Scholarlink

<p>To reveal the paths, effects and differences of the Digital Economy (DE)’s impact on the technological innovation of SMEs, this paper deeply explores the heterogeneous impact of the development of the DE on the technological innovation of SMEs and its internal mechanisms. By adopting methods including two-way FEM, threshold regression, and SDM, a comprehensive evaluation system covering digital economic carriers, industry digitization, and digital industrialization is constructed. This paper utilizes the superSBM model including undesired output and the BP neural network to measure the technical efficiency of enterprises. The results show that industrial digitalization and digital industrialization have significant promoting effects, with coefficients of 0.186 and 0.115. In the heterogeneity analysis, the eastern region has the strongest impact, with a coefficient of 0.252, the central region has a coefficient of 0.151, and the western region is not significant. Enterprises in high-tech industries are more incentivized, with a coefficient of 0.268. The impact coefficients of enterprises in the growth stage and maturity stage are 0.191 and 0.134, and there is no significant impact in the recession stage. This article provides systematic empirical evidence for understanding how DE enables technological innovation in China’s listed SMEs, and has reference significance for promoting the balanced development of regional digitalization and building a differentiated innovation support system.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Heterogeneous impact of China’s digital economy development on technological innovation of small and medium-sized enterprises

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月5日

查看全文
  • Zixuan Hu

摘要

To reveal the paths, effects and differences of the Digital Economy (DE)’s impact on the technological innovation of SMEs, this paper deeply explores the heterogeneous impact of the development of the DE on the technological innovation of SMEs and its internal mechanisms. By adopting methods including two-way FEM, threshold regression, and SDM, a comprehensive evaluation system covering digital economic carriers, industry digitization, and digital industrialization is constructed. This paper utilizes the superSBM model including undesired output and the BP neural network to measure the technical efficiency of enterprises. The results show that industrial digitalization and digital industrialization have significant promoting effects, with coefficients of 0.186 and 0.115. In the heterogeneity analysis, the eastern region has the strongest impact, with a coefficient of 0.252, the central region has a coefficient of 0.151, and the western region is not significant. Enterprises in high-tech industries are more incentivized, with a coefficient of 0.268. The impact coefficients of enterprises in the growth stage and maturity stage are 0.191 and 0.134, and there is no significant impact in the recession stage. This article provides systematic empirical evidence for understanding how DE enables technological innovation in China’s listed SMEs, and has reference significance for promoting the balanced development of regional digitalization and building a differentiated innovation support system.