臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：信用合作社分行區位評選之研究：應用整合式多評準決策模式
作者：許筱吟
作者(外文)：HSU, HSIAO-YIN
校院名稱：國立臺北科技大學
系所名稱：管理學院管理博士班
指導教授：廖森貴
學位類別：博士
出版日期：2020
主題關鍵詞：區位評選；多評準決策；模糊德菲法；決策實驗室法；網絡分析法；理想解類似度偏好順序法；信用合作社；Location Selection；MCDM；The Fuzzy Delphi Method；DEMATEL；ANP；TOPSIS；Credit Cooperative Bank
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:1
  • 共同引用共同引用:0
  • 點閱點閱:2
在金融體系中，信用合作社因囿於臺灣現行金融法規的規定，限制其經營業務種類、營業區域及交易對象，故造成信用合作社的營運範疇、可用的資源、事業網絡、營業額、客戶數及市佔率等面向均無法與金控公司或商業銀行抗衡，而逐步朝向發展小而美的社區銀行，也逐步運用銀行經營模式，試圖增加營業據點，藉此降低營運成本，而提升其企業經營規模經濟。
有別於過去有關金融機構分行據點選址的研究大都是以商業銀行為對象，本研究以信用合作社為研究對象，在研究方法上不同於過往大多採用單一決策模式之方法，整合運用模糊德菲法、決策實驗室法、網絡分析法，以及理想解類似度偏好順序法等方法，建構信用合作社在設立分行時的決策模型，以提升決策的完整性與精確性。
首先，收集過往有關金融機構對於區位評選的文獻且針對信用合作社主席、總經理，以及副總經理進行深度訪談萃取次準則，進而設計出第一波問卷，將問卷發放給全國信用合作社主管，一共回收38份問卷。再透過模糊德菲法求取38位主管們對於各次準則的重要性評價，利用重心法計算出其重要性程度，保留重要性為前12名的次準則，分別為：人口密度、就業人口、人口成長率、停車方便性、臨停方面性、消費者與銀行距離考量、競爭者距離遠近、依賴銀行業務之需求、產業密集度、地區之存放款量、觀察新設金融機構之存放款表現、地區金融機構之存放款量趨勢。再根據過去文獻以及訪談信用合作社主管的意見，將次準則分類進而建立本論文之層級架構。
根據層級架構中的4個準則設計出決策實驗室法問卷，以宜蘭市的信用合作社做為案例，由其4位主管判定兩準則的影響程度建立關係矩陣，進而建立準則的相互影響關係。此外，網絡分析法的部分，4位主管根據信用合作社分行區位評選之關係層級架構，建立準則以及次準則的配對比較矩陣，以幾何平均數彙整成群體決策的綜合分數，4位主管的問卷一致性比率皆<0.1，根據極限超級矩陣，即可得知各準則之權重。最後，在理想解類似度偏好順序法的部分，決策者依據各準則分別以1-9分為方案給分，個案信用合作社分行區位包含4個方案，新北市是因為法規限制，要去外縣市設分社只能先去臨近的鄉鎮設分社，五結、員山、南澳是因為目前宜蘭縣這幾各縣市尚未有信用合作社、人口數也相對較多、銀行家數較少。最後得知，個案信用合作社分行最佳區位為新北市。
藉由質性訪談與量化的專家調查，研究結果指出信用合作社與一般金融銀行機構在區位評選考量要素上有所不同，而整合上述4種決策科學所建立之決策模型，能有效提升信用合作社在分行區位評選上的效度。
Within the financial system of Taiwan, the regulations of the current financial laws limit the business operation types, operation zones, and trade parties of credit cooperativebanks. Consequently, credit cooperative banks are unable to compete with financial holding companies and commercial banks in terms of scope of operations, usable resources, utility networks, turnover, customer volume, and market share. Therefore, credit cooperative banks are progressing toward developing into small but comprehensive community banks. Additionally, they are gradually adopting the bank operation mode, which means that they increase their operation scale economy by increasing the amount of branches and lowering operational costs.
