Exploring Factors Influencing Gen Z's Acceptance and Adoption of AI and Cloud-Based Applications and Tools in Academic Attainment
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Doi: 10.28991/ESJ-2024-08-03-02
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Tayan, O., Hassan, A., Khankan, K., & Askool, S. (2024). Considerations for adapting higher education technology courses for AI large language models: A critical review of the impact of ChatGPT. Machine Learning with Applications, 15, 100513. doi:10.1016/j.mlwa.2023.100513.
Hiran, K. K., & Dadhich, M. (2024). Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach. Technological Forecasting and Social Change, 199, 122979. doi:10.1016/j.techfore.2023.122979.
Wu, W., & Plakhtii, A. (2021). E-Learning Based on Cloud Computing. International Journal of Emerging Technologies in Learning, 16(10), 4–17. doi:10.3991/ijet.v16i10.18579.
Wang, N., Xue, Y., Liang, H., Wang, Z., & Ge, S. (2019). The dual roles of the government in cloud computing assimilation: an empirical study in China. Information Technology and People, 32(1), 147–170. doi:10.1108/ITP-01-2018-0047.
Haque, A., Pulok, R. A., Rahman, M. M., Akter, S., Khan, N., & Haque, S. (2023). Recognition of Bangladeshi Sign Language (BdSL) Words using Deep Convolutional Neural Networks (DCNNs). Emerging Science Journal, 7(6), 2183-2201. doi:10.28991/ESJ-2023-07-06-019.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. doi:10.1186/s41239-023-00392-8.
Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S. M. (2022). Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review. IEEE Access, 10, 61905–61921. doi:10.1109/ACCESS.2022.3179356.
Al-Madhagy Taufiq-Hail, G., Alanzi, A. R. A., Yusof, S. A. M., & MadallahAlruwail, M. A. (2021). Software as a service (SAAS) cloud computing: An empirical investigation on university students’ perception. Interdisciplinary Journal of Information, Knowledge, and Management, 16, 213–253. doi:10.28945/4740.
Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32(3), 444–452. doi:10.1007/s10956-023-10039-y.
Strzelecki, A. (2023). Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education, 49(2), 223–245. doi:10.1007/s10755-023-09686-1.
Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. doi:10.1016/j.techfore.2023.123076.
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. doi:10.1016/j.chb.2022.107468.
Seufert, S., Guggemos, J., & Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552. doi:10.1016/j.chb.2020.106552.
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40. doi:10.37074/jalt.2023.6.1.17.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. doi:10.1080/14703297.2023.2190148.
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education, 9, 45312. doi:10.2196/45312.
Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2). doi:10.53761/1.20.02.07.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. doi:10.2307/30036540.
Prater, M. R. (2019). Teaching millennials and generation Z: New opportunities in undergraduate medical education. Handbook of Research on the Efficacy of Training Programs and Systems in Medical Education, 72–91. doi:10.4018/978-1-7998-1468-9.ch004.
Mahmoud, A. B., Fuxman, L., Mohr, I., Reisel, W. D., & Grigoriou, N. (2021). “We aren’t your reincarnation!” workplace motivation across X, Y and Z generations. International Journal of Manpower, 42(1), 193–209. doi:10.1108/IJM-09-2019-0448.
Le, T. D., Duc Tran, H., & Hoang, T. Q. H. (2022). Ethically minded consumer behavior of Generation Z in Vietnam: The impact of socialization agents and environmental concern. Cogent Business and Management, 9(1), 2102124. doi:10.1080/23311975.2022.2102124.
Djafarova, E., & Foots, S. (2022). Exploring ethical consumption of generation Z: theory of planned behaviour. Young Consumers, 23(3), 413–431. doi:10.1108/YC-10-2021-1405.
Elshami, W., Saravanan, C., Taha, M. H., Abdalla, M. E., Abuzaid, M., & Kawas, S. Al. (2021). Bridging the gap in online learning anxiety among different generations in health professions education. Sultan Qaboos University Medical Journal, 21(4), 539–548. doi:10.18295/squmj.4.2021.040.
