scholarly journals Does Herding Undermine the Trust Enhancing Effect of Reputation? An Empirical Investigation with Online-Auction Data

Social Forces ◽  
2020 ◽  
Author(s):  
Wojtek Przepiorka ◽  
Ozan Aksoy
Keyword(s):  
Reputation Systems ◽  
Online Auction ◽  
Large Set ◽  
Market Exchange ◽  
Online Markets ◽  
Reputation Mechanism ◽  
Competition And Cooperation ◽  
Trading Partners ◽  
Online Reputation ◽  
Organizational Features

Abstract In today’s online markets, the reputation mechanism undergoes its most successful propagation in human history. Online reputation systems substitute informal sanctioning mechanisms at work in close-knit groups and enable complete strangers to trade with each other across large geographic distances. The organizational features of online markets support actors in solving three problems that hamper mutually beneficial market exchange: the value, competition, and cooperation problems. However, due to the plethora of trading opportunities available online, actors face a problem of excess, i.e., the difficulty of choosing a trading partner. Imitation of other actors’ choices of trading partners (i.e., herding) can solve the problem of excess but at the same time lead to the neglect of information about these trading partners’ trustworthiness. Using a large set of online-auction data (N ≈ 88 k), we investigate whether herding as a strategy for solving the problem of excess undermines the reputation mechanism in solving the cooperation problem. Our analysis shows that although buyers follow others in their decisions of which offers to consider, they do not follow others at any price and refer to sellers’ reputations to establish seller trustworthiness. Our results corroborate that reputation systems are viable organizational features that promote mutually beneficial exchanges in anonymous online markets.

Online Reputation Systems

2019 ◽  
pp. 478-495
Author(s):  
Chris Snijders ◽  
Uwe Matzat
Keyword(s):  
Empirical Evidence ◽  
Regression Method ◽  
Effect Sizes ◽  
Reputation Systems ◽  
Single Shot ◽  
Hedonic Regression ◽  
Online Markets ◽  
Online Marketplace ◽  
Order Of Magnitude ◽  
Online Reputation

Potentially, reputation systems in online markets are ways of safeguarding hazardous single-shot transactions between traders, by artificially creating a network of connections between all users of an online marketplace. Given that online markets exist and use such reputation systems, this has triggered the question how large the value of reputation is. This question has been analyzed in previous research with mixed results. After introducing the issue in some more detail, the chapter posits two arguments that may put research into the value of reputation in a somewhat different light: (1) the fact that the most often used “hedonic regression” method, which considers actual sales only, does not lead to estimates that can be straightforwardly connected to the value of reputation, neither for the seller nor for the buyer, and (2) the empirical evidence from the experimental literature on the effects of semantic feedback, which suggests effect sizes that may well be an order of magnitude larger than the effects found in reputation research. The chapter concludes with some implications for research in the field.

Trust and Reputation in Markets

2019 ◽  
pp. 381-400
Author(s):  
Andreas Diekmann ◽  
Wojtek Przepiorka
Keyword(s):  
Essential Element ◽  
Reputation Systems ◽  
Online Markets ◽  
Rating Systems ◽  
Reputation Mechanism ◽  
The Third ◽  
Historical Societies ◽  
Trust And Reputation ◽  
Business Relations

Economic transactions are subject to trust problems, which can be solved by means of long-term business relations, by institutional regulations or by reputational incentives. In this chapter, we first deal with the trust problem and the various ways of its solution. We then discuss the functioning of the reputation mechanism in markets in historical and contemporary societies. Although similar mechanisms are at work in contemporary societies as in historical societies, the technologies available to communicate information about reputation have changed. This is particularly evident in online markets, in which electronic rating systems have become an essential element. In the third part of this chapter, we focus on the development, forms and consequences of reputation in online markets. Reputation information promotes cooperation in markets if it is credible. This requires that simple means of deception are unavailable. We conclude this chapter by dealing with the down side of reputation systems.

scholarly journals The emergence and selection of reputation systems that drive cooperative behaviour

2018 ◽  
Vol 285 (1886) ◽  
pp. 20181508 ◽  
Author(s):  
Alain Schlaepfer
Keyword(s):  
Computer Simulations ◽  
Relevant Information ◽  
Reputation Systems ◽  
Cooperative Behaviour ◽  
Social Image ◽  
Reputation Mechanism ◽  
Initial Evolution ◽  
Group Members ◽  
Number Of Individuals ◽  
Selection Of

Reputational concerns are believed to play a crucial role in explaining cooperative behaviour among non-kin humans. Individuals cooperate to avoid a negative social image, if being branded as defector reduces pay-offs from future interactions. Similarly, individuals sanction defectors to gain a reputation as punisher, prompting future co-players to cooperate. But reputation can only effectively support cooperation if a sufficient number of individuals condition their strategies on their co-players' reputation, and if a sufficient number of group members are willing to record and transmit the relevant information about past actions. Using computer simulations, this paper argues that starting from a pool of non-cooperative individuals, a reputation system based on punishment is likely to emerge and to be the driver of the initial evolution of cooperative behaviour. However, once cooperation is established in a group, it will be sustained mainly through a reputation mechanism based on cooperative actions.

Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 32-62 ◽  
Author(s):  
Elshrif Ibrahim Elmurngi ◽  
Abdelouahed Gherbi
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Statistical Technique ◽  
Machine Learning Techniques ◽  
Reputation Systems ◽  
Analysis Model ◽  
Learning Techniques ◽  
Reputation Score ◽  
Reporting Systems ◽  
Online Reputation

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.

Legal Challenges of Online Reputation Systems

2011 ◽  
pp. 84-111 ◽  
Author(s):  
Jennifer Chandler ◽  
Khalil el-Khatib ◽  
Morad Benyoucef ◽  
Gregor Von Bochmann ◽  
Carlisle Adams
Keyword(s):  
United States ◽  
Common Law ◽  
The United States ◽  
The Other ◽  
Reputation Systems ◽  
Online Interactions ◽  
Canadian Law ◽  
Legal Challenges ◽  
Online Reputation

Online reputation systems have become important tools for supporting commercial as well as noncommercial online interactions. But as online users become more and more reliant on these systems, the question of whether the operators of online reputation systems may be legally liable for problems with these systems becomes both interesting and important. Indeed, lawsuits against the operators of online reputation systems have already emerged in the United States regarding errors in the information provided by such systems. In this chapter, we will take the example of eBay’s Feedback Forum to review the potential legal liabilities facing the operators of online reputation systems. In particular, the applicability of the Canadian law of negligent misrepresentation and of defamation will be covered. Similar issues may be expected to arise in the other common law jurisdictions

Strategic Behavior in Online Reputation Systems: Evidence from eBay

2011 ◽  
Author(s):  
Shun Ye ◽  
Guodong (Gordon) Gao ◽  
Siva Viswanathan
Keyword(s):  
Strategic Behavior ◽  
Reputation Systems ◽  
Online Reputation

scholarly journals On the ethical and political agency of online reputation systems

First Monday ◽  
2019 ◽  
Author(s):  
Anna Wilson ◽  
Stefano De Paoli
Keyword(s):  
Methodological Individualism ◽  
Digital Data ◽  
Reputation Systems ◽  
Political Agency ◽  
Main Approach ◽  
Trust And Reputation ◽  
Alternative Approaches ◽  
Online Reputation ◽  
Digital Spaces

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We undertake a disclosive archaeology (Introna, 2014) of typical reputation systems, identifying relevant figuration agencies including affordances and prohibitions, (cyborg) identities, (cyborg) practices and discourses, in order to examine their ethico-political agency. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm within whatever space they operate. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts.

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