Reputation Systems Research Articles

Securing IoT Sensors Using Sharding-Based Blockchain Network Technology Integration: A Systematic Review

Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.

Blockchain-based Anonymous Reputation System for Performance Appraisal

Blockchain-based Anonymous Reputation System for Performance Appraisal

Examining the Empirical Relationship Between Quality of Service (QoS) and Trust Mechanisms of Cloud Services

Service selection has emerged as a prominent challenge due to the flourishing demand for computing services and the dynamic nature of its resources. The increasing demand for cloud services makes it challenging to choose a provider offering equal services and facilities at costs that match those of competing providers. Apart from educating customers in the process of choosing cloud services, trust mechanisms include user reviews, reputation systems, and certifications assist to boost consumers’ confidence in cloud services. The service measurement index (SMI) offers a disciplined framework combining both functional and non-functional quality of service indicators concurrently, therefore easing decision-making. The main emphasis of the research is on the fundamental elements influencing the choice of cloud services in the present environment, the identification of extra characteristics of cloud services transcending SMI, and the identification of the most suitable approach for some services. By means of the measurement of customer enjoyment and experience, QoS traits provide some insight on the impact of trust mechanisms on service acceptance. Comparisons of SMI and QoS measurements before and after trust mechanism deployment provide insightful analysis. Empirical research guides these comparisons. This study aims to clarify the interactions among QoS, trust mechanisms, and cloud service adoption as well as highlight the implications these elements have for customers and service providers. Furthermore, presented in this paper is an algorithm using a comprehensive method to trust estimation in order to ascertain the degree of confidence worthiness of certain people.

Reputational Discrimination and Fairness in China’s Social Credit System

As the first nationwide reputation system, the Chinese Social Credit System (SCS) employs a plethora of information technologies to rate citizens according to a state-defined notion of trustworthiness. Complementing extant research about the SCS that highlights important concerns regarding privacy and surveillance, our work explores the economic facets of the system on the basis of microeconomic and informational theories. We identify reputational discrimination as the basic working principle of the SCS, and propose that in addition to carrots and sticks, which are often highlighted as the main tools of the SCS to incentivize individuals to trustworthy behavior, status is another important variable the planner disposes of. Using a contest model approach, we find that when status is used as a complementary instrument to rewards and punishments, a planner who cares about efficiency, morality, and strictness treats most unfair those that are already bad off. Our work adds another dimension to the policy analysis of the SCS, and advances the understanding of the informational design of the SCS by investigating the fairness-related concerns that the system raises for individuals. Our findings underscore the importance of incorporating fairness principles into the SCS, to not further widen existing economic and societal gaps.

How Do Reputation Systems Affect Commitment and Social Cohesion in Economic Exchange?

With the rise of the platform economy, economic interaction increasingly takes place under the regime of online reputation systems, which reduce uncertainty by publicizing others’ past behavior. Uncertainty, however, is central to the development of stable and cohesive relationships. The fundamental concerns are that reputation systems render personal, stable relationships obsolete and erode social cohesion. Grounded in social exchange theory, we propose two mechanisms through which reputation systems reduce commitment and inhibit social cohesion. These hypotheses are tested in a lab experiment simulating economic exchange with and without reputation systems. Contrary to our theoretical expectations, we find that reputation systems slightly reduce interactions between strangers and do not inhibit the development of cohesive ties. Although reputation systems reduce the expressive value of cooperation, they offset this undesired effect by increasing cooperation. Alleviating concerns about the social ramifications of the platform economy, the relationship structure appears largely unaffected by the reputation system. We conclude that actors interpret acts of cooperation differently in the presence of a reputation system, and market participants develop relationships not for purely functional reasons but as emotion-based by-products of economic exchange.

A Systematic Review of Blockchain-Based Privacy-Preserving Reputation Systems for IoT Applications

With the growing popularity of the Internet of Things (IoT), billions of devices are anticipated to be deployed in various industries without establishing trust between them. In environments without pre-established trust, reputation systems provide an effective method of assessing the trustworthiness of IoT devices. There has been considerable literature on deploying reputation systems in industries that have not yet established trust among themselves. Therefore, the article reviews published studies on reputation systems for IoT applications to date, focusing on decentralised systems and decentralised systems using blockchain technology. These studies are evaluated regarding security (including integrity and privacy) and non-security requirements to highlight open research challenges. In alignment with this, an analysis and summary of the existing review studies on reputation systems for particular IoT applications are presented, demonstrating the need for a review article to consider all IoT applications and those that have not been explored. The IoT applications and sub-applications are described, and their problem statement, literature to date and research gap are comprehensively evaluated. Finally, the open research challenges concerning reputation systems are reviewed and addressed to provide the researcher with a road map of potential research directions.

