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Author: 高侑成
Yu-Cheng Kao
Thesis Title: 傳統金融機構運用混合決策方法導入供應鏈金融的金融科技策略以提升競爭優勢—以台灣中小企銀為例
Incumbent Financial Institutions use a Hybrid Decision Approach Adopting Fintech Strategy to Enhance Competitive Advantages. A case of Taiwan Business Bank
Advisor: 謝劍平
Joseph C. P. Shieh
Committee: 謝劍平
Joseph C. P. Shieh
劉代洋
Day-Yang Liu
陳俊男
Chun-Nan Chen
曾國雄
Gwo-Hshiung Tzeng
鄭宗記
Tsung-Chi Cheng
Degree: 博士
Doctor
Department: 管理學院 - 管理研究所
Graduate Institute of Management
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 中文
Pages: 167
Keywords (in Chinese): 供應鏈金融銀行區塊鏈金融科技混合多準則決策模糊理論多準則決策
Keywords (in other languages): supply chain finance, banking, blockchain, Fintech, hybrid multiple criteria decision-making, fuzzy set theory, Multiple Criteria Decision-Making
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  • 傳統的金融機構對於中小企業 (Small and Medium Enterprises, 簡稱SMEs) 面臨Covid-19疫情的大封鎖,以及供應鏈斷鏈造成金融機構違約增加和信貸緊縮相當憂心,再加上新競爭對手Google、Amazon、Alibaba等科技巨獸試圖運用雲端計算、人工智慧和機器學習等金融科技技術搶進原有的供應鏈金融市場,造成傳統的金融機構市場占有率不斷流失。過去的研究主要著重於技術提升,或是偏重在供應鏈效率的優化;有關傳統金融機構如何建構一個決策模型來評估其供應鏈金融的金融科技策略,現有文獻中幾無具體的指導方針。

    本研究主要混合了BWM (Best-Worst Method) 和Modified-VIKOR方法來建構「HMCDM決策模型」,它將用來產生傳統金融機構導入供應鏈金融金融科技策略的替代方案優先排序,以提供高階主管進行決策制定。此外,本研究從現有的文獻和實務中總結出五種可行的金融科技策略,並且強調區塊鏈策略的優勢,未來它將逐步發展。

    在評估階段,本研究以臺灣一家國內中小企業金融機構作為研究個案。為了避免專家對於測試項目猜測,設計了明確 (Crisp)、模糊 (Fuzzy)、信心加權 (Confidence Weighted) 和信心加權模糊 (Confidence Weighted Fuzzy) 的問卷來進行評估,以提升整體問卷的信效度。

    研究結果顯示區塊鏈供應鏈金融的「領導運營商」應是該金融機構的理想選擇。「HMCDM決策模型」還揭示了每個評估因素的重要程度,為金融機構導入金融科技服務提供了全新的想法。這些發現有助於金融機構發展金融科技的供應鏈融資業務,供應鏈的成員也可能受益於縮短貸款的審核時間以加快其營運效率。


    Incumbent financial institutions are quite worried about the increase in bank defaults and credit crunch caused by SMEs (Small and Medium Enterprises) facing the blockade of the Covid-19 epidemic and supply chain disruptions, coupled with their competitors Google, Amazon, Alibaba and other tech giants trying to use Fintech such as cloud computing, artificial intelligence and machine learning have entered the original supply chain financial market, causing the market share of Incumbent financial institutions to gradually lose. Past research has mainly focused on technological improvements, or on the optimization of supply chain efficiency; there are few specific guidelines on how incumbent financial institutions can develop a holistic model to evaluate their fintech strategies for supply chain finance.

    This study mainly hybrids the BWM (Best-Worst Method) and Modified-VIKOR methods to construct the "HMCDM Decision Model", which will be used to generate the incumbent financial institutions to adpot prioritization of alternatives to supply chain financial Fintech strategies to provide the top managements with decision-making. In addition, this study summarizes five feasible fintech strategies from existing literature and practice, and emphasizes the advantages of blockchain-based strategies, which will be gradually developed in the future.

