This paper was presented at the Research Symposium on Marketing and Entrepreneurship, held at the University of Illinois at Chicago, August 2009.

Authors

• G. Christopher Crawford (University of Louisville)

Overview

This paper models emerging ventures as complex adaptive systems. These systems exhibit avalanche-type dynamics, where the non-linear outcomes cannot be understood in a description of the individual parts. This was tested with a power law distribution of the baseline year of both total revenue and total number of owners per firm from the data enclave.

There are five inherent properties of complex adaptive systems that can be attributed to the study of entrepreneurial action: schemata, structural and periodic attractors, recombination, and energy. Schemata is the cognitive makeup of the entrepreneur that facilitates speed and heuristics for decision-making; changes in schemata can reduce self-deviating feedback loops and path-dependency, both of which can hinder new venture growth (Arthur, 1989; Schindehutte & Morris, 2009). Structural attractors are the primary drivers of venture growth and include the human, social, and financial capital in the new firm. Periodic attractors, like a business plan or patent, can compensate for weak structural attractors, but lose value over time and context. Recombination describes the formation of more diverse teams, beyond the lone entrepreneur. Higher levels of energy flow into and out of the system through repeated patterns of interactions with potential stakeholders.

Complexity theory’s inherent postmodern epistemology (McKelvey, 2002) implies that the interdependent nature of these components have an underlying optimal structure that can improve the performance of a young venture.