Interpreting Innovation Dynamics with Complexity Theory

Capturing and understanding innovation dynamics is a continuous challenge due to the difficulty of collecting process data and because it often involves multiple levels and units of analysis. Interest has been increasingly focused on applying complexity theory in explaining temporal and nonlinear characteristics of innovation because of its systematic paradigm for examining change in complex systems. Until recently, however, there is relatively little empirical support. This paper fulfills this objective by examining longitudinal data of Nylon innovation. Results reveal an attractor-shifting pattern accompanied by four adaptive cycles in the Nylon innovation process.