Factors Shaping Students' Attitudes and Adoption Intentions Toward Artificial Intelligence Applications: A Case Study at a Private University in Zhanjiang, China
Article Sidebar
Main Article Content
Abstract
Purpose: This research aimed to investigate the key factors impacting students' attitudes and adoption intentions toward artificial intelligence applications at a private university in Zhanjiang, China. The conceptual framework delineates the cause-and-effect relationships among Perceived Usefulness, Perceived Ease of Use, Trust, Effort Expectancy, Performance Expectancy, Attitude, and Adoption Intention. Research design, data, and methodology: The researcher employed a quantitative methodology with a sample consisting of 500 students from a private university in Zhanjiang, China. Non-probability sampling methods were used, including judgmental sampling to select four colleges, quota sampling to determine the sample size, convenience sampling for data collection, and online questionnaire distribution. Data analysis was conducted using structural equation modeling (SEM) and confirmatory factor analysis (CFA) to evaluate model fit, reliability, and construct validity. Results: The findings reveal that attitude is a significant mediating variable that substantially impacts adoption intention. Among the factors affecting attitude, Trust has the most pronounced effect, followed by perceived usefulness. additionally, performance expectancy, effort expectancy, and perceived ease of use also influence attitude. Conclusions: These results indicate that enhancing students' trust, perceived usefulness, perceived ease of use, performance expectancy, and effort expectancy regarding artificial intelligence applications is an effective strategy for promoting their acceptance and use in educational settings.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.014
Ahmad, T. (2019). Scenario based approach to re-imagining future of higher education which prepares students for the future of work. Higher Education, Skills and Work-Based Learning, 10(1), 217-238. https://doi.org/10.1108/HESWBL-12-2018-0136
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
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., & Al-Maroof, R. S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics, 11(20), 20.
https://doi.org/10.3390/electronics11203291
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6, 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268
Almarashdeh, I., & Alsmadi, M. (2016). Investigating the acceptance of technology in distance learning program. Proceedings of the 2016 International Conference on Information Science and Communications Technologies (ICISCT), 1-5.
https://doi.org/10.1109/ICISCT.2016.7777404
Alshare, K. A., & Lane, P. L. (2011). Predicting student-perceived learning outcomes and satisfaction in ERP courses: An empirical investigation. Communications of the Association for Information Systems, 28.
https://doi.org/10.17705/1CAIS.02834
Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: The modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589-1605. https://doi.org/10.1007/s10639-020-10317-x
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
https://doi.org/10.1037/0033-2909.107.2.238
Bervell, B., Nyagorme, P., & Arkorful, V. (2020). LMS-enabled blended learning use intentions among distance education tutors: Examining the mediation role of attitude based on technology-related stimulus-response theoretical framework (TR-SR-TF). Contemporary Educational Technology, 12(2), 273. https://doi.org/10.30935/cedtech/8317
Bhosale, S., Salunkhe, A., & Sutar, S. (2020). Artificial intelligence and its application in different areas. Conference: International Journal of Advance and Innovative Research, 7, 34-39.
Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context - A survey study. International Journal of Information Management, 60, 102375. https://doi.org/10.1016/j.ijinfomgt.2021.102375
Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101.
Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
Chang, Y., Lee, S., Wong, S. F., & Jeong, S. (2021). AI-powered learning application use and gratification: An integrative model. Information Technology & People, 35(7), 2115-2139. https://doi.org/10.1108/ITP-09-2020-0632
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
Chien, S.-E., Chu, L., Lee, H.-H., Yang, C.-C., Lin, F.-H., Yang, P.-L., Wang, T.-M., & Yeh, S.-L. (2019). Age difference in perceived ease of use, curiosity, and implicit negative attitude toward robots. ACM Transactions on Human-Robot Interaction, 8(2), 1-19. https://doi.org/10.1145/3311788
Chu, M.-W., Guo, Q., & Leighton, J. P. (2014). Students’ interpersonal trust and attitudes towards standardized tests: Exploring affective variables related to student assessment. Assessment in Education: Principles, Policy & Practice, 21(2), 167-192. https://doi.org/10.1080/0969594X.2013.844094
Coppin, B. (2004). Artificial intelligence illuminated (1st ed.). Jones and Bartlett Publishers.
