Examining Utilization of Online Learning Platforms: A Case of Undergraduates in Vocational Colleges in Sichuan, China

Main Article Content

Lihua Zhu
Sansanee Aranyanak Khlaewkhla

Abstract

Purpose: Due to the COVID-19 pandemic, online learning has become the trend of education development. This study examined the effects of perceived ease of use, perceived usefulness, attitude, social influence, facilitating conditions, and behavioral intention toward undergraduates’ use behavior of online learning platforms in Sichuan, China. Research design, data, and methodology: This research adopted a quantitative method, and questionnaires were utilized to collect data. There were 500 copies of questionnaires used in the analysis. The IOC (Item-Objective Congruence) and Pilot test were applied to measure the reality and validity of the constructs prior to collecting data. The data was analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: The relationships between perceived ease of use and perceived usefulness, perceived ease of use and attitude, and perceived usefulness and attitude were confirmed. Attitude, social influence, and facilitating conditions were significant predictors of behavioral intention. Behavioral intention significantly affected use behavior. Nevertheless, perceived usefulness had no significant impact on behavioral intention. Conclusion: Henceforth, to improve the learning platform's utilization rate, the developer of the online learning platforms should improve the simplicity and convenience of the platform usage. Academic practitioners can make online learning one of the compulsory tasks for vocational undergraduates.

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Zhu, L., & Aranyanak Khlaewkhla, S. (2025). Examining Utilization of Online Learning Platforms: A Case of Undergraduates in Vocational Colleges in Sichuan, China . AU-GSB E-JOURNAL, 18(1), 94-105. https://doi.org/10.14456/augsbejr.2025.10
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Articles
Author Biographies

Lihua Zhu

Ph.D. Candidate in Technology, Education and Management, Graduate School of Business and Advanced Technology Management, Assumption University, Thailand.

Sansanee Aranyanak Khlaewkhla

Office of Graduate Studies, Assumption University of Thailand.

References

Ab Jalil, H., Ma'rof, A. M., & Omar, R. (2019). Attitude and behavioral intention to develop and use MOOCs among academics. International Journal of Emerging Technologies in Learning (iJET), 14(24), 31. https://doi.org/10.3991/ijet.v14i24.12105

Adeoye, I. A., Adanikin, A. F., & Adanikin, A. (2020). COVID-19 and E-Learning: Nigeria Tertiary Education System Experience. International Journal of Research and Innovation in Applied Science, 28-30. http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/1063

Aggelidis, V., & Chatzoglou, P. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115-126. https://doi.org/10.1016/j.ijmedinf.2008.06.006

Alam, M. Z., Hu, W., Hoque, M. R., & Kaium, M. A. (2020). Adoption intention and usage behavior of mHealth services in Bangladesh and China. International Journal of Pharmaceutical and Healthcare Marketing, 14(1), 37-60. https://doi.org/10.1108/ijphm-03-2019-0023

Albarracin, D., & Shavitt, S. (2018). Attitudes and Attitude Change. Annual Review of Psychology 69(1), 299-327. https://doi.org/10.1146/annurev-psych-122216-011911.

Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50. https://doi.org/10.1016/j.aci.2014.09.001.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the Technology Acceptance Model in Context of Yemen, Mediterranean Journal of Social Sciences, 6(4). https://doi.org/10.5901/mjss.2015.v6n4s1p268

Anderson, T. (2008). The theory and practice of online learning (1st ed.). Athabasca University Press.

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 (1st ed.). Psychological

Blackwell, R. D., Engel, J. F., & Miniard, P. W. (1995). Consumer Behavior (6th ed.). Dryden Press.

Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the Thai elderly intention to use smartphone for e-Health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research. https://doi.org/10.1109/shuser.2012.6268881

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105. https://doi.org/10.1037/h0046016

Cao, Y. (2022). Factors Influencing Online Learning System Usage Among Fourth-Year Students in Higher Education in Sichuan, China. ABAC ODI JOURNAL Vision. Action. Outcome, 9(3), 123-143.

Cao, Y., & Jittawiriyanukoon, C. (2022). Factors Impacting Online Learning Usage during Covid-19 Pandemic Among Sophomores in Sichuan Private Universities. AU-GSB e-Journal, 15(1), 152-163.

Carter, E. (2008). Marketing “smart” medical innovation: Physicians' attitudes and intentions. International Journal of Pharmaceutical and Healthcare Marketing, 2(4), 307-320. https://doi.org/10.1108/17506120810922349

Chang, I., Li, Y., Hung, W., & Hwang, H. (2005). An empirical study on the impact of quality antecedents on tax payers' acceptance of internet tax-filing systems. Government Information Quarterly, 22(3), 389-410.

