Service-Oriented Factors Affecting the Adoption of Smartphones

Youngmo Kang, Mingook Lee, Sungjoo Lee


This research investigates the adoption factors of smartphones focusing on the differences of smartphone and feature phone users. We used Technology Acceptance Model (TAM) which incorporates service-oriented and device-oriented functional attributes as exogenous variables for a product-service system such as smartphones. In addition, Decision Tree (DT) and customer surveys were conducted. As a study results, we found that the service-oriented functional attributes - ‘wireless internet’ and ‘mobile applications’ - affect the adoption of smartphones regardless of users. However, the DT results revealed that the more important factor is 'mobile applications' to smartphone users but 'wireless internet' for feature phone users. In conclusion, we discovered that a strategy emphasis on the service-oriented attributes is needed for the adoption of smartphones.


partial least square; technology acceptance model; decision tree; smartphone; comparison analysis; cellular phone market

Full Text:

PDF [en]


ADAMS, D. A., Nelson, R. R., Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly, 6(2), 227-247.

AJZEN, I., Fishbein, M. (1980). Understanding Attitudes and Predicting Behavior. Prentice-Hall Inc. Englewood Cliffs, New Jersey.

ARMSTRONG, N., Nugent, C., Moore, G., Finlay, D. (2010). Developing smartphone applications for people with Alzheimer’s disease. 10th International Conference on Information Technology and Applications in Biomedicine. doi: 10.1109/ITAB.2010.5687795

BARCLAY, D., Hiffins, C. and Tompson, R. (1995). The partial least square (PLS) approach to causal modelling, personal computer adoption and use as an illustration. Technology Studies, 2, 285-309.

BODKER, M., Gimpel, G., Hedma, J. (2009). The user experience of smart phones: a consumption values approach. [Accessed October 22, 2010]

CAROLINA, L-N., Francisco, J.M.C., Harry, B. (2008). An assessment of advanced mobile services acceptance: contributions from TAM and diffusion theory models. Information and Management, 4(6), 359-364. doi: 10.1016/

CHANG, Y. F., Chen, C.S., Zhou, H. (2009). Smart phone for mobile commerce, Computer Standards and Interfaces. 31(4), 740-747. doi: 10.1016/j.csi.2008.09.016

CHEN, J. V., Yen, D. C., Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: a case study of a delivery service company in logistics. Information and Management, 46(4), 241-248. doi: 10.1016/

CHEN, K., Chen J.V., Yen, D.C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards and Interfaces, 33(4), 422-431. doi: 10.1016/j.csi.2011.01.003

CHEONG, J.H., Park, M.C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 206-222. doi: 10.1108/10662240510590324

CHIN, W., Newsted, P. (1999). Structural equation modelling analysis with small samples using partial least squares. In: Hoyle, R.H. (Eds.), Statistical Strategies for Small Sample Research. SAGE, California, pp. 307-341.

CHOI, J., Seol, H., Lee, S., Cho, H., Park, Y. (2008). Customer satisfaction factors of mobile commerce in Korea. Internet Research, 18(3), 313-335. doi: 10.1108/10662240810883335

COHEN, J. (1988). Statistical Power Analysis for the Behavioural Sciences. Lawrence Erlbaum, Hillsdale.

DAVIS, F.D., Bagozzi, R., Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982

ENGEL, J. E., Blackwell, R.D. (1982). Consumer Behavior. The Dryden Press, New York.

FORNELL, C., Bookstein, F. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440-452. [Accessed October 22, 2010]

GANTI, V., Gehrke, J., Ramakrishnan, R. (1999). Mining very large databases. IEEE Computer, 32(6), 38-45. doi: 10.1109/2.781633

GEFEN, D., Straub, D., Boudreau, M. (2000). Structural equation modelling and regression: guidelines for research practice. MIS Quarterly, 27, 51-90. [Accessed October 22, 2010]

GOLDBERGER, A. (1972). Structural equation models in the social sciences. Econometrica, 40, 979-1001. [Accessed October 22, 2010]

GREEN, P.E., Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing, 54(4), 1-19. [Accessed October 22, 2010]

HAENLEIN, M., Kaplan, A. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283-297. [Accessed October 22, 2010]

HANNU, V., Carolina, L.N., Francisco, J.M.C., Harry, B. (2010). Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3), 242-255. doi: 10.1016/j.tele.2009.11.001

IGBARIA, M., Zinatelli, N., Cragg, P., Cavaye, A.L.M. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS Quarterly, 21(3), 279-305. [Accessed October 22, 2010]

JAN, T.S., Hsiao, C.T. (2004). A four-role model of the automotive industry development in developing countries: a case in Taiwan. Journal of the Operational Research Society, 55(11), 1145-1155. [Accessed October 22, 2010]

KHAN, K., Kim, H. (2009). Factors affecting consumer resistance to innovation: a study of smartphones. Master’s Thesis in JONKOPING University. [Accessed October 22, 2010]

KIM, S.H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: adoption of a smartphone by individuals. Information and Management, 45(6), 387-393. doi: 10.1016/

LEUNG, L., Wei, R. (2000). More than just talk on the move: uses and gratifications of the cellular phone. Journalism and Mass Communication Quarterly, 77(2), 308-320. [Accessed October 22, 2010]

LIM, S., Xue, L., Yen, C.C., Chang, L., Chan, H.C., Tai, B.C., Duh, H.B.L., Choolani, M. (2011). A study on Singaporean women’s acceptance of using mobile phones to seek health information. International Journal of Medical Informatics, 80(12), 813-884. doi: 10.1016/j.ijmedinf.2011.08.007

LU, J., Wang, L.Z., Yu, C.S. (2007). Is TAM for wireless mobile data services applicable in China? A survey report from Zhejiang. China, International Journal of Mobile Communications, 5(1), 11-31. 10.1504/IJMC.2007.011487

NG-KRUELLE, G., Swatman, P., Rebne, D., Hampe, F. (2002). The price of convenience: Privacy and mobile commerce. Quarterly Journal of Electronic Commerce, 3(3), 273-285. [Accessed October 22, 2010]

NUNNALLY, J.C. (1967). Psychometric Theory. McGraw-Hill, New York.

PODSAKOFF, P.M., Organ, D. (1986). Self-reports in organizational research: problems and prospects. Journal of Management, 12(4), 531-544. doi: 10.1177/014920638601200408

Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.

SARKER, S., Wells, J.D. (2003). Understanding mobile handheld device use and adoption. Communications of the ACM, 46(12), 35-40. doi: 10.1145/953460.953484

TAYLOR, S., Todd, P.A. (1995). Understanding information technology usage: a test of competing models. Information System Research, 6(2), 144-176. doi: 10.1287/isre.6.2.144

WERTS, C., Lin, R., Jöreskog, K. (1974). Intra-class reliability estimates: testing structural assumptions. Educational and Psychological Measurement, 34, 25-33. doi: 10.1177/001316447403400104


Copyright (c)

2017 © Universidad Alberto Hurtado - Facultad de Economía y Negocios. 
Erasmo Escala 1835 - Santiago, Chile.
Economic Analysis Review | Observatorio Económico | Gestión y Tendencias 

Journal Supported by Chimera Innova Group