I., Arthur, F., Arkorful, V., Abam Nortey, S., & Solomon Osei-Yaw, R. (2024). Economics students’ behavioural intention and usage of ChatGPT in higher education: A hybrid structural equation modelling-artificial neural network approach. Cogent Social Sciences, 10(1), Article 2300177. https://doi.org/10.1080/23311886.2023.2300177

Shivdas, A., Menon, D. G., & Nair, C. S. (2020). Antecedents of acceptance and use of a digital library system. The Electronic Library, 38(1), 170-185. https://doi.org/10.1108/el-03-2019-0074

Şimşek, A. S., & Ateş, H. (2022). The extended technology acceptance model for Web 2.0 technologies in teaching. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 165-183. https://doi.org/10.24310/innoeduca.2022.v8i2.15413

Soni, N., Kumar, S., & Singh, P. (2022). Generative AI: Transforming industries and society. AI & Society, 37(4), 1231-1245. https://doi.org/10.1007/s00146-021-01234-5

Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209-1230. https://doi.org/10.1111/bjet.13425

Talan, T., Doğan, Y., & Kalinkara, Y. (2024). Digital natives' mobile learning adoption in terms of UTAUT-2 model: A structural equation model. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 100-123. https://doi.org/10.24310/ijtei.101.2024.17440

Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019, June 1). The battle of Brain vs. Heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222-235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008

Thongsri, N., Shen, L., Bao, Y., & Alharbi, I. M. (2018). Integrating UTAUT and UGT to explain behavioural intention to use M-learning. Journal of Systems and Information Technology, 20(3), 278-297. https://doi.org/10.1108/jsit-11-2017-0107

Tseng, T. H., Lin, S., Wang, Y., & Liu, H. (2019). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635-650. https://doi.org/10.1080/10494820.2019.1674888

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2023). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339. https://doi.org/10.1016/j.dsx.2020.04.012

Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412

Venkatesh, V. (2021). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1-2), 641-652. https://doi.org/10.1007/s10479-020-03918-9

Vishwanath, A., & Goldhaber, G. M. (2003). An examination of the factors contributing to adoption decisions among late-diffused technology products. New Media & Society, 5(4), 547-572. https://doi.org/10.1177/146144480354005

Wang, W., & Wang, C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774. https://doi.org/10.1016/j.compedu.2009.02.021

Wijaya, T., Cao, Y., Weinhandl, R., Yusron, E., & Lavicza, Z. (2022). Applying the UTAUT model to understand factors affecting micro-lecture usage by mathematics teachers in China. Mathematics, 10(7), Article 1008. https://doi.org/10.3390/math10071008

Yeop, M. A., Wong, K. T., & Noh, N. M. (2016). Blended learning in selected journals: A content analysis using the complex adaptive blended learning systems. International Journal of Instructional Technology and Distance Learning, 13(10), 47–58.

Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning value’. Education and Information Technologies, 27(9), 12065-12082. https://doi.org/10.1007/s10639-022-11116-2

Zwain, A. A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system. Interactive Technology and Smart Education, 16(3), 239-254. https://doi.org/10.1108/itse-09-2018-0065