To mitigate inefficiencies arising from asymmetric information, some markets rely on government interventions, whereas others rely on reputation systems, warranties, or guarantees. This paper explores the impact of two mechanisms, namely, reputation badges and buyer protection programs, and their interaction on eBay’s marketplace. Adding buyer protection reduces the premium for the reputation badge and increases efficiency in the marketplace. These efficiency gains are achieved by reducing moral hazard through an increase in sellers´ quality and by reducing adverse selection through a higher exit rate for low-quality sellers. Our estimates suggest buyer protection increases the total welfare by 2.9%. This paper was accepted by Matt Shum, marketing.
Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies.
The widespread use of mobile devices brings opportunities to capture large-scale, continuous information about human behavior. Mobile data has tremendous value, leading to business opportunities, market strategies, security concerns, etc. Visual analytics systems that support interactive exploration and discovery are needed to extracting insight from the data. However, visual analysis of complex social-spatial-temporal mobile data presents several challenges. We have created MobiVis, a visual analytics tool, which incorporates the idea of presenting social and spatial information in one heterogeneous network. The system supports temporal and semantic filtering through an interactive time chart and ontology graph, respectively, such that data subsets of interest can be isolated for close-up investigation. "Behavior rings," a compact radial representation of individual and group behaviors, is introduced to allow easy comparison of behavior patterns. We demonstrate the capability of MobiVis with the results obtained from analyzing the MIT Reality Mining dataset.
Web clickstream data are routinely collected to study how users browse the web or use a service. It is clear that the ability to recognize and summarize user behavior patterns from such data is valuable to e-commerce companies. In this paper, we introduce a visual analytics system to explore the various user behavior patterns reflected by distinct clickstream clusters. In a practical analysis scenario, the system first presents an overview of clickstream clusters using a Self-Organizing Map with Markov chain models. Then the analyst can interactively explore the clusters through an intuitive user interface. He can either obtain summarization of a selected group of data or further refine the clustering result. We evaluated our system using two different datasets from eBay. Analysts who were working on the same data have confirmed the system's effectiveness in extracting user behavior patterns from complex datasets and enhancing their ability to reason.
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