Determinants of Behavioral Intention to Learn Arts Education of Postgraduate Students in Chengdu, China

Authors

  • Yuhang Fu
  • Sutthisak Inthawadee
  • Taminee Shinasharkey

DOI:

https://doi.org/10.14456/shserj.2024.38
CITATION
DOI: 10.14456/shserj.2024.38
Published: 2024-08-20

Keywords:

Social Sphere, Academic Sphere, Education Satisfaction, Attitude, Social Influence, Behavioral Intention

Abstract

Purpose: This research delves into the factors that impact the behavioral intention of university students to engage in arts education. The conceptual framework encompasses social sphere, academic sphere, educational satisfaction, attitude, social influence, self-efficacy, effort expectancy, and behavioral intention. Research design, data, and methodology: The target population and sample size are 500 postgraduate students who have experienced arts education at top three universities in Chengdu, China. A quantitative research approach was adopted, using a questionnaire. The sampling techniques employed in this study include judgmental, quota, convenience, and snowball sampling. 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. Furthermore, education satisfaction, self-efficacy and effort expectancy significantly impact behavioral intention. Nevertheless, the relationship between attitude, social influence and behavioral intention is not supported. Conclusions: Understanding these determinants can inform the development of strategies and interventions to promote arts education and enhance students' engagement and intention to pursue arts-related fields.

Author Biographies

Yuhang Fu

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

Sutthisak Inthawadee

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

Taminee Shinasharkey

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

References

Al-Gahtani, S. S. (2016). Empirical Investigation of e-Learning Acceptance and Assimilation: A Structural Equation Model. Applied Computing and Informatics, 12(1), 27-50. https://doi.org/10.1016/j.aci.2014.09.001

Bardakcı, S. (2019). Exploring High School Students' Educational Use of YouTube. International Review of Research in Open and Distributed Learning, 20(2), 260-278. https://doi.org/10.19173/irrodl.v20i2.4074

Bashir, I., & Madhavaiah, C. (2015). Consumer Attitude and Behavioral 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

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

Birch, D., & Burnett, B. (2009). Bringing academics on board: Encouraging institution-wide diffusion of e-learning environments. Australasian Journal of Educational Technology, 25(1), 1-10. https://doi.org/10.14742/ajet.1184

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

Burton, J., Horowitz, R., & Abeles, H. (2000). Learning in and through the arts: The question of transfer. Studies in Art Education, 41(3), 228-257. https://doi.org/10.2307/1320379

Cai, Z., Zhu, J., & Tian, S. (2022). Preservice teachers’ teaching internship affects professional identity: Self-efficacy and learning engagement as mediators. Frontiers in Psychology, 13(2), 1070763.

http://doi.org/10.3389/fpsyg.2022.1070763

Castro, M., Expósito-Casas, E., López-Martín, E., Lizasoain, L., Navarro-Asencio, E., & Gaviria, J. L. (2015). Parental involvement on student academic achievement: A meta-analysis. Educational research review, 14(2), 33-46.

https://doi.org/10.1016/j.edurev.2015.01.002

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.

Cheung, R., & Vogel, D. (2013). Predicting User Acceptance of Collaborative Technologies: An Extension of The Technology Acceptance Model for e-Learning. Computers & Education, 63(1), 160-175.

https://doi.org/10.1016/j.compedu.2012.12.003

Cigdem, H., & Ozturk, M. (2016). Factors Affecting Students’ Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School. International Review of Research in Open and Distributed Learning, 17(3), 276-295.

https://doi.org/10.19173/irrodl.v17i3.2253

Deasy, R. J. (2002). Critical links: Learning in the arts and student academic and social development. http://www. aep-arts.org/

Deng, P., Chen, B., & Wang, L. (2023). Predicting students’ continued intention to use E-learning platform for college English study: the mediating effect of E-satisfaction and habit. Frontiers in Psychology, 14, 1182980.

http://doi.org/10.3389/fpsyg.2023.1182980

Divaris, K., Barlow, P. J., Chendea, S. A., Cheong, W. S., Dounis, A., Dragan, I. F., & Vrazic, D. (2008). The academic environment: the students’ perspective. European Journal of Dental Education, 12(1), 120-130.

https://doi.org/10.1111/j.1600-0579.2007.00494.x

Eisner, E. (2002). The arts and the creation of mind. Yale University Press. National Council of Teachers of English, 80(5), 1-6. https://doi.org/10.12987/9780300133578

Ferrer-Balas, D., Lozano, R., Huisingh, D., Buckland, H., Ysern, P., & Zilahy, G. (2010). Going beyond the rhetoric: system-wide changes in universities for sustainable societies. Journal of Cleaner Production, 18(7), 607-610. https://doi.org/10.1016/j.jclepro.2009.12.009

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

Gellad, W. F., Thorpe, C. T., Steiner, J. F., & Voils, C. I. (2017). The myths of medication adherence. Pharmacoepidemiology and drug safety, 26(12), 1437-1441. https://doi.org/10.1002/pds.4334

Golova, E., Baetova, D., Zaitseva, O., & Novikov, Y. (2020, January). Modernization of the Social Sphere in Terms of the Development of the System of Preschool Education of Children in Rural Areas. In International Scientific Conference the Fifth Technological Order: Prospects for the Development and Modernization of the Russian Agro-Industrial Sector (TFTS 2019), 103-108.

