PEMODELAN DETERMINAN NIAT KEBERLANJUTAN MAHASISWA UNIVERSITAS UNTUK MENGGUNAKAN KECERDASAN BUATAN GENERATIF: UTAUT 2 YANG DIMODIFIKASI
Keywords:
Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Habit, Satisfaction, dan Continuance Intention
Abstract
Penelitian ini bertujuan untuk mengeksplorasi continuance intention penggunaan generative artificial intelligence dengan menggunakan pendekatan modifikasi United Theory of Acceptance and Use of Technology (UTAUT) 2. Penelitian menggunakan pendekatan kuantitatif dengan menggunakan 279 responden yang merupakan mahasiswa di Kota Padang yang menggunakan generative artificial intelligence. Data dikumpulkan dengan melakukan penyebaran kuesioner secara online dan offline yang berskala likert 1-5. Analisis data menggunakan bantuan aplikasi SmartPLS4. Hasilnya menunjukkan bahwa performance expectancy dan effort expectancy berpengaruh secara positif dan signifikan terhadap satisfaction. Performance expectancy, effort expectancy, dan hedonic motivation berpengaruh secara negatif dan tidak signifikan terhadap continuance intention. Social influence, habit, satisfaction berpengaruh secara positif dan signifikan terhadap continuance intention. Terakhir, facilitating conditions berpengaruh secara positif dan tidak signifikan terhadap continuance intention.
References
Al Halbusi, H., Al-Sulaiti, K., Abdelfattah, F., Ahmad, A. B., & Hassan, S. (2024). Understanding consumers’ adoption of e-pharmacy in Qatar: applying the unified theory of acceptance and use of technology. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-03-2023-0042
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101325
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1). https://doi.org/10.1016/j.jjimei.2022.100143
Al-Hawamleh, A. (2024). Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems: An Integration of the Information Systems Success Model and the Technology Acceptance Model. International Journal of Electrical and Computer Engineering Systems, 5(2), 201–214.
Ali, M. B., Tuhin, R., Alim, M. A., Rokonuzzaman, M., Rahman, S. M., & Nuruzzaman, M. (2022). Acceptance and use of ICT in tourism: the modified UTAUT model. Journal of Tourism Futures. https://doi.org/10.1108/JTF-06-2021-0137
Alzahrani, L. (2023). Analyzing students’ attitudes and behavior toward artificial intelligence technologies in Higher Education. International Journal of Recent Technology and Engineering (IJRTE), 11(6), 65–73. https://doi.org/10.35940/ijrte.F7475.0311623
Armenia, S., Franco, E., Iandolo, F., Maielli, G., & Vito, P. (2024). Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management. Technological Forecasting and Social Change, 200. https://doi.org/10.1016/j.techfore.2023.123131
Azzuhri, A. (2024). Berdamai dengan AI di Ruang Akademik Baca artikel detiknews, “Berdamai dengan AI di Ruang Akademik” selengkapnya https://news.detik.com/kolom/d-7439852/berdamai-dengan-ai-di-ruang-akademik. Https://News.Detik.Com/Kolom/d-7439852/Berdamai-Dengan-Ai-Di-Ruang-Akademik.
Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180. https://doi.org/10.1016/j.techfore.2022.121692
Bhatnagr, P., & Rajesh, A. (2023). Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model. South Asian Journal of Marketing. https://doi.org/10.1108/sajm-06-2022-0040
Binekas, H., & Belgiawan, P. F. (2023). Factors Influence Satisfaction and Continuance Intention of Chatbot Users (pp. 102–116). https://doi.org/10.2991/978-94-6463-216-3_9
Bin-Nashwan, S. A., Shah, M. H., Abdul-Jabbar, H., & Al-Ttaffi, L. H. A. (2023). Social-related factors in integrated UTAUT model for ZakaTech acceptance during the COVID-19 crisis. Journal of Islamic Accounting and Business Research, 14(8), 1383–1403. https://doi.org/10.1108/JIABR-02-2022-0038
Bouteraa, M., Bin-Nashwan, S. A., Al-Daihani, M., Dirie, K. A., Benlahcene, A., Sadallah, M., Zaki, H. O., Lada, S., Ansar, R., Fook, L. M., & Chekima, B. (2024). Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students’ integrity matter? Computers in Human Behavior Reports, 14. https://doi.org/10.1016/j.chbr.2024.100402
Chhonker, M. S., Verma, D., & Kar, A. K. (2017). Review of Technology Adoption frameworks in Mobile Commerce. Procedia Computer Science, 122, 888–895. https://doi.org/10.1016/j.procs.2017.11.451
Dhoni, R. (2022). Ratusan Perguruan Tinggi Ada di Sumbar, Berikut Rinciannya. Ratusan Perguruan Tinggi Ada Di Sumbar, Berikut Rinciannya.
Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: a stimulus–organism–response approach. Journal of Research in Innovative Teaching and Learning. https://doi.org/10.1108/JRIT-01-2024-0006
Edo, O. C., Ang, D., Etu, E. E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2). https://doi.org/10.1016/j.jjimei.2023.100186
Elkefi, S., Tounsi, A., & Kefi, M. A. (2024). Use of ChatGPT for education by engineering students in developing countries: a mixed-methods study. Behaviour and Information Technology. https://doi.org/10.1080/0144929X.2024.2354428
Elshaer, I. A., Hasanein, A. M., & Sobaih, A. E. E. (2024). The moderating effects of gender and study discipline in the relationship between university students’ acceptance and use of chatGPT. European Journal of Investigation in Health, Psychology and Education, 14(7), 1981–1995. https://doi.org/10.3390/ejihpe14070132
Esawe, A. T. (2022). Understanding mobile e-wallet consumers’ intentions and user behavior. Spanish Journal of Marketing - ESIC, 26(3), 363–384. https://doi.org/10.1108/SJME-05-2022-0105
Ferri, L., Maffei, M., Spanò, R., & Zagaria, C. (2023). Uncovering risk professionals’ intentions to use artificial intelligence: empirical evidence from the Italian setting. Management Decision. https://doi.org/10.1108/MD-02-2023-0178
Gao, B. (2023). Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory. Acta Psychologica, 234. https://doi.org/10.1016/j.actpsy.2023.103856
GoodStats. (2024a). 10 Negara Pengguna AI Terbanyak, Indonesia Salah Satunya. Https://Data.Goodstats.Id/Statistic/10-Negara-Pengguna-Ai-Terbanyak-Indonesia-Salah-Satunya-RLlmC.
GoodStats. (2024b). 185,3 Juta Pengguna Internet Tercatat di Indonesia pada 2024. Https://Data.Goodstats.Id/Statistic/1853-Juta-Pengguna-Internet-Tercatat-Di-Indonesia-Pada-2024-JFNoa.
Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1). https://doi.org/10.1016/j.jjimei.2024.100232
Hair, J. F. J., Hult, G. T. M., Rigle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second Edition). SAGE Publications Ltd.
Hair, J. F. J., Hult, G. T. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Iqbal, U. P., Jose, S. M., & Tahir, M. (2023). Integrating trust with extended UTAUT model: a study on Islamic banking customers’ m-banking adoption in the Maldives. Journal of Islamic Marketing, 14(7), 1836–1858. https://doi.org/10.1108/JIMA-01-2022-0030
Ivana, & Soeherman, B. (2024). Ancaman Darurat Text Generative Artificial Intelligence pada pendidikan akuntansi. Jurnal Riset Akuntansi Dan Keuangan, 12(1), 667–682. https://doi.org/10.17509/xxxx.xxx
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102521
Jayarathne, P. G. S. A., Chathuranga, B. T. K., Dewasiri, N. J., & Rana, S. (2022). Motives of mobile payment adoption during COVID-19 pandemic in Sri Lanka: a holistic approach of both customers’ and retailers’ perspectives. South Asian Journal of Marketing. https://doi.org/10.1108/sajm-03-2022-0013
Jo, H. (2022). Continuance intention to use artificial intelligence personal assistant: type, gender, and use experience. Heliyon, 8(9). https://doi.org/10.1016/j.heliyon.2022.e10662
Jurnalissumbar. (2024). Seminar nasional di universitas negeri padang: pemanfaatan artificial intelligence di lembaga pendidikan. Https://Www.Jurnalissumbar.Com/2024/07/Seminar-Nasional-Di-Universitas-Negeri.Html?M=1.
