The Assessment on Significant Factors of Undergraduate Students’ Behavioral Intention to Learn Arts Education in Chengdu, China

Main Article Content

Yuhang Fu
Witsaroot Pariyaprasert
Poonphone Suesaowaluk

Abstract

Purpose: This study explores the determinants of university students' behavioral intention to learn arts education. The conceptual framework includes factors from the social sphere, academic sphere, education satisfaction, attitude, social influence, self-efficacy, effort expectancy, and behavioral intention. Research design, data, and methodology: The target population are those who have experienced arts education at Chengdu, China. Participants are categorized into undergraduate students, with a sample size of 500. A quantitative research approach was adopted, and data were collected using a questionnaire as the primary instrument. The sampling techniques employed in this study include judgmental, quota, convenience, and snowball sampling. To ensure the validity and reliability of the questionnaire, a pilot test was conducted with 50 participants, and both the item-objective congruence (IOC) index and Cronbach's alpha were used for validity and reliability testing, respectively. The collected data were analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM), which served as the main statistical techniques for this research. Results: Social sphere and academic sphere significantly impact education satisfaction. Behavioral intention is significantly impacted by education satisfaction, self-efficacy and effort expectancy, but not by attitude and social influence. Conclusions: These analyses provide valuable insights into the factors influencing university students' behavioral intention to engage in arts education.

Downloads

Download data is not yet available.

Article Details

How to Cite
Fu, Y., Pariyaprasert, W., & Suesaowaluk, P. (2024). The Assessment on Significant Factors of Undergraduate Students’ Behavioral Intention to Learn Arts Education in Chengdu, China. AU-GSB E-JOURNAL, 17(2), 32-41. https://doi.org/10.14456/augsbejr.2024.26
Section
Articles
Author Biographies

Yuhang Fu

Ph.D. Candidate in Technology, Education and Management, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.

Witsaroot Pariyaprasert

Full-Time Lecturer, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.

Poonphone Suesaowaluk

Full-Time Lecturer, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand.

References

Ahghar, G. (2016). Chapter Five School Climate and Teachers. The Psychology of School Climate, 74(1), 1-10.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (1st ed.). Prentice-Hall.

Alamri, M. M. (2021). Using blended project-based learning for students’ behavioral intention to use and academic achievement in higher education. Education Sciences, 11(5), 207. https://doi.org/10.3390/educsci11050207

Allen, M., Bourhis, J., Burrell, N., & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta-analysis. The American journal of distance education, 16(2), 83-97. https://doi.org/10.1207/s15389286ajde1602_3

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. https://doi.org/10.1037/0033-295x.84.2.191

Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of Consumer Susceptibility to Interpersonal Influence. Journal of Consumer Research, 15(4), 473-481. http://dx.doi.org/10.1086/209186

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

Blotnicky, K. A., Franz-Odendaal, T., French, F., & Joy, P. (2018). A study of the correlation between STEM career knowledge, mathematics self-efficacy, career interests, and career activities on the likelihood of pursuing a STEM career among middle school students. International journal of STEM education, 5(1) 1-15. https://doi.org/10.1186/s40594-018-0118-3

Catterall, J. S., Dumais, S. A., & Hampden-Thompson, G. (2012). The arts and achievement in at-risk youth: Findings from four longitudinal studies. National Endowment for the Arts, 1(2), 1-28.

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

Deasy, R. J., Catterall, J. S., Hetland, L., & Winner, E. (2013). The arts and the creation of mind. In J. S. Catterall, R. Chapleau, J. Iwanaga, & C. Freeman (Eds.), Critical links: Learning in the arts and student academic and social development (pp. 3-22). Arts Education Partnership.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

Driver, M. (2002). Exploring student perceptions of group interaction and class satisfaction in the web-enhanced classroom. The Internet and Higher Education, 5(1), 35-45. https://doi.org/10.1016/s1096-7516(01)00076-8

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

Fowler, F. J. (2013). Survey Research Methods (4th ed.). SAGE Publications.

Fry, P. S., & Coe, K. J. (1980). Interaction among dimensions of academic motivation and classroom social climate: A study of the perceptions of junior high and high school pupils. British Journal of Educational Psychology, 50(1), 33-42. https://doi.org/10.1111/j.2044-8279.1980.tb00795.x

Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of e-banking services in Iran: The moderating role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801-807.

Goldsmith, R., Flynn, L., & Goldsmith, E. (2003). Innovative Consumers and Market Mavens. Journal of Marketing Theory and Practice, 11(4), 54-64. https://doi.org/10.1080/10696679.2003.11658508

Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Harlow, England: Pearson Education.

Johnson, C. C., Walton, J. B., Strickler, L., & Elliott, J. B. (2023). Online Teaching in K-12 Education in the United States: A Systematic Review. Review of Educational Research, 93(3), 353–411. https://doi.org/10.3102/00346543221105550

Kim, K. C., & Song, J. H. (2021). The Impact of Interaction of Art Education in the Era of Pandemic on Satisfaction and Behavioral Intent: Focusing on Online and Offline Comparisons. Journal of the Korea Convergence Society, 12(9), 99-111.

