Systematic development of new knowledge is as important in the developing field of knowledge management (KM) as in other social science and technological domains. Careful research is essential for the development of new knowledge in a systematic manner (e.g., avoiding the process of trial and error). The problem is, throughout the era of modern science, a chasm has persisted between laboratory and field research that impedes knowledge development about knowledge management. This article combines and builds upon recent results to describe a research approach that bridges the chasm between laboratory and field methods in KM: computational experimentation. As implied by the name, computational experiments are conducted via computer simulation. But such experiments can go beyond most simulations (e.g., incorporating experimental controls, benefiting from external model validation). And they can offer simultaneously benefits of laboratory methods (e.g., internal validity, lack of confounding) and fieldwork (e.g., external validity, generalizability). Further, computational experiments can be conducted at a fraction of the cost and time associated with either laboratory experiments or field studies. And they provide a window to view the kinds of meta-knowledge that are important for understanding knowledge management. Thus, computational experimentation offers potential to mitigate many limitations of both laboratory and field methods and to enhance KM research. We discuss computational modeling and simulation as a complementary method to bridge the chasm between laboratory and field methods—not as a replacement for either of these methods.
Computational Experimentation
