In Situ Technological Innovation Diffusion Rate Accuracy Assessment

At present, the accuracy of diffusion rate forecasting, at a macro-level, in research literature, is nonexistent. This reveals underlying macro-level trends assessment using historical technological innovation data to explore statistical characteristics percent-error Bass and logistic model time stepped through its lifecycle. A quantitative exploratory analysis (EDA) based approach was employed uncover macro-perspective patterns insights on innovation’s forecasted 42 matured U.S. consumer innovations. An objective this effort determine (mean, median, variance, standard deviation, skewness, kurtosis) various points lifecycle reveal directional associative insights. Specifically, explores development knowledge quantifying forecasting partial data. Developing such framework for accessing situ (real-time) would benefit an organization’s decision makers maximizing gains minimizing losses. These include identifying whether models are more likely overestimate or underestimate when assessed Practitioners can use information set resource investment strategies policies risk tolerance utility weighted outcomes via theory tools.