IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v33y2017i1p244-253.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207015001193
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijforecast.2015.08.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mary Tripsas & Giovanni Gavetti, 2000. "Capabilities, cognition, and inertia: evidence from digital imaging," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1147-1161, October.
    2. Daniel Kahneman & Dan Lovallo, 1993. "Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking," Management Science, INFORMS, vol. 39(1), pages 17-31, January.
    3. Varho, Vilja & Tapio, Petri, 2013. "Combining the qualitative and quantitative with the Q2 scenario technique — The case of transport and climate," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 611-630.
    4. Wright, George & Bradfield, Ron & Cairns, George, 2013. "Does the intuitive logics method – and its recent enhancements – produce “effective” scenarios?," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 631-642.
    5. Willy Aspinall, 2010. "A route to more tractable expert advice," Nature, Nature, vol. 463(7279), pages 294-295, January.
    6. Soste, Leon & Wang, Q.J. & Robertson, David & Chaffe, Robert & Handley, Selina & Wei, Yongping, 2015. "Engendering stakeholder ownership in scenario planning," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 250-263.
    7. Meissner, Philip & Wulf, Torsten, 2013. "Cognitive benefits of scenario planning: Its impact on biases and decision quality," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 801-814.
    8. Warth, Johannes & von der Gracht, Heiko A. & Darkow, Inga-Lena, 2013. "A dissent-based approach for multi-stakeholder scenario development — The future of electric drive vehicles," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 566-583.
    9. Schoemaker, Paul J.H. & Day, George S. & Snyder, Scott A., 2013. "Integrating organizational networks, weak signals, strategic radars and scenario planning," Technological Forecasting and Social Change, Elsevier, vol. 80(4), pages 815-824.
    10. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    11. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    12. Gerard P. Hodgkinson & Nicola J. Bown & A. John Maule & Keith W. Glaister & Alan D. Pearman, 1999. "Breaking the frame: an analysis of strategic cognition and decision making under uncertainty," Strategic Management Journal, Wiley Blackwell, vol. 20(10), pages 977-985, October.
    13. Bonaccio, Silvia & Dalal, Reeshad S., 2006. "Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(2), pages 127-151, November.
    14. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    2. Rowe, Emily & Wright, George & Derbyshire, James, 2017. "Enhancing horizon scanning by utilizing pre-developed scenarios: Analysis of current practice and specification of a process improvement to aid the identification of important ‘weak signals’," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 224-235.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.
    2. Fritschy, Carolin & Spinler, Stefan, 2019. "The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    3. Förster, Bernadette, 2015. "Technology foresight for sustainable production in the German automotive supplier industry," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 237-248.
    4. Ramboarison-Lalao, Lovanirina & Gannouni, Kais, 2019. "Liberated firm, a leverage of well-being and technological change? A prospective study based on the scenario method," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 129-139.
    5. Philip Meissner & Torsten Wulf, 2016. "Debiasing illusion of control in individual judgment: the role of internal and external advice seeking," Review of Managerial Science, Springer, vol. 10(2), pages 245-263, March.
    6. Rowe, Emily & Wright, George & Derbyshire, James, 2017. "Enhancing horizon scanning by utilizing pre-developed scenarios: Analysis of current practice and specification of a process improvement to aid the identification of important ‘weak signals’," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 224-235.
    7. Tiberius, Victor & Siglow, Caroline & Sendra-García, Javier, 2020. "Scenarios in business and management: The current stock and research opportunities," Journal of Business Research, Elsevier, vol. 121(C), pages 235-242.
    8. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    9. Önkal, Dilek & Sinan Gönül, M. & Goodwin, Paul & Thomson, Mary & Öz, Esra, 2017. "Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility," International Journal of Forecasting, Elsevier, vol. 33(1), pages 280-297.
    10. Makkonen, Marika & Hujala, Teppo & Uusivuori, Jussi, 2016. "Policy experts' propensity to change their opinion along Delphi rounds," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 61-68.
    11. Tobias Meyer & Heiko A. von der Gracht & Evi Hartmann, 2022. "Technology foresight for sustainable road freight transportation: Insights from a global real‐time Delphi study," Futures & Foresight Science, John Wiley & Sons, vol. 4(1), March.
    12. Metz, Ashley & Hartley, Paul, 2020. "Scenario development as valuation: Opportunities for reflexivity," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    13. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    14. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.
    15. Frevel, Nicolas & Beiderbeck, Daniel & Schmidt, Sascha L., 2022. "The impact of technology on sports – A prospective study," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    16. Hussain, M. & Tapinos, E. & Knight, L., 2017. "Scenario-driven roadmapping for technology foresight," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 160-177.
    17. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    18. Phadnis, Shardul & Caplice, Chris & Singh, Mahender & Sheffi, Yossi, 2014. "Axiomatic foundation and a structured process for developing firm-specific Intuitive Logics scenarios," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 122-139.
    19. Gebhardt, Maximilian & Spieske, Alexander & Kopyto, Matthias & Birkel, Hendrik, 2022. "Increasing global supply chains’ resilience after the COVID-19 pandemic: Empirical results from a Delphi study," Journal of Business Research, Elsevier, vol. 150(C), pages 59-72.
    20. Lehr, Thomas & Lorenz, Ullrich & Willert, Markus & Rohrbeck, René, 2017. "Scenario-based strategizing: Advancing the applicability in strategists' teams," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 214-224.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:244-253. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.