User Intention towards a Music Streaming Service: A Thailand Case Study

Abstract

This paper presents a novel acceptance model for an online music streaming scenario of Thailand. The music streaming industry has been gaining in popularity in the recent times.  This research has been conducted in order to measure the user attitude towards the use of this relatively new service using a modified version of the popular Technology Acceptance Model. We try to identify the most popular music-streaming service of Thailand and also the factors that affect the use of such a service. Data has been collected in the form of an online questionnaire survey from more than 300 participants for the purpose of model building and validation. A subsequent regression analysis carried out on the proposed model explains more than 60 percent of the variance of the dependent variable i.e. Behavioral Intention in our case to the predictor variables Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment and Perceived Satisfaction Level. The results show that Perceived Enjoyment and Perceived satisfaction are the two strongest predictors for Behavioral Intention which is quite different from that of the utilitarian type of information systems.

Keywords: Music streaming, TAM, hedonic information systems, regression

References
[1] Cisco Global Mobile Data Traffic Forecast Update Report, 2014-2019, Cisco White Paper (2016).


[2] Sari Komulainen, Minna Karukka, and Jonna Häkkilä. Social music services in teenage life: a case study. In Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on ComputerHuman Interaction (OZCHI ’10). ACM, New York, NY, USA, 2010, 364-367.


[3] Leena Arhippainen and Seamus Hickey. Classifying music user groups and identifying needs for mobile virtual music services. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (MindTrek ’11). ACM, New York, NY, USA, 2011, 191-196.


[4] Fred D. Davis. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 3 (September 1989), 319-340.


[5] Lee, Younghwa, Kenneth A. Kozar, and Kai RT Larsen. The technology acceptance model: Past, present, and future. Communications of the Association for information systems 12.1 (2003): 50.


[6] Marina Abad, Itxaso Díaz, and Markel Vigo. Acceptance of mobile technology in hedonic scenarios. In Proceedings of the 24th BCS Interaction Specialist Group Conference (BCS ’10). British Computer Society, Swinton, UK, 2010, 250-258.


[7] Hans Heijden. User acceptance of hedonic information systems. MIS Q. 28, 4 (December 2004), 695-704.


[8] Sheppard, Blair H., Jon Hartwick, and Paul R. Warshaw. The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer research 15.3 (1988): 325-343.


[9] Moon, Ji-Won, and Young-Gul Kim. Extending the TAM for a World-Wide-Web context. Information & management 38.4 (2001): 217-230.


[10] Mun Y. Yi and Yujong Hwang. Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. Int. J. Hum.-Computer. Studies. 59, 4 (October 2003), 431-449.


[11] Ritu Agarwal and Elena Karahanna. Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage1. MIS Q. 24, 4 (December 2000), 665-694.


[12] Venkatesh, V. and Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 2008, 39: 273–315.


[13] Andrew L, Peter W, Shaun F, Nigel T and Louise C. On the reproduction of the musical economy after the Internet. Media, Culture and Society, 2005, 27(2), 289-290.


[14] International Federation of the Phonographic Industry Digital Music Report, last accessed 8th August, 2017 at http://www.ifpi.org/news/IFPI-GLOBAL-MUSICREPORT-2017


[15] Suvi Silfverberg, Lassi A. Liikkanen, and Airi Lampinen. I’ll press play, but I won’t listen: profile work in a music-focused social network service. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (CSCW ’11). ACM, New York, NY, USA, 2011, 207-216.


[16] Kurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, and Romain Yon. Music Personalization at Spotify. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16). ACM, New York, NY, USA, 2016, 373-373.


[17] Bruce Ferwerda, Emily Yang, Markus Schedl, and Marko Tkalcic. Personality Traits Predict Music Taxonomy Preferences. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ’15). ACM, New York, NY, USA, 2015, 2241-2246.


[18] William Odom, John Zimmerman, and Jodi Forlizzi. Teenagers and their virtual possessions: design opportunities and issues. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11). ACM, New York, NY, USA, 2011, 1491-1500.


[19] William Odom, John Zimmerman, and Jodi Forlizzi. Virtual possessions. In Proceedings of the 8th ACM Conference on Designing Interactive Systems (DIS ’10). ACM, New York, NY, USA, 2010, 368-371.


[20] Rebecca D. Watkins, Abigail Sellen, and Siân E. Lindley. Digital Collections and Digital Collecting Practices. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). ACM, New York, NY, USA, 2015, 3423-3432.


[21] Joy Ng and Jude Yew. Why Download When You Can Stream?: The Experience of Collecting Music in the Streaming Age. In Proceedings of the 3rd International Conference on Human Computer Interaction and User Experience in Indonesia (CHIuXiD ’17), ACM, New York, NY, USA, 2017, 28-33.


[22] Zoonky Lee, JaeKyung Lee, Sang-goo Lee, HeungSun Park and Hyunsoo Kim. The effect of psychological ownership on the possession attachment and willingness to share the Internet content. 24th IEEE International Conference on Management of Innovation and Technology, Bangkok, 2008, pp. 722-726