The Diffusion of Innovations: A Methodological Reappraisal
Abstract
Studies of diffusion have traditionally relied on specific distributions --prinRrily the logistic --to characterize and estimate those processes.We argue here that such approach gives rise to serious problems of comparability and interpretation, and may result in large biases in the estimates of the para- meters of interest.We propose instead the Ginits expected mean difference as a measure of diffusion speed, discuss its advantages over the traditional approach, and tackle with it the problems of truncated processes, inter-group comparisons, and related issues.We also elaborate on the use of the hazard rate, and suggest some possible extensions.The diffusion of CT scanners is presented as an illustration.
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