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The Theory and Measurement of Expertise-Based Problem Solving in Organizational Teams: Revisiting Demonstrability

Author

Listed:
  • Bryan L. Bonner

    (David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112)

  • Daniel Shannahan

    (School of Business, College of Professional Studies, Northern State University, Aberdeen, South Dakota 57401)

  • Kristin Bain

    (Saunders College of Business, Rochester Institute of Technology, Rochester, New York 14623)

  • Kathryn Coll

    (David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112)

  • Nathan L. Meikle

    (Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556)

Abstract

The current paper revisits and builds upon task demonstrability, which defines the criteria necessary for groups to choose a correct response if any member prefers that response. We identify boundary conditions of the current conceptualization of task demonstrability with respect to its use in understanding modern organizational teams. Specifically, we argue that, in its current form, task demonstrability is not optimally suited to studying ongoing teams in which member expertise varies and teams work to complete complex multifaceted tasks. To address this issue, we provide a revisited perspective on demonstrability. We specify the nomological network of revisited demonstrability and recast each of its criteria in a form that preserves the original intent of the construct, but has broader applicability, particularly to organizational contexts. We then discuss theoretical implications and managerial applications of the construct. Finally, noting that there is no standard assessment tool for demonstrability (original or revisited), we develop and validate a measure to facilitate future research.

Suggested Citation

  • Bryan L. Bonner & Daniel Shannahan & Kristin Bain & Kathryn Coll & Nathan L. Meikle, 2022. "The Theory and Measurement of Expertise-Based Problem Solving in Organizational Teams: Revisiting Demonstrability," Organization Science, INFORMS, vol. 33(4), pages 1452-1469, July.
  • Handle: RePEc:inm:ororsc:v:33:y:2022:i:4:p:1452-1469
    DOI: 10.1287/orsc.2021.1481
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