Assessment of data quality and validity of composite innovation indicators with a neutrosophic multi-criteria approach

In light of the emphasis on innovation as a driver of economic growth, new tools are needed to measure and compare national innovation systems. Improving methodological approaches to constructing composite innovation indicators also requires exploring issues related to aggregation, weighting, and reviewing the quality and validity of the data. This paper aims to support this debate by assessing the process and outcomes of innovation using fuzzy theory combined with a multicriteria technique to address the uncertainty attached to the underlying data used in constructing the composite indicator. Mainly, we deal with the problems related to the uniformity of the period covered by the indicator and the origin and reliability of the source of information by incorporating degrees of truth, indeterminacy, and falsity into the assessment process by applying neutrosophic numbers. To test the effectiveness of our approach, an empirical analysis is carried out based on the European Innovation Scoreboard and the assessment of the elementary criteria over the period 2020–2023.