Abstract Purpose In the context of deepening the cooperation of Industry-University-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... Purpose In the context of deepening the cooperation of Industry-University-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 "Situation-Action-Cognition" (S- A-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. Read More Read Less

Abstract Purpose Innovation failure knowledge sharing plays an important role in reducing the probability of repeated failure of subsequent innovation and improving innovation ability of virtual research organization. However, it is very difficult for members to actively share the innovation failure knowledge without incentives. This study aims to establish the incentive mechanism of innovation failure knowledge sharing, and promote the sharing behavior of... Purpose Innovation failure knowledge sharing plays an important role in reducing the probability of repeated failure of subsequent innovation and improving innovation ability of virtual research organization. However, it is very difficult for members to actively share the innovation failure knowledge without incentives. This study aims to establish the incentive mechanism of innovation failure knowledge sharing, and promote the sharing behavior of innovation failure knowledge in virtual research organization. Design/methodology/approach By using game theory, considering the risk aversion degree of organization members and the negative effect of innovation fault-tolerance environment, the incentive model of innovation failure knowledge sharing of virtual research organization was constructed, and the incentive relationship of innovation failure knowledge sharing between organization and its members under the influence of different states was analyzed. Findings Without considering the negative effect of innovation fault-tolerant environment, the optimal incentive coefficient of innovation failure knowledge sharing is positively related to the sharing rate of innovation failure knowledge and the transformation ability of innovation failure knowledge of organization members, and negatively related to the sharing cost and risk aversion degree of organization members. Considering the negative effect of innovation fault-tolerant environment, virtual research organization should make a corresponding modification of sharing incentive intensity according to the estimation of tolerance degree to innovation fault-tolerant environment by itself and its members, so as to reduce the knowledge input of organization. Originality/value This study proposes an incentive mechanism of innovation failure knowledge sharing considering the negative effect of innovation fault-tolerant environment, which provides a novel idea and method for the design of incentive mechanism of innovation failure knowledge sharing in virtual research organization. Read More Read Less

Abstract In the medical field the decision represents an extremely important asset because the risk must be zero. This is why decision approaches which are based on a predictive vision are a must solution. The decision is generally based on the exploitation of a large volume of medical data. The processing and analysis of mass data is only possible through an extraction... In the medical field the decision represents an extremely important asset because the risk must be zero. This is why decision approaches which are based on a predictive vision are a must solution. The decision is generally based on the exploitation of a large volume of medical data. The processing and analysis of mass data is only possible through an extraction of knowledge allowing the medical experts to make the best decision. Thus to meet this need, data mining has become the most promising approach. There are several techniques of datamining, and although they are quite developed the y still remain even less efficient notably the classical meta-heuristics. In this paper, we are exploiting a new meta-heuristic called symbiotic organisms search (SOS) that is based on a biological process. In this paper, we develop the formal model of the SOS based data mining process in the medical field with a comparative study with other metaheuristics to show its performance and credibility of treatment. Read More Read Less