Consensus Clustering for Simulation-Based Reputation Measurement for Online Services

User ratings for online services can be considered a categorization of the services, forming an initial clustering of them. Online service reputation measurement based on user ratings can be viewed as a further clustering of services based on the users' categorization. However, current reputation measurement methods do not consider the consistency between service reputation and users' categorization of services, leading to unreasonable outcomes. In this paper, we propose a reputation measurement method for online services based on consensus clustering. First, a user-service rating matrix is established to obtain the categorization of services by users. Next, a similarity graph is constructed using a designed similarity measurement function, which is then segmented to generate meta-clusters. Finally, the correlation between clusters and user ratings is computed to determine service reputations with maximum consistency. Simulation experiments on the MovieLens dataset are conducted to verify the effectiveness and efficiency of the proposed method.

Quality of working life in remote platform work: a systematic review of a fragmented field of research

PurposeRevitalisation of quality of working life (QWL) research points to non-standard work such as remote platform work as a compelling setting where research on QWL is needed. Whereas the literature on working conditions in remote work platforms is rich, knowledge on the topic is fragmented. This systematic review aims to synthesize and integrate findings from existing literature to offer an encompassing and multidimensional understanding of QWL and the managerial practices linked to it in remote work platforms.Design/methodology/approachA systematic review of 24 empirical qualitative studies selected based on a multiple-database search using Boolean search engines. The selection of studies to be included in the review was performed through a four-steps procedure, following the PRISMA protocol. A thematic analysis of the studies was performed to synthesize findings.FindingsWe synthesize and show how remote platform workers experience a degrading QWL along five QWL dimensions, and we illustrate how these QWL dimensions are influenced by platforms’ managerial practices such as client-biased systems, rate-setting mechanisms, reputational systems, global competition schemes, lock-in systems, monitoring and nudging systems and information asymmetry.Originality/valueThe study contributes to reinvigorating QWL literature by producing a systematic synthesis of workers’ experience of QWL in the non-standard work context of remote platform work and the managerial practices that influence QWL. Our study overcomes two main shortcomings of the existing empirical studies published: (1) studies examine only a few QWL dimensions and/or (2) examine some platforms’ managerial practices that influence QWL, overlooking others.

Navigating the Gig Economy: Talent identification in tripartite work arrangements in Morocco

The gig economy is transforming the traditional employment landscape in Morocco, introducing both challenges and opportunities for talent identification. As online labor platforms such as Upwork, Fiverr, and Uber become increasingly prevalent, they facilitate the connection between freelance workers and SMEs seeking flexible labor solutions. This paper delves into the intricacies of tripartite work arrangements that encompass these platforms, the requesters (SMEs), and the gig workers themselves. By examining these relationships, we uncover the unique aspects of talent identification within the Moroccan gig economy. Our study employs a mixed-method approach, combining qualitative interviews and quantitative surveys to gather comprehensive data from various stakeholders.We identify that while online platforms employ standardized algorithms for talent identification, these methods often overlook local cultural and linguistic nuances critical to the Moroccan context. SMEs in Morocco tend to heavily rely on platform-mediated ratings and reviews, which underscores the trust placed in these digital reputation systems. Furthermore, our findings reveal that gig workers who invest in skill development and actively manage their online reputations are more likely to secure consistent and lucrative opportunities.This research provides a conceptual framework for understanding the complexities of talent identification in Morocco's gig economy, offering valuable insights for enhancing talent management practices. We propose several recommendations for platforms to refine their algorithms, for SMEs to develop more robust evaluation criteria, and for gig workers to focus on continuous skill enhancement. This paper not only contributes to the academic discourse on gig work but also provides practical guidelines for stakeholders to navigate and optimize talent identification processes in Morocco's evolving labor market.

