Research On Knowledge Sharing And Competition Behavior In Crowdsourcing Platform Based On Social Network Analysis

With the competition in global market becoming increasingly fierce,companies have been exploring new ways to improve production and innovation models.As an open innovation model,crowdsourcing provides an effective method for companies to make full use of external resources to improve internal innovation performance.Crowdsourcing is the act of taking a job traditionally performed by a designated agent(usually an employee)and outsourcing it to an undefined,generally large group of people in the form of an open call.Numerous crowdsourcing platforms,such as Task CN,Inno Centive,Treadless,etc.,have emerged at home and abroad,attracting attention from industry and academia.The success of crowdsourcing depends on the sustained participation and qualitysubmissions of the individuals.Therefore,based on social network analysis and data from crowdsourcing competition platform Kaggle,this study examined individual's knowledge sharing and participation behavior in crowdsourcing contests and provided theoretical support for the development of crowdsourcing platform.Firstly,this study analyzes structural characteristics and evolution trends of knowledge sharing network divided into three stages.Secondly,how network structure and node attributes affecting knowledge sharing network are examined by applying exponential random graph model.Thirdly,the effect of knowledge sharing experience on sustained participation and competition performance in crowdsourcing contest are analyzed by using regression analysis method.Small-world effects,reciprocity and transitivity are observed in knowledge sharing network.The results show that activeness,popularity and level increase the likelihood of knowledge sharing formation.Moreover,the quantity of knowledge sharing and individual's influence have a significant effect on sustained participation.The quantity and quality of knowledge sharing and individual's influence have a significant effect on competition performance.The findings provide insights into improving sustained participation and performance in crowdsourcing communities.