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This study reviews the academic literature on global reputations and its implications for meritocracy. Over the years, systems of measuring and visualizing reputation have proliferated globally with organizations competing for talented employees, clients, and resources in a situation of limited supply, resulting in the emergence of reputation systems as a device to position businesses in the international market and to contend cross-nationally for prestige. Yet, the tangible utilities of these systems for promoting a meritocratic culture remain contested. Notwithstanding their utility as cognitive heuristics, global reputation systems can distort information and become dysfunctional when consumers embedded in vastly different cultures and institutional environments navigate these systems. This chapter identifies gaps in extant knowledge and suggests number of ways of improving our theoretical and analytical frameworks on the association between reputation and meritocracy. Specifically, it advances the concept of “reputation work” to understand how reputations are built, evaluated, maintained, communicated, consumed, and deconstructed and calls for attention to each of these dimensions to forge a stronger coupling between reputations and meritocracy.

IP reputation systems, which filter e‐mail based on the sender's IP address, are located at the perimeter – before the messages reach the mail server's anti‐spam filters. To increase IP reputation system efficacy and overcome the shortcomings of individual IP‐based filtering, recent studies have suggested exploiting the properties of IP clusters, such as those of Autonomous Systems (AS). Cluster‐based techniques can enhance accuracy and reduce false negative rates. However, clusters generally contain enormous amounts of IP addresses, which hinder cluster‐based systems from reaching their full spam filtering potential. The purpose of this paper is exploitation of social network metrics to obtain a more granular, i.e. sub‐divided, view of cluster‐based reputation, and thus enhance spam filtering accuracy.

The authors examined the performance of various social network metrics, including nodal degree, betweenness centrality, closeness centrality and valued graphs, to find an optimal element that enhances IP reputation prediction in AS clusters.

It was found that all measures contributed to prediction, yet the best predictor of spam reputation was the out‐degree metric, which showed a strong positive correlation with spam reputation prediction. This implies that more granular information can increase the accuracy of IP reputation prediction in AS clusters.

Used in conjunction with other technologies, the granular cluster‐based reputation system can be a valuable addition to commercial and open‐source spam filtering systems, or to standalone DNS‐based blacklists.

The authors' approach can promote mitigation of larger spam volumes at the perimeter, save bandwidth, and conserve valuable system resources.

In e-commerce, consumers have begun to rely on the opinions of fellow consumers who posted through online consumer reviews to a reputation management system. An ethical concern has arisen in the use and abuse of these new systems. We examine the underlying ethical issues that entrepreneurs are confronting in this time of surging e-commerce. Using 32 vignettes, one for each cross-section of our research construct framework, followed by two Likert scales for respondents to indicate their agreement with the action described from both the perspective of ethicality and professional acceptability, we received responses for 1,252 vignettes, which generated a dataset of 2,504 data points. The results of our pilot study suggest that the ethical considerations for business professionals conducting business online are more nuanced and complex than conventional wisdom on the subject might suggest. While 60 research subjects are small, the use of paired vignettes in our survey allowed us to measure at least 1,000 paired responses for each research construct. The results have the potential of revealing how young professionals have been conditioned by the prevalence of web-based interactions and the anonymity they afford participants, as well as the degree to which they rationalize the misrepresentation of information by business professionals for the purpose of manipulating consumers’ purchasing decisions in order to drive sales. If consumers’ trust in reputation management systems erodes, the result could be a collapse of the entire system as a meaningful source of information. We also demonstrate the tolerance of what is deemed ethical versus professionally acceptable with online business practices.

The purpose of this study is to examine the role of customer experience moderating the relationship between reputation (online consumer reviews) and price premium.

This paper collected half-year period transaction of Nokia 5230XM and Kingston SD card from Taobao.com, the largest e-commerce platform in China. This paper combined theoretical analysis and empirical analysis together. Two-stage regression and logistic regression analysis was applied in this empirical analysis. The sensitivity analyses (robustness check) were also conducted in this paper.

Customer experience negatively moderates reputation price premium; thus, the positive effect of the reputation system is weaker for the experienced customer than for the naïve customer. Customers with more experience are less likely to pay the price premium and rely on a reputation system.

The results help sellers to strategize in the online marketplace. Sellers that wish to compete in the e-market must understand the type of customers they are addressing and differentiate the way they treat customers based on the level of customer experience.

