Factors Impacting the Perceived Usefulness and Behavioral Intention toward Blended Learning System in Higher Education

Main Article Content

Zhijian Lin

Abstract

Purpose: This study aimed to assess the factors that impact students’ perceived usefulness and behavioral intentions toward blended learning systems (BLS) in Chinese higher education. Research design, data, and methodology: This study adopts quantitative research methods, using Item-Objective Congruence and Pilot Tests to estimate the validity and reliability of the questionnaire. Based on various sampling methods, an electronic questionnaire was used to collect data, and Cronbach’s Alpha was used to evaluate the reliability of the data. According to the proposed research model, Confirmatory Factor Analysis (CFA) was used to analyze the structural validity, and Structural Equation Modeling (SEM) was used to test the structural correlation. Results: It was found that the perceived usefulness of BLS was significantly affected by information quality, system quality, and collaboration quality (CBQ). Perceived usefulness, hedonic motivation, facilitating condition, and effort expectancy significantly drove behavioral intention to use BLS. Conclusions: This study proposed a composite research framework to analyze the influence of college students’ behavior and intention to use BLS more completely and effectively. The researchers believed that improving the quality factors and external promotion conditions of BLS could improve students’ PU for BLS and promote students’ enthusiasm and intention to use BLS.

Downloads

Download data is not yet available.

Article Details

How to Cite
Lin, Z. (2025). Factors Impacting the Perceived Usefulness and Behavioral Intention toward Blended Learning System in Higher Education. AU-GSB E-JOURNAL, 18(4), 84-96. Retrieved from https://assumptionjournal.au.edu/index.php/AU-GSB/article/view/8456
Section
Articles
Author Biography

Zhijian Lin

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

References

Abbas, T. (2016). Social factors affecting students' acceptance of e-learning environments in developing and developed countries: a structural equation modeling approach. Journal of Hospitality & Tourism Technology, 7(2), 200-212. https://doi.org/10.1108/jhtt-11-2015-0042

Abdekhoda, M., Dehnad, A., Mirsaeed, S., & Gavgani, V. (2016). Factors influencing the adoption of e-learning in Tabriz University of Medical Sciences. Medical Journal of the Islamic Republic of Iran, 30, 457.

Abdekhoda, M., Maserat, E., & Ranjbaran, F. (2020). A conceptual model of flipped classroom adoption in medical higher education. Interactive Technology and Smart Education, 17(4), 393-401. https://doi.org/10.1108/itse-09-2019-0058

Abu-Gharrah, A., & Aljaafreh, A. (2021). Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education, 11(1), 53-66. https://doi.org/10.3926/jotse.1081

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

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 429-444. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Alrousan, M., AlMadadha, A., AlKhasawneh, M., & Adel Tweissi, A. (2022). Determinants of virtual classroom adoption in jordan: the case of princess sumaya university for technology. Interactive technology and smart education, 19(2), 121-144. https://doi.org/10.1108/itse-09-2020-0211

Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behaviour, 64, 843-858. https://doi.org/10.1016/j.chb.2016.07.065

Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Abdullah, A., Ming, G. L., Nurbiha, A. S., Shukri, M., & Baba, S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: an empirical investigation. Education and Information Technologies, 24(6), 3433-3466.

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model. BMC Medical Education, 20(367), 1-9. https://doi.org/10.1186/s12909-020-02302-2

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

Bliuc, A. M., Goodyear, P., & Ellis, R. A. (2007). Research focus and methodological choices in studies into students’ experiences of blended learning in higher education. The Internet and Higher Education, 10(4), 231-244. https://doi.org/10.1016/j.iheduc.2007.08.001

Bokolo, A., Jr., Kamaludin, A., Romli, A., Mat Raffei, A., ALe Phon, D., Abdullah, A., Ming, G. L., Nurbiha, A. S., Shukri, M., & Baba, S. (2020). A managerial perspective on institutions’ administration readiness to diffuse blended learning in higher education: Concept and evidence. Journal of Research on Technology in Education, 52(1), 37-64.

