ABSTRACT
Improvising is an emergent process of self-organization, replenished of sudden transitions, disruptive moments, and exponential process improvement and decline. Hence, improvisation is a nonlinear process by nature, and this chapter aims to spark interest in the contribution of the Nonlinear Dynamic Systems (NDS) theory the expand current research about improvisation in organizations. We do so by first briefly integrating improvisation theory and NDS theory, followed by a description of how easy-to-implement methodologies (i.e., cusp catastrophe modeling, and artificial neural networks), can be used by improvisation scholars to study improvisation as the complex process it really is.