Kadhim, R. J. (2024). The role of artificial intelligence technologies in increasing awareness and educating users through electronic social networks (survey study on a sample of thi qar university students). Conhecimento & Diversidade, Niterói, 16(41).
Kang, S., Choi, Y., & Kim, B. (2024). Impact of Motivation Factors for Using Generative AI Services on Continuous Use Intention: Mediating Trust and Acceptance Attitude. Social Sciences, 13(9), 475. https://doi.org/10.3390/socsci13090475
Kominfo. (2024). Penggunaan AI Makin Masif, Wamenkominfo Ajak Mahasiswa Terapkan Prinsip Kejujuran. Https://Www.Kominfo.Go.Id/Content/Detail/54396/Siaran-Pers-No-67hmkominfo012024-Tentang-Penggunaan-Ai-Makin-Masif-Wamenkominfo-Ajak-Mahasiswa-Terapkan-Prinsip-Kejujuran/0/Siaran_pers.
Kompas. (2024). Kontroversi AI Generatif dan Dampaknya terhadap Pendidikan. Https://Amp.Kompas.Com/Tren/Read/2024/01/26/165255365/Kontroversi-Ai-Generatif-Dan-Dampaknya-Terhadap-Pendidikan.
Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict chatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100246
Li, W. (2021). The role of trust and risk in Citizens’ E-Government services adoption: A perspective of the extended UTAUT model. Sustainability (Switzerland), 13(14). https://doi.org/10.3390/su13147671
Li, Y., & Zhao, M. (2021). A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.528259
Lukman, Agustina, R., & Aisy Rihadatul. (2023). Problematika penggunaan artificial intelligence (AI) untuk pembelajaran di kalangan mahasiwa STIT Pemalang. Jurnal Madaniyah, 13(2), 242–255.
Menon, D., & Shilpa, K. (2023). “Chatting with chatGPT”: analyzing the factors influencing users’ intention to use the open AI’s chatGPT using the UTAUT model. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e20962
Negm, E. M. (2023). Consumers’ acceptance intentions regarding e-payments: a focus on the extended unified theory of acceptance and use of technology (UTAUT2). Management & Sustainability: An Arab Review. https://doi.org/10.1108/msar-04-2023-0022
Pak, T. Y., Bae, B., Lee, C., Jung, I., & Jang, B. J. (2023). Modeling public acceptance of demand-responsive transportation: An integrated UTAUT and ITM framework. Journal of Public Transportation, 25. https://doi.org/10.1016/j.jpubtr.2023.100067
Pamungkas, B. (2024). AI di Perkuliahan: Membantu atau “Mem-buntukan” Mahasiswa? Https://Ftmm.Unair.Ac.Id/Ai-Di-Perkuliahan-Membantu-Atau-Mem-Buntukan-Mahasiswa/.
Patil, H., & Undale, S. (2023). Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model. Education and Information Technologies, 28(11), 14943–14965. https://doi.org/10.1007/s10639-023-11778-6
Purwanto, A., Asbari, M., & Santoso, T. I. (2024). Analisis data penelitian marketing: perbandingan hasil antara Amos, SmartPLS, WarpPLS, dan SPSS untuk jumlah sampel besar. Journal of Industrial Engineering & Management Research, 2(4). https://doi.org/10.7777/jiemar
Rahman, P., Ningsih, N., & Rahim, A. (2024). Tampilan potensi ancaman kecerdasan buatan bagi perguruan tinggi. Jurnal Manajemen Pendidikan Islam, 10(1), 63–72.
