Virtual communities are a significant source of information for consumers and businesses. This research examines how users value virtual communities and how virtual communities differ in their value propositions. In particular, this research examines the nature of trade-offs between information quantity and quality, and explores the sources of positive and negative externalities in virtual communities. The analyses are based on more than 500,000 postings collected from three large virtual investing-related communities (VICs) for 14 different stocks over a period of four years. The findings suggest that the VICs engage in differentiated competition as they face trade-offs between information quantity and quality. This differentiation among VICs, in turn, attracts users with different characteristics. We find both positive and negative externalities at work in virtual communities. We propose and validate that the key factor that determines the direction of network externalities is posting quality. The contributions of the study include the extension of our understanding of the virtual community evaluation by users, the exposition of competition between virtual communities, the role of network externalities in virtual communities, and the development of an algorithmic methodology to evaluate the quality (noise or signal) of textual data. The insights from the study provide useful guidance for design and management of VICs.

Millions of people participate in online social media to exchange and share information. Presumably, such information exchange could improve decision making and provide instrumental benefits to the participants. However, to benefit from the information access provided by online social media, the participant will have to overcome the allure of homophily—which refers to the propensity to seek interactions with others of similar status (e.g., religion, education, income, occupation) or values (e.g., attitudes, beliefs, and aspirations). This research assesses the extent to which social media participants exhibit homophily (versus heterophily) in a unique context—virtual investment communities (VICs). We study the propensity of investors in seeking interactions with others with similar sentiments in VICs and identify theoretically important and meaningful conditions under which homophily is attenuated. To address this question, we used a discrete choice model to analyze 682,781 messages on Yahoo! Finance message boards for 29 Dow Jones stocks and assess how investors select a particular thread to respond. Our results revealed that, despite the benefits from heterophily, investors are not immune to the allure of homophily in interactions in VICs. The tendency to exhibit homophily is attenuated by an investor’s experience in VICs, the amount of information in the thread, but amplified by stock volatility. The paper discusses important implications for practice.

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of move data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential dataanalytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data preprocessing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases-scientific data compression and remote visualization have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improve the application End-to-End transfer performance.

No abstract

No abstract