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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52072

    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。
    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:[Graduate Institute & Department of Transportation Management] Thesis

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