ABSTRACT The main purpose of this paper is to analyze the dynamic process when two competitive innovations diffuse simultaneously in the small world network. To illustrate the micro diffusion process, an agent-based modeling and simulation method is applied. In the agent based model, there are two competitive innovations. Agents make decisions to adopt one of the innovations according to the utility value. The sensitivity of the parameters of the utility function is analyzed. The result indicates that in the early stage and the late stage the advertisement strategy is better; while in the middle stage the word-of-mouth will be better.
Cite this paper
Y. Yan and Y. Li, "Competitive Innovation Diffusion in Small-World Network: Agent-Based Modeling and Simulation," Technology and Investment, Vol. 3 No. 3, 2012, pp. 198-202. doi: 10.4236/ti.2012.33028.
 E. M. Rogers and M. Everett, “Diffusion of innovations,” 4th Edition, The Free Press, New York, 1995.
 L. Riccardo, “Segmentation and Increasing Returns in the Evolutionary Dynamics of Competing Techniques,” Metroeconomica, Vol. 52, No. 2, 2001, p. 2.
 C. Watanabe, “Reiko Kondo: A Substitution Orbit Model of Competitive Innovations,” Technological Forecasting & Social Change, Vol. 71, No. 4, 2004, pp. 365-390.
 B. Zhang, L. Fang and R. B. Zhang, “A Dynamic Diffusion Model of Competitive Multi-innovations and Its Applacation,” Technology Economics, Vol. 27, No. 9, 2008, pp. 5-9, 19.
 R. Nie, K. M. Qian and D. H. Pan, “The Innovative Technology Diffuse Models and Their Stability Analysis Base on Logistic Equation,” Journal of Industrial Engineering and Engineering Management, Vol. 20, No. 1, 2006, pp. 41-45.
 Y. H. Liu and J. R. Dong, “Innovation Produce and Technology Comparative Diffusion,” Journal of Industrial Technological Economics, Vol. 25, No. 7, 2006, pp. 48-52.
 F. M. Bass, “A New Product Growth Model for Consumer Durables,” Management Science, Vol. 15, No. 5, 1969, pp. 215-227. doi:10.1287/mnsc.15.5.215
 D. J. Watts and S. H. Strogatz, “Collective Dynamics of ‘Small-World’ Networks,” Nature, Vol. 393, No. 6684, 1998, pp. 440-442. doi:10.1038/30918
 D. Maienhofer, “Finding Optimal Targets for Change Agents: A Computer Simulation of Innovation Diffusion,” Computational & Mathematical Organization Theory, Vol. 8, No. 4, 2002, pp. 259-280.