Reputation Systems Research Articles

A variational formulation for a trust and reputation system

A variational formulation for a trust and reputation system

Service Reputation and Customer Loyalty in Ocean Freight Forwarders: An Exporters’ Perspective in Mindanao Regions

Customer loyalty is a vital component for businesses to achieve success and growth and that gaining customer loyalty can be sustainable by building a positive reputation. This study investigated the influence of customer satisfaction and trust as dimensions of the reputation of Ocean Freight Forwarders on customer loyalty. Data were collected from 180 exporting companies in Mindanao and a survey questionnaire was administered. A descriptive correlational design was employed, and the results showed positive and statistically significant relationship between customer loyalty, satisfaction, and trust. The findings revealed that exporters perceive ocean freight forwarders' reputation as highly satisfactory in terms of technical and functional qualities, and they have a very high level of trust in forwarders' benevolence and credibility. The results also showed strong loyalty in all dimensions of customer loyalty. Thus, the practical implications of this study are clear: prioritizing customer satisfaction and maintaining trust by the forwarders are essential for establishing a strong reputation. Therefore, ocean freight forwarders should consider these findings and continuously evaluate and improve their trust image and customer satisfaction strategies to enhance customer loyalty and maintain a competitive position in the dynamic export industry.

ARS-Chain: A Blockchain-Based Anonymous Reputation-Sharing Framework for E-Commerce Platforms

E-commerce platforms incorporate reputation systems that allow buyers to rate sellers after transactions. However, existing reputation systems face challenges such as privacy leakage, linkability, and multiple rating attacks. The feedback data can inadvertently expose user information privacy because they reveal the buyers’ identities and preferences, which deters a significant number of users from providing their ratings. Moreover, malicious actors can exploit data analysis and machine learning techniques to mine user privacy from the rating data, posing serious threats to user security and trust. This study introduces ARS-Chain, a pioneering and secure blockchain-driven anonymous reputation-sharing framework tailored for e-commerce platforms. The core of ARS-Chain is a dynamic ring addition mechanism with linkable ring signatures (LRS), where the number of LRS rings is dynamically added in alignment with the evolving purchase list, and LRS link tags are constructed with the LRS rings and item identifiers. Further, a consortium blockchain is introduced to store these anonymous ratings on e-commerce platforms. As a result, ARS-Chain ensures full anonymity while achieving cross-platform reputation sharing, making rating records unlinkable, and effectively countering multiple rating attacks. The experimental results confirm that ARS-Chain significantly enhances user information privacy protection while maintaining system performance, having an important impact on the construction of trust mechanisms for e-commerce platforms.

Bengali fake reviews: A benchmark dataset and detection system

The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.

OPTIMIZING VISITATION SERVICES: A CASE STUDY OF FEMALES DORMOF URDANETA DISTRICT JAIL

Jail visitation services lies in recognizing the inherent value of human connection, support, and accountability in promoting rehabilitation, maintaining human rights, and fostering successful reintegration into society for incarcerated individuals. This study focused on the assessment of the visitation services offered at the Womens Dorm of Urdaneta District Jail.The study made use of mixed method of quantitative and qualitative descriptive research design among the 27 Person Deprived of Liberty in Urdaneta District Jail Female Dorm. The research findings suggest that the visitation services at Urdaneta District Jail Female Dorm are highly satisfactory, with an overall rating of 3.74, indicating a high level of contentment among female Persons Deprived of Liberty (PDLs). This underscores the exceptional quality of visitation services provided by the jail, attributed to the provision of basic and exemplary services aimed at ensuring the well-being of PDLs and their families. Despite this overall satisfaction, specific areas for improvement have been identified. The E-dalaw area received the lowest mean rating of 3.41, indicating a need for enhancement in its equipment and facilities. Additionally, concerns related to sudden visitation cancellations, vaccination requirements for children visitors, and ventilation in the visitation area highlight key challenges that require attention to further optimize the visitation experience for both PDLs and their families. Addressing these issues will contribute to maintaining and enhancing the high standard of visitation services offered by Urdaneta District Jail Female Dorm.By addressing these challenges, Urdaneta District Jail Female Dorm can continue to uphold its reputation for exceptional visitation services while fostering a supportive environment for rehabilitation and reintegration.

Indirect reciprocity with Bayesian reasoning and biases.

Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation.

