Factors Affecting College Students' Intention to Use English U-learning in Sichuan, China
Article Sidebar
Main Article Content
Abstract
Purpose: This research aimed to evaluate the effects of perceived ease of use, social influence, service quality, perceived usefulness, satisfaction, and attitude toward using and intention to use English u-learning on college students. Research design, data and methodology: This study was a quantitative study and the researcher obtained data for analysis by distributing questionnaires to the target population. the index of Item–Objective Congruence (IOC), pilot test, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were methods utilized to analyze the data and test research hypotheses proposed. Results: The results showed that perceived ease of use and perceived usefulness of English u-learning, social influence, service quality, and satisfaction had positive direct and/or indirect effect on college students’ intention to use English u-learning. Satisfaction exerted the most significant influence on intention to use English u-learning. However, attitude showed no causal relationship with intention to use English u-learning. Conclusions: For English u-learning system developers, they should focus on improving perceived ease of use, perceived usefulness, and service quality of the system. For system promoters and management of education institutions, they ought to increase social influence of English u-learning and raise students’ satisfaction to improve their intention to use English u-learning.
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
Abbasi, M.S., Chandio, F.H., Soomro, A.F., & Shah, F. (2011). Social influence, voluntariness, experience and the internet acceptance: an extension of technology acceptance model within a south-Asian country context. Journal of Enterprise Information Management, 24(1), 30-52.
Aboelmaged, M. G. (2018). Predicting the success of twitter in healthcare: a synthesis of perceived quality, usefulness and flow experience by healthcare professionals. Online Information Review, 42(6), 898-922.
Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405-420.
Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information and Management, 44(3), 263-275.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.
Akbar, F. (2013). What affects students’ acceptance and use of technology? Fig Share. https://figshare.com/articles/What_affects_students_acceptance_and_use_of_technology_/6686654
Anderson, E.W., Fornell, C., & Lehmann, D.R. (1994). Customer satisfaction, market share, and profitability: findings from Sweden. Journal of Marketing, 58(3), 53-65. https://doi.org/10.2307/1252310
Arbuckle, J. J. (1995). AMOS user's guide. Small Waters.
Athiyaman, A. (1997). Linking student satisfaction and service quality perceptions: the case of university education. European Journal of Marketing, 31(7), 528–540.
Ayeh, J.K. (2015). Travellers’ acceptance of consumer-generated media: an integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48 (7), 173-180.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Bashir, I, & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/JIBR-02-2014-0013
Benjangjaru, B., & Vongurai, R. (2018). Behavioral intention of Bangkokians to adopt mobile payment services by type of users. AU-GSB e-Journal, 11(1), 34-46. http://www.assumptionjournal.au.edu/index.php/AU-GSB/article/view/3299
Bolton, R. (1998). A dynamic model of the duration of the customer’s relationship with a continuous service provider: the role of satisfaction. Marketing Science, 17(1), 45-65.
Bolton, R., & Lemon, K. (1999). A dynamic model of customers’ usage of services: usage as an antecedent and consequence of satisfaction. Journal of Marketing Research, 36(2), 171-86.
Brady L.W., & Yaeger T.E. (2013). Population. In Encyclopedia of Radiation Oncology. Springer. https://doi.org/10.1007/978-3-540-85516-3_647
Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford Press.
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor & Francis Group.
Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: an empirical examination. Industrial Management and Data Systems, 105(5), 645-651.
Carlson, J & O’Cass, A. (2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviors in content-driven e-service web sites. Journal of Services Marketing, 24(2), 112-127.
Chang, M.K., & Cheung, W. (2001). Determinants of the intention to use Internet/WWW at work: a confirmatory study. Information and Management, 39(1), 1-14.
Cenfetelli, R.T., Benbasat, I., & Al-Natour, S. (2008). Addressing the what and how of online services: positioning supporting-services functionality and service quality for business-to-consumer success. Information Systems Research, 19(2), 161-81. https://doi.org/10.1287/isre.1070.0163
Chen, C.F. (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: evidence from Taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709-717.
Cheng, Y.M. (2012). The effects of information systems quality on nurses’ acceptance of the electronic learning system. Journal of Nursing Research, 20(1), 19-30.
China Daily. (2016). English Education in China. http://www.chinadaily.com.cn/kindle/2016-12/26/content_27777395.htm
Choi, C., Greenwell, T.C., & ve Lee, K. (2018). Effects of service quality, perceived value, and consumer satisfaction on behavioral intentions in virtual golf. Journal of Physical Education and Sport, 18(3), 1459-1468.
Claar, C., Dias, L.P., & Shields, R. (2014). Student acceptance of learning management systems: a study on demographics. Issues in Information Systems, 15(1), 409-417.
Cooksey R., & McDonald G. (2019). How Do I Manage the Sampling Process? In Surviving and Thriving in Postgraduate Research (pp. 868). Springer. https://doi.org/10.1007/978-981-13-7747-1_19
Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. [Doctoral dissertation]. MIT Sloan School of Management.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.
