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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

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  • Stanislav Birko
  • Edward S Dove
  • Vural Özdemir

Abstract

The extent of consensus (or the lack thereof) among experts in emerging fields of innovation can serve as antecedents of scientific, societal, investor and stakeholder synergy or conflict. Naturally, how we measure consensus is of great importance to science and technology strategic foresight. The Delphi methodology is a widely used anonymous survey technique to evaluate consensus among a panel of experts. Surprisingly, there is little guidance on how indices of consensus can be influenced by parameters of the Delphi survey itself. We simulated a classic three-round Delphi survey building on the concept of clustered consensus/dissensus. We evaluated three study characteristics that are pertinent for design of Delphi foresight research: (1) the number of survey questions, (2) the sample size, and (3) the extent to which experts conform to group opinion (the Group Conformity Index) in a Delphi study. Their impacts on the following nine Delphi consensus indices were then examined in 1000 simulations: Clustered Mode, Clustered Pairwise Agreement, Conger’s Kappa, De Moivre index, Extremities Version of the Clustered Pairwise Agreement, Fleiss’ Kappa, Mode, the Interquartile Range and Pairwise Agreement. The dependency of a consensus index on the Delphi survey characteristics was expressed from 0.000 (no dependency) to 1.000 (full dependency). The number of questions (range: 6 to 40) in a survey did not have a notable impact whereby the dependency values remained below 0.030. The variation in sample size (range: 6 to 50) displayed the top three impacts for the Interquartile Range, the Clustered Mode and the Mode (dependency = 0.396, 0.130, 0.116, respectively). The Group Conformity Index, a construct akin to measuring stubbornness/flexibility of experts’ opinions, greatly impacted all nine Delphi consensus indices (dependency = 0.200 to 0.504), except the Extremity CPWA and the Interquartile Range that were impacted only beyond the first decimal point (dependency = 0.087 and 0.083, respectively). Scholars in technology design, foresight research and future(s) studies might consider these new findings in strategic planning of Delphi studies, for example, in rational choice of consensus indices and sample size, or accounting for confounding factors such as experts’ variable degrees of conformity (stubbornness/flexibility) in modifying their opinions.

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  • 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.
    • Handle: RePEc:plo:pone00:0135162
    DOI: 10.1371/journal.pone.0135162
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    References listed on IDEAS

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    1. 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.
    2. Ulucanlar, S. & Faulkner, A. & Peirce, S. & Elwyn, G., 2013. "Technology identity: The role of sociotechnical representations in the adoption of medical devices," Social Science & Medicine, Elsevier, vol. 98(C), pages 95-105.
    3. Alessandro Di Mare & Vito Latora, 2007. "Opinion Formation Models Based On Game Theory," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(09), pages 1377-1395.
    4. Matthijs Warrens, 2010. "Inequalities between multi-rater kappas," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(4), pages 271-286, December.
    5. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
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    2. M. K. Dhillon & P. M. Rafi-Ul-Shan & H. Amar & F. Sher & S. Ahmed, 2023. "Flexible Green Supply Chain Management in Emerging Economies: A Systematic Literature Review," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 1-28, March.
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    4. 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).
    5. Bolger, Fergus & Rowe, Gene & Belton, Ian & Crawford, Megan M & Hamlin, Iain & Sissons, Aileen & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina & Wright, George, 2020. "The Simulated Group Response Paradigm: A new approach to the study of opinion change in Delphi and other structured-group techniques," OSF Preprints 4ufzg, Center for Open Science.
    6. 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.
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    8. Zhangyuan He & Hans-Dietrich Haasis, 2020. "A Theoretical Research Framework of Future Sustainable Urban Freight Transport for Smart Cities," Sustainability, MDPI, vol. 12(5), pages 1-28, March.
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