A Microstructural Approach to Self-Organizing: The Emergence of Attention Networks

Abstract

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants’ resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large open-source software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost.Funding: Financial support for this work was provided by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 100018_150126] (“Relational event modes for bipartite networks with application to collaborative problem solving,” P.I. Alessandro Lomi) and by the Deutsche Forschungsgemeinschaft [Grant 321869138] (“Statistical analysis of time-stamped multi-actor events in social networks,” P.I. Jüergen Lerner).Supplemental Material: The supplemental video containing the dynamic visualization of the data is available at https://zenodo.org/record/7564503 and in the e-companion (available at https://doi.org/10.1287/orsc.2023.1674 ).