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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.

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.

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.

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.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

Cross-border e-commerce in China has been booming in recent years. This paper aims to study pricing in Chinese cross-border e-commerce companies and focuses on the baby food market, which is simply examined as a case study to highlight broader implications. In this intensely competitive sector, the biggest challenge faced by such companies is ensuring that they are in a position to be able set prices in the short-term to maximize their competitive advantage and profitability. The study of pricing will help management to make correct operational decisions.

This study utilizes transaction data, which were obtained from the Taobao e-commerce platform. Taobao is the largest e-commerce retail platform in the world. We analyzed factors, including business models, homogeneity, reputation ratings and sales volumes, which may affect pricing.

This study found that consumers in the baby food sector of Chinese cross-border e-commerce are not price-sensitive. Consumers are reputation-rating-sensitive. The reputation ratings of sellers affect the price dispersion in e-commerce markets. The Core Price Dispersion Rate Model not only considers the prices but also takes sales volumes into account in the calculations. Finally, based on Gaussian processes, a model was developed for price forecasting in the area of cross-border e-commerce. The experimental results show that the proposed method is highly valuable for price forecasting.

This study provides a novel understanding of the baby food sector in the Chinese cross-border e-commerce market by examining the business model, price dispersion, reputation rating and correlation between the reputation of sellers, prices and sales volume. Furthermore, a model for price forecasting is proposed.

The potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.

The black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.

Through the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.

All things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

The purpose of this paper is to investigate the varying ideologies guiding the cultural dimensions of family business and to examine the cultural sensitivity of these varying ideologies.

The research relies upon the CASE framework of nine cultural dimensions of family business. First, the literature pertaining to varying ideologies associated with each of the family business cultural dimensions is reviewed to form a conceptual analysis. Second, hypotheses are generated regarding the anticipated relationships between the two major dimensions of societal culture (power distance and in‐group collectivism) and the nine family business cultural dimensions. Data from the GLOBE program and the CASE project are then used to conduct non‐parametric tests.

The nine family business dimensions are shown as ideologies intersecting three systems of family business (family, business and social) and three social interaction elements (structural, relational and cognitive). Empirical support is found for the cultural sensitivity of the family business dimensions, in terms of the two major societal culture characteristics (power distance and in‐group collectivism).

This work provides insights into a broader conceptualization of family business in an increasingly global context. By virtue of the cultures in which they are formed, nurtured, and grow, family firms are influenced by a number of ideologies. Ideological differences – both quantitative and qualitative – mean that the forms and formats of family businesses also differ, as a reflection of their ideological and cultural underpinnings. In particular, it is useful to consider how family businesses differ, depending on their proportional support for the family, business and social system ideologies.