Service-Led Model for the Activation of Smart TV: Case Study in Korea

Purpose: This study explores the characteristics of STV service to empirically examine effects of the services on adoption and usage of STV to lead sustained growth of the STV industry. Methodology/Approach: This study employs structural equation modeling as a quantitative approach, to examine causal relationships between service characteristics and user intentions. The survey collects 212 data only from actual users of STV, who have experienced STV functions or services, in South Korea. Findings: The results of service-oriented model based on extended Technology Acceptance Model (TAM) indicate that ‘interactivity’, ‘content quality’, and ‘simplicity’ as service characteristics influence intention to use STV. Research Limitation/implication: First, the STV industry should establish a distribution structure that generates sufficient profits for content providers as done in Smartphone market. Second, Services of STV should be provided to allow two-way communication and to allow users to engage in active interactions with other users. Originality/Value of paper: This study makes contributions to research on both new products and service adoption by providing richer explanations of the mechanisms acting on the actual use of STV. Given that STV is considered a key appliance for the next generation of social media and smart appliance, our findings offer new directions on how to realize high quality services in the STV industry.

[1]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[2]  Matthew K. O. Lee,et al.  Understanding user acceptance of multimedia messaging services: An empirical study , 2007, J. Assoc. Inf. Sci. Technol..

[3]  Junseok Hwang,et al.  A socio-technical analysis of factors affecting the adoption of smart TV in Korea , 2016, Comput. Hum. Behav..

[4]  Harry Bouwman,et al.  Analysis of users and non-users of smartphone applications , 2010, Telematics Informatics.

[5]  Gurpreet Dhillon,et al.  A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research , 2014, Inf. Syst. Res..

[6]  Aaron Bere,et al.  Exploring Determinants for Mobile Learning User Acceptance and Use: An Application of UTAUT , 2014, 2014 11th International Conference on Information Technology: New Generations.

[7]  Ching-Chiang Yeh,et al.  Using a hybrid model to evaluate development strategies for digital content , 2015 .

[8]  Laura Lucia-Palacios,et al.  Enemies of cloud services usage: inertia and switching costs , 2016 .

[9]  Ibrahim M. Al-Jabri,et al.  Mobile Banking Adoption: Application of Diffusion of Innovation Theory , 2012 .

[10]  Myeong-Cheol Park,et al.  Mobile internet acceptance in Korea , 2005, Internet Res..

[11]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

[12]  I. Ajzen The theory of planned behavior , 1991 .

[13]  G. Premkumar,et al.  Adoption of new information technologies in rural small businesses , 1999 .

[14]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[15]  Stephen L. Vargo,et al.  Evolving to a New Dominant Logic for Marketing , 2004 .

[16]  Byoungsoo Kim,et al.  The difference of determinants of acceptance and continuance of mobile data services: A value perspective , 2011, Expert Syst. Appl..

[17]  Won-Moon Song,et al.  Efficient Recommendation for Smart TV Contents , 2012, BDA.

[18]  Terry S. Overton,et al.  Estimating Nonresponse Bias in Mail Surveys , 1977 .

[19]  Tom Evens,et al.  Smart TV in Germany: how does convergence impact market structure industry and business model venturing in digital television broadcasting? , 2013 .

[20]  Pei-Hsuan Tsai,et al.  Comparing the Apple iPad and non-Apple camp tablet PCs: a multicriteria decision analysis , 2014 .

[21]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[22]  Yiwen Gao,et al.  An empirical study of wearable technology acceptance in healthcare , 2015, Ind. Manag. Data Syst..

[23]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[24]  Judy Chuan-Chuan Lin,et al.  Towards an understanding of the behavioural intention to use a web site , 2000, Int. J. Inf. Manag..

[25]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[26]  Yung-Hsiang Cheng,et al.  Exploring radio frequency identification technology's application in international distribution centers and adoption rate forecasting , 2011 .

[27]  Pablo César,et al.  The Evolution of TV Systems, Content, and Users Toward Interactivity , 2009, Found. Trends Hum. Comput. Interact..

[28]  Niina Mallat,et al.  Exploring consumer adoption of mobile payments - A qualitative study , 2007, J. Strateg. Inf. Syst..

[29]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

[30]  Jinwoo Hong,et al.  Research of Social TV service technology based on smart TV platform in next generation infrastructure , 2010, 5th International Conference on Computer Sciences and Convergence Information Technology.

[31]  Tao Zhou,et al.  Industrial Management & Data Systems Understanding continuance usage of mobile sites , 2016 .

[32]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[33]  Yu-Chen Chen,et al.  Extrinsic versus intrinsic motivations for consumers to shop on-line , 2005, Inf. Manag..

[34]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[35]  Imsook Ha,et al.  Determinants of adoption of mobile games under mobile broadband wireless access environment , 2007, Inf. Manag..

[36]  Wynne W. Chin,et al.  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 , 2003, Inf. Syst. Res..

[37]  Hyunseung Choo,et al.  Smart TV: are they really smart in interacting with people? Understanding the interactivity of Korean Smart TV , 2013, Behav. Inf. Technol..

[38]  Dong Hee Shin,et al.  An empirical investigation of a modified technology acceptance model of IPTV , 2009, Behav. Inf. Technol..

[39]  Moon-Koo Kim,et al.  Factors influencing the low usage of smart TV services by the terminal buyers in Korea , 2016, Telematics Informatics.

[40]  Mary C. Whitton,et al.  Using innovation diffusion theory to guide collaboration technology evaluation: work in progress , 2001, Proceedings Tenth IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE 2001.

[41]  Youngim Bae,et al.  Adoption of smart TVs: a Bayesian network approach , 2012, Ind. Manag. Data Syst..

[42]  Sergio Toral,et al.  The moderating role of prior experience in technological acceptance models for ubiquitous computing services in urban environments , 2015 .