The Assessment of Attitude and Behavioral Intention of E-Learning Among Art and Design Students of Chengdu Textile College in China
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Abstract
Purpose: With the pandemic outbreak worldwide, electronic learning has been increasing in higher education. It is critical to survey students’ willingness to utilize e-learning. Thus, the purpose of the research is to study the factors significantly impacting on perceived usefulness, attitude, and behavioral intention of e-learning in college education among art and design significant students at Chengdu Textile College (CTC) of Sichuan Province in China. Research design, data, and methodology: A quantitative approach was applied with 500 samples and distributed questionnaires to target art school students at Chengdu Textile College. The sampling methods for data collection involve judgmental, quota and convenience sampling. The Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were applied in statistical analysis, including model fits, validity and reliability of constructs, and hypothesis testing. Results: The results of the study confirm that the causal relationships among self-efficacy, perceived ease of use, social influence, and performance expectancy on perceived usefulness, attitude, and behavioral intention toward e-learning utilization. Conclusion: This study contributes to educators to put forward suggestions for college education management, curriculum designers, and researchers to get better acquainted with e-learning and make active implementation due to students’ higher perceived usefulness and active attitude and willingness of electronic learning utilization.
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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. (2005). Attitudes, Personality, and Behavior (1st ed.). Open University Press, Berkshire.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1st ed.) Prentice-Hall.
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110. https://doi.org/10.1016/j.techsoc.2018.06.007
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
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics' Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1). https://doi.org/10.14569/ijacsa.2014.050120
Ali, M., Raza, S. A., Qazi, W., & Puah, C.-H. (2018). Assessing e-learning system in higher education institutes: Evidence from structural equation modelling. Interactive Technology and Smart Education, 15(1), 59-78. Al-Okaily, M., Alqudah, H., Matar, A., Lutfi, A., & Taamneh, A. (2020). Dataset on the Acceptance of e-learning System among Universities Students' under the COVID-19 Pandemic Conditions. Data in Brief, 32, 106176. https://doi.org/10.1016/j.dib.2020.106176
Alokaily, M., Alqudah, H., matar, A., & Lutfi, A. (2020). Impact of COVID-19 Pandemic on Acceptance of e-learning System in Jordan: A Case of Transforming the Traditional Education Systems. Humanities & Social Sciences Reviews, 8(4), 840-851. https://doi.org/10.18510/hssr.2020.8483
Alotaibi, S. J., & Wald, M. (2013). Acceptance Theories and Models for Studying the Integrating Physical and Virtual Identity Access Management Systems. International Journal for e-Learning Security, 3(1), 226-235. https://doi.org/10.20533/ijels.2046.4568.2013.0029
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562.
Bagozzi, R. P., Baumgartner, H., & Yi, Y. (1992). State versus Action Orientation and the Theory of Reasoned Action: An Application to Coupon Usage. Journal of Consumer Research, 18(4), 505. https://doi.org/10.1086/209277
Bagozzi, R. P., & Lee, K.-H. (2002). Multiple Routes for Social Influence: The Role of Compliance, Internalization, and Social Identity. Social Psychology Quarterly, 65(3), 226. https://doi.org/10.2307/3090121
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/bf02723327
Bailey, D. R., Almusharraf, N., & Almusharraf, A. (2022). Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model. Education and Information Technologies, 27(6), 7679-7698. https://doi.org/10.1007/s10639-022-10949-1
Bajaj, A., & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information & Management, 33(4), 213-224. https://doi.org/10.1016/s0378-7206(98)00026-3
Bakar, A., Razak, F., & Abdullah, W. (2013). Assessing the effects of UTAUT and self-determination predictor on students’ continuance intention to use student portal. World Applied Sciences Journal, 21(10), 1484-1489.
Bandura, A. (1978). Social Learning Theory of Aggression. Journal of Communication, 28(3), 12-29. https://doi.org/10.1111/j.1460-2466.1978.tb01621.x
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147. https://doi.org/10.1037/0003-066x.37.2.122
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory (1st ed.). Prentice-Hall, Englewood Cliffs.
Bandura, A. (1997). Self-Efficacy: The Exercise of Control, (1st ed.). Freeman, New York.
