Students’ behavioral intention on interactive video in primary Cinematography of Art Universities in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2023.15Keywords:
Interactive Video, Perceived Usefulness, Performed Ease of Use, Attitude, Self-EfficacyAbstract
The emergence of the COVID-19 has made some traditional classroom teaching impossible. Therefore, online teaching has become a compelling choice for higher education in China. However, the shortcomings of the weak sense of communication in online teaching leads to poor teaching quality. Especially in the cinematography major, as a highly practical major, online teaching methods cannot achieve the purpose of teaching. The emergence of interactive video technology has brought a turning point for this kind of practical professional network teaching. The study was to explore the effect of using interactive video in major cinematography classrooms and the acceptance of students. The theoretical basis of this research is the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Unified Theory of Acceptance and Use of Technology (UTAUT). The variables are perceived usefulness, perceived ease of use, attitude, social influence, self-efficacy, and behavioral intention. The 480 questionnaires were distributed to students from three universities. The data analysis was based on 451 valid questionnaires returned. The Structural Equation Model (SEM) SEM was used to validate the research hypothesis to determine the relationship between variables. The findings indicate that all six hypotheses proposed in this study are supported. The results showed that students' perceived ease of use when using interactive videos for learning directly affect their perceptions on the usefulness of interactive videos. Perceived ease of use and perceived usefulness directly impact attitude; that is, when students perceived that the interactive video is easy to operate or helpful, it positively affect their attitude towards interactive video. In addition, attitude, self-efficacy, and social influence are the three influencing factors in predicting students' behavioral intentions. The research results would promote the widespread use of interactive video in higher education by investigating cinematography students' behavioral intentions to use interactive video in professional.
References
Ali, M., Raza, S. A., Qazi, W., & Puah, C. H. (2018). Assessing e-learning system in higher education institutes Evidence from structural equation modelling. Interactive Technology and Smart Education, 15(1), 59-78.
Alleyne, P., & Lavine, M. (2013). Factors influencing accountants’ behavioral intentions to use and actual usage of enterprise resource planning systems in a global development agency. Journal of Financial Reporting and Accounting, 11(2), 179-200.
Arora, S., & Kaur, H. (2019). Exploring the bank selection criteria in India: scale development and validation. International Journal of Bank Marketing, 37(3). 666-690.
Ashaduzzaman, M., Jebarajakirthy, C., Weaven, S. K., Maseeh, H. I., Das, M., & Pentecost, R. (2022). Predicting collaborative consumption behaviour: a meta-analytic path analysis on the theory of planned behaviour. European Journal of Marketing, 56(4), 968-1013.
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.
Batara, E., Nurmandi, A., Warsito, T., & Pribadi, U. (2017). Are government employees adopting local e-government transformation? The need for having the right attitude, facilitating conditions and performance expectations. Transforming Government: People, Process and Policy, 11(4), 612-638.
Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. Emerald Publishing Limited, 22(4), 483-504.
Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM on using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30(11), 3192-3210.
Chou, S., & Chen, C. W. (2018). The influences of relational benefits on repurchase intention in service contexts: the roles of gratitude, trust and commitment. Journal of Business & Industrial Marketing, 33(5), 680-692.
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results [Ph.D. Dissertation]. MIT Sloan School of Management.
Demoulin, N. T. M., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management, 44(5), 540-559.
Dwivedi, Y. K., Choudrie, J., & Brinkman, W. P. (2006). Development of a survey instrument to examine consumer adoption of broadband. Industrial Management & Data Systems, 106(5), 700-718.
Erdogan, N., Ovali, P. K., & Kayapinar, O. (2022). Housing satisfaction in a traditional neighborhood of Izmit, Turkey. International Journal of Housing Markets and Analysis, 13(2), 251-266.
Fatima, J. K., Ghandforoush, P., Khan, M., & Masico, R. D. (2017). Role of innovativeness and self-efficacy in tourism m-learning. Tourism Review, 72(3), 344-355.
Folorunso, C. L., & Ahmad, M. H. (2014). One city; two conditions: exigent parameters for paint performance in Lagos. Nigeria, 61(1),10-19.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015-1033.
Getz, M. (1991). Interactive Video. The Bottom Line, 4(4), 40-42.
Ghazali, E. M., Mutum, D. S., Chong, J. H., & Nguyen, B. (2018). Do consumers want mobile commerce? A closer look at M-shopping and technology adoption in Malaysia. Asia Pacific Journal of Marketing and Logistics, 30(4), 1064-1086.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2015). Multivariate Data Analysis. (7th ed.). Prentice Hall.