Brown, S., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399-426. https://doi.org/10.2307/25148690

Chen, C. C., Lee, C. H., & Hsiao, K. L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan: effects of interactivity and openness. Library Hi Tech, 36(4), 705-719. https://doi.org/10.1108/lht-11-2016-0129

Chen, S. W., Stocker, J., Wang, R. H., Chung, Y. C., & Chen, M. F. (2009). Evaluation of self-regulatory online learning in a blended course for post-registration nursing students in Taiwan. Nurse Education Today, 29(7), 704-709.

https://doi.org/10.1016/j.nedt.2009.03.002

Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390.

https://doi.org/10.1108/10662241211235699

Cheng, Y. M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses continued blended e-learning intention. Information technology & people, 27(3), 230-258.

Cheng, Y. M. (2022). Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success. Library Hi Tech, 41(6), 1748-1780.

Chiu, C. M., Wang, E. T., Shih, F. J., & Fan, Y. W. (2011). Understanding knowledge sharing in virtual communities: an integration of expectancy disconfirmation and justice theories. Online Information Review, 35(1), 134-153.

https://doi.org/10.1108/14684521111113623

Cho, V., Cheng, T., & Lai, W. (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

Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a web-based electronic learning system: the flow theory perspective. International Journal of Human-Computer Studies, 65(3), 223-243. https://doi.org/10.1016/j.ijhcs.2006.10.002

Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students' perspective: a structural equation model (sem) approach. Interactive Technology and Smart Education, 16(2), 94-116. https://doi.org/10.1108/itse-05-2018-0027

Cidral, W. A., Oliveira, T., Felice, M. D., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers and Education, 122, 273-290. https://doi.org/10.1016/j.compedu.2017.12.001

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

DeLone, W. H., & McLean, E. R. (2003). Information systems success: The quest for the dependent variable. Information Systems Research, 12(1), 60-95.

Dobbs, K. (2000). Who’s in charge of e-learning?. Training, 37(6), 54-58.

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/s11423-017-9526-1

Fabianic, D. (2002). Online instruction and site assessment. Criminal Justice Education, 13(1), 173-186. https://doi.org/10.1080/10511250200085401

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104

Friesen, N. (2012, August 12). Defining blended learning. Learning spaces. https://www.normfriesen.info/papers/Defining_Blended_Learning_NF.pdf

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential. Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated mode. Management Information Systems Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519

Goodwin, L. D., & Leech, N. L. (2003). The meaning of validity in the New Standards for Educational and Psychological Testing: Implications for measurement courses. Measurement and Evaluation in Counseling and Development, 36(3), 181-191. https://doi.org/10.1080/07481756.2003.11909741

Gustavsson, M., & Wanstrom, C. (2009). Assessing information quality in manufacturing planning and control processes. International Journal of Quality and Reliability Management, 26(4), 325-340. https://doi.org/10.1108/02656710910950333

Hadullo, K., Oboko, R., & Omwenga, E. (2017). A model for evaluating E-Learning systems quality in higher education in developing countries. International Journal of Education and Development Using Information and Communication Technology, 13(2), 185.

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

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall.

Harriman, G. (2004, March 23). What is blended learning?. E-Learning Resources. http://www.grayharriman.com/blended_learning.htm

Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology (1st ed.). Sage.

Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 1, 1-23.

Huang, J., & Duangekanong, S. (2022). Factors Impacting the Usage Intention of Learning Management System in Higher Education. AU-GSB E-JOURNAL, 15(1), 41-51. https://doi.org/10.14456/augsbejr.2022.59

Kim, H., & Niehm, L. S. (2009). The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of Interactive Marketing, 23(3), 221-233. https://doi.org/10.1016/j.intmar.2009.04.009

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

Kisanjara, S. B., Tossy, T. M., Sife, A. S., & Masanjila, S. S. (2019). E-Learning uptake among academicians and students. International Online Journal of Education and Teaching, 6(1), 18-35. https://doi.org/10.3991/ijet.v15i01.11349

Klem, L. (2000). Structural equation modeling. American Psychological Association.

