FACTORS INFLUENCING UNIVERSITY ANIMATION STUDENT’S BEHAVIORAL INTENTION TO LEARN THREE - DIMENSIONAL ANIMATION SOFTWARE IN CHENGDU CHINA

Authors

  • Ming Yang Assumption University
  • Somsit Duangekanong Graduate School of Advanced Technology Management, Assumption University

DOI:

https://doi.org/10.14456/lsej.2022.8

Keywords:

Three-dimensional animation software, Learning, Behavioral intention

Abstract

The purpose of this study is to explore the factors that influence the behavioral intention in Learning Three-dimensional animation Software of animation majors in universities of Chengdu, China. The conceptual framework constructs the relationship among Perceived ease of use (PEOU), Perceived usefulness (PU), Self-efficacy (SE), Satisfaction (SAT), Enjoyment (ENJ), Social influence (SI), Behavioral intention (BI).The researchers used a quantitative method (n=500) to distribute questionnaires to students of three grades majoring in animation. Non-probabilistic sampling includes purposive sampling when selecting target students of animation major, Probabilistic sampling includes cluster sampling when selecting public universities in Chengdu, and stratified sampling of students of different grades when distributing surveys. The Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used for the data analysis including model fit, reliability and validity of the constructs. The results of this paper show that perceived usefulness is the biggest factor that affects animation majors' behavioral intention of learning Three-dimensional animation software, while perceived ease of use has a significant impact on perceived usefulness, but has no significant direct impact on behavioral intention. Among other factors, social influence has been proved to have impact behavioral intention, satisfaction has a significant impact on students' behavioral intention of using Three-dimensional animation software, and self-efficacy directly affects satisfaction. Enjoyment factor has limited influence on students' learning Three-dimensional animation software. Therefore, the research suggests that in the course design and teaching of Three-dimensional animation major, we should start from the related factors of students' behavioral intention to create a better learning environment of technology and art.

References

Agrebi S, Jallais J. Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services 2015;22:16-23.

Balouchi M, Aziz YA, Hasangholipour T, Khanlari A, Abd Rahman A, Raja-Yusof RN. Explaining and predicting online tourists behavioural intention in accepting consumer generated contents. Journal of Hospitality and Tourism Technology 2017;8(2):168-189.

Bandura A. Social foundations of thought and action: A social cognitive theory. Eaglewood Cliffs, NJ: Prentice Hall; 1986.

Bardakci S. Exploring high school students' educational use of youtube. International Review of Research in Open and Distance Learning 2019;20(2):260-278.

Bashir I, Madhavaiah C. Consumer attitude and behavioural intention towards internet banking adoption in india. Journal of Indian Business Research 2015;7(1):67-102.

Bollen K. A new incremental fit index for general structural equation models. Sociological Methods and Research. 1989;17(3):303-316.

Bolton RN, Drew JH. A multistage model of consumers’ assessments of service quality and value. Journal of Consumer Research 1991;17(4):375-384.

Çelik H. What determines Turkish consumers’ acceptance of Internet banking?. International Journal of Bank Marketing 2008;26(5):353-370.

Cigdem H, Topcu A. Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example[J]. Computers in Human Behavior 2015;52:22-28.

Compeau DR, Higgins CA. Computer self-efficacy: development of a measure and initial test. Management Information Systems Quarterly 1995;19(2):189-211.

Damnjanovic V, Jednak S, Mijatovic I. Factors affecting the effectiveness and use of Moodle: Students' perception. Interactive Learning Environments 2013;23(4):496-514.

Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Management Science 1989;35(8):982-1003.

Davis FD. Perceived usefulness, ease of use and user acceptance of information technology. Management Information Systems Quarterly 1989;13(3):319-339.

Drury H, Kay J, Losberg W. Student satisfaction with groupwork in undergraduate computer TOJET. The Turkish Online Journal of Educational Technology 2003;13(3):77-85

El-Masri M, Tarhini A. 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 2017;65(3):743-763.

Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Philosophy Rhetoric; 1975.

Fokides E. Greek pre-service teachers' intentions to use computers as in-service teachers. Contemporary Educational Technology 2017;8(1):56-75.

Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research 1981;18(3):328-388.

Gallego MD, Luna P, Bueno S. User acceptance model of open source software. Computers in Human Behavior 2008;24(5):2199-2216.

