Application of Multiattribute Decision-Making for Evaluating Regional Innovation Capacity

Abstract EN

The growing imbalance in regional innovation development has become an urgent issue in China’s strategy to build an innovative country.

To enrich the regional innovation capacity evaluation system, scientifically assess regional innovation capacity, and explore available pathways to improve regional innovation capacity, this paper introduces a multiattribute decision-making method for evaluating regional innovation capacity.

First, a random forest model and the DEMATEL-based analytic network process (DANP) method are applied to calculate the weights of the evaluation attributes.

Second, the multiobjective optimization by the ratio analysis method based on the maximum and minimum (MOORA-min-max method) is used to calculate the evaluation attribute gap ratios and regional innovation capacity of each region.

Finally, the limitations of regional innovation development are identified based on the evaluation attribute gap ratios and the critical influence strength roadmap (CISR) to explore the regional innovation capacity improvement pathways.

The results show that “output capacity of R&D personnel in universities and research institutes” is the most fundamental evaluation attribute in the regional innovation capacity evaluation, while “output efficiency of R&D funds in universities and research institutes” is the most influential evaluation attribute.

Research in Sichuan and Inner Mongolia reveals that regions need to identify critical constraints in four aspects: knowledge creation, knowledge acquisition, enterprise innovation, and innovation environment, to improve regional innovation capacity.