Factors Impacting Behavioral Intention of Animation Major Undergraduates Towards Hybrid Education in Chengdu
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Published:
Dec 28, 2021
Keywords:
Perceived Ease of Use, Perceived Usefulness, Self-Efficacy, Social Influence, Effort Expectancy.
Main Article Content
Abstract
This study examines the essential factors that significantly impact the behavioral intention of hybrid education among animation undergraduate students from three public universities in Chengdu of China. The researchers conducted the quantitative research methodology with 500 samples and distributed the questionnaire online and offline to the target respondents. The multistage sampling technique was executed and constructed using judgmental sampling and stratified random sampling to gather data. The Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were utilized for statistical analysis, including the goodness of model fits, validity and reliability for each construct’s examination and hypotheses test. The results have been proven to accomplish research objectives and revealed that all variables have a significant impact among its pairs in which attitude towards use presented the strongest most influence on behavioral intention. Therefore, office of academic affairs of public universities is recommended to evaluate the essential influencers for the current hybrid education implementation pattern to strengthen students’ acceptance and learning achievements.
Article Details
Section
บทความวิจัย (research article)
References
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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.
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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.
Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8 (1), 56-75.
Golnaz, R., Zainulabidin, M., Mad Nasir, S., & Eddie Chiew, F.C. (2010). Non-Muslim Perception Awareness of Halal Principle and Related Food Products in Malaysia. International Food Research Journal, 17(3), 667-674.
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Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation: Pearson Education Australia.
Mansour, B., & Mupinga, D. M. (2007). Students’ Positive and Negative Experiences in Hybrid and Online Classes. College Student Journal, 41(1), 242-248.
Mazman, S., Usluel, Y., & Çevik, V. (2009). Social Influence in The Adoption Process and Usage of Innovation: Gender Differences. International Journal of Behavioral, Cognitive, Educational and Psychological Sciences, 1(4), 229-232.
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Pintrich, R. (1999). The Role of Motivation in Promoting and Sustaining Self-Regulated Learning. International Journal of Educational Research, 31(6), 459-470.
Popma, J. (2012). Is Hybrid Education and Videoconferencing the Wave of the Future for Online Courses? Journal of Instructional Research, 67-72.
Preacher, K. & Hayes, A. (2008). Asymptotic And Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behavior Research Methods, 40 (3), 879-891. https://doi.org/10.1145/3361785.3361808.
Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis: IGI Global Press.
Soper, D. S. (2019). A-priori Sample Size Calculator for Structural Equation Models [Software]. http://www.danielsoper.com/statcalc.
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. https://doi.org/10.1080/10494820.2011.641674.
Oh, S., Ahn, J., & Kim, B. (2003). Adoption of Broadband Internet in Korea: The Role of Experience in Building Attitudes. Journal of Information Technology, 18. https://doi.org/10.1080/0268396032000150807.
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), 96-115. https://doi.org/10.28945/4085.
Venkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (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.
Xiang, C. (2019). The Current Issue of the Digital Game Concept Art Education for Chinese Undergraduates. International Journal of Information and Education Technology, 9 (6), 419 - 422. https://doi.org/10.18178/ijiet.2019.9.6.1238.
Yang, D., Yang, A., & Hang, S. (2020). Annual report on China’s education 2020. Social Sciences Academic press.
Asadi, S., Nilashi, M., Husin, A.R.C. & Yadegaridehkordi, E. (2016). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management, 18 (4), 305-330. https://doi.org/10.1007/s10799-016-0270-8.
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.
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Inc.
Benjangjaru, B., & Vongurai, R. (2018). Behavioral Intention of Bangkokians to Adopt Mobile Payment Services by Type of Users. AU-GSB E-JOURNAL, 11(1), 34.
Browne, W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models: Newbury Park, CA: Sage.
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.
Celik, V., & Yeşilyurt, E. (2013). Attitudes To Technology, Perceived Computer Self-Efficacy, And Computer Anxiety as Predictors of Computer Supported Education. Computers & Education, 60(1), 148-158.
Chauhan, S. (2015). Acceptance Of Mobile Money by Poor Citizens of India: Integrating Trust into The Technology Acceptance Model. Info, 17(3), 58-68.
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.
Davis, F. (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.
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.
Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8 (1), 56-75.
Golnaz, R., Zainulabidin, M., Mad Nasir, S., & Eddie Chiew, F.C. (2010). Non-Muslim Perception Awareness of Halal Principle and Related Food Products in Malaysia. International Food Research Journal, 17(3), 667-674.
Hair, J. F., Black., W. C., Babin., B. J., Anderson., R. E., & L.Tatham., R. (2006). Multivariant Data Analysis. Pearson International Edition.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Análise Multivariada de Dados. Bookman
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.
Herbart, J. (1806) Allgemeine Pädagogik Aus Dem Zweck der Erziehung Abgeleitet: Johann Friedrich Röwer Ort Göttingen.
Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB e-Journal, 14(1), 61-70.
Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation: Pearson Education Australia.
Mansour, B., & Mupinga, D. M. (2007). Students’ Positive and Negative Experiences in Hybrid and Online Classes. College Student Journal, 41(1), 242-248.
Mazman, S., Usluel, Y., & Çevik, V. (2009). Social Influence in The Adoption Process and Usage of Innovation: Gender Differences. International Journal of Behavioral, Cognitive, Educational and Psychological Sciences, 1(4), 229-232.
Mtebe, J., & Raisamo, R. (2014). Investigating Students’ Behavioral Intention to Adopt and Use Mobile Learning in Higher Education in East Africa. International Journal of Education and Development using Information and Communication Technology, 10(3). 5-20.
Ozgen, O., & Kurt, S. D. K. (2013). Purchasing Behavior of Islamic Brands: An Experimental Research. Paper presented at the 42nd Annual Conference of EMAC European Marketing Academy, Istanbul. https://doi.org/10.1145/3361785.3361808.
Pintrich, R. (1999). The Role of Motivation in Promoting and Sustaining Self-Regulated Learning. International Journal of Educational Research, 31(6), 459-470.
Popma, J. (2012). Is Hybrid Education and Videoconferencing the Wave of the Future for Online Courses? Journal of Instructional Research, 67-72.
Preacher, K. & Hayes, A. (2008). Asymptotic And Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behavior Research Methods, 40 (3), 879-891. https://doi.org/10.1145/3361785.3361808.
Sarmento, R., & Costa, V. (2016). Comparative Approaches to Using R and Python for Statistical Data Analysis: IGI Global Press.
Soper, D. S. (2019). A-priori Sample Size Calculator for Structural Equation Models [Software]. http://www.danielsoper.com/statcalc.
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. https://doi.org/10.1080/10494820.2011.641674.
Oh, S., Ahn, J., & Kim, B. (2003). Adoption of Broadband Internet in Korea: The Role of Experience in Building Attitudes. Journal of Information Technology, 18. https://doi.org/10.1080/0268396032000150807.
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), 96-115. https://doi.org/10.28945/4085.
Venkatesh, V., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (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.
Xiang, C. (2019). The Current Issue of the Digital Game Concept Art Education for Chinese Undergraduates. International Journal of Information and Education Technology, 9 (6), 419 - 422. https://doi.org/10.18178/ijiet.2019.9.6.1238.
Yang, D., Yang, A., & Hang, S. (2020). Annual report on China’s education 2020. Social Sciences Academic press.