產品及服務的品質對顧客滿意度有直接的影響，但對於顧客及消費者而言，各項服務或產品特性所造成的感受不盡相同，因此公司企業在發展經營策略前，認知各項服務與產品特性的品質屬性(quality attribute)始終極為重要；以適當的方法判別出服務或產品特性的品質屬性不僅能夠減少企業資源的浪費、更能確保企業發展利基，甚至能夠發掘出造成差異化、吸引顧客的重要項目。在判斷產品及服務的品質屬性的方法中，Kano模式普遍受到大家的歡迎，且已被廣泛的應用在各種領域及產業上，但如何能夠快速且有效的應用Kano模式找出正確的品質屬性也引發廣泛的討論。

本研究應用資料挖礦(data mining)中的群集分析之概念並結合信號雜音比發展出一套簡單快速且具相當可靠度、用於判別Kano模式中的品質屬性的方法。利用群集分析中相似度的概念配合適當的門檻劃分做出分群，接著計算信號雜音比判斷，進一步做出確切的分類；利用本研究所建構之方法於實際案例做驗證，並比較其他針對Kano模式的品質屬性分類方法，結果顯示本研究所建構之方法可靠度優於其他方法。

Service and product quality have the most significant and direct impact on customer satisfaction. However, customers have different impressions on various service or product quality attributes. Therefore, it is essential for enterprises to fully understand the quality attributes of their service or products. This study implemented Kano’s model to evaluate the quality attributes. The concept of Kano’s two-dimensional model evaluates quality attributes with the asymmetric and nonlinear relationship. In addition, classifying quality attributes in the Kano model with typical satisfaction data is another issue that people keen to know. The main objective of this study is to identify the quality attributes in the Kano model with the relationship between the attribute performance and customer satisfaction.
This study applied the clustering analysis and signal-to-noise ratio to determine the quality attribute of service or product characteristics in the Kano model. First, the related data were collected and the similarities of attributes were calculated. Second, the thresholds to group the attributes were defined. Finally, the signal-to-noise ratio of each attribute was computed and the quality attributes were identified. The proposed approach was validated using data collected from a food and beverage industry, showing that the proposed approach performs better than the regression methods and the other methods.

目錄
【摘要】 I
【Abstract】 II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章 文獻探討 4
2.1 Kano二維品質模式 4
2.2 Kano品質屬性分類法 8
2.2.1 Kano問卷法 8
2.2.2 懲罰報酬比對分析法 12
2.2.3 其他Kano模式品質分類方法 15
2.3 群集分析 15
2.3.1 相似度計算 16
第三章 研究方法 21
3.1 建構問題並蒐集資料 21
3.2 相似度計算 22
3.3 建立分群劃分門檻 24
3.4 計算信號雜音比 25
3.5 判別品質屬性 27
第四章 個案研究 28
4.1 案例背景 28
4.2 實施過程 28
4.2.1 建構問題並蒐集資料 28
4.2.2 相似度計算 31
4.2.3 建立分群劃分門檻 33
4.2.4 計算信號雜音比 33
4.2.5 判別品質屬性 34
4.3 結果比較與分析 36
4.3.1 懲罰報酬比對分析法 36
4.3.2 其他方法 38
第五章 結論與建議 40
5.1 結論 40
5.2 未來研究方向 42
參考文獻 43
附錄一 問卷問題 45
附錄二 PRCA資料數據(Chen, 2012) 46
附錄三 Chen(2014)之資料數據 49
附錄四 混淆矩陣(confusion matrix) 50

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