An Assessment on Behavioral Intention to Use Chaoxing Learning Platform in The Post-Pandemic Among Third-Year Undergraduates in Anhui, China
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Abstract
Purpose: This study investigates the factors that impact assessment on behavioral intention to use Chaoxing Learning Platform in the post-pandemic among third-year undergraduates in Anhui, which are determined by perceived ease of use, perceived usefulness, attitude, behavior intention, facilitating conditions, self-efficacy and subjective norm. Research design, data, and methodology: The population are 500 third-year undergraduate students who have at least one year experience, using Chaoxing Learning Platform at three universities in Anhui, China, including Anhui University of Finance and Economics, Bengbu University, and Tongling University. Confirmatory factor analysis and structural equation modeling are statistical techniques used to confirm validity, reliability, model fit and hypotheses testing. Results: The results show the supported relationship of perceived usefulness and behavioral intention. Facilitating conditions significantly impact perceived usefulness and behavioral intention. Furthermore, subjective norms significantly impact attitude and behavioral intention. There are non-supported relationships between perceived ease of use, perceived usefulness, attitude, self-efficacy and behavioral intention. Conclusions: The results of this study show that educational institutions can enhance the adoption and usage of the Chaoxing Learning Platform among third-year undergraduates in Anhui, China. This will ultimately improve students' overall learning experience and support their academic success in the post-pandemic era.
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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., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1st ed.). Prentice-Hall, Englewood Cliffs.
Ajzen, I., & Fishbein, M. (2005). The Influence of Attitudes on Behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Lawrence Erlbaum Associates Publishers.
Akbar, F. (2013). What affects students’ acceptance and use of technology. Dietrich College of Humanities and Social Sciences, 15(1), 1-10.
Alkhadim, M., Gidado, K., & Painting, N. (2019). Perceived crowd safety in large space buildings: The confirmatory factor analysis of perceived risk variables. Journal of Engineering, Project, and Production Management, 8(1), 22-39. https://doi.org/10.32738/jeppm.201801.0004
Alkhanak, S. A. A. K., & Azmi, I. A. G. (2011). University Students Information Technology Experience and Its Role towards e-Learning Orientation. New Educational Review, 24, 231-242.
Al-Rafee, S., & Cronan, T. (2006). Digital Piracy: Factors that Influence Attitude Toward Behavior. Journal of Business Ethics, 63(3), 237-259. https://doi.org/10.1007/s10551-005-1902-9
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory (1st ed.). Prentice-Hall, Inc.
Bandura, A. (1997). Self-efficacy: The exercise of control (1st ed.). W H Freeman/Times Books/ Henry Holt & Co.
Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107(2), 238-246. http://dx.doi.org/10.1037/0033-2909.107.2.238
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Booker, B. (2021). A psychological perspective of agency and structure within critical realist theory: a specific application to the construct of self-efficacy. Journal of Critical Realism, 20(3), 239-256. https://doi.org/10.1080/14767430.2021.1958281
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
Buabeng-Andoh, C., & Baah, C. (2020). Pre-Service Teachers' Intention to Use Learning Management System: An Integration of UTAUT and TAM. Interactive Technology and Smart Education, 17(4), 455-474. https://doi.org/10.1108/itse-02-2020-0028
Chang, E. C. (1998). Hope, Problem-Solving Ability, and Coping in a College Student Population: Some Implications for Theory and Practice. Journal of Clinical Psychology, 54, 953-962.
Chaoxing Corporation. (2021). AnHui ChaoXing New Material Technology Co. Ltd https://www.chinaplasonline.com/eMarketplace/exhibitorinfo/eng/supplier_info/AnHui__ChaoXing_New__Material___Technology__CoLtd?compid=285793&src=11
Chen, T., Peng, L., Jing, B., Wu, C., Yang, J., & Cong, G. (2020). The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China. Sustainability, 12(18), 7329. https://doi.org/10.3390/su12187329
Cheng, T.-M., Wu, H., Wang, J., & Wu, M. R. (2019). Community Participation as a mediating factor on residents’ attitudes towards sustainable tourism development and their personal environmentally responsible behavior. Current Issues in Tourism, 22(14), 1764-1782. https://doi.org/10.1080/13683500.2017.1405383
Chowdhury, S., & Endres, M. (2005). Gender Difference and the Formation of Entrepreneurial Self-Efficacy. Research gate, 1, 1-10.
