Intelligent Vehicle Leasing Recommender Mechanism Based on Requirements Engineering

• Main Authors: Wen-Hsien Chien, 簡文顯
• Other Authors: Chen–Shu Wang
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/ve77s9

碩士 === 國立臺北科技大學 === 管理學院資訊與財金管理EMBA專班 === 103 === The value creation of enterprise mainly comes from customers. The customer value mainly comes from the customer interaction, the service innovation of business-to-customer, and business investment in new technology. Therefore, enterprise must increase its competitive advantages by identifying market segments and core values as well as being innovative on customer value to survive in the competitive environment. In this study, the research scope is on the internet rental service of the short-term car rental in Taiwan. Gathered data shown that there are many car rental industry have using internet to service customers, however most of them only provide their customers with standard car selection for rental condition setting. It makes no obviously difference and no competitive at all. To segment the market and increase innovate value and fulfill the customer’s need, the study builds an Intelligent Vehicle Leasing Recommender Mechanism (IVLRM) to improve the car rental service on internet, and help enterprises enhance core competitive edge, and then reached the business continuity. According to preiovus literature analysis of customer value, it shows a consideration degree of influence on service innovation, interaction and situational considerations demand. Also, to fullfill the customer demand for self-affirmation is an important part of customer value. Therefore, the study has acted on the premise of service innovation, interaction and situational considerations demand, and has begun to consider how to fulfill the customer’s needs and to improve business earnings by using Requirement Engineering, Data Mining, Knowledge Discovery in Database, and Recommender Mechanism to build an IVLRM prototype system. Finally, to verify the IVLRM on this study, an experiment is implemented. According to the experiment results shows, from the customer perspective, 2/3 of customers were successfully recommended on the first trial regardless old or new members. And, nearly 96% of customers successfully accepted the recommendation results of IVLRM on second rounds. The average satisfaction is 97.3%. From a business perspective, the recommended target is based on the highest level of return and therefore the business profit can be increasing accordingly. The experiment results in the first two-thirds of the recommended are succeeded and it did help on enterprise’s earnings. The experimental results show that the proposed IVLRM prototype in this study can indeed increase customer satisfaction, innovative value and the competitiveness of enterprises, improve corporate earnings, achieve win-win customer and business benefits.