Earticle

The ubiquitous computing system is expected to be widely utilized in digital home, logistics control, environment/disaster management, medical/health-care services and other applications. The ubiquitous sensor network (USN) is a key infra-structure of this system. Nodes in the USN are exposed to adverse environments and required to perform their missions with very limited power supply only. Also the sensor network is composed of much more nodes. In case some node consumes up its power capacity under a certain required level, the network topology should change and re-routing/re-transmission of data is necessitated. Resultantly communication protocols studied for conventional wireless networks or ad-hoc networks are not suitable for the sensor network. Schemes should be devised to control the efficient usage of node power in the sensor network. This paper proposes a routing algorithm to enhance the efficiency of power consumption for USN node and analyzes its performance by simulation.

In this paper we suggest a context-driven configuration model of virtual businesses to form a business network model consisting of role-based, interaction-centered business partners. The model makes use of the subcontext concept which explicitly represents actors and interactions in virtual business (VB) context. We separate actors who have capacities on tasks in a specific kind of role and actor subcontext which models requirements in specific interaction subcontext. Three kinds of actors are defined in virtual service chains, service user, service provider, and external service supporter. Interaction subcontext models a service exchange process between two actor subcontexts with consideration of context dependencies like task and quality dependencies. Each subcontext may be modeled in the form of a situation network which consists of a finite set of situation nodes and transitions. A specific situation is given in a corresponding context network of actors and interactions. It is illustrated with a simple example.

In an effort to apply IT to practices of learning, universities are trying to implement e-Learning systems and expanding the extent of their usage. Nowadays, e-Learning systems are not only used for conducting web-based distance learning, but also used for supporting traditional classes education by encouraging communication and information sharing between instructors and learners or among the learners. There is relatively lack of studies on the exploitation of e-Learning systems in the traditional classes, in comparison with a distance education. Specifically, how e-Learning systems could support the traditional class and/or which benefits e-Learning systems could offer in the classes are among the important issues. In this study, we suggest that e-Learning systems would be the rich media to communicate and exchange information among people who participate in a class. We derive key variables like media richness and media experience from Media Richness Theory and from Channel Expansion Theory. Moreover, Media usefulness and Satisfaction of a learner with e-Learning system is drawn from the literature on IS success. We examine the effects of perceived media richness, media usefulness, and media experience on leaner"s satisfaction with e-Learning systems. In addition, we also investigate learner"s media usefulness perception which is positively related to media richness and media experience. Finally, learner"s experience with e-Learning systems affects perceived media richness. Based on the results of an empirical test, we first suggest that perceived media richness with e-Learning systems contributes to increase media usefulness and satisfaction of a learner. Second, media experience is an important predictor of media richness and media usefulness perception. Consequently, the result can support Channel Expansion Theory. Finally, media usefulness perception affects learner"s satisfaction with e-Learning systems.

There found limitations of mobile advertising researches in methodology, theory building, and research implication. Existing researches were basic and some exploratory studies on mobile advertising and its effects. Especially, there was not a systematic typology research on mobile advertising. This paper tried to develop a systematic and empirical typology of mobile advertising and show proper advertising cases. We classified mobile advertising using mobile channels and methods (Text, Icon, Media, and Contents). Also we used advertising effects like information offering, leading transaction and response, and channel connection in classification of mobile advertising. Finally we suggested mobile advertising cases based on this typology and analysed the performance of moile advertising.

The objectives of this study are to investigate factors influencing trust on internet shopping mall and to examine whether these factors on trust have differences between Korean and Chinese users. Based on the literature reviews, this study employs six factors in two groups of influencing factors, web-site characteristics and individual characteristics as key determinants of trust in internet shopping mall. Analysis of 470 responses(Korean: 320 users, Chinese: 150 users) of survey questionnaire indicates the following: In terms of the relationship between web-site characteristics factors and the trust, the results showed significant differences between Korean and Chinese samples. For the Korean samples, transaction security and interactivity were the only significant influencing factors. For the Chinese samples, perceived reputation and perceived size were the only significant factors. In terms of individual characteristics factors, the influencing factors were found identical for the samples. Both propensity to trust and familarity were significant factors in both Korean and Chinese samples. Based on the findings, managerial implications are discussed in building user trust in internet shopping mall in Korean and Chinese market.

The present paper investigates the technological and conceptional characteristics of peering and transit in internet interconnection. Especially, the market with two ISP competing for customers is modeled and the outcome with peering is compared with transit. This paper also analyze the congestion effects on retail price and network capacity. When there is congestion effects occurring from network sharing, the retail price is increased and the fixed fee is decreased with the degree of congestion. Finally, the optimal access charge assuring the socially optimal retail price and network capacity is derived.

Using an exchange standard, we can design an open architecture for the interchange of decision models and data. XML (eXtensible Markup Language) provides a general framework for creating such a standard. Although XML-based model representation languages such as OOSML were proposed, they are partly limited in expression capability, flexibility, generality, etc. This paper proposes a new method for expressing and sharing decision models and data based on XBRL (eXtensible Business Reporting Language), which is a XML language specialized in business reporting. We have developed a XBRL taxonomy for decision models with the concepts and relationships of a representative modeling framework, SM (Structured Modeling). The method allows for expressing data as well as decision models in a consistent and flexible manner. Diverse dependencies between components of SM models can also be affluently expressed.

The purpose of this study is to examine the effect of relationship orientation factors on customer satisfaction and loyalty in Internet shopping malls. Based on previous exploratory work and a review of the literature of relationship marketing, six key factors of relationship orientation construct are identified : trust, bonding, communication, shared value, empathy and reciprocity. And a conceptual model is developed and seven research hypotheses are empirically examined using structural equation modelling. The results show that bonding, shared value and reciprocity has statistically significant effect on the trust of online customers and trust has a positive influence on customer satisfaction and loyalty in Internet shopping malls. Theoretical, managerial and research implications are discussed.

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first, we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

Most private and public organizations use, for their works, information systems that change their organizational structures. Nowadays, many organizations have team-based structures where typically a cross functional team is in charge of a business process. The effective communication among the members of a team is an important factor of team performance in that communication facilitates trust building, knowledge exchange, change acceptance, and safety improvement. This paper reviews methods for analyzing communication among team members from three aspects such as the content, the amount, and the structure of communication.