Reputation Value Research Articles

Impact of Student Attachment on University Reputation: An Analysis of Perceived Quality and Perceived Value

Many studies have referred to campus reputation but have not used the mediating variable of student attachment. This study aims to examine the impact of perceived quality and value on students and analyze how perceived quality and value affect university reputation through student attachment. The statistical population comprised students at campus X. The sampling technique used was simple random sampling on 212 students from Campus X in Bandung, Indonesia. The results show that value perception is positively and significantly influenced by perceived quality. Perceived quality and perceived value have a positive and significant impact on student attachment, student attachment has a positive and significant impact on university reputation, perceived quality has a positive and significant impact on university reputation through student attachment, and perceived quality has a positive and significant impact on perceived value through perception of quality. In the literature on consumer behavior, there are several studies that examine perceived quality on perceived value, perceived quality on student attachment, perceived value on student attachment, and student attachment on university reputation. The primary contribution of this research is to examine perceived quality and value of university reputation through student attachment. This research adds value to the consumer behavior literature by providing new insights into the determinants of university reputation in a private university.

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

A Blockchain Cross-Chain Transaction Method Based on Decentralized Dynamic Reputation Value Assessment

A Blockchain Cross-Chain Transaction Method Based on Decentralized Dynamic Reputation Value Assessment

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.

Media Review: The Value of Reputation

Media Review: The Value of Reputation

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

Partisans neither expect nor receive reputational rewards for sharing falsehoods over truth online.

A frequently invoked explanation for the sharing of false over true political information is that partisans are motivated by their reputations. In particular, it is often argued that by indiscriminately sharing news that is favorable to one's political party, regardless of whether it is true-or perhaps especially when it is not true-partisans can signal loyalty to their group, and improve their reputations in the eyes of their online networks. Across three survey studies (total N = 3,038), and an analysis of over 26,000 tweets, we explored these hypotheses by measuring the reputational benefits that people anticipate and receive from sharing different content online. In the survey studies, we showed participants actual news headlines that varied in (ⅰ) veracity, and (ⅱ) favorability to their preferred political party. Across all three studies, participants anticipated that sharing true news would bring more reputational benefits than sharing false news. Critically, while participants also expected greater reputational benefits for sharing news favorable to their party, the perceived reputation value of veracity was no smaller for more favorable headlines. We found a similar pattern when analyzing engagement on Twitter: among headlines that were politically favorable to a user's preferred party, true headlines elicited more approval than false headlines.

DBPBFT: A hierarchical PBFT consensus algorithm with dual blockchain for IoT

The Internet of Things (IoT) is composed of smart devices connected to a network that can send and receive large amounts of data with other devices, generating a lot of data for processing and analysis. Due to the fact that every transaction in blockchain is recorded, placed in a data block, and added to an immutable and secure data chain, blockchain is becoming one of the most promising solutions for enhancing IoT security issues. As more devices become intelligent, the scale of IoT systems, including residential IoT and industrial IoT, is on the rise. Consequently, the issue of resource consumption, stemming from the escalating system communication overhead, is becoming more pronounced. In order to improve the efficiency of the consensus process for residential IoT and reduce the overhead caused by the consensus process, this paper proposes a hierarchical PBFT consensus algorithm With Dual Blockchain for IoT (DBPBFT). Compared to industrial IoT, DBPBFT is more suitable for residential IoT with small scope and clear data classification. DBPBFT separates the responsibilities of dual chains, improving system scalability while also enhancing blockchain security. A chain is divided into several small groups, each responsible for a type of data, reducing system overhead and communication overhead. To avoid unnecessary view-change as much as possible, before consensus begins, each group will select the current view primary node based on reputation values. The simulation results show that the DBPBFT algorithm is superior to traditional algorithms. In terms of reducing communication overhead, compared with EPBFT and DPNPBFT, DBPBFT has increased by 73.8% and 53.1%, respectively. In terms of consensus efficiency, DBPBFT has improved by 34% compared to DPNPBFT.

On a security scheme against collusive attacks in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) offer a promising solution to bring drivers comfortable driving experiences and also improve road safety in intelligent transportation systems, but also faces many security issues. Collusive attack is one of the most challenging threats in VANETs because it violates the fundamental assumption made by VANET-based applications that all received information be correct and trustworthy. Collusive attackers can not only generate and send false or forged messages, but also purposely manipulate the reputation value of normal or malicious vehicular nodes. To address these issues, we analyze the behaviors characteristics of collusive attacks and propose a generic, lightweight, and fully distributed detection scheme against collusive attacks in VANETs. This scheme integrates two methods to identify different collusive attacks for fraud reputation and fraud message, respectively, as well as an incentive method to restrain collusive nodes. Simulation-based experiments are conducted and the results illustrate the superiority of the proposed security scheme over state-of-the-art methods.

