• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.59-72
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• 2008

Computing systems in the modern era are expanding rapidly to include mobile-based businesses that make us of the various convergence distributed business process. This has lead to growing interest in the field of mobile embedded software development methodology, which has in turn lead to the proliferation of the embedded mobility. The use of CBD (Component Based Development) provides reusability, maintainability and portability, all of which are very important and focus issues to the business process. It also comes with the inherent productivity, quality and reliability of CBD. To make efficient use of CBD, though, clarified interface definitions for component integration are necessary. These definitions should be made up of collaborative hierarchical and horizontal architecture layers. Successful definitions should apply an effective framework made up of the architecture and process. In this paper, we describe an interface specification for small grained mobile embedded components(MEC) for the mobile embedded domain to meet maximum user requirements. We build and deploy the reconfigurable design patterns and components (in business domain categories) to make a component hierarchy and business logics for mobile embedded software. Proposed components specification plays a major role in development of the software for handling inconsistency in existing specification. It also includes plenty of specification information, using semantics and modeling based mechanisms to support business processes. We propose a development model of mobile embedded software using CBD for very complex and dynamic mobile business. We can apply it in a plug and play manner to develop the software. We verify that our framework supports very good productivity, quality and maintainability to meet the user's requirements in mobile business.

• Journal of Distribution Science
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• v.12 no.10
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• pp.85-97
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• 2014

Purpose - This study aims to clarify through which process asset specificity and supplier development project (SDP) affect performance. Cooperation, partnership, the level of information exchange, and the importance of information sharing are considered significant variables as mediators related to the process. Finally, the performance formation model of the supply chain through asset specificity and supplier development project would be suggested as being a result of this study. Research design, data, and methodology - Data collection was as follows: questionnaires were distributed to 250 companies that have business ties with H Company. The empirical study to test our hypothesis was based on statistical analysis (using SPSS 19.0 and AMOS 19.0). The hypothesis of this paper is that the asset specificity and supplier development project variables have positive effects on the following variables: mediators such as cooperation and partnership (reliability and dependence); and the cooperation and partnership variables have a positive effect on the following variables: level of information sharing, the importance of information sharing, and level of information sharing; the importance of information sharing has a positive effect on supply chain performance. We tested our hypothesized model utilizing path analysis with latent variables. Results - First, it was found that asset specificity has significant positive effects on cooperation (H1), reliability (H2), and dependability (H3). Second, it was proved that the level of comprehension on the purpose of SDP has positive effects significantly on cooperation (H4), reliability (H5), and dependability (H6). Third, the hypotheses related to cooperation were all significantly accepted. The relationships of cooperation with the level (H7) and importance (H8) of information sharing were significant. Fourth, the hypotheses related to reliability were all significantly accepted. The relationships of reliability with the level (H9) and importance (H10) of information sharing were significant. In terms of dependability, however, the hypotheses were partially accepted. The effect of dependability was significant on the importance of information sharing (H12), but insignificant on the level of information sharing (H11). Finally, the causal relationships from the level of information sharing to SCM performance (H13) and from the importance of information sharing to SCM performance (H14) were both significantly accepted. Conclusion - First, with rapid changes in the business environment, enterprises should acquire the right information to properly implement SCM. For successful SCM, firms should understand the supplier development project. Second, asset specificity and the level of comprehension on SDP have significant effects both on cooperation and partnership (reliability and dependability). Third, mediators such as cooperation, reliability, and dependability significantly affect the level and importance of information sharing. Fourth, the level and importance of information sharing have significant impacts on SCM performance. This paper makes a meaningful contribution to further the understanding of how SDP affects SCM performance. Finally, successful SCM performance is achieved by information sharing through a collaborative environment and partnership (confidence & dependence) rather than by investing only in setting up an information system.