The use of a flexible diffusion model for forecasting national-level mobile telephone and internet diffusion

Much attention has been given to adoption and diffusion, defined as the degree of market penetration, of Information and Communications Technologies (ICT) in recent years ( Carter, Jambulingam, Gupta, & Melone, 2001 ; Kiiski & Pohjola, 2002 ; Milner, 2003 ; Benhabib & Spiegel, 2005 ). The theory of diffusion of innovations considers how a new idea spreads throughout the market over time. The ability to accurately predict new product diffusion is of concern to designers, marketers, managers, and researchers alike. However, although the diffusion process of new products is generally accepted as following an s-curve pattern, where diffusion starts slowly, grows exponentially, peaks, and then declines (as shown in Fig. 1 ), there is considerable disagreement about what factors affect diffusion and how to measure diffusion rates ( Bagchi, Kirs, & Lopez, 2008 ).