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Research on Science & Technology Talents

Research on the Influencing Mechanism of Knowledge Associa⁃ tion Integration on University Interdisciplinary Team Innovation Ability 

Jin Ziqi1 , Fu Yining2 , Liu Qili3 , Zhong Bowen4 

（1.Advanced Technology Department, Soochow University, Suzhou 215021, China; 2.School of Education, So‐ ochow University, Suzhou 215021, China; 3.School of Social Science, Soochow University, SuZhou 215021, China; 4.School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China）

Abstract: Interdisciplinary research team is an important organizational forms for solving vari‐ ous complex problems and scientific tasks faced by human society. It's also an important way to adapt and serve the regional economy. It takes knowledge as the basic unit for the production and application of new knowledge. It is the synthesis of scientific structure, social group and cre‐ ative thinking, and also the epitome of knowledge innovation activities. This study construct a theoretical model of knowledge association integration -cross-level deep learning -dual innova‐ tion ability, expands the front-end inducing factors of innovation capability and improves the analysis of the formation mechanism of innovation capability. Based on the theory of knowledge innovation and cognitive psychology, starting from the connotation of knowledge association inte‐ gration, this study discusses three dimensions of knowledge association integration: subject fac‐ tor, knowledge factor and cooperation factor. It explains the mechanism of direct influence on breakthrough innovation ability and progressive innovation ability respectively, and further ex‐ amines the mediating role of cross-level deep learning. With cross-level deep learning as the mediating variable and hierarchical regression analysis as the empirical test, this paper analyzes the influencing mechanism of knowledge association and integration on innovation ability through the mediating role of cross-level deep learning. Through the analysis of questionnaires from 331 members by relying on 39 interdisciplinary research teams of research universities or research institutions in Jiangsu Province, it is concluded that knowledge association integration can effectively promote the improvement of dual innovation ability, and has a significant positive impact on both breakthrough innovation ability and progressive innovation ability. Three dimen‐ sions of knowledge association integrates have a significant positive impact on the innovation ability of interdisciplinary teams in universities. Cross-level deep learning plays a partially me‐ diating role in the influence mechanism of knowledge relevance and integration on the innova‐ tion ability of interdisciplinary teams and has a significant positive impact on both breakthrough innovation ability and progressive innovation ability. The research shows that the team should form a unified value and goal vision in the cooperation, and form a unique conceptual motiva‐ tion. Secondly, based on the importance of knowledge association integration for cross-level deep learning and team innovation ability, the team should form a tolerant cultural atmosphere and provide strong environmental support and policy support for interdisciplinary members, set a flexible time period to ensure that members can fully identify exploration innovation points and generate solution solutions, and increase members' deep learning opportunities. In addition, the interdisciplinary platform and management operation mechanism are added to coordinate the re‐ sources of all parties and shape the centripetal force of the team, which plays a role in the inte‐ gration of knowledge and effectively improve the innovation ability. This study enriched the re‐ search on knowledge relevance and integration and interdisciplinary team innovation ability, and brought enlightenment to the practice of interdisciplinary team scientific research management. 

Key words: interdisciplinary team; knowledge association integration; innovation ability; in‐ fluencing mechanism