Social Search with Missing Data: Which Ranking Algorithm?

Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods

BEYOND INSTITUTION-BASED TRUST: BUILDING EFFECTIVE ONLINE MARKETPLACES WITH SOCIAL MECHANISMS

Researchers have devoted considerable efforts to design effective online marketplaces, especially with respect to the institutional structures believed to establish buyer trust in the community of sellers. Comparatively speaking, the effectiveness of social mechanisms, although practically evidenced as important, has received much less attention in e-commerce research. In the current study we explore the contribution of social mechanisms–specifically IT-enabled instant messaging, the message box, online community and informal coalition programme–to effective online marketplaces. We propose that these mechanisms facilitate social relationships and trust building processes during transactions, in addition to the existing institutional structures. When consumerto- consumer (C2C) transactions are considered risky, the buyer-seller social relationship is more critical for buyers when forming their transaction intentions. The research model is largely supported by a pilot study of 104 buyers of TaoBao.com, China’s C2C leading marketplace. We discuss the findings, implications, and our preparations for a large-scale study

DIGITAL TEHNOLOGIES. AN OVERVIEW OF CURRENT EVOLUTIONS AND IMPACT

Various observers describe today's global economy as one in transition to aknowledge economy, as an extension of an information society. The transition requires thatthe rules and practices that determined success in the industrial economy need rewriting in aninterconnected, globalized economy where knowledge resources such as know-how andexpertise are as critical as other economic resources. According to analysts of the knowledgeeconomy, these rules need to be rewritten at the levels of firms and industries in terms ofknowledge management and at the level of public policy as knowledge policy or knowledge-related policy. The digital and ICT revolutions are twin revolutions. Information andcommunications technology (ICT) refers to a broad field encompassing computers,communications equipment and the services associated with them. It includes the telephone,cellular networks, satellite communication, broadcasting media and other forms ofcommunication.digital revolutions, communication equipment, broadcasting media

Structural balance emerges and explains performance in risky decision-making.

Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize

Instant Messaging and Employee\u27s Performance: A Text Mining Approach

The adoption of Instant Messaging (IM) applications in the workplace remains contentious due to difficulties in adequately quantifying organizational benefits and how it affects individual performance. Previous research on the impact of IM usage on employee performance has been limited to analyzing primary data (i.e., survey methods), making it difficult to extrapolate the findings to a constantly changing workplace. In contrast, we investigate the relationships between these individuals\u27 IM usage at the workplace and their primary assessment metric in their organization, performance evaluation, using longitudinal data of employees\u27 IM activities and their performance evaluation collected from a US Fortune 500 financial company. Using cutting-edge text-mining techniques, we identify the primary purposes of IM utilization in organizations and assess the impact of those attributes on employee performance. Our findings show that IM in the workplace can improve team communication, knowledge-sharing experience, and social networking among employees, but it can also be disruptive. However, the combined effect of team communication and knowledge sharing on employee performance can overshadow the negative impact of IM interruption on employee performance