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Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

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  • Jesse Shore

    (Information Systems Department, Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Ethan Bernstein

    (Organizational Behavior Unit, Harvard Business School, Boston, Massachusetts 02163)

  • David Lazer

    (Department of Political Science, Northeastern University, Boston, Massachusetts 02115)

Abstract

Using data from a novel laboratory experiment on complex problem solving in which we varied the structure of 16-person networks, we investigate how an organization’s network structure shapes the performance of problem-solving tasks. Problem solving, we argue, involves both exploration for information and exploration for solutions . Our results show that network clustering has opposite effects for these two important and complementary forms of exploration. Dense clustering encourages members of a network to generate more diverse information but discourages them from generating diverse theories; that is, clustering promotes exploration in information space but decreases exploration in solution space. Previous research, generally focusing on only one of those two spaces at a time, has produced an inconsistent understanding of the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we bring disparate results under a single theoretical roof and clarify the effects of network clustering on problem-solving behavior and performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one determinant of successful problem solving may harm the other.

Suggested Citation

  • Jesse Shore & Ethan Bernstein & David Lazer, 2015. "Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces," Organization Science, INFORMS, vol. 26(5), pages 1432-1446, October.
  • Handle: RePEc:inm:ororsc:v:26:y:2015:i:5:p:1432-1446
    DOI: 10.1287/orsc.2015.0980
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    References listed on IDEAS

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    Cited by:

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    4. Andrew Mao & Winter Mason & Siddharth Suri & Duncan J Watts, 2016. "An Experimental Study of Team Size and Performance on a Complex Task," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-22, April.
    5. Gengjun Yao & Jingwei Wang & Baoguo Cui & Yunlong Ma, 2022. "Quantifying effects of tasks on group performance in social learning," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    6. Joshua Becker & Douglas Guilbeault & Ned Smith, 2021. "The Crowd Classification Problem: Social Dynamics of Binary Choice Accuracy," Papers 2104.11300, arXiv.org.
    7. Rojas, Mariana Giovanna Andrade & Solis, Edgar Rogelio Ramirez & Zhu, John JianJun, 2018. "Innovation and network multiplexity: R&D and the concurrent effects of two collaboration networks in an emerging economy," Research Policy, Elsevier, vol. 47(6), pages 1111-1124.
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    9. Boris Maciejovsky & David V. Budescu, 2020. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets," Organization Science, INFORMS, vol. 31(6), pages 1497-1514, November.
    10. Svenja C. Sommer & Elliot Bendoly & Stylianos Kavadias, 2020. "How Do You Search for the Best Alternative? Experimental Evidence on Search Strategies to Solve Complex Problems," Management Science, INFORMS, vol. 66(3), pages 1395-1420, March.
    11. Kim, Dennie & Funk, Russell & Zaheer, Aks, 2020. "Structure in Context: A Morphological View of Whole Network Performance," SocArXiv x6q7g, Center for Open Science.
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