Essays on the time-dependent analysis of new product forecastin

Essay I. This essay examines the time between product development and market launch, which is the beginning of the product life cycle, and its relation to the subsequent diffusion of consumer durables. I find that this "incubation time" is long. Further, it is an important predictor of the shape of the diffusion curve. Using the Bass (1969) model as a base, I find that the longer the incubation time, the lower the coefficient of innovation (p) and the longer the time to peak sales. As powerful a descriptive tool as the Bass model is, however, it is fairly unreliable as a predictor until several periods (typically years) of sales data are available. Consequently, efforts have been undertaken to use information than sales to forecast the diffusion curve. Using the incubation time in a Bayesian forecasting model significantly improves forecasts early in the life cycle.;Essay II. This essay will addresses the concept of "intergeneration time" in order to assess the impact of new technology entry on the diffusion of old and new technologies. One of the criticism raised in using Bass-type diffusion model is that the product life cycle curve is known only after the curve is fully developed or terminated. The intergeneration time, however, can compensate the lack of the actual sales history for the new technology in forecasting the diffusion process for a next generation of technology. Typical product prediction neglects to take into account the competition from old technology. In this essay, the carryover demand for old technology and its dissipating sales are analyzed. The evidence of carryover demand for old technology is sufficiently suggestive to make one suspect that old technology will normally lose its demand early in the existence of new one. Moreover, the shorter the intergeneration time, the faster the old technology loses market share.