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Data Mining in Franchising

Data Mining in Franchising
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Author(s): Ye-Sho Chen (Louisiana State University, USA), Grace Hua (Louisiana State University, USA) and Bob Justis (Louisiana State University, USA)
Copyright: 2009
Pages: 9
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch148

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Abstract

Franchising has been a popular approach given the high rate of business failures (Justis & Judd, 2002; Thomas & Seid, 2000). Its popularity continues to increase, as we witness an emergence of a new business model, Netchising, which is the combination power of the Internet for global demand-andsupply processes and the international franchising arrangement for local responsiveness (Chen, Justis, & Yang, 2004). For example, Entrepreneur magazine—well known for its Franchise 500 listing—in 2001 included Tech Businesses into its Franchise Zone that contains Internet Businesses, Tech Training, and Miscellaneous Tech Businesses. At the time of this writing, 40 companies are on its list. Netchising is an effective global e-business growth strategy (Chen, Chen, & Wu, 2006), since it can “offer potentially huge benefits over traditional exporting or foreign direct investment approaches to globalization” and is “a powerful concept with potentially broad applications” (Davenport, 2000, p. 52). In his best seller, Business @ the Speed of Thought, Bill Gates (1999) wrote, “Information technology and business are becoming inextricably interwoven. I don’t think anybody can talk meaningfully about one without talking about the other” (p. 6). Gates’ point is quite true when one talks about data mining in franchise organizations. Despite its popularity as a global e-business growth strategy, there is no guarantee that the franchising business model will render continuous success in the hypercompetitive environment. This can be evidenced from the constant up-and-down ranking of the Franchise 500. Thus, to see how data mining can be “meaningfully” used in franchise organizations, one needs to know how franchising really works. In the next section, we show that (1) building up a good “family” relationship between the franchisor and the franchisee is the real essence of franchising, and (2) proven working knowledge is the foundation of the “family” relationship. We then discuss in the following three sections the process of how to make data mining “meaningful” in franchising. Finally, future trends of data mining in Netchising are briefly described.

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