Understanding Behavioral Intentions and Use Behavior of Students Towards Online Learning Systems in Jiangxi, China
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Purpose: This research paper aims to investigate the factors impacting behavioral intentions and use behavior of students in vocational collages towards online learning systems in Jiangxi, China. The conceptual framework proposed a causal relationship among perceived usefulness, perceived ease of use, attitude, perceived behavioral control, social influence, behavioral intentions, and use behavior. Research design, data, and methodology: The researcher used a quantitative method (n=500) to distribute questionnaires to students. The nonprobability sampling includes judgmental sampling in selecting five vocational colleges, quota sampling in proportion of sample size, and convenience sampling in collecting data and distributing surveys by the online platform. The Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) were used for the data analysis, including model fit, reliability, and validity of the constructs. Results: The results explicated that perceived usefulness, perceived ease of use, Attitude, perceived behavioral control, and social influence have a significant impact on behavioral intentions Furthermore, behavioral intentions significantly impact use behavior. Conclusions: Six hypotheses were proven to fulfill research objectives. Hence, future research can expand educational technology and impact school management performance, manifested as new products, services, or processes created by new technological behaviors, providing schools with a better digital environment and more convenient management processes.
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References
Aaker, J. L., & Maheswaran, D. (1997). The effect of cultural orientation on persuasion. Journal of Consumer Research, 24(3), 315–328. https://doi.org/10.1086/209513
Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Sciences, 30, 361-391.http://dx.doi.org/10.1111/j.1540-5915.1999.tb01614.x
Agrebi, S., & Jallais, J. (2014). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22(6), 16-23. https://doi.org/10.1016/j.jretconser.2014.09.003
Ajzen, I. (1988). Attitudes, Personality, and Behavior (1st ed.). Dorsey.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
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.
http://dx.doi.org/10.1111/j.1559-1816.2002.tb00236.x
Ajzen, I. (2012). The Theory of Planned Behaviour, In P. A. M. Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of Theories of Social Psychology (pp. 438-459). Sage.
https://doi.org/10.4135/9781446249215.n22
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior (1st ed.). Prentice- Hall.
Akroush, M., & Al-Debei, M. (2015). An integrated model of factors affecting consumer attitudes towards online shopping. Business Process Management Journal, 21(6), 1353-1376. https://doi.org/10.1108/bpmj-02-2015-0022
Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54. https://doi.org/10.1016/j.dss.2012.12.032
Alexandersson, M., & Limberg, L. (2012). Changing Conditions for Information Use and Learning in Swedish Schools: A Synthesis of Research. HumanIT, 11(2), 131-154.
Ali, S., Ahmad Ganai, B., Kamili, A. N., & Ali Bhat, A. (2018). Pathogenesis-related proteins and peptides as promising tools for engineering plants with multiple stress tolerance. Microbiological Research, 212, 29-37. https://doi.org/10.1016/j.micres.2018.04.008
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Adoption of Management Information Systems in Context of Yemeni Organizations: A Structural Equation Modeling Approach. Journal of Digital Information Management, 13(6), 429-444.
Al-Rahmi, W., Alias, A. P., Norma, D. R., Othman, M., Marín, V., & Tur, G. (2018). A model of factors affecting learning performance through the use of social media in Malaysian higher education. Computers & Education, 121(1), 59-72. https://doi.org/10.1016/j.compedu.2018.02.010
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
Asiegbu Ikechukwu, F., Powei Daubry, M., & Iruka Chijindu, H. (2012). Consumer Attitude: Some Reflections on Its Concept, Trilogy, Relationship with Consumer Behavior, and Marketing Implications. European Journal of Business and Management, 4(3), 1-10.
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Bag, S., & Omrane, A. (2020). Corporate Social Responsibility and Its Overall Effects on Financial Performance: Empirical Evidence from Indian Companies. Journal of African Business, 23(1), 264-280. https://doi.org/10.1080/15228916.2020.1826884
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
Belanche, D., Casaló Ariño, L., & Guinalíu, M. (2012). Website usability, consumer satisfaction and the intention to use a website: The moderating effect of perceived risk. Journal of Retailing and Consumer Services, 19(1), 124-132. https://doi.org/10.1016/j.jretconser.2011.11.001
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
Bhatt, K. (2021). Adoption of online streaming services: moderating role of personality traits. International Journal of Retail & Distribution Management, 50(4), 437-457. https://doi.org/10.1108/ijrdm-08-2020-0310
Bidin, Z., & Al-Rahamneh, N.-M. (2020). The Effect of Tax Fairness, Peer Influence, and Moral Obligation on Sales Tax Evasion among Jordanian SMEs. J. Risk Financial Manag, 15(9), 407. https://doi.org/10.3390/jrfm1509040
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/03-2017-000
Cheng, K. Y., Colbath, R. A., & Frye, M. A. (2019). Olfactory and Neuromodulatory Signals Reverse Visual Object Avoidance to Approach in Drosophila. Curr. Biol, 29(12), 2058-2065.
