LAUSR

A recurrent refrain among technology management researchers and practitioners is the need to adapt to the increasing pace and unpredictability of change within many industry environments. Yet, both the characterization of industry environments and evidence of how they evolve over time has been decidedly mixed. This reflects the limitations and overreliance on linear methods in management research. In this paper, we introduce nonlinear dynamical system methods from complexity theory as an alternative to characterize and operationalize industry environments. We identify three measures employed regularly in disciplines ranging from medicine to physics to identify nonlinear patterns in data. Using data from 19 key industry sectors over 36 years, we demonstrate how this method can be used to examine how industry environments evolve. Our results indicate that industry environments evolve as chaotic systems with unpredictability commensurate with characterizations of hypercompetition. Furthermore, we observe that this unpredictability is widespread across multiple industry sectors, and not relegated to a selected group of industry environments. However, we do observe variation in the level of complexity and sensitivity to initial conditions across industry sectors. Finally, we find no evidence that the level of unpredictability of these environments was increasing over the time period studied.