This research contributes to the reputation management and customer behavior literature by identifying the effects of customer experience on the relationship between the reputation system and price premium. The results address the conflicts found in previous studies by extending the explanation of the negative reputation price premium.

Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

This paper aims to solve the problem of information overload and reduce search costs. It proposes a social e-commerce online reputation formation model and community state-introduced model. A system dynamics trend simulation has been run to capture the relationship among the sellers, buyers, social e-commerce platforms and external environment to obtain an online reputation.

Empirical research relating to social e-commerce reputation has been used to confirm the influencing factors in social e-commerce, and a conceptual framework is developed for social e-commerce reputation formation. Thereafter, a trend simulation is generated to classify the relationship among the factors based on system dynamics. Also, the improved algorithm for community detection and a state-introduced model based on a Markov network are proposed to achieve better network partition for better online reputation management.

The empirical model captures the interaction effect of social e-commerce reputation and the state-introduced model to guide community public opinion and improve the efficiency of social e-commerce reputation formation. This helps minimize searching cost thereby improving social e-commerce reputation construction and management.

There is no appropriate online reputation system to be constructed to test the relationship proposed in the study for a field experiment. Also, deeper investigation for the nodes’ attributes in social networks should be made in future research. Besides, researchers are advised to explore measurement for the reputation of a given seller by using social media data as from Twitter or micro blogs.

Investigations that study online reputation in the social e-commerce are limited. The empirical research figured out the factors which can influence the formation of online reputation in social e-commerce. An SD model was proposed to explain the factors interaction and trend simulation was run. Also, a state-introduced model was proposed to highlight the effect of nodes’ attributes on communities’ detection to give a deeper investigation for the online reputation management.

The term “crowdwork” describes a new form of digital work that is organized and regulated by internet-based platforms. This chapter examines how crowdwork platforms ensure their virtual workforce’s commitment and control its performance despite its high mobility, anonymity, and dispersion. The findings are based on a case study analysis of 15 microtask and macrotask platforms, encompassing 32 interviews with representatives of crowdwork platforms, and crowdworkers, as well as an analysis of the platforms’ homepages and community spaces. The chapter shows that performance control on crowd platforms relies on a combination of direct control, reputation systems, and community building, which have until now been studied in isolation or entirely ignored. Moreover, the findings suggest that while all three elements can be found on both microtask and macrotask platforms, their functionality and purpose differ. Overall, the findings highlight that platforms are no neutral intermediaries but organizations that adopt an active role in structuring the digital labor process and in shaping working conditions. Their managerial structures are coded and objectified into seemingly neutral technological infrastructures, whereby the underlying power relations between capital and labor become obscured.

Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues.

In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users’ personal preferences.

The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method.

The recommendation approach modifies CBF method by considering the target user's group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group's preferences may also reflect an individual's preferences. The reputation-based group preferences of the target user's group can be used to complement the target user's preferences.

The purpose of this paper is to provide an argument for implementing a reputation system into virtual knowledge sharing programs.

The author formulates opinions based on workplace realities, professional insights and personal experience.

To help knowledge workers be more generous with their insights and information in a virtual knowledge sharing platform, personal reputation must be examined, assessed, and leveraged as part of the intrinsic value of the program.

Several practical implications emerge in this article: knowledge sharing should be virtual; it should occur outside of functional boundaries; systems should include reputation assessments; power should be in the hands of the individual participants; and no monetary incentive should be given to participate.

The ideas and methods for implementing a reputation system are new. Readers will discover the latest trend emerging in the area of virtual knowledge sharing and collaborative learning.

The purpose of this study is to decompose common reputation measurement systems into behavioural organisation–public relationship outcomes, cognitive representations of an organisation in the minds of publics and evaluations of organisational performance. In the proposed model, propensity for active communication behaviour and familiarity are suggested as correlated precursors of organisation–public relationship outcomes (eg trust, satisfaction, commitment and control mutuality) and organisation–public relationship outcomes are hypothesised to have a direct effect on evaluations of organisational performance as well as an indirect effect via the mediation of cognitive representations of the organisation. The authors investigated different types of five Korean‐based organisations )two domestic corporations in different industries, a multinational corporation, a sports association and a non‐profit organisation) to validate the model across different types of organisations. The findings of this study suggest that relationship outcomes lead to favourable representations of an organisation and positive evaluations of performance of the organisation.