Chiang, T.-C. (2018). Housing Constraints and Spatial Misallocation. American Economic Journal: Macroeconomics, 11(2), 1-39.
Childers, T. L., Carr, C. L., & Peck, J. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior. Journal of Retailing, 77, 511-535. https://doi.org/10.1016/S0022-4359(01)00056-2
Clark, V. L. P., & Ivankova, N. V. (2016). Mixed Methods Research: A Guide to the Field (1st ed.). SAGE Publications.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340. https://doi.org/10.2307/249008
Davis, F., Bagozzi, R., & Warshaw, P. (2006). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology, 22(14), 1111 - 1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
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
EDUCAUSE. (2021, April 26). Horizon Report| Teaching and Learning Edition. https://library.educause.edu/resources/2021/4/2021-educause-horizon-report-teaching-and-learning-edition
Eyun, J. K. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journalism & Mass Communication Quarterly, 89(2), 279-298. https://doi.org/10.1016/j.jbusres.2011.02.043
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research (1st ed.). Reading, MA: Addison-Wesley.
Fu, H. (2023). The Assessment of Attitude and Behavioral Intention of E-Learning Among Art and Design Students of Chengdu Textile College in China. AU-GSB E-JOURNAL, 16(1), 57-69. https://doi.org/10.14456/augsbejr.2023.7
Gerhardt, T., & Mackenzie, L. (2018). The Challenges and Opportunities of ICT in WIL: A case study among priests exploring the correlation and trajectory between effective WIL and ICT Pervasiveness. Journal of Higher Education, 8(4), 408-421. https://doi.org/10.1108/heswbl-07-2017-0045
Guriting, P., & Ndubisi, N. O. (2006). Borneo online banking: Evaluating customer perceptions and behavioral intention. Management Research News, 29(1/2), 6-15. https://doi.org/10.1108/01409170610645402
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.
Ho, Y.-S. (2012). The top-cited research works in the Science Citation Index Expanded. Scientometrics, 94, 1297-1312. https://doi.org/10.1007/s11192-012-0837-z
Hoyle, R. H. (2012). Handbook of structural equation modeling (1st ed.). The Guilford Press.
John, H. (2013). Men in Black (1st ed.). Reaktion Books
Kedar, O., Liran, H., & Tuttnauer, O. (2021). Permissibility of Electoral Systems: A New Look at an Old Question. Journal of Politics, 83(2), 439-452. https://doi.org/10.1086/709835
Kelman, H. C. (1958). Compliance, Identification, and Internalization: Three Processes of Attitude Change. Journal of Conflict Resolution, 2, 51-60. https://doi.org/10.1177/002200275800200106
Kim, H., & Han, T. I. (2020). Body Image Concerns among South Korean Kindergarteners and Relationships to Parental, Peer, and Media Influences. Early Childhood Education Journal, 49, 177-184. https://doi.org/10.1007/s10643-020-01059-z
Kim, H.-W., & Kwahk, K.-Y. (2007). Comparing the Usage Behavior and the Continuance Intention of Mobile Internet Services. Management of eBusiness, 1(1), 1-15.
King, W. R., & He, J. (2006). A Meta-Analysis of the Technology Acceptance Model. Information and Management, 43, 740-755. http://dx.doi.org/10.1016/j.im.2006.05.003
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (1st ed.). Guilford Press.
Kropp, F., Lavack, A., & Silvera, D. (2005). Values and Collective Self-Esteem as Predictors of Consumer Susceptibility to Interpersonal Influence among University Students. International Marketing Review, 22(1), 7-33. https://doi.org/10.1108/02651330510581154
Lee, M. D. P. (2008). A Review of the Theories of Corporate Social Responsibility: Its Evolutionary Path and the Road Ahead. International Journal of Management Reviews, 10(1), 53-73. https://doi.org/10.1111/j.1468-2370.2007.00226.x
Lee, S., Mogle, J., Jackson, C., & Buxton, O. (2019). What's not fair about work keeps me up Social Science Research. Social Science Research, 81(1). 11-20.
Leenders, R. T. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21-47. https://doi.org/10.1016/S0378-8733(01)00049-1
Linnenbrink, E. A. (2007). The role of affect in student learning: A multi-dimensional approach to considering the interaction of affect, motivation, and engagement. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 107–124). Elsevier Academic Press. https://doi.org/10.1016/B978-012372545-5/50008-3
Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2015). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29(4), 40-48. https://doi.org/10.1016/j.iheduc.2015.12.003
Liyanagunawardena, T., Williams, S., & Adams, A. (2013). The impact and reach of MOOCs:a developing countries’ perspective. eLearning Papers, 1(33), 1-8.
Madhurima, D., & Ewuuk, L.-D. (2014). An empirical examination of customers' adoption of m-banking in India. Marketing Intelligence & Planning, 32(4), 475-494.
