Decision Support for Smart Cities
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ItemDistraction or Connection? An Investigation of Social Media Use at Work( 2018-01-03)The use of social media in the workplace is controversial. In order to develop a good understanding of social media use at work, this study examines the effects of social media use from both positive and negative sides. Based on two-factor theory, this study proposes that social media use at work engenders distraction and perceived relatedness, which in turn influence job performance. This study further draws on resource matching theory to posit that the perceptual load of the job moderates the effects of social media use at work on distraction and perceived relatedness. A survey will be conducted to collect data and test the research hypotheses. In theoretical terms, this study is expected to contribute to information systems research by investigating both positive and negative outcomes of social media usage. In practical terms, this study sheds light on the usage and management of social media in the workplace.
ItemA Novel Personalized Academic Knowledge Sharing System in Online Social Network( 2018-01-03)Information overload is a major problem for both readers and authors due to the rapid increase in scientific papers in recent years. Methods are proposed to help readers find right papers, but few research focuses on knowledge sharing and dissemination from authors’ perspectives. This paper proposes a personalized academic knowledge sharing system that takes advantages of author’s initiatives. In our method, we combine the user-level and document-level analysis in the same model, it works in two stages: 1) user-level analysis, which is used to profile users in three dimensions (i.e., research topic relevance, social relation and research quality); and 2) document-level analysis, which calculates the similarity between the target article and reader’s publications. The proposed method has been implemented in the ScholarMate, which is a popular academic social network. The experiment results show that the proposed method can effectively promote the academic knowledge sharing, it outperforms other baseline methods.
ItemUtilizing Geospatial Information in Cellular Data Usage for Key Location Prediction( 2018-01-03)Previous research on the identification of key locations (e.g., home and workplace) for a user largely relies on call detail records (CDRs). Recently, cellular data usage (i.e., mobile internet) is growing rapidly and offers fine-grained insights into various human behavior patterns. In this study, we introduce a novel dataset containing both voice and mobile data usage records of mobile users. We then construct a new feature based on the geospatial distribution of cell towers connected by mobile users and employ bivariate kernel density estimation to help predict users’ key locations. The evaluation results suggest that augmented features based on both voice and mobile data usage improve the prediction precision and recall.