In this study, the researchers used credit cooperative banks as the study sample, which distinguishes the current study from previous research on branch location selection that used commercial banks as a sample. In terms of methodology, the current study constructed a decision-making model for the location selection of credit cooperative banks’ branches. To enhance the integrity and accuracy of the decisions made using the model, the researchers constructed the model by combining the fuzzy Delphi method, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and technique for order preference by similarity to ideal solution (TOPSIS).This distinguishes the current study from past studies, the majority of which used a single-criterion decision-making method.
The researchers began the data collection by identifying relevant sub-criteria: this was achieved by reviewing the literature related to financial institutions’ choice of location and conducting in-depth interviews with presidents, chief operating officers, and deputy chief operating officers of credit cooperative banks. An initial questionnaire was designed on the basis of the literature review and interview data. The designed questionnaire was then distributed to credit cooperative banks supervisors across Taiwan; 38 valid questionnaires were retrieved. The 38 supervisors’ importance ratings of each sub-criterion were obtained using the fuzzy Delphi method, and the extent of importance of each sub-criterion was calculated using the center-of-gravity method. The 12 sub-criteria with the highest level of importance were retained and are as follows: population density, employed population, population growth rate, parking convenience, temporary parking convenience, distance between consumers and banks, distance of competitors, need for bank-dependent services, industrial density, regional deposit and loan volume, deposit and loan performance of newly established financial institutions, and deposit volume trends of local financial institutions. On the basis of relevant studies and the opinions of the interviewees, the researchers then classified the sub-criteria into groups and established the tiered framework of the current study.
The researchers designed a DEMATEL questionnaire according to the four criteria in the tiered framework and used a credit cooperative bank in Yilan City as a case study. Four supervisors were selected to judge the level of influence of two criteria for establishing the relation matrix; the influence relationships between the criteria were then established accordingly. In ANP, the four supervisors constructed the pairwise comparison matrix of criteria and sub-criteria and compiled the geometric average values into the comprehensive scores of group decisions. The consistency ratio was <0.1. Thus, the weight of each criterion could be obtained using the limitingsupermatrix. RegardingTOPSIS, the decision makers were required to score each alternative on a scale of 1-9 according to the stipulated sub-criteria.The credit cooperative bank in the case study has four alternatives for branch location selection: New Taipei City, because relevant laws stipulate that if a credit cooperative bank is to establish a branchin another county or city, it can only do so in a neighboring township; and Wujie, Yuanshan, and Nan-ao Townships of Yilan County, for the reasons that credit cooperative banks still do not exist in these townships, these townships have higher populations than others, and there are fewer banks in these townships. The final conclusion reached is that the best location for the credit cooperative bank in the case study is New Taipei City.
The study results obtained through qualitative interviews and a quantitative expert survey indicate that credit cooperative banks differ from conventional banking institutions in terms of which factors are considered when making location selection decisions. The integrated decision-making model constructed by combining the aforementioned decision-making approaches effectively enhanced the validity of branch locations selected bya credit cooperative bank.
