关键词: 研究型大学, 科技成果转化过程, 网络排序方法, 网络DEA

Abstract: Under the innovation-driven development strategy, promoting the transfer of knowledge and technology from research universities to industries is the key to realize national strategies and optimize industrial transformation and upgrading. As a key component of the national innovation system, universities play a critical role in the complex technology transfer process that facilitates technology transformation from pure research activities to commercialization. Concerning the growing importance of universities in providing research outputs, numerous recent studies have explored how universities make technology transfer activities more efficient. In this paper, we applied a novel integrated method to evaluate the efficiency of technology transfer of research universities in China. First, the network DEA model was used to evaluate and compare the overall staged and sub-staged efficiency of 28 research universities in China. Based on efficiency results, a network-based ranking method was further applied to compare the transformation advantages of each research university and evaluate the contribution of individual factors to efficiency. Compared with previous research, our research provides a novel method to seek efficiency improvement strategies within the system and identify benchmark universities for each factor, so that we can compare the “unique attributes” of each university. The results provide policymakers with implications on how to improve the technology transfer efficiency of research universities in China.According to several past studies, technology transfer is a complex process that includes knowledge and technology accumulation and dissemination. Taking this systematic view, we disassemble the universities′ technology transfer process into a two-stage process, which constitutes the R&D creation stage and value creation stage. To explore the efficiency in different stages of technology transfer, we apply a two-stage network DEA model. The model consider two initial input variables (R&D funds and R&D personnel), three intermediate variables (the number of scientific papers, monographs, and applied patents), two intermediate input variables (technology service funds and technology service personnel), and two ultimate output variables (the amount of revenue form patents sell and transfer). We examined the efficiency in the R&D creation stage and the value creation stage, the results show that most research universities performed relatively efficient in the R&D creation stage with a mean efficiency score of 0.87, but the efficiency of the second stage is considered low with a mean score of 0.43. Among all the universities, 14 are efficient in the first stage, but only 4 are efficient in the second stage, and merely 3 universities, which are Tsinghua University, Ocean University of China, and Northwest A&F University, are efficient in both stages.Further, we conduct the network-based ranking method after the preliminary efficiency analysis. This requires a total of 81 runs of the two-stage efficiency analysis, with each bearing different combination of I/M/O factors, we get the accumulated reference networks for the R&D creation and value creation stages, respectively. In the R&D creation stage, Xiamen University and the Ocean University of China lead other universities. To further investigate the information on what have they done right to be ranked at the top. Xiamen University is the benchmark for the factors of R&D personnel, the number of published papers and monographs. Xiamen University has been very efficient in managing R&D researchers, though a mall-scale of R&D personnel, it has managed to rank number three and four in the number of published papers and monographs. Xiamen University only owned 8% of R&D personnel compared to Jilin University, while the number of published papers is almost doubled the number of Jilin University. The Ocean University of China is the benchmark for the factors of R&D fund and the number of published papers, which means that though a small-scale of financial support, Ocean University of China has managed to efficiently produce scientific papers. In the value creation stage, Nankai University and Tsinghua University significantly lead other universities. Tsinghua University is the benchmark for the factors of the number of applied patents, the technology service funds and the revenue of sold patents. On the other hand, Nankai University is the benchmark for the factors of the technology service personnel and funds. The number of technology service personnel and funds of Nankai University are only 1% of those of Northeastern University, however, the revenues from sold patents are 1.3 times that of Northeastern University, and the revenues from transferred patents are 2.2 times of that of Northeastern University.Next, we group the universities with similar strengths together. Universities, such as Fudan University, Zhejiang University, Huazhong University of S&T, Chongqing University, etc., are characteristic of being efficiently management R&D personnel to maximize the R&D outputs, while they paid less attention to the commercialization of technological inventions. Universities, like Tsinghua University and Nankai University, are good at commercializing patents invented by R&D personnel, while their efficiency in the first stage needs some improvements. All these suggested that the technology transfer is a complex system, and all process factors should be taken into consideration to improve the efficiency in seed-stage and the whole stage for those low efficient universities. As for the common weakness in the transfer process, the efficiency of technology service personnel and technology service funds are relatively low for most of the universities, which indicate that the unreasonable fund structure of the university, as well as the challenge for research universities in China to balance the two distinct R&D and commercial activities.Previous literature paid more attention to the macro-external factors which affect technology transfer efficiency. In this paper, we shifted to a different perspective to investigate the micro-internal efficiency-improvement path where every university could find a benchmark to improve its transfer efficiency.

Key words: research university, technology transfer process, network-based ranking, network DEA