CLASSIFYING QUALITY ATTRIBUTES IN THE KANO MODEL USING CLUSTERING ANALYSIS: AN EMPIRICAL STUDY

利用群集分析探索品質屬性分類問題

陳麗妃(Li-Fei Chen) ； 蘇朝墩(Chao-Ton Su) ； 黃偉家(Wei-Chia Huang) ； Dimitri Cayard

《品質學報》 24卷5期 (2017/10) Pp. 335-347

https://doi.org/10.6220/joq.2017.24(5).03

摘要

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. The concept of Kano's two-dimensional model evaluates quality attributes with the asymmetric and nonlinear relationship. 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. The result shows that the proposed approach performs better than the regression methods and the other methods.

關鍵字

service quality ； customer satisfaction ； Kano model ； clustering analysis ； signal-to-noise ratio

並列摘要

產品及服務的品質對顧客滿意度有直接的影響，對於顧客而言，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，有效辨識各項服務與產品特性的品質屬性(quality attribute)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，同時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。然而，傳統的Kano正反項問卷常因冗長且繁瑣的問項，導致有效性與可行性受到質疑。因此，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(clustering analysis)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。本研究並利用依實際案例，驗證所建構方法之有效性。此外，並進一步比較其他針對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分類績效優於其他方法。

並列關鍵字

服務品質 ； 顧客滿意度 ； Kano模式 ； 群集分析 ； 信號雜音比

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