一、中文部分
1.丁一倫、陳耀竹、張桂綸（2016）。應用模糊德菲法與網絡分析法評選民宿之區位。環球科技人文學刊，22，1-11。
2.丁亞中、謝孟宏（2009）。台北市百貨公司地理資訊系統自動化選址之研究。地圖，19，71-95。
3.王啟安（2009）。多角化對績效及風險的影響：以銀行與信用合作社為例。國立臺北科技大學商業自動化與管理研究所，臺北市。
4.金融監督管理委員會（2016）。金融科技發展策略白皮書。新北市：金融監督管理委員會。
5.林建山（1987）。商情預測：技術與實務。臺北市：環球經濟社。
6.林俊良（1997）。我國銀行業增設分行之探討。國立中正大學國際經濟研究所，嘉義縣。
7.林柏宏（2019）。台灣上市銀行業經營績效的決定因素：國際化所扮演的角色。國立政治大學行政管理碩士學程，臺北市。
8.洪娟娟（2005）。區位因素與經營績效關係之研究－台灣銀行之實證。國立東華大學企業管理研究所，花蓮縣。
9.吳建德（2002）。論現階段我國信用合作社之改革與發展。三民主義學報，24，115-132。
10.梁連文（2001）。信用合作社未來經營方向之探討。臺北市：台灣金融研訓院。
11.張有恆（1998）。運輸計畫評估與決策─模糊理論之探討與應用。臺北市：華泰文化事業股份有限公司。
12.張有恆、徐村和、陳曉玲（1997）。航空站區位選擇評估程序之研究。運輸計畫季刊，26(1)，37-68。
13.張桂綸（2011）。台灣百年企業新產品開發案評選之研究。國立臺北科技大學工商管理研究所，臺北市。
14.陳重光（2001）。考量網路經濟特性下影響台灣地區商業銀行分行設立地點區位因素研究。國立雲林科技大學工業工程與管理研究所，雲林縣。
15.陳惠文（2013）。銀行分行搬遷選址之關鍵要素分析：網路程序法之應用。東吳大學企業管理學研究所，臺北市。
16.麥勝剛（2015）。我國信用合作社之發展與願景。信用合作，125，4-15。
17.黃萬發（2014）。信用合作社經營管理之成功關鍵因素。開南大學商學院，桃園市。
18.黃達源（2016）。Bank 3.0時代影響銀行分行據點決策之關鍵因素分析。東吳大學管理研究所，臺北市。
19.楊勝剛（2003）。台灣地區公營銀行民營化的進展與問題。國際金融研究，3，28-32。
20.溫宗憲（1992）。新銀行設立分支機構決策模型之研究。國立台灣大學商學研究所，臺北市。
21.鄧振源（1997）。國防科研計畫評選之研究。新北市：華梵大學。
22.鄧振源（2005）。計畫評估─方法與應用第二版。新北市：海洋大學運籌規劃與管理研究中心。
23.周秀霞、沈中華（2009）。外國銀行追隨企業顧客或勞工顧客到台灣嗎?追隨顧客理論在台灣之實證。臺大管理論叢，29(2)，1-36。
24. 鄧振源（1997）。國防科研計畫評選之研究。新北市：華梵大學。
25. 鄧振源（2005）。計畫評估─方法與應用第二版。新北市：海洋大學運籌規劃與管理研究中心。
二、外文部分
1.Abbasi, G. Y. (2003).A Decision Support System for Bank Location Selection.International Journal ofComputer Applications in Technology, 16,202-210.
2.Abo-Sinna, M. A.,&Amer, A. H.(2005).Extensions of TOPSIS for Multi-objective Large-scale Nonlinear Programming Problems.Applied Mathematics and Computation, 162, 243-256.
3.Aksoy, S.,& Ozbuk, M. Y. (2017). Multiple Criteria Decision Making in Hotel Location: Does it Relate to Postpurchase Consumer Evaluations?Tourism Management Perspectives, 22, 73-81.
4.Azimi, R., Yazdani-Chamzini, A., Fouladgar, M.M., Zavadskas, E.K.,& Basiri, M.H.,(2011). Ranking the Strategies of Mining Sector through ANP and TOPSIS in a SWOT Framework.Journal of Business Economics and Management,12, 670-689.
5.Bagchi-Sen, S. (1995). FDI in US Producer Services: A Temporal Analysis of Foreign Direct InvestmentintheFinance,Insurance and Real Estate Sectors.Regional Studies,29, 159-170.
6.Bass, F.,& Campbell, D. (2013). Bank Branches Disappear from Poor Neighborhoods like Longwood.Bronx.Bloomberg Businessweek.
7.Berger, A. N., Leusner, J. H.,& Mingo, J. J. (1997). The Efficiency of Bank Branches.Journalof Monetary Economics,40,141-162.
8.Berger, A. N.,& Robert D. Y. (2001). The Effects of Geographic Expansion on Bank Efficiency.Journal of Financial Services Research, 9,163-184.
