Predicting Electronic Commerce Growth: An Integration Of Diffusion And Neural Network Models


Somnath Mukhopadhyay
Subhashish Samaddar
Satish Nargundkar


There is a growing recognition that e-market planners and various planning agencies in Information Technology sectors have a significant interest in measuring and forecasting the growth of e-commerce. The difficulties lie in finding a forecasting model that can incorporate both internal and external influences on diffusion, as well as an acceptable measure for e-commerce growth. This study uses models based on the knowledge of traditional diffusion theories as well as artificial neural networks. Additionally, it integrates the two into a hybrid model in order to study e-commerce growth. A count of dot-com hosts is used as a reliable measure of e-commerce growth in all the models. Our study demonstrates that a simple Neural Network model, if properly calibrated, can create a very flexible response function to forecast e-commerce diffusion growth. The neural network model successfully modeled both the internal and external influences in the data, while the traditional formulations could only model the internal influences. The predictive validation of the results was enhanced by replicating the comparisons on simulated data with various degrees of external influence. The study suggests that when external influences are present, the neural network model will be superior to the best traditional diffusion model.

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December, 2008

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