Multicriteria decision model for constructing composite indicators and its application in representing multidimensional governance

Governance is a complex, multidimensional construct difficult to measure and has economic, social, and sus- tainability implications. This study revisits the literature on governance, examining its conceptual frameworks and its relationships with economic, social, and sustainability factors. The study reveals several gaps in the representation of multidimensional governance. First, the almost exclusive use of Principal Component Analysis (PCA) and Simple Additive Weighting with Equal Weights (SAW-EW) to operationalize the construct. Second, the limited use of tests to verify the quality and reliability of the representations. Third, the analysis of governance as a unidimensional construct. This research aims to address these gaps by incorporating quality and reliability parameters into a new multicriteria decision model for constructing composite indicators. The multicriteria decision model uses ranking uncertainty, links to other variables, and the composite indicator's informational and discriminant power to identify the best method for representing multidimensional governance. The use of this framework across OECD countries shows that the Entropy method represents multidimensional governance with greater quality and reliability than the PCA, SAW-EW, Benefit of the Doubt (BoD), and Ordered Weighted Average (OWA) methods. The Entropy method ensures a more accurate alignment between the concept of multidimensional governance and economic, social, and sustainability aspects, providing robust information for political and investment decisions. The quality and reliability framework is adaptable to other multidimensional phenomena, with the best method defined for each context.