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研究生:鄭妍茵
研究生(外文):Cheng, Yen-Yin
論文名稱:結合模糊多屬性決策方法與模糊C均值法探討重要性–績效分析問題
論文名稱(外文):A hybrid approach of fuzzy multiple-attribute decision making methods and Fuzzy C-means to Importance-Performance Analysis problems
指導教授:陳梁軒陳梁軒引用關係
指導教授(外文):Chen, Liang-Hsuan
口試委員:王泰裕謝中奇
口試委員(外文):Wang, Tai-YueHsieh, Chung-Chi
口試日期:2021-05-29
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:73
中文關鍵詞:重要性–績效分析決策實驗室分析法VIKOR法模糊C均值法
外文關鍵詞:Importance-performance analysis (IPA)Decision making trial and evaluation laboratory (DEMATEL)VIKORFuzzy C-means
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重要性-績效分析(Importance-Performance Analysis;IPA)為一種協助組織了解自身競爭優、劣勢之決策工具,組織透過IPA能找出優越之處以及迫切待改善之重要屬性,因此被廣泛應用於各領域之研究。IPA是由重要性和績效表現兩面向所構成,並將屬性落點於四個象限進行特性分析,組織藉此能更加了解目前的策略營運狀況,並針對劣勢部分進行改善。在IPA的應用上,相關文獻根據屬性重要性或績效表現進行深入探討,並提出調整方法,其中之一為決策實驗室分析法(DEcision MAking Trial and Evaluation Laboratory;DEMATEL),考慮部分屬性存在相互關係而產生相對重要性並藉此進行重要性之調整。另外,VIKOR法用於處理評估績效表現欠缺與理想值比較而造成評估不準確的問題。對於決定象限界值之方式,在不同的IPA象限分界方法下可能造成分析結果不一致的情況。在執行IPA上,由於決策者處於複雜環境使人為主觀認知存在不明確性,故明確值已無法完整表達決策者之意見。
本研究主要根據(1)屬性資訊之模糊不確定性、(2)屬性之相對重要性以及績效差距以及(3)象限分界問題,三個面向建立本研究之IPA方法,將模糊理論、DEMATEL、VIKOR法以及模糊C均值法加入IPA的方法中。有別於過去文獻,本研究加以考慮屬性相對重要性與績效差距問題,使屬性資訊更符合現實環境,接著透過模糊C均值法針對屬性特性進行分群,並決定屬性落點之特性區域,避免不同象限分界方法造成分析結果不一致的問題。最後,根據IPA分析結果,本研究針對具有競爭劣勢特性之屬性進行改善排序,藉此提供決策者更明確的參考資訊,期望組織在未來更能有效的執行資源分配與策略規劃,且於產業中更具競爭優勢。
Importance-performance analysis (IPA) is a decision making method that is widely used to assist organizations with planning their corporate strategies. Although IPA has been used in several fields by modifying the original IPA in order to overcome the disadvantages inherent in the traditional approach, there are still some shortcomings and that can be improved.
In this study, a new hybrid approach for IPA is proposed that covers three aspects of a problem. First, considering the fuzziness of the obtained attribute information in decision makers’ (DMs’) subjective opinions, fuzzy set theory is combined with the modified IPA. Second, to deal with the relative importance and the performance gap among attributes which resulting from the mutual relationship between the attributes and the lack of a comparison with an ideal performance value, the proposed approach uses the Decision Making Trial and Evaluation Laboratory (DEMATEL) and VIKOR to analyze such problems. Third, fuzzy C-means is used to classify attributes according to their characteristics. In addition, the proposed approach ranks the priority of attributes in the areas in which they need to be concentrated, where higher priority attributes can be defined as the most important but less effective than less important attributes, so DMs can clearly understand what attributes need to be improved right away and can allocate resources effectively.
Using a comparison with the fuzzy IPA reference, the proposed approach further involves the modification of attribute information and revises some drawbacks related to distributing attributes to specific areas to provide a more appropriate result with the expectation of more suitable strategic approaches to problem solving in organizations.
摘要 I
Abstract II
誌謝 VI
目錄 VII
表目錄 IX
圖目錄 X
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究範圍與假設 2
第四節 研究流程 3
第五節 論文架構 4
第二章 文獻探討 5
第一節 模糊集合理論 5
第二節 重要性-績效分析 8
第三節 決策實驗室分析法 15
第四節 VIKOR法 16
第五節 模糊C均值法 19
第三章 研究方法 25
第一節 研究構想 25
第二節 方法建立 27
第三節 小結 36
第四章 範例演算 38
第一節 範例說明 38
第二節 範例演算 42
第三節 結果比較與分析 56
第五章 結論與建議 66
第一節 研究成果 66
第二節 未來研究方向 67
參考文獻 68
Azzopardi, E., & Nash, R. (2013). A critical evaluation of importance–performance analysis. Tourism Management, 35, 222-233.
Ban, O. I., Ban, A. I., & Tuşe, D. A. (2016). Importance–performance analysis by fuzzy C-means algorithm. Expert Systems with Applications, 50, 9-16.
Bellman, R., Kalaba, R., & Zadeh, L. (1966). Abstraction and pattern classification. Journal of Mathematical Analysis and Applications, 13(1), 1-7.
Bezdek, J. C. (1981). Models for Pattern Recognition. In Pattern Recognition with Fuzzy Objective Function Algorithms (pp. 1-13). Springer US.
Boley, B. B., McGehee, N. G., & Hammett, A. T. (2017). Importance-performance analysis (IPA) of sustainable tourism initiatives: The resident perspective. Tourism Management, 58, 66-77.
