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While many scholars of organizational innovations have examined characteristics of innovations such as relative advantage and complexity and how they facilitate the adoption of an innovation by organizations, others have used mathematical models to fit diffusion patterns. In this study, the authors attempt to integrate these two areas of inquiry and explore the possibilities to predict diffusion patterns based on characteristics of the innovation and the adopting entities. Based on a cross-sectional sample of 313 large American firms, 20 information technology (IT) innovations were examined and their diffusion patterns assessed with respect to models that espoused internal and external influence. The mixed influence model (Bass model) was chosen as a robust common representation for the set of diffusion patterns. However, the external influence as represented by the coefficient of innovation was found to be extremely small and the internal influence dominates for all innovations. The other two parameters of the model, the saturation level and the coefficient of imitation, which represents internal influence, were then used to perform a cluster analysis. Five clusters of technologies emerged, and the potential relationships between their innovation characteristics and diffusion patterns were explored. Rigorous examination of these potential relationships by future researchers may result in practical methods for predicting patterns of IT innovation diffusion based on innovation and technology characteristics.
Teng et al. (Tue,) studied this question.