Chauhan, S., & Jaiswal, M. (2016). Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. The International Journal of Management Education, 14(3), 248-262. https://doi.org/10.1016/j.ijme.2016.05.005

Chen, C., & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28(4), 1115-1122. https://doi.org/10.1016/j.tourman.2006.07.007

Chen, H., & Tseng, H. (2012). Factors that influence acceptance of web-based E-lEarning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), 398-406. https://doi.org/10.1016/j.evalprogplan.2011.11.007

Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705-719. https://doi.org/10.1016/s0378-7206(01)00127-6

Cheng, E. W., Chu, S. K., & Ma, C. S. (2019). Students’ intentions to use PBWorks: A factor-based PLS-SEM approach. Information and Learning Sciences, 120(7/8), 489-504. https://doi.org/10.1108/ils-05-2018-0043

Chin, W. W. (1998). Issues and Opinion on Structural Equation Modeling. MIS Quarterly, 22(1), 7-16.

Chua, P. Y., Rezaei, S., Gu, M., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions. Nankai Business Review International, 9(2), 118-142. https://doi.org/10.1108/nbri-01-2017-0003

Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.2307/3150876

CNNIC. (2020, April 22). The 45th statistical report on Internet development in China -China Internet Network Information Center. https://www.cac.gov.cn/202004/27/c_1589535470378587.htm

Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis (1st ed.). Psychology Press.

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, 475-487.

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.

Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449. https://doi.org/10.1007/s11747-011-0300-3

DiStefano, C., & Hess, B. (2005). Using Confirmatory Factor Analysis for Construct Validation: An Empirical Review. Journal of Psychoeducational Assessment, 23(3), 225-241. https://doi.org/10.1177/073428290502300303

Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192. https://doi.org/10.7763/ijiet.2016.v6.683.

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of E-lEarning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763. https://doi.org/10.1007/s11423-016-9508-8

Fan, X., Duangekanong, S., & Xu, M. (2022). Factors Affecting College Students’ Intention to Use English U-learning in Sichuan, China. AU-GSB E-JOURNAL, 14(2), 118-129. https://doi.org/10.14456/augsbejr.2021.20

Fenech, T. (1998). Using perceived ease of use and perceived usefulness to predict acceptance of the World Wide Web. Computer Networks and ISDN Systems, 30(1-7), 629-630. https://doi.org/10.1016/s0169-7552(98)00028-2

Fogg, B. (2009). The behavior grid. Proceedings of the 4th International Conference on Persuasive Technology, 42, 1-5. https://doi.org/10.1145/1541948.1542001

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. https://doi.org/10.2307/3151312

Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of Internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231. https://doi.org/10.1108/apjml-06-2013-0061

Given, L. M. (2008). The sage encyclopedia of qualitative research methods (1st ed.). Thousand Oaks, CA: Sage.

Handoko, B. L. (2019). Application of UTAUT theory in higher education online learning. Proceedings of the 2019 10th International Conference on E-business, Management and Economics. https://doi.org/10.1145/3345035.3345047

Hao, M. (2023). The College Students’ Behavioral Intention to Use Mobile Reading Apps in Sichuan, China. AU-GSB e-Journal, 16(1), 121-130.

Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. International Marketing (Advances in International Marketing), 20, 277-319. https://doi.org/fbbzjt

Hossain, M. M., Islam, K. Z., Al Masud, A., Biswas, S., & Hossain, M. A. (2021). Behavioral intention and continued adoption of Facebook: an exploratory study of graduate students in Bangladesh during the Covid-19 pandemic. Management, 25(2), 153-186.

Hsiao, C., & Tang, K. (2014). Explaining undergraduates’ behavior intention of E-tExtbook adoption. Library Hi Tech, 32(1), 139-163. https://doi.org/10.1108/lht-09-2013-0126

Huang, J., Lin, Y., & Chuang, S. (2007). Elucidating user behavior of mobile learning. The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569

Hubley, A. M. (2014). Discriminant Validity. In Michalos, A.C. (Ed.) Encyclopedia of Quality of Life and Well-Being Research. Springer Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_751

Ismail, S. (2010). International students' acceptance on using social networking site to support learning activities. International Journal for the Advancement of Science and Arts, 1(2), 81-90.

Jati, N. J., & Laksito, H. (2012). Analisis faktor-faktor yang mempengaruhi minat pemanfaatan dan penggunaan sistem E-learning. Media Riset Akuntansi, Auditing Dan Informasi, 12(2), 22-56.

Keengwe, J., & Kidd, T. T. (2010). Towards Best Practices in Online Learning and Teaching in Higher Education. MERLOT Journal of Online Learning and Teaching, 6(2), 533-541.

