This research approached the negotiation problem from the seller's perspective in the make-to-order manufacturing industry in order to solve the multi-attribute automated negotiation problem. To this end, more than 'two negotiation attributes are defined and a preferred value of each negotiation alternative is calculated based on the entropy value of each attribute. And then, the best option is selected according to the utility function of each negotiator, a penalty of negotiation delay, and conviction of possibility of negotiation success. The suggested automated negotiation methods in this study can be employed for the development of advanced automated negotiation systems.

Software companies try to upgrade the level of technology and quality of their products by adopting Software Process Improvement (SPI). First of all, the companies must understand affecting factors based on various environmental view points in order to acquire competitive advantage. In this study, a research model gets started on the basis of literature reviews, and identifies SPI factors that affect the maturity levels of organizations. Organizational members and assessment types are chosen as moderating factors. An empirical research has been made for analyzing the causal relationships among affecting factors and CMM levels. The results of this study will suggest several implications to decision makers who handle SPI issues.

In recent, Open markets are booming up rapidly. Open markets are one of the online marketplaces which mainly concentrated on spot transactions of commodities, and are differentiated from integrated internet shopping stores with market participants, trading rules and so on. This study investigated on factors affecting satisfaction with and preference on open markets by comparing open markets with integrated internet shopping stores, and aimed to figure out the reasons why open markets are growing up so rapidly and to forecast the future of open markets. To investigate the factors affecting buyers' satisfaction with and preference on internet shopping channel. I extracted several factors through literature reviews. The factors include the pros (cost saving and time saving), the cons (security concerns and privacy concerns), and decision making support suggested by Simon's research as well. Then, I constructed research model and related research hypotheses. To verify research hypotheses, I conducted field survey targeting on online buyers and analyzed research data using structural equation model. According to data analysis result, open markets have competitive advantages over integrated internet shopping stores with respect to cost saving, time saving, and decision making support. However, online buyers are still concerning privacy issues within open markets. In summary, buyers are considering that open markets are cheaper, faster, and more efficient internet shopping channel, compared with integrated internet shopping stores. However, open markets are required to dedicate to lessen buyers' privacy concerns to rebirth as more satisfying and preferable internet shopping channel and to prosper in the future.

Modern companies have heavily invested in developing CRM systems without explicitly knowing how the systems improve customer relationship management. Many researchers have attempted to find the factors that contribute to the success of CRM systems. However, most research have investigated the direct relationships between CRM success factors and system success without explaining how the factors contribute to the system success. It seems to be logical to think that success factors enhance the customer equity value management (CEVM) capability of the firms and then these capabilities are the foundation for the success of systems. In this study, we developed a CRM success model that includes CEVM capability as a mediating variable. The research followed the following steps: (1) CRM success factors that have theoretical relationship with CEVM capability are identified and categorized, (2) the theoretical relationship between CEVM capability and CRM performance is established in the perspective of strategic CRM, and (3) the research model is tested with the survey data collected from Korean firms.

One of the most important reasons for using online game is intrinsic motivation which relates the drive to perform a behavior for perceptions of pleasure and satisfaction from the behavior itself. Therefore, most studies for using online game have been based on theories for intrinsic motivation such as flow. However, such theories have some limitations for explaining social aspects of online games such as Massively Multiplayer Online Role Game (MMORPG), which provides a society for game players by using a virtual space for playing games so called 'persistent world'. Adaptive structuration theory (AST) is one of trying to capture the change of using IT due to social interactions between users and system. In order to explain online game players' behavior considering social interaction, we suggest a research model based on AST. Our model aims to investigate the relationship between appropriation which is determined by faithfulness of appropriation (FOA) and consensus on appropriation (COA) and performance which is represented by the relationship among perceived winning, flow, and intention of reuse. An empirical test of our model for 125 MMORPG users which is analyzed by Partial Least Square method shows very satisfactory and interesting results. While hypotheses suggested in our model are supported, the influence of COA on game performance is shown to be stronger than that of FOA.

This study tries to explain how the relationship-specificity of invested assets affects E-collaboration and consequently on performance of Small and Medium Enterprises (SMEs). We classify the relationship-specificity of invested assets into four types such as business process specificity, physical asset specificity, domain knowledge specificity, and site specificity. We define E-collaboration as composed of Electronic Information Sharing (EIS) and Electronic Cooperation (E-Co). In addition, we articulate firm's performance as operational and strategic one, and investigate the impacts of EIS and E-Co on its performance. The data were collected from 187 SMEs and used for analysis. Based on the survey results, we find the following: (1) EIS is directly influenced by business process specificity and physical asset specificity, (2) E-Co is affected by site specificity and domain knowledge specificity, (3) EIS has a positive and significant impact on E-Co, (4) EIS affects firm's operational performance, (5) E-Co influences on firm's strategic performance. In conclusions, the higher the level of EIS, SMEs seem to get greater operational performance, Respectively, the higher the level of E-Co, they tend to get greater strategic performance.

The objective of this research is to develop a formal framework and methodology to facilitate real-time data integration, thus enabling semantic interoperability among distributed and heterogeneous information systems. The proposed approach is based on the concepts of "ontology" and "semantic mediators." An ontology is developed and used to capture the intension (including structure, integrity rules and meta-properties) of the database schema. We also develop the agent communication protocol for semantic reconciliation, which is based on the theory of speech acts and agent communication language. This protocol is used by a set of semantic mediators, which automatically detect and resolve various semantic conflicts at the data- and schema-levels by referring to the ontology. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous information sources. Prototype tools are being implemented to provide proof of concept for this work.

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

Location-based advertising or application has been one of the drivers of third-generation mobile operators' marketing efforts in the past few years. As a result, many studies on location-based marketing or advertising have been proposed for recent several years. However, these approaches have two common shortcomings. First. most of them just suggested the theoretical architectures, which were too abstract to apply it to the real-world cases. Second, many of these approaches only consider service provider (seller) rather than customers (buyers). Thus, the prior approaches fit to the automated sales or advertising rather than the implementation of CRM. To mitigate these limitations, this study presents a novel advertisement recommendation model for mobile users. We call our model MAR-CF (Mobile Advertisement Recommender using Collaborative Filtering). Our proposed model is based on traditional CF algorithm, but we adopt the multi-dimensional personalization model to conventional CF for enabling location-based advertising for mobile users. Thus, MAR-CF is designed to make recommendation results for mobile users by considering location, time, and needs type. To validate the usefulness of our recommendation model. we collect the real-world data for mobile advertisements, and perform an empirical validation. Experimental results show that MAR-CF generates more accurate prediction results than other comparative models.