# Robust measurement of innovation performances in Europe with a hierarchy of interacting composite indicators

## TL;DR

This paper introduces a hierarchical multicriteria decision-making approach to improve the robustness and interaction considerations in composite innovation indicators, enhancing benchmarking of European countries' innovation performance.

## Contribution

It proposes a novel hierarchical-SMAA-Choquet integral method for aggregating innovation indicators, addressing weighting and interaction issues in composite indicators.

## Key findings

- Enhanced robustness in innovation measurement
- Better capturing of indicator interactions
- Improved country benchmarking accuracy

## Abstract

For long time the measurement of innovation has been in the forefront of policy makers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. However, there is no consensus about the appropriate methodology to aggregate the varying dimensions of innovation into a single summary indicator. One of the best known examples of composite innovation indicators is the European Innovation Scoreboard (EIS). It is a relevant tool for benchmarking innovation in Europe. Still, the EIS lacks a proper scheme for weighting the included indicators according to their relative importance. In this context, we propose an appraisal methodology permitting to take into consideration the interaction of criteria and robustness concerns related to the elicitation of the weights assigned to the elementary indicators. With this aim, we apply the hierarchical-SMAA-Choquet integral approach. This integrated multicriteria decision making (MCDM) method helps the users to rank and benchmark countries' innovation performance taking into account the importance and interaction of criteria assigned by themselves, rather than equal weights or weights exogenously fixed by external experts.