The successful introduction of new durable products is important in helping companies to stay ahead of their competitors. Decisions relating to these products can be improved by the availability of reliable pre-launch forecasts of their adoption time series. However, producing such forecasts is a difficult, complex and challenging task mainly because of non-availability of past time series data relating to the product and the multiple factors that can affect adoptions such as customer heterogeneity, macro-economic conditions following the product launch and technological developments which may lead to the product's premature obsolescence. This paper critically reviews the literature to examine what it can tell us about the relative effectiveness of three fundamental approaches to filling the data void : i) management judgment, ii) analysis of judgments by potential customers and iii) formal models of the diffusion process. It then shows that the task of producing pre-launch timeseries forecasts of adoption levels involves a set of sub-tasks, which involve either quantitative estimation or choice, and argues that the different nature of these tasks means that forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models, rather than unstructured judgment should be at the core of the forecasting process.Gaps in the literature are identified and the paper concludes by suggesting a research agenda to indicate where future research efforts might be most profitably employed.