An Empirical Study of Millennial Customers’ Buying Intentions for Entertainment Ticket Online Purchases According to the Technology Acceptance Model
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Published:
Dec 6, 2018
Keywords:
Buying intention Perceive usefulness Perceived ease of use Millennial customers
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Abstract
This research considered the factors influencing targets of millennial customers’ online buying intentions, and aimed to give suggestions concerning the business implications and opportunities for entertainment ticket online sales. The study involved millennial generation respondents (n=394) who were experienced in purchasing entertainment tickets online. The approach taken was through confirmatory factor analysis using five constructs: website quality, perceived ease of use, perceived usefulness, trust, and attitude toward online buying. The Structural Equation Modelling (SEM) technique was used to analyse the causal relationships among 10 hypotheses based on the empirical data. The results supported six out of ten hypotheses. The Technology Acceptance Model (TAM) was a useful theoretical tool to understand and predict millennial users’ buying intentions for entertainment ticket online purchases. The managerial implication for online ticket companies is to focus on perceived ease of use of their websites or applications that – along with other features – influence trust and attitudes toward online purchases and affect online buying intentions.
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Research Articles
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References
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Zhao, Q., Chen, C., & Wang, J. (2016). The effects of psychological ownership and TAM on social media loyalty. Telematics and Informatics, 33(4), 959-972. Retrieved from https://dl.acm.org/citation.cfm?id= 2937248.2937369
Comegys, C., & Brennan, M. (2003). Students’ online shopping behavior: A dual-country perspective. Journal of Internet Commerce, 2(2), 69-87.
Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, Massachusetts Institute of Technology, Sloan School of Management, Cambridge: MA. Retrieved from https://dspace.mit.edu/handle/1721.1/15192
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Retrieved from https://www.jstor.org/stable/249008?seq=1#page_ scan_tab_contents
Delone, W., & McLean, E. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. Retrieved from https://pdfs.semanticscholar.org/e086/a205220b7746e08ce9b7e031f32e1714d25d.pdf
Ganguly, B., Dash, S., Cyr, D., & Head, M. (2010). The effects of website design on purchase intention in online shopping: The mediating role of trust and the moderating role of culture. International Journal of Electronic Business, 8(4), 302-330. Retrieved from https://www.researchgate.net/publication/ 220300397_The_effects_of_website_design_on_purchase_intention_in_online_shopping_The_mediating_role_of_trust_and_the_moderating_role_of_culture
Entertainment and Media Outlook 2016-2020. (2016). 7th annual edition 2016, an in-depth analysis of the trends shaping the entertainment and media industry in South Africa, Nigeria and Kenya. Retrieved from https://www.pwc.co.za/en/assets/pdf/enm/entertainment-and-media-outlook-2016-2020.pdf
Hair, J., Black, W., Babin, B., & Anderson, R. (2009). Multivariate data analysis (7th ed.). New York, NY: Prentice Hall.
Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), (2nd edition). Sage Publications: Thousand Oaks.
Hajli, N. (2015). Social commerce constructs and customer’s intention to buy. International Journal of Information Management, 35(2), 183-191. Retrieved from https://www.sciencedirect.com/ science/article/pii/S026840121400125X?via%3Dihub
Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structure Equation Modeling, 6(1), 1-55. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/10705519909540118
Karvounis, N. (2015). 4 Gen Y trends that affect your practice. Journal of Financial Planning, 17(185), 54-56. Retrieved from https://www.onefpa.org/journal/Pages/MAR15-4-Gen-Y-Trends-that-Affect-Your-Practice.aspx
Lee, S. (2009). How do online reviews affect purchasing intention? African Journal of Business Management, 3(10), 576-581. Retrieved from http://academicsjournals.org/ajbm
Manager Online News. (2017). Bangkok Entertainment Fest 2017. Manager Online. Retrieved from http://www.manager.co.th.
Pando-Garcia, J., Perianez-Canadillas, I., & Charterina, J. (2016). Business simulation games with and without supervision: An analysis based on the TAM model. Journal of Business Research, 69, 1731-1736. Retrieved from https://ideas.repec.org/a/eee/jbrese/v69y2016i5p1731-1736.html
Park, S. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162. Retrieved from https://www.researchgate.net/publication/308173476_
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614-622. Retrieved from https://www.sciencedirect.com/science/article/pii/S0747563213002847?via%3Dihub
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Journal Computers & Education, 71, 247-256. Retrieved from https://www.sciencedirect.com/ science/article/pii/S0360131513002790?via%3Dihub
Suwunniponth, W. (2014). Factor driving consumer intention in online shopping. International Journal of Economics and Management Engineering, 8(6), 1949-1953. Retrieved from https://waset.org/ publications/9998734/factor-driving-consumer-intention-in-online-shopping
Wang, W., & Liu, Y. (2009). Attitude, behavioral intention and usage: An empirical study of Taiwan railway’s internet ticketing system. In South West Decision Sciences Institute 2009 Proceedings, 25-28. Retrieved from https://www.semanticscholar.org/paper/Attitude%2C-Behavioral-Intention-and-Usage%3A-An-Study-Wang-Liu/1092484206f158d00376b5d16fbd700db1d3bb3a
Wixom, B., & Watson, H. (2001). An empirical investigation of the factors affecting data warehousing success. MIS quarterly, 25(1), 17-41. Retrieved from https://www.researchgate.net/publication/220260021_ An_Empirical_Investigation_of_the_Factors_Affecting_Data_Warehousing_Success
Zhao, Q., Chen, C., & Wang, J. (2016). The effects of psychological ownership and TAM on social media loyalty. Telematics and Informatics, 33(4), 959-972. Retrieved from https://dl.acm.org/citation.cfm?id= 2937248.2937369