Thangavel, P., Pathak, P., & Chandra, B. (2021). Millennials and Generation Z: a generational cohort analysis of Indian consumers. Benchmarking, 28(7), 2157–2177. doi:10.1108/BIJ-01-2020-0050.
Chaney, D., Touzani, M., & Ben Slimane, K. (2017). Marketing to the (new) generations: summary and perspectives. Journal of Strategic Marketing, 25(3), 179–189. doi:10.1080/0965254X.2017.1291173.
Kalpathi, S. S. (2016). The Millennials: exploring the world of the largest living generation. Penguin Random House India, Gurugram, India.
Twenge, J. M. (2017). iGen: Why today's super-connected kids are growing up less rebellious, more tolerant, less happy--and completely unprepared for adulthood--and what that means for the rest of us. Atria Books, New York, United States.
Srisathan, W. A., Ketkaew, C., Jitjak, W., Ngiwphrom, S., & Naruetharadhol, P. (2022). Open innovation as a strategy for collaboration-based business model innovation: The moderating effect among multigenerational entrepreneurs. PLoS ONE, 17(6), 265025. doi:10.1371/journal.pone.0265025.
Rue, P. (2018). Make Way, Millennials, Here Comes Gen Z. About Campus: Enriching the Student Learning Experience, 23(3), 5–12. doi:10.1177/1086482218804251.
Turner, A. (2015). Generation Z: Technology and Social Interest. The Journal of Individual Psychology, 71(2), 103–113. doi:10.1353/jip.2015.0021.
Wang, L. Y. K., Lew, S. L., & Lau, S. H. (2020). An empirical study of students’ intention to use cloud e-learning in higher education. International Journal of Emerging Technologies in Learning, 15(9), 19–38. doi:10.3991/ijet.v15i09.11867.
Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. doi:10.1007/s10639-021-10573-5.
Nguyen, D. T., Vu, T. H. N., & Kim, H. T. (2021). Factors affecting e-learning based cloud computing acceptance: an empirical study at Vietnamese universities. Journal of International Economics and Management, 20(3), 118–133. doi:10.38203/jiem.020.3.0019.
Kumar, V., & Sharma, D. (2021). E-learning theories, components, and cloud computing-based learning platforms. International Journal of Web-Based Learning and Teaching Technologies, 16(3), 1–16. doi:10.4018/IJWLTT.20210501.oa1.
Koh, J. H. L., & Kan, R. Y. P. (2021). Students’ use of learning management systems and desired e-learning experiences: are they ready for next generation digital learning environments? Higher Education Research and Development, 40(5), 995–1010. doi:10.1080/07294360.2020.1799949.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982.
Al-Rahmi, W. M., & Zeki, A. M. (2017). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences, 29(4), 526–535. doi:10.1016/j.jksuci.2016.09.002.
Zhang, Z., Cao, T., Shu, J., & Liu, H. (2022). Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments, 30(8), 1388–1401. doi:10.1080/10494820.2020.1723113.
Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at University as a Tool for Complex Thinking: Students’ Perceived Usefulness. Journal of New Approaches in Educational Research, 12(2), 323–339. doi:10.7821/naer.2023.7.1458.
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1). doi:10.14569/ijacsa.2014.050120.
Al-Rahmi, W. M., Othman, M. S., Yusof, L. M., & Musa, M. A. (2015). Using social media as a tool for improving academic performance through collaborative learning in Malaysian higher education. Review of European Studies, 7(3), 265–275. doi:10.5539/res.v7n3p265.
Thavi, R., Jhaveri, R., Narwane, V., Gardas, B., & Jafari Navimipour, N. (2024). Role of cloud computing technology in the education sector. Journal of Engineering, Design and Technology, 22(1), 182–213. doi:10.1108/JEDT-08-2021-0417.
