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Modelling and forecasting national introduction times for successive generations of mobile telephony
Authors:Nigel Meade  Towhidul Islam
Abstract:An accurate prediction of the timing of a country's introduction of a new generation of mobile telephony benefits numerous agents including suppliers of network and consumer equipment, regulators, and network planners. We consider the estimation and prediction of the time interval between the international introduction of a generation of mobile telephony and its introduction into a specific country when a decision maker judges the introduction of a newer technology a worthwhile investment. Using literature-based socio-economic and geographical variables, we examine how well variation in international introduction times of four generations of mobile telephony in 172 countries can be explained and forecast. We model and forecast introduction times at two levels of granularity: we use Cox's proportional hazards model for the introduction time; we partition countries into introduction time-based segments and model segment membership using multinomial logistic regression. Our modelling of each generation considers three subsets of explanatory variables: All variables, socio-economic Covariates only, Regional dummies only. Over successive generations, the Covariates only models reveal the changing relevance of each socio-economic covariate. Model-based forecasting of the introduction time of the next generation is performed under three hypotheses making different uses of the information available at the time the relevant generation is launched internationally. However, changing socio-economic environments coupled with changing models impair forecasting accuracy, the lower accuracy of modelled introduction times is concentrated in 20% of countries. We speculate about the nature of the unobserved factors affecting these countries' decision processes.
Keywords:Mobile telecommunications  Technological forecasting  Technological generations  Forecasting accuracy  Proportional hazard models  Multinomial logistic regression
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