Understanding Factors Impacting Behavioral Intention and Use Behavior of Online Art Exhibitions Among Art Students in Sichuan, China


  • Yitao Zhai Mr


DOI: 10.14456/abacodijournal.2024.18
Published: 2024-04-24


art college, online art exhibition, subjective norms, behavioral intention, use behavior


This study aims to explore the factors impacting students in art majors in Chengdu universities to use online art exhibitions. The framework proposes seven variables of causal relationships, including subjective norms, perceived ease of use, perceived usefulness, behavioral intention, perceived behavioral control, social impact, and behavior. The researcher applied quantitative methods to distribute questionnaires to 517 participants. Before issuing the questionnaire, the validity and reliability of the data were tested using the Index of item objective congruence (IOC) and Cronbach’s alpha for the pilot tests (n=50). The data are analyzed by confirmatory factor analysis (CFA) and structural equation model (SEM) to verify the model's goodness of fit and confirm the causal relationship between the hypothesis test variables. The results show that subjective norms have a significant impact on perceived usefulness, perceived ease of use has a significant impact on perceived usefulness, perceived ease of use has a significant impact on behavioral intention, perceived usefulness has a significant impact on behavioral intention, perceived behavioral control has a significant impact on behavioral intention, social impact has a significant impact on behavioral intention. The behavioral intention has a significant impact on behavior. The seven hypotheses have been proven to meet the research objectives. Therefore, the study of conceptual models can predict and explain the behavioral intention of using online art exhibitions in higher education.


Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683.


Ajzen, I. (2006). Constructing a Theory of Planned Behavior Questionnaire (1st ed.). Psychology Press.

Akbar, F. (2013). What affects students’ acceptance and use of technology?. https://figshare. com/articles/What_affects_students_acceptance_and_use_of_technology_/6686654

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of internet banking in Jordan: examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(2), 145-157. https://doi.org/10.1057/fsm.2015.5

Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983-1998. https://doi.org/10.1007/s10639-019-10062-w

Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students’ acceptance of Google classroom: An exploratory study using PLS-SEM approach. International Journal of Emerging Technologies in Learning, 13(6), 112-123. https://doi.org/10.3991/ijet.v13i06.8275

Avci, U., & Askar, P. (2012). The comparison of the opinions of the university students on the usage of blog and Wiki for their courses. Educational Technology and Society, 15(2), 194-205.

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Sciences, 16(1), 74-94. http://dx.doi.org/10.1007/BF02723327

Calisir, F., Gumussoy, C. A., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531.


CCPT. (2020). Overview of the Development of China's Exhibition Industry. China Council for the promotion of international trade.


Chau, P. Y. K., & Hu, P. J.-H. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management, 39(4), 297-311. https://doi.org/10.1016/s0378-7206(01)00098-2

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers and education, 59(3), 1054-1064. https://doi.org/10.1016/j.compedu.2012.04.015

Chiu, Y.-B., Lin, C.-P., & Tang, L.-L. (2005). Gender differs: assessing a model of online purchase intentions in e-tail service. International Journal of Service Industry Management, 16(5), 416-435. https://doi.org/10.1108/09564230510625741

Chu, S.-C., Chen, H.-T., & Sung, Y. (2016). Following brands on Twitter: an extension of theory of planned behavior. International Journal of Advertising, 35(3), 421-437. https://doi.org/10.1080/02650487.2015.1037708

Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2015). Further Understanding Factors that Explain Freshman Business Students’ Academic Integrity Intention and Behavior: Plagiarismand Sharing Homework. Journal of Business Ethics, 147(1), 197-220

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-390.


Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand communities: a social media perspective. Journal of Product and Brand Management, 24(1), 28-42. https://doi.org/10.1108/jpbm-06-2014-0635

Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology, 24(1), 11-24. https://doi.org/10.1057/jit.2008.31

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: extending the unified theory of acceptance and use of technology 2(UTAUT2). Educational Technology Research and Development, 65(3), 743-763. https://doi.org/10.1007/s11423-017-9526-1

Filippini, R., Forza, C., & Vinelli, A. (1998). Trade-off and compatibility between performance: Definitions and empirical evidence. International Journal of Production Research, 36(12), 3379- 3406.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research (1st ed). 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. https://doi.org/10.2307/3151312

Gefen, D. (2003). TAM or just plain habit: a look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13. https://doi.org/10.4018/joeuc.2003070101

Habelsberger, B. E. M., & Bhansing, P. V. (2021). Art Galleries in Transformation: Is COVID-19 Driving Digitisation? Arts, 10(3), 48. http://dx.doi.org/10.3390/arts10030048

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2003). Essentials of business research methods (2nd ed.). John Wiley and Sons.


Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers and Education, 102, 79-89.


Hollebeek, L. D., Glynn, M., & Brodie, R. J. (2014). Consumer brand engagement in social media: conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149-165. https://doi.org/10.1016/j.intmar.2013.12.002

Hollebeek, L. D., Juric, B., & Tang, W. (2017). Virtual Brand community engagement practices: a refined typology and model. Journal of Services Marketing, 31(3), 204-2017. https://doi.org/10.1108/jsm-01-2016-0006

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, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional Criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. http://dx.doi.org/10.1080/10705519909540118

Huang, J.-T. (2011). Application of planned behavior theory to account for college students' occupational intentions in contingent employment. The Career Development Quarterly, 59(5), 455-466. https://doi.org/10.1002/j.2161-0045.2011.tb00971.x

Joo, Y. J., Kim, N., & Kim, N. H. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development, 64(4), 611-630. https://doi.org/10.1007/s11423-016-9436-7

Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212

Killingsworth, M. A., Kahneman, D., & Mellers, B. (2020). Income and emotional well-being: A conflict resolved. Authors Info & Affiliations, 120(10), e2208661120.

