IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v60y2017i2p229-236.html

Finding the future: Crowdsourcing versus the Delphi technique

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S000768131630129X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2016.11.007?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Armstrong, J Scott, 1991. "Prediction of Consumer Behavior by Experts and Novices," Journal of Consumer Research, Journal of Consumer Research Inc., 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, Journal of Consumer Research Inc., 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.
    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. Prommer, Lisa & Tiberius, Victor & Kraus, Sascha, 2020. "Exploring the future of startup leadership development," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    2. 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.
    3. Magnier-Watanabe, Rémy & Lemaire, Jean-Paul, 2018. "Inbound foreign direct investment in Japan: A typology," International Business Review, Elsevier, vol. 27(2), pages 431-442.
    4. Kamran-Disfani, Omid & Mantrala, Murali, 2024. "Can crowdsourcing improve prediction accuracy in fashion retail buying?," Journal of Retailing, Elsevier, vol. 100(3), pages 404-421.
    5. Simone Wurster, 2021. "Creating a Circular Economy in the Automotive Industry: The Contribution of Combining Crowdsourcing and Delphi Research," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    6. Schlecht, Laura & Schneider, Sabrina & Buchwald, Arne, 2021. "The prospective value creation potential of Blockchain in business models: A delphi study," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    7. Steininger, Dennis M. & Gatzemeier, Simon, 2019. "Digitally forecasting new music product success via active crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 167-180.
    8. 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.
    9. Chatterjee, Subimal & Dalman, M. Deniz & Mookherjee, Satadruta, 2020. "To short or not to short? Improving morality judgments of short trades and short traders," Journal of Business Research, Elsevier, vol. 114(C), pages 173-185.
    10. Nina Gorovaia & Dildar Hussain, 2025. "From Strategy to Storefront: CSR and Brand Competitiveness in Franchise Networks," Post-Print hal-05233649, HAL.

    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. Steininger, Dennis M. & Gatzemeier, Simon, 2019. "Digitally forecasting new music product success via active crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 167-180.
    2. Meirowitz, Adam, 2005. "Deliberative Democracy or Market Democracy: Designing Institutions to Aggregate Preferences and Information," Papers 03-28-2005, Princeton University, Research Program in Political Economy.
    3. Byung-Yeon Kim & Gerard Roland, 2011. "Are the Markets Afraid of Kim Jong-Il?," KIER Working Papers 789, Kyoto University, Institute of Economic Research.
    4. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    5. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    6. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting Prediction Market Prices as Probabilities," Working Paper Series 2006-11, Federal Reserve Bank of San Francisco.
    7. McKenzie, Jordi, 2013. "Predicting box office with and without markets: Do internet users know anything?," Information Economics and Policy, Elsevier, vol. 25(2), pages 70-80.
    8. Carlos Devece & Daniel Palacios-Marqués & D. Enrique Ribeiro-Soriano, 2021. "IT-based strategy, capabilities, and practices: crowdsourcing implementation in market-oriented firms," Review of Managerial Science, Springer, vol. 15(1), pages 15-32, January.
    9. Albert N. Link & John T. Scott, 2013. "Private Investor Participation and Commercialization Rates for Government-sponsored Research and Development: Would a Prediction Market Improve the Performance of the SBIR Programme?," Chapters, in: Public Support of Innovation in Entrepreneurial Firms, chapter 11, pages 157-174, Edward Elgar Publishing.
    10. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    11. Atanasov, Pavel & Witkowski, Jens & Ungar, Lyle & Mellers, Barbara & Tetlock, Philip, 2020. "Small steps to accuracy: Incremental belief updaters are better forecasters," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 19-35.
    12. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    13. Wolfers, Justin & Zitzewitz, Eric, 2006. "Prediction Markets in Theory and Practice," CEPR Discussion Papers 5578, C.E.P.R. Discussion Papers.
    14. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    15. Fabio Milani, 2010. "Public option and private profits," Applied Health Economics and Health Policy, Springer, vol. 8(3), pages 155-165, May.
    16. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    17. Oechssler, Jörg & Schmidt, Carsten & Schnedler, Wendelin, 2007. "Asset bubbles without dividends : an experiment," Papers 07-01, Sonderforschungsbreich 504.
    18. Sjöberg, Lennart, 2006. "Are all crowds equally wise? A comparison of political election forecasts by experts and the public," SSE/EFI Working Paper Series in Business Administration 2006:9, Stockholm School of Economics, revised 08 Sep 2008.
    19. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    20. Gibson, Rajna & Habib, Michel A. & Ziegler, Alexandre, 2014. "Reinsurance or securitization: The case of natural catastrophe risk," Journal of Mathematical Economics, Elsevier, vol. 53(C), pages 79-100.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:bushor:v:60:y:2017:i:2:p:229-236. 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/bushor .

    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.