产学研合作驱动高校学者学术绩效提升的组态效应:基于动机-行为匹配视角

Configuration Effect of University-industry Collaboration Driving Academic Performance Improvement of Researchers Among University: Based on the Motivational-behavior Matching Perspective

摘要:

高校学者在有限的资源约束下，如何有效地平衡科学活动和商业活动的时间和精力是大学产学研合作管理的核心问题。立足产学研行为主体的个人层次，以360位高校学者为样本，运用模糊集定性比较分析方法从产学研合作动机-行为匹配的视角分析了6个前因条件对学者学术绩效的组态效应。研究发现：（1）产生高学术绩效的10种适配组态可以总结为4种核心模式：学习驱动-门当户对型、学习驱动-两情相悦型、使命驱动-门当户对型和资助驱动-两情相悦型；（2）在学术生产力维度下，学习驱动-门当户对型、学习驱动-两情相悦型、使命驱动-门当户对型是核心驱动模式，在后两种模式中，还需要学者在产学研合作中投入大量的资源；（3）在学术影响力维度下，学习驱动-两情相悦型、使命驱动-门当户对型和资助驱动-两情相悦型是核心驱动模式，在资助动机驱动下的产学研合作，学者不进行资源的大量投入更容易促进学术影响力的提升；（4）对比分析发现，在学习动机或使命动机驱动下的学者与“门当户对”的企业开展产学研合作的过程中进行高的资源投入让学者更容易提升学术生产力；在学习动机或资助动机驱动下的学者与“两情相悦”的企业开展产学研合作，并且在资助动机存在的情况下不进行大量的产学研合作资源投入，更容易提升学术影响力。本文研究结论一方面丰富了产学研合作驱动高校学者学术绩效提升研究的方法和视角，另一方面也为我国高校通过产学研合作反哺科学研究提供了驱动路径。

Abstract:

Under the constraint of limited resources, how to effectively balance the time and energy between scientific and commercial activities is the core issue of the management of university-industry collaboration. Based on the individual level of university-industry collaboration subjects, this paper takes 360 researchers from university as samples and uses fuzzy set qualitative comparative analysis method to analyze the configuration effects of six antecedent conditions on academic performance from the perspective of motivation-behavior matching of university-industry collaboration. The findings are as follows: (1) The 10 adaptation configurations that generate high academic performance can be summarized into four core patterns: learning driven-family matching type, learning driven-mutual affection type, mission driven-family matching type and funding driven-mutual affection type; (2) From the dimension of academic productivity, the core driving models are: learning driven-family matching type, learning driven-mutual affection type and mission driven-family matching type. In the latter two models, researchers need to invest a lot of resources in university-industry collaboration; (3) From the dimension of academic influence, the core driving models are: learning driven-mutual affection type, mission driven-family matching type and and funding driven-mutual affection type. Researchers with funding motivation are more likely to promote the improvement of academic influence without a large amount of resource input; (4) A comparative analysis shows that it is easier for researchers to enhance academic productivity when they are driven by learning motivation or mission motivation to invest high resources in the process of university-industry collaboration with ‘family matching’, and researchers who are driven by learning motivation or funding motivation to carry out university-industry collaboration with ‘mutual affection’. In addition, it is easier to enhance academic influence without a large amount of input of university-industry collaborative resources under the motivation of funding. On the one hand, the conclusions of this paper enrich the methods and perspectives of research on academic performance improvement of university driven by university-industry collaboration; on the other hand, they provide a driving path for university to feed scientific research through university-industry collaboration.