Measuring Fourth-Year Undergraduates’ Behavioral Intention to Use Chaoxing Learning Platform in The Post-Pandemic in Anhui, China
DOI:
https://doi.org/10.14456/shserj.2024.36Keywords:
Attitude, Behavior Intention, Facilitating Conditions, Self-efficacyอ, Post-PandemicAbstract
Purpose: This study investigates the factors that measuring undergraduates’ behavioral intention to use Chaoxing learning platform in the post-pandemic 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 study's target population comprises 500 fourth-year undergraduate students who have a minimum of one year of experience using the Chaoxing Learning Platform. These students are drawn from three universities in Anhui, China, namely Anhui University of Finance and Economics, Bengbu University, and Tongling University. To assess validity, reliability, model fit, and test hypotheses, the researchers utilized statistical techniques such as Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). Results: The results show that perceived usefulness is significantly impacted by perceived ease of use and facilitating conditions. Behavioral intention is significantly impacted by perceived usefulness, self-efficacy and subjective norm. Attitude is significantly impacted by self-efficacy and subjective norm. There are non-supported relationships between perceived usefulness, perceived ease of use attitude, facilitating conditions and behavioral intention. Conclusions: The recommendations focus on improving user experience, addressing concerns, leveraging social influence, and providing ongoing support, ultimately leading to increased intention and engagement with the platform.
References
Abramson, J., Dawson, M., & Stevens, J. (2015). An Examination of the Prior Use of E-Learning within an Extended Technology Acceptance Model and the Factors That Influence the Behavioral Intention of Users to Use M-Learning. Sage Journals, 2(2), 1-10.
Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. Journal of Applied Social Psychology, 32, 665-683.
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
Anderson, J., & Gerbing, D. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103, 411-423. http://dx.doi.org/10.1037/0033-2909.103.3.411
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499. https://doi.org/10.1348/014466601164939
Bajaj, A., & Nidumolu, S. R. (1998). A Feedback Model to Understand Information System Usage. Information & Management, 33, 213-224. https://doi.org/10.1016/S0378-7206(98)00026-3
Bandura, A. (1977). Social learning theory (1st ed.). Prentice Hall.
Banerjee, S., & Dey, D. (2013). Ecosystem & livelihood support: The story of East Kolkata wetlands. Environment and Urbanization Asia, 4(2) 325-337.
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
Booker, J. F., Howitt, R. E., Michelsen, A. M., & Young, R. A. (2012). Economics and the Modeling of Water Resources and Policies. Natural Resource Modeling Journal, 25(1), 168-218. https://doi.org/10.1111/j.1939-7445.2011.00105.x
Bu, X. (2019). The Blended Teaching Mode Based on Chaoxing Learning APP —Taking Advanced English Course as an Example. Advances in Social Science, Education and Humanities Research, 357, 247-249.
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
Byrne, B. M. (2010). Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming (2nd ed.). Taylor and Francis Group.
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.
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
Chow, M., Ling, C., Jian, Y., & Wong, P. W. (2012). Conceptual Paper: Factors Affecting the Demand of Smartphone among Young Adult. International Journal on Management, Economics and Finance, 37, 245-253.
Clark-Carter, D. (2010). Quantitative Psychological Research: The Complete Student’s Companion (1st ed.). Taylor & Francis.
Compeau, D. R., & Huff, S. (1999). Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study. MIS Quarterly, 23(2), 145-158.
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
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research (1st ed.). Addison-Wesley Publishing.
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
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
Glen, S. (2015). Snowball Sampling: Definition, Advantages and Disadvantages.
http://www.statisticshowto.com/snowball-sampling/
Guo, L., Huang, J., & Zhang, Y. (2019). Education Development in China: Education Return, Quality, and Equity. Sustainability, 11(13), 3750. http://dx.doi.org/10.3390/su11133750
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
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of Efficacy Expectations in Predicting the Decision to Use Advanced Technologies: A Case of Computers. Journal of Applied Psychology, 72, 307-318.
https://doi.org/10.1037/0021-9010.72.2.307
Hsiao, C. H., & Tang, K. Y. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption: Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163.
Hsu, M., Chang, C., Chu, K., & Lee, Y. (2014). Determinants of Repurchase Intention in Online Group-Buying: The Perspectives of DeLone & McLean IS Success Model and Trust. Computers in Human Behavior, 36, 234-245.
https://doi.org/10.1016/j.chb.2014.03.065
Hu, T., Zhang, D., & Wang, J. (2015). A Meta-Analysis of the Trait Resilience and Mental Health. Personality and Individual Differences, 76, 18-27. https://doi.org/10.1016/j.paid.2014.11.039
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, Z., & Cai, L. A. (2015). Modeling Consumer-Based Brand Equity for Multinational Hotel Brands: When Hosts Become Guests. Tourism Management, 46, 431-443. https://doi.org/10.1016/j.tourman.2014.07.013
IiMedia Research. (2020). CMDAS China Mobile Internet Big Data Monitoring System.
https://www.iimedia.com.cn/en/monitor.jsp
Jackson, S. (2006). Heterosexuality, Sexuality and Gender: Re-thinking the Intersections. Palgrave.
Jaiyeoba, O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: apathy to adoption. International Journal of Information and Learning Technology, 36, 1-10.
Lai, C. K., Hoffman, K. M., & Nosek, B. A. (2013). Reducing implicit prejudice. Social and Personality Psychology Compass, 7(5), 315-330. https://doi.org/10.1111/spc3.12023
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 Ed ucation (Chap. 4, pp. 61-87). Edmonton: Athabasca University Press.
