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Online knowledge-based peer-production communities, like question and answer sites (Q&A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q&A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.

This paper concerns the applicability of reputations systems for assessing Quality of Experience (QoE) for web services in the Future Internet. Reputation systems provide mechanisms to manage subjective opinions in societies and yield a general scoring of a particular behavior. Thus, they are likely to become an important ingredient of the Future Internet. Parameters under evaluation by a reputation system may vary greatly and, particularly, may be chosen to assess the users’ satisfaction with (composite) web services. Currently, this satisfaction is usually expressed by QoE, which represents subjective users’ opinions. The goal of this paper is to present a novel framework of web services where a reputation system is incorporated for tracking and predicting of users’ satisfaction. This approach is a beneficial tool which enables providers to facilitate service adaptation according to users’ expectations and maintain QoE at a satisfactory level. Presented reputation systems operate in an environment of composite services that integrate client and server-side. This approach is highly suitable for effective QoE differentiating and maximizing user experience for specific customer profiles as even the service and network resources are shared.

To provide social exchange on a global level, sharing-economy companies leverage interpersonal trust between their members on a scale unimaginable even a few years ago. A challenge to this mission is the presence of social biases among a large heterogeneous and independent population of users, a factor that hinders the growth of these services. We investigate whether and to what extent a sharing-economy platform can design artificially engineered features, such as reputation systems, to override people’s natural tendency to base judgments of trustworthiness on social biases. We focus on the common tendency to trust others who are similar (i.e., homophily) as a source of bias. We test this argument through an online experiment with 8,906 users of Airbnb, a leading hospitality company in the sharing economy. The experiment is based on an interpersonal investment game, in which we vary the characteristics of recipients to study trust through the interplay between homophily and reputation. Our findings show that reputation systems can significantly increase the trust between dissimilar users and that risk aversion has an inverse relationship with trust given high reputation. We also present evidence that our experimental findings are confirmed by analyses of 1 million actual hospitality interactions among users of Airbnb.

Reputation systems have been recognized as successful online review communities and word-of-mouth channels. Our study draws upon the elaboration likelihood model to analyze the extent that the characteristics of reviewers and their early reviews reduce or worsen the bias of subsequent online reviews. Investigating the sources of this bias and ways to mitigate it is of considerable importance given the previously established significant impact of online reviews on consumers' purchasing decisions and on businesses' profitability. Based on a panel data set of 744 individual consumers collected from Yelp, we used the Markov chain Monte Carlo simulation method to develop and empirically test a system of simultaneous models of consumer review behavior. Our results reveal that male reviewers or those who lack experience, geographic mobility, or social connectedness are more prone to being influenced by prior reviews. We also found that longer and more frequent reviews can reduce online reviews' biases. This paper is among the first to examine the moderating effects of reviewer and review characteristics on the relationship between prior reviews and subsequent reviews. Practically, this study offers businesses effective customer relationship management strategies to improve their reputations and expand their clientele.

This article studies the question and answer (Q&A) technology of electronic commerce platforms, an increasingly common form of user-generated content that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, the authors show that Q&As complement consumer reviews: unlike reviews, questions are primarily asked prepurchase and focus on clarification of product attributes rather than discussion of quality; answers convey fit-specific information in a predominantly sentiment-free way. Drawing on these observations, the authors hypothesize that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers and, therefore, improved product ratings. Indeed, when products suffering from fit mismatch start receiving Q&As, their subsequent ratings improve by approximately .1 to .5 stars, and the fraction of negative reviews that discuss fit-related issues declines. The extent of the rating increase due to Q&As is proportional to the probability that purchasers will experience fit mismatch without Q&A. These findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings of products that have incurred low ratings due to customer–product fit mismatch.

