Capturing Structural Complexity of Innovation Diffusion through System Dynamics: A Discussion on Model Development, Calibration, and Simulation Results

Several shortcomings of traditional innovation diffusion research create major impediments to the ‘holistic’ understanding of diffusion of innovations as well as to the development of effective strategies of policy intervention to facilitate diffusion process. Among the major criticisms of diffusion research are the ‘selection biases’ of many diffusion studies and the futility of curve fitting as an adequate test of theoretical relevance. These limitations can be avoided by substantive and methodological changes in diffusion research; incorporating and integrating into selective critical structural variables to map their interaction, and explaining the inherent dynamism. There are several research articles that discuss different approaches to modeling innovation diffusion over time. Conventionally, the approaches in explaining the innovation diffusion process assume that the process takes place in a stable and homogeneous system in which the innovation diffuses or spreads without being affected by the system’s structural variables even under external influences. Many studies have established that the presence of symmetry is not the general rule in innovation diffusion process. Also examined are these models, and the need to further modify in order to improve the holistic understanding of the dynamic structural complexities and forces driving the processes of innovation and diffusion is recognized.