A Study on Behavioral Intention and Self-Learning Attitude of Internet Base E-Learning Among Liberal Arts Students in Chengdu, China

Main Article Content

Wang Xiang

Abstract

Purpose: This study aims to investigate factors impacting students on the behavioral intention to self-learning and self-learning attitude of internet base e-learning for Liberal arts students in Chengdu, China including system quality, information quality, service quality, perceived usefulness, perceived ease of use, perceived enjoyment, self-learning attitude, and behavioral intention. Research design, data, and Methodology: The sample size involves 500 students in liberal arts in the first to third year. A questionnaire is designed, investigated and statistically analyzed. The sample techniques are judgmental, quota and convenience sampling. The index of item-objective congruence and the Cronbach's Alpha test were conducted before the data collection. Data analysis involved employing confirmatory factor analysis and structural equation modeling techniques. Results: The findings revealed that system quality and service quality significantly influence percived usefulness. Perceived ease of use has a significant influence on perceived usefulness and perceived enjoyment. Perceived usefulness significantly influences behavioral intention. In contrast, perceived ease of use and perceived usefulness have no significant influence on self-learning attitude. Additionally, information quality has no significant influence on perceived usefulness. Conclusions: These findings have significant implications for educators and policymakers in designing and implementing effective e-learning programs that foster a positive attitude toward self-directed learning.

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How to Cite
Xiang, W. (2025). A Study on Behavioral Intention and Self-Learning Attitude of Internet Base E-Learning Among Liberal Arts Students in Chengdu, China. AU-GSB E-JOURNAL, 18(1), 106-115. https://doi.org/10.14456/augsbejr.2025.11
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Articles
Author Biography

Wang Xiang

School of Sichuan Vocational College of Cultural Industry, China.

References

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

Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1st ed.). Prentice-Hall.

Arnab, R. (2017). Survey Sampling Theory and Applications (1st ed.). Elsevier Ltd.

Babbie, E. R. (1990). Survey research methods (1st ed.). Cengage Learning Press.

Bajaj, A., & Nididumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33(4), 213-224. https://doi.org/10.1016/s0378-7206(98)00026-3

Bosnjak, M., Obermeier, D., & Tuten, T. (2006). Predicting and explaining the propensity to bid in online auctions: A comparison of two action-theoretical models. Journal of Consumer Behavior, 5(2), 102-116. https://doi.org/10.1002/cb.38

Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T. (2010). Students' perceptions about the use of video games in the classroom. Computers & Education, 54(4), 1145-1156. https://doi.org/10.1016/j.compedu.2009.10.022

Buabeng-Andoh, C. (2018). Predicting students’ intention to adopt mobile learning: A combination of theory of reasoned action and technology acceptance model. Journal of Research in Innovative Teaching & Learning, 11(2), 178-191. https://doi.org/10.1108/JRIT-03-2017-0004.

Calisir, F., Altin, G., Bayraktaroglu, A., & Karaali, D. (2014). Predicting the Intention to Use a Web-Based Learning System: Perceived Content Quality, Anxiety, Perceived System Quality, Image, and the Technology Acceptance Model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531. https://doi.org/10.1002/hfm.20548

Chang, Y.-S., & Yang, C. (2013). Why do we blog? From the perspectives of technology acceptance and media choice factors. Behaviors and Information Technology, 32(4), 371-386. https://doi.org/10.1080/0144929x.2012.656326

Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53(3), 877-889. https://doi.org/10.1016/j.compedu.2009.05.007

Cheng, K. H., & Tsai, C. C. (2013). Affordances of Augmented Reality in Science Learning: Suggestions for Future Research. Journal of Science Education and Technology, 22, 449-462. http://dx.doi.org/10.1007/s10956-012-9405-9

Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-227. https://doi.org/10.1016/j.compedu.2009.01.014

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., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982- 1003. https://doi.org/10.1287/mnsc.35.8.982

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

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, 19(4), 9-30.

Flavian, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction, and consumer trust on website loyalty. Information & Management, 43(1), 1-14. https://doi.org/10.1016/j.im.2005.01.002

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

Gray, D. E. (2017). Doing Research in the Business World (1st ed.). SAGE Publications Ltd.

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.

Hong, S.-J., Thong, J. Y. L., & and Tam, K. Y. (2006). Understanding continued information technology usage behaviors: a comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819-1834. https://doi.org/10.1016/j.dss.2006.03.009

Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. The Electronic Journal of Business Research Methods, 6, 53-60.

Hoyle, R. H. (1995). The Structural Equation Modeling Approach: Basic Concepts and Fundamental Issues. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 1-15). Sage Publications.

Israel, G. D. (2003). Determining Sample Size. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS, Florida.

Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections. Acquisition & Technical Services, 35, 45-60. https://doi.org/10.1080/14649055.2011.10766298

Kaplan, S. (2008). Framing contests: Strategy making under uncertainty. Organization Science, 19(5), 729–752. https://doi.org/10.1287/orsc.1070.0340

Keating, B., Rugimband, R., & Quzai, A. (2003). Differentiating between service quality and relationship quality in cyberspace. Managing Service Quality, 13(3), 217-232. https://doi.org/10.1108/09604520310476481

Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviors of hotel front office systems: an extended technology acceptance model. Tourism Management, 29(3), 500-513. https://doi.org/10.1016/j.tourman.2007.05.016

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd edition) New York: The Guilford Press

Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53(4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014

Lee, G. G., & Lin, H. F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail and Distribution Management, 33(2), 161-176. https://doi.org/10.1108/09590550510581485

Lee, M. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104. https://doi.org/10.1016/j.im.2003.10.007

Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406

Lee, Y.-C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423-1438. https://doi.org/10.1016/j.compedu.2007.01.001

Letchumanan, M., & Tarmizi, A. R. (2011). Assessing the intention to use e‐book among engineering undergraduates in Universiti Putra Malaysia. Malaysia. Library Hi Tech, 29(3), 512-528. https://doi.org/10.1108/07378831111174459

Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioral intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933-948. https://doi.org/10.1111/j.1467-8535.2011.01261.x

Lin, F. Y., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of e-government initiatives in the Gambia: a validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271-279. https://doi.org/10.1016/j.giq.2010.09.004

Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138. https://doi.org/10.1108/10662240710736997

Lin, L. (2013). Multiple dimensions of multitasking phenomenon. International Journal of Technology and Human Interaction, 9(1), 37-49. https://doi.org/10.4018/jthi.2013010103

Liu, C., & Arnett, K. (2000). Exploring the Factors Associated with Web Site Success in the Context of Electronic Commerce. Information & Management, 38, 23-33.

http://dx.doi.org/10.1016/S0378-7206(00)00049-5

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. https://doi.org/10.1016/j.compedu.2009.09.009

Miller, J., & Khera, O. (2010). Digital library adoption and the technology acceptance model: a cross-country analysis. The Electronic Journal of Information System in Developing Countries, 40(6), 1-19. https://doi.org/10.1002/j.1681-4835.2010.tb00288.x

Ndubisi, N. O. (2006). Factors of online learning adoption: a comparative juxtaposition of the theory of planned behaviour and the technology acceptance model. International Journal on ELearning, 5(4), 571-591.

Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of information and system quality: an empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199-235. https://doi.org/10.1080/07421222.2005.11045823

Ong, C.-S., & Lai, J.-Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829. https://doi.org/10.1016/j.chb.2004.03.006

Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795-804. https://doi.org/10.1016/j.im.2003.08.012

Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation. Computers & Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.01

Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: an exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385. https://doi.org/10.1016/j.tele.2013.11.008

Petty, R. E., & Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change (1st ed.). Springer-Verlag.

Privitera, G. J. (2014). Research Methods for the Behavioral Sciences, Psychology and Health, 13(4), 623-649.

Rigdon, E. E. (1998). Structural Equation Modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 251-294). Erlbaum.

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 HumanComputer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003

Roca, J. C., & Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: a self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. https://doi.org/10.1016/j.chb.2007.06.001

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. https://doi.org/10.1016/j.im.2003.12.013

Sánchez-Muros, M.-J., Barroso, F. G., & Manzano-Agugliaro, F. (2014). Insect meal as renewable source of food for animal feeding: a review. J. Cleaner Prod., 65, 16-27. https://doi.org/10.1016/j.jclepro.2013.11.068

Tao, T., & Wen, L. (2015). New Media and Future Education [J]. China Audio-visual Education, 1(1) 34-38.

Teo, H., Wei, K., & Benbasat, I. (2003). Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective. MIS Quarterly, 27(1), 19-49. https://doi.org/10.2307/30036518

Teo, T. S. H., & Lim, V. K. G. (1997). Usage patterns and perceptions of the internet: the gender gap. Equal Opportunities International, 16(6/7), 1-8. https://doi.org/10.1108/eb010696

Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001

Thong, J. Y. L., Hong, W., & Tam, K. (2002). Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context and individual differences?. International Journal of Human-Computer Studies, 57(1), 215-242. https://doi.org/10.1016/s1071-5819(02)91024-4

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

Wei, L. (2011). Moment Analysis and translanguaging space: Discursive construction of identities by multilingual Chinese youth in Britain. Journal of Pragmatics, 43(5), 1222-1235. https://doi.org/10.1016/j.pragma.2010.07.035

Wojciechowski, R., & Cellary, W. (2013). Evaluation of Learners’ Attitude toward Learning in ARIES Augmented Reality Environments. Computers & Education, 68, 570-585. http://dx.doi.org/10.1016/j.compedu.2013.02.014