Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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
Recommended Citation
Franklin, Christopher; Sreedharan, Jayashree; and Bezak, Victor, "Enhanced Business Location Intelligence by Forecasting Innovation Adopter Types and Predictive Space-Time Locations: An Integrated Approach" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 6.
https://aisel.aisnet.org/hicss-58/li/data_analytics/6
Enhanced Business Location Intelligence by Forecasting Innovation Adopter Types and Predictive Space-Time Locations: An Integrated Approach
Hilton Waikoloa Village, Hawaii
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
https://aisel.aisnet.org/hicss-58/li/data_analytics/6