Does Chinese Ownership Policy of Sci‐Tech Achievements Promote the University Technology Transfer Efficiency?

Qian Luo et al.

Higher Education Quarterly2026https://doi.org/10.1111/hequ.70103article
AJG 2ABDC C
Weight
0.50

What the paper says

ABSTRACT With scholars paying increasing attention to the effects of ownership policy of sci‐tech achievements, further discussion on the nexus between the ownership policy and the university technology transfer (UTT) efficiency is particularly useful for improving UTT system. Accordingly, this paper investigates the impact mechanism of Chinese ownership policy of sci‐tech achievements on UTT efficiency using the regression models based on the OLS method and Tobit model. By proposing a comprehensive evaluation index, this paper quantifies the ownership policy of sci‐tech achievements by using the ownership policy system construction capability of sci‐tech achievements (PSCC) in 30 provinces. A dynamic two‐stage data envelopment analysis model with considering ownership policy heterogeneity is constructed to assess and analyse the UTT efficiency. We find that regardless of the overall system and sub‐stages, the efficiency of leading areas classified by PSCC tends to be higher than that of other areas. Among policy support dimensions, element support level exerts a significantly positive impact on UTT efficiency and its sub‐stage efficiencies, serving as the core driving factor, while subject support level has no significant effect.

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https://doi.org/https://doi.org/10.1111/hequ.70103

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@article{qian2026,
  title        = {{Does Chinese Ownership Policy of Sci‐Tech Achievements Promote the University Technology Transfer Efficiency?}},
  author       = {Qian Luo et al.},
  journal      = {Higher Education Quarterly},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/hequ.70103},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.