Gruber, T., Fuß, S., Voss, R., & Gläser-Zikuda, M. (2010). Examining Student Satisfaction with Higher Education Services: Using A New Measurement Tool. International Journal of Public Sector Management, 23(2), 105-123.

https://doi.org/10.1108/09513551011022474

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

Hosizah, H., Kuntoro, K., & Basuki, N. (2016). Intention and Usage of Computer Based Information Systems in Primary Health Centers. International Journal of Evaluation and Research in Education, 5(2), 113-118.

https://doi.org/10.11591/ijere.v5i2.4529

Hsu, W. H. (2015). Transitioning to a communication-oriented pedagogy: Taiwanese university freshmen's views on class participation. System, 49(1), 61-72. https://doi.org/10.1016/j.system.2014.12.002

Khan, M. J., Reddy, L. K. V., Khan, J., Narapureddy, B. R., Vaddamanu, S. K., Alhamoudi, F. H., Vyas, R., Gurumurthy, V., Altijani, A. A. G., & Chaturvedi, S. (2023). Challenges of E-Learning: Behavioral Intention of Academicians to Use E-Learning during COVID-19 Crisis. Journal of Personalized Medicine, 13(3), 555. http://dx.doi.org/10.3390/jpm13030555

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, Y.-J., & Lee, S.-H. (2021). The Relationships among Quality of Online Education, Learning Immersion, Learning Satisfaction, and Academic Achievement in Cooking-Practice Subject. Sustainability, 13(21), 12152.

http://dx.doi.org/10.3390/su132112152

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

Llorent García, V. J., González Gómez, A. L., Farrington, D. P., & Zych, I. (2022). Improving literacy competence and social and emotional competencies in Primary Education through Cooperative Project-Based Learning. Psicothema, 34(1), 102-109.

Ludlow, P. (1991). The New European Community (1st ed.). Routledge

Luszczynska, A., Schwarzer, R., & Schwarzer, C. (2005). Self-efficacy and social support predict benefit finding 12 months after cancer surgery: The mediating role of coping strategies. Psychology, Health & Medicine, 10(4), 365-375.

https://doi.org/10.1080/13548500500093738

McClure, M., Tarr, P., Thompson, C. M., & Eckhoff, A. (2017). Defining quality in visual art education for young children: Building on the position statement of the Early Childhood Art Educators. Arts Education Policy Review, 118(3), 154-163.

https://doi.org/10.1080/10632913.2016.1245167

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.

National Endowment for the Arts. (2012). The arts and achievement in at-risk youth: Findings from four longitudinal studies. https://www.arts.gov/sites/default/files/Arts-At-Risk-Youth.pdf

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

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

Power, S., & Taylor, C. (2013). Social justice and education in the public and private spheres. Oxford Review of Education, 39(4), 464-479. https://doi.org/10.1080/03054985.2013.821854

Rohrbach, M. (2011). Learning Styles in the Art Room. Arts & Activities, 150(2), 28-29.

Ryan, R. M., Deci, E. L., & Vansteenkiste, M. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definition, theory, practices, and future directions. Contemporary Educational Psychology, 44, 1-68.

https://doi.org/10.1016/j.cedpsych.2020.101860

School Education Gateway. (2018). Poll on arts for learning: Results. https://www.schooleducationgateway.eu/en/pub/viewpoints/surveys/poll-on-arts-for-learning.htm

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. https://doi.org/10.1016/j.jfoodeng.2005.02.010

She, L., Ma, L., Jan, A., Sharif Nia, H., & Rahmatpour, P. (2021). Online Learning Satisfaction During COVID-19 Pandemic Among Chinese University Students: The Serial Mediation Model. Frontiers in Psychology, 12, 743936.

https://doi.org/10.3389/fpsyg.2021.743936

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

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. www.danielsoper.com/statcalc/default.aspx

Stevens, J. P. (1992). Applied multivariate statistics for the social sciences (2nd ed.). Erlbaum.

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

Tsang, S. K., Hui, E. K., & Law, B. (2012). Self-efficacy as a positive youth development construct: a conceptual review. The Scientific World Journal, 2(9), 1-20.

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.

Wilder, S. (2014). Effects of parental involvement on academic achievement: a meta-synthesis. Educational Review, 66(3), 377-397. https://doi.org/10.1080/00131911.2013.780009

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

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

Downloads

Published

2024-08-20

How to Cite

Fu, Y., Inthawadee, S., & Shinasharkey, T. (2024). Determinants of Behavioral Intention to Learn Arts Education of Postgraduate Students in Chengdu, China. Scholar: Human Sciences, 16(2), 122-131. https://doi.org/10.14456/shserj.2024.38