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Exploring user acceptance of streaming media devices: an extended perspective of flow theory

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Abstract

Streaming media devices have recently become one of the innovative IT devices used to replace traditional smart TV sets. In order to examine user acceptance of streaming media device, this study proposes an extended research model based upon flow theory and investigates the relationship among flow, perceived usefulness, product-related characteristics (i.e., content quality, functionality, ease of use, portability), and a manufacturer-related characteristic (i.e., trust). Partial least square methodology was employed to test the proposed model and corresponding hypotheses on data collected from 305 survey samples. The results showed that flow and perceived usefulness, two mediating variables, has a significant influence on usage intention. Among the four antecedents reflecting product-related attributes, content quality has the strongest effect on flow. Interestingly, functionality and ease of use affected only perceived usefulness in an indirect way through flow. Thus, flow mediates the effect of functionality and ease of use on perceived usefulness. This study discusses a number of implications and offers insights useful for both researchers and practitioners.

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References

  • Agarwal R, Karahanna E (2000) Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Q 24:665–694

    Article  Google Scholar 

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211

    Article  Google Scholar 

  • Bae Y, Chang H (2012) Adoption of smart TVs: a Bayesian network approach. Ind Manag Data Syst 112:891–910

    Article  Google Scholar 

  • Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25:351–370

    Article  Google Scholar 

  • Bradley J (2012) If we build it they will come? The technology acceptance model. In: Dwivedi YK, Wade MR, Schneberger SL (eds) Information systems theory. Springer, New York, pp 19–36

    Chapter  Google Scholar 

  • Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6:3–5

    Article  Google Scholar 

  • Chae M, Kim J, Kim H, Ryu H (2002) Information quality for mobile internet services: a theoretical model with empirical validation. Electron Mark 12:38–46

    Article  Google Scholar 

  • Chang HH, Wang IC (2008) An investigation of user communication behavior in computer mediated environments. Comput Hum Behav 24:2336–2356

    Article  Google Scholar 

  • Chin WW, Marcolin BL, Newsted PR (2003) A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf Syst Res 14:189–217

    Article  Google Scholar 

  • Chin WW, Thatcher JB, Wright RT (2012) Assessing common method bias: problems with the ULMC technique. MIS Q 36:1003–1019

    Google Scholar 

  • Choi DH, Kim J, Kim SH (2007) ERP training with a web-based electronic learning system: the flow theory perspective. Int J Hum Comput Stud 65:223–243

    Article  Google Scholar 

  • Chung J, Tan FB (2004) Antecedents of perceived playfulness: an exploratory study on user acceptance of general information-searching websites. Inf Manag 41:869–881

    Article  Google Scholar 

  • Csikszentmihalyi M (2000) Beyond boredom and anxiety: experiencing flow in work and play. Jossey-Bass, San Francisco, CA

  • Csikszentmihalyi M (1990) Flow: the psychology of optimal experience. Harper Collins, New York

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340

    Article  Google Scholar 

  • Fam KS, Foscht T, Collins RD (2004) Trust and the online relationship—an exploratory study from New Zealand. Tour Manag 25:195–207

    Article  Google Scholar 

  • Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18:39–50

    Article  Google Scholar 

  • Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27:51–90

    Article  Google Scholar 

  • Ghani JA (1995) Flow in human computer interactions: test of a model. In: Carey JM (ed) Human factors in information systems: emerging theoretical bases. Ablex, New Jersy, pp 291–311

    Google Scholar 

  • Guo YM, Poole MS (2009) Antecedents of flow in online shopping: a test of alternative models. Inf Syst J 19:369–390

    Article  Google Scholar 

  • Hair JF Jr, Hult GTM, Ringle C, Sarstedt M (2016) A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Washington, DC

    Google Scholar 

  • Hausman AV, Siekpe JS (2009) The effect of web interface features on consumer online purchase intentions. J Bus Res 62:5–13

    Article  Google Scholar 

  • Hoffman DL, Novak TP (1996) Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 60:50–68

    Article  Google Scholar 

  • Hong S-J, Tam KY (2006) Understanding the adoption of multipurpose information appliances: the case of mobile data services. Inf Syst Res 17:162–179

    Article  Google Scholar 

  • Hsu C-L, Lin JC-C (2015) What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electron Commer Res Appl 14:46–57

    Article  Google Scholar 

  • Hsu C-L, Lu H-P (2004) Why do people play on-line games? An extended TAM with social influences and flow experience. Inf Manag 41:853–868

    Article  Google Scholar 

  • Hsu C-L, Wu C-C, Chen M-C (2013) An empirical analysis of the antecedents of e-satisfaction and e-loyalty: focusing on the role of flow and its antecedents. Inf Syst e-Bus Manag 11:287–311