9.Boufounoua, P. V.(1995). Evaluating Bank Branch Location and Performance: ACase Study.EuropeanJournalofOperational Research, 87,389-402.
10.Chang, K. L., Liao, S. K., Tseng, T. W.,& Liao, C. Y. (2015). An ANP based TOPSIS Approach for Taiwanese Service Apartment Location Selection.Asia Pacific Management Review, 20,49-55.
11.Chang, P. L., Hsu, C. W.,& Chang, P. C.(2011). FuzzyDelphi Methodfor Evaluating Hydrogen Production Technologies.InternationalJournal of Hydrogen Energy,36, 14172-14179.
12.Chao,C. C.,& Kao, K. T.(2015). Selection of Strategic Cargo Allianceby Airlines.Journal of Air Transport Management, 43, 29-36.
13.Chen, H. (2013). Analysis of Key Elements of Bank Branch Relocation: Application of Network Procedure.The 16th Conference on Interdisciplinary and Multifunctional Business Management& High Education Forum on Business Management.
14.Chen, J., Wang, J., Baležentis, T., Zagurskaitė, F., Streimikiene, D.,& Makutėnienė, D. (2018). Multicriteria Approach Towards the Sustainable Selection of a Teahouse Location with Sensitivity Analysis.Sustainability, 10, 2926, doi:10.3390/su10082926.
15.Chou, H. H.,& Shen, C. H. (2009).Do Banks Follow their Corporate or Non-corporate Customers to Taiwan? A Test of “Follow the Customers” Hypothesis.NTU Management Review, 19(2), 1-36.
16.Cinar, N.(2009).A Decision Support Model for Bank Branch Location Selection.International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 3(12), 1092-1097.
17.Dağdeviren, M.(2010). A Hybrid Multi-criteria Decision-making Model for Personnel Selection in Manufacturing Systems.Journal of Intelligent Manufacturing,21, 451-460.
18.Dalkey, N.,& Helmer, O.(1963). An Experimental Application of theDelphi Method to the Use of Experts.Management Science,9(3), 458-467.
19.Deng, S.,&Elyasiani, E. (2008). Geographic Diversification, Bank Holding Company Valueand Risk.Journal of MoneyCredit and Banking,40,1217-1238.
20.Eldemir, F.,& Onden, I.(2016). Geographical Information Systems and Multicriteria Decisions Integration Approach for Hospital Location Selection.International Journal of Information Technology & Decision Making, 15(5),975-997.
21.Erdin, C.,& Akbaş, H. E. (2019). A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey.Sustainability, 11, 3837, doi:10.3390/su11143837.
22.Federal Deposit Insurance Corporation (FDIC)(2015). Brick-and-mortar Banking Re- mains Prevalent in an Increasingly Virtual World.FDICQ,9(1), 37-51.
23.Goldberg, L.G.,& Grosse, R. (1994). Location Choice of Foreign Banks in the United States.JournalofEconomics and Business, 46,367-379.
24.Goldberg, L. G.,& Johnson, D. (1990). The Determinants of US Banking Activity Abroad.Journal of International Money and Finance, 9,123-137.
25.Goldberg, M. A., Helsley, R. W.,& Levi, M. D. (1989). The Location of International Financial Activity: An Interregional Analysis.Regional Studies,23,1-7.
26.Goodstein, R. M.,& Rhine, S. L. (2017). The Effects of Bank and Nonbank Provider Locations on Household Use of Financial Transaction Services.Journal of Banking & Finance, 78, 91-107.
27.Gross, M. B., Hogarth, J. M., Manohar, A.,& Gallegos, S. (2012). Who Uses Alternative Financial Servicesand Why?Consumer Interests Annual, 58,2012-2057.
28.Hale, T. S.,& Moberg, C. R. (2003). Location Science Research: AReview.Annals of Operations Research,123,21-35.
29.Herrin, A., &Perniae, M. (1987). Factors Influencing the Choice of Location: Local and Foreign Firms in the Philippines.Regional Studies, 21, 531-541.