Krishnasamy, S., Ling, L. S., & Kim, T. C. (2020). Improving learning experience of probability and statistics using multimedia system. International Journal of Emerging Technologies in Learning (IJET), 15(1), 77-87.

Kuo, Y. C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20(1), 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001

Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioral intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computer in Higher Education, 25, 93-121.

doi:10.1007/s12528-013-9069-3

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, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Bioinformatics, 31(5), 517-541. https://doi.org/10.1108/14684520610706406

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

Lu, D. N., Le, H. Q., & Vu, T. H. (2020, June 20). The factors affecting acceptance of e-learning: a machine learning algorithm approach. https://files.eric.ed.gov/fulltext/EJ1272761.

Marandu, E. E., Mathew, I. R., Svotwa, T. D., Machera, R. P., & Jaiyeoba, O. (2023). Predicting students' intention to continue online learning post-COVID-19 pandemic: extension of the unified theory of acceptance and usage technology. Journal of Applied Research in Higher Education, 15(3), 681-697.

Mirabolghasemi, M., Shasti, R., & Choshaly, S. H. (2021). An investigation into the determinants of blended learning satisfaction from EFL learners’ perspectives. Interactive Technology and Smart Education, 18(4), 391-408. https://doi.org/10.1108/ITSE-06-2021-0075.

Molinillo, S., Aguilar-Illescas, R., Anaya-Sanchez, R., & Vallespın-Aran, M. (2018). Exploring the impacts of interactions, social presence, and emotional engagement on active collaborative learning in a social web-based environment. Computers and Education, 123(8), 41-52. https://doi.org/10.1016/j.compedu.2018.04.012

Moorthy, K., Yee, T., T'ing, L., & Kumaran, V. (2019). Habit & hedonic motivation are the strongest influences in mobile learning behaviors among higher education students in Malaysia. Aust J Educ Technol, 35(4), 174-191. https://doi.org/10.14742/ajet.4432

Nguyen, T. D., Nguyen, D. T., & Cao, T. H. (2014). Acceptance and use of information system: e-learning based on cloud computing in Vietnam (1st ed.). Springer.

Okaz, A. (2015). Integrating blended learning in higher education. Procedia - Social and Behavioral Sciences, 186, 600-603. https://doi.org/10.1016/j.sbspro.2015.04.086

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

Owston, R., Wideman, H., Murphy, J., & Lupshenyuk, D. (2008). Blended teacher professional development: a synthesis of three program evaluations. The Internet and Higher Education, 11(3), 201-210. https://doi.org/10.1016/j.iheduc.2008.07.003

Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation. Computers and Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.011

Pai, H. H., Sears, D. A., & Maeda, Y. (2015). Effects of small-group learning on transfer: a metanalysis. Educational Psychology Review, 27(1), 79-102. https://doi.org/10.1007/s10648-014-9260-8

Pallant, J. (2010). SPSS survival manual: A step-by-step guide to data analysis using SPSS (4th ed). McGraw Hill.

Passey, D. (2006). Technology enhancing: analyzing use of information and communication technology by primary and secondary school publish with learner’s framework. The curriculum journal, 16(2), 139-166. https://doi.org/10.1080/09585170600792761

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.

Perera, R., & Abeysekera, N. (2022). Factors affecting learners’ perception of e-learning during the COVID-19 pandemic. Asian Association of Open Universities, 17(1), 84-100. https://doi.org/10.1108/aaouj-10-2021-0124

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.

Polites, G. L., Williams, C. K., Karahanna, E., & Seligman, L. (2012). A theoretical framework for consumer e-satisfaction and site stickiness: an evaluation in the context of online hotel reservations. Journal of Organizational Computing and Electronic Commerce, 22(1), 1-37. https://doi.org/10.1080/10919392.2012.642242

Raees, F. L. (2002). Applications and benefits of information technology. Educational Technology, 2, 16.

