Composite indicators and Machine Learning techniques. An Application to the tourism industry

Abstract

[EN] Composite indicators are essential tools for summarizing complex and multidimensional phenomena into a single measure, aiding decision-making in various fields, including tourism. This paper preliminary reviews the main composite indicators used in the literature to assess the competitiveness of tourism destinations, along with their sustainability. Then the paper proposes the construction of composite indicators for the tourism sector, leveraging on Principal Component Analysis and Factor Analysis as key statistical methodologies enhanced with machine learning methods to improve accuracy, robustness, and interpretability. Through an empirical analysis on Italian tourism data, we compare standard and regularized methodologies, demonstrating how machine learning-enhanced approaches can improve the reliability and interpretability of composite indicators. Our findings provide valuable insights for researchers and policymakers seeking to develop robust and data-driven tourism performance measures.