@article {773,
	title = { Formation Mechanism of Knowledge Stickiness in the Collaborative Innovation of Industry{\textendash}University{\textendash}Research-},
	journal = {Journal of Digital Information Management},
	volume = {18},
	year = {2020},
	month = {10/2020,12/2020},
	pages = {173-183},
	type = {Research},
	chapter = {173},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">\&nbsp;Purpose In the context of deepening the cooperation of Industry{\^a}{\texteuro}{\textquotedblleft}University{\^a}{\texteuro}{\textquotedblleft}Research (IUR), enterprises must gain competitive advantage by transferring external knowledge to the enterprise for knowledge appreciation. On the basis of the knowledge stickiness encountered in the process of knowledge transfer in the collaborative innovation of IUR, the formation process and causes of knowledge stickiness are analyzed. Design/methodology/approach In this study, the knowledge flow model based on heat conduction theory was proposed. The dynamic simulation was carried out using MATLAB software. The knowledge flow and evolution process in IUR were analyzed from the macro level, whereas the role and influence of various factors in knowledge stickiness was analyzed from the micro level. Finally, the preliminary mechanism of knowledge management (sharing and transfer) between IUR was established. Findings Results show that: the process of knowledge transfer between IUR is the process of continuously realizing knowledge increment and knowledge creation, and the process of knowledge evolution between IUR is a process that the Industry, the University and the Research approach continuously; and knowledge stickiness has a direct impact on the efficiency of knowledge transfer. Strengthening the cognition between IUR, increasing the number of activities between IUR, and creating a collaborative innovation atmosphere between IUR will reduce knowledge stickiness and improve the efficiency of knowledge transfer. Originality/value: Knowledge stickiness is classified from the perspective of {\^a}{\texteuro}{\oe}Situation{\^a}{\texteuro}{\textquotedblleft}Action{\^a}{\texteuro}{\textquotedblleft}Cognition{\^a}{\texteuro} (S{\^a}{\texteuro}{\textquotedblleft} A{\^a}{\texteuro}{\textquotedblleft}C) and the generation process of knowledge stickiness in knowledge transfer is explored by using heat conduction theory, which makes a positive contribution to the exploration of the internal mechanism and transfer path of knowledge stickiness.</span></p>
},
	doi = {https://doi.org/10.6025/jdim/2020/18/5-6/173-183},
	url = {http://www.dline.info/download.php?sn=3104},
	author = {Zhang Feng, and Liu Guoxin}
}
