BRIDGING THE GAP BETWEEN ARTIFICIAL MARKET SIMULATIONS AND QUALITATIVE RESEARCH IN DIFFUSION OF INNOVATION

Abstract

Artificial markets are an emerging form of agent-based simulation in which agents represent individual industries, firms, or consumers interacting under simulated market conditions. While artificial markets demonstrate considerable potential for advancing innovation research, the validity of the method depends on the ability of researchers to construct agents that faithfully capture the key behavior of targeted entities. To date, few such methods have been documented in the academic literature. This article describes a novel method for combining qualitative innovation research (case studies, grounded theory, and sequence analysis) with software engineering techniques to synthesize simulation-ready theories of adoption behavior. A step-by-step example is provided from the transportation domain. The result was a theory of adoption behavior that is sufficiently precise and formal to be expressed in Unified Modeling Language (UML). The article concludes with a discussion of the limitations of the method and recommendations future applications to the study of diffusion of innovation.