Searching Unanswered Questions A Review of Knowledge Management Processes in Virtual Teams

This article provides a review of the empirical literature on knowledge management processes in virtual teams in an effort to keep the stock of the current state of knowledge. The review is organized according to the four basic knowledge management processes outlined by Alavi and Leidner (2001) (i.e., creation/acquisition, sharing/transferring, storage/retrieval and application). Factors influencing the effectiveness of knowledge management processes studied in the existing literature are examined and discussed. Building on this review, we critically evaluate this stream of research and propose avenues for future work on knowledge management in virtual teams

A Critical Analysis Of The State-Of-The-Art On Automated Detection Of Deceptive Behavior In Social Media

Recently, a large body of research has been devoted to examine the user behavioral patterns and the business implications of social media. However, relatively little research has been conducted regarding users’ deceptive activities in social media; these deceptive activities may hinder the effective application of the data collected from social media to perform e-marketing and initiate business transformation in general. One of the main contributions of this paper is the critical analysis of the possible forms of deceptive behavior in social media and the state-of-the-art technologies for automated deception detection in social media. Based on the proposed taxonomy of major deception types, the assumptions, advantages, and disadvantages of the popular deception detection methods are analyzed. Our critical analysis shows that deceptive behavior may evolve over time, and so making it difficult for the existing methods to effectively detect social media spam. Accordingly, another main contribution of this paper is the design and development of a generic framework to combat dynamic deceptive activities in social media. The managerial implication of our research is that business managers or marketers will develop better insights about the possible deceptive behavior in social media before they tap into social media to collect and generate market intelligence. Moreover, they can apply the proposed adaptive deception detection framework to more effectively combat the ever increasing and evolving deceptive activities in social medi

E-Document Management Based on Web services and XML

Document management plays an important role in R&D project management for government funding agencies, universities, and research institutions. The advent of Web services and XML presents new opportunities for e-document management. This paper describes a novel solution for processing large quantities of electronic documents in multiple formats within a short timeframe. The solution is based on Web services for integrating two-tiered distributed processing. It also involves a document extraction process for handling multiple document formats, with XML as the intermediate for information exchange. The application of the solution at the National Natural Science Foundation of China (NSFC) proved successful, and the general approach may be applied to a broad range of e-document management settings

Semi-Supervised Text Mining For Dynamic Business Network Discovery

Recently, much research effort has been devoted to the discovery and analysis of online social networks. However, relatively little research has been done for business network discovery and analysis. Although named entity recognition (NER) tools are available to identify basic entities in texts, there are still challenging research problems, such as co-reference resolution and the identification of abbreviations of organization names. Guided by the design science methodology, the main contribution of this paper is the design and development of a novel semi-supervised method for the identification of business entities (e.g., companies) and their relationships. Based on the automatically mined business networks, financial analysts can then predict the business prestige of companies for better financial investment decision making. Initial experiments show that the proposed business entity identification method is more effective than other baseline methods. Moreover, the proposed semi-supervised business relationship mining method is more effective than the state-of-the-art supervised machine learning classifier when a large number of manually labeled training examples are not available. The managerial implication is that business managers can apply the design artifacts to promptly identify potential business partners and competitors, and hence improve their strategic business decision marking processes