DOI QR코드

DOI QR Code

An Empirical Analysis of Smartphone Diffusions in a Global Context

  • Cho, Daegon (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
  • Published : 2015.05.31

Abstract

This paper examines the diffusion of smartphones with a special emphasis on the diffusive interactions between Apple iOS and Google Android in a global context. Since the two mobile platforms were first introduced in the market, the use of smartphones has skyrocketed, suggesting that the dramatic diffusion of smartphones may be explained in part by the growth and competition of these two platforms. To study this, an extended Bass model is applied to a data set of quarterly smartphone sales between 2008 and 2013 for 15 countries. Our findings suggest that the innovation effect was more salient for iOS than for Android in developed countries, whereas the imitation effect was more striking for Android than for iOS in developing countries. Furthermore, our results from the co-diffusion model suggest that the diffusion of Android negatively affected by the diffusion of iOS, but not vice versa.

References

  1. Bass, F. M. 1969. A new product growth model for consumer durables. Management Science, 15(1), 215-227. https://doi.org/10.1287/mnsc.15.5.215
  2. Bucklin, L. P., S. Sengupta. 1993. The co-diffusion of complementary innovations: Supermarket scanners and UPC symbols. Journal of Product Innovation Management, 10, 148-160. https://doi.org/10.1016/0737-6782(93)90006-C
  3. Bohlin, A., H. Gruber, P. Koutroumpis. 2010. Diffusion of new technology generations in mobile communications. Information Economics and Policy, 22, 51-60. https://doi.org/10.1016/j.infoecopol.2009.11.001
  4. Brynjolfsson, E., C.F. Kemerer. 1996. Network externalities in microcomputer software: An econometric analysis of the spreadsheet market. Management Science, 42, 1627-1647. https://doi.org/10.1287/mnsc.42.12.1627
  5. Chandrasekaran, D., G. Tellis. 2006. New product growth models in marketing: A critical review of models and findings. N. Malhotra, eds. Review of Marketing Research. M. E. Sharpe, Inc., Armonk, NY, 39-80.
  6. Danaher, P. J., B. G. S. Hardie, W. P. Putsis, Jr. 2001. Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research, 38(4) 501-514. https://doi.org/10.1509/jmkr.38.4.501.18907
  7. DeGusta, M. 2012. Are Smart Phones Spreading Faster than Any Technology in Human History? MIT Technology Review. Accessed at http://www.technologyreview.com/news/427787/are-smart-phones-spreading-faster-than-any-technology-in-human-history/ on October 12, 2014.
  8. Dewan, S., D. Ganley, K. L. Kraemer. 2010. Complementarities in the diffusion of personal computers and the Internet: Implications for the global digital divide. Information Systems Research, 21(4), 925-940. https://doi.org/10.1287/isre.1080.0219
  9. Gruber, H., F. Verboven. 2001. The evolution of markets under entry and standards regulation: the case of global mobile telecommunications. International Journal of Industrial Organization, 19, 1189-1212. https://doi.org/10.1016/S0167-7187(01)00069-8
  10. Farrell, J., G. Saloner. 1986. Installed base and compatibility: Innovation, product pronouncements and predation. American Economic Review, 76, 940-955.
  11. Katz, M., C. Shapiro. 1985. Network Externalities, Competition and Compatibility. American Economic Review, 75(3), 424-440.
  12. Lee, S., S. Lee. 2014. Early diffusion of smartphones in OECD and BRICS countries: An examination of the effects of platform competition and indirect network effects, Telematics and Informatics, 31, 345-355. https://doi.org/10.1016/j.tele.2013.12.002
  13. Mahajan, V., E. Muller. 1996. Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe case. Technology Forecasting and Social Change, 51, 109-132. https://doi.org/10.1016/0040-1625(95)00225-1
  14. Meade, N., T. Islam. 2006. Modeling and forecasting the diffusion of innovation - A 25 year review. International Journal of Forecasting, 22(3), 519-545. https://doi.org/10.1016/j.ijforecast.2006.01.005
  15. Merrill Lynch. 2013. Global Wireless Matrix.
  16. Nair, H, P. Chintagunta, J-P. Dube. 2004. Empirical analysis of indirect network effects in the market for personal digital assistants. Quantitative Marketing and Economics, 2(1), 23-58. https://doi.org/10.1023/B:QMEC.0000017034.98302.44
  17. Ohashi, H. 2003. The role of network effects in the U.S. VCR market, 1978-1986. Journal of Economics & Management Strategy, 12(4), 447-496. https://doi.org/10.1162/105864003322538929
  18. Park, Y., M. Ueda. 2011. A Comparative Study on the Diffusion of Smartphones in Korea and Japan, in 2011 IEEE/IPSJ 11th International Symposium on Applications and the Internet (SAINT). IEEE, pp. 545-549.
  19. Rochet, J. C., J. Tirole. 2003. Platform competition in two-sided markets. Journal of European Economic Association, 1(4), 990-1029.
  20. Rogers, E. M. 2003. Diffusion of Innovations. Free Press, New York.
  21. Shapiro C., H. Varian. 1998. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, Boston, MA.
  22. Sun, B., J. Xie, H. H. Cao. 2004. Product strategy for innovators in markets with network effects. Marketing Science, 23(2), 243-254. https://doi.org/10.1287/mksc.1040.0058
  23. Takada, H., D. Jain. 1991. Cross-national analysis of diffusion of consumer durable goods in Pacific rim countries. Journal of Marketing, 55, 48-54. https://doi.org/10.2307/1252237
  24. Xie, J., M. Sirbu. 1995. Price competition and compatibility in the presence of positive demand externalities, Management Science. 41(2), 909-926. https://doi.org/10.1287/mnsc.41.5.909
  25. Zellner, A. 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298):348-368. https://doi.org/10.1080/01621459.1962.10480664

Cited by

  1. Non-R&D-based innovation activities and performance in Chinese SMEs: the role of absorptive capacity vol.25, pp.1, 2017, https://doi.org/10.1080/19761597.2017.1302548