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Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process

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  • Meissner, Philip
  • Brands, Christian
  • Wulf, Torsten

Abstract

The integration of expert judgment is a fundamental pillar of most scenario planning processes. In particular, the systematic scanning of external expert opinions has been shown to be effective for the early detection of emerging threats and opportunities in an organization’s environment. However, organizations tend to focus on internal advice more than on advice from external experts. This can be critical for organizations if it leads to an inertia in internal judgment, resulting in blind spots or a failure to see weak signals in the firm’s periphery. In this article, we introduce a structured framework for the collection and structuring of internal and external expert judgment. This so-called 360∘ Stakeholder Feedback tool provides a structured and quantitative approach for the detection and discussion of blind spots and weak signals in scenario planning processes. Thus, it can contribute to a better and more holistic judgment in the strategic process. We demonstrate the methodology based on a case from the German construction industry, in which we aggregate and analyze expert judgments from different stakeholder groups regarding the future of the industry.

Suggested Citation

  • Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:244-253
    DOI: 10.1016/j.ijforecast.2015.08.002
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    Cited by:

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    3. Wright, George & Cairns, George & O'Brien, Frances A. & Goodwin, Paul, 2019. "Scenario analysis to support decision making in addressing wicked problems: Pitfalls and potential," European Journal of Operational Research, Elsevier, vol. 278(1), pages 3-19.
    4. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    5. Arbrie Jashari & Victor Tiberius & Marina Dabić, 2022. "Tracing the progress of scenario research in business and management," Futures & Foresight Science, John Wiley & Sons, vol. 4(2), June.
    6. Jodlbauer, Herbert & Tripathi, Shailesh & Brunner, Manuel & Bachmann, Nadine, 2022. "Stability of cross impact matrices," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    7. Kim, Sehoon & Connerton, Timothy Paul & Park, Cheongyeul, 2021. "Exploring the impact of technological disruptions in the automotive retail: A futures studies and systems thinking approach based on causal layered analysis and causal loop diagram," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    8. Ilya Kuzminov & Irina Loginova & Elena Khabirova, 2018. "Stress Scenario Development: Global Challenges For The Russian Agricultural Sector," HSE Working papers WP BRP 88/STI/2018, National Research University Higher School of Economics.

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