Lee, Y.-C. (2006). An Empirical Investigation into Factors Influencing the Adoption of an e-Learning System. Online Information Review, 30, 517-541.https://doi.org/10.1108/14684520610706406
Legris, P., Ingham, J., & Collerette, P. (2003). Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model. Information & Management, 40, 191-204. http://dx.doi.org/10.1016/S0378-7206(01)00143-4
Liao, Y., & Soltani, E. (2010). Training interventions: fulfilling managerial ends or proliferating invaluable means for employees? Some evidence from Iran. European Business Review, 22(2), 128-152.
https://doi.org/10.1108/09555341011023498
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
Maher, A. A., & Mady, S. (2010). Animosity, subjective norms, and anticipated emotions during an international crisis. International Marketing Review, 27(6), 630-651. https://doi.org/10.1108/02651331011088263
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web Context. Information and Management, 38, 217-230. http://dx.doi.org/10.1016/S0378-7206(00)00061-6
Nicol, S., & Bice, S. (2022). Negotiating transboundary crises in higher education: Tsinghua University’s shift to online learning during the COVID-19 pandemic. International Journal of Chinese Education, 11(1), 1-10.
https://doi.org/10.1177/22125868211073147
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
O’Leary, Z. (2017). The Essential Guide to Doing Your Research Project (1st ed.). SAGE.
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
Okyere-Kwakye, E., & Nor, K. M. (2020). Examining individual intention to share knowledge with people from other tribes. Knowledge Management & E-Learning, 12(3), 315-343.
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17, 460-469.http://dx.doi.org/10.2307/3150499
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
Park, J. H., Yoon, S. H., Kim, S. W., Shin, D., Jin, S. K., Yang, B. S., & Cho, Y. M. (2012). Hematological and serum biochemical parameters of Korean native goats fed with spent mushroom substrate. Asian J. Anim. Vet. Adv., 7(11), 1139-1147.
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
Peterson, C., & Park, N. (2009). Classifying and Measuring Strengths of Character. In S. J. Lopez, & C. R. Snyder (Eds.), Oxford Handbook of Positive Psychology (pp. 25-33). Oxford University Press.
Ren, L. (2000). A Multivariate GARCH-M Model for Exchange Rates in the US, Germany, and Japan (1st ed.). Classification and Information Processing at the Turn of the Millennium.
Salloum, S., & Shaalan, K. (2019). Factors Affecting Students’ Acceptance of E-Learning System in Higher Education Using UTAUT and Structural Equation Modeling Approaches. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 469-480.
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, B., Kumar, A., & Sarin, J. (2016). Academic Stress, Anxiety, Remedial Measures Adopted and Its Satisfaction among Medical Students: A Systematic Review. International Journal of Health Sciences and Research, 6, 368-376.
https://doi.org/10.5958/2249-7137.2017.00099.4
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
Shi, X., & Chen, Z., Wang, H., Yeung, D.-Y., Wong, W. K., & WOO, W.-C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. PubMed, 1(2), 1-10.
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. www.danielsoper.com/statcalc/default.aspx
Šumak, B., Hericko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior 27(6), 2067-2077.
Tarhini, A., Hassouna, M., Abbasi, M., & Orozco, J. (2015). Towards the Acceptance of RSS to Support Learning: An empirical study to validate the Technology Acceptance Model in Lebanon. The Electronic Journal of e-Learning, 13(1), 30-41.
Tarhini, A., Masa'deh, R., Al-Busaidi, K. A., Bany Mohammed, A., & Maqableh, M. (2017). Factors influencing students' adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 65(3), 743-763.
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. (2006). Attitudes toward computers: A study of post-secondary students in Singapore. Interactive Learning Environments, 14(1), 17-24. https://doi.org/10.1080/10494820600616406
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006
Teo, T. (2010). Ontology and Scientific Explanation: Pluralism as an a Priori Condition of Psychology. New Ideas in Psychology, 28, 235-243. https://doi.org/10.1016/j.newideapsych.2009.09.017
Teo, T. (2011). Theory and empirical research: Can scientific ideas be violent? In P. Stenner, J. Cromby, J. Motzkau, J. Yen, & Y. Haosheng (Eds.), Theoretical psychology: Global transformations and challenges (pp. 239-246). Captus.
Teo, T., & Lee, C. B. (2010). Explaining the Intention to Use Technology among Student Teachers: An Application of the Theory of Planned Behavior (TPB). Campus-Wide Information Systems, 27(2), 60-67.
https://doi.org/10.1108/10650741011033035
Teo, T., Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of pre-service teachers' technology acceptance in Turkey: A structural equation modeling approach. Asia-Pacific Education Researcher, 21(1), 191-202.
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.
Van der Heijden, H. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41-48. https://doi.org/10.1057/palgrave.ejis.3000445
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
Waldman, I. D. (2003). A developmental propensity model of the origins of conduct problems during childhood and adolescence. In B. B. Lahey, T. E. Moffitt, & A. Caspi (Eds.), Causes of conduct disorder and juvenile delinquency (pp. 76-117). The Guilford Press.
Wang, Z., & Duangekanong, S. (2022). Understanding Freshmen’s Persistence in A Chinese Private Higher Education. AU-GSB E-JOURNAL, 15(2), 126-138. https://doi.org/10.14456/augsbejr.2022.78
Williams, C., Kaylene, C., & Williams, C. C. (2011). Five Key Ingredients for Improving Student Motivation. Research in Higher Education Journal, 12, 104-122.
Wong, S. L., & Teo, T. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 315-360.
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, Y., & Espinoza, S. (1998). Relationships among computer self-efficacy, attitudes toward computers, and desirability of learning computing skills. Journal of Research on Technology in education, 30(4), 420-436.
https://doi.org/10.1080/08886504.1998.10782236
Zolait, A. H. S. (2014). The nature and components of perceived behavioural control as an element of theory of planned behaviour": Erratum. Behaviour & Information Technology, 33(3), 302-303.
