JOURNAL ARTICLE

A Cognitive Network Perspective on Creativity: Theorizing Network Mobilization Scripts.

  • Published In: Organization Science (INFORMS), 2025, v. 36, n. 2. P. 626 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Biscaro, Claudio; Montanari, Fabrizio 3 of 3

Abstract

This article examines how individuals' subjective perceptions of their interpersonal networks—referred to as cognitive networks—influence the ways they mobilize these networks during the elaboration of creative ideas. Based on an inductive case study of a small creative studio, the authors identify two distinct network mobilization scripts linked to creators' cognitive network types: those who perceive their networks as loose tend to activate a small inner circle of intimate colleagues and engage in direct, humorous interactions ("creating in a studio"), while those who see their networks as cohesive engage a broader set of ties with courteous and inclusive communication ("orchestrating the network"). Both scripts support idea elaboration effectively, but their success depends on the alignment (resonance) between an individual's cognitive network and their mobilization practices, as well as the compatibility of these practices with those of their collaborators. The study contributes to creativity and cognitive network research by highlighting the subjective and socially embedded nature of network mobilization, suggesting multiple viable pathways for leveraging social networks in creative work.

Additional Information

  • Source:Organization Science (INFORMS). 2025/03, Vol. 36, Issue 2, p626
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2022.16899
  • Accession Number:184328947
  • Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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