    In the evaluation stage, this study takes a domestic SME financial institution in Taiwan as a case study. In order to avoid experts' guesses, the Crisp, Fuzzy, Confidence Weighted and Confidence Weighted Fuzzy questionnaires were designed for evaluation to improve the reliability and validity of the overall questionnaire.

    The findings show that the "leading operator" of blockchain-based supply chain finance should be the ideal choice for the financial institution. The "HMCDM Decision Model" also reveals the importance of each evaluation factor, and provides a new idea for financial institutions to adopt Fintech services. These findings not only help financial institutions to develop Fintech supply chain financing business, but also members of the supply chain may also benefit from shortening loan approval times to speed up their operational efficiency.

    中文摘要 i Abstract ii 誌謝 iii 目錄 iv 圖目錄 vii 表目錄 ix 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機 3 第三節 研究目的與方法簡介 17 第四節 研究的重要性 19 第二章 文獻探討 20 第一節 供應鏈金融的介紹 20 一、 供應鏈金融的發展 20 二、 區塊鏈技術應用於供應鏈金融平台 29 三、 供應鏈金融在全球發展狀況 29 第二節 傳統金融機構導入供應鏈金融之關鍵因素 35 一、 法遵 35 二、 組織和營運管理 36 三、 技術 37 四、 財務 39 第三節 MCDM應用在金融機構導入金融科技的相關研究 40 第四節 供應鏈金融在實務和學術上的研究狀況與重大發現 41 第三章 研究方法 42 第一節 德菲法(Delphi Method) 44 第二節 明確集合vs 模糊集合 (Crisp sets vs Fuzzy sets) 46 第三節 三角模糊法 (Triangular Fuzzy method) 48 第四節 解模糊法 (Defuzzification method) 49 第五節 信心加權法 (Confidence-Weighted) 50 第六節 信心加權模糊法 (Confidence-Weighted Fuzzy) 51 第七節 最佳-最差法(Best-Worst Method, 簡稱BWM) 52 第八節 簡單加權法 (Simple Additive Weighting, 簡稱SAW) 55 第九節 修正折衷排序法 (Modified-VIKOR method) 56 第四章 研究過程和結果 58 第一節 專家背景介紹 59 第二節 研究個案介紹 61 第三節 金融科技策略介紹 62 第四節 個案施作流程 63 第五節 建立MCDM決策模型和BWM&個案問卷設計 65 一、 專家問卷施測 65 二、 專家問卷施測過程 68 三、 專家問卷的信效度檢測 68 四、 專家問卷施測結果和建立MCDM決策模型 69 五、 BWM&個案問卷設計 69 第六節 建立BWM評估矩陣 79 一、 BWM問卷施測 79 二、 問卷施測過程和結果 79 第七節 建立研究個案評估矩陣 82 一、 個案問卷施測 82 二、 問卷施測過程和結果 83 第八節 建立BWM整體權重矩陣 93 一、 施作流程 93 二、 一致性檢定檢測 94 三、 施測結果 95 四、 產生BWM整體權重矩陣 103 第九節 建立BWM + SAW聚合評估方案 104 一、 施作流程 104 二、 施測結果 104 第十節 建立BWM + Modified-VIKOR聚合評估方案 108 一、 施作流程 108 二、 施測結果 108 第十一節 綜合評估結果與討論 112 第五章 結論 114 第一節 研究發現與貢獻 115 一、 研究發現 115 二、 研究貢獻 115 第二節 在實務上的管理意涵 118 第三節 研究限制與未來研究 119 一、 研究限制 119 二、 未來研究 119 參考文獻 121 附錄 專家問卷1 134 附錄 專家問卷2 137 附錄 BWM問卷 139 附錄 Crisp問卷 144 附錄 Triangular Fuzzy問卷 146 附錄 Confidence-Weighted 問卷 149 附錄 Confidence-Weighted Fuzzy問卷 152

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