Learning by Connecting: How Rule Networks Evolve Through Discovery of Relevance

Martin Schulz () and Kejia Zhu ()
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Martin Schulz: Organizational Behaviour and Human Resources Division, Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
Kejia Zhu: Department of Management Sciences, Faculty of Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

Organization Science, 2022, vol. 33, issue 5, 2018-2040

Abstract: Learning-by-connecting, the formation of connections between lessons, is a fairly common phenomenon, but how does it evolve? We argue that learning-by-connecting unfolds as the relevance of lessons to other lessons is gradually discovered over time. The process of “relevance discovery” unfolds through a dynamic interplay between lessons and their context that provides opportunities to discover the relevance of lessons to other lessons. We develop a theoretical model in which the availability of these opportunities and their sorting in time drive the formation of connections. We explore and test our model in the context of organizational rules that we conceptualize, following rule-based learning theories, as repositories of lessons learned. Our empirical context is the formation of citation ties between clinical practice guidelines (CPGs), a type of organizational rules in healthcare, in a Canadian regional healthcare organization. We find that citation tie formation intensifies when opportunities to discover relevance become available. We also find that learning-by-connecting creates rule networks in which the formation of new ties slows down due to the sorting of opportunities in time. Our findings support our assumption that learning-by-connecting is shaped by relevance discovery. Our study extends models of rule-based learning and contributes to discussions on the formation of connections in contexts of dispersed learning and knowledge.

Keywords: organizational learning; archival research; knowledge-based view; organizational evolution and change; organizational processes; dynamic analysis/event history methods (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ororsc:v:33:y:2022:i:5:p:2018-2040

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