Bandura, A. (2009). Social cognitive theory goes global. The Psychologist, 22(6), 504–506.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(4), 411-420. https://doi.org/10.1109/3468.852435
Boddy, C. (2012). The Nominal Group Technique: an aid to Brainstorming ideas in research. Qualitative Market Research: An International Journal, 15(1), 6-18. https://doi.org/10.1108/13522751211191964
Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: integrating technology adoption and collaboration research, Journal of Management Information Systems, 27(2), 9-54.
Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research, 21(2), 230-258. https://doi.org/10.1177/0049124192021002005
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.), Routledge Taylor & Francis Group.
Chang, S.-S., Lou, S.-J., Cheng, S.-R., & Lin, C.-L. (2015). Exploration of usage behavioral model construction for university library electronic resources. The Electronic Library, 33(2), 292-307. https://doi.org/10.1108/el-10-2013-0195
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003
Chu, A., & Mastel-Smith, B. (2010). The Outcomes of Anxiety, Confidence, and Self-efficacy With Internet Health Information Retrieval in Older Adults: A Pilot Study. CIN: Computers, Informatics, Nursing, 28(4), 222-228. https://doi.org/10.1097/ncn.0b013e3181e1e271
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study. MIS Quarterly, 23(2), 145. https://doi.org/10.2307/249749
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189. https://doi.org/10.2307/249688
Cooper, D., & Schindler, P. (2011). Business research methods (11th ed.). McGraw Hill.
Cruz-Cárdenas, J., Zabelina, E., Deyneka, O., Guadalupe-Lanas, J., & Velín-Fárez, M. (2019). Role of demographic factors, attitudes toward technology, and cultural values in the prediction of technology-based consumer behaviors: A study in developing and emerging countries. Technological Forecasting and Social Change, 149, 119768. https://doi.org/10.1016/j.techfore.2019.119768
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
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
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
Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis's Perceived Usefulness and Ease-of-use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis. Decision Sciences, 29(4), 839-869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x
Farah, M. F. (2014). An Expectancy-Value Approach to the Study of Beliefs Underlying Consumer Boycott Intention. International Journal of Business and Management, 9(10). https://doi.org/10.5539/ijbm.v9n10p101
Fogg, B. (2009). A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology - Persuasive '09 https://doi.org/10.1145/1541948.1541999
Fokides, E. (2017). Greek Pre-service Teachers' Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1). https://doi.org/10.30935/cedtech/6187
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
Gilbert, A. (2015). An exploration of the use of and the attitudes toward technology in first-year instrumental music. Student Research, Creative Activity, and Performance – School of Music, 4(1), 174-186.
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis: A Global Perspective (7th ed.). Pearson Education.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, L. R. (2006). Multivariant Data Analysis (6th ed.). Pearson International Edition.
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101-123. https://doi.org/10.1007/s11423-016-9465-2
Hartshorne, R., & Ajjan, H. (2009). Examining student decisions to adopt Web 2.0 technologies: theory and empirical tests. Journal of Computing in Higher Education, 21(3), 183–198.
Hsiao, C.-H., & Tang, K.-Y. (2015). Investigating factors affecting the acceptance of self-service technology in libraries: The moderating effect of gender. Library Hi Tech, 33(1), 114-133. https://doi.org/10.1108/lht-09-2014-008
Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological Bulletin, 77(5), 361-372. https://doi.org/10.1037/h0032590
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. https://doi.org/10.1016/j.compedu.2012.10.001
Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277-283. https://doi.org/10.1016/j.iheduc.2010.08.002
Liaw, S.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
Limongelli, C., Sciarrone, F., Temperini, M., & Vaste, G. (2009). Virtual Cultural Tour Personalization by Means of an Adaptive E-Learning System: A Case Study. Visioning and Engineering the Knowledge Society. A Web Science Perspective, 40-49. https://doi.org/10.1007/978-3-642-04754-1_5
Lin, K.-M., Chen, N.-S., & Fang, K. (2011). Understanding e-learning continuance intention: a negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77-89. https://doi.org/10.1080/01449291003752948
Lu, H.-P., Hsu, C.-L., & Hsu, H.-Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106-120. https://doi.org/10.1108/09685220510589299
Lucas, H. C., & Spitler, V. K. (1999). Technology Use and Performance: A Field Study of Broker Workstations. Decision Sciences, 30(2), 291-311. https://doi.org/10.1111/j.1540-5915.1999.tb01611.x
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
Maio, G., & Haddock, G. (2009). The Psychology of Attitudes and Attitude Change (1st ed.). SAGE Publications.
