Complexity as an empirical tendency: Promoting non-measurement as a means to enhanced understanding

In this conceptual paper, I seek to provide an organising framework for conducting qualitative research in complexity studies in management. Building upon the underlying logic of Kauffman's NK(C) model and the notion of second-order complexity, I urge management researchers interested in complex adaptive systems to capture, understand, and articulate complexity as an empirical tendency as opposed to the measurement-driven orientation of many scholars. I contend that the latter orientation's illusion for numerical precision, predictive accuracy and generalizable truthfulness is not only undoable but also unnecessary in the context of providing practically meaningful and realistic recommendations to those interested in complexity. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This conceptual paper proposes an organizing framework for qualitative research in complexity studies in management, emphasizing the importance of capturing, understanding, and articulating complexity as an empirical tendency rather than a measurement-driven orientation. It also argues that the pursuit of numerical precision and generalizable truthfulness is unnecessary in providing practically meaningful and realistic recommendations in the context of complexity.