Behavioral Intention of Male Students to Use 5G Internet Network to Use Online Education Platforms in Sichuan, China
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Abstract
Purpose: This study explores the factors influencing male students’ behavioral intention to use 5G internet network to use online education platforms in Sichuan, China. The conceptual framework is based on the relationships between a perceived ease of use, system quality, information quality, service quality, attitude, social influence, perceived usefulness and behavioral intention. Research design, data, and methodology: This study used a questionnaire; the target respondents were 650 male university students from three universities in Sichuan. Results: The results of the study showed that attitude was the strongest predictor of behavioral intention to use, followed by social influence and perceived usefulness. Perceived ease of use was the most significant influence on attitude, so the system's ease of use would increase positive feedback from learners to make learning effective. Conclusions: Therefore, in the process of selecting and using 5G for online education platforms at the University, it is important to try to generate as much interest in learning as possible, to increase the quality of the system, the quality of the service, and the quality of the information, to improve the ease of use and to create a positive experience so that male students will be better able to use the platform system for continuous learning.
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References
Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & Management 44(3), 263-275. https://doi.org/10.1016/j.im.2006.12.008
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
Al-Mamary, Y. H., Shamsuddin, A., Hamid, N. A., & Al-Maamari, M. H. (2015). Adoption of management information systems in context of Yemeni organizations: A structural equation modeling approach. Journal of Digital Information Management, 13(6), 429–444.
Arbaugh, J. B. (2000). An exploratory study of the effects of gender on student learning and class participation in an internet- based MBA course. Management Learning, 31(4), 503-519. https://doi.org/10.1177/1350507600314006
Awang, Z. (2012). Structural equation modeling using AMOS graphic (5th ed.). Penerbit Universiti Teknologi MARA
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail and Distribution Management, 45(6), 626-640. https://doi.org/10.1108/ijrdm-08-2016-0144
Baroudi, J. J., & Orlikowski, W. J. (1988). A short-form measure of user information satisfaction: a psychometric evaluation and notes on use. Journal of Management Information Systems, 4(4), 44-59. https://doi.org/10.1080/07421222.1988.11517807
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
Camarero, C., Rodríguez, J., & Jose, R. S. (2010). An exploratory study of online forums as a collaborative learning tool. Online Information Review, 36(4), 568-586. https://doi.org/10.1108/14684521211254077.
Cenfetelli, R. T., Benbasat, I., & Al-Natour, S. (2005, December 11-14). Information technology mediated customer service: a functional perspective [Paper Presentation]. Proceedings of the International Conference on Information Systems, Las Vegas, NV, USA.
Chatzoglou, P. D., & Vraimaki, E. (2009). Knowledge-sharing behaviour of bank employees in greece. Business Process Management Journal, 15(2), 245-266. https://doi.org/10.1108/14637150910949470
Chennamaneni, A., Teng, J. T. C., & Raja, M. K. (2012). A unified model of knowledge sharing behaviors: theoretical development and empirical test. Behavior & Information Technology, 31(11), 1097-1115. https://doi.org/10.1080/0144929x.2011.624637
Choi, M., Han, K., & Choi, J. (2015). The effects of product attributes and service quality of transportation card solutions on service user’s continuance and word-of-mouth intention. Service Business, Heidelberg, 9(3), 463-490. https://doi.org/10.1007/s11628-014-0235-0
Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: an exploratory study on user acceptance of general information-searching web sites. Information and Management, 41(7), 869-81. https://doi.org/10.1016/j.im.2003.08.016
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201. https://doi.org/10.1177/00970302030003001
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten -year update. Journal of Management Information Systems, 9(4), 9-30.
Elmorshidy, A. (2018). The impact of knowledge management systems on innovation—An empirical investigation in Kuwait. Journal of Information and Knowledge Management Systems, 48(3), 388-403. https://doi.org/10.1108/vjikms-12-2017-0089
Fishbein, M. A., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Addison Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51.https://doi.org/10.2307/30036519
Ghazali, E. M., Mutum, D. S., Chong, J. H., & Nguyen, B. (2018). Do consumers want mobile commerce? A closer look at M-shopping and technology adoption in Malaysia. Asia Pacific Journal of Marketing and Logistics, 30(4), 1064-1086. https://doi.org/10.1108/apjml-05-2017-0093
Holsapple, C., & Wu, J. (2008). Building effective online game websites with knowledge-based trust. Information Systems Frontiers, 10(1), 47-60. https://doi.org/10.1007/s10796-007-9060-5
Kalini, Z., & Marinkovi, V. (2019). The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems. International Journal of Bank Marketing, 38(1), 138-1584.
