A spatial model of the diffusion of mobile communications within the European Union

Innovation diffusion studies have been popular. However, usually the focus has been on two dimensions: Either the innovation's diffusion is studied on the micro level by examining the individual's adoption of an innovation, or on the macro-level by modelling the sigmoid diffusion curve. The third dimension of the diffusion of an innovation, spatial diffusion, has gained less attention. Spatial diffusion models mostly base on the effect of distance on an innovation's diffusion process. Generally, it is seen that the innovation is adopted later in places further away from the innovation centre, even though there are also other, mainly complementary, hypotheses about the diffusion of an innovation. One common approach for studying the spatial diffusion of an innovation, the spatial gravity model, builds on the distance of places in explaining the diffusion process. As the name of the model implies, the hypothesis is that the diffusion is due to different pulling forces of regions. In other words, regions adopt the innovation at a different time because of a different pulling force. In this study, a gravity-based spatial diffusion model is employed for studying the spatial diffusion of mobile communications within the European Union. The model considers the diffusion process on a national level, the adoption units being the member countries of the European Union. The countries' annual penetration rates of mobile subscribers are used as the diffusion data. Also, a variable testing the effect of the country's economic situation, measured by GDP per capita, on the spatial diffusion process is included in the model. Moreover, by extrapolating the model may be used for forecasting purposes, thus giving annual information about the diffusion of mobile communications in the near future. The European Union is a leader in the mobile field. This kind of first-mover position makes the studying of its diffusion process interesting: The results might give information, which could be utilized in controlling the followers' diffusion processes. Also, if there is a significant effect of the economic situation on the diffusion process, it comes with some interesting implications: If an economic recession is found to hinder the diffusion of mobile communications, is the current timing of launch of the third generation the optimal one, noting that the EU simultaneously faces a slowdown in growth? For judging this, the effect of different economic growth scenarios on the diffusion of mobile communications could be estimated, as the model might be used for forecasts.