Enhanced Business Location Intelligence by Forecasting Innovation Adopter Types and Predictive Space-Time Locations: An Integrated Approach

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Presented at HICSS 2025 by

Christopher Franklin

Jayashree Sreedharan

Victor Bezak

An integrated temporal forecast with Bayesian predicted spatial presence of Rogers innovation diffusion adopters is introduced. A “Bass-Bayes-Spatial-Extension” (BBSE) model forms the foundation of the ensemble; with Rogers’ Innovation Adopter Behavioral Types added. We discover the simpatico relationship between Bass “Innovation Adopters”, and Rogers “Innovation Adopters”. When they are synchronized, we provide new information on WHEN, WHO and WHERE Adopter types enter the market. Demand chain management’s strategic marketing-mix decisions benefit dynamically from this parsimonious combined geospatial big data – enhanced Location Intelligence approach. The (a) Rogers’ psycho-social profiles and (b) evidence of ongoing geocoded random empirical adoptions (from actual ongoing geocoded adopter sales), iteratively update and inform their hierarchical posteriors and prior distributions. Bayesian probabilistic predictions of spatial distribution over the Census areal units-of-analysis emerge with advanced demographic and social analysis. A simple hypothetical example shows the practitioner the basic operationalization of the model. A summary and conclusion complete the paper