@article{he2026persistent,
  title = {Persistent Differences in Innovation Efficiency: Evidence from Chinese Listed Firms},
  author = {Hao HE},
  year = 2026,
  url = {https://ibimapublishing.com/articles/IBIMABR/2026/523128/},
  journal = {IBIMA Business Review},
  volume = 2026,
  pages = 12,
  doi = 10.5171/2026.523128,
  abstract = {Innovation efficiency reflects a firm's ability to transform innovation inputs into technological outputs. While prior studies have identified various determinants of innovation performance, little attention has been paid to whether differences in innovation efficiency persist over time due to stable firm-specific characteristics. This study addresses this gap by examining the structural persistence of innovation efficiency among Chinese listed firms. Using a panel dataset of Chinese A-share listed firms from 2012 to 2023, comprising 37,658 firm-year observations, innovation efficiency is estimated through Stochastic Frontier Analysis (SFA) based on a Cobb–Douglas innovation production function. The persistence of innovation efficiency is then assessed using an intraclass correlation coefficient (ICC) framework. The results indicate that the average innovation efficiency of Chinese listed firms is 0.787, with substantial variation across firms. More importantly, approximately 66.4% of the total variation in innovation efficiency can be attributed to firm-specific and time-invariant factors, providing strong evidence of structural persistence. Additional analyses show that firm size is positively associated with innovation efficiency, whereas financial leverage has a negative effect. In contrast, the effective tax rate does not exhibit a robust relationship with innovation efficiency after controlling for firm fixed effects. These findings suggest that long-term innovation efficiency is primarily shaped by persistent organizational capabilities and internal resources rather than short-term external factors. The study contributes to the literature on innovation efficiency and firm heterogeneity and offers policy implications for fostering sustainable innovation capacity.},
  keywords = {Innovation efficiency; Structural persistence; Stochastic frontier analysis; Chinese listed firms},
  note = Article ID: 523128
}