    Article  Google Scholar 

  • Hsu M-H, Chang C-M, Chu K-K, Lee Y-J (2014) Determinants of repurchase intention in online group-buying: the perspectives of DeLone & McLean IS success model and trust. Comput Hum Behav 36:234–245

    Article  Google Scholar 

  • Jackson J (2013) Google posts Chromecast development kit for third-party apps. PCWorld. http://www.pcworld.com/article/2045140/google-posts-chromecast-development-kit-for-thirdparty-apps.html. Accessed 12 Mar 2017

  • Joo J, Sang Y (2013) Exploring Koreans’ smartphone usage: an integrated model of the technology acceptance model and uses and gratifications theory. Comput Hum Behav 29:2512–2518

    Article  Google Scholar 

  • Jung Y, Perez-Mira B, Wiley-Patton S (2009) Consumer adoption of mobile TV: examining psychological flow and media content. Comput Hum Behav 25:123–129

    Article  Google Scholar 

  • Kim K, Hwang J, Zo H, Lee H (2016) Understanding users’ continuance intention toward smartphone augmented reality applications. Inf Dev 32:161–174

    Article  Google Scholar 

  • Kirs P, Bagchi K (2012) The impact of trust and changes in trust: a national comparison of individual adoptions of information and communication technologies and related phenomenon. Int J Inf Manag 32:431–441

    Article  Google Scholar 

  • Koufaris M (2002) Applying the technology acceptance model and flow theory to online consumer behavior. Inf Syst Res 13:205–223

    Article  Google Scholar 

  • Kumar P, Dass M, Topaloglu O (2011) Exploring satisfaction in business-to-business services: a path-analytic approach. Serv Bus 5:13–27

    Article  Google Scholar 

  • Lee DY, Lehto MR (2013) User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Comput Educ 61:193–208

    Article  Google Scholar 

  • Lee M-C, Tsai T-R (2010) What drives people to continue to play online games? An extension of technology model and theory of planned behavior. Int J Hum Comput Interact 26:601–620

    Article  Google Scholar 

  • Li D, Browne GJ (2006) The role of need for cognition and mood in online flow experience. J Comput Inf Syst 46:11–17

    Google Scholar 

  • Lin A (2006) The acceptance and use of a business-to-business information system. Int J Inf Manag 26:386–400

    Article  Google Scholar 

  • Lu Y, Zhou T, Wang B (2009) Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Comput Hum Behav 25:29–39

    Article  Google Scholar 

  • Luhmann N (ed) (2000) Familiarity, confidence, trust: Problems and alternatives. Trust: Making and breaking cooperative relations, D. G. Gam-betta edn. Basil Blackwell, New York

  • Mason W, Suri S (2012) Conducting behavioral research on Amazon’s Mechanical Turk. Behav Res Methods 44:1–23

    Article  Google Scholar 

  • Merhi MI (2015) Factors influencing higher education students to adopt podcast: an empirical study. Comput Educ 83:32–43

    Article  Google Scholar 

  • Novak TP, Hoffman DL, Yung Y-F (2000) Measuring the customer experience in online environments: a structural modeling approach. Mark Sci 19:22–42

    Article  Google Scholar 

  • Pagani M (2004) Determinants of adoption of third generation mobile multimedia services. J Interact Mark 18:46–59

    Article  Google Scholar 

  • Park E, Joon Kim K (2013) User acceptance of long-term evolution (LTE) services: an application of extended technology acceptance model. Program 47:188–205

    Article  Google Scholar 

  • Park E, Kim KJ (2011) The effects of mobility on handheld device in text reading. In: Proceedings of the 13th IASTED international conference on control and applications (IASTED-CA’11). ACTA Press, Anaheim, CA, pp 287–291

  • Park E, Ohm J (2014) Factors influencing users’ employment of mobile map services. Telemat Inf 31:253–265

    Article  Google Scholar 

  • Park N, Roman R, Lee S, Chung JE (2009) User acceptance of a digital library system in developing countries: an application of the technology acceptance model. Int J Inf Manag 29:196–209

    Article  Google Scholar 

  • Park E, Sung J, Cho K (2015) Reading experiences influencing the acceptance of e-book devices. Electron Libr 33:120–135

    Article  Google Scholar 

  • Parks-Associates (2015) The Streaming Media Device Landscape. https://www.parksassociates.com/report/streaming-2015

  • Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88:879–903

    Article  Google Scholar 

  • Shin D-H, Shin Y-J (2011) Why do people play social network games? Comput Hum Behav 27:852–861

