Reputation

We explain what reputation effects are, how they arise and the factors that limit or strengthen them

Acceptance of feedbacks in reputation systems: the role of online social interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

Network-aware Evaluation Environment for Reputation Systems

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation

Specifying and analysing reputation systems with coordination languages

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models

Reputation Management: Corporate Image and Communication

Reputation was, is, and always will be of immense importance to organisations, whether commercial, governmental or not-for-profit. To reach their goals, stay competitive and prosper, good reputation paves the organisational path to acceptance and approval by stakeholders. Even organisations operating in difficult ethical environments - perhaps self-created - need to sustain a positive reputation where possible. Argenti & Druckenmiller argue that, “organisations increasingly recognize the importance of corporate reputation to achieve business goals and stay competitive” (Argenti & Druckenmiller 2004, p.368). While there are many recent examples of organisations whose leadership and business practice behaviours have destroyed their reputation, such as Enron, Arthur Andersen, Tyco and WorldCom, the positive case for reputation is that it has fostered continued expansion of old stagers like Johnson & Johnson and Philips and innovators such as Cisco Systems, who top recent rankings of the most respected organisations in the US and Europe. What is evident is that reputation does not occur by chance. It relates to leadership, management, and organisational operations, the quality of products and services, and - crucially - relationships with stakeholders. It is also connected to communication activities and feedback mechanisms. This chapter will consider the definitions and nature of reputation and its management, best practice and evaluation. It will also discuss the boundaries between branding, image and reputation

Observable Reputation Trading

Is the reputation of a firm tradable when the change in ownership is observable? We consider a competitive market in which a share of owners must retire in each period. New owners bid for the firms that are for sale. Customers learn the owner’s type, which reflects the quality of the good or service provided, through experience. After observing an ownership change they may want to switch firm. However, in equilibrium, good new owners buy from good old owners and retain high-value customers. Hence reputation is a tradable intangible asset, although ownership change is observable

Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.Comment: 14 pages, 5 figures, 5 tables, submitted to ICACCI 2013, Mysore, indi

Managing Corporate Reputation

{Excerpt} Newly minted approaches to corporate reputation are already obsolete. Beyond gaining control of issues, crises, and corporate social responsibility, organizations need to reconceptualize and manage reputation in knowledge-based economies. Reputation is not about likability: it is the aggregate estimation in which a person or entity is held by individuals and the public against a criterion, based on past actions and perceptual representation of future prospects, when compared to other persons or entities. Since we cannot develop a personal relationship with every entity in the world, the regard in which a party is held is a proxy indicator of predictability and the likelihood the party will meet expectations, a useful earmark that facilitates sense and decision making against alternatives. Every day, through what amounts to a distributed means of social control, we assess and judge with effect the competence of individuals and organizations to fulfill expectations based on such social evaluation

Flow-based reputation: more than just ranking

The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information. We study, both analytically and numerically, the properties of the proposed metric and the effect of attacks on reputation values