Search Results (277)

This study examines the impact of Environmental, Social, and Governance (ESG) disclosures on the firm valuation of non-financial firms listed in South Africa, using Tobin’s Q as a firm value proxy. Using a panel data approach of 642 firm-year observations from 2017 to 2022, the study applies Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimators to address possible endogeneity concerns. The results consistently show that, for the whole sample, ESG disclosures are positively and significantly related to firm value, thus supporting the view that markets reward transparency and sustainability initiatives. Firm size and liquidity also have positive impacts, while financial leverage has an inverse relationship with firm value. Subgroup regression analysis shows significant sectoral differences: ESG disclosure in non-manufacturing companies has a positive and significant relationship with firm value, in line with stakeholder and signaling theories, emphasizing the premium for intangible assets like reputation and trust. However, in manufacturing companies, ESG disclosure is negatively and significantly associated with firm value, implying concerns among investors regarding compliance costs, strategic misalignment, or possible “greenwashing.” The study contributes to the emerging-market literature by (i) introducing a PCA-based ESG index specific to JSE-listed non-financials, (ii) triangulating results across static and dynamic specifications to ensure robustness, and (iii) uncovering sectoral heterogeneity that has been largely overlooked. The research also has practical implications for corporate managers, policymakers, and investors on the alignment of ESG practices to industry attributes for long-term value optimization. Full article

The metaverse represents a transformative integration of virtual and physical worlds, offering unprecedented opportunities for social interaction, commerce, education, healthcare, and entertainment. Establishing trust in these expansive and decentralized environments remains a critical challenge. Blockchain technology, with its decentralized, secure, and immutable nature, is emerging as an essential pillar of trust and digital asset ownership within the metaverse. This paper provides an extensive review of blockchain-enabled trust and reputation frameworks specifically tailored to metaverse ecosystems. We present an in-depth analysis of existing blockchain solutions across diverse metaverse domains, including gaming, virtual real estate, healthcare, and education. Our core contributions include a comprehensive taxonomy that classifies current trust and reputation schemes by their underlying mechanisms, threat models addressed, and their architectural strategies. We provide a comparative benchmark analysis evaluating key performance metrics such as security robustness, scalability, user privacy, and cross-platform interoperability, revealing critical trade-offs inherent in current designs. Our analysis finds that score-based designs trade scalability for nuanced reputation representation, while SSI- and SBT-based approaches improve Sybil-resistance but introduce significant privacy governance challenges. Finally, we outline unresolved research challenges, including cross-platform reputation portability, privacy-preserving computation, real-time trust management, and standardized governance structures. Full article

To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing. Full article

Plastic recycling is critical to transitioning toward a circular economy (CE), yet traceability systems for recycled plastics remain unevenly adopted. While effective traceability supports transparency, compliance, and supply chain accountability, its implementation is shaped not only by technological readiness but also by organisational behaviours and strategic priorities. This study explores how traceability adoption is influenced by company size, internal CE strategy, and perceptions of cost, risk, and regulatory demand. A survey of 65 Australian industry stakeholders reveals that 76% of companies with a CE strategy have implemented traceability systems, compared to 42% without. Larger firms report higher adoption rates than small and medium enterprises, largely due to resource advantages and differing interpretations of traceability’s value. Key barriers include high perceived costs, lack of standardised frameworks, and scepticism toward digital tools. Conversely, motivations such as reputational benefits, regulatory alignment, and inter-organisational trust were identified as enablers, alongside emerging technologies like blockchain and chemical tracers. The findings underscore the role of organisational context in shaping traceability practices and highlight the need for tailored interventions. Recommendations include financial incentives, harmonised standards, and sector-specific guidance that address not only technical gaps but behavioural and structural factors limiting uptake. Positioning traceability as an integrated organisational strategy may accelerate its adoption and support broader circular economy outcomes across the plastics value chain. Full article

The rapid evolution of cyber threats requires intelligence sharing between organizations while ensuring data privacy and contributor credibility. Existing centralized cyber threat intelligence (CTI) systems suffer from single points of failure, privacy concerns, and vulnerability to adversarial manipulation. This paper introduces TrustFed-CTI, a novel trust-aware federated learning framework designed for privacy-preserving CTI collaboration across distributed organizations. The framework integrates a dynamic reputation-based trust scoring system to evaluate member reliability, along with differential privacy and secure multi-party computation to safeguard sensitive information. A trust-weighted model aggregation mechanism further mitigates the impact of adversarial participants. A context-aware trust engine continuously monitors the consistency of threat patterns, authenticity of data sources, and contribution quality to dynamically adjust trust scores. Extensive experiments on practical datasets including APT campaign reports, MITRE ATT&CK indicators, and honeypot logs demonstrate a 22.6% improvement in detection accuracy, 28% faster convergence, and robust resistance to up to 35% malicious participants. The proposed framework effectively addresses critical vulnerabilities in decentralized CTI collaboration, offering a scalable and privacy-preserving mechanism for secure intelligence sharing without compromising organizational autonomy. Full article

