• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.197-220
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• 2011

The smartphone is simply beyond the means of communication equipment, to line up the turning point of mobile convergence, is recognized as a service tool of new concept the camera, game, multimedia function, digital multimedia broadcasting, mobile internet etc, that use of smartphone is working toward developed a variety and new business models. The study is empirically studied casualties that self-determination influences flow and satisfaction which is intrinsic motivation of smartphone. There are many studies on flow, is intrinsic motivation, influencing satisfaction and Loyalty, but there are little studies which variables influences flow. this study is explore causality of autonomy, competence, relatedness which are major variables of self-determination theory that studied factors effecting intrinsic motivation influencing flow and satisfaction. This study developed a research model to explain the use of smartphone, and collected 670 survey responses from the office workers of seoul S company who had experiences with such smartphone. To prove the validity of the proposed research model, SEM analysis is applied with valid 670 questionnaires. The results, firstly, autonomy positively influences flow. secondly, competence significantly influences flow. thirdly, relatedness significantly influenced flow. also, upper above results shows that flow influences satisfaction.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.