30.Hirtle, B. (2007). The Impact of Network Size on Bank Branch Performance.Journal of Banking & Finance,31,3782-3805.
31.Hsu,T. H.,& Yang, T. H.(2000). Application of Fuzzy AnalyticHierarchy Process in the Selection of Advertising Media.Journalof Management and Systems, 7, 19-40.
32.Hsu, Y. L., Lee, C. H.,&Kreng, V. B.(2010). The Application ofFuzzy DelphiMethod and Fuzzy AHP in Lubricant RegenerativeTechnologySelection.Expert Systems with Applications, 37, 419-425.
33.Huang, C. J., Lee, W. R.,& Tsou, S. H. (2008).International Expansion and Performance of the Taiwanese Banks.Asia Academy of Management 2008 Conference.
34.Hultman, C. W.,& McGee, L. R. (1989). Factors Affecting the Foreign Banking Presence in the US. Journal of Banking & Finance, 13, 383-396.
35.Hultman, C. W.,& McGee, L. R. (1990). The Japanese Banking Presence in the United States and its Regional Distribution.Growth and Change, 21,69-79.
36.Hwang, C. L.,& Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
37.Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R.,& Mieno, H.(1993).The Max-min Delphi Method and Fuzzy DelphiMethod via Fuzzy Integration.Fuzzy Sets and Systems,55,241-253.
38.Kataj, R. K., Ranjbar, H.,& Pirzad, A. (2016). Recognize and Ranking Effective Factors on Pasargad Bank Location using MCDM and AHP and GIS in Iran (Case study: Yasouj City).International Business Management, 10,4927-4937.
39.Liao, S. K., Hsu, H. Y.,& Chang, K. L. (2108). A Hybrid Multiple Criteria Decision Making Model for Selecting the Location of Women’s Fitness Centers.Mathematical Problems in Engineering, 2018, Article ID 9780565, 11 pages, https://doi.org/10.1155/2018/9780565.
40.Ma, Z., Shao, C., Ma, S., &Ye, Z.(2011). Constructing Road Safety Performance Indicators Using Fuzzy Delphi Method and GreyDelphi Method.Expert Systems with Applications, 38, 1509-1514.
41.McCauley, R., McGuire, P.,& von Peter, G. (2012). After the Global Financial Crisis: From International to Multinational Banking?Journal of Economics and Business,64,7-23.
42.Murray, T. J., Pipino, L. L.,& van Gigch, J. P. (1985). A Pilot Study of Fuzzy Set Modification of Delphi.Human SystemsManagement,5(1),76-80.
43.Nie, S., Liao, H., Wu, X., Tang, M.,& Al-Barakati, A.(2019). Hesitant Fuzzy Linguistic DNMA Method with Cardinal Consensus Reaching Process for Shopping Mall Location Selection.International Journal of Strategic Property Management, 23(6),420-434.
44.Nigh, D., Cho, K. R.,& Krishnan, S. (1986). The Role of Location-related Factors in US Banking Involvement Abroad: An Empirical Examination.Journal of International Business Studies, 17, 59-72.
45.Opricovic, S.,&Tzeng, G. H.(2004). Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS.European Journal of Operational Research, 156,445-455.
46.Owen, S. H.,&Daskin, M. S. (1998). Strategic Facility Location: A review.European Journal of Operational Research,111,423-447.
47.Popovic, G., Stanujkic, D., Brzakovic, M.,&Karabasevic, D.(2019).A Multiple-criteria Decision-making Model for the Selection of a Hotel Location.Land Use Policy, 84, 49-58.
48.Porter, M. E. (1990). The Competitive Advantage of Nations.Competitive Intelligence Review,11, 12-14.
49.ReVelle, C. S.,&Eiselt, H. A. (2005).Location Analysis: A Synthesis and Survey.EuropeanJournal of Operational Research,165,1-19.
50.Roberts, R.,&Arnander, C. (2001). Take Your Partners.London: Palgrave Macmillan.
51.Saaty, T.L. (1980). TheAnalytic Hierarchy Processes.New York: McGraw-Hill.
