本研究採用混合模糊多準則決策(Multiple Criteria Decision Making; MCDM)方法，探討台灣銀行業者對Fintech人才聘僱方面，對於以人工智慧(AI)求職推薦系統為核心的數位化平台，作為面談聘任的主要影響因素。經由文獻回顧與專家討論，本研究架構包含四個主構面(履歷、媒合條件、軟體即服務與平台即服務)、十五個準則與四個方案。本研究利用決策實驗室分析法(Decision Making Trial and Evaluation Laboratory; DEMATEL)，分析構面間的因果關係，再利用模糊網絡分析法(Fuzzy Analytic Network Process, FANP)及模糊層級分析法(Fuzzy Analytic Hierarchy Process, FAHP)，對主構面及準則作權重計算以及排名。根據30位專家訪問的實證結果顯示，「媒合條件 (Job Attributes)」是最重要的構面，而最重要的準則是「人格特質 (Personality)」。根據理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution; TOPSIS)及折衷排序法(VlseKriterijumska Optimizacija I Kompromisno Resenje, VIKOR)對四個方案排名，結果均顯示「先建立資料庫與平台 (Establish Database and Platform)」為銀行最優先的策略方案，委外建構AI資訊系統次之，直接建立AI系統第三，先訓練人員再引進AI最不重要。最後，敏感性分析顯示除非決策機制偏低(高個別遺憾)或為一(以多數決策機制為主)，上述排序呈現穩定的狀態。

This study utilizes the hybrid fuzzy Multiple Criteria Decision Making (MCDM) approach to analyze the main factors underlying an AI platform with an AI job recommendation system as its core for recruiting Fintech talents in the Taiwanese banking sector. By means of a literature review and discussions with experts, the evaluation framework includes four main dimensions (the resume, job attributes, Software-as-a-Service, and Platform-as-a-Service), fifteen criteria and four alternatives. In addition, this study utilizes the Decision Making Trial and Evaluation Laboratory (DEMATEL) method in order to analyze the causal relationship between the dimensions, and then use the Fuzzy Analytic Network Process (FANP) and Fuzzy Analytic Hierarchy Process (FAHP) approaches to calculate the weights and rank the main dimensions and criteria. The empirical results according to the 30 experts reveal that the “Job Attributes” is the most important of the main dimensions, and the most important criterion is “Personality”. We rank the four alternatives based on the advantages of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) separately. The results indicate that the banks tend to use “Establish Database and Platform” as the main strategy, followed by “Outsource AI System”, then “Directly Establish AI System”, with “Train Employees before Introducing AI” as the least important. Finally, sensitivity analysis shows that unless the decision-making mechanism is low (high individual regrets) or one (mainly the majority decision-making mechanism), the above ranking exhibits a stable state.

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