| 研究生: |
宋杰蓉 Sung, Chieh-Jung |
|---|---|
| 論文名稱: |
證券商數位轉型與多角化策略之動態二階段 DEA 效率分析:2020–2024 年臺灣證券商 Digital Transformation and Diversification Strategies in Taiwanese Securities Firms: A Dynamic Two-Stage DEA Efficiency Analysis, 2020–2024 |
| 指導教授: |
林泰宇
Lin, Tai-Yu 黃瀞瑩 Huang, Ching-Ying |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 企業管理學系 Department of Business Administration |
| 論文出版年: | 2026 |
| 畢業學年度: | 114 |
| 語文別: | 中文 |
| 論文頁數: | 103 |
| 中文關鍵詞: | 證券商 、數位轉型 、多角化經營 、動態二階段RDM-DDF-DEA 、營運與獲利能力效率 |
| 外文關鍵詞: | securities firms, digital transformation, business diversification, Dynamic Two-Stage RDM-DDF-DEA, operational efficiency and profitability creation capability |
| 相關次數: | 點閱:2 下載:0 |
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隨著金融科技快速發展、手續費競爭加劇及資本市場交易型態轉變,臺灣證券商面臨傳統經紀業務獲利空間壓縮與經營模式調整之壓力。在此情況下,數位轉型與多角化經營已成為證券商提升競爭力與經營韌性的重要策略。然而,既有研究多聚焦於銀行業,較少針對證券商進行系統性分析,亦缺乏同時從營運效率與獲利能力兩階段切入之實證研究。
本研究以臺灣 25 家主要證券商為研究對象,分析 2020 年至 2024 年間之經營資料,並採用動態二階段 RDM DDF DEA 資料包絡分析法(Two Stage Dynamic RDM DDF DEA)建構效率評估模型。模型區分為營運效率與獲利能力兩個階段,並以總資產作為跨期結轉變數,且將第一階段之交易量與營業收入設定為連結兩階段之中介產出(link)。第一階段投入變數包括員工人數、分公司數及營運費用,產出變數為交易量及營業收入;第二階段則以業務範圍項目數與技術及研發支出為投入,並以 EPS、ROE 與市占率作為最終產出。
本研究透過動態二階段 RDM DDF DEA 方法,建構一套結合數位轉型、多角化經營與效率評估之分析架構。結果顯示,臺灣證券商整體平均總效率為 0.6579,顯示多數券商在資源配置與效率轉化上仍有改善空間。進一步分析發現,營運效率與獲利能力之表現並不一致,數位轉型不是「有做就有效」,而是需投入至一定強度,才會出現較明顯的效率改善;多角化經營之效益亦具情境依賴性,未必必然帶來效率提升。研究結果說明,證券商之效率表現並非僅由規模決定,而更取決於數位投入、多角化策略與組織整合能力之配適程度。研究成果可作為證券商調整資源配置與轉型策略之參考,亦可提供監理機關於推動證券業數位發展與制度規劃時之實證依據。
This study examines how digital transformation and business diversification jointly shape the efficiency of Taiwanese securities firms. Using panel data for 25 major securities firms from 2020 to 2024, the research applies a Dynamic Two-Stage Range Directional Model–Directional Distance Function Data Envelopment Analysis (Dynamic Two-Stage RDM-DDF-DEA) framework that decomposes the intermediation process into an operational efficiency stage and a profitability creation stage, with total assets specified as a carry-over variable across periods. In the first stage, number of employees, number of branches, and operating expenses are used as inputs, and trading volume and operating revenue as outputs. In the second stage, the number of business lines and technology and R&D expenditures serve as inputs, while EPS, ROE, and market share are modeled as final outputs. The empirical results show that the average overall efficiency of the sample firms is 0.6579, indicating substantial scope for improving resource allocation and output generation. The evidence further reveals that digital investment exhibits a non-linear threshold effect and that the benefits of diversification are highly context-dependent rather than automatically efficiency-enhancing. Overall, the findings suggest that efficiency depends less on firm size than on the strategic fit among digital investment, diversification choices, and organizational integration capabilities.
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