52.Saaty, T.L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. Pittsburgh: RWS Publications.
53.Sabi, M. (1988). An Application of the Theory of Foreign Direct Investment to Multinational Banking in LDCs.Journal of International Business Studies, 19, 433-447.
54.Samani, Z. N., Karimi, M.,&Alesheikh, A. A.(2018). A Novel Approach to Site Selection: CollaborativeMulti-criteria Decision Making through Geo- socialNetwork (Case Study: Public Parking).International Journal ofGeo-Information, 7, 82, doi:10.3390/ijgi7030082.
55.Saremi, M., Mousavi, S. F.,& Sanayei, A. (2009). TQM Consultant Selection in SMEs with TOPSIS under Fuzzy Environment.Expert Systems with Applications, 36, 2742-2749.
56.Saunders, A.(1994). Banking and Commerce: An Overview of the Public Policy Issues.Journal of Banking & Finance, 8,231-254.
57.Shilton, L., & Webb. J.(1995).Office Employment and Headquarters Location in the New York City Region.Journal of Real Estate Economics andFinance, 10, 145-159.
58.Shyur, H. J. (2006). COTS Evaluation using Modified TOPSIS and ANP.Applied Mathematics and Computation, 177, 251-259.
59.Tzeng, G. H., Chiang, C. H.,& Li, C. W. (2007). Evaluating Intertwined Effects in E-learning Programs: A Novel Hybrid MCDM Model based on Factor Analysis and DEMATEL.Expert systems with Applications, 32, 1028-1044.
60.U.S. Postal Service USPS (2014). Providing Non-Bank Financial Services for the Un-deserved. Office of the Inspector General United States Postal Service White Paper, RARC-WP-14-007.
61.Wang,S. (2009). A Review of Western Regional Economic Theory.Journal of Chifeng College Natural Science Edition, 259, 79-81.
62.Wu, C. C., Chang, D. S.,& Wang, R. (2018). Identifying Key Factorsof Adopting an RFID System in Nursing Care using DEMATELbasedANP.Corporate Management Review,38(1),63-98.
63.Yang,Y.F., Sun, Z. H.,& Rong J. D. (2007). The Development of the Bank Location Selection Decision Support System is based on the Example of Taipei City.Journal of the Chinese Geographical Society, 38, 45-65.
64.Yang, Y. P. O., Shieh, H. M., Leu, J. D.,&Tzeng, G. H.(2008).A Novel Hybrid MCDM Model Combined with DEMATEL and ANPwith Applications.International Journal of Operations Research, 5(3),160-168.
65.Zhou, J., Feng, X.,& Feng,X. (2018). Research and Application of ATM Location Model based on Market Reality.Computer Application and Software, 352, 117- 120.
66.Zhou, X. X.,& Shen, Z. H. (2009). Analysis of the Conditions for Taipei, Hong Kong and Shanghai to Become International Financial Centers-Determinants of Establishing Branches in Foreign Banks.Vision Foundation Quarterly, 102, 49-100.
三、參考網站
1.中華民國信用合作社聯合社，https://www.nfcc.org.tw/default.htm。
2.金融監督管理委員會，https://stat.fsc.gov.tw/FSCChartShow_Restore/CRPages/MS_Chart_Show.aspx。
3.金融監督管理委員會銀行局，http://www.banking.gov.tw。
4.宜蘭信用合作社，https://ilan.scu.org.tw/index.htm。
5.台灣經濟研究院產經資料庫，https://tie.tier.org.tw/search/index.aspx?keyword=%abH%a5%ce%a6X%a7%40%aa%c0。
6.Federal Deposit Insurance Corporation (FDIC).FDIC national survey of unbanked and underbanked households, http://www.economicinclusion.gov/surveys/2011household/.
7.Financial Service Centers of America (FiSCA). Theunbanked &underbanked consumer in America, http://www.fisca.org/.


 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：869（臺灣：865，外國：4） ／ 本年度總使用人次：2111296 ／ 訪客人次（自102年09月）：67911109
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上