Chen, K. Y. (2014). Improving importance-performance analysis: The role of the zone of tolerance and competitor performance. The case of Taiwan's hot spring hotels. Tourism Management, 40, 260-272.
Chen, Z. & Yang, W. (2011). A new multiple attribute group decision making method in intuitionistic fuzzy setting. Applied Mathematical Modelling, 35(9), 4424-4437.
Cheng, S. H. (2018). Applying Importance-Performance Analysis By Fuzzy C-means To Evaluate Tourist Friendly Destination. In 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 480-484). IEEE.
Chu, C. H., & Guo, Y. J. (2015). Developing similarity based IPA under intuitionistic fuzzy sets to assess leisure bikeways. Tourism Management, 47, 47-57.
Chung, C. C., Chao, L. C., & Lou, S. J. (2016). The establishment of a green supplier selection and guidance mechanism with the ANP and IPA. Sustainability, 8(3), 259.
Deng, W.-J. & Pei, W. (2009). Fuzzy neural based importance-performance analysis for determining critical service attributes. Expert Systems with Applications, 36(2), 3774-3784.
Deng, W.-J. (2008). Fuzzy importance-performance analysis for determining critical service attributes. International Journal of Service Industry Management, 19(2), 252-270.
Dubois, D., & Prade, H. (1978). Operations on fuzzy numbers. International Journal of Systems Science, 9(6), 613-626.
Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.
Fukuyama, Y., Sugeno, M.(1989). A new method of choosing the number of clusters for the fuzzy c-mean method. In Proc. 5th Fuzzy Syst. Symp.,247-250.
Geng, X. & Chu, X. (2012). A new importance- performance analysis approach for customer satisfaction evaluation supporting PSS design. Expert Systems with Applications, 39(1), 1492-1502.
Ho, L.-H., Feng, S.-Y., Lee, Y.-C. & Yen, T.-M. (2012). Using modified IPA to evaluate supplier's performance: Multiple regression analysis and DEMATEL approach. Expert Systems with Applications, 39(8), 7102- 7109.
Höppner, F., Klawonn, F., Kruse, R., & Runkler, T. (1999). Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. John Wiley & Sons.
Hsu, W. K. K., Yu, H. F., & Huang, S. H. S. (2015). Evaluating the service requirements of dedicated container terminals: a revised IPA model with fuzzy AHP. Maritime Policy & Management, 42(8), 789-805.
Hu, H.-Y., Lee, Y.-C., Yen, T.-M. & Tsai, C.-H. (2009). Using BPNN and DEMATEL to modify importance-performance analysis model - A study of the computer industry. Expert Systems with Applications, 36(6), 9969-9979.
Islam, M. S., Tseng, M. L., Karia, N., & Lee, C. H. (2018). Assessing green supply chain practices in Bangladesh using fuzzy importance and performance approach. Resources, Conservation and Recycling, 131, 134-145.
Kano, N., Seraku, N., Takahashi, F. & Tsuji, S. (1984). Attractive quality and must-be quality. The Journal of the Japanese Society for Quality Control, 14(2), 147-156
Klir, G., & Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic (Vol. 4). New Jersey: Prentice hall.
Lin, C.-J. & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
Martilla, J. A. & James, J. C. (1977). Importance-Performance analysis. Journal of Marketing, 41(1), 77-79.
Matzler, K., & Sauerwein, E. (2002). The factor structure of customer satisfaction. International Journal of Service Industry Management.
Matzler, K., Bailom, F., Hinterhuber, H. H., Renzl, B., & Pichler, J. (2004). The asymmetric relationship between attribute-level performance and overall customer satisfaction: A reconsideration of the importance–performance analysis. Industrial Marketing Management, 33(4), 271-277.
Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.
Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514-529.
Park, H. R., Kim, H. M., Lee, Y., Jeong, S. Y., & Lim, Y. S. (2017). Sanitation management of cooks in childcare centers in South Korea according to working duration and the type of childcare center: Importance-Performance Analysis. Food Control, 73, 1452-1458.
Phadermrod, B., Crowder, R. M., & Wills, G. B. (2019). Importance-performance analysis based SWOT analysis. International Journal of Information Management, 44, 194-203.
Ruspini, E. H. (1969). A new approach to clustering. Information and Control, 15(1), 22-32.
Sato, M., Sato, Y., Jain, L. C., & Kacprzyk, J. (1997). Fuzzy Clustering Models and Applications. Physica-Verlag.
Seng Wong, M., Hideki, N., & George, P. (2011). The use of importance-performance analysis (IPA) in evaluating Japan’s e-government services. Journal of Theoretical and Applied Electronic Commerce Research, 6(2), 17-30.
Silva, F., & Fernandes, P. (2010, November). Using importance-performance analysis in evaluating institutions of higher education: A case study. In 2010 International Conference on Education and Management Technology (pp. 121-123). IEEE.
Trang, S. T. N., Zander, S., de Visser, B., & Kolbe, L. M. (2016). Towards an importance–performance analysis of factors affecting e-business diffusion in the wood industry. Journal of Cleaner Production, 110, 121-131.
Tsai, W. H., Hsu, W., & Chou, W. C. (2011). A gap analysis model for improving airport service quality. Total Quality Management & Business Excellence, 22(10), 1025-1040.
Tseng, M. L., & Bui, T. D. (2017). Identifying eco-innovation in industrial symbiosis under linguistic preferences: A novel hierarchical approach. Journal of Cleaner Production, 140, 1376-1389.
Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095-2117.
Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507.
Xie, X. L., & Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8), 841-847.
Yang, M. S., & Ko, C. H. (1996). On a class of fuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets and Systems, 84(1), 49-60.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199-249.
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