為了更有效地招聘和管理人才，從管理者的角度研究人工智慧職位推薦系統的意願和感知至關重要。因此，本研究使用混合模糊多準則決策（MCDM）模型，評估關於建構人工智能職位推薦系統的三種不同方案。測量指標包含四個主要構面（履歷、媒合條件、平台即服務和軟體即服務）和十六項標準。本研究結果顯示: (1)平台即服務和軟體即服務為主構面中影響程度最高的兩項要素; (2)軟體即服務存在自我影響，其中「AI API」是最為關鍵的因素，而「介面及資料庫系統」屬於影響力最高的因素; (3) FANP分析顯示「平台即服務和軟體即服務」為主構面的前兩項關鍵要素，「經驗、AI研發能力、AI 技術掌握度、數位化和個人特質」為標準中的前五個重要因素; (4)TOPSIS和VIKOR方法皆顯示電動車產業建構人工智能職位推薦系統的最佳方案是「建立完整資料庫」。

In order to be more efficient in recruiting and managing talents, it is critical to study the willingness and perceptions for the AI openings recommendation platform from the perspective of managers. The hybrid fuzzy Multi-Criteria Decision-Making (MCDM) model is utilized to assess three different alternatives for constructing the AI openings recommendation platform. The measurement indicators incorporate four major criteria (Resume, Job attributes, PaaS, and SaaS) and sixteen sub-criteria. The results show that: (1) the two most influential factors are PaaS and SaaS; (2) “AI API” is the most crucial factor and “Interface& database system” is the most influential factor in terms of the self-influencing performance of SaaS; (2) “PaaS” and “SaaS” are the most important factors among the major criteria and “Experience,” “R&D capability of AI,” “AI maturity,“ “Digitalization,” and “Personality“ are the top five key factors among the sub-criteria based on the FANP; and (3) the TOPSIS and VIKOR approaches reveal that “Build up the complete database” is the most optimal alternative.

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