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研究生: 陳眉秀
Mei-Hsiu Chen
論文名稱: 影響台灣豪華車廠商利用智能化平台提升銷售主管聘雇效率之因素分析
Factors and Determinants for Recruiting Marketing Executives Using an AI platform in Taiwan’s Luxury Car Industry
指導教授: 張順教
Shun-Chiao Chang
口試委員: 吳克振
Cou-Chen Wu
張光第
Kuang-Ti Chang
鄭政秉
Cheng-Ping Cheng
學位類別: 碩士
Master
系所名稱: 管理學院 - 企業管理系
Department of Business Administration
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 138
中文關鍵詞: AI推薦系統多準則決策分析方法模糊網路層級分析法
外文關鍵詞: AI recommendation system
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  • 本研究採用混合模糊多準則決策(MCDM)方法,分析台灣豪華車廠商對人才聘雇方面,對於以人工智慧(AI)求職推薦系統為核心的數位平台作為面談聘任的主要影響因素。基於相關文獻和專家建議,本研究根據2020年豪華車新車銷售前九大品牌找尋16位專家做訪談,而研究模型衡量指標包括4個主構面(履歷、媒合條件、軟體即服務以及平台即服務)及16個準則。另外,本研究中的MCDM方法包括DEMATEL,用於找出構面之間的因果關係,再利用FANP以及FAHP方法,分別對主構面及準則作權重計算以及排名,實證結果顯示平台即服務 (Platform-as-a-Service) 是最重要的構面,而最重要的準則是「資料精確度(information accuracy)」。根據VIKOR(VlseKriterijumska Optimizacija I Kompromisno Resenje)跟TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)對四個方案進行排名,結果均表示「委外客製化系統 (Outsourcing Customized System)」為廠商優先的策略方案,尋求與人力銀行合作次之,培訓AI人才第三,建立AI求職推薦系統最不重要。最後,敏感性分析顯示除非決策機制偏低(高個別遺憾)或最高(以多數決機制為主)時,上述排序呈現穩定的狀態。


    This study utilizes the hybrid fuzzy Multi-Criteria Decision Making (MCDM) method to evaluate the determinants of face-to-face interactions with digital platforms such as AI job position recommendation systems for recruiting talents in Taiwan’s luxury car industry. According to the top nine manufacturers of new cars sold in 2020, we found sixteen experts as the target to do the interview. The measurement indicators include four main dimensions (resume, job attribute, Software-as-a-Service, and Platform-as-a-Service) and sixteen criteria. The DEMATEL method is used to find the causal relationship among the dimensions, and the FANP and FAHP methods are employed to calculate and rank the weights of the main dimensions and criteria. The empirical results reveal that the “Platform-as-a-Service” is the most important dimension, and the top three key criteria are “information accuracy, experience, and the degree of AI technology.” The VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) show that the “Outsourcing Customized System” is the priority strategy for firms. The “Cooperation with Job Bank” is ranked second, and “Looking for AI Technical Talents” is ranked third. Finally, the sensitivity analysis indicates that, unless the coefficients of analysis mechanism is low (high individual regrets) or the highest (maximum group utility), the above rankings are in a stable state.

    Contents 摘 要 III Abstract IV 誌 謝 V List of Tables VIII List of Figures X Chapter 1 Introduction 1 1.1. Research Background and Motivation 1 1.2. Research Objectives and Purpose 2 1.3. Research Process 3 Chapter 2 Literature Review 6 2.1. Artificial Intelligence 6 2.2. The Determinant Factors of the Job Position Recommendation System 8 2.2.1. The Resume Determinant 9 2.2.2. The Job Attribute Determinant 10 2.2.3. The Software as a Service (SaaS) Determinant 12 2.2.4. The Platform as a Service (PaaS) Determinant 13 2.3. The Literature on MCDM Methods 17 Chapter 3 The Luxury car market in Taiwan 22 3.1. Luxury Cars 22 3.2. Automobile Industry Chain in Taiwan 22 3.3. Luxury car market in Taiwan 25 Chapter 4 Methodology 28 4.1. Fuzzy Set Theory and Fuzzy Numbers 28 4.2. Multiple-Criteria Decision-Making (MCDM) Methodology 30 4.2.1. The DEMATEL Methodology 31 4.2.2. Fuzzy Analytic Hierarchy Process (FAHP) 31 4.2.3. Fuzzy Analytic Network Process (FANP) 32 4.2.4. The TOPSIS method 33 4.2.5. The VIKOR method 34 Chapter 5 Evaluation Model 36 Chapter 6 Empirical Results 42 6.1. Analysis of the DEMATEL method 42 6.1.1. The DEMATEL method for the main dimensions 42 6.1.2. The DEMATEL method for the dimension of SaaS 44 6.2. The Results of the FANP 46 6.3. The Results for VIKOR and TOPSIS 54 6.3.1. The Results of VIKOR 54 6.3.2. The Results of TOPSIS 59 6.4. Sensitivity analysis 62 Chapter 7 Conclusions, Limitations, and Recommendations 66 7.1. Conclusions 66 7.2. Limitations and Recommendations for Future Research 71 Reference 72 Appendix A 82 Appendix B 88 B.1 The definition of membership function μM(X) 88 B.2 The main steps of each method used in this study 89 B.2.1 The main steps of DEMATEL 89 B.2.2 The major steps of the FAHP 92 B.2.3 The main steps of FANP 93 B.2.4 The main steps of TOPSIS 95 B.2.5 The main steps of the VIKOR 96 Appendix C 99

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