Reaching the public with Twitter: The reputation value of CEOs

In this study, we investigate whether CEOs' use of Twitter influences their compensation. The results show that CEOs benefit from being active on social media because this participation helps reduce information asymmetry. Further, the effect of this Twitter usage becomes stronger when firms face competitive markets. We also use earnings announcements to show how CEOs' tweets can lower the deviations in stock prices around these announcements. Overall, a CEO's personal use of social media can help to reduce the information asymmetry between their firm and investors. This strategic tool benefits the CEO when negotiating compensation.

Calculation and selection scheme of node reputation values for notary mechanism in cross-chain

Calculation and selection scheme of node reputation values for notary mechanism in cross-chain

Optimizing Collaborative Crowdsensing: A Graph Theoretical Approach to Team Recruitment and Fair Incentive Distribution.

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism.

Improvement of PBFT Consensus Algorithm Based on Affinity Propagation Clustering in Intellectual Property Transaction Scenarios

In response to the problems of random selection of primary nodes, high communication complexity, and low consensus efficiency in the current consensus mechanism for intellectual property transactions, a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on the Affinity-Propagation (AP) clustering algorithm, termed AP-PBFT, is proposed. Firstly, the election strategy of the leader node is constructed based on the reputation mechanism; the reward and punishment mechanism is designed to achieve the dynamic adjustment of the reputation value of the nodes in the PBFT consensus process, and the number of votes among the nodes is introduced to determine the node’s reputation value in collaboration with the reward and punishment mechanism to guarantee the precise ordering of the nodes. Secondly, nodes with high reputation values are selected as cluster centers to run the AP clustering algorithm, and clustering groups of knowledge property transaction nodes are constructed based on responsibility and availability. Finally, the three-stage consensus process of the PBFT consensus algorithm is optimized, and the consensus task is decomposed into two layers: the intra-consensus group and the inter-leader node group, reducing the communication complexity of transaction data in the blockchain. Experimental findings indicate a significant performance improvement of the algorithm over the PBFT consensus algorithm in communication complexity, throughput, and consensus efficiency in the simulation environment of multiple types of transactions in intellectual property transactions, including different types of large-scale transaction scenarios, such as purchases, sales, licenses, and transfers.

A COMPARATIVE STUDY ON COMSUMER LOYALTY OF E-COMMERCE COMPANIES FLIPKART AND AMAZON

This study compares Flipkart and Amazon in particular to delve into the nuances of client loyalty in the Indian e-commerce business. It uses a mixed-method approach to find the key variables affecting customer loyalty by combining surveys with other methods. According to the study, developing a following of devoted clients requires a variety of factors. On the one hand, it is essential to have concrete features like a user-friendly platform, competitive pricing, variety, and high-quality products delivered quickly. The report does stress, though, that loyalty is more than simply business. Strong brand identity and ethical business practices are critical, as demonstrated by the value of brand reputation and trust. The study demonstrates how Flipkart and Amazon use various approaches to foster customer loyalty. Flipkart creates a feeling of community by customizing the shopping experience for its customers through engaging marketing campaigns, special deals, and tailored suggestions. On the other hand, in order to expedite the shopping experience and encourage recurring transactions, Amazon places a high priority on a wide range of products, a strong logistical network, and reward programs like Amazon Prime. Notably, both businesses use artificial intelligence and data analytics to improve customer satisfaction and personalize services.One of the main challenges facing the e-commerce industry is security issues. While consumers like the ease of online purchasing, the survey notes that distrust and loyalty can be hampered by worries about data privacy, cybersecurity, and fake goods. According to the study, Flipkart and Amazon have taken steps to allay these worries, including putting strong security measures in place, guaranteeing authenticity, and keeping open lines of communication with their patrons. KEY WORDS: INDIA, E-COMMERCE, LOYALTY, FLIPKART, AMAZON, STRATEGY, FACTORS, CUSTOMER.

Nested Markets and the Transition of the Agro-Marketing System towards Sustainability

We are currently witnessing a global transition (albeit slow) towards new, more sustainable models of development and consumption. This transition activates and highlights a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight that the global agri-marketing system does not provide adequate responses to the principles of sustainability. This is due to a mixture of opportunism, information asymmetries, and ‘lock-in effects’, which create serious market failures. This, in turn, brings structural holes, in which new forms of exchange are born. We identify these as nested markets: hybrid market forms that often use new information technologies and create a new form of proximity in which reciprocity and reputation play a central role. In this article, we argue that the market is not only the place where prices and quantities are assessed. Markets are complex social spaces, where more-or-less stable relationships are formed, based on values of reciprocity and reputation that contain opportunism. This article discusses the many well-documented cases of new markets. This article argues that these new markets are characterized by a strong specificity of the resources used (that include territory, sustainability, and solidarity).