Kim, H. W., Xu, Y., & Koh, J. (2004). A comparison of online trust building factors between potential customers & repeats customers. Journal of the Association for Information Systems, 5(10), 392-420. https://doi.org/10.17705/1jais.00056
Kwon, O., Choi, K., & Kim, M. (2007). User acceptance of context-aware services: self-efficacy, user innovativeness and perceived sensitivity on contextual pressure. Behaviour and Information Technology, 26(6), 483-498. https://doi.org/10.1080/01449290600709111
Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South korea: theories and results. Computers and Education, 53(4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, J. W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277-83.
Lee, Y., Shih-Pang, T., & Liu, F. (2007). Antecedents of learner satisfaction towards e-learning. Journal of American Academy of Business, 11(2), 161-8.
Li, C., & Sun, Z. (2019). The effects of blended learning on knowledge, skills, and satisfaction in nursing students: A meta-analysis. Nurse Education Today, 82(1), 51-57.
Maity, M., Bagchi, K., Shah, A., & Misra, A. (2019). Explaining normative behavior in information technology use. Information Technology & People, 32(1), 94-117. https://doi.org/10.1108/itp-11-2017-0384
Mead, E. L., Rimal, R. N., Ferrence, R., & Cohen, J. E. (2014). Understanding the sources of normative influence on behavior: the example of tobacco. Social Science Medicine, 115, 139-143. https://doi.org/10.1016/j.socscimed.2014.05.030
Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students’ Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB E-JOURNAL, 15(2), 150-158. https://doi.org/10.14456/augsbejr.2022.80
Mohammadi, H. (2015). A study of mobile banking usage in Iran. International Journal of Bank Marketing, 33(6), 733-759. https://doi.org/10.1108/ijbm-08-2014-0114
Mpinganjira, M. (2019). Cognitive absorption and behavioral intentions in virtual health communities: A focus on content posters. Journal of Systems and Information Technology, 21(1), 122-145. https://doi.org/10.1108/jsit-06-2017-0044
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Rashotte, L. S. (2007). Social influence. In G. Ritzer (Ed.), Blackwell Encyclopedia of Sociology, Blackwell (pp. 4426-4429). Oxford. https://doi.org/10.1002/9781405165518.wbeoss154
Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: an extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-96. https://doi.org/10.1016/j.ijhcs.2006.01.003
Ryu, S., Ho, S. H., & Han, I. (2003). Knowledge sharing behavior of physicians in hospitals. Expert Systems with Applications, 25(1), 113-122. https://doi.org/10.1016/s0957-4174(03)00011-3
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286. https://doi.org/10.1016/j.jfoodeng.2005.02.010
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge psychological tests and testing research (pp. 27-50). Nova.
Soper, D. S. (2023). A-Priori Sample Size Calculator for Structural Equation Models [Software]. http://wwwdanielsopercom/statcalc
Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Pearson Education.
Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61(8), 233-244. https://doi.org/10.1016/j.chb.2016.03.016
Tsu Wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management and Data Systems, 109(3), 370-388. https://doi.org/10.1108/02635570910939399
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), 425–478.
Venkatesh, V., Thong, J. Y. L., & 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
Wang, Y. S., & Liao, Y. W. (2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381-398. https://doi.org/10.1016/s0747-5632(04)00174-8
Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85-102. https://doi.org/10.1287/isre.1050.0042
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002
Wu, M. L. (2010). Structural equation model -Operation and application of amos (2nd ed.). Chong Qing: CHONGQING UNIVERSITY PRESS,30.
Zhang, M., Zhu, A., & Zhang, F. (2020). Research on Influencing Factors of Online Education Platform Users’ Continuous Use Behavior. Library Forum, 40(5), 1-10.
Zhang, N. X., Sun, J. H., Wang, C. C., & Gui, Q. P. (2021). The Status Quo and Further Development Strategy of China’s Online Higher Education in the Perspective of International Comparison. China Higher Education Research, 1, 48-55.
Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527-540. https://doi.org/10.1108/10662241111176353