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Research Article

JCR. 2020; 7(19): 10164-10177


Morteza Gholizadeh, Sina Fard Moradinia.

Human resource risks have a major influence on the performance of an organization, especially project-based organizations, and the issue of identifying and managing the risk is very significant in this process. Given the risk of human resource embraces a wide range of concepts, including economic, political, cultural, strategic, environmental, process, psychological, and operational risks, therefore, both theoretically and practically, the necessity of identifying human resource risks and evaluating them through a broad perspective and considering various aspects of the problem, realizing the concepts and requirements of human resource risk management and prioritizing the human resource risks according to specific organizational conditions and facilities, is of great importance. To this end, in the current study, an attempt was made to provide an integrated model based on the adaptive neural fuzzy inference system (ANFIS) to assess the identified risks through detecting the most important risks affecting human resource management in the country's civil engineering projects. Based on the conducted analysis of risk using fuzzy inference system in the previous section and determining the probability, severity of the effect, and human resource management risks rate, a total of 9 risk factors were mentioned as the most important risks with high risk (unauthorized) with the critical importance. It should be noted that in the fourth phase, the plans to respond to each of the risks have been presented. The results also showed that the qualitative and quantitative analysis of risks with two methods of fuzzy inference and ANFIS according to experts’ opinions and random data presented that if the risks do not exist in projects that are implemented for the first time or there is no access to the required number of experts, instead of using the opinion of experts and their analysis by ANFIS method, acceptable results can be obtained by adopting and selecting input data based on random data chosen from fuzzy intervals

Key words: risk management, human resources, civil engineering projects, response programs, ANFIS method

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