Kitcharoen, K., & Vongurai, R. (2021). Factors influencing customer attitude and behavioral intention towards consuming dietary supplements. AU-GSB E-JOURNAL, 13(2), 94-109.

Krueger, N. F. J., Reilly, M. D., & Carsrud, A. L. (2000). Competing models of entrepreneurial intentions. Journal of Business Venturing, 15(5/6), 411-432.


Lee, J.-K., & Lee, W.-K. (2008). The relationship of e-Learner's self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behavior, 24(1), 32-47. https://doi.org/10.1016/j.chb.2006.12.001

Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees' e-learning systems acceptance. Knowledge-Based Systems, 24(3), 355-366.


Li, F., & Wang, Z. (2021). Application of digital media interactive technology in post-production of film and television animation. Journal of Physics: Conference Series, 1966(1), 1-7.

Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioural intention to reuse e-learning systems in rural china. British Journal of Educational Technology, 43(6), 933-948. https://doi.org/10.1111/j.1467-8535.2011.01261.x

Liggett, S., & Corcoran, M. (2020). Framing the Conversation: The Role of the Exhibition in Overcoming Interdisciplinary Communication Challenges. Springer.


Lin, H.-F. (2006). Understanding behavioral intention to participate in virtual communities. CyberPsychology & Behavior, 9(5), 540-547. https://doi.org/10.1089/cpb.2006.9.540

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, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29-39.


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

Mathieson, K. (2006). Predicting user intentions: comparing the Technology Acceptance Model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173

McKinnon, K., & Igonor, A. (2008). Explaining eLearning perceptions using the technology acceptance model and the theory of planned behavior. E-learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Association for the Advancement of Computing in Education (AACE), 1, 2994-2999.

Mead, E. L., Rimal, R. N., Ferrence, R., & Cohen, J. E. (2014). Understanding the sources of normative influence on behavior: the example of tobacco. Social Science Medicine, 115, 139-143. https://doi.org/10.1016/j.socscimed.2014.05.030

Navratilova, T. (2013). Analysis and comparison of factors influencing university choice. Journal of Competitiveness, 5(3), 90-100.

Patrick, M., Ilias, O. P., & Michail, G. (2016). An integrative adoption model of video-based learning. The International Journal of Information and Learning Technology, 33(4), 2056-4880.

Patterson, P., Yu, T., & De Ruyter, K. (2006). Understanding customer engagement in services. In Advancing Theory, Maintaining Relevance, 1(2), 4-6.

Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: a cross-sectional study. Computers in Human Behavior, 27(1), 568-575.


Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers and Education, 145, 103732.

Roca, J. C., & Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: a self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604.

Sanchez, R. A., & Hueros, A. D. (2010). Motivational Factors that Influence the Acceptance of Moodle Using TAM. Computers in Human Behavior, 26, 1632-1640.


Stone, T. H., Jawahar, I. M., & Kisamore, J. L. (2010). Predicting academic misconduct intentions and behavior using the theory of planned behavior and personality. Basic and Applied Social Psychology, 32(1), 35-45. https://doi.org/10.1080/01973530903539895

Tandon, U., & Kiran, R. (2019). Factors impacting customer satisfaction: an empirical investigation into online shopping in India. Journal of Information Technology Case and Application Research, 21(1), 13-34.


Teo, A. C., Tan, G. W. H., Cheah, C. M., Ooi, K. B., & Yew, K. T. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578-597. https://doi.org/10.1504/ijmc.2012.049757

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), 51-66.


Teo, T., Zhou, M. M., Fan, C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: a study in Macau. Educational Technology Research and Development, 67(3), 749-766. https://doi.org/10.1007/s11423-019-09650-x

Tosuntas, S. B., Karadag, B. E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: a structural equation model based on the unified theory of acceptance and use of technology. Computers & Education, 81(2), 169-178. https://doi.org/10.1016/j.compedu.2014.10.009

Tselios, N., Daskalakis, S., & Papadopoulou, M. (2011). Assessing the Acceptance of a Blended Learning University Course. Educational Technology & Society, 14, 224-235.

Tseng, T. H., Lin, S., Wang, Y. S., & Liu, H. X. (2019). Investigating teachers’ adoption of MOOCs: the perspective of UTAUT2. Interactive Learning Environments, 30(4), 635-650. https://doi.org/10.1080/10494820.2019.1674888

Valtonen, T., Sointu, E., Makitalo, K., & Kukkonen, J. (2015). Developing a TPACK measurement instrument for 21st century pre-service teachers. International journal of media, technology, and lifelong learning, 11(5), 88-100.

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.


Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences Journal, 27(3), 451-481.


Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 2, 186-204.


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. 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. https://doi.org/10.2307/41410412

Wai, C. C., & Seng, E. L. K. (2015). Measuring the effectiveness of blended learning environment: a case study in Malaysia. Education and Information Technologies, 20(3), 429-443. https://doi.org/10.1007/s10639-013-9293-5

Ya-Ching, L. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.

Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300-318. https://doi.org/10.1177/0047239515618464




How to Cite

Zhai, Y. (2024). Understanding Factors Impacting Behavioral Intention and Use Behavior of Online Art Exhibitions Among Art Students in Sichuan, China . ABAC ODI JOURNAL Vision. Action. Outcome, 11(2), 316-336. https://doi.org/10.14456/abacodijournal.2024.18