    Article  Google Scholar 

  • Shin J, Park Y, Lee D (2015) Google TV or Apple TV? The reasons for smart TV failure and a user-centered strategy for the success of smart TV. Sustainability 7:15955–15966

    Article  Google Scholar 

  • Strategy-Analytics (2015) Global Connected TV Device Vendor Share: Q3 2015. Strategy Analytics

  • Tao Y, Chang J, Rau P-LP (2014) When China encounters smart TV: exploring factors influencing the user adoption in China. In: International conference on cross-cultural design. Springer International Publishing, pp 696–706

  • Technavio (2015) Global Streaming Media Device Market 2015–2019. http://www.technavio.com/report/global-streaming-media-device-market-2015-2019. Accessed 12 Mar 2017

  • Tekeoglu A, Tosun AS Blackbox security evaluation of chromecast network communications. In: Performance Computing and Communications Conference (IPCCC), 2014 IEEE International, 2014. IEEE, pp 1–2

  • Teo TS, Liu J (2007) Consumer trust in e-commerce in the United States, Singapore and China. Omega 35:22–38

    Article  Google Scholar 

  • Teo TS, Lim VK, Lai RY (1999) Intrinsic and extrinsic motivation in Internet usage. Omega 27:25–37

    Article  Google Scholar 

  • Tsou C-W, Liao C-H Investigating the antecedents of intentions to purchase a netbook as a second laptop. In: 2010 International Conference on Education and Management Technology (ICEMT), 2010. IEEE, pp 285–291

  • Tung F-C, Chang S-C, Chou C-M (2008) An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform 77:324–335

    Article  Google Scholar 

  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46:186–204

    Article  Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478

    Article  Google Scholar 

  • Verto-Analytics (2015) Verto Index: Streaming Media Services. http://www.vertoanalytics.com/verto-index-streaming-media-services/

  • Wired (2013) What Google’s Chromecast Means to Business. http://www.wired.com/insights/2013/07/what-googles-chromecast-means-to-business/

  • Wong R (2015) Google’s new Chromecast vs. Apple TV, Amazon Fire TV and Roku 3. http://mashable.com/2015/09/29/chromecast-vs-competition/#kJKGpPIMFgqC. Accessed 12 Mar 2017

  • Wu J-J, Chang Y-S (2005) Towards understanding members’ interactivity, trust, and flow in online travel community. Ind Manag Data Syst 105:937–954

    Article  Google Scholar 

  • Wu K, Zhao Y, Zhu Q, Tan X, Zheng H (2011) A meta-analysis of the impact of trust on technology acceptance model: investigation of moderating influence of subject and context type. Int J Inf Manag 31:572–581

    Article  Google Scholar 

  • Xu H, Teo H-H, Tan BC, Agarwal R (2009) The role of push-pull technology in privacy calculus: the case of location-based services. J Manag Inf Syst 26:135–174

    Article  Google Scholar 

  • Yang H, Yu J, Zo H, Choi M (2016) User acceptance of wearable devices: an extended perspective of perceived value. Telemat Inform 33:256–269

    Article  Google Scholar 

  • Yoffie DB (1997) Introduction: CHESS and competing in the age of digital convergence. In: Yoffie DB (ed) Competing in the age of digital convergence. Harvard Business Press, Boston, MA, pp 1–35

  • Yoon HS, Occeña LG (2015) Influencing factors of trust in consumer-to-consumer electronic commerce with gender and age. Int J Inf Manag 35:352–363

    Article  Google Scholar 

  • Yoon U-N, Ga M-H, Jo G-S Aligned thumbnails-based video browsing system with Chromecast. In: 2015 IEEE International Conference on Consumer Electronics (ICCE), 2015. IEEE, pp 86–87

  • Yu J, Lee H, Ha I, Zo H (2015) User acceptance of media tablets: an empirical examination of perceived value. Telemat Inform 34(4):206–223

    Article  Google Scholar 

  • Zhou T (2012) Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf Technol Manag 13:27–37

    Article  Google Scholar 

  • Zhou T (2013) The effect of flow experience on user adoption of mobile TV. Behav Inf Technol 32:263–272

    Article  Google Scholar 

  • Zhou T (2014) Examining continuance usage of mobile Internet services from the perspective of resistance to change. Inf Dev 30:22–31

    Article  Google Scholar 

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Acknowledgements

The present research was conducted by the research fund of Dankook University in 2017.

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Correspondence to Hwansoo Lee.

Appendix 1

Appendix 1

See Table 8.

Table 8 Variance of the substantive and method construct

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Yang, H., Lee, H. Exploring user acceptance of streaming media devices: an extended perspective of flow theory. Inf Syst E-Bus Manage 16, 1–27 (2018). https://doi.org/10.1007/s10257-017-0339-x

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