A nonintuitive, decision support modeling ensemble approach is developed and tested for hypothetical, business-to-consumer (B2C,), innovative product data. The approach demonstrates the potential for gaining competitive advantage by integrating a unique permutation of innovation diffusion theory, sustainability, IS and location. The basic assumption is that early adopters of an innovation “pay more and buy less” i.e., which supports a firm’s sustainable profitable growth while demanding fewer units due to their small proportion among all adopters which translates to reduced CO2 for their purchases. The behavioral rationale for early adopters is derived from Innovation Diffusion Theory. Locating all potential early adopters using the modeling ensemble (i.e., the Bass Bayes Spatial Extension) in a SOM (serviceable and obtainable market) temporally (i.e., quickly) and spatially (i.e., within qualified census blocks) is critical to optimum early adopter maximum target market penetration. The decision support modeling ensemble approach generates expected results subject to limits and delimits, ceteris paribus.