Abstract:Government research institutions are the important force of national science and technology innovation and the second largest executive body of R&D activities. Exploring the influencing factors affecting the resource allocation efficiency of government research institutions and the path choice can provide a reference for effectively improving the resource allocation efficiency of government research institutions. The three-stage data envelopment analysis (DEA) model is used to measure the allocation efficiency of science and technology resources allocation efficiency of government research institutions in 31 provinces of China. Different from the traditional DEA model, we select the foreign direct investment, proportion of tertiary industry, and level of economic development as the environmental variables that affect the allocation efficiency of science and technology resources but cannot be controlled subjectively. The configuration analysis of the influencing factors of allocation efficiency is carried out with the fuzzy-set qualitative comparative analysis (fsQCA) method for exploring the necessary conditions to explain the efficiency of high-level and non-high-level allocation efficiency of science and technology resources allocation efficiency of government research institutions. The study finds that: after the environmental factors are excluded, the allocation efficiency of scientific and technological resources of government research institutions in the western region decreases obviously, and the impact of environmental factors on the western region is much greater than that in the eastern region. No single condition constitutes a necessary condition for a high (non-high) level of allocation efficiency of scientific and technological resources of government research institutions, and the allocative efficiency of science and technology resources is the result of the complex effects of multiple condition variables. There are three driving paths for high-level allocative efficiency: talent-environment co-drive development (S1), allocation-scale drive(S2), and fundamental research-oriented(S3). Configuration paths S1 and S3 emphasize the core role of industry-university-research cooperation level, talent intensive, and scientific and technological innovation atmosphere, Path S2 emphasizes the configuration-scale level, non-basic research-oriented core conditioning. For the non-high-level allocative efficiency path, the absence of allocative scale, the absence of industry-university-research cooperation, and the absence of an atmosphere of scientific and technological innovation are all the core conditions affecting the improvement of efficiency. In both NS1 and NS2 paths, high government support plays a general role, but high government support does not bring high technology resource allocation efficiency. It’s worth noting that in addition to the lack of necessary environmental conditions, excessive government funding is the key to restricting the improvement of resource allocation efficiency. Therefore, governments in different regions should tailor their resource allocation systems to suit the needs of their local government research institutions. The government should focus on the improvement of the scale of allocation and the level of industry-university-research cooperation, and constantly strengthen the construction of a talent resource pool. Regions with low levels of allocation efficiency of scientific and technological resources should not rely excessively on government financial funds, but should continuously improve the depth and breadth of the level of industry-university-research cooperation; at the same time. The research conclusion not only expands the theory in the field of science and technology resource allocation efficiency but also provides decision support for institutions to optimize science and technology resource allocation.
