Document Type : علمی - پژوهشی
Authors
1
PhD student of Strategic Management, Civil Engineering, Urban Development, & Crisis Management Faculty, Malek Ashtar University of Technology, Tehran, Iran.
2
Ph.D. student of Strategic Management, Department of Civil Engineering, Urban Development and Crisis Management, Defense University Complex, Malek Ashtar University of Technology, Tehran, Iran
3
Assistant Professor, Department of International Relations, National Defense University, Tehran, Iran.
10.48308/jbmp.2024.234972.1584
Abstract
Objective: In recent years, the issue of governance has entered the national sphere and emphasized the importance of strategic decisions. But making these decisions is always accompanied by errors and cognitive biases. the aim of identifying the cognitive biases of policy makers in governance strategic decision-making.
Methodology: The current research has a applicative purpose and in terms of the type of data, it is a qualitative research. By searching for research keywords in foreign databases such as Emerald, Web of Science, Scopus, etc. and domestic databases such as Nurmagz, Mogiran , Civilica and... (researches from 1980 to 2024 and 1390 to 1402) the desired sources were found and the validity and reliability of the findings were confirmed by the face validity method by the expert center group and Kappa reliability equal to 0.72. The obtained sources have been identified by using the seven-step metacombination method of Sandelowski and Barroso (2007) coding and the desired components of the research.
Findings: According to the findings of the 22 selected final studies, 134 codes lead to 40 cognitive biases and in 5 main groups, they include biases related to decision making and judgment, information processing biases, awareness and perception biases, self-centered biases, and social biases. and a group has been identified.
Conclusion: The research results showed how decision-making is influenced and activated by internal and external biases. This research emphasizes that policy makers must recognize and manage their cognitive biases in each category and select appropriate information from a wide set of data.
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