Malhotra, N. K., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of marketing research: an applied orientation (2nd ed.). Pearson Education Frenchs Forest
Masrom, M. (2007). Technology acceptance model and E-learning (6th ed.). Conference on Education.
Mikhaylov, N. S., & Fierro, I. (2015). Social capital and global mindset. Journal of International Education in Business, 8(1), 59-75. https://doi.org/10.1108/jieb-09-2014-0018
Mulenga, E. M., & Marbán, J. M. (2020). Is COVID-19 the Gateway for Digital Learning in Mathematics Education? Contemporary Educational Technology, 12(2), ep269. https://doi.org/10.30935/cedtech/7949
Nagy, J. T. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. The International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2886
Ngampornchai, A., & Adams, J. (2016). Students' acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13(1). https://doi.org/10.1186/s41239-016-0034-x
Pintrich, P. R. (2003). A Motivational Science Perspective on the Role of Student Motivation in Learning and Teaching Contexts. Journal of Educational Psychology, 95(4), 667-686. https://doi.org/10.1037/0022-0663.95.4.667
Poon, W.-C. (2007). Users' adoption of e-banking services: the Malaysian perspective. Journal of Business & Industrial Marketing, 23(1), 59-69. https://doi.org/10.1108/08858620810841498
Rogers, E. M. (1993). The Diffusion of Innovations Model. Diffusion and Use of Geographic Information Technologies, 9-24. https://doi.org/10.1007/978-94-011-1771-5_2
Roscoe, J. (1975). Fundamental Research Statistics for the Behavioral Sciences. (1st ed.), Holt Rinehart and Winston Publishers.
Russo, A., Watkins, J., & Groundwater‐Smith, S. (2009). The impact of social media on informal learning in museums. Educational Media International, 46(2), 153-166. https://doi.org/10.1080/09523980902933532
Sahin, F., Dogan, E., Okur, M. R., & Şahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 27(6), 7827-7849. https://doi.org/10.1007/s10639-022-10930-y
Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216. https://doi.org/10.1016/j.elerap.2009.07.005
Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2016). Mobile Shopper Marketing: Key Issues, Current Insights, and Future Research Avenues. Journal of Interactive Marketing, 34, 37-48. https://doi.org/10.1016/j.intmar.2016.03.002
Sharma, S. K., Joshi, A., & Sharma, H. (2016). A multi-analytical approach to predict the Facebook usage in higher education. Computers in Human Behavior, 55, 340-353. https://doi.org/10.1016/j.chb.2015.09.020
Shin, D.-H. (2012). 3DTV as a social platform for communication and interaction. Information Technology & People, 25(1), 55-80. https://doi.org/10.1108/09593841211204344
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, Leading - Edge Psychological Tests and Testing Research. 2(3), 27-50.
Sripalawat, J., Thongmak, M., & Ngarmyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3),
Tan, P. J. B. (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., Hone, K., & Liu, X. (2014). The effects of individual differences 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
Tarhini, A., Masa’deh, R. e., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students' adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/jieb-09-2016-0032
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
Tsu Wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K.-B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370-388. https://doi.org/10.1108/02635570910939399
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 438-502.
Venkatesh, V., & Davis, F. D. (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., 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.
Wang, L.-Y.-K., 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. https://doi.org/10.1016/j.heliyon.2019.e01788
Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4), 245-258. https://doi.org/10.1046/j.1360-3736.2003.00184.x
Wu, M.-Y., Chou, H.-P., Weng, Y.-C., & Huang, Y.-H. (2011). TAM2-based study of website user behavior-using web 2.0 websites as an example. WSEAS Transactions on Business and Economics, 8(4), 133-151.
Yueh, H.-P., Huang, J.-Y., & Chang, C. (2015). Exploring factors affecting students' continued Wiki use for individual and collaborative learning: An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1). https://doi.org/10.14742/ajet.170.
Zhao, T. (2021). Construction and application suggestions of evaluation model of digital learning tools in artificial intelligence era. China Audio-visual Education. 8, 85-86.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013
Zulherman, Z., Zain, F. M., Napitupulu, D., & Sailin, S. N. (2021). Analyzing Indonesian Students’ Google Classroom Acceptance during COVID-19 Outbreak: Applying an Extended Unified Theory of Acceptance and Use of Technology Model. European Journal of Educational Research, 10(4), 1697-1710.