Is the ebay feedback system really efficient ? an experimental study

The eBay Feedback Forum is claimed to be a crucial component of the success of eBay. Many empirical studies have found that this feedback system exerts a deterrent effect on the opportunistic behavior the Internet's anonymity may incite buyers and sellers to adopt. The feedback system in place on eBay is however far from being perfect and may be especially vulnerable to strategic ratings (or nonratings) that might reduce the informational content of feedback profiles. This article aims to examine the efficiency of the eBay feedback system, through a set of experiments based on the trust game. Our experimental design consists of four different treatments. The baseline treatment corresponds to a finite repeated simultaneous trust game. The second treatment, called “eBay rating” is identical to the baseline treatment except that we added a second stage in which the players have the opportunity of rating their partner. In this treatment, each participant is given the choice to either evaluate immediately or wait, knowing that only one rating will be accepted. The third treatment, called "Sequential rating" is identical to the “eBay rating” treatment, except that the order in which players evaluate one another is randomly determined by the computer. Finally in the fourth treatment, called “Simultaneous rating”, both players are required to make their rating decisions simultaneously. Our experimental results indicate that the eBay feedback system could be improved by either constraining partners to leave ratings simultaneously or by predetermining the rating sequence.

Aggregating partial, local evaluations to achieve global ranking

We analyze some voting models mimicking online evaluation systems intended to reduce the information overload. The minimum number of operations needed for a system to be effective is analytically estimated. When herding effects are present, linear preferential attachment marks a transition between trustful and biased reputations.Comment: 9 pages, 5 figures, accepted for publication in Physica

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust

Risk analysis of Android applications: A user-centric solution

Android applications (apps) pose many risks to their users, e.g., by including code that may threaten user privacy or system integrity. Most of the current security countermeasures for detecting dangerous apps show some weaknesses, mainly related to users' understanding and acceptance. Hence, users would benefit from an effective but simple technique that indicates whether an app is safe or risky to be installed. In this paper, we present MAETROID (Multi-criteria App Evaluator of TRust for AndrOID), a framework to evaluate the trustworthiness of Android apps, i.e., the amount of risk they pose to users, e.g., in terms of confidentiality and integrity. MAETROID performs a multi-criteria analysis of an app at deploy-time and returns a single easy-to-understand evaluation of the app's risk level (i.e., Trusted, Medium Risk, and High Risk), aimed at driving the user decision on whether or not installing a new app. The criteria include the set of requested permissions and a set of metadata retrieved from the marketplace, denoting the app quality and popularity. We have tested MAETROID on a set of 11,000 apps both coming from Google Play and from a database of known malicious apps. The results show a good accuracy in both identifying the malicious apps and in terms of false positive rate

Portfolios of Exchange Relationships: An Empirical Investigation of an Online Marketplace for IT Services

Small firms face distinct problems and opportunities when procuring IT resources. Whereas previous work focused at the level of firm or buyer-supplier dyad, we address portfolios of buyer-supplier relationships at an online marketplace for IT services. Using the portfolio approach, we develop a buyers taxonomy and analyze properties of resulting clusters.Our investigation reveals four clusters of buyers with distinct mixes of long-term and short-term supplier relationships. Although reverse auctions are found to be associated with short-term relationships and negotiations support long-term relationships, buyers in different clusters use the two mechanisms in combination to a different extent.Performance;Buyer-supplier relationships;IT services;Online markets;Outsourcing;Reverse auctions

The Impact of Visibility in Innovation Tournaments: Evidence From Field Experiments

Contests have a long history of driving innovation, and web-based information technology has opened up new possibilities for managing tournaments. One such possibility is the visibility of entries – some web-based platforms now allow participants to observe others’ submissions while the contest is live. Seeing other entries could broaden or limit idea exploration, redirect or anchor searches, or inspire or stifle creativity. Using a unique data set from a series of field experiments, we examine whether entry visibility helps or hurts innovation contest outcomes and (in the process) also address the common problem of how to deal with opt-in participation. Our eight contests resulted in 665 contest entries for which we have 11,380 quality ratings. Based on analysis of this data set and additional observational data, we provide evidence that entry visibility influences the outcome of tournaments via two pathways: (1) changing the likelihood of entry from an agent and (2) shifting the quality characteristics of entries. For the first, we show that entry visibility generates more entries by increasing the number of participants. For the second, we find the effect of entry visibility depends on the setting. Seeing other entries results in more similar submissions early in a contest. For single-entry participants, entry quality “ratchets up” with the best entry previously submitted by other contestants if that entry is visible, while moving in the opposite direction if it’s not. However, for participants who submit more than once, those with better prior submissions improve more when they cannot see the work of others. The variance in quality of entries also increases when entries are not visible, usually a desirable property of tournament submissions

A Multi-Criteria-Based Evaluation of Android Applications

Android users can face the risk of downloading and installing bad applications on their devices. In fact, many applications may either hide malware, or their expected behavior do not fully follow the user\u27s expectation. This happens because, at install-time, even if the user is warned with the potential security threat of the application, she often skips this alert message. On Android this is due to the complexity of the permission system, which may be tricky to fully understand. We propose a multi-criteria evaluation of Android applications, to help the user to easily understand the trustworthiness degree of an application, both from a security and a functional side. We validate our approach by testing it on more than 180 real applications found either on official and unofficial markets