Greenwashing refers to the fabrication of environmental claims or the exploitation of unreliable data to support an unjustified green image. This study examines how greenwashing undermines sustainable marketing communication and consumer management by eroding trust-based brand–consumer relationships. Grounded in an integrated framework that combines the Theory of Planned Behavior, Expectation Confirmation Theory, and Consumer–Brand Relationship Theory, the research develops a cohesive model linking brand expectations, belief disconfirmation, consumer confusion, brand trust, and loyalty. Survey data from 375 Lebanese consumers were analyzed using structural equation modeling, confirming that subjective norms, perceived behavioral control, and behavioral beliefs significantly shape expectations toward green brands. When greenwashing is perceived, these expectations result in belief disconfirmation, which in turn heightens confusion, reduces trust, and weakens brand loyalty. The findings highlight that while greenwashing may offer short-term reputational benefits, it functions as a critical barrier to sustainable consumption by discouraging authentic engagement with environmentally responsible products. Theoretically, the study advances sustainable marketing literature by identifying expectation disconfirmation and confusion as psychological mechanisms that obstruct progress toward SDG 12 (Responsible Consumption and Production). The study’s innovation lies in integrating three behavioral and relational theories into a unified framework that captures both cognitive (disconfirmation, confusion) and relational (trust, loyalty) mechanisms. This theoretical integration offers a transferable analytical model that can be replicated across markets, generating broader insights into how deceptive sustainability communication affects consumer–brand dynamics. It also contextualizes these mechanisms within a developing-market setting, where weak regulation and fragile institutional trust amplify the risks of greenwashing. Practically, the study emphasizes the need for transparent sustainability communication as both an ethical responsibility and a consumer management strategy essential for fostering loyalty. For policymakers, the results underscore the importance of stronger regulatory oversight, eco-labeling standards, and consumer protection frameworks to mitigate deceptive sustainability claims. Full article

Federated learning (FL) is a foundational technology for enabling collaborative intelligence in vehicular edge computing (VEC). However, the volatile network topology caused by high vehicle mobility and the profound security risks of model poisoning attacks severely undermine its practical deployment. This paper introduces DTB-FL, a novel framework that synergistically integrates digital twin (DT) and blockchain technologies to establish a secure and efficient learning paradigm. DTB-FL leverages a digital twin to create a real-time virtual replica of the network, enabling a predictive, mobility-aware participant selection strategy that preemptively mitigates network instability. Concurrently, a private blockchain underpins a decentralized trust infrastructure, employing a dynamic reputation system to secure model aggregation and smart contracts to automate fair incentives. Crucially, these components are synergistic: The DT provides a stable cohort of participants, enhancing the accuracy of the blockchain’s reputation assessment, while the blockchain feeds reputation scores back to the DT to refine future selections. Extensive simulations demonstrate that DTB-FL accelerates model convergence by 43% compared to FedAvg and maintains 75% accuracy under poisoning attacks even when 40% of participants are malicious—a scenario where baseline FL methods degrade to below 40% accuracy. The framework also exhibits high resilience to network dynamics, sustaining performance at vehicle speeds up to 120 km/h. DTB-FL provides a comprehensive, cross-layer solution that transforms vehicular FL from a vulnerable theoretical model into a practical, robust, and scalable platform for next-generation intelligent transportation systems. Full article

In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This paper proposes a trust assessment mechanism for vehicular federated learning based on graph neural network (GNN) edge weight similarity. An explainable asynchronous federated learning data sharing framework is designed, consisting of permissioned asynchronous federated learning and a locally verifiable directed acyclic graph (DAG). The GNN connection weights perform reputation assessment on edge devices through DAG-based verification, while deep reinforcement learning (DRL) enables explainable node selection to improve asynchronous federated learning efficiency. The proposed explainable incentive mechanism based on GNN edge weight similarity and DAG can not only effectively prevent malicious node attacks but also improve the fairness and explainability of federated learning. Extensive experiments across different participant scales (30–200 nodes), various asynchrony degrees ($\alpha$ = 1–5), and malicious node attack scenarios (up to 50% malicious nodes) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving up to 99.2% accuracy with significant improvements of 1.3–3.1% over existing trust-based federated learning methods and maintaining 95% accuracy even under severe attack conditions. The results show that the proposed scheme performs well in terms of learning accuracy and convergence speed. Full article

This study explores the role of social trust, a critical informal institution, in mitigating corporate brownwashing—the strategic concealment of positive environmental performance. Drawing on a panel of 15,081 firm-year observations from Chinese A-share listed firms between 2010 and 2022, we operationalize brownwashing as a strategy where firms demonstrate substantive environmental compliance (i.e., no environmental penalties) while simultaneously practicing symbolic verbal conservatism (below-median environmental disclosure). Our empirical analysis reveals that higher regional social trust significantly curbs the propensity for firms to engage in brownwashing. This effect is not only statistically significant but also economically meaningful: a one-standard-deviation increase in social trust is associated with a 1.85 percentage point decrease in the likelihood of brownwashing. This effect operates through two key channels: enhancing stakeholder monitoring and reinforcing internal governance for environmental accountability. The impact of trust is significantly amplified under specific conditions: its role is more pronounced where formal sustainability regulations are weaker, highlighting trust as a crucial informal pillar of the sustainability governance architecture, and its inhibitory effect is strengthened when firms face higher reputational risks tied to their environmental performance. This study makes several contributions: it provides broad, cross-industry evidence on a key challenge in sustainability reporting; offers empirical support for the “trust fidelity” theory in the context of environmental disclosure; and develops a ‘channel-amplifier’ framework that advances our understanding of the complex institutional interplay required to foster corporate environmental transparency. Full article

