Research On The Identification Of Key Knowledge Source In Crowdsourcing Innovation Based On Knowledge Acquisition

It is hard for enterprises to gain more competitive edges from the traditional enclosed innovation pattern in the era of knowledge economy. Enterprises are striving to seek new innovation pattern to make full use of external knowledge and innovation resources. Crowdsourcing innovation is becoming the main choice of enterprise to Innovation. It can help enterprise get creative knowledge from the masses, cover the shortage of the inadequacy of internal innovation resources, decrease the cost of enterprise innovation effectively and enhance enterprise's innovation performance.Though the practice of crowdsourcing innovation has got good applying result, but it is still in the exploratory stage as a new business pattern. Because of the uncertainty of the users involved and the complexity of innovation tasks, it is the most difficult problem of continued progress about how to get knowledge from the vast amounts of user knowledge and select the key knowledge source. Based on the above background, great deals of research works have been done in this thesis.1. This thesis analyzes the knowledge flow in the crowdsourcing innovation. Because of the peculiarity and complexity of crowdsourcing innovation model, it is necessary to study the knowledge flow in innovation pattern based on the perspective of knowledge management and build key knowledge source recognition model. Thus, this thesis analyzes knowledge dimensions and knowledge flow progress based on analysis of crowdsourcing innovation types. Crowdsourcing innovation mode can be divided into wide choices crowdsourcing innovation mode, integration crowdsourcing innovation mode and fusion crowdsourcing innovation mode. Through in-depth analysis, user's knowledge in crowdsourcing innovation consists of four dimensions, knowledge used in the users, about the users, knowledge from the users and knowledge co-creating with users. Based on this, this study builds a knowledge flow model in crowdsourcing innovation mode to analyze the knowledge flow process in crowdsourcing innovation mode2. This thesis analyzes knowledge acquisition factors under crowdsourcing innovation mode. Qualitative research method—grounded theory is used to study the factors affecting knowledge acquisition. This thesis derives theoretical model about the factors affecting knowledge acquisition according to open coding, axial coding, and selective coding. After frequency statistics, we can find that specialized knowledge and skills, feeling interactions, task price and participants' credit level are the initial four mentioned frequency coding. Compared comprehensively. characteristics of tacit knowledge is the most important factor in affecting knowledge acquisition, Knowledge acquisition situation, task properties and characteristics of knowledge acquisition also have important influence on knowledge acquisition.3. This thesis builds the key knowledge source recognition model under crowdsourcing innovation mode. Characteristics of tacit knowledge is the most important factor in affecting knowledge acquisition, it is necessary to identify the key knowledge sourcefrom the participation of a large number of users to enhance the knowledge acquisition performance. Considering that the essence of key knowledge identification in crowdsourcing innovation is the matching of knowledge requirements and knowledge supply knowledge. A key knowledge source recognition system under crowdsourcing innovation is built which is composed of user's knowledge ability, participation willingness of two sides and Knowledge demand factors from bilateral perspective. Considering the defects of BP neural network, it was optimized by genetic algorithm, thus a key knowledge source recognition model under crowdsourcing innovation mode is built and verified through empirical study.This thesis not only has great guiding significance for avoiding excessive competition between public participants and reduce the waste of resources, but also has higher theoretical significance and practical value for promoting knowledge acquisition performance and strengthening the management and guidance of crowdsourcing innovation mode.