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<p>The elicitation of expert consensus in Future Studies often results in complex, high-dimensional datasets characterized by ordinal variables and non-linear dependencies. Traditional hard clustering techniques frequently fail to capture the intrinsic ambiguity and the overlapping nature of future scenarios derived from Delphi studies. This paper introduces a novel hybrid methodological framework that integrates correlation network analysis with fuzzy clustering to detect latent structures in ordinal Delphi data. We model the consensus space as a weighted graph where edge weights represent rank correlations between projections. A topological modularity maximization is first applied to identify the backbone of the community structure, followed by a Fuzzy Analysis Clustering (FANNY) algorithm on the correlation distance space to assign membership degrees to transitional variables. The methodology is applied to a dataset of 57 items regarding the future of the tourism ecosystem in the Apulia region, evaluated by a panel of experts. Results reveal a tripartite latent structure comprising structural assets, sustainability governance, and economic risks. The validity of the extracted partitions is confirmed via a Monte Carlo permutation test, demonstrating that the detected modularity significantly exceeds that of random networks. This approach offers a robust quantitative tool for policy planning, capable of distinguishing between core drivers and ambiguous bridge factors in scenario building.</p>

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A hybrid network-fuzzy framework for sustainable tourism: identifying latent structures from Delphi data

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Yuri Calleo,
  • Laura Antonucci,
  • Simone Di Zio

摘要

The elicitation of expert consensus in Future Studies often results in complex, high-dimensional datasets characterized by ordinal variables and non-linear dependencies. Traditional hard clustering techniques frequently fail to capture the intrinsic ambiguity and the overlapping nature of future scenarios derived from Delphi studies. This paper introduces a novel hybrid methodological framework that integrates correlation network analysis with fuzzy clustering to detect latent structures in ordinal Delphi data. We model the consensus space as a weighted graph where edge weights represent rank correlations between projections. A topological modularity maximization is first applied to identify the backbone of the community structure, followed by a Fuzzy Analysis Clustering (FANNY) algorithm on the correlation distance space to assign membership degrees to transitional variables. The methodology is applied to a dataset of 57 items regarding the future of the tourism ecosystem in the Apulia region, evaluated by a panel of experts. Results reveal a tripartite latent structure comprising structural assets, sustainability governance, and economic risks. The validity of the extracted partitions is confirmed via a Monte Carlo permutation test, demonstrating that the detected modularity significantly exceeds that of random networks. This approach offers a robust quantitative tool for policy planning, capable of distinguishing between core drivers and ambiguous bridge factors in scenario building.