Live chat tools have emerged as a channel for fostering synchronous communication between sellers and buyers. However, a positive link between live chat and conversion does not suggest causation because customers with high purchase intention are more likely to initiate live chat in the first place. Our research accounts for such selection and studies the impact of live chat with the presence of information cues: product sales volumes and seller feedback scores. Using granular data from Alibaba, we find that live chat can increase purchase probability of tablets by 15.99%. Further, the substitutional or complementary patterns between live chat and the information cues allow us to understand how live chat tools change the ways consumers evaluate product quality and seller credibility. We observe a substitutional effect of live chat on seller feedback score such that sellers with low feedback scores benefit more from live chat conversations than sellers with high scores. On the contrary, products with a high past sales volume sell better after live chat, indicating a reinforcement effect. Such findings deepen our understanding of the difference between various information cues in electronic markets and delineate the specific type of benefit live chat brings if put into best use by sellers and online marketplace designers. Live chat tools have emerged as a channel for fostering synchronous communication between sellers and buyers. The role of live chat in the e-commerce environment, however, is largely underexplored. Using granular data from Alibaba, we examine the effect of live chat on consumers’ purchase decisions. After controlling for the selection process that customers with high purchase intention are more likely to initiate live chat in the first place, we find that live chat can increase purchase probability of tablets by 15.99%. We also investigate how the effect of live chat is moderated by existing information cues: product sales volume and seller feedback score. The substitutional or complementary patterns allow us to understand how live chat tools change the ways consumers evaluate product quality and seller credibility. We observe a substitutional effect of live chat on seller feedback score such that sellers with low feedback score benefit more from live chat conversations than sellers with high score. On the contrary, products with high past sales volume sell better after live chat, indicating a reinforcement effect. Such findings deepen our understanding of the difference between various information cues in electronic markets. As one of the first systematic studies to investigate live chat, our paper contributes to web trust conceptual frameworks with empirical analyses and sheds light on practical decisions faced by e-vendors and platform designers.

Purpose The purpose of this paper is to investigate how the effectiveness of systems for ensuring cooperation in online transactions is impacted by a positivity bias in the evaluation of the work that is produced. The presence of this bias can reduce the informativeness of the reputation system and negatively impact its ability to ensure quality. Design/methodology/approach This research combines survey and experimental methods, collecting data from 1,875 Mechanical Turk (MTurk) workers in five studies designed to investigate the informativeness of the MTurk reputation system. Findings The findings demonstrate the presence of a positivity bias in evaluations of workers on MTurk, which leaves them undifferentiated, except at the extremity of the reputation system and by status markers. Research limitations/implications Because MTurk workers self-select tasks, the findings are limited in that they may only be generalizable to those who are interested in research-related work. Further, the tasks used in this research are largely subjective in nature, which may decrease their sensitivity to differences in quality. Practical implications For researchers, the results suggest that requiring 99 per cent approval rates (rather than the previously advised 95 per cent) should be used to identify high-quality workers on MTurk. Originality/value The research provides insights into the design and use of reputation systems and demonstrates how design decisions can exacerbate the effect of naturally occurring biases in evaluations to reduce the utility of these systems.

In recent years, consumer-to-consumer (C2C) marketplaces such as eBay and Taobao have adopted a component rating system, and run it simultaneously with but independent of a binary rating system. This paper investigates the extent to which binary rating and component rating systems are able to provide consistent signals of sellers’ quality, focusing on the reputation system design under the Chinese context. Using field data from Taobao, we performed canonical correlation analyses and found that the reputation signals of the two systems are generally correlated. As expected, negative and neutral ratings accurately reveal buyer dissatisfaction. Our results, however, show that positive ratings exhibit negative correlations with the three component ratings (i.e., item-as-described, customer service, and on-time delivery), suggesting that large numbers of positive ratings on Taobao may encourage trust in the platform but do not help to choose credible sellers. Our results elucidate the role of cultural difference in explaining the negative relationship in China and provide important implications for the design of reputation systems.

Digital platforms facilitate the coordination, match making, and value creation for large groups of individuals. In consumer-to-consumer (C2C) online sharing platforms specifically, trust between these individuals is a central concept in determining which individuals will eventually engage in a transaction. The majority of today’s online platforms draw on various types of cues for group coordination and trust building among users. Current research widely accepts the capacity of such cues but largely ignores their changing effectiveness over the course of a user’s lifetime on the platform. To address this gap, we conduct a laboratory experiment, studying the interplay of cognitive and affective trust cues over the course a multi-period trust experiment for the coordination of groups. We find that the trust-building capacity of affective trust cues is time-dependent and follows an inverted u-shape form, suggesting a dynamic complementarity of cognitive and affective trust cues.