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A microstructural approach to self-organizing: the emergence of attention networks

A microstructural approach to self-organizing: the emergence of attention networks

Tonellato, Marco, Tasselli, Stefano, Conaldi, Guido ORCID: 0000-0003-3552-7307, Lerner, Jürgen and Lomi, Alessandro (2023) A microstructural approach to self-organizing: the emergence of attention networks. Organization Science. ISSN 1047-7039 (Print), 1526-5455 (Online) (In Press)

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A recent stream of theoretical development investigates new forms of organizing as bundles of novel solutions to the universal problems of organizing: how to allocate organizational problems to organizational participants, and how to integrate participants’ resulting efforts. In this paper, we add to this debate by reframing organizational attention allocation as a concatenation of self-organizing, micro-structural mechanisms linking multiple participants to multiple problems, thereby forming an attention network. In particular, we argue that, when managerial hierarchies are absent and authority is decentralized, visible acts of attention allocation become a fundamental coordination mechanism by which participants provide information to each other on how to integrate their efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micro-mechanisms: Focusing, Reinforcing, Mixing, and Clustering. In a statistical analysis of attention networks in a large open-source software project, we found support for the four hypotheses about the self-organizing dynamics of the observed network associating organizational problems (software bugs) and 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 wide set of organizations where participants can choose which problems to allocate attention to and where the outcomes of these choices are publicly visible.

Item Type: Article
Uncontrolled Keywords: attention networks; social networks; micro-structural mechanisms; social mechanisms; attention allocation; relational event models
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
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Last Modified: 29 Mar 2023 08:16

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