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| Recommendation Method for Enterprise R&D Partners Using Technology Matching and Typological Optimization |
| Zhao Zhanyi1,2, Zhong Yongheng2,3,4, Li Zhenzhen3,4, Liu Jia2,3,4, Xi Chongjun1,2 |
1.National Science Library, Chinese Academy of Sciences, Beijing 100190
2.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
3.National Science Library (Wuhan), Chinese Academy of Sciences, Wuhan 430071
4.Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071 |
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Abstract Accurately recommending compatible partners among diverse innovation entities is crucial for reducing innovation risks and overcoming key core technological challenges. This study proposes a method for recommending enterprise R&D partners based on technology matching and typological optimization to enhance precision and interpretability. The method integrates technological matching, including technological similarity and complementarity to identify large-scale potential cooperation pairs using machine learning algorithms. The Boston Matrix is employed to categorize these recommendations across two dimensions: technological similarity and complementarity. Additionally, indicators such as innovation strength, cooperation preferences, proximity, and brand effect are combined to evaluate the typological results and optimize the entire process. Using the field of fuel cells for an empirical application, the results depict that the model's identification algorithm achieves an F1 value of 93%, outperforming category and semantic dimensions-based algorithms by 2 and 4 percentage points, respectively. This model accurately reflects technological matching between innovation entities and supports the subdivision of partners into four categories: priority cooperation, key focus, transformative complement, and diversified expansion. The evaluation and optimization results can effectively distinguish and provide various options for enterprises, increasing the likelihood of successful cooperation.
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Received: 20 November 2023
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1 Banerjee T, Siebert R. Dynamic impact of uncertainty on R&D cooperation formation and research performance: evidence from the bio-pharmaceutical industry[J]. Research Policy, 2017, 46(7): 1255-1271.
2 余辉, 夏文蕾, 黄炜, 等. 基于文本匹配-组态优化的两阶段技术供需匹配方法研究[J]. 情报学报, 2024, 43(3): 261-273.
3 翟东升, 郭程, 张杰, 等. 基于专利的企业潜在研发伙伴推荐方法研究[J]. 数据分析与知识发现, 2017, 1(3): 10-20.
4 刘晓燕, 张淑伟, 单晓红. 面向技术融合的合作伙伴识别研究[J]. 科学学研究, 2023, 41(12): 2164-2174.
5 Ahn J M, Lee W, Mortara L. Do government R&D subsidies stimulate collaboration initiatives in private firms?[J]. Technological Forecasting and Social Change, 2020, 151: 119840.
6 Pommerening S, Al Wawi B, Dahlqvist J. Factors and drivers of partner selection and formation within open innovation in SMEs[R/OL]. [2021-11-18]. http://www.diva-portal.org/smash/get/diva2:1106208/FULLTEXT01.pdf.
7 赵展一, 钟永恒, 王辉, 等. 基于技术关联关系的企业研发潜在合作伙伴识别方法研究综述[J]. 现代情报, 2023, 43(10): 152-163, 177.
8 吴菲菲, 李睿毓, 黄鲁成. 企业跨产业研发合作伙伴识别与选择[J]. 情报杂志, 2019, 38(9): 42-48, 29.
9 张金柱, 施佳璐, 章成志. 专利技术互补性研究综述: 概念、测度及应用[J]. 情报学报, 2023, 42(10): 1251-1264.
10 何喜军, 董艳波, 武玉英, 等. 基于异构信息网络嵌入的专利技术主体间交易推荐模型[J]. 情报学报, 2020, 39(1): 57-67.
11 李冰, 丁堃, 孙晓玲. 企业潜在技术合作伙伴及竞争者预测研究——以燃料电池技术为例[J]. 情报学报, 2021, 40(10): 1043-1051.
12 汪志兵, 韩文民, 孙竹梅, 等. 基于网络结构特征的技术创新合作网络链路可预测性研究[J]. 情报理论与实践, 2022, 45(3): 165-172.
