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Consensus in the delphi method: What makes a decision change?

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  • Barrios, Maite
  • Guilera, Georgina
  • Nuño, Laura
  • Gómez-Benito, Juana

Abstract

We examined whether giving feedback to participants in a Delphi study about the level of agreement across the expert panel had an effect on opinion change between rounds. We also considered the potential influence of participants’ sociodemographic and professional characteristics. Five three-round Delphi studies were conducted independently, in which a total of 628 mental health experts responded to all three rounds. In each study, participants had to decide, based on their experience, whether a series of categories were relevant. The percentage of group agreement (i.e., percentage of participants who considered each category as relevant) in round 2 was shown as feedback in round 3, and responses in rounds 2 and 3 were considered to analyze opinion change. Results showed that when the feedback given in round 3 indicated that ≥75% of experts considered a category to be relevant, there was a further shift in opinion towards the group opinion (i.e., the category then yielded even greater consensus), whereas if the feedback indicated <75% group agreement, individual opinions tended to shift against the group opinion (i.e., consensus over the category decreased). Neither sociodemographic nor professional variables had a significant effect in explaining opinion shift. These results show that in Delphi studies, feedback has an influence on individual responses and the achievement of consensus.

Suggested Citation

  • Barrios, Maite & Guilera, Georgina & Nuño, Laura & Gómez-Benito, Juana, 2021. "Consensus in the delphi method: What makes a decision change?," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:tefoso:v:163:y:2021:i:c:s004016252031310x
    DOI: 10.1016/j.techfore.2020.120484
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    References listed on IDEAS

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    1. Meijering, Jurian Vincent & Tobi, Hilde, 2018. "The effects of feeding back experts’ own initial ratings in Delphi studies: A randomized trial," International Journal of Forecasting, Elsevier, vol. 34(2), pages 216-224.
    2. Meijering, J.V. & Kampen, J.K. & Tobi, H., 2013. "Quantifying the development of agreement among experts in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1607-1614.
    3. Jiang, Ruth & Kleer, Robin & Piller, Frank T., 2017. "Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 84-97.
    4. Kauko, Karlo & Palmroos, Peter, 2014. "The Delphi method in forecasting financial markets— An experimental study," International Journal of Forecasting, Elsevier, vol. 30(2), pages 313-327.
    5. Meijering, Jurian V. & Tobi, Hilde, 2016. "The effect of controlled opinion feedback on Delphi features: Mixed messages from a real-world Delphi experiment," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 166-173.
    6. 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.
    7. Rowe, Gene & Wright, George, 1996. "The impact of task characteristics on the performance of structured group forecasting techniques," International Journal of Forecasting, Elsevier, vol. 12(1), pages 73-89, March.
    8. Zimmermann, Martin & Darkow, Inga-Lena & von der Gracht, Heiko A., 2012. "Integrating Delphi and participatory backcasting in pursuit of trustworthiness — The case of electric mobility in Germany," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1605-1621.
    9. Landeta, Jon & Barrutia, Jon, 2011. "People consultation to construct the future: A Delphi application," International Journal of Forecasting, Elsevier, vol. 27(1), pages 134-151, January.
    10. 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.
    11. Landeta, Jon & Barrutia, Jon, 2011. "People consultation to construct the future: A Delphi application," International Journal of Forecasting, Elsevier, vol. 27(1), pages 134-151.
    12. Laura Nuño & Georgina Guilera & Michaela Coenen & Emilio Rojo & Juana Gómez-Benito & Maite Barrios, 2019. "Functioning in schizophrenia from the perspective of psychologists: A worldwide study," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.
    13. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    14. 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.
    15. Yaniv, Ilan & Milyavsky, Maxim, 2007. "Using advice from multiple sources to revise and improve judgments," Organizational Behavior and Human Decision Processes, Elsevier, vol. 103(1), pages 104-120, May.
    16. Stanislav Birko & Edward S Dove & Vural Özdemir, 2015. "Evaluation of Nine Consensus Indices in Delphi Foresight Research and Their Dependency on Delphi Survey Characteristics: A Simulation Study and Debate on Delphi Design and Interpretation," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-14, August.
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