Taufiq-Hail, A. M., Yusof, S. A. B. M., Al Shamsi, I. R. H., Bino, E., Saleem, M., Mahmood, M., & Kamran, H. (2023). Investigating the impact of customer satisfaction, trust, and quality of services on the acceptance of delivery services companies and related applications in Omani context: A Predictive model assessment using PLSpredict. Cogent Business and Management, 10(2), 2224173. doi:10.1080/23311975.2023.2224173.
Chang, J. H., Chiu, P. S., & Lai, C. F. (2023). Implementation and evaluation of cloud-based e-learning in agricultural course. Interactive Learning Environments, 31(2), 908–923. doi:10.1080/10494820.2020.1815217.
Utami, I. Q., Fahmiyah, I., Ningrum, R. A., Fakhruzzaman, M. N., Pratama, A. I., & Triangga, Y. M. (2022). Teacher’s acceptance toward cloud-based learning technology in Covid-19 pandemic era. Journal of Computers in Education, 9(4), 571–586. doi:10.1007/s40692-021-00214-8.
Chang, M., Walimuni, A. C. S. M., Kim, M. cheol, & Lim, H. soon. (2022). Acceptance of tourism blockchain based on UTAUT and connectivism theory. Technology in Society, 71, 102027. doi:10.1016/j.techsoc.2022.102027.
Utami, A. D. W., Arif, S., & Satrio, P. U. D. (2021). Understanding usability and user experience cloud-based learning management system from teacher review. In 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 262-267. doi:10.1109/ICEEIE52663.2021.9616959.
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. doi:10.1080/10494820.2015.1122635.
Khechine, H., Lakhal, S., Pascot, D., & Bytha, A. (2014). UTAUT Model for Blended Learning: The Role of Gender and Age in the Intention to Use Webinars. Interdisciplinary Journal of E-Skills and Lifelong Learning, 10, 033–052. doi:10.28945/1994.
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. doi:10.1177/0266666918765907.
Shaqrah, A. A. (2015). Explain the behavior intention to use e-learning technologies: A unified theory of acceptance and use of technology perspective. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 10(4), 19-32. Doi:10.4018/IJWLTT.2015100102
Abushakra, A., & Nikbin, D. (2019). Extending the UTAUT2 Model to Understand the Entrepreneur Acceptance and Adopting Internet of Things (IoT). Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, vol 1027, Springer, Cham, Switzerland. doi:10.1007/978-3-030-21451-7_29.
Seo, J., Cho, Y. W., Jung, K. J., & Gim, G. Y. (2019). A Study on Factors Affecting the Intension to Use Human Resource Cloud Service. Big Data, Cloud Computing, Data Science & Engineering. BCD 2018, Studies in Computational Intelligence, vol 786, Springer, Cham, Switzerland. doi:10.1007/978-3-319-96803-2_12.
Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption of mobile banking. Information Development, 32(4), 799–814. doi:10.1177/0266666915570764.
Giovanis, A., Athanasopoulou, P., Assimakopoulos, C., & Sarmaniotis, C. (2019). Adoption of mobile banking services: A comparative analysis of four competing theoretical models. International Journal of Bank Marketing, 37(5), 1165-1189. doi:10.1108/IJBM-08-2018-0200.
Samsudeen, S. N., Selvaratnam, G., & Hayathu Mohamed, A. H. (2022). Intention to use mobile banking services: an Islamic banking customers’ perspective from Sri Lanka. Journal of Islamic Marketing, 13(2), 410-433. doi:10.1108/JIMA-05-2019-0108.
Al-Mamary, Y. H. S. (2022). Understanding the use of learning management systems by undergraduate university students using the UTAUT model: Credible evidence from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), 100092. doi:10.1016/j.jjimei.2022.100092.
Huang, Q., Chen, X., Ou, C. X., Davison, R. M., & Hua, Z. (2017). Understanding buyers’ loyalty to a C2C platform: the roles of social capital, satisfaction and perceived effectiveness of e-commerce institutional mechanisms. Information Systems Journal, 27(1), 91–119. doi:10.1111/isj.12079.