Clark-Carter, D. (2010). Quantitative Psychological Research: The Complete Student’s Companion (1st ed.). Taylor & Francis.
Compeau, D., & Higgins, C. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19, 189-211. https://doi.org/10.2307/249688
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. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes (1st ed.). Harcourt Brace Jovanovich College Publishers.
Fan, X., Duangekanong, S., & Xu, M. (2021). 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
Festinger, L. (1957). An Introduction to the Theory of Dissonance. In L. Festinger (Ed.), A Theory of Cognitive Dissonance (pp. 1-30). Stanford, CA: Stanford University Press.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Addison-Wesley Publishing Co, Inc.
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
Gallagher, S., & Palmer, J. (2020, September 29). The Pandemic Pushed Universities Online. The Change Was Long Overdue. Harvard Business Review. https://hbr.org/2020/09/the-pandemic-pushed-universities-online-the-change-was-long-overdue
Gao, L. L., & Bai, X. S. (2014). A Unified Perspective on the Factors Influencing Consumer Acceptance of Internet of Things Technology. Asia Pacific Journal of Marketing and Logistics, 26, 211-231. http://dx.doi.org/10.1108/APJML-06-2013-0061
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS quarterly, 27, 51-90. https://doi.org/10.2307/30036519
Gefen, D., & Straub, D. (1997). Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model. MIS Quarterly, 21(4), 389-400. https://doi.org/10.2307/249720
Gefen, D., & Straub, D. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption a Study of E-Commerce Adoption. Journal of the Association for Information Systems, 1(1), 1-30. https://doi.org/10.17705/1jais.00008
Glen, S. (2015). Snowball Sampling: Definition, Advantages and Disadvantages. http://www.statisticshowto.com/snowball-sampling/
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall, Upper Saddle River.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson, New York.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46, 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
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, 853-868.
http://dx.doi.org/10.1016/j.im.2003.08.014
Huang, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. International Journal of Mobile Communications, 5(4), 409-422. https://doi.org/10.1504/ijmc.2007.012788
Huang, Y.-M., Huang, Y.-M., & Lin, Y. T. (2012). A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Computers & Education. 58(1), 273-282. https://doi.org/10.1016/j.compedu.2011.08.008
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. (1997). Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly, 21, 279-305. http://dx.doi.org/10.2307/249498
Jackson, S. (2006). Heterosexuality, Sexuality and Gender: Re-thinking the Intersections. In D. Richardson, M. Casey, & J. McLaughlin (Eds.), Feminist and Queer Intersections: Sexualities, Cultures and Identities (pp. 38-39). Palgrave Macmillan.
Jolaee, A., Nor, K., Khani, N., & Mdyusoff, R. (2014). Factors affecting knowledge sharing intention among academic staff. International Journal of Educational Management, 28(4), 413-431. https://doi.org/10.1108/ijem-03-2013-0041
Joreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/BF02289343
Kankanhalli, A., Tan, B., & Wei, K. K. (2005). Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation. MIS Quarterly, 29(1), 113-143. https://doi.org/10.2307/25148670
Khan, R. A., & Qudrat-Ullah, H. (2021). Adoption of LMS in Higher Educational Institutions of the Middle East (1st ed.). Springer.
Lee, M. J., & McLoughlin, C. (2010). Beyond Distance and Time Constraints: Applying Social Networking Tools and Web 2.0 Approaches to Distance Learning. In G. Veletsianos (Ed.), Emerging Technologies in Distance Education (pp. 61-87). Athabasca University Press.
