Research On Influencing Factors Of User Contribution Level And User Classification In OIC Allowing Remix

At present,with the intensification of global competition and the shortening of innovation cycle,more and more enterprises and other organizations realize that closed innovation can no longer meet the current needs.They must break through their own organizational boundaries and seek external knowledge sources and cooperation opportunities.With the transformation from closed innovation to open innovation and the rapid development of network technology,a large number of Open Innovation Community(OIC)based on Internet have emerged.In order to promote cooperation within the community,OIC incorporates different systems and technologies into its online platform,including a remix technology based on Creative Commons license.In the OIC which allowig remix,users can freely reuse others' innovations to form their own innovations.This remix way of innovation has greatly activated the innovative atmosphere of the community.A large number of derivatives have doubled the innovative achievements of the community,and remix has become the second largest source of innovation besides the original innovation.However,remix has also been facing problems such as the low enthusiasm of users and the low quality of remix results.In this context,based on existing researches,this paper first constructs a model of influencing factors of users' remix contribution level in OIC allowing remix.Then,it takes Thingiverse,a well-known OIC as the research object to empirically analyze the validity of the model.On this basis,we continue to construct the influencing factors model of users' original innovation contribution level,and through further empirical research,we compare the difference between the original innovation contribution level and the remix contribution level in influencing factors.Finally,according to the remix contribution level and original innovation contribution level of users in this community,different user roles and contributions in OIC allowing remix are discussed.The main conclusions are as follows:(1)User's individual factors: Self-presentation desire has a positive impact on popularity of remix and original innovation;Hobbies and interests have more and stronger negative effects on the original innovation contribution level;Return expectation promotes users to produce more original innovations but inhibits users from producing more remixes;Advanced professional knowledge level promotes users to produce more original innovations but inhibits users to produce more remixes;Open attitude of remix has more positive impact on remix contribution level.(2)Community environmental factors: The positive impact of community recognition on remix contribution level is more and stronger;The effect of social network relationship on remix quantity and original innovation quantity is completely opposite,and the effect of it on remix quality and original innovation quality is exactly the same;Observational learning mainly promotes remix contribution quantity and original innovation contribution quality,and the three methods of observational learning have their own biases;Reinforcement learning can significantly promote users to produce more remixes,but has no significant impact on the number of original innovations.(3)Knowledge factors: The quality of knowledge itself significantly promotes users to produce more remixes,but significantly inhibits users to produce more original innovations;The high quality of knowledge sources of remix significantly inhibits the production and the popularity of remix.(4)User classification: There are 12 kinds of user types in OIC which allow remix,such as edge remixer and effective remixer.The conclusion of this paper can help to solve the gap between remix and original innovation,help remix play a greater role,and better manage those OICs that are allowing remix.