上海地區行動電話使用者之服務品質認知與滿意度分析

The Analysis of the Service Quality Perceptions and Satisfactions of Mobile Phone User in Shanghai

10.30066/JCS.200703.0001

楊朝堂(Tsau-Tang Yang)；唐資文(Tzy-Wen Tang)；王美慧(Mei-Huey Wang)

服務品質 ； 滿意度 ； 市場區隔 ； 人口統計變數 ； 類神經網路 ； Service Quality ； Satisfaction ； Market Segment ； Demographic Variables ； Neural Network

顧客滿意學刊

3卷1期（2007 / 03 / 01）

1 - 24

繁體中文

自中國大陸加入WTO後，大陸行動電話服務市場之龐大商機已成為各方矚目的焦點。台灣行動電話服務在面臨台灣市場已趨飽和下，台灣廠商目前對於前往中國大陸拓展新市場乃躍躍欲試。行動電話服務產業之重點在於無形服務的提供，因此服務品質成為行動電話廠商鞏固市場或搶佔市場的重要關鍵因素。有鑑於此，本研究欲探討中國大陸行動電話使用者在各項服務之服務品質認知狀況，包括重視度與滿意度，並按滿意度將顧客進行分類，希望瞭解各個顧客區隔群之服務認知以及人口統計變數之差異。本研究採用問卷調查法，探討對象為大陸上海地區的行動電話使用者，有效樣本數為264份。研究結果發現，大陸行動電話使用者在各項服務品質之重視度與滿意度間呈現差距，顯示現有業者應儘快修正服務方向。此外，不同人口統計變數在服務品質重視度與滿意度呈現顯著差異，而進行市場區隔後也發現不同區隔群在服務品質認知與人口統計變數也呈現顯著差異，其中，應用區別分析之辨識率達97%，應用類神經網路模式進行市場區隔的辨識率則高達100%。

Since Mainland China joined the World Trade Organization (WTO) in 2001, the tremendous market of mobile phone service has been the common concern of various communication companies. As the mobile phone market, on the contrary, is being saturated, Taiwanese communication companies appear highly interest in expanding the market in Mainland China. Since that Service quality is the key point for mobile phone service industry to maintain or even extend their market, this study investigate the perception toward service quality such as the degree of perceived importance and perceived satisfaction of mobile phone users in Mainland China. We segment them by the degree of satisfaction, and then elucidated the difference of service perception and demographic variables across segment. This study conducted the questionnaire survey from the users in Shanghai and regained 264 valid questionnaires. The result shows that there is a gap between importance perception and satisfaction perception, which indicates that existing companies should amend their service content. Besides, there is significant difference in service quality perception across demographic variables, and various segments exhibit variety in service quality perception and demographic variables. Furthermore, the recognition rate of segmentation is 97% with discriminating analysis. The ratio is even higher, 100%, by means of neural network model.

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