| Summary: | 碩士 === 國立高雄第一科技大學 === 資訊管理所 === 94 === ABSTRACT
Due to the development of mobile technologies has become mature, mobile commerce is considered as the next important application of information technologies in business. Direct selling is the technique of approaching a customer on a person-to-person basis, either with a group approach or individually, one-to-one, to offer products or services conveniently with a personal services emphasis (Bernstein, 1984). Therefore, the salesperson in direct selling industry requires the capabilities of mobilization and customer-orientation. Mobile technologies, such as wireless communication and PDA, can help salesperson conduct business anywhere and anytime, and record the attributes of customers conveniently. That is, using mobile technologies can help salespersons do their business more effectively and efficiently.
This thesis proposed a framework on combining mobile devices and decision support technologies to recommend products for direct selling agents. The framework uses mobile devices and data mining techniques to find suitable customers and products for direct selling agents. The framework is named mobile-based product recommendation decision support system (MPRDSS). In MPRDSS, mobile devices are used to collect customer attributes and transaction, and wireless network is used to transmit collected data into central database immediately. MPRDSS also is furnished with classification and association mechanism to recommend products and customers for salesperson via mobile devices.
In this thesis, empirical transaction records collected from direct selling industry are used to simulate the rules of customer purchasing behaviors and a prototype system is build based on the MPRDSS framework to verify the proposed method. The research results indicate that MPRDSS can really benefit the direct selling industry.
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