基于专利合作网络的研发团队识别及创新产出影响研究

Identifying R&D Teams and Innovations with Patent Collaboration Networks

【目的】利用专利发明人合作网络识别技术研发团队,并对团队创新产出的影响因素进行统计分析。【方法】设计核心研发人员检测算法,提出基于核心研发人员的研发团队识别算法。以专利产出数量作为研发团队创新产出的数量指标,以专利被引数和专利权要求数作为研发团队创新产出的质量指标,利用负二项回归模型分析研发团队特征对团队创新产出的影响。【结果】在语音识别技术领域的实证研究表明,所提研发团队识别算法可有效识别出研发团队演化序列566个,包含各时间片段的研发团队共1 827个,研发团队平均规模为16.670;研发团队作为子网络,平均聚类系数为0.856,平均最短路径长度为1.646,表现出明显的小世界特性。【局限】研发团队识别算法对于一些规模较小且缺少技术领域知名发明人的研发团队识别效果不佳;还需进一步扩大实证研究样本,以验证研究结果的普适性。【结论】基于语音识别技术领域样本数据分析了研发团队特征对创新产出的影响,负二项回归模型结果表明:团队规模、团队网络平均最短路径长度对创新产出数量和质量均有显著正向影响;团队持续时间、团队稳定性、团队网络密度对创新产出数量和质量均有显著负向影响;团队聚类系数对创新产出数量有显著负向影响,对创新产出质量无显著性影响。

[Objective] This paper tries to identify technology R&D teams based on the patent holders’ collaboration networks, aiming to analyze factors influencing these teams’ innovations. [Methods] First, we identified the core R&D personnel and their team members. Then, we used the number of patents as the quantity index of innovation outputs, and the number of patent citations and claims as the quality index of innovation outputs. Finally, we used the negative binomial regression model to analyze the impacts of team characteristics on their innovations. [Results] We conducted an empirical study in the field of speech recognition technology and the proposed algorithm effectively identified 566 evolutionary sequences of R&D teams, including 1 827 R&D teams in each snapshot, with an average size of 16.670. These teams form a small world sub-network with an average clustering coefficient of 0.856 and an average shortest path length of 1.646. [Limitations] The proposed algorithm could not effectively find technology R&D teams from the fields with few well-known experts. The sample size also needs to be expanded. [Conclusions] The team size and average shortest path length of team network have significant positive impacts on the quantity and quality of innovations. The persistence, stability and network density of these teams have significant negative effects on the quantity and quality of innovations. The team clustering coefficient has significant negative effects on the quantity of innovations, but no significant impacts on the quality of innovations.