Factors Affecting Behavioral Intention and Usage Behavior of Mixed Painting Education of Students in Chengdu, China

Main Article Content

Xiaoyan Zhan

Abstract

Purpose: This study investigates factors affecting students’ behavioral intention and actual use of mixed painting education in Chengdu, China. The research model is perceived ease of use, perceived usefulness, attitude, social influence, facilitation conditions, behavioral intention, and actual usage. Research design, data, and methodology: The researchers adopted a quantitative approach (n=500) and sent questionnaires to 9–11-year-old students' parents. The sampling techniques are purposive, quota, convenience sampling, when collecting data and distributing online and offline surveys. Before the data collection, the index of item-objective congruence (IOC) and Cronbach’s Alpha for pilot test (n=50) were employed. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) are used for data analysis, including model fit, reliability, and structure validity. Results: Perceived ease of use has a significant effect on perceived usefulness. Perceived usefulness and perceived ease of use significantly affect attitude. Perceived usefulness, perceived ease of use, social influence, and facilitating conditions significantly affect behavioral intention. Behavioral intention has a significant effect on actual usage. Conclusion: The teaching plan of non-academic art schools should pay more attention to the behavior intention and practical application of students and parents. Therefore, operators should also pay attention to cultivating parents' awareness of investment in art education.

Downloads

Download data is not yet available.

Article Details

How to Cite
Zhan, X. (2024). Factors Affecting Behavioral Intention and Usage Behavior of Mixed Painting Education of Students in Chengdu, China. AU-GSB E-JOURNAL, 17(3), 102-111. https://doi.org/10.14456/augsbejr.2024.53
Section
Articles
Author Biography

Xiaoyan Zhan

Jintang Branch School of Chengdu Normal Affiliated Primary School, China.

References

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

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

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

Balakrishnan, V. (2017). Key determinants for intention to use social media for learning in higher education institutions. Universal Access in the Information Society, 16(2), 289–301. https://doi.org/10.1007/s10209-016-0457-0

Bardakcı, S. (2019). Exploring high school students' educational use of YouTube. International Review of Research in Open and Distributed Learning, 20(2), 1-10. 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

Betsy McCoach, D., & Newton, S. D. (2016). Confirmatory factor analysis. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 851– 872). Sage Publications.

Bonnes, M., & Bonaiuto, M. (2002). Environmental psychology: From spatial-physical environment to sustainable development. In R. B. Bechtel & A. Churchman (Eds.), Handbook of environmental psychology (pp. 28–54). John Wiley & Sons.

Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies-Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage Publications.

Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.

Chang, A. (2012). UTAUT and UTAUT 2: A review and Future Research agenda. Winners, 13(2), 106-114.

Chauhan, S. (2015). Acceptance of mobile money by poor citizens of India: Integrating trust into the technology acceptance model. Info, 17(3), 58–68. https://doi.org/10.1108/info-02-2015-0018

Clark, V. L. P., & Ivankova, N. V. (2016). Mixed methods research: A guide to the field (1st ed.). Sage Publications.

Cooper, D., & Schindler, P. (2014). Business research methods (12th ed.). McGraw Hill.

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

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982−1003.

DeLone, W. H., & McLean, E. R. (2016). Statistics systems achievement size. In series in data technology manipulate (1st ed.). Now Publishers.

Do, T.-T., Huang, K. C., Do, T., & Trinh, T. P. T. (2021). Factors influencing teachers’ intentions to use realistic mathematics education in Vietnam: An extension of the theory of planned behavior. Journal on Mathematics Education, 12(2), 331–348. https://doi.org/10.22342/jme.12.2.14094.331-348

Efiloğlu, K. Ö., & Tingöy, Ö. (2017). The acceptance and use of a virtual learning environment in higher education: An empirical study in Turkey, and the UK. International Journal of Educational Technology in Higher Education, 14(26), 1–15. https://doi.org/10.1186/ s41239-017-0064-z

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. https://doi.org/10.1080/002075498192111

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research (1st ed.). Addison-Wesley.

Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional Students' Life Satisfaction Scale. Personality and Individual Differences, 25(5), 965–971. https://doi.org/10.1016/S0191-8869(98)00115-9

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed). Pearson.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant data analysis (6th ed.). Pearson Education.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results, and higher acceptance. Long Range Planning: International Journal of Strategic Management, 46(1-2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology, 51(6), 2306–2325. https://doi.org/10.1111/bjet.12905

Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based totally absolutely adoption of cell net: Empirical research. Choice support structures, 43(1), 111–126. https://doi.org/10.1016/j.Dss.2005.05.009

Lee, D. K., In, J., & Lee, S. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220–223. https://doi.org/10.4097/kjae.2015.68.3.220

Liao, S.-H., Fei, W.-C., & Chen, C.-C. (2007). Knowledge sharing, absorptive capacity, and innovation capability: An empirical study of Taiwan's knowledge-intensive industries. J. Information Science, 33(3), 340-359. https://doi.org/10.1177/0165551506070739

Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2017). Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the Association for Information Science and Technology, 68(3), 623–637. https://doi.org/10.1002/asi.23699

McCauley, R., & Kilgour, D. (1990). Effect of Air Temperature on Growth of Largemouth Bass in North America. Transactions of the American Fisheries Society, 119, 276-281. https://doi.org/10.1577/1548-8659(1990)119<0276:EOATOG>2.3.CO;2

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206 (00)00061-6

Moon, M. A., Habib, M. D., & Attiq, S. (2015). Analyzing the sustainable behavioral intentions: Role of norms, beliefs and values on behavioral intentions. Pakistan Journal of Commerce and Social Sciences, 9(2), 524–539.

Mtebe, J. S., & Raisamo, R. (2014). Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. The International Review of Research in Open and Distance Learning, 15(1), 251–271. https://doi.org/10.19173/irrodl.v15i1.1687

Pando-Garcia, J., Periañez-Cañndillas, I., & Charterina, J. (2016). Business simulation games with and without supervision: An analysis based on the TAM model. Journal of Business Research, 69(5), 1731–1736. https://doi.org/10.1016/j.jbusres.2015.10.046

Petter, S., & McLean, E. R. (2009). A metanalytic evaluation of the DeLone and McLean IS success model: An exam of IS achievement at the individual degree. Records & manage, 46(3), 159–166. Https://doi.org/10.1016/j.Im.2008.12.006

Rubaai, N., & Hashim, H. (2019). Polytechnic ESL lecturers’ acceptance of using massive open online course (MOOC) for teaching English as a second language (ESL). International Journal of Innovative Technology and Exploring Engineering, 8(9), 114–121. https://doi.org/10.35940/ijitee.I7530.078919

Shiue, M., & Hsu, Y. (2017). Digital game’s impacts on students’ learning effectiveness of correct medication. International Journal of Management, Economics and Social Sciences Special Issue-International Conference on Medical and Health Informatics, 6(1), 157–165.

Sife, A., Lwoga, E., & Sanga, C. (2007). New technologies for teaching and learning: Challenges for higher learning institutions in developing countries. International Journal of Education and Development Using ICT, 3(2), 57–67. https://www. learntechlib.org/p/42360

Stommel, M., Wang, S., Given, C. W., & Given, B. (1992). Focus on psychometrics confirmatory factor analysis (CFA) as a method to assess measurement equivalence. Research in Nursing & Health 15(5), 399–405. https://doi.org/10.1002/nur.4770150508

Taherdoost, H. (2016). Sampling methods in research methodology; How to choose a sampling technique for research. International Journal of Academic Research in Management, 5(2), 18–27. https://doi.org/10.2139/ssrn.3205035

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

Teel, C., & Verran, J. A. (1991). Factor comparison across studies. Research in Nursing & Health, 14(1), 67–72. https://doi.org/10.1002/nur.4770140110

Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using Information and Communication Technology. 93(3), 71–85.

Usluel, Y. K., & Mazman, S. G. (2010). Eğitimde yeniliklerin yayılımı, kabulü ve benimsenmesi sürecinde yer alan öğeler: bir içerik analizi çalışması. Çukurova University Faculty of Education Journal, 3(39), 60–74.

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.

Wang, C., Jeng, Y., & Huang, Y. (2016). What influences teachers to continue using cloud services? The role of facilitating conditions and social influence. The Electronic Library, 35(3), 520–533. https://doi.org/10.1108/el-02-2016-0046

Wozney, I., Venkatesh, V., & Abrama, P. (2006). Implementing computer technologies: Teachers’ perception and practice. Journal of Technology and Teacher Education, 14(1), 173–187.

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

Xie, Y. (2012). Sociological methodology and quantitative research (2nd ed.). Social Science Academic Press.

Zait, A., & Bertea, P. E. (2014). Methods for testing discriminant validity. Management & Marketing, 9(2), 217–224.

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