The e-Readiness Measurement for Travel Agents

碩士 === 元智大學 === 資訊管理研究所 === 91 === After the maturity of e-commerce technology (B2B,B2C) and more corporations begin process reengineering, the information flow, cooperation and competition models between companies lead into new confines. The evaluation of Electronic Business (e-business) urges the...

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Bibliographic Details
Main Authors: Yuan-Lang Goan, 龔原瑯
Other Authors: Yao-Chin Lin
Format: Others
Language:zh-TW
Published: 2003
Online Access:http://ndltd.ncl.edu.tw/handle/64601547722859950924
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Summary:碩士 === 元智大學 === 資訊管理研究所 === 91 === After the maturity of e-commerce technology (B2B,B2C) and more corporations begin process reengineering, the information flow, cooperation and competition models between companies lead into new confines. The evaluation of Electronic Business (e-business) urges the industries to redefine the business models, making members in supply chain to cooperate more closely, and improve the competence of whole industry. Travel agents in Taiwan face a great compact of electronic commerce because of more transparent and faster information transformation in new channels, new competitors and depression influence. E-business which can improve industry competence then become a hot topic. But the travel service have some special properties cause the implement method is not as sure as other industries like manufacturing industry and semiconductor industry. This study chose “travel agents in Taiwan” as our object of research. We tried to establish a “e-readiness measurement model” to research key competencies that should be measured before implementing e-business and how travel agents perform on these key competencies. Through the method of factor analysis, six factors were extracted. The factors are “level of e-business and information sharing”, ”adoption of new e-commerce models”, ”supply of production and information”, ”business environment”, ”level of customize” and “information infrastructure”. After getting these factors, we processed cluster analysis. Using six factors as input variables generated five clusters. There were “conservative”, “negative”, “wait and see”, “trier” and “highly developer”. Finally we generalize the required level of every factor through interviews with experts and leader quadrant, making compare and suggestions for every cluster of travel agents.