Reza, M. D. S. binti M., Tan, S. H., Chong, L. L., & Ong, H. B. (2024). Continuance usage intention of e-wallets: Insights from merchants. International Journal of Information Management Data Insights, 4(2). https://doi.org/10.1016/j.jjimei.2024.100254
Savitri, D. (2024). Kemendikbudristek Godok Regulasi Penggunaan Generative AI di Perguruan Tinggi. Https://Www.Detik.Com/Edu/Perguruan-Tinggi/d-7310158/Kemendikbudristek-Godok-Regulasi-Penggunaan-Generative-Ai-Di-Perguruan-Tinggi.
Sekaran, U., & Bougie, R. (2016). Research methods for business : a skill-building approach (Seventh edition). www.wileypluslearningspace.com
Sharma, A., Mohan, A., Johri, A., & Asif, M. (2024). Determinants of fintech adoption in agrarian economy: Study of UTAUT extension model in reference to developing economies. Journal of Open Innovation: Technology, Market, and Complexity, 10(2). https://doi.org/10.1016/j.joitmc.2024.100273
Venkatesh, V., Smith, R. H., Morris, M. G., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User acceptance of information technology: toward a unified view. Management Information Systems Research Center, 27(3), 425–478.
Venkatesh, V., Walton, S. M., & Thong, J. Y. L. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. Management Information System Research Center, 157–178. http://about.jstor.org/terms
Wan, L., Xie, S., & Shu, A. (2020). Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model. SAGE Open, 10(3). https://doi.org/10.1177/2158244020941858
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.870777
Yu, X., Yang, Y., & Li, S. (2024). Users’ continuance intention towards an AI painting application: An extended expectation confirmation model. PLoS ONE, 19(5 May). https://doi.org/10.1371/journal.pone.0301821
Sari, M. Y., Pujani, V., & Yulihasri. (2024). Eksplorasi niat penggunaan e-government di Kota Padang melalui aplikasi SI RANCAK. Journal Publicuho, 7(1), 336–352. https://doi.org/10.35817/publicuho.v7i1.366
Yunus, Y., Saputri, R. P., & Fransisca, M. (2024). Implementasi Teknologi Artifical Intelligence (AI) sebagai Penunjang Pembelajaran Siswa di SMK Nusatama Kota Padang Implementation of Artificial Intelligence (AI) Technology to Support Student Learning at Nusatama Vocational School, Padang City Universitas Putra Indonesia YPTK Padang. 4(2), 142–151. https://doi.org/10.55606/kreatif.v4i2.3562
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00453-6
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101325
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1). https://doi.org/10.1016/j.jjimei.2022.100143
Al-Hawamleh, A. (2024). Exploring the Satisfaction and Continuance Intention to Use E-Learning Systems: An Integration of the Information Systems Success Model and the Technology Acceptance Model. International Journal of Electrical and Computer Engineering Systems, 5(2), 201–214.
Ali, M. B., Tuhin, R., Alim, M. A., Rokonuzzaman, M., Rahman, S. M., & Nuruzzaman, M. (2022). Acceptance and use of ICT in tourism: the modified UTAUT model. Journal of Tourism Futures. https://doi.org/10.1108/JTF-06-2021-0137
Alzahrani, L. (2023). Analyzing students’ attitudes and behavior toward artificial intelligence technologies in Higher Education. International Journal of Recent Technology and Engineering (IJRTE), 11(6), 65–73. https://doi.org/10.35940/ijrte.F7475.0311623
Armenia, S., Franco, E., Iandolo, F., Maielli, G., & Vito, P. (2024). Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management. Technological Forecasting and Social Change, 200. https://doi.org/10.1016/j.techfore.2023.123131
Azzuhri, A. (2024). Berdamai dengan AI di Ruang Akademik Baca artikel detiknews, “Berdamai dengan AI di Ruang Akademik” selengkapnya https://news.detik.com/kolom/d-7439852/berdamai-dengan-ai-di-ruang-akademik. Https://News.Detik.Com/Kolom/d-7439852/Berdamai-Dengan-Ai-Di-Ruang-Akademik.
Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180. https://doi.org/10.1016/j.techfore.2022.121692
Bhatnagr, P., & Rajesh, A. (2023). Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model. South Asian Journal of Marketing. https://doi.org/10.1108/sajm-06-2022-0040
Binekas, H., & Belgiawan, P. F. (2023). Factors Influence Satisfaction and Continuance Intention of Chatbot Users (pp. 102–116). https://doi.org/10.2991/978-94-6463-216-3_9
Bin-Nashwan, S. A., Shah, M. H., Abdul-Jabbar, H., & Al-Ttaffi, L. H. A. (2023). Social-related factors in integrated UTAUT model for ZakaTech acceptance during the COVID-19 crisis. Journal of Islamic Accounting and Business Research, 14(8), 1383–1403. https://doi.org/10.1108/JIABR-02-2022-0038
Bouteraa, M., Bin-Nashwan, S. A., Al-Daihani, M., Dirie, K. A., Benlahcene, A., Sadallah, M., Zaki, H. O., Lada, S., Ansar, R., Fook, L. M., & Chekima, B. (2024). Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students’ integrity matter? Computers in Human Behavior Reports, 14. https://doi.org/10.1016/j.chbr.2024.100402
Chhonker, M. S., Verma, D., & Kar, A. K. (2017). Review of Technology Adoption frameworks in Mobile Commerce. Procedia Computer Science, 122, 888–895. https://doi.org/10.1016/j.procs.2017.11.451
Dhoni, R. (2022). Ratusan Perguruan Tinggi Ada di Sumbar, Berikut Rinciannya. Ratusan Perguruan Tinggi Ada Di Sumbar, Berikut Rinciannya.
Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: a stimulus–organism–response approach. Journal of Research in Innovative Teaching and Learning. https://doi.org/10.1108/JRIT-01-2024-0006
Edo, O. C., Ang, D., Etu, E. E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2). https://doi.org/10.1016/j.jjimei.2023.100186
Elkefi, S., Tounsi, A., & Kefi, M. A. (2024). Use of ChatGPT for education by engineering students in developing countries: a mixed-methods study. Behaviour and Information Technology. https://doi.org/10.1080/0144929X.2024.2354428
Elshaer, I. A., Hasanein, A. M., & Sobaih, A. E. E. (2024). The moderating effects of gender and study discipline in the relationship between university students’ acceptance and use of chatGPT. European Journal of Investigation in Health, Psychology and Education, 14(7), 1981–1995. https://doi.org/10.3390/ejihpe14070132
Esawe, A. T. (2022). Understanding mobile e-wallet consumers’ intentions and user behavior. Spanish Journal of Marketing - ESIC, 26(3), 363–384. https://doi.org/10.1108/SJME-05-2022-0105
Ferri, L., Maffei, M., Spanò, R., & Zagaria, C. (2023). Uncovering risk professionals’ intentions to use artificial intelligence: empirical evidence from the Italian setting. Management Decision. https://doi.org/10.1108/MD-02-2023-0178
Gao, B. (2023). Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory. Acta Psychologica, 234. https://doi.org/10.1016/j.actpsy.2023.103856
GoodStats. (2024a). 10 Negara Pengguna AI Terbanyak, Indonesia Salah Satunya. Https://Data.Goodstats.Id/Statistic/10-Negara-Pengguna-Ai-Terbanyak-Indonesia-Salah-Satunya-RLlmC.
GoodStats. (2024b). 185,3 Juta Pengguna Internet Tercatat di Indonesia pada 2024. Https://Data.Goodstats.Id/Statistic/1853-Juta-Pengguna-Internet-Tercatat-Di-Indonesia-Pada-2024-JFNoa.
Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1). https://doi.org/10.1016/j.jjimei.2024.100232
Hair, J. F. J., Hult, G. T. M., Rigle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second Edition). SAGE Publications Ltd.
Hair, J. F. J., Hult, G. T. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Iqbal, U. P., Jose, S. M., & Tahir, M. (2023). Integrating trust with extended UTAUT model: a study on Islamic banking customers’ m-banking adoption in the Maldives. Journal of Islamic Marketing, 14(7), 1836–1858. https://doi.org/10.1108/JIMA-01-2022-0030
Ivana, & Soeherman, B. (2024). Ancaman Darurat Text Generative Artificial Intelligence pada pendidikan akuntansi. Jurnal Riset Akuntansi Dan Keuangan, 12(1), 667–682. https://doi.org/10.17509/xxxx.xxx
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102521
Jayarathne, P. G. S. A., Chathuranga, B. T. K., Dewasiri, N. J., & Rana, S. (2022). Motives of mobile payment adoption during COVID-19 pandemic in Sri Lanka: a holistic approach of both customers’ and retailers’ perspectives. South Asian Journal of Marketing. https://doi.org/10.1108/sajm-03-2022-0013
Jo, H. (2022). Continuance intention to use artificial intelligence personal assistant: type, gender, and use experience. Heliyon, 8(9). https://doi.org/10.1016/j.heliyon.2022.e10662
Jurnalissumbar. (2024). Seminar nasional di universitas negeri padang: pemanfaatan artificial intelligence di lembaga pendidikan. Https://Www.Jurnalissumbar.Com/2024/07/Seminar-Nasional-Di-Universitas-Negeri.Html?M=1.
Kadhim, R. J. (2024). The role of artificial intelligence technologies in increasing awareness and educating users through electronic social networks (survey study on a sample of thi qar university students). Conhecimento & Diversidade, Niterói, 16(41).
Kang, S., Choi, Y., & Kim, B. (2024). Impact of Motivation Factors for Using Generative AI Services on Continuous Use Intention: Mediating Trust and Acceptance Attitude. Social Sciences, 13(9), 475. https://doi.org/10.3390/socsci13090475
Kominfo. (2024). Penggunaan AI Makin Masif, Wamenkominfo Ajak Mahasiswa Terapkan Prinsip Kejujuran. Https://Www.Kominfo.Go.Id/Content/Detail/54396/Siaran-Pers-No-67hmkominfo012024-Tentang-Penggunaan-Ai-Makin-Masif-Wamenkominfo-Ajak-Mahasiswa-Terapkan-Prinsip-Kejujuran/0/Siaran_pers.
Kompas. (2024). Kontroversi AI Generatif dan Dampaknya terhadap Pendidikan. Https://Amp.Kompas.Com/Tren/Read/2024/01/26/165255365/Kontroversi-Ai-Generatif-Dan-Dampaknya-Terhadap-Pendidikan.
Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict chatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100246
Li, W. (2021). The role of trust and risk in Citizens’ E-Government services adoption: A perspective of the extended UTAUT model. Sustainability (Switzerland), 13(14). https://doi.org/10.3390/su13147671
Li, Y., & Zhao, M. (2021). A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.528259
Lukman, Agustina, R., & Aisy Rihadatul. (2023). Problematika penggunaan artificial intelligence (AI) untuk pembelajaran di kalangan mahasiwa STIT Pemalang. Jurnal Madaniyah, 13(2), 242–255.
