An ordered weighted averaging-based framework to build and select better composite indicators

Social, economic, business, and environmental phenomena are multidimensional in nature. In particular, poverty, economic development, innovation, and sustainability cannot be measured by a single indicator. In this regard, the composite indicators framework provides tools for addressing the multidimensional nature of phenomena. Its implementation offers Polycemaker a one-dimensional synthesis measure, easy to understand, that encompasses the multiple underlying indicators of the multidimensional phenomenon, without loss of analytical scope. Despite its advantages, composite indicators have drawbacks. There are many methods for constructing composite indicators, but some methods remain underexplored. The different ways of constructing composite indicators lead to uncertainty about which method to use. Uncertainty analyses and linkages with external variables have predominantly verified the quality of the composite indicators. This study presents a Software and a Decision Support System to address these gaps. The novel Ordered Weighted Averaging (OWA)-based Software facilitates the construction of composite indicators, leveraging OWA's flexibility to control the sub-indicators' mutual compensation levels and bias composite indicator scores in different directions (positive or negative) and intensities. The Software includes a wide range of tests (robustness, explanatory power, informational power, discriminating power, and the ratio of atypical measurements) to measure the quality of OWA-based composite indicators. Furthermore, the novel Decision Support System enables identifying the composite indicator that achieves higher satisfaction levels in quality tests, preventing decision-makers from using weak solutions. Finally, the Software and the Decision Support System are applied to identify the composite indicator that best represents Global Innovation, Potential Economic Development, and Affordable and Clean Energy. • Few publications explore OWA in the construction of composite indicators. • OWA adjusts the compensation between sub-indicators in composite indicators. • New software tests the quality of indicators built with OWA. • System identifies the best indicator based on multiple tests. • The system offers greater precision and reliability for decisions.