How to Explain CouchSurfing's Success?

In this paper we describe the social network of CouchSurfing as an innovative, non-monetary model for the traditional international hospitality and travel market. This paper is written to describe our understanding of how it can operate and expand in spite of potential risks and uncertainties. We present the results of an exploratory qualitative research project that was conducted in Hungary in 2012/13. The findings indicate that: (1) a high level of trust as a personality trait characterizes members; (2) trust can be interpreted as a strong cultural rule; (3) members perceive a low level of risk and have limited practical knowledge about the safety features of the system; and (4) trust towards a given member is not based on rational calculation but on emotions

Trust and reputation management in decentralized systems

In large, open and distributed systems, agents are often used to represent users and act on their behalves. Agents can provide good or bad services or act honestly or dishonestly. Trust and reputation mechanisms are used to distinguish good services from bad ones or honest agents from dishonest ones. My research is focused on trust and reputation management in decentralized systems. Compared with centralized systems, decentralized systems are more difficult and inefficient for agents to find and collect information to build trust and reputation. In this thesis, I propose a Bayesian network-based trust model. It provides a flexible way to present differentiated trust and combine different aspects of trust that can meet agents’ different needs. As a complementary element, I propose a super-agent based approach that facilitates reputation management in decentralized networks. The idea of allowing super-agents to form interest-based communities further enables flexible reputation management among groups of agents. A reward mechanism creates incentives for super-agents to contribute their resources and to be honest. As a single package, my work is able to promote effective, efficient and flexible trust and reputation management in decentralized systems

An E-Business Model Facilitating Service Provider Selection in B2C E-Commerce

The advent and expansion of the Internet and its applications, among them e-commerce, has provided new opportunities for the emergence of novel e-business models. A portion of these models are in the form of performing a mediatory role to provide some services for customers or businesses, and to facilitate transactions between them. In B2C e-commerce, often, a service consumer may supply his service demand from a range of providers and when he doesn\u27t have any transaction with many of them making an accurate decision becomes challenging. Therefore, he would need to interact with others to acquire relevant information. Current approaches for addressing this issue are generally rating-based and perform poorly. Recently, an experience-based approach has been proposed by ensoy et al [1]. This paper reviews this approach, analyzes its weaknesses and problems and proposes a new model to eliminate those problems, in which a third party assists the consumers in choosing their desired service providers

How Online Social Networks Create Value for Organizations: A Resource-Based Perspective

Online Social Network (OSN) is an emerging communication technology which has increasingly been used. Todayorganizations are paying more attention to the importance of these networks and are trying to create value with theseemerging technologies. Current literature focuses on the information and network implications of these networks at theindividual and group levels, leaving the organizational level impact understudied. This study examines how these networkscreate value for organizations. We propose a model based on Sambamurthy et al. (2003) framework conceptualizing the OSNand explaining how OSN capabilities bring about customer agility through digital options of communication facilitation

Advisor Networks and Referrals for Improved Trust Modelling in Multi-Agent Systems

This thesis relates to the usage of trust modelling in multi-agent systems - environments in which there are interacting software agents representing various users (for example, buyers and sellers exchanging products and services in an electronic marketplace). In such applications, trust modelling may be crucial to allow one group of agents (in the e-commerce scenario, buyers) to make effective decisions about which other agents (i.e., sellers) are the most appropriate partners. A number of existing multi-agent trust models have been proposed in the literature to help buyers accurately select the most trustworthy sellers. Our contribution is to propose several modifications that can be applied to existing probabilistic multi-agent trust models. First, we examine how the accuracy of the model can be improved by limiting the network to a portion of the population consisting of the most trustworthy agents, such that the less trustworthy contributions of the remaining agents can be ignored. In particular, we explore how this can be accomplished by either setting a maximum size for a buyer's advisor network or setting a minimum trustworthiness threshold for agents to be accepted into that advisor network, and develop methods for appropriately selecting the values to limit the network size. We demonstrate that for two models, both the Personalized Trust Model (PTM) developed by Zhang as well as TRAVOS, these approaches will yield significant improvements to the accuracy of the trust model, as opposed to using an unrestricted advisor network. Our final proposed modification is to use an advisor referral system in combination with one of the network-limiting approaches. This would ensure that if a particular agent within the advisor network had not met a specified level of experience with the seller under consideration, it could be replaced by another agent that had greater experience with that seller, which should in turn allow for a more accurate modelling of the seller's trustworthiness. We present a particular approach for replacing advisors, and show that this will yield additional improvements in trust-modelling accuracy with both PTM and TRAVOS, especially if the limiting step were such that it would yield a very small advisor network. We believe that these techniques will be very useful for trust researchers seeking to improve the accuracy of their own trust models, and to that end we explain how other researchers could apply these modifications themselves, in order to identify the optimal parameters for their usage. We discuss as well the value of our proposals for identifying an "optimal" size for a social network, and the use of referral systems, for researchers in other areas of artificial intelligence