Hospital Customer Competition Issues are being addressed through Confirmation Factor Analysis on Experiential Marketing, Brand Trust, and Loyalty

Introduction The post-COVID-19 pandemic has caused an epidemiological transition that has pushed the pace of development of the health service system even further. Issues and revolutions in the health service industry have made all health service industry providers compete with each other to get service users with competitive advantages in the potential global health market. The hospital has improved revenue sustainability while promoting quality assurance practices. These healthcare industry managers have become adept at introducing business practices into business operations to increase the market for hospital customers. Exploratory factor analysis on important marketing dimensions such as experiential marketing, brand trust, and patient loyalty is very much needed in the issue of competition in the health service industry in hospitals. Materials and Methods This research was quantitative research with a survey approach to outpatients with a sample size of n=255 patients according to Lemeshow (1997). Identification of loading factor analysis includes experiential marketing variables, brand trust, and loyalty. Data analysis used IBM SPSS Statistics 26 Results The research results showed 1) an important loading factor on the experiential marketing variable is the Think dimension indicator (0.845), 2) an important loading factor on the brand trust variable is the brand characteristic dimension (0.945) and 3) an important loading factor on variable loyalty is Repeat Purchase (0.925) Conclusion Dimension of brand trust is the most important dimension in the issue of competition in the health service industry in hospitals. Strengthening and expanding hospital branding as a promotion strategy to add positive value to the reputation of patient and community services at the hospital.

Research on Traffic Optimization and Growth Strategy under Precision Marketing -- A Case Study of Social Media Xiaohongshu

With the development and progress of social media, digital marketing has ushered in a new era. With over 200 million monthly active users, Xiaohongshu has shown brands the potential of precision marketing and traffic optimization on social media. Major brands have joined Xiaohongshu to improve their communication matrix. Therefore, this article adopts the literature analysis method to analyze the marketing status and reasons and problems for the existence of traffic on the Xiaohongshu platform, and explores strategies for optimizing traffic on the Xiaohongshu platform based on the 4C template. In the era of digital marketing, the Xiaohongshu brand needs to reposition itself accurately, promote personalized marketing, use technological methods to create creative content, attract user attention, and use diverse interactive methods to establish emotional resonance with users. In order to improve the drawbacks of Xiaohongshu’s traffic growth, enhance its traffic utilization rate, improve Xiaohongshu’s user reputation, and provide more accurate theoretical basis and development direction for social media precision marketing, ushering in long-term and healthy development.

Strategic insights for sustainable tourism development in Kashmir Valley: SWOT and QSPM analysis

Promoting sustainable tourism entails fostering environmentally responsible growth that endures, achieving a delicate balance between honoring the rich history, preserving culture, and navigating the challenges posed by diverse altitudes in the Kashmir Valley. A knowledge test was created to measure the stakeholders' understanding of sustainable tourism to guide further analysis with the selected stakeholders. Our research delved into this objective by conducting real-world analysis, including questionnaires and interviews with key stakeholders. A Quantitative Strategic Planning Matrix (QSPM) was utilized and built on the foundation of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) framework to drive sustainable tourism effectively. The results underscore the intricate landscape the valley operates within, revealing varied levels of stakeholder satisfaction across different aspects. Understanding these nuanced perspectives is crucial to charting a sustainable course for the region. In this light, the existing strategies aimed at managing the area, with a focal point on promoting sustainable tourism. The emphasis is on recognizing and tackling the challenges while seizing the weaknesses and threats that could impact the industry's reputation for excellent service and environmental stewardship. It is about aligning strategies to ensure a harmonious coexistence between tourism growth and the natural, cultural, and social fabric of this adorable valley.

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.

Fake It Till You Make It—A Statistical Assessment of the Proportion of Fake Reviews in Closed Reputation Systems

ABSTRACT Fake reviews have become an increasingly relevant issue in electronic commerce. Regulators and platform operators have begun to take action against fake reviews through regulation and litigation. While there has emerged an ample body of research on fake reviews in open reputation systems (i.e. where everyone can write reviews), little is known about the frequency and nature of fake reviews in closed reputation systems (i.e. where the completion of a transaction is a necessary precondition for reviewing). This article assesses the frequency of fake reviews in this particular domain of the platform economy. To do so, we draw on data from Airbnb and investigate systematic differences between first and second rating score distributions, estimating the proportion of potentially fake five-star ratings using KL-divergence. In doing so, we advance the understanding of fake reviews in closed reputation systems, offering theoretical insights into review dynamics and practical strategies for detecting fraudulent behavior using score distributions and user attributes. This shows that early-on reputation is crucial for complementors to overcome the cold-start problem, and platform operators will have to think of ways to allow them to do so (e.g. reputation imports) in order not to jeopardize the overall credibility of their reputation systems.

