Stepping into the era of knowledge economy, knowledge has become the most important strategic resource of enterprises. Enterprise′s innovation performance and competitive advantage depend on its knowledge flow and innovation level. Especially in the collaborative innovation process, the efficient knowledge flow across organizational boundaries plays vital role for the success of collaborative innovation, which is conducive to the effective promotion and sharing of best practice and experience, and the efficient recycling of knowledge from tacitness to explicitness, and from the individual level to the organizational level. Therefore, measuring and analyzing the knowledge flow efficiency in collaborative innovation knowledge network (CIKN) is an important issue for the effective management and operation of CIKN. However, we have discovered few formally operationalized researches on the knowledge flow efficiency, especially the systematic and quantitative studies on the measurement of knowledge flow efficiency. 

To address this research gap, this paper aims to propose a new quantitative measurement method for the knowledge flow efficiency in CIKN by adopting the complex network theory and method. For CIKN, the characteristics of its members and the connections among members both influence the network properties, especially the knowledge flow efficiency in the network. Therefore, the CIKN should be regarded as a weighted network to study the issue of measuring knowledge flow efficiency. Motivated by this consideration, in this work, an undirected weighted network (UWN) based method is proposed to measure the knowledge flow efficiency in CIKN. Firstly, according to the node properties and the relationship properties in collaborative innovation knowledge network, the UWN model of collaborative innovation knowledge network is built. Secondly, synthesizing the multiple factors of knowledge flow efficiency in CIKN, a new knowledge flow efficiency measurement model is developed, and an extended application of this model is further proposed for member management. Finally, the feasibility and effectiveness of the proposed model and method is verified by a case of a smartphone-development company.

Operational measurement of knowledge flow efficiency in CIKN is significant to improve the quality of decision making in CIKN management. For the contribution on knowledge management theories, this study adopts a systematic and quantitative complex network method to develop a new measurement model of knowledge flow efficiency in CIKN. It must be pointed out that most of the existing works on knowledge flow efficiency are qualitative in nature, and the quantitative and effective measurement method of knowledge flow efficiency is lacking in the knowledge management literature. Therefore, this study is beneficial to researchers to better understand knowledge flow theoretically, and thus add value to the literature on knowledge management.

For decision-makers of CIKN, this research can support them to make effective decisions and strategies to improve the knowledge flow efficiency and knowledge management performance of CIKN. Firstly, decision-makers can achieve a reasonable and quantitative measurement of the real status of knowledge flow efficiency in CIKN. The research results indicate the factors, i.e. CIKN structure and properties, knowledge sharing willingness and ability, and knowledge collaboration relationship, all have great influence on knowledge flow efficiency, which should be paid much attention by decision makers. Additionally, via the application of knowledge flow efficiency in member management, this work can assist to identify and rank the important members with great influence and importance on knowledge flow efficiency. Such identification and ranking is very significant in the context of the systematic and targeted management of members, in order to encourage the important members to use their advantages to improve the knowledge flow efficiency in CIKN.

collaborative innovation knowledge network, UWN, knowledge flow efficiency, measurement model