A Reputation System for Provably-Robust Decision Making in IoT Blockchain Networks

A Reputation System for Provably-Robust Decision Making in IoT Blockchain Networks

What drives consumers to post more photos in online reviews? A trait activation theory perspective

PurposeThis study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.Design/methodology/approachThis study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.FindingsThe reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.Practical implicationsAs a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.Originality/valueThis research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

Do Word-of-Mouth and Quality Have the Same Influence in Choosing a Doctor Online? A Study from Elaboration Likelihood Model Perspective

From the theoretical perspective of the elaboration likelihood model and service quality, this study attempts to provide an empirical study of patients’ medical choice behaviour and the moderating effect of disease risk under different paths with a real data set from an online healthcare community. The results demonstrate that factors from the central and peripheral routes affect patients’ behavioural intention regarding online medical choices. Disease risk plays an important moderating role. Low-risk patients are mainly influenced by factors from the peripheral route including doctors’ service reputation and disclosure of service qualifications, while high-risk patients are mainly influenced by factors from the central route including service quality of treatment and reviews quality.

Brand Ambassadors and Online Influencers on The Consumer’s Purchase Intentions in The Social Media Landscape

In recent years, the emergence of social media has altered the way consumers interact with digital marketing on a global scale. It also deals with the effect of social media marketing and management on product and service reputation. The use of endorser marketing by online influencers and brand ambassadors in digital marketing has skyrocketed. The current study takes a preliminary approach to determine marketing endorsers on the TikTok site that influence consumer’s intentions. It provides an integrated credibility VisCAP Model that takes into account accessing advertising functions valued among endorsers that can influence consumers' purchase intentions, such as attractiveness, trustworthiness and expertise. Consequently, those are the three criteria to determine the validity of this research. As a consequence of this present research, a theoretical framework can anticipate the relationship between online influencers and brand ambassadors in social media scenarios. The proposed framework is supplemented with a basic guideline for other corporations, advertising agencies and industries for future understanding of endorsement marketing.

OpenFL: A scalable and secure decentralized federated learning system on the Ethereum blockchain

Decentralized Federated Learning (FL) offers a paradigm where independent entities collaboratively train a machine learning model while preserving the privacy of their datasets. Integrating blockchain technology into decentralized FL frameworks is critical to establishing the trust necessary for user participation. However, existing FL systems using blockchain often struggle with scalability, latency, and privacy issues, particularly in permissionless blockchain contexts. This paper proposes OpenFL, a novel, collateral-backed reputation system implemented on the Ethereum blockchain. This system aims to foster trust among participants in a decentralized FL environment. We present a fully autonomous smart contract platform specifically tailored to facilitate FL processes among anonymous users. Furthermore, we address potential security concerns by detailing our strategies to mitigate various attack vectors. To validate our system’s efficacy, we conducted experiments on the Ethereum Ropsten testnet using the MNIST and CIFAR-10 datasets. Our findings demonstrate OpenFL’s capability to overcome the inherent limitations of permissionless blockchains while highlighting the significance of open-access protocols in this context. OpenFL can potentially broaden the participant base in trust-sensitive applications by reducing entry barriers, thus substantially contributing to decentralized machine learning.

The duality of reputation portability: Investigating the demand effect of imported ratings across online labor markets

Online labor platforms have been criticized for fueling precarious working conditions. Due to their platform-bound reputation systems, switching costs are prohibitively high and workers are locked-in to the platforms. One widely discussed approach to addressing this issue and improving workers’ position is the portability of reputational data. In this study, we conduct an online experiment with 239 participants to test the effect of introducing reputation portability and to study the demand effect of imported ratings. We find that the volume of imported ratings stimulates demand, although to a lower degree than onsite ratings. Specifically, the effect of imported ratings corresponds to about 35% of the effect of onsite ratings. The results imply the possibility of unintended cross-market demand concentration effects that especially favor workers with high rating volumes (“superstars”).

An intrinsic integrity-driven rating model for a sustainable reputation system

In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets – where misleading practices like fake ratings are rampant – remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions.Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system’s foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain.

Revolutionizing collaborative auditing: A dynamic blockchain-based cloud storage framework for data updates and assurance

Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services.

Blockchain Technology: A Framework for Endless Applications

Over the last decade, Blockchain Technology has evolved from being a peer-to-peer implementation of digital currency into a framework for numerous applications. That is, blockchain is widely recognized as the technology behind cryptocurrencies such as Bitcoin, but recent research indicates that it can be exploited way more than that. Among the most notable characteristics of the Blockchain Technology are system decentralization, data immutability, transaction transparency, and user privacy. Thus, it becomes apparent that it can significantly benefit many consumer applications, where trust and security are crucial aspects of the implementation. In this paper, we review and present, in a tutorial manner, blockchain's fundamental postulates and characteristics, and we introduce a novel point of view, where we visualize a layered representation of the blockchain infrastructure, so as to analyze it as a framework. Thinking of Blockchain Technology as a framework may assist consumers to better understand its capabilities and limitations. Additionally, we review emerging blockchain-based applications that span across many different scientific fields. We also discuss the relative merits and restrictions. Namely, we have identified applications in finance, smart contracts, internet of things, e-voting systems, storage and data protection, reputation systems, healthcare, and transportation, indicating that the Blockchain Technology is becoming a framework for endless applications.

