The Measurement of Patent Conversion Efficiency in China’s High-Tech Industry Regions Based on a Shared Input Two-Stage Network DEA Model

In the era of technological revolution, high-tech industries have gained prominence in national innovation systems. However, China’s high-tech sector faces challenges such as late development, weak foundations, and regional disparities. To address these issues, this study proposes a shared-input two-stage network DEA model. This model, based on an input-output perspective, considers resources that circulate and collaboratively function across multiple stages in the form of shared inputs. This paper analyzes data from 25 provinces (including municipalities) in China from 2011 to 2020 and divides the patent conversion process into two sub-stages: the upstream technology research and development stage and the downstream achievement transformation stage, measuring the stage efficiency values and overall efficiency values, respectively. To align with reality, this paper incorporates the intensity of the strength of intellectual property protection, strength of government financial support, and the expenditure on technology import as regional shared input variables. Meanwhile, expenditure on technological transformation is treated as a capital-type intermediate input variable. This approach unveils the “black box” of single-stage DEA, enabling more accurate efficiency measurement. Key findings reveal: (1) China’s high-tech research and development of patent technology, the achievement transformation and overall conversion efficiency show annual improvement, yet overall efficiency remains low with regional imbalances; (2) Achievement transformation efficiency exerts a greater impact on overall conversion efficiency than research and development of patent technology efficiency. Comparative analyses with single-stage and chained two-stage DEA models confirm the necessity of phased evaluation and shared-input variables, supported by input-output elasticity tests. The findings validate the applicability and interpretability of the proposed model in efficiency evaluation.