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Finding the future: Crowdsourcing versus the Delphi technique

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  • Flostrand, Andrew

Abstract

When managers are unable to use quantifiable time series data to make forecasts or decide on uncertainties, they can either rely on their own intuition and judgment or resort to the insights of others. The Delphi technique is a well-known forecasting technique that relies on the pooled perspectives of experts to predict uncertain quantities or the outcomes of events. This relies on polling the opinions of experts, aggregating these opinions, feeding them back to the responding experts along with their own estimates, and having them repeat their judgment calls until some level of consensus is reached. More recently, however, the opinions of many others who are not experts have been sought on a range of topics in a loose assembly of similar techniques bundled under the title of crowdsourcing. This article compares Delphi and crowdsourcing as prediction and estimation tools for managers. It notes their differences and similarities, and provides a simple tool for executives to use in deciding whether or not to use these tools, and if so, which tool or combination of them will work best in a given situation.

Suggested Citation

  • Flostrand, Andrew, 2017. "Finding the future: Crowdsourcing versus the Delphi technique," Business Horizons, Elsevier, vol. 60(2), pages 229-236.
    • Handle: RePEc:eee:bushor:v:60:y:2017:i:2:p:229-236
    DOI: 10.1016/j.bushor.2016.11.007
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    1. Armstrong, J Scott, 1991. "Prediction of Consumer Behavior by Experts and Novices," Journal of Consumer Research, Oxford University Press, vol. 18(2), pages 251-256, September.
    2. Prpić, John & Shukla, Prashant P. & Kietzmann, Jan H. & McCarthy, Ian P., 2015. "How to work a crowd: Developing crowd capital through crowdsourcing," Business Horizons, Elsevier, vol. 58(1), pages 77-85.
    3. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    4. Wood, Stacy L & Lynch, John G, Jr, 2002. "Prior Knowledge and Complacency in New Product Learning," Journal of Consumer Research, Oxford University Press, vol. 29(3), pages 416-426, December.
    5. George T. Milkovich & Anthony J. Annoni & Thomas A. Mahoney, 1972. "The Use of the Delphi Procedures in Manpower Forecasting," Management Science, INFORMS, vol. 19(4-Part-1), pages 381-388, December.
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    4. Kawamoto, Carlos Tadao & Wright, James Terence Coulter & Spers, Renata Giovinazzo & de Carvalho, Daniel Estima, 2019. "Can we make use of perception of questions' easiness in Delphi-like studies? Some results from an experiment with an alternative feedback," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 296-305.
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