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

• 成果类型: Article
• 署名作者: Schulz, Martin; Zhu, Kejia
• 署名单位: University of British Columbia; University of Waterloo
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2021.1524
• 发表日期: 2022
• 页码: 2018-2040
• 关键词: organizational learning; archival research; knowledge-based view; organizational evolution and change; organizational processes; dynamic analysis/event history methods

摘要

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.