An Ensemble Method for the Fraud Detection in Transactions

In today's world, bank fraud has become one of the significant threats to the financial stability and security of clients of financial institutions. The development of technologies, in particular in the field of machine learning, opens up wide opportunities for building effective systems for detecting and preventing fraud in the banking sector [1, 2]. Detecting fraudulent transactions is an important task that requires thoughtful and technological solutions. One of these methods is the use of machine learning approaches and methods. This paper proposes the use of an ensemble method that combines several machine learning models at once. This approach will reduce the probability of false positives and increase classification accuracy. In addition, for the optimal operation of the model, preprocessing of the data will be carried out, in particular, their normalization, balancing of classes, as well as the selection of features. During the research, it is important not only to achieve high accuracy, but also to reduce as much as possible the number of fraudulent transactions that will be mistakenly classified as normal [3]. This is related to the business requirements of the banking sector, as each such transaction causes losses to the system's reputation, as well as direct financial losses. Within the framework of the study, it is substantiated that the use of this approach gives better classification results than single models due to the compensation of the shortcomings of each of them. The choice of this approach is also due to high practicality, compatibility with financial systems, as well as ease of integration. This paper analyzes the proposed model, its advantages and disadvantages in comparison with analogues. The ensemble method helps to combine the advantages of simple models and reduce the impact of their shortcomings on the final result. In general, the choice of software should depend on the technical requirements of the project and to obtain better results, different models and approaches should be analyzed.

Blockchain-based Reputation System for Smart Farm Anomaly Detection and Prevention

While smart farming is becoming increasingly popular as a way to improve the efficiency and sustainability of agroecosystems and global food security, its vulnerability to cyber-attacks is rapidly increasing. A distributed reputation system can help detect and prevent malicious activities by tracking the reputation of IP addresses of devices, products, and entities in a smart farming system, thereby enhancing its security and trust. In this paper, we propose a blockchain-based reputation system for IP addresses in smart farms for real-time intrusion detection and prevention. The study uses the Ethereum blockchain smart contracts, Oracles, Intrusion Detection System (IDS), and a Proof-of-Reputation (PoR) consensus mechanism. The system was evaluated on two datasets, the Spamhaus Blocklist, and the Malware Domain List datasets. It achieved high accuracy, recall, and precision rates, F1 score, as well as low false positive and false negative rates. This shows that the proposed system has high potential to be an effective tool for improving malware detection and prevention in real-time, creating a trusted supply chain for a smart farming ecosystem against a wide range of attack types, including denial-of-service attacks, probe attacks, and user-to-root attacks.

Legitimacy of Entrepreneurs in the Iron Triangle Concept. Which is Prioritized? Price, Quality or Service?

This study aims to understand how entrepreneurs, especially members of the Indonesian Young Entrepreneurs Association (HIPMI) Denpasar branch, prioritize and balance price, quality, and service factors in running their businesses. Using an interpretive approach and in-depth interview method, this study explores entrepreneurs' experiences and strategies in applying the Iron Triangle Concept to face market competition. The research findings show that the priority of one factor is influenced by market conditions, consumer needs, and the business's position in the growth cycle. Entrepreneurs who prioritize price tend to use competitive strategies to attract consumers, while those who prioritize quality focus on improving the reputation of products or services. On the other hand, superior service is an important factor in building long-term customer loyalty.This study confirms that business success lies in the ability to balance these three factors adaptively according to market dynamics. In addition, these findings provide practical insights for novice entrepreneurs in understanding how to manage the basic elements that are important for building business sustainability and legitimacy. This study contributes to the entrepreneurship literature by offering strategic guidance based on real experiences for entrepreneurs in managing the complexity of competitive markets.

Trusting the Untrustworthy: How trust is established and maintained in illicit markets and organised crime groups

Illicit markets and organised crime groups are characterised by risk and uncertainty. Inherent information asymmetry and the ubiquitous threat of law enforcement and adversaries, like scammers, create an environment that where cooperation and exchange would seem unlikely. To profit in illicit markets, however, cooperation is necessary and, given the potential payoffs, illicit actors find means to overcome the barriers to collaboration. This article explores how criminal actors cooperate in what would be assumed to be an uncooperative environment. Without the usual legal mechanisms, they establish trust to mitigate against such risks. This is done by leveraging social capital, emitting signals that are cheap to emit but costly to fake and using the threat of violent sanctions. In online markets, where anonymity generates further problems for cooperation, actors also use built-in reputation systems and chat forums to mitigate risk.

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.