Ringle, C. M. Wende, S., & Becker, J. M. (2022). Smart PLS, Bönningstedt, Germany. Available online: http://www.smartpls.com (accessed on May 2024).
Seemiller, C., & Grace, M. (2017). Generation Z: Educating and engaging the next generation of students. About campus, 22(3), 21-26. doi:10.1002/abc.21293.
Arkhipova, M. V., Belova, E. E., Gavrikova, Y. A., Pleskanyuk, T. N., & Arkhipov, A. N. (2019). Reaching generation Z. Attitude toward technology among the newest generation of school students. In Perspectives on the use of New Information and Communication Technology (ICT) in the Modern Economy, 1026-1032. doi:10.1007/978-3-319-90835-9_114.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial Least Squares Structural Equation Modeling. Handbook of Market Research. Springer, Cham, Switzerland. doi:10.1007/978-3-319-57413-4_15.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. doi:10.3758/brm.41.4.1149.
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT Model to Explain the Students’ Acceptance of Mobile Learning System in Higher Education. IEEE Access, 7, 174673–174686. doi:10.1109/ACCESS.2019.2957206.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. doi:10.1016/j.jretconser.2017.08.026.
Khan, M. A., Zubair, S. S., & Malik, M. (2019). An assessment of e-service quality, e-satisfaction and e-loyalty: Case of online shopping in Pakistan. South Asian Journal of Business Studies, 8(3), 283–302. doi:10.1108/SAJBS-01-2019-0016.
Kaya, B., Behravesh, E., Abubakar, A. M., Kaya, O. S., & Orús, C. (2019). The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction and e-loyalty. Journal of Internet Commerce, 18(4), 369-394. doi:10.1080/15332861.2019.1668658.
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115. doi:10.1016/j.jfbs.2014.01.002.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. doi:10.2307/3151312.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336, Lawrence Erlbaum Associates Publishers, Mahwah, United States.
Kenny, D. A. (2016). Moderation. Available online: http://davidakenny.net/cm/moderation.htm (accessed on March 2024).
Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874. doi:10.1016/j.ijinfomgt.2013.07.001.
Yeh, N. C., Lin, J. C. C., & Lu, H. P. (2011). The moderating effect of social roles on user behaviour in virtual worlds. Online Information Review, 35(5), 747–769. doi:10.1108/14684521111176480.
Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677. doi:10.1108/EJM-08-2020-0636.
Astatke, M., Weng, C., & Chen, S. (2023). A literature review of the effects of social networking sites on secondary school students’ academic achievement. Interactive Learning Environments, 31(4), 2153-2169. doi:10.1080/10494820.2021.1875002.
Ngoc Hoi, V. (2023). Augmenting student engagement through the use of social media: The role of knowledge sharing behaviour and knowledge sharing self-efficacy. Interactive Learning Environments, 31(7), 4021-4033. doi:10.1080/10494820.2021.1948871
Miraz, M. H., Hasan, M. T., Rekabder, M. S., & Akhter, R. (2022). Trust, transaction transparency, volatility, facilitating condition, performance expectancy towards cryptocurrency adoption through intention to use. Journal of Management Information and Decision Sciences, 25, 1-20.
de Blanes Sebastián, M. G., Antonovica, A., & Sarmiento Guede, J. R. (2023). What are the leading factors for using Spanish peer-to-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model. Technological Forecasting and Social Change, 187, 1–16. doi:10.1016/j.techfore.2022.122235.
Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., De Vet, H. C., & Van Der Beek, A. J. (2014). Construct validity of the individual work performance questionnaire. Journal of occupational and environmental medicine, 56(3), 331-337. doi:10.1097/JOM.0000000000000113.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. doi:10.2307/249008.
DOI: 10.28991/ESJ-2024-08-03-02
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