Mangleburg, T., Doney, P., & Bristol, T. (2004). Shopping with friends and teens’ susceptibility to peer influence. Journal of Retailing, 80(2), 101-116. https://doi.org/10.1016/j.jretai.2004.04.005
Mika, I., & Sanna, S. (2015). Evolution of technology perceptions over time. Information Technology & People, 28(3), 589-606.
Moorthy, M., Ling, C., Fatt, Y., Yee, C., Yin, E., Yee, K., & Wei, L. (2017). Barriers of Mobile Commerce Adoption Intention: Perceptions of Generation X in Malaysia. Journal of theoretical and applied electronic commerce research, 12(2), 37-53. https://doi.org/10.4067/s0718-18762017000200004
Mourali, M., Laroche, M., & Pons, F. (2005). Individualistic orientation and consumer susceptibility to interpersonal influence. Journal of Services Marketing, 19(3), 164-173. https://doi.org/10.1108/08876040510596849
Nakayama, M., & Leon, S. (2019). Demystifying the Communication-Driven Usefulness Hypothesis: The Case of Healthcare Insurance Applications. International Journal of Healthcare Information Systems and Informatics, 14(4), 1-17. https://doi.org/10.4018/ijhisi.2019100104
Nathalie, T. M., & Souad, D. M. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management, 44(5) 1-10.
Nelson, K. (2003). Narrative and self, myth, and memory: Emergence of the cultural self. In R. Fivush & C. A. Haden (Eds.), Autobiographical memory and the construction of a narrative self: Developmental and cultural perspectives (pp. 3–28). Lawrence Erlbaum Associates Publishers.
Oertzen, A.-S., & Schröder, G.-O. (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
Park, Y. K., Song, J. H., Yoon, S. W., & Kim, J. (2014). Learning Organization and Innovative Behavior: The Mediating Effect of Work Engagement. European Journal of Training and Development, 38, 75-94. https://doi.org/10.1108/EJTD-04-2013-0040
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. https://doi.org/10.1590/0101-60830000000081
Pipitwanichakarn, T., & Wongtada, N. (2020). The role online review on mobile commerce adoption: an inclusive growth context. Journal of Asia Business Studies, 14(5), 759-778. https://doi.org/10.1108/jabs-02-2019-0060
Riquelme, H. E., & Rios, R. E. (2010). The Moderating Effect of Gender in the Adoption of Mobile Banking. The International Journal of Bank Marketing, 28, 328-341. http://dx.doi.org/10.1108/02652321011064872
Samuel, A., Susan, M., & Biniecki, Y. (2017). Using concept mapping as a tool for conducting research: An analysis of three approaches. Cogent Social Sciences, 3(1), 1404753. https://doi.org/10.1080/23311886.2017.1404753
Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis (1st ed.). IGI Global Press.
Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses- a review. The J. Am. Sci., 1(1), 4-16
Shiau, W. L., & Chau, P. Y. (2016). Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Information & Management, 53, 355-365. https://doi.org/10.1016/j.im.2015.10.004
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 Science Publishers.
Sripalawat, J., Thongmak, M., & Ngarmyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems (JCIS). 51(1), 1-10.
Stark, J. S., & Lattuca, L. R. (1997). Shaping the College Curriculum: Academic Plans in Action (1st ed.). Evaluative.
Sudin, T., & Budiarto, Y. (2021). Meaningful Work, Quality of Work-life, and Job Satisfaction: A Study of Nightlife Workers. Open Journal for Psychological Research, 5(1), 9-20. https://doi.org/10.32591/coas.ojpr.0501.02009s
Sun, Y., & Jeyaraj, A. (2013). Information technology adoption and continuance: A longitudinal study of individuals’ behavioral intentions. Information & Management, 50(7), 457-465. https://doi.org/10.1016/j.im.2013.07.005
Tenorio, M. L., Dekker, M., & Verkerk, R. (2017). Evaluating the effect of storage conditions on the shelf life of cape gooseberry. LWT, 80, 523-530. https://doi.org/10.1016/j.lwt.2017.03.027
Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk, and usage intention across customer groups for mobile payment services in India. Internet Research: Electronic Networking Applications and Policy, 24(3), 369-392. https://doi.org/10.1108/intr-12-2012-0244
Van Manen, M. (1997). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy (2nd ed.). Althouse Press, London.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 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: Towards a Unified View. MIS Quarterly, 27, 425-478.
Weng, F., Yang, R.-J., Ho, H.-J., & Su, H.-M. (2018). A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers. Applied System Innovation, 1(3), 36. https://doi.org/10.3390/asi1030036
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43(6), 728-739. http://dx.doi.org/10.1016/j.im.2006.05.002
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I. A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., & Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326-9331. https://doi.org/10.1073/pnas.1701762114
Zhou, T. (2011). Understanding mobile internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207-218. https://doi.org/10.1177/0266666911414596