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4,000원
원문정보
초록
영어
Modern businesses increasingly depend on large and continuously growing volumes of data to inform strategic decision-making. However, the rapid pace of data generation presents significant challenges for traditional analytical methods to remain timely and effective. This has led to a growing need for AI-based Decision Support System (AI-DSS) to assist business management processes. The effectiveness of an AI-DSS extends beyond its technical capabilities; it also depends on alignment with business goals, ease of use, and its support for ethical and responsible decision-making. This paper evaluates and compares AI-DSS tools within business contexts using Multi-Criteria Decision-Making (MCDM) frameworks. They have been reliably used in selecting systems tailored to given priorities. The proposed MCDM framework considers key evaluation dimensions, including technical performance, strategic compatibility, usability, cost-effectiveness and other factors. By integrating these factors, the study offers a systematic approach to help organizations make more informed and context-sensitive decisions when selecting AI-DSS tools.
목차
Abstract Introduction Literature review Methodology 3.1 Phase I: FAHP-Based Criteria Weighting 3.2 Phase II: Ranking AIDSS Tools Using TOPSIS Results 4.1 Fuzzy AHP Results for Criteria Weighting 4.2 TOPSIS Results for AIDSS Tools Ranking Conclusion References
키워드
AI-based Decision Support System (AI-DSS)Multi-Criteria Decision-Making (MCDM)Evaluation FrameworksBusiness Management.
저자
Yusupova Fotima Javlon kizi [ Interdisciplinary Program of Digital Future Convergence, Chonnam National University ]
Park, So Ra [ Collage of Business Administration, Chonnam National University ]