Hair JF, Black WC, Babin BJ, Anderson RE, Tatham LR. Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall; 2006.

Hair JF, Hult GTM, Ringle CM, Sarstedt M. Multivariate data analysis: A global perspective (7/e), Pearson Education, New Delhi; 2013.

Hair JF, Black WC, Babin BJ. Multivariate data analysis. Pearson Prentice Hall, New Jersey; 2010.

Harun C, Mustafa O. Factors affecting students' behavioral intention to use lms at a turkish post-secondary vocational school. International Review of Research in Open & Distributed Learning 2016;17(3):276-295.

Hayduk LA. Lisrel issues, debates, and strategies. London: The John Hopkins University Press; 1996.

Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 1999;6(1):1-55.

Johnson RD, Hornik S, Salas E. An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies 2008; 66(5):356-369.

Jöreskog K, Sörbom D. LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International Inc., Chicago, IL; 1998.

Jung TH, Lee H, Chung N, Dieck MC. Cross-cultural differences in adopting mobile augmented reality at cultural heritage tourism sites. International Journal of Contemporary Hospitality Management 2018;30(3):1621-1645.

Kaplan D. Structural equation modeling: foundations and extensions. 2nd ed. Los Angeles: SAGE Press; 2009.

Kline, Rex B. Principles and practice of structural equation modeling. 4th ed. New York. ISBN 978-1-4625-2334-4. OCLC 934184322; 2016.

Lee GG, Lin HF. Customer perceptions of e-service quality in online shopping. International Journal of Retail and Distribution Management 2005;33(2):161-175.

Lee MC. Factors influencing the adoption of Internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications 2009;8(3): 130-141.

Lewis-Beck M, Bryman A, Liao T. Encyclopedia of social science research methods. Thousand Oaks, CA: Saga Publications; 2004.

Liaw SS, Huang HM. Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education 2013;60(1):14-24.

Lisa X. Analysis of the development status of China's animation industry in 2019. Available at: https://www.qianzhan.com/analyst/detail/220/200228-0ea5b576.html. Accessed February 8, 2021.

Malhotra N, Hall J, Shaw M, Oppenheim P. Essentials of marketing research, An applied orientation: Pearson Education Australia; 2004.

Mikalef P, Pappas IO, Giannakos M. An integrative adoption model of video-based learning[J]. The International Journal of Information and Learning Technology 2016;33(4):219-235.

Nunnally JC. Psychometric theory, McGraw-Hill. New York, NY; 1978.

Püschel J, Mazzon A, Hernandez JMC. Mobile banking: proposition of an integrated adoption intention framework. International Journal of Bank Marketing 2010;28(5):389-409.

Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online 2003;8:23-74.

Teo T. Examining the intention to use technology among preservice teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments 2012;20(1):3-18.

Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 1973; 38(1):1-10.

Wang HY, Wang SH. User acceptance of mobile internet based on the unified theory of acceptance and use of technology: Investigating the determinants and gender differences. Social Behavior and Personality: An International Journal 2010;38(3):415-426.

Warshaw P, Davis F. Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology 1985;21:213-228.

Womble J. E-learning: the relationship among learner satisfaction, self-efficacy, and usefulness. The Business Review 2008;10(1):182-188.

Wu X, Gao Y. Applying the extended technology acceptance model to the use of clickers in student learning: some evidence from macroeconomics classes. American Journal of Business Education 2011;4(7):43-50.

Zhang JC, Byon KK, Xu K, Huang H. Event impacts associated with residents' satisfaction and behavioral intentions: a pre-post study of the nanjing youth olympic games. International Journal of Sports Marketing and Sponsorship 2020;21(3):487-511.

Zhou T. Understanding continuance usage of mobile sites. Industrial Management & Data Systems. 2013;113(9):1286-1299.

Downloads

Published

2022-01-31

How to Cite

Yang, M., & Duangekanong, S. (2022). FACTORS INFLUENCING UNIVERSITY ANIMATION STUDENT’S BEHAVIORAL INTENTION TO LEARN THREE - DIMENSIONAL ANIMATION SOFTWARE IN CHENGDU CHINA. Life Sciences and Environment Journal, 23(1), 94–111. https://doi.org/10.14456/lsej.2022.8

Issue

Section

Research Articles