Reputation is not enough: Ensuring strong order-fairness in Byzantine consensus

With the development of blockchain technology, Byzantine Fault Tolerance (BFT) is becoming an important research topic. The rPBFT consensus algorithm was introduced to address a range of shortcomings in the PBFT consensus algorithm, including high communication complexity, limited scalability, and a significant decline in performance when the system reaches approximately 100 nodes. Although rPBFT has been widely applied in the FISCO consortium chain, existing methods fail to ensure fairness in the power distribution among consensus member nodes and fine-grained node classification in rPBFT consensus. This work proposes a rPBFT consensus mechanism based on reputation value evaluation and supervision of consensus members. By implementing hierarchical management of nodes based on their reputation values, malicious nodes are eliminated, and supervision of consensus members is realized. The simulation experiment simulates the decision process of a variety of different nodes and consensus member jury with different proportions of 60%, 80% and 100% judges. The results show that the proposed scheme can dynamically update the node reputation value and classify various nodes. On the premise that the jury judges cast no less than 50% of the verdict votes, the malicious nodes in the consensus members can also be eliminated from the group of consensus member nodes. The scheme proposed in this paper effectively improves the fault tolerance of the rPBFT consensus mechanism, maintains the stability of the consortium chain network and ensures the security of the system.

Enabling Vehicle-to-Vehicle Trust in Rural Areas: An Evaluation of a Pre-Signature Scheme for Infrastructure-Limited Environments

This research investigates the deployment and effectiveness of the novel Pre-Signature scheme, developed to allow for up-to-date reputation being available in Vehicle-to-Vehicle (V2V) communications in rural landscapes, where the communications infrastructure is limited. We discuss how existing standards and specifications can be adjusted to incorporate the Pre-Signature scheme to disseminate reputation. Addressing the unique challenges posed by sparse or irregular Roadside Units (RSUs) coverage in these areas, the study investigates the implications of such environmental factors on the integrity and reliability of V2V communication networks. Using the widely used SUMO traffic simulation tool, we create and simulate real-world rural scenarios. We have conducted an in-depth performance evaluation of the Pre-Signature scheme under the typical infrastructural limitations encountered in rural scenarios. Our findings demonstrate the scheme’s usefulness in scenarios with variable or constrained RSUs access. Furthermore, the relationships between the three variables, communication range, amount of RSUs, and degree of home-to-vehicle connectivity overnight, are studied, offering an exhaustive analysis of the determinants influencing V2V communication efficiency in rural contexts. The important findings are (1) that access to accurate Reputation Values increases with all three variables and (2) the necessity of Pre-Signatures decreases if the amount and range of RSUs increase to high numbers. Together, these findings imply that areas with a low degree of adoption of RSUs (typically rural areas) benefit the most from our approach.

A framework for efficient cross-chain token transfers in blockchain networks

The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.

An Assessment of Digitalization Techniques in Contact Centers and Their Impact on Agent Performance and Well-Being

The role of contact centers in improving the operational efficiency of numerous organizations is of utmost importance. Presently, digitalization technology has enabled contact centers to deliver exceptional customer service and support, while minimizing the adverse impact on agent well-being. Artificial intelligence techniques such as topic modeling and sentiment analysis can aid agents in addressing specific queries, providing real-time support and feedback, and helping them build stronger relationships with customers. This study aims to investigate the advantages of integrating these techniques in the analysis of customer–agent conversations within contact centers. This study examines whether there is a discernible advantage in analyzing customer–agent conversations in real-time and whether it is worth using this type of digitization to enhance agent performance and well-being. Furthermore, this study explores the impact of these technologies on European privacy, business, real-time agent support, the value of conversation data, brand reputation, and customer satisfaction. The results of this study demonstrate the significance of incorporating topic modeling and sentiment analysis into the analysis of customer–agent conversations at contact centers.

RPRA: Reputation-based prioritization and resource allocation leveraging predictive analytics and vehicular fog computing

Predictive analytics and vehicle fog computing (VFC) have emerged as important methodologies for enabling seamless and instantaneous access to storage and information in real-time and facilitating the execution of resource-hungry smart applications. VFC-enabled IoT devices that use predictive analytics efficiently can process data in a timely manner. However, besides time sensitivity, better security and privacy solutions for several such smart systems, such as smart healthcare systems, are essential but have rarely been developed so far. With the nature of healthcare information being personal-specific and trust being of paramount significance, our research aims to fill a gap by proposing reputation-based prioritization and resource allocation (RPRA). It is a VFC-enabled framework for time-sensitive, computationally resource-deficient smart healthcare IoT devices that uses latency and energy as key constraints. RPRA is a subjective logic-based heuristic approach that uses context-dependent, indirect, and perceived reputation-based trust. Our method minimizes trust bias and trust-based assaults and uses static weight allocation and a specialized algorithm that guarantees utility maximization. We employed three distinct key metrics, i.e., individual perception, real-time perception, and marginal perception, to identify rogue nodes with hostile intent and remove them from active participation. In a dynamic VFC-enabled healthcare system, our approach outperforms in terms of utility-based reputation values with the highest range on the scale of [0,70] and the least algorithmic complexity.