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系統識別號 U0026-1407201617411000
論文名稱(中文) 直覺式模糊環境下之重要性-績效分析
論文名稱(英文) Approach for Importance-Performance Analysis under Intuitionistic Fuzzy Environment.
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系
系所名稱(英) Department of Industrial and Information Management
學年度 104
學期 2
出版年 105
研究生(中文) 徐慧穎
研究生(英文) Hui-Ying Hsu
學號 r36034090
學位類別 碩士
語文別 中文
論文頁數 73頁
口試委員 指導教授-陳梁軒
口試委員-王泰裕
口試委員-施勵行
中文關鍵字 重要性-績效分析  決策實驗室分析法  直覺式模糊集  相似度法 
英文關鍵字 Importance-performance analysis (IPA)  Intuitionistic fuzzy set (IFS)  Decision making trial and evaluation laboratory (DEMATEL)  Similarity 
學科別分類
中文摘要 重要性-績效分析(Importance-Performance Analysis;IPA)為一幫助組織規劃營運策略的決策工具,組織透過重要性-績效分析能找出最需關切的焦點及重要屬性待改善之優先次序(priority),因此受各產業廣泛應用。IPA是由重要性和績效表現兩面向,將屬性劃分出四個象限進而了解屬性之特性,組織能依據屬性之特性,了解目前的營運策略其優缺之處,對缺失之部分進行調整改善。之後在IPA的應用上,關於決定象限界值之方式各有不同,Chu & Guo(2015)提出IPA會因選擇象限界值的方式不同,造成最後分析結果不一致的情況,因此以相似度法決定屬性之落點改善之。IPA之相關文獻多由屬性重要性或績效表現切入進行深入探討,提出IPA的修正方法,其中之一為利用決策實驗室分析法(Decision Making Trial and Evaluation Laboratory;DEMATEL),其主要考慮部分屬性存有相關性,對於屬性重要性的影響,而需調整屬性重要性。而在執行IPA與DEMATEL,首先必須獲得屬性資訊,資訊過去常以明確值呈現,然而資訊因評估者之主觀認知存在不確定性,但明確值並無法表現出資訊的不確定性。雖然有IPA與DEMATEL之模糊方法的探討提出,但常以解模糊化的方式,將模糊數或直覺式模糊數(Intuitionistic fuzzy set;IFS)轉為明確值進行後續計算,雖計算上較方便卻也因此造成不確定性資訊的遺失。
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、(3)象限分界問題,三個面向建立本研究之IPA方法。針對此三個面向,本研究將IFS、DEMATEL以及相似度法加入IPA的方法中,有別於過去之IPA模糊方法,本研究不需將模糊數解模糊化轉成明確值即可進行後續之數學運算,因此能將不確定性資訊完整保留,不會有原先IPA模糊方法因數值型態改變而造成不確定性資訊遺失的問題。此外,根據IPA之結果,本研究針對第四象限之關鍵屬性提出兩種方法進行排序,期望對於決策者未來思考投入資源的分配上能有所幫助。
英文摘要 Importance-performance analysis (IPA) has been widely used in assisting organizations to plan their corporate strategies since it was first proposed by Martilla and James (1977). Even though there have been many scholars who have proposed revised IPA methods in order to overcome the drawbacks of traditional IPA, the modified IPA methods still have some shortcomings and so can be improved.

A new approach for IPA is proposed in this study in three points. First, considering the fuzziness of the obtained attribute information, which arises from assessors’ subjective opinions, intuitionistic fuzzy set (IFS) is combined with the modified IPA. The new approach will avoid the loss of uncertain information which derives from defuzzification that previous fuzzy IPA methods used for simplifying the complexity of calculation. Second, to deal with the implicit importance of attributes, which results from the mutual influence relationship between the attributes, the proposed approach uses the decision making trial and evaluation laboratory (DEMATEL) to analyze the cause-effect relationship and degree of influence between the attributes. Third, a similarity measure of IFS called, “dual bipolar similarity of IFS”, is used to classify attributes into the four quadrants which conventional IPA has defined. In addition, the proposed approach reveals the priority of attributes in Quadrant IV. These higher priority attributes in Quadrant IV can be defined as Critical Attributes which are most important but with less effectiveness. As known, the priority of problems in Quadrant IV shall be carried out urgently than the other three quadrants. Making one step further by revealing of these Critical Attributes in Quadrant IV, it helps the decision-maker (DM) allocates resources even more effectively.

