JOURNAL ARTICLE

When Does External Knowledge Benefit Team Creativity? The Role of Internal Team Network Structure and Task Complexity.

  • Published In: Organization Science (INFORMS), 2024, v. 35, n. 1. P. 92 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Venkataramani, Vijaya; Tang, Chaoying 3 of 3

Abstract

The article examines how team creativity is influenced by the interplay between teams' external knowledge acquisition capabilities—measured by the breadth of their interactions with diverse external parties—and their internal knowledge integration capabilities, indicated by the decentralization of problem-solving interactions among team members. Two field studies involving research and development (R&D) and project teams in Chinese organizations found that external knowledge acquisition enhances creativity primarily when internal problem-solving networks are decentralized, allowing balanced participation and effective information elaboration. This effect is especially pronounced for teams facing complex tasks, with centralized internal structures limiting the beneficial impact of external knowledge by constraining open discussion and integration of diverse perspectives. The research highlights the mediating role of information elaboration processes and underscores the importance of considering both external and internal team capabilities, as well as task complexity, in fostering creativity.

Additional Information

  • Source:Organization Science (INFORMS). 2024/01, Vol. 35, Issue 1, p92
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2023.1661
  • Accession Number:175119298
  • 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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