Exploring the preference of customers between financial companies and agents based on TCA

  • Authors:

  • Sheng-Kai Fang;Jhieh-Yu Shyng;Wen-Shiung Lee;Gwo-Hshiung Tzeng

  • Affiliations:

  • Department of Banking and Finance, Kainan University, No. 1, Kainan Road, Luchu Shiang, Taoyuan 33857, Taiwan;Department of Information Management, Lan-Yang Institute of Technology, No. 79, Fu-Shin Road, To-Chen, I-Lan 621, Taiwan;Department of Business Administration, Tamkang University, No. 151, Yingzhuan Road, Danshui Dist, New Taipei City, Taiwan;Department of Business and Entrepreneurial Management, Institute of Project Management, Kainan University, No. 1, Kainan Road, Luchu, Taoyuan 338, Taiwan and Institute of Management of Technology, ...

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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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.