National Kaohsiung First University of Science and Technology

About: National Kaohsiung First University of Science and Technology is a education organization based out in Kaohsiung City, Taiwan. It is known for research contribution in the topics: Fuzzy logic & Taguchi methods. The organization has 2981 authors who have published 3762 publications receiving 78093 citations. The organization is also known as: National Institute of Technology at Kaohsiung.

Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories

Abstract: The biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to snare Knowledge with other members. This paper integrates the Social Cognitive Theory and the Social Capital Theory to construct a model for investigating the motivations behind people's knowledge sharing in virtual communities. The study holds that the facets of social capital -- social interaction ties, trust, norm of reciprocity, identification, shared vision and shared language -- will influence individuals' knowledge sharing in virtual communities. We also argue that outcome expectations -- community-related outcome expectations and personal outcome expectations -- can engender knowledge sharing in virtual communities. Data collected from 310 members of one professional virtual community provide support for the proposed model. The results help in identifying the motivation underlying individuals' knowledge sharing behavior in professional virtual communities. The implications for theory and practice and future research directions are discussed.

What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction

Abstract: E-learning is emerging as the new paradigm of modern education. Worldwide, the e-learning market has a growth rate of 35.6%, but failures exist. Little is known about why many users stop their online learning after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with e-Learning. This study developed an integrated model with six dimensions: learners, instructors, courses, technology, design, and environment. A survey was conducted to investigate the critical factors affecting learners' satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners' perceived satisfaction. The results show institutions how to improve learner satisfaction and further strengthen their e-Learning implementation.

Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations

Abstract: There has been a growing interest in examining the factors that support or hinder one's knowledge sharing behavior in the virtual communities. However, still very few studies examined them from both personal and environmental perspectives. In order to explore the knowledge sharing behaviors within the virtual communities of professional societies, this study proposed a social cognitive theory (SCT)-based model that includes knowledge sharing self-efficacy and outcome expectations for personal influences, and multi-dimensional trusts for environmental influences. The proposed research model was then evaluated with structural equation modeling, and confirmatory factor analysis was also applied to test if the empirical data conform to the proposed model.

A GA-based feature selection and parameters optimizationfor support vector machines

Abstract: Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the classification accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.