Designing a Fuzzy Expert System to Predict the Level of Social Responsibility of Managers

Document Type : Original Article

Authors

1 PhD Student, Department of Public Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran (Corresponding Author) Email: farshid.elmi@yahoo.com

2 Assosiated Professor, Department of Management, Kharazmi University, Tehran, Iran

3 Assosiated Professor, Department of Public Management, Islamic Azad University, Mashhad Branch, Mashhad, Iran

Abstract

Focusing on the role of managers in fulfilling the mission of social responsibility of organizations, the purpose of this research was to present the model of social responsibility of managers in the electricity distribution organization of Razavi Khorasan province. For this purpose, the researcher chose the mixed research method to carry out the research. Therefore, the results of the current research were based on the collection and analysis of the opinions of academic and professional experts and members of a statistical sample consisting of managers and employees of the Electricity Distribution Organization of Razavi Khorasan Province. In the qualitative part, the research model was designed based on the study of the theoretical foundations and existing history in the field of corporate social responsibility, as well as interviews with experts in 6 dimensions, 28 components and 75 indicators. In the quantitative part, the hierarchical model of the social responsibility of managers was formed using the fuzzy inference system, which is a practical model in the field of predicting the social responsibility of top managers in the electricity distribution organization of Razavi Khorasan Province. The most important achievement of this research is the construction of a hierarchical model- It is a level in the field of social responsibility of managers that can be used to predict and make decisions in the field of social responsibility of managers in the organization.

Keywords

References

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Interdisciplinary Studies on Strategic Knowledge
Volume 9, Issue 36 - Serial Number 36
February 2026
Pages 111-152

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