Analysis of collaborative innovation behaviour and its influencing factors in scientific research crowdsourcing platforms: based on the fsQCA method

Abstract

Introduction. This study aims to explore the influencing factors and their combined effects on the benefits of knowledge innovation, and to explore the impact of factors on the effects of knowledge innovation from a configuration perspective. Method. This study constructed a knowledge innovation ecosystem for scientific research crowdsourcing platforms, as well as a configuration model that affects the knowledge innovation benefits of scientific research crowdsourcing. Based on this, we collected data through a survey questionnaire. Then, we used the method of fuzzy set qualitative comparative analysis to identify the configuration effects of influencing factors and analyse the core configuration. Analysis. Five core configurations were constructed, which are shown as internal and external linkage based on environmental dynamics, individual and environment interlocking based on team maintenance, individual initiative to supplement weaknesses, external drive driven, and individual led based on team and platform support. Results. The configurations have different focuses, but all highlight the core conditions for individual innovation investment as the configuration. Conclusion. The results indicate that individual driving factors are worth considering. Meanwhile, by referring to the core components of the five configurations, researchers can combine various factors to better form knowledge innovation.