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This study examines empirically the interaction effects of national culture and contextual factors (nature of the knowledge and the relationship between the knowledge sharer and recipient) on employees' tendency to share knowledge with co-workers. Quantitative and open-ended responses to two scenarios were collected from 142 managers (104 from the U.S. and 38 from the People's Republic of China). These two nations were selected due to their divergence on salient aspects of national culture, as well as their global political and economic importance. The focus on interaction effects was aimed at providing a more powerful test of culture's effects than simple comparisons of means typical of prior related research. Consistent with culture-based expectation, the quantitative results indicated that Chinese vs. U.S. nationals' openness of knowledge sharing was related to their different degrees of collectivism—the relative emphasis on self vs. collective interests—as well as whether knowledge sharing involved a conflict between self and collective interests. Also consistent with prediction, Chinese relative to U.S. nationals shared knowledge significantly less with a potential recipient who was not a member of their “ingroup.” Content analysis of the open-ended responses further showed that the quantitative results are the aggregated outcomes of trade-offs across cultural attributes and their interactions with contextual factors.

Abstract. Existing location-based services provide specialized services to their customers based on the knowledge of their exact locations. With untrustworthy servers, location-based services may lead to several privacy threats ranging from worries over employers snooping on their workers' whereabouts to fears of tracking by potential stalkers. While there exist several techniques to preserve location privacy in mobile environments, such techniques are limited as they do not distinguish between location privacy (i.e., a user wants to hide her location) and query privacy (i.e., a user can reveal her location but not her query). Such distinction is crucial in many applications where the locations of mobile users is publicly known. In this paper, we go beyond the limitation of existing cloaking algorithms as we propose a new robust spatial cloaking technique for snapshot and continuous location-based queries that clearly distinguishes between location privacy and query privacy. By such distinction, we achieve two main goals: (1) supporting private location-based services to those customers with public locations, and (2) performing spatial cloaking on-demand basis only (i.e., when issuing queries) rather than exhaustively cloaking every single location update. Experimental results show that the robust spatial cloaking algorithm is scalable and efficient while providing anonymity for large numbers of continuous queries without hiding users' locations.