Nowadays, with the expansion of economic businesses and also the dependency of economic activists on the insurance industry to provide the capitals security, there is now a growing need to identify and evaluate risks of the insurance industry. Therefore, in this study, a comprehensive model was developed to evaluate and manage business risk by reviewing the research literature, extensively. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS) was developed for every business risk after identifying the relevant dimensions and the criteria and collecting the necessary data from the central insurance databases. Finally, a general model was presented to evaluate and manage risks of the insurance industry. Four major problems were also considered: optimal and efficient normalization, optimal training for testing ratio for every neural network, model validation, and the easiness of user communication with the system. The results show that the model can provide an accurate estimation for risk evaluation and management. Thus, this system can be considered as an appropriate tool for business risk evaluation and management of insurance companies. Furthermore, the effectiveness of this method in evaluating and managing the risk at insurance companies can be turned into a neural network and such a neural network can be used as an appropriate decision-making support tool.

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Since today's world is moving ahead rapidly, the condition for the survival and durability of institutions and organizations depend on research, development, innovation, and communication, the realization of which requires the presence of a new and creative model. Therefore, this paper aims to formulate and explain an open successful innovation management model by seeking the factors affecting the diffusion of innovation model at sports federations. The study in term of goal is applied and in term of data analysis method is descriptive correlational study. The statistical population consists of sports federations of Iran in 2017. The sample size was 70 individuals selected using Morgan table and the simple random sampling method from 10 federations. The research collection data tool was a questionnaire. The correlation matrix was used to determine the relationships between independent and dependent variables. Furthermore, a novel hybrid technique of fuzzy DEMATEL and fuzzy ANP was used to rank the factors affecting open innovation. The results showed that structural factors, interorganizational joint venture, customer relationships, research and development department, and new technologies affected the open innovation. Moreover, structural factors were ranked the most effective factors. Then, customer relationships and interorganizational joint venture were known as the weakest factors affecting the creation of open innovation.