Kim, T. T., Suh, Y., Lee, G., & Choi, B. (2010). Modelling roles of task‐technology fit and self‐efficacy in hotel employees' usage behaviors of hotel information systems. International Journal of Tourism Research, 12(6), 709 - 725. http://doi.org/10.1002/jtr.787

Liu, L., Schuster, G. L., Moosmüller, H., Stamnes, S., Cairns, B., & Chowdhary, J. (2022). Optical properties of morphologically complex black carbon aerosols: Effects of coatings. J. Quant. Spectrosc. Radiat. Transfer, 281, 108080. https://doi.org/10.1016/j.jqsrt.2022.108080

Liu, L., Zhao, X., Liu, Y., Zhao, H., & Li, F. (2019). Dietary addition of garlic straw improved the intestinal barrier in rabbits. J. Anim. Sci., 97(10), 4248-4255

Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students' Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB e-JOURNAL, 15(2), 150-158.

Nebor, J. N. (1986). Parental Influence and Involvement on Reading Achievement. Eric, 1-13.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

Onaolapo, S., & Oyewole, O. (2018). Performance expectancy, effort expectancy, and facilitating conditions as factors influencing smart phones use for mobile learning by postgraduate students of the University of Ibadan, Nigeria. Interdisciplinary Journal of e-Skills and Lifelong Learning, 14(1), 95-115. https://doi.org/10.28945/4085

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.

Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for Testing and Evaluating Survey Questions. Public Opinion Quarterly, 68(1), 109–130. https://doi.org/10.1093/poq/nfh008

Ramírez-Montoya, M. S., Castillo-Martínez, I. M., Sanabria-Z, J., & Miranda, J. (2022). Complex thinking in the framework of Education 4.0 and Open Innovation—A systematic literature review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 4.

Ramsden, P. (1979). Student learning and perceptions of the academic environment. Higher education, 8(4), 411-427. https://doi.org/10.1007/bf01680529

Reinke, W. M., Herman, K. C., & Newcomer, L. (2016). The Brief Student–Teacher Classroom Interaction Observation: Using dynamic indicators of behaviors in the classroom to predict outcomes and inform practice. Assessment for effective intervention, 42(1), 32-42.

Russell, D. R. (1997). Rethinking genre in school and society: An activity. Written Communication, 14(4), 504-554. https://doi.org/10.1177/0741088397014004004

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286.

Sheeran, P., Maki, A., Montanaro, E., Avishai-Yitshak, A., Bryan, A., Klein, W. M., Miles, E., & Rothman, A. J. (2016). The impact of changing attitudes, norms, and self-efficacy on health-related intentions and behavior: A meta-analysis. Health psychology: official journal of the Division of Health Psychology, American Psychological Association, 35(11), 1178–1188. https://doi.org/10.1037/hea0000387

Shen, C. W., Ho, J. T., Kuo, T. C., & Luong, T. H. (2017, April 3-7). Behavioral intention of using virtual reality in learning [Paper Presentation]. In 26th International World Wide Web Conference, WWW 2017 Companion (pp. 129-137), Perth, Australia.

Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the Technology Acceptance Model in Examining Students’ Behavioural Intention to Use an e-portfolio System. Australasian Journal of Educational Technology, 27(4), 600-618. https://doi.org/10.14742/ajet.940

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.

Soper, D. S. (2023). A-priori Sample Size Calculator for Structural Equation Models [Software]. www.danielsoper.com/statcalc/default.aspx

Stevens, B. F. (1992). Price Value Perceptions of Travelers. Journal of Travel Research, 31(2), 44-48. http://dx.doi.org/10.1177/004728759203100208

Sudhana, P., Noermijati, N., Hussein, A., & Indrawati, N. (2020). The mediating role of self-congruity in transnational higher education choice: a proposed framework. Journal of Applied Research in Higher Education, 13(3), 811-829. https://doi.org/10.1108/JARHE-05-2020-0141

Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203-220. https://doi.org/10.1016/s0022-4359(01)00041-0

Upitis, R., Smithrim, K., & Souto-Manning, M. (2016). Learning through the arts: Lessons of engagement. In R. Upitis, K. Smithrim, & M. Souto-Manning (Eds.), Learning through the arts: A resource guide for teachers and teaching artists (pp. 1-8). Queen's Printer for Ontario.

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

Vululleh, P. (2018). Determinants Of Students’ E-Learning Acceptance in Developing Countries: An Approach Based on Structural Equation Modeling (SEM). International Journal of Education and Development Using Information and Communication Technology, 14(1), 141- 151.

Weerasinghe, I. S., & Fernando, R. L. (2017). Students' satisfaction in higher education. American journal of educational research, 5(5), 533-539.

Winner, E., Goldstein, T. R., & Vincent-Lancrin, S. (2013). Art for art's sake? The impact of arts education. Educational Research and Innovation, 1(1), 1-28.

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002

Zhang, C. (2023). A Model for Developing Student Satisfaction for Undergraduate Students in Private Higher Education Institutions in Singapore. International Journal of Sociologies and Anthropologies Science Reviews, 3(2), 165–180. https://doi.org/10.14456/jsasr.2023.25

Zhong, K., Feng, D., Yang, M., & Jaruwanakul, T. (2022). Determinants of Attitude, Satisfaction and Behavioral Intention of Online Learning Usage Among Students During COVID-19. AU-GSB E-JOURNAL, 15(2), 49-57. https://doi.org/10.14456/augsbejr.2022.71