Ramayah, T., Ahmad, N. H., & Lo, M. C. (2010). The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia-Social and Behavioral Sciences, 2(2), 5422-5426. https://doi.org/10.1016/j.sbspro.2010.03.885

Rivera, J. L. (2019). Blended learning-effectiveness and application in teaching and learning foreign languages. Open Journal of Modern Linguistics, 9, 129-144. https://doi.org/10.4236/ojml.2019.92013

Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15-36. https://doi.org/10.1108/AAOUJ-08-2021-0084

Rughoobur, S., & Hosanoo, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422-444. https://doi.org/10.1108/qae-03-2021-0043

Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems. Interactive Technology and Smart Education, 16(3), 219-238. https://doi.org/10.1108/itse-11-2018-0092

Sattari, A., Abdekhoda, M., & Zarea, G. (2017). Determinant factors affecting web-based training acceptance by health students: Applying the UTAUT model. International Journal of Emerging Technology and Learning, 12(10), 112-126.

https://doi.org/10.3991/ijet.v12i10.7421

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

Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behavior and Information Technology, 36(10), 1053-1066. https://doi.org/10.1080/0144929x.2017.1340973

Shin, D. H. (2015). Effect of the customer experience on satisfaction with smartphones: assessing smart satisfaction index with partial least squares, Telecommunications Policy, 39(8), 627-641. https://doi.org/10.1016/j.telpol.2014.10.001

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.

Singh, H. (2003). Building effective blended learning programs. Educational Technology, 43(6), 51-54. https://doi.org/10.4018/978-1-7998-7607-6.ch002

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

Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: an historical perspective. In Sawyer, R. K. (Ed.), Cambridge Handbook of the Learning Sciences (pp. 409-426). Cambridge University Press. https://doi.org/10.1017/9781108888295.025

Strau, S., & Rummel, N. (2020). Promoting interaction in online distance education: designing, implementing, and supporting collaborative learning. Information and Learning Sciences, 121(5/6), 251-260. https://doi.org/10.1108/ILS-04-2020-0090

Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169. https://doi.org/10.2307/248922

Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202.

Tajuddin, R. A., Baharudin, M., & Hoon, T. S. (2013). System quality and its influence on students’ learning satisfaction in UiTM Shah Alam. Procedia - Social and Behavioral Sciences, 90, 677-685. https://doi.org/10.1016/j.sbspro.2013.07.140

Twum, D., Smith, J., & Brown, L. (2022). Exploring new trends in digital education. Journal of Educational Technology, 35(3), 45-60. https://doi.org/10.1016/j.jedutech.2022.05.007

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.

Venkatesh, V., Thong, J. Y., & 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.

Wang, J. (2019). Application of blending learning based on network learning space in teaching design of digital art. International Journal of Educational Technology, 14(9), 177-189. https://doi.org/10.3991/ijet.v14i03.10107

Wang, Q. (2008). A generic model for guiding the integration of ICT in to teaching and learning. Innovation Education and Teaching International, 45(4), 411-419. https://doi.org/10.1080/14703290802377307

Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers and Education, 53(3), 761-774. https://doi.org/10.1016/j.compedu.2009.02.021

Wong, L., Tatnall, A., & Burgess, S. (2014). A framework for investigating blended learning effectiveness. Education Training, 56(2/3), 233-251. https://doi.org/10.1108/et-04-2013-0049

Wu, J., & Liu, W. (2013). An empirical investigation of the critical factors affecting students’ satisfaction in EFL blended learning. Journal of Language Teaching Research, 4(1), 176-185. https://doi.org/10.4304/jltr.4.1.176-185

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

Yiong, B. L. C., Sam, H. K., & Wah, T. K. (2008). Acceptance of e-learning among distance learners: a Malaysian perspective (1st ed.). Australasian Society for Computers in Learning in Tertiary Education.

Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science & Technology, 13(3), 312-317. https://doi.org/10.1016/s1007-0214(08)70050-6