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In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

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This paper proposed the hybrid model using rough set theory (RST), neural networks (NN), and data envelopment analysis (DEA) to predict the corporate performance directly. First, to evaluate corporate performance, the DEA was employed. Second, integrated RST with BPN techniques, which is one of the popular used models of NN, named RST+BPN, was used to build the corporate performance-prediction model and the corporate governance variables are used as predictive variables. This hybrid method enabled us to evaluate an individual firm and provided performance information without comparing it with other companies. The experimental result showed that the proposed model outperforms the NN model with nonextracted predictive variables and provides a promising alternative in corporate performance prediction.

Supply chains have received considerable interest from businesses and academic communities in recent years. It is especially an issue of substantial importance in an industry such as semiconductor manufacturing which possesses a uniquely vertical integrated structure and demands costeffective and timely flow of production and information. There are four major segments in the semiconductor industry and its supply chain: design, fabrication (fab), packaging and testing, and support. This article attempts to provide an integrated perspective on the information exchange process and propose an agent-based information exchange framework to facilitate free flow of information across the supply chain. Four types of information exchange, namely production, capacity, logistics, and engineering, are defined and three information-exchange methods adopted in the semiconductor supply chain are examined as well. This paper further locates the bottlenecks in information sharing within the semiconductor supply chain, such as IT barriers, data format, integration, timeliness, and accuracy, and moves on to discuss the implications of the proposed framework.

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