Grouping interdependent tasks: Using spectral graph partitioning to study complex systems

Research summary: This article uses spectral graph partitioning to advance strategic management research, and focuses on the study of complex systems that contain strongly connected components with component interactions that are weighted and directed. The spectral graph partitioning method complements existing methods, especially, when external architectural artifacts do not exist or are less than certain. We illustrate this methodology using a U.S. airline's production system. We highlight some useful metrics and show how researchers can apply this method to generate additional architectural insights.Managerial summary: We describe a method for analyzing the architecture of complex systems that contain directed and weighted component interactions, and in which each component is interdependent (directly or indirectly) on every other component. Using the spectra of a complex system, the method is not only tractable but also provides good and predictable groupings. We illustrate this method using the firm-level production task system of a firm found in the U.S. passenger airline sector. The method is useful if the system architecture is hidden, in flux, or both. The method may also permit a holistic comparison of different systems and their architectures. We discuss metrics and illustrate how they can provide additional architectural insights. Copyright (c) 2015 John Wiley & Sons, Ltd.