Positive Experience and Motivation towards the Acceptance of Streaming Entertainment Service Application of Government University Students
Article Sidebar
Main Article Content
Abstract
The objective of this research was to investigate positive experience as well as motivation that affected the acceptance of the streaming entertainment service applications of government university students. The sample in this research was comprised of the government university students who used the streaming entertainment applications. The total of 400 questionnaires were distributed using the quota sampling method. The results showed that the motivation, positive emotional experience, positive social experience, and positive functional experience affected the perceived ease of use and perceived usefulness of streaming entertainment service applications. In addition, the perceived ease of use affected the perceived usefulness of streaming entertainment service applications. Moreover, both the perceived ease of use and perceived usefulness affected the intention to use streaming entertainment applications.
When considering the results of the influence path analysis- both direct and indirect, it was found that the motivation, positive social experience, and positive emotional experience had the direct influence on the perceived ease of use of the streaming entertainment applications which were statistically significant at the 0.001 level and had the indirect influence on the perceived usefulness of the streaming entertainment applications with the significance level at 0.05.
The positive functional experience had the direct influence on the perceived ease of use of the streaming entertainment applications which was statistically significant at the 0.01 level and had the direct and indirect influences on the perceived usefulness of streaming entertainment applications using with the significance at 0.001 level.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Alben, L. (1996). Quality of experience: Defining the criteria for effective interaction design. Interactions, 3(3), 11-15
Amonwiwat, S., Rattanaphinyowong, T., Homchampa, T., Minthakhin, N., Phaophongphaibun, S. and Arakwichanan, N. (2014). Strategies for Gen Y Consumers. Economic Intelligence Center (EIC).
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.
Chen, H.R. and Tseng, H.F. (2012). Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), 398–406.
Cho, C. Y. (2015). Exploring factors that affect usefulness, ease of use, trust, and purchase intention in the online environment. International Journal of Management & Information Systems, 19(1), 21-36
Cocosila, M. and Igonor, A. (2012). Perceived value of social media : An empirical investigation. In Proceedings of the International Conference on Information Resources Management. Vienna, Austria.
Davis, F. D. (1985). A Technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, B.S., Massachusetts Institute of Technology, Massachusetts
Deci, E. L. and Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Publishing Co. Electronic Transactions Development Agency. (2019). Report of the survey of internet user behavior in Thailand 2019. Ministry of Digital Economy and Society.
Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fu, F., Yu, S. C. and Ting, C. J. (2012), The ignored concept on development of educational information technology. Procedia-Social and Behavioural Sciences, 64, 447-456.
Hair, J. F., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998). Multivariate data analysis (5th ed.). New Jersey: Prentice-Hall.
Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Prentice Hall.
Hair, J. F., Blak, W. C., Barbin, B. J., Anderson, R. E. and Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, NJ: Prentice-Hall.
Hamid, A. A., Razak, F. Z. A., Bakar, A. A. and Abdullah, W. S. W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Economics and Finance, 35, 644-649.
Holbrook, M. B. (1999). Introduction to consumer value. In: Holbrook, M.B. (Ed.), Consumer Value: A Framework for Analysis and Research. Routledge, London: 1–28.
Hu, L. and Bentler, P. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. A Multidisciplinary Journal, 6(1), 1-55.
Huang, F., Teo, T. and Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591.
Inan D. I., Abidin, Z., Hidayanto, A. N., Rianto, M. E., Zakiri, F., Praharsa, M. D., et al. (2020). Factors influencing mobile tourism recommender systems adoption by smart travellers: Perceived value and parasocial interaction perspectives. International Conference on Human - Computer Interaction HCII 2020: Design (pp. 52-62). Operation and Evaluation of Mobile Communications.
Jay, A. (2020). Number of Netflix subscribers in 2020/2021: Growth, Revenue and Usage Finances online. Retrieved March 24, 2020, from https://financesonline.com/number-of-netflix-subscribers
Kiattipong, N. (2015). Behaviorial study of consumer’s decision making in using car service application. Independent research Faculty of Commerce and Accountancy, M.Sc., Thammasat University. Retrieved January 18, 2020, from https://doi.nrct.go.th//ListDoi/listDetail?Resolve_DOI=10.14457/TU.the.2015.1355
Lindstrom, M. (2005). Brand sense: build powerful brands through touch, taste, smell, sight, and sound. New York: Free Press.
Liwatthanakit, W. (2009). Behavioral Intention of the Users of a University Human Resource Management System. Chulalongkorn Business Review, 31(121), 109-133.
Loo, R. and Thorpe, K. (2000). Development and application of the life roles inventory–values scale. Canadian Journal of Counseling, 34(4), 297–308.
Maneewong, P. (2020). Factors affecting the intention in using the express program of accountants in higher education. Journal of the Society of Researchers, 25(1), 475-493.
Md, A. A. D. M. N., Ayub, A. F. M. and Jaafar, W. M. W. (2017). Influence of students’ perceived ease of use, perceived usefulness and time spent towards students’ continuance intention using MOOC among public university students. In International conference on Education Muslim Society (ICEMS2017), 264-268.
Mongkolsubkul, W., Distanont, A., Khongmalai, O. and Noppakunthammachart, J. (2016). Factors affecting adoption of e-Government service: A case study of e-Revenue. KMUTT Research and Development Journal, 39(1), 3-9.
Nithisiripong, S. and Thongmak. M. (2017). Factors influencing behavioral intention to use QR code app for shopping in a virtual store. Chulalongkorn Business Review, 39(2), 90-121.
Park, N., Lee, K. M. and Cheong, P.H. (2008). University instructors’ acceptance of electronic courseware:An application of the Technology Acceptance Model. Journal of Computer-Mediated Communication, 13(1), 163–186.
Rodthong. S. (2013). Behavioral intention in downloading mobile application among smartphone users. Retrieved January 26, 2020, from http://www.repository.rmutt.ac.th/dspace/bitstream/123456789/2008/1/139314.pdf
Ryan, R. M. and Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
Sheth, J. N., Newman, B. I. and Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170.
Suriyapaitool, W. (2017). The Effects of Perceived Usefulness and Ease of Use on Attitude and Consumers’ Purchased Intentions of Fashion Products via M-commerce. Retrieved February 9, 2020, from http://kb.psu.ac.th/psukb/handle/2016/11710
Thong, J. Y. L., Hong, S. J. and Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
Wang, C. Y., Chou, S. C. T. and Chang, H. C. (August 12-15, 2009). Examining the impacts of perceived value and perceived quality on users’ intention to join web 2.0 communities. In Proceedings of the 11th International Conference on Electronic Commerce (329–334). Taipei: Association for Computing Machinery.
Wang, L. Y. K., Lew, S. L., Lau, S. H. and Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Retrieved November 12, 2021, from https://doi.org/10.1016/j.heliyon.2019.e01788
Wu, B. and Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
Yeemalee. N. (2018). The influence of perceived ease of use, perceived benefits and electronic word-of-mouth (E-word of mouth) on generation Y consumers’ intention to use a movie application and series in Bangkok. Retrieved January 8, 2020, from http://dspace.bu.ac.th/jspui/handle/123456789/3071
Youn, S. Y. and Lee, K. H. (2019). Proposing value-based technology acceptance model: testing on paid mobile media service. Fashion and Textiles, 6(1), 1-16
Zikmund, W., Babin, B., Carr, J. and Griffin, M. (2013). Business Research Methods (9th ed.). Mason, OH: South-Western.