Hamdani, M. R., Valcea, S., & Buckley, M. R. (2016). The MTMM matrix approach: implications of HRM research. Personnel Review, 45(6), 1156-1175.
Hati, S. R. H., Fitriasih, R., & Safira, A. (2020). E-textbook piracy behavior: An integration of ethics theory, deterrence theory, and theory of planned behavior. Journal of Information, Communication and Ethics in Society, 18(1), 105-123.
He, J. J. (2020). Characteristics and countermeasures of Interactive video in China. News Tribune, 2020(6), 29-31.
Heeler, R. M., & Ray, M. L. (1972). Measure validation in marketing. Journal of Marketing Research (JMR), 9(4), 361-70.
Hsiao, C., & Tang, K. (2014). Explaining undergraduates’ behavior intention of e-textbook adoption Empirical assessment of five theoretical models. Library Hi Tech, 32(1), 139-163.
Hu, J., & Zhang, Y. (2016). Chinese students’ behavior intention to use mobile library apps and effects of education level and discipline. Library Hi Tech, 34(4), 639-656.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Huang, J. M., Ho, T. K., Liu, Y. C., & Lin, Y. H. (2015). A discussion on the user intention of golfers toward golf GPS navigation. Journal of Hospitality and Tourism Technology, 6(1), 26-39.
Kim, J., Erdem, M., Byun, J. W., & Jeong, H. (2011). Training soft skills via e-learning: international chain hotels. International Journal of Contemporary Hospitality, 23(6), 739-763.
Lee, J., & Lee, Y. (2019). Does online shopping make consumers feel better? Exploring online retail therapy effects on consumers’ attitudes towards online shopping malls. Asia Pacific Journal of Marketing and Logistics, 31(2), 464-479.
Liu, L. G. (2004). The contribution of public libraries to countries’ economic productivity: a path analysis. Library Review, 53(9), 435-441.
Lu, Z. Y., & Ying, J. (2020). Research on the development problems and Strategies of interactive video under the background of industrialization. Yi Hai, 12, 147-150.
Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7),7383-756.
Moghavvemi, S., & Salleh, N. A. M. (2014). Effect of precipitating events on information system adoption and use behaviour. Journal of Enterprise Information Management, 27(5),599-622.
Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society, 12(3), 150-162.
Parsloe, E. (1983). Interactive Video. Journal of European Industrial Training. 7(3), 28-32.
Perry, A. (2017). Factors comprehensively influencing acceptance of 3D-printed apparel. Journal of Fashion Marketing and Management, 21(2), 219-234.
Science and Technology Department. (2020, August 25). 5G High-tech Video-Interactive Video Technology White Paper (2020). China Capital Beijing：National Radio and Television Administration. http://www.nrta.gov.cn/art/2020/8/25/art_3713_53728.html
Soper, D. S. (2019). A-priori Sample Size Calculator for Structural Equation Models. http://www.danielsoper.com/statcalc.
Suki, N. M. (2016). Willingness of patrons to use library public computing facilities: insights from Malaysia. The Electronic Library, 34(5), 823-845.
Tarhini, A., Masa’deh, R., Al-Busaidi, K. A., Mohammed, A. B., & Mahmoud, M. (2017). Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182.
Teo, T. (2011). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology. Wide Information Systems, 28(2), 93-101.
Ukut, I. I. T., & Krairit, D. (2019). Justifying students’ performance: A comparative study of both ICT students’ and instructors’ perspective. Interactive Technology and Smart Education,16(1), 18-35.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
Wang, Y. S., Tseng, T. H., Wang, Y. M., & Chun, C. W. (2020). Development and validation of an internet entrepreneurial self-efficacy scale. Internet Research, 30(2), 635-675.
Watjatrakul, B. (2013). Intention to use a free voluntary service the effects of social influence, knowledge and perceptions. Journal of Systems and Information Technology, 15(2), 202-220.
Wu, G., Zheng, J., Zhao, X., & Zuo, J. (2020). How does strength of ties influence project performance in Chinese megaprojects? A conflict-based perspective. International Journal of Conflflict, 31(5), 753-780.
Zhao, Y. (2021). Narrative and Immersion: Interaction types and user experience in Bilibili’s interactive short videos. Journal of Southwest University for Nationalities, 2021(2),129-134.
Zimmerman, R. D., & Darnold, T. C. (2009). The impact of job performance on employee turnover intentions and the voluntary turnover process A meta-analysis and path model. Personnel Review, 38(2), 142-158.
