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題名:應用類神經網路與連續分段迴歸解析二維品質模式
作者:蕭銀城
作者(外文):HSIAO, YIN-CHENG
校院名稱:中華大學
系所名稱:科技管理博士學位學程
指導教授:李友錚
學位類別:博士
出版日期:2014
主題關鍵詞:Kano模型類神經網路分段迴歸二維品質模式Kano's modelNeural NetworkPiecewise Regressiontwo-dimensional quality model
原始連結:連回原系統網址new window
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Kano(1984)所提出的二維品質模型的確能貼近真實品質性能與客戶滿意度之間的關係,已成功地運用在各個領域,並且在整合其它品質管理方法上都有不錯的成果。陸陸續續有許多學者提出不同的方法加以改善傳統的Kano模型,但卻只有極少數的研究是將重點放在發展Kano模型或提出一些新方法來取代Kano原有的評估表,這些方法多數是從操作面的角度,並不能充分地反映品質屬性和客戶滿意度之間的非線性且不對稱關係,而且一維品質模型可能低估魅力品質屬性,而高估了必須品質以及無差異品質屬性。在本研究中,我們提出了修正Kano二維品質屬性分類評估表的方法。以台北市殯葬業者為調查對象,並對其客戶進行滿意度調查。調查結果分別以類神經網路與連續分段迴歸之修正模式加以驗證。研究發現,根據本研究提出之修正模式所得到的結果,能將各品質屬性與滿意度之間關係做一釐清,提出從顧客滿意度增量之角度來選擇改善之優先順序,透過案例之驗證更能反映現實的狀況與需求,此修正二維品質模型具備適用性及有效性。
Kano’s model of two-dimension quality can display the correlation between the actual quality performance and customer satisfaction, and has been widely applied in various fields, and also its efficient integration with other quality management methods. Since its introduction, many different methods have been proposed to improve the original model. However, too little attention is paid to the development of the Kano’s model or the design of new methods to replace the original evaluation table. Most of the existing methods are based on the operational validity and cannot fully reflect the non-linear relationship between the quality attributes and customer satisfaction. In addition, one-dimension quality model might underestimate the attractive quality attributes but overestimate the must-be quality and indifferent quality attributes. In this study, we propose a modified evaluation sheet of Kano’s model. Funeral Service Providers in Taipei City are investigated and its customer satisfaction. And then applying Artificial Neural Network (ANN) and Piecewise Regression to be analysis and verified. The result is found that, we propose an enhanced quality model to clarify the correlation between quality attributes and customer satisfaction. Moreover, the priorities of quality improvement should be decided for optimizing customer satisfaction. Through our case study, the proposed enhanced quality model is proved to be feasible and valid.
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