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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.

The purpose of this paper is to contribute to understanding innovation dynamics in services, in particular the link between innovation and productivity. A methodology to explain this link is suggested. Instead of establishing a single, direct connection between innovation and labor productivity, as in earlier approaches in the services literature, a simultaneous equations model is used. We put forward an extended version of the CDM (Crepon, Duguet and Mairesse) model, incorporating two feedback effects and using innovation activities rather than the more restrictive R&D proxy. Activities prior to the innovation implementation are also taken into account allowing for direct and indirect effects on labor productivity. Moreover, we discuss and handle the oftentimes overlooked methodological problems affecting this relationship. Micro data for ten service sectors in Portugal are used to estimate the model. The existence of a Schumpeterian virtuous cycle is confirmed, pointing to a mechanism reinforcing innovation investment returns. We find that innovation activities have a positive impact on labor productivity, but no evidence was found of a significant direct effect of innovation output. Labor productivity also improves with management capabilities. Relationships with customers, suppliers and cooperation partnerships significantly increase the probability of innovating, suggesting that stimulating organizational networking is a key element in a service firm’s innovation strategy.