Lim, H., & Dubinsky, A. (2005). The theory of planned behavior in E-Commerce: Making a case for interdependencies between salient beliefs. Psychology and Marketing, 22(10), 833- 855. https://doi.org/10.1002/mar.20086
Lin, B. B. (2011). Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change. BioScience, 61, 183-193. http://dx.doi.org/10.1525/bio.2011.61.3.4
Lin, C. L., Jin, Y. Q., Zhao, Q., Yu, S.-W., & Su, Y.-S. (2021). Factors Influence Students’ Switching Behavior to Online Learning under COVID-19 Pandemic: A Push–Pull–Mooring Model Perspective. The Asia-Pacific Education Researcher, 30, 229–245. https://doi.org/10.1007/s40299-021-00570-0
Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52, 599–607.
Lourenco, F., & Jayawarna, D. (2011). Enterprise Education: The Effect of Creativity on Training Outcomes. International Journal of Entrepreneurial Behaviour & Research, 17(3), 224-244. https://doi.org/10.1108/13552551111130691
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
Oertzen, A.-S., & Odekerken-Schröder, G. (2019). Achieving continued usage in online banking: a post-adoption study. International Journal of Bank Marketing, 37(6), 1394-1418. https://doi.org/10.1108/IJBM-09-2018-0239
O’Leary, Z. (2017). The Essential Guide to Doing Your Research Project (1st ed.). SAGE Publications Ltd., London.
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(6), 795-804. https://doi.org/10.1016/j.im.2003.08.012
Pavlou, P. A., & Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115-143. https://doi.org/10.2307/25148720
Pedroso, C. B., Silva, A. L. D., & Tate, W. L. (2016). Sales and Operations Planning (S&OP): Insights from a multi-case study of Brazilian Organizations. International Journal of Production Economics, 182, 213-229. https://doi.org/10.1016/j.ijpe.2016.08.035
Qin, M. (2020). An Antioxidant Enzyme Therapeutic for COVID-19. Willey online library, 32(43), 1-43. https://doi.org/10.1101/2020.07.15.205211
Robey, D., & Dana, F. (1982). User Involvement in Information System Development: A Conflict Model and Empirical Test. Institute for Operations Research and the Management Sciences, 28(1), 73-85. https://doi.org/10.1287/mnsc.28.1.73
Salloum, S., & Shaalan, K. (2019). Factors Affecting Students’ Acceptance of E-Learning System in Higher Education Using UTAUT and Structural Equation Modeling Approaches (1st ed.). Proceedings of the International Conference on Advanced Intelligent Systems and Informatics.
Sam, H., & Othman, A., & Nordin, Z. (2005). Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas. Educational Technology & Society. 8, 205-219.
Sanchez, R. A., Duarte, H. A., & Garcia, O. M. (2013). E-Learning and the University of Huelva: A Study of WebCT and the Technological Acceptance Model. Campus-Wide Information Systems, 30(2), 135-160.
Sharma, S., Kaufman, P., & Raman, P. (2005). The Role of Relational Information Processes and Technology Use in Customer Relationship Management. Journal of Marketing, 69, 177-192. https://doi.org/10.1509/jmkg.2005.69.4.177
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, (pp. 27-50). Nova Science Publishers.
Soper, D. S. (2023). A-priori Sample Size Calculator for Structural Equation Models [Software]. www.danielsoper.com/statcalc/default.aspx
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., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 1-16. https://doi.org/10.1080/10494820.2011.641674
Tucker, N. R., Chaffin, M., Fleming, S. J., & Hall, A. W. (2020). Transcriptional and Cellular Diversity of the Human Heart. Circulation, 142(5), 466-482. https://doi.org/10.1161/circulationaha.119.04540
Ukut, I., & Krairit, D. (2019). Justifying students’ performance, A comparative study of ICT students’ and instructors’ perspectives. Interactive Technology and Smart Education, 16(1), 18-35.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
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., 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
Venkatesh, V., & Thong, J., & 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. https://doi.org/10.2307/41410412
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43, 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002
Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending Classes Without Stopping Learning: China’s Education Emergency Management Policy in the COVID-19 Outbreak. Journal of Risk and Financial Management, 13(3), 55. http://dx.doi.org/10.3390/jrfm13030055