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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75021

    Title: Exploring the preference of customers between financial companies and agents based on TCA
    Authors: Fang, Sheng-Kai;Shyng, Jhieh-Yu;Lee, Wen-Shiung;Tzeng, Gwo-Hshiung
    Contributors: 淡江大學企業管理學系
    Keywords: Transaction cost analysis;TCA;Rough set theory;RST;Formal concept analysis;FCA;Data mining;Flow graph;Customer relationship management;CRM
    Date: 2012-03
    Issue Date: 2012-03-02 21:16:08 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: Based on transaction cost analysis (TCA), this research explores the customers’ loyalty to either the financial companies or the company financial agents with whom they have established relationship. In the past, consumers were divided into those who rely on agents and those who do not. In this study, we use two processes (pre-process and post-process) to select suitable rules, and to explore into the relationship among attributes. In the pre-process, we utilized factor analysis (FA) to choose the variable and rough set theory (RST) that found decision table to construct the decision rules, and approach to data mining and knowledge discovery based on information flow distribution in a flow graph. The post-process applies the formal concept analysis (FCA) from these suitable rules to explore the attribute relationship and the most important factors affecting the preference of customers for deciding whether to choose companies or agents. The degree of the customers’ dependence on agents was affected by the TCA, customer satisfaction and loyalty. The principal findings were that the different degrees of dependence of customers have various characteristics. The RST and FCA were two complementary mathematical tools for data analysis. Following an empirical analysis, we use two hit testes that incorporate 30 and 36 validated sample object into the decision rule. The hitting rate of two testes, were reached 90%. The results of the empirical study indicate that the generated decision rules can cover most new objects. Consequently, we believe that the result can be fully applied in financial research.
    Relation: Knowledge-Based Systems 27, pp.137–151
    DOI: 10.1016/j.knosys.2011.09.003
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

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