Performance evaluation of “Open Bidding for Selecting the Best Candidates” research teams based on big data analysis

Purpose This study aims to assess the research performance of scientific research teams under the “Open Bidding for Selecting the Best Candidates” policy in Beijing by constructing a comprehensive evaluation system. It focuses on analyzing the teams’ capabilities in research output capacity, direction continuity, integration of disciplines and tightness of team cooperation, providing theoretical support and strategic guidance for the selection and optimization of research teams. Design/methodology/approach This study takes 33 university team leaders as the research sample, chosen randomly from the “Open Bidding for Selecting the Best Candidates” policy database. Data regarding their publications in Peking University Core Journals and Chinese Social Sciences Citation Index (CSSCI) Journals between 2000 and February 2024 are collected through the CNKI database. Using social network analysis methods, research teams are identified and a comprehensive performance evaluation index system is developed, which includes research output capacity, direction continuity, integration of disciplines and tightness of team cooperation. Findings This study finds that the research direction of most team leaders remains consistent before and after being selected for the talent title. They are able to guide and motivate their team members to participate in high-quality research activities, playing an important role in enhancing team cohesion and actively training team members. However, the interdisciplinary integration capability of the research teams still needs improvement. Therefore, for the key technology projects which are “neck-breaking” in the system of “Open Bidding for Selecting the Best Candidates,” it is essential to break free from the conventional mindset of prioritizing “background, titles, and networks.” Instead, the focus is on professional capabilities, selecting interdisciplinary teams with diverse professional backgrounds and expertise. Originality/value This study contributes to the field by constructing a research team performance evaluation system based on bibliometric characteristics, enriching the methodology for assessing scientific research teams. It uniquely combines social network analysis with multidimensional performance evaluation indicators, revealing the research performance and influencing factors of teams under the “Open Bidding for Selecting the Best Candidates” policy in Beijing. This provides new perspectives and theoretical foundations for the selection and optimization of scientific research teams.