SCFS-securing flying ad hoc network using cluster-based trusted fuzzy scheme

AbstractFlying Ad hoc Networks have emerged as a promising technology for number of real-time applications. However, the flexible and unstructured characteristics of these networks make them vulnerable to security threats posed by malicious nodes, such as denial of service attacks, node impersonation, and information breaches. Another major issue is the consideration of those nodes being unable to prove their trustworthiness due to factors like hardware or software failure, or by link interruptions, during the processing of detection of false nodes in the network. The existing mechanisms encompassing encryption, authentication, and intrusion detection highlight limitations to secure real-time applications and services due to the high speed of flying nodes and the absence of fixed network structures. To overcome these constraints, this research paper incorporates a novel framework for evaluating and improving the security of network by introducing an innovative cluster-based approach. Moreover, it presents a fuzzy model that dynamically estimates the trust levels of both individual nodes and clusters, by assigning weight to the parameters to address vulnerabilities. Additionally, a trust reconfiguration mechanism is further proposed to address the issue of nodes unable to substantiate their trust by providing them with additional chances based on the collective trust from previous evaluations. Further, the paper incorporates a dynamic reputation system to proficiently identify and separate malicious and selfish nodes from the network. Simulation results indicate a significant improvement in performance metrics, with a considerable reduction in delay and drop ratio by 41.46% and 36.37%, respectively, while the sufficient rise of 54.71% and 46.05% in throughput and coverage, respectively, comparing with the considered state-of-art.

The impact of fraud on reputation systems

Reputation systems can mitigate opportunistic behavior and improve efficiency on online marketplaces. However, rating fraud is a major problem on many websites. We experimentally investigate the circumstances under which rating fraud undermines the functioning of a reputation system and reduces efficiency. We find that the ability to manipulate feedback received from others generally reduces the reliability of displayed ratings compared to a control treatment where rating fraud is not possible. When manipulation is possible and costless, rating fraud is widespread, ratings become less reliable, expectations are lower, and efficiency is significantly reduced. However, when there is a cost to manipulation, there is less fraud, ratings are more reliable and efficiency increases, even when the cost is comparatively low.

User credibility evaluation for reputation measurement of online service

PurposeUsers often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.Design/methodology/approachIn this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.FindingsTheoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.Originality/valueThe proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

Evolution of Human Cooperation: An Archaeological Approach Focusing on Hunter-Gather Society

The history and development of cooperation between people is a major topic in many areas. The most popular ideas about how cooperation started are cultural group selection and the idea of charity. Scientists have different responses to them, though. People have evolved and survived in many places over a very long time, so they must have left behind a lot of proof. This paper looks into the main reasons why people work together and how cooperation has evolved by collecting historical samples and ethnic records and analysing them along with other research. The poll results show that the main reason people work together is to get what they want. To stay alive in natural settings that are very complicated, people work together to build trust and reputation systems. The creation of ways to respond to different cultures and punish people who don't follow the rules has helped to keep socialised systems of cooperation that were formed when cultures clashed or mixed. Cooperation is something that everyone does, but different places have their own religious beliefs and cultural practices that shape how people work together. A lot of different things affect how cooperative people are, and it's hard to come up with a good theoretical model just from the studies that have been done so far.

TRUSTWORTHINESS ASSESSMENT OF USERS IN SOCIAL REVIEWING SYSTEMS

Social networks represent a cornerstone of our daily life, where the so-called social reviewing systems (srss) play a key role in our daily lives and are used to access data typically in the form of reviews. due to their importance, social networks must be trustworthy and secure, so that their shared information can be used by the people without any concerns, and must be protected against possible attacks and misuses. one of the most critical attacks against the reputation system is represented by mendacious reviews. as this kind of attacks can be conducted by legitimate users of the network, a particularly powerful solution is to exploit trust management, by assigning a trust degree to users, so that people can weigh the gathered data based on such trust degrees. trust management within the context of srss is particularly challenging, as determining incorrect behaviors is subjective and hard to be fully automatized. several attempts in the current literature have been proposed; however, such an issue is still far from been completely resolved. in this study, we propose a solution against mendacious reviews that combines fuzzy logic and the theory of evidence by modeling trust management as a multicriteria multiexpert decision making and exploiting the novel concept of time-dependent and content-dependent crown consensus. we empirically proved that our approach outperforms the main related works approaches, also in dealing with sockpuppet attacks.