Menon, D., & Shilpa, K. (2023). “Chatting with chatGPT”: analyzing the factors influencing users’ intention to use the open AI’s chatGPT using the UTAUT model. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e20962
Negm, E. M. (2023). Consumers’ acceptance intentions regarding e-payments: a focus on the extended unified theory of acceptance and use of technology (UTAUT2). Management & Sustainability: An Arab Review. https://doi.org/10.1108/msar-04-2023-0022
Pak, T. Y., Bae, B., Lee, C., Jung, I., & Jang, B. J. (2023). Modeling public acceptance of demand-responsive transportation: An integrated UTAUT and ITM framework. Journal of Public Transportation, 25. https://doi.org/10.1016/j.jpubtr.2023.100067
Pamungkas, B. (2024). AI di Perkuliahan: Membantu atau “Mem-buntukan” Mahasiswa? Https://Ftmm.Unair.Ac.Id/Ai-Di-Perkuliahan-Membantu-Atau-Mem-Buntukan-Mahasiswa/.
Patil, H., & Undale, S. (2023). Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model. Education and Information Technologies, 28(11), 14943–14965. https://doi.org/10.1007/s10639-023-11778-6
Purwanto, A., Asbari, M., & Santoso, T. I. (2024). Analisis data penelitian marketing: perbandingan hasil antara Amos, SmartPLS, WarpPLS, dan SPSS untuk jumlah sampel besar. Journal of Industrial Engineering & Management Research, 2(4). https://doi.org/10.7777/jiemar
Rahman, P., Ningsih, N., & Rahim, A. (2024). Tampilan potensi ancaman kecerdasan buatan bagi perguruan tinggi. Jurnal Manajemen Pendidikan Islam, 10(1), 63–72.
Reza, M. D. S. binti M., Tan, S. H., Chong, L. L., & Ong, H. B. (2024). Continuance usage intention of e-wallets: Insights from merchants. International Journal of Information Management Data Insights, 4(2). https://doi.org/10.1016/j.jjimei.2024.100254
Savitri, D. (2024). Kemendikbudristek Godok Regulasi Penggunaan Generative AI di Perguruan Tinggi. Https://Www.Detik.Com/Edu/Perguruan-Tinggi/d-7310158/Kemendikbudristek-Godok-Regulasi-Penggunaan-Generative-Ai-Di-Perguruan-Tinggi.
Sekaran, U., & Bougie, R. (2016). Research methods for business : a skill-building approach (Seventh edition). www.wileypluslearningspace.com
Sharma, A., Mohan, A., Johri, A., & Asif, M. (2024). Determinants of fintech adoption in agrarian economy: Study of UTAUT extension model in reference to developing economies. Journal of Open Innovation: Technology, Market, and Complexity, 10(2). https://doi.org/10.1016/j.joitmc.2024.100273
Venkatesh, V., Smith, R. H., Morris, M. G., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User acceptance of information technology: toward a unified view. Management Information Systems Research Center, 27(3), 425–478.
Venkatesh, V., Walton, S. M., & Thong, J. Y. L. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. Management Information System Research Center, 157–178. http://about.jstor.org/terms
Wan, L., Xie, S., & Shu, A. (2020). Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model. SAGE Open, 10(3). https://doi.org/10.1177/2158244020941858
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.870777
Yu, X., Yang, Y., & Li, S. (2024). Users’ continuance intention towards an AI painting application: An extended expectation confirmation model. PLoS ONE, 19(5 May). https://doi.org/10.1371/journal.pone.0301821
Sari, M. Y., Pujani, V., & Yulihasri. (2024). Eksplorasi niat penggunaan e-government di Kota Padang melalui aplikasi SI RANCAK. Journal Publicuho, 7(1), 336–352. https://doi.org/10.35817/publicuho.v7i1.366
Yunus, Y., Saputri, R. P., & Fransisca, M. (2024). Implementasi Teknologi Artifical Intelligence (AI) sebagai Penunjang Pembelajaran Siswa di SMK Nusatama Kota Padang Implementation of Artificial Intelligence (AI) Technology to Support Student Learning at Nusatama Vocational School, Padang City Universitas Putra Indonesia YPTK Padang. 4(2), 142–151. https://doi.org/10.55606/kreatif.v4i2.3562
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00453-6
Published
2024-10-20
Section
Articles
Copyright (c) 2024 Fadhilah Idris, Vera Pujani, Yulia Hendri Yeni
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