Kirat Rai, S., Ramamritham, K., & Jana, A. (2020). Identifying factors affecting the acceptance of government-to-government system in developing nations – empirical evidence from Nepal. Transforming Government: People, Process and Policy, 14(2), 283-303. https://doi.org/10.1108/tg-05-2019-0035

Kleijinen, M., Wetzels, M., & de Ruyter, K. (2004). Consumer acceptance of wireless finance. Journal of Financial Services Marketing, 8(3), 206- 217. https://doi.org/10.1057/palgrave.fsm.4770120

Kuo, Y., & Yen, S. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110. https://doi.org/10.1016/j.chb.2008.07.007

Lee, C., Tsao, C., & Chang, W. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services. Journal of Enterprise Information Management, 28(5), 680-697. https://doi.org/10.1108/jeim-07-2014-0077

Lee, Y.-H., Hsiao, C., & Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5), 562–579. https://doi.org/10.14742/ajet.381

Lei, P., & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33-43. https://doi.org/10.1111/j.1745-3992.2007.00099.x

Lew, S.-L., Lau, S.-H., & Leow, M.-C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788.

Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600-610. http://dx.doi.org/10.1016/j.compedu.2009.09.009

Liu, Y., Li, H., Kostakos, V., Goncalves, J., Hosio, S., & Hu, F. (2014). An empirical investigation of mobile government adoption in rural China: A case study in Zhejiang province. Government Information Quarterly, 31(3), 432-442. https://doi.org/10.1016/j.giq.2014.02.008

Lupton, D. (2014). Health promotion in the Digital Era: A critical commentary. Health Promotion International, 30(1), 174-183. https://doi.org/10.1093/heapro/dau091

MacKenzie, E. J., Siegal, J. H., Shapiro, S., Moody, M., & Smith, R. T. (1986). Functional recovery and medical costs of trauma. The Journal of Trauma: Injury, Infection, and Critical Care, 26(7), 678. https://doi.org/10.1097/00005373-198607000-00060

Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use E-lEarning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077. https://doi.org/10.1007/s10639-021-10557-5

Min, G. (2020). Factors Affecting Yi Ethnic Minority EFL Learners’ English Pronunciation Learning in Leshan Normal University, Sichuan, China. English Language Teaching, 13(6), 104-108. https://doi.org/10.5539/elt.v13n6p104

Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-Web context. Information & Management, 38(4), 217-230. https://doi.org/10.1016/s0378-7206(00)00061-6

Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning (1st ed.). Cengage Learning

Moran, M., Hawkes, M., & Gayar, O. E. (2010). Tablet personal computer integration in higher education: Applying the unified theory of acceptance and use technology model to understand supporting factors. Journal of Educational Computing Research, 42(1), 79-101. https://doi.org/10.2190/ec.42.1.d

Moses, P., Wong, S. L., Bakar, K. A., & Mahmud, R. (2013). Perceived usefulness and perceived ease of use: Antecedents of attitude towards laptop use among science and mathematics teachers in Malaysia. The Asia-Pacific Education Researcher, 22(3), 293-299. https://doi.org/10.1007/s40299-012-0054-9

Moshagen, M. (2012). The model size effect in SEM: Inflated goodness-of-fit statistics are due to the size of the covariance matrix. Structural Equation Modeling a Multidisciplinary Journal, 19(1), 86-98. https://doi.org/fzxh7m

Neuman, W. L. (2003). Social Research Methods: Qualitative and Quantitative Approaches (1st ed.). Allyn and Bacon.

Nikou, S., & Bouwman, H. (2014). Ubiquitous use of mobile social network services. Telematics and Informatics, 31(3), 422-433. https://doi.org/10.1016/j.tele.2013.11.002.

Nunnally, J. C. (1978). Psychometric theory (1st ed.). McGraw-Hill Companies.

Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004

Pallant, J. F. (2000). Development and validation of a scale to measure perceived control of internal states. Journal of Personality Assessment, 75(2), 308-337. https://doi.org/10.1207/s15327752jpa7502_10

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

Perry, A. (2017). Factors comprehensively influencing acceptance of 3D-printed apparel. Journal of Fashion Marketing and Management, 21(2), 219-234. https://doi.org/10.1108/jfmm-03-2016-0028

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235. https://doi.org/10.1108/10662240410542652

Raman, A., Don, Y., Khalid, R., Hussin, F., Omar, M. S., & Ghani, M. (2014). Technology acceptance on smart board among teachers in Terengganu using UTAUT model. Asian Social Science, 10(11), 84-91. https://doi.org/10.5539/ass.v10n11p84

Rashotte, L. (2007). Social influence. In Everitt B. & Howell D. (Eds.) The Blackwell Encyclopedia of Sociology. JohnWiley & Son. https://doi.org/10.1002/9781405165518.wbeoss154

Rotchanakitumnuai, S., & Speece, M. (2009). Modeling electronic service acceptance of an E‐sEcuritiEs trading system. Industrial Management & Data Systems, 109(8), 1069-1084. https://doi.org/10.1108/02635570910991300

Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management 42(2), 317–327. doi:10.1016/j.im.2003.12.013

Salim, B. (2012). An application of UTAUT model for acceptance of social media in Egypt: A statistical study. International Journal of Information Science, 2(6), 92-105. https://doi.org/10.5923/j.ijis.20120206.05

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. (2006). Calculator: A-priori Sample Size for Structural Equation Models. Daniel Soper https://www.danielsoper.com/statcalc/calculator.aspx?id=89

Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science, 41(8), 1328-1342. https://doi.org/10.1287/mnsc.41.8.1328

Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app-based shopping: Evidences from India. Journal of Indian Business Research, 9(3), 248-264. https://doi.org/10.1108/jibr-11-2016-0132

Talukder, S., Chiong, R., Dhakal, S., Sorwar, G., & Bao, Y. (2019). A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of M-governMent service adoption. Journal of Systems and Information Technology, 21(4), 419-438. https://doi.org/10.1108/jsit-10-2017-0096

Tan, P. J. (2013). Applying the UTAUT to understand factors affecting the use of English E-learning websites in Taiwan. SAGE Open, 3(4), 215824401350383. https://doi.org/10.1177/2158244013503837

Tarhini, A., Elyas, T., Akour, M. A., & Al-Salti, Z. (2016). Technology, demographic characteristics, and e-learning acceptance: A conceptual model based on extended technology acceptance model. Higher Education Studies, 6(3), 72–89. https://doi.org/10.5539/hes.v6n3p72.

Tarhini, A., Hassouna, M., Abbasi, M. S., & Orozco, J. (2015). Towards the acceptance of RSS to support learning: An empirical study to validate the technology acceptance model in Lebanon. Electronic. Journal of e-Learning, 13(1), 30–41.

Tarhini, A., Hone, K., & Liu, X. (2014). The efects of individual diferences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163. https://doi.org/10.1016/j.chb.2014.09.020

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144

Teo, T., & Van Schalk, P. (2009). Understanding technology acceptance in pre-service teachers: A structural-equation modeling approach. The Asia-Pacific Education Researcher, 18(1), 47-66. https://doi.org/10.3860/taper.v18i1.1035

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443

Triandis, H. C. (1977). Interpersonal behavior (1st ed.). Brooks/Cole Pub.

Ukut, I. I., & Krairit, D. (2019). Justifying students’ performance. Interactive Technology and Smart Education, 16(1), 18-35. https://doi.org/10.1108/itse-05-2018-0028

Unal, E., & Uzun, A. M. (2020). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619-637. https://doi.org/10.1111/bjet.13046

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. https://doi.org/10.2307/30036540

Wang, Y. (2023). Learning Satisfaction of Online Art Education: A Case of Undergraduates in Public Colleges in Sichuan. AU-GSB e-Journal, 16(1), 38-47.

Watjatrakul, B. (2013). Intention to use a free voluntary service. Journal of Systems and Information Technology, 15(2), 202-220. https://doi.org/10.1108/13287261311328903

Weng, F., Yang, R., Ho, H., & Su, H. (2018). A TAM-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1(3), 36. https://doi.org/10.3390/asi1030036

Wiafe, I., Koranteng, F. N., Tettey, T., Kastriku, F. A., & Abdulai, J. (2019). Factors that affect acceptance and use of information systems within the maritime industry in developing countries. Journal of Systems and Information Technology, 22(1), 21-45. https://doi.org/10.1108/jsit-06-2018-0091.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A specification 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, X. W., Li, D., & Lv, J. (2010). Research on the User Behaviors in the Network Competitive Intelligence System Based on TAM Model and Perceived Risk. Information Science, 28(6), 931-935.

Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing, 27(3), 262-270. https://doi.org/10.1108/07363761011038338

Yao, A., Wu, J., & Yang, Y. (2022). Research on a strategy of improving students’ satisfaction with teaching: An empirical study based on undergraduate universities in Sichuan. Wireless Communications and Mobile Computing, 2, 1-8. https://doi.org/10.1155/2022/3115584

Yu, K., & Huang, G. (2020). Exploring consumers’ intent to use smart libraries with technology acceptance model. The Electronic Library, 38(3), 447-461. https://doi.org/10.1108/el-08-2019-0188

Yuan, K. H., Zhang, Z., & Zhao, Y. (2017). Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 1(2) 1-16. https://doi.org/10.1080/10705511.2016.1276836

Zhang, T., Qu, Y., Wen, Y., Wang, C., & Zhang, X. (2019). Research on the Reform of ID Course under the Background of 3E—A Case Study of ID in Sichuan Normal University (1st ed.). Proceedings of the 2019 3rd International Conference on Education, Economics and Management Research (ICEEMR 2019).

Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 43, 342-350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006