The Function of Artificial Intelligence for Transcendent Governance with a Strategic Management Approach
سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 100
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شناسه ملی سند علمی:
JR_MSESJ-7-2_014
تاریخ نمایه سازی: 25 اسفند 1403
چکیده مقاله:
This study follows a qualitative-quantitative method, employing textual content analysis in the qualitative section. Data collection tools include past research documents and library resources in this field. The data analysis method is based on open coding. According to the study’s findings, among ۱۸۷ relevant articles and books, ۴۲ studies were analyzed using a systematic review. After screening the indicators through the Delphi method, the remaining indicators were categorized into four groups for strengths and weaknesses and three groups for threats and opportunities, resulting in a total extraction of ۵۵ indicators. The statistical population of this research consists of ۱۰ government officials in governance who simultaneously held faculty positions in public administration and had authored works in this domain. In the quantitative section, utilizing the AHP-SWOT approach and analyzing internal and external factor matrices, the study sought to identify the most significant strengths and opportunities. It then developed and prioritized aggressive strategies to provide a practical framework for implementing artificial intelligence in governance. Ultimately, the strategies were ranked using the QSPM method. Based on the research findings, the most significant strength was facilitating informed decision-making based on logic, reason, and intuition, with a weight of ۰.۰۵۷, while the most important opportunity was enhancing democracy, with a weight of ۰.۱۰۵. The strategy of leveraging strengths in informed decision-making to improve democracy was ranked as the top strategy, with a score of ۳.۲۳۴. The results indicated that adopting informed decision-making based on logic and rationality as a superior strategy has significant potential for strengthening democracy and transparency in governance. This study follows a qualitative-quantitative method, employing textual content analysis in the qualitative section. Data collection tools include past research documents and library resources in this field. The data analysis method is based on open coding. According to the study’s findings, among ۱۸۷ relevant articles and books, ۴۲ studies were analyzed using a systematic review. After screening the indicators through the Delphi method, the remaining indicators were categorized into four groups for strengths and weaknesses and three groups for threats and opportunities, resulting in a total extraction of ۵۵ indicators. The statistical population of this research consists of ۱۰ government officials in governance who simultaneously held faculty positions in public administration and had authored works in this domain. In the quantitative section, utilizing the AHP-SWOT approach and analyzing internal and external factor matrices, the study sought to identify the most significant strengths and opportunities. It then developed and prioritized aggressive strategies to provide a practical framework for implementing artificial intelligence in governance. Ultimately, the strategies were ranked using the QSPM method. Based on the research findings, the most significant strength was facilitating informed decision-making based on logic, reason, and intuition, with a weight of ۰.۰۵۷, while the most important opportunity was enhancing democracy, with a weight of ۰.۱۰۵. The strategy of leveraging strengths in informed decision-making to improve democracy was ranked as the top strategy, with a score of ۳.۲۳۴. The results indicated that adopting informed decision-making based on logic and rationality as a superior strategy has significant potential for strengthening democracy and transparency in governance.
کلیدواژه ها:
Transcendent governance ، artificial intelligence ، strategic management of artificial intelligence implementation ، opportunities for artificial intelligence implementation ، mixed-methods analysis
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