Evaluating innovation capability of Chinese listed companies based on comprehensive methods

Abstract

Purpose This paper aims to evaluate innovation capability of companies based on comprehensive methods. Design/methodology/approach This paper used principal component analysis (PCA), kernel principal component analysis (KPCA) and principal component cluster (PCC) analysis to analyze the listed companies’ innovation capability. On these bases, mean method, Borda method, Copeland method, alienation coefficient method and fuzzy Borda method were used separately for the comprehensive evaluation. Findings The results show that the comprehensive evaluation can overcome the shortage of the single method and improve the reliability of the innovation ability evaluation. In addition, the method also reveals that the innovation ability of the listed companies is closely related to the innovation investment and their industry and further regional economic development level of each province (city and area). Originality/value This paper uses PCA, KPCA and PCC to evaluate and study their innovation ability. On the basis of these, five methods (mean method, Copeland method, Borda method, divorced coefficient method and fuzzy Borda method) are applied respectively to combine the sort results of PCA, KPCA and PCC. The results show that combination methods have better theoretical and practical significance for innovation ability.

Evaluating the innovation capability of cluster-based firms: a graph-theoretic approach

“…When evaluating this capability, the literature remains scarce (Zhang et al , 2019; Tavares et al , 2021). This can be explained by the fact that assessing IC is a complex problem (Hu and Liu, 2021).…”