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Comparing parameter estimation methods for service-based diffusion models

This paper compares methods for parameter estimation of diffusion models when seeking to extend these to service industry contexts from the traditional product focus. In the marketing science and economics literature, parameter estimation is dominated by econometric methods. This presents certain limitations as well as advantages compared to calibration in system dynamics modelling, which emphasises estimation of parameters by direct observation. But this poses a problem for industry or market-level diffusion models where deriving aggregate parameters observationally is impractical, especially for launches of new products or services which lack direct market knowledge. One solution is to use judgemental bootstrapping, entailing the estimation of parameters from an expert’s forecast time series. Models parameterised this way can then be used as a basis for simulated structural experiments of proposed market architectures. Some interim results from three service industry case studies are presented.