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.
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.
DeLone, W.H., & McLean, E.R. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95.
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.
Dick, A.S., & Basu, K. (1994). Customer loyalty: toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 22(2), 99-113.
Didyasarin, H., Vongurai, R., & Inthawadee, S. (2017). The factors impact attitude toward using and customer satisfaction with elderly health care mobile application services: a case study og people in Bangkok Metropolitan, Thailand. AU-GSB e-Journal, 10(1), 167-176. http://www.assumptionjournal.au.edu/index.php/AU-GSB/article/view/2870
Dikko, M. (2016). Establishing Construct Validity and Reliability: Pilot Testing of a Qualitative Interview for Research in Takaful (Islamic Insurance). Qualitative Report, 21(3).
Du, H., Ge, Z., Zhao, L., & Lv, T. (2012). An empirical study of consumer adoption on 3g value-added services in china. Nankai Business Review International, 3(3), 257-283.
Ellis, D.R., & Kurniawan, S.H. (2000). Increasing the usability of online information for older users: a case study in participatory design. Industrial and Systems Engineering, 12(2), 263-276.
Fazio, R.H. (1989). On the power and functionality of attitudes: the role of attitude accessibility. In A. R. Pratkanis, S. J. Breckler, & A.G. Greenwald (Eds.), Attitude Structure and Function (pp. 153-179). Lawrence Erlbaum Associates.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. 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.
Foroughi, B., Iranmanesh, M. & Hyun, S.S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015-1033. https://doi.org/10.1108/JEIM-10-2018-0237
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing & Logistics, 26(2), 211-231.
Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (2010). Multivariate Data Analysis (6th ed., pp. 137-142). Prentice Hall.
Haubl, G. (1996). A cross-national investigation of the effects of country of origin and brand name on the evaluation of a new car. International Marketing Review, 13(5), 76-97.
Hong, S.-J., & Tam, K.Y. (2006). Understanding the adoption of multipurpose information appliances: the case of mobile data services. Information Systems Research, 17(2), 162-179.
Hong, W., Thong, J.Y.L., Wong, W.M., & Tam, K.-Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124.
Hsu, C.L., & Lu, H.P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
Hsu, H.H. (2012). The acceptance of Moodle: an empirical study based on UTAUT. Creative Education, 3(8), 44-46.
Hu & Bentler. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
Hu, X., & Lai, C. (2019). Comparing factors that influence learning management systems use on computers and on mobile. New library world, 120(7/8), 468-488.
Igbaria, M., Iivari, J., & Maragah, H. (1995). Why do individuals use computer technology? A Finnish case study. Information & Management, 29(5), 227-238.
Kashive, N., Powale, L., & Kashive, K. (2021). Understanding user perception toward artificial intelligence (AI). The International Journal of Information and Learning Technology, 38(1), 1-19.
Kesharwani, A., & Tripathy, T. (2012). Dimensionality of perceived risk and its impact on Internet banking adoption: an empirical investigation. Services Marketing Quarterly, 33(2), 177-193.
Kim, T.G., Lee, J.H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: an extended technology acceptance model. Tourism Management, 29(3), 500-513.
Kim, J.-H., Kim, M., & Kandampully, J. (2007). The impact of buying environment characteristics of retail websites. The Service Industries Journal, 27(7), 865-880.
Kleijnen, M., Wetzels, M., & de Ruyter, K. (2004). Consumer acceptance of wireless finance. Journal of Financial Services Marketing, 8(3), 206-17.
Lederer, A.L., Maupin, D.J., Sena, M.P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29(3), 269-82. https://doi.org/10.1016/S0167-9236(00)00076-2
Lee, Y. (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.-K., Park, J.-H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590-1599.
Li S.Z., & Jain A. (2009). Convenience Sample. In Encyclopedia of Biometrics (pp. 11-65). Springer. https://doi.org/10.1007/978-0-387-73003-5_787
Lin, H.F. (2009). Examination of cognitive absorption influencing the intention to use a virtual Community. Behaviour & Information Technology, 28(5), 421-431.
Lin, H.F. (2013). The effect of absorptive capacity perceptions on the context-aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249-265.
Liu, C.L., & Forsythe, S. (2011). Examining drivers of online purchase intensity: moderating role of adoption duration in sustaining post-adoption online shopping. Journal of Retailing & Consumer Services, 18(1), 101-109.
Liu, Y.D., Taylor, P., & Shibli, S. (2009). Measuring customer service quality of English public sport facilities. International Journal of Sport Management and Marketing, 6(3), 229-252.
Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet research, 24(2), 134-159. https://doi.org/10.1108/IntR-05-2012-0100
Mallat, N., Rossi, M., Tuunainen, V.K., & Öörni, A. (2008). An empirical investigation of mobile ticketing service adoption in public transportation. Personal and Ubiquitous Computing volume, 12, 57-65. https://doi.org/10.1007/s00779-006-0126-z
Megan, S. (2020). What is Ubiquitous Learning? Practical Adult Insights. https://www.wisegeek.com/what-is-ubiquitous-learning.htm
Messmer, D.J. (1979). Repetition and attitudinal discrepancy effects on the affective response to television advertising. Journal of Business Research, 7(1), 75-93.