According to the comparison of the proposed method with the IFS-similarity-IPA approach proposed by Chu and Guo (2015), the proposed approach is more flexible to use in that it takes the attitude of the DM into account. The proposed approach can obtain a more correct result and provide more useful information to the DM as well as still maintain the uncertainty of attribute information which was ignored from previous fuzzy methods. This study introduces a new approach for IPA which revises some drawbacks previous approaches have had to provide a more appropriate result. Additional information is also provided to the DMs, with the expectation of helping them make more suitable strategies.
論文目次 摘要…… I
Abstract…. II
致謝…… 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 3
第四節 研究流程 3
第五節 論文架構 5
第二章 文獻探討 6
第一節 直覺式模糊集 6
第二節 重要性-績效分析 13
第三節 決策實驗室分析法 18
第四節 小結 20
第三章 研究方法 21
第一節 研究構想 21
第二節 方法建立 24
第三節 小結 37
第四章 範例演算 39
第一節 範例說明 39
第二節 範例演算 42
第三節 結果比較與分析 50
第五章 結論與建議 65
第一節 研究成果 65
第二節 未來研究方向 66
參考文獻 67
附錄…… 71
參考文獻 Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Atanassov, K. T. & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343-349.
Chen, S. M. (1997). Similarity measures between vague sets and between elements. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 27(1), 153-158.
Chen, L.-H. & Tu, C.-C. (2014). Dual Bipolar Measure of Atanassov's Intuitionistic Fuzzy Sets. IEEE Transactions on Fuzzy Systems, 22(4), 966-982.
Chen, Z. & Yang, W. (2011). A new multiple attribute group decision making method in intuitionistic fuzzy setting. Applied Mathematical Modelling, 35(9), 4424-4437.
Chu, C.-H. & Guo,Y.-J. (2015). Developing similarity based IPA under intuitionistic fuzzy sets to assess leisure-bikeways. Tourism Management, 47, 47-57.
Deng, W.-J. (2008). Fuzzy importance-performance analysis for determining critical service attributes. International Journal of Service Industry Management, 19(2), 252-270.
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.
Dengfeng, L. & Chuntian, C. (2002). New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions. Pattern Recognition Letters, 23(1-3), 221–225.
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.
Govindan, K., Khodaverdi, R. & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42(20), 7207-7220.
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.
Hong, D. H., Kim, C. (1999). A note on similarity measures between vague sets and between elements. Information Sciences, 115(1), 83-96.
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.
Hung, K.-C. (2015). A novel decision approach: Intuitionistic fuzzy importance performance analysis. Proc. MakeLearn and TIIM Joint International Conference, Bari, Italy, May 2015, 1521-1529.
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.
Li ,Y., Chi, A. & Yan, D. (2002). Similarity measures between vague sets and vague entropy. J. Computer Sci. 29(12), 129-132.
Li, F. & Xu, Z. (2001). Similarity measures between vague sets. Journal of Software, 12(6), 922–927.
Li, Y., Hu, Y., Zhang, X., Deng, Y. & Mahadevan, S. (2014). An evidential DEMATEL method to identify critical success factors in emergency management. Applied Soft Computing, 22, 504-510.
Li, Y., Olson, D. L. & Qin, Z. (2007). Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis. Pattern Recognition Letters, 28(2), 278-285.
Liang, Z. & Shi, P. (2003). Similarity measures on intuitionistic fuzzy sets. Pattern Recognition Letters, 24(15), 2687-2693.
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., 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.
Mitchell, H. B. (2003). On the Dengfeng–Chuntian similarity measure and its application to pattern recognition. Pattern Recognition Letters, 24(16), 3101-3104.
Nikjoo, A. V. & Saeedpoor, M. (2014). An intuitionistic fuzzy DEMATEL methodology for prioritising the components of SWOT matrix in the Iranian insurance industry. Int. J. Operational Research, 20(4), 439-452.
Song, P., Liang, J. & Qian, Y. (2012). A two-grade approach to ranking interval data. Knowledge-Based Systems, 27, 234-244.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Zheng, H.-Z., Chu, D.-H. & Xu, X.-F. (2009). Importance Performance Analysis Based Fuzzy Neural for Determining Critical Service Attributes. Proc. IEEE International Conference on Artificial and Computational Intelligence, AICI'09, Shanghai, China, Nov 2009, 4, 7-10.
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