Why does diffusion of innovation sometimes propagate throughout the whole population, and why at other times does it halt in its interim process? The current paper provides a potential answer to this question by developing a simple computational model of social networks. The proposed computational approach incorporating small-world graphs enables the authors to find that diffusion of innovation is more likely to fail in a random network than in a highly clustered network of consumers. A marketing implication is that the choice of initial target groups and their network structures matter in influencing whether an innovation makes full or partial penetration, in markets where network effects plays a role. (C) 2008 Elsevier Inc. All rights reserved.