Cox, A. M. (2021). Exploring the impact of artificial intelligence and robots on higher education through literature-based design fictions. International Journal of Educational Technology in Higher Education, 18(1), 3.
https://doi.org/10.1186/s41239-020-00237-8
Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the technology acceptance model in a three-countries study of smartwatch adoption. The Journal of High Technology Management Research, 30(1), 1-14.
https://doi.org/10.1016/j.hitech.2019.02.001
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes (pp. xxii, 794). Harcourt Brace Jovanovich College Publishers.
El-Gayar, O., & Moran, M. (2006). College students’ acceptance of tablet PCs: An application of the UTAUT model. Faculty Research & Publications, 2(3), 1-7.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention and behavior: An introduction to theory and research. Contemporary Sociology, 6(2), 244. https://doi.org/10.2307/2065853
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?. Psychology Learning & Teaching, 21(1), 37-56. https://doi.org/10.1177/14757257211037149
Gatzioufa, P., & Saprikis, V. (2022). A literature review on users’ behavioral intention toward chatbots’ adoption. Applied Computing and Informatics, 2(4), 12-15. https://doi.org/10.1108/ACI-01-2022-0021
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners’ intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381-390. https://doi.org/10.1111/bjet.12148
Huang, C.-H. (2020). The influence of self-efficacy, perceived usefulness, perceived ease of use, and cognitive load on students’ learning motivation, learning attitude, and learning satisfaction in blended learning methods. 2020 3rd International Conference on Education Technology Management, 29-35. https://doi.org/10.1145/3446590.3446595
Jairak, K., Praneetpolgrang, P., & Mekhabunchakij, K. (2009). An acceptance of mobile learning for higher education students in Thailand. International Conference on Mobile Business, 2(1), 17-20.
Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19. https://doi.org/10.1108/IJILT-05-2020-0090
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human-Computer Interaction, 36(20), 1902-1911.
https://doi.org/10.1080/10447318.2020.1801227
Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
Liebenberg, J., Benade, T., & Ellis, S. (2018). Acceptance of ICT: Applicability of the Unified Theory of Acceptance and Use of Technology (UTAUT) to South African students. Journal of Information Technology Education: Research, 17, 160-173.
Lien, C. H., & Cao, Y. (2014). Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China. Computers in Human Behavior, 41, 104-111. https://doi.org/10.1016/j.chb.2014.08.013
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
Loeckx, J. (2016). Blurring boundaries in education: Context and impact of MOOCs. The International Review of Research in Open and Distributed Learning, 17(3). https://doi.org/10.19173/irrodl.v17i3.2395
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
Moorman, C., Zaltman, G., & Deshpande, R. (1992). Relationships between providers and users of market research: The dynamics of trust within and between organizations. Journal of Marketing Research, 29(3), 314-328.
https://doi.org/10.1177/002224379202900303
Nam, C. W. (2014). The effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments. Computers in Human Behavior, 37, 237-248. https://doi.org/10.1016/j.chb.2014.05.007
Nassuora, A. (2013). Students’ acceptance of mobile learning for higher education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1-9. https://doi.org/10.12785/ijlms/010101
Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2022). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512-520.
https://doi.org/10.1016/j.procs.2021.12.168
Ndubisi, N. O., & Chukwunonso, N. C. (2004, July). Online learning adoption intention: Comparing the predictive power of two competing models. Proceedings of HERDSA conference, 1-9.
Osman, Z., Alwi, N. H., & Khan, B. N. A. (2016). A study of mediating effect of attitude on perceived ease of use and students’ intention to use online learning platform among online learninginstitutions in Malaysia. Journal of Education and Practice, 7(9), 49-55.
Pangaribuan, C., & Wulandar, Y. (2019). A crowdfunding platform user acceptance: An empirical examination of performance expectancy, effort expectancy, social factors, facilitating condition, attitude, and behavioral intention. Proceedings of the 1st Sampoerna University-AFBE International Conference, SU-AFBE 2018, 6-7 December 2018, Jakarta Indonesia, 346-352. https://doi.org/10.4108/eai.6-12-2018.2286301
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Pillai, A., Smith, B., & Jones, C. (2023). Examining the impact of effort expectancy on technology acceptance among students. Journal of Educational Technology, 15(2), 123-145.
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence‐based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693-1710. https://doi.org/10.1111/bjet.12994