This study aims to develop and evaluate an AI- and big-data-based innovation system for proactively managing ESG (Environmental, Social, and Governance) risks within the tourism supply chain. Drawing on heterogeneous data sources including supply chain records, news articles, social media, and public databases, the research employs advanced methodologies such as network analysis, anomaly detection, natural language processing (including greenwashing detection), and predictive modeling. Through this comprehensive approach, the study demonstrates the feasibility and effectiveness of a dynamic AI-driven ESG risk management system that delivers reliable risk identification and quantitative performance evaluation. The theoretical contribution lies in bridging AI-driven ESG evaluation frameworks with sustainable tourism and hospitality literature, moving beyond static, indicator-based assessments toward a more systematic, replicable, and predictive methodology capable of capturing the dynamic, multiscalar, and networked nature of tourism supply chains. Ultimately, this research provides tourism and hospitality firms with a powerful tool to enhance transparency, mitigate ethical and reputational risks, and strengthen stakeholder trust, while offering actionable insights for managers and policymakers developing data-driven ESG integration strategies. Full article

Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem. Full article

Mobile banking applications play a crucial role in providing users with access to financial services, and the quality of user experience is a key factor for their sustainability. This study investigates the predictability of application quality signals derived from user ratings of five leading mobile banking apps in Türkiye. The main problem addressed is understanding how these user-driven quality indicators evolve over time and identifying effective methods for forecasting them. This research problem is critical for understanding how banks can monitor customer satisfaction and reputational risk in real time, as fluctuations in app ratings directly affect user trust and engagement. For this purpose, daily average rating series collected from the Google Play Store were analyzed using LSTM-based time series models, and the results were benchmarked against the seasonal naïve (SNaive) method. The findings show that LSTM consistently achieved lower error rates across all banks, with particularly reliable forecasts for YapıKredi and Akbank, where MAPE values ranged between 16% and 28%. However, low R² values for some banks suggest limitations in long-term forecasting. The contribution of this study lies in demonstrating that user experience signals in mobile banking can be systematically monitored from a time series perspective, and that LSTM-based approaches provide a more effective method for capturing these quality dynamics. Full article

Using platform self-operation, customer reviews, and compensation commitments as traditional benchmarks, this study foregrounds blockchain traceability as a technology-enabled authenticity signal in cross-border cosmetic e-commerce (CBEC). Using an 8-scenario orthogonal experiment, we test a model in which perceived risk mediates the effects of authenticity signals on purchase intention. We probe blockchain boundary conditions by examining their interactions with traditional signals. Our results show that blockchain is the only signal with a significant direct effect on purchase intention and that it also exerts an indirect effect by reducing perceived risk. While customer reviews show no consistent effect, self-operation and compensation influence purchase intention indirectly via risk reduction. Moderation tests indicate that blockchain is most effective in low-trust settings—i.e., when self-operation, reviews, or compensation safeguards are absent or weak—while this marginal impact declines when such safeguards are strong. These findings refine signaling theory by distinguishing a technology-backed signal from institutional and social signals and by positioning perceived risk as the central mechanism in CBEC cosmetics. Managerially speaking, blockchain should serve as the anchor signal in high-risk contexts and as a reinforcing signal where traditional assurances already exist. Future work should extend to field/transactional data and additional signals (e.g., brand reputation, third-party certifications). Full article

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article

Supply chain financing offers advantages over traditional channels such as bank loans and equity financing, including greater flexibility, lower transaction costs, and simplified approval procedures. However, when a firm’s sustainability faces uncertainty, access to supply chain financing may become constrained by multiple factors, including the risk tolerance of supply chain partners, market transparency, and corporate reputation. ESG, representing Environmental, Social, and Governance standards, is a critical framework for assessing corporate sustainability performance. Given that divergent ESG evaluations reflect disparate market assessments of a firm’s sustainable development capabilities, such divergence may affect supply chain financing by altering stakeholder trust dynamics. This research examines A-share listed firms in China (2016–2022) and reveals that divergence in ESG evaluations significantly inhibits firms’ access to supply chain financing. Mechanism validation suggests that divergent ESG evaluations amplify informational opacity, operational risks, and negative reputation, thereby influencing supply chain partners’ risk perceptions and trust levels. Heterogeneity analysis shows that corporate governance quality, regional trust levels, and ESG awareness modulate the negative impact of divergent ESG evaluations on supply chain financing. The asymmetric effects of divergent ESG evaluations on supply chain financing are further confirmed, with distinct manifestations between upstream suppliers and downstream customers. By bridging gaps in existing research on divergent ESG evaluations and supply chain finance, this work offers regulatory guidelines, operational recommendations for firms, and investment decision frameworks. Full article