13 温芳芳. 专利分类号耦合分析在企业潜在合作关系识别中的应用[J]. 现代情报, 2018, 38(7): 142-147.
14 傅俊英, 彭喆, 郑佳, 等. 基于专利异构网络的中小企业潜在合作伙伴研究——以石墨烯领域为例[J]. 情报学报, 2019, 38(4): 391-401.
15 席笑文, 郭颖, 宋欣娜, 等. 基于word2vec与LDA主题模型的技术相似性可视化研究[J]. 情报学报, 2021, 40(9): 974-983.
16 Zhou J H, Liu Z X, Li J Z, et al. Technology complementarity and collaborative innovation: the moderating effects of IT adoption[C]// Proceedings of the 2019 IEEE International Symposium on Innovation and Entrepreneurship. Piscataway: IEEE, 2019: 1-7.
17 伊惠芳, 刘细文, 陆婕, 等. 基于问题—解决方案(P-S)的技术机会发现研究[J]. 图书情报工作, 2023, 67(17): 102-117.
18 Wang X F, Li R R, Liu Y Q, et al. A morphology-driven method for measuring technology complementarity: empirical study involving Alzheimer’s disease[J]. Journal of Data and Information Science, 2022, 7(3): 20-48.
19 Sun L, Xu W Z. Enterprise complementarity based on patent information[J]. Wireless Communications and Mobile Computing, 2022, 2022: 5797285.
20 茹丽洁, 张娴, 许海云, 等. 面向技术转移的专利组合技术关联性测度研究[J]. 情报杂志, 2017, 36(2): 73-78.
21 赵展一, 李贞贞, 钟永恒, 等. 融合专利类别与语义信息的企业潜在技术关系测算方法研究[J]. 情报理论与实践, 2023, 46(3): 200-208.
22 赵健宇, 董振杰, 余乐安, 等. 基于知识—合作相依网络的技术融合预测与潜在合作伙伴识别研究[J/OL]. 系统工程理论与实践, (2024-05-15) [2024-06-25]. https://kns.cnki.net/kcms/detail/11.2267.N.20240515.1251.021.html.
23 向姝璇, 李睿. 基于专利文献整体相似度计算的竞争对手发现——以5G领域为例[J]. 情报理论与实践, 2021, 44(5): 100-105.
24 万冬. 基于技术互补性的潜在技术合作伙伴选择研究[D]. 北京: 北京理工大学, 2021.
25 Wang X F, Qiao Y L, Hou Y J, et al. Measuring technology complementarity between enterprises with an hLDA topic model[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1309-1320.
26 冉从敬, 宋凯, 赵倩蓉, 等. 合作什么, 去哪合作, 与谁合作?——专利视角下的校企合作对象选择系统构建[J]. 图书馆论坛, 2020, 40(8): 113-121.
27 吴菲菲, 苍思吉, 苗红, 等. 研发合作研究现状述评与展望——基于管理全过程视角[J]. 科技进步与对策, 2021, 38(19): 152-160.
28 Das T K, Teng B S. A resource-based theory of strategic alliances[J]. Journal of Management, 2000, 26(1): 31-61.
29 Vaez-Alaei M, Deniaud I, Marmier F, et al. A decision-making framework based on knowledge criteria for network partner selection[C]// Proceedings of the 2019 International Conference on Industrial Engineering and Systems Management. Piscataway: IEEE, 2019: 377-382.
30 崔晶, 丁文晴, 马俊红, 等. 基于技术同心多元化的企业研发合作伙伴识别与选择——以中药企业为例[J]. 科技管理研究, 2021, 41(4): 133-139.
31 Turner P. Complementarity and competence-knowledge, skills, attitudes, and behaviours[M]// Complementarity in Organizations. Cham: Palgrave Macmillan, 2022: 203-231.
32 王超, 许海云, 方曙. 产学研潜在合作对象识别方法研究[J]. 科学学研究, 2018, 36(1): 101-113. |
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