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    Title: 都會區小客車駕駛人對車載資通訊服務之消費認知與市場區隔研究
    Other Titles: A study on consumption cognition and market segmentation of urban car drivers toward telematics services
    Authors: 邱劉中;Chiu, Liu-chung
    Contributors: 淡江大學運輸管理學系碩士班
    陶治中;Tao, Chi-chung
    Keywords: 車載資通訊服務;支持向量機;市場區隔;Telematics Services;Support Vector Machine;Market Segmentation
    Date: 2010
    Issue Date: 2010-09-23 16:40:04 (UTC+8)
    Abstract: 本研究係以Telematics服務供給者立場,了解具備何種特性之都會區小客車駕駛人會需要何種服務內容?並願意支付多少費用以獲取服務?有鑑於此,探討Telematics服務之相關背景與研究,以及涉入程度、生活型態、產品屬性等理論,依據理論內容建立研究架構,以確定都會區小客車駕駛人對於Telematics服務需求程度以及價值觀,進而探究小客車駕駛人願意支付多少費用以換取服務內容。本研究將利用產品屬性、涉入程度、生活型態等量表以及付費意願調查,達成上述研究目的。
    彙整Telematics服務內容後,即進行問卷設計與調查,共發出400份問卷,其中318份為有效問卷。實證結果如下:
    1.首先利用因素分析萃取五種服務構面屬性:即時線上服務、緊急救援服務、消費代訂服務、車體診斷服務與行車輔助服務。
    2.利用k-means法將318個觀察值予以分類,分別為創新採用型、即時享用型、經濟實用型與安全應用型。
    3.使用支持向量機方法,判別行車反應與生活型態兩種因子,分析何種顯著因子在分類族群上,可獲得較高之分類準確率。
    4.行車反應認知狀況相較於生活型態因子在小客車駕駛人使用Telematics服務內容有顯著影響。當兩種變數同時納入,分類準確率可提高81%以上。
    5.支持向量機分析結果顯示:分類準確率並不會因為訓練樣本與測試樣本之比例調配不同而有顯著差異。
    6.綜整三次支持向量機分析結果,創新採用型分類準確率最高,且樣本數最少,可知支持向量機確實支持小樣本之分類特性。
    7.依據每一族群消費特性、消費行為,研擬適合各族群之服務策略與服務費用。
    From a Telematics Service Prodiver’s (TSP) viewpoint, this study aims at identifying urban car drivers’ need towards Telematics services including their consumption of cognition and willingness to pay.
    First, this study reviews available literature concerning Telematics services, theories of involvement, life style and product attributes. Then, a research framework is proposed to put forward hypotheses among urban car drivers, value judgment of Telematics service contents and willingness to pay based on above theories. Some key findings from 318 effective samples are suumarized as follows:
    1.Five Telematics service attributes are identified by using factor analysis: on-line services, emergency services, reservation services, vehicle diagnosis services, and driver-aided services.
    2.Four user groups are classified by using K-means clustering: innovation adoption users, on-line users, safety concerned users and cost saving users.
    3.It is found with the help of SVM that both of driving response and life style factor are included into clustering process simultaneously can obtain higher accuracy of discrimination.
    4.There is no significant difference between proportional combinations of training samples and testing samples in accuracy of discrimination by using SVM.
    5.Three runs of SVM experimental results show that the accuracy of discrimination of innovation adoption user group is the highest with the minimal sample size. It supports that SVM is suitable for clustering with small samples.
    6.Corresponding product strategies are proposed for 4 user groups as well as their consumption characteristics.
    Appears in Collections:[運輸管理學系暨研究所] 學位論文

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