Milic N. (2008) Sample Size Determination. In Encyclopedia of Public Health (pp. 155-192). Springer. https://doi.org/10.1007/978-1-4020-5614-7_3090Dordrecht.
Oh, J.C., & Yoon, S.J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour & Information Technology, 33(7), 716-729. https://doi.org/10.1080/0144929X.2013.872187
Oliver, R.L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-9. https://doi.org/10.2307/3150499
Oliver, R.L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 18-30. https://doi.org/10.1086/209358
Pan, K.Y. (2019). Research on English ubiquitous learning in the mobile Internet environment. Campus English, 7, 154.
Perea, T., Benedict, M., Dellaert, G.C., & de Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 5(1), 102-21.
Ram, S., & Jung, H. (1991). How product usage influences customer satisfaction. Marketing Letters, 2, 403-11.
Rashotte, L.S. (2007). Social influence. In G. Ritzer (Ed.), Blackwell Encyclopedia of Sociology (pp. 4426-4429), Blackwell.
Rice, R.E., Grant, A.E., Schmitz, J., & Torobin, J. (1990). Individual and network influences on the adoption and perceived outcomes of electronic messaging. Social Networks, 12(1), 27-55.
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), 683-696.
Roest, H., & Pieters, R. (1997). The nomological net of perceived service quality. International Journal of Service Industry Management, 8(4), 336-51.
Rui-Hsin, K., & Lin, C.-T. (2018). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98-112. https://doi.org/10.1108/PIJPSM-10-2016-0157
Shao, Z. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOCs: The moderating effect of gender. Internet Research, 28(1), 232-250. https://doi.org/10.1108/IntR-11-2016-0335
Shen, D., Laffey, J., Lin, Y., & Huang, Z. (2006). Social influence for perceived usefulness and ease-of-use of course delivery systems. Journal of Interactive Online Learning, 5(3), 270-282.
Sheppard, B.H., Hartwick, J., & Warshaw, P.R. (1988). The theory of reasoned action: a meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325-343.
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
Šumak, B., Polancic, G., & Hericko, M. (2010). An empirical study of virtual learning environment adoption using UTAUT (pp. 17-22). 2nd International conference on Mobile, Hybrid, and On-Line Learning, (ELML 2010). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/eLmL.2010.11
SWJTU. (2019). SWJTU at a glance. https://en.swjtu.edu.cn/ABOUT/SWJTU_at_a_Glance.htm
Szymanski, D.M., & Henard, D.H. (2001). Customer satisfaction: a meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16-35.
Teo, H.H., Chan, H.C., Wei, K.K., & Zhang, Z. (2003). Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities. International Journal of Human-Computer Studies, 59(5), 671-697.
Venkatesh, V. (2000). Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342-365.
Wan‐Ling Hu, A., & Ming‐Hone Tsai, W. (2009). An empirical study of an enjoyment‐based response hierarchy model of watching MDTV on the move. Journal of Consumer Marketing, 26(2), 66-77. https://doi.org/10.1108/07363760910940438
Wang, K., & Lin, C.L. (2012). The adoption of mobile value-added services. Managing Service Quality, 22(2), 184-208.
Wang, Y.S., Wang, Y.M., Lin, H.H., & Tang, T.I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management, 14(5), 501-519.
Watjatrakul, B. (2013). Intention to use a free voluntary service. The effects of social influence, knowledge and perceptions. Journal of Systems and Information Technology, 15(2), 202-220.
Woodruff, R.B., & Schumann, D.W. (1993). Understanding value and satisfaction from the customer’s point of view. Survey of Business, 29(1), 33-42.
Wu, I.L., & Chen, J.L. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. International Journal of Human-Computer Studies, 62(6), 784-808.
Xue, W. (2007). From e-learning to U-Learning. China Education Informatization, 12, 7-9.
Yu, T.K., & Yu, T.Y. (2010). Modelling the factors that affect individuals’ utilisation of online learning systems: an empirical study combining the task technology fit model with the theory of planned behavior. British Journal of Educational Technology, 41(6), 1003-1017. https://doi.org/10.1111/j.1467-8535.2010.01054.x
Yu, X., Roy, S.K., Quazi, A., Nguyen, B. & Han, Y. (2017). Internet entrepreneurship and “the sharing of information” in an Internet-of-Things context: The role of interactivity, stickiness, e-satisfaction and word-of-mouth in online SMEs’ websites. Internet Research, 27(1), 74-96. https://doi.org/10.1108/IntR-02-2015-0060
Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for online learning systems: an intrinsic motivation perspective. Tsinghua Science and Technology, 13(3), 312-17.
Zhou, T. (2011). An empirical examination of users’ post-adoption behaviour of mobile services. Behaviour & Information Technology, 30(2), 241-250