Estimating the Innovation Efficiency of the Artificial Intelligence Industry in China Based on the Three-Stage DEA Model

Abstract

The innovation efficiency of the artificial intelligence (AI) industry is underexplored. The aim of this article is to effectively evaluate the innovation efficiency of the regional AI industry to promote the allocation of resources and the development of this industry. This article proposes a new comprehensive evaluation model based on the synergy of science and technology, education, and venture capital to examine the AI industrial innovation efficiency by adopting the three-stage data envelopment analysis method. There are three main aspects of the results presented in this work: 1) The AI industry in China achieved improvements in scale efficiency and technical efficiency from 2015 to 2018, experienced cultivation and development in two periods, and the average pure technical efficiency level reached 0.906. 2) The AI industry in China shows interregional heterogeneity. The technical efficiency and scale efficiency of East and Central China are higher than those of other regions. 3) Three environmental factors, including the economic development level, government innovation support, and technology market maturity, have impacts on innovation efficiency. This research contributes to the development of the AI industry by formulating a new evaluation model aimed at innovation efficiency, and its innovation conceptual framework can be used to evaluate other emerging industries.