Search Results (21)

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies. Full article

With the growth of the urban population and economic level, the issue of urban parking space shortages (UPSSs) has assumed growing prominence. This persistent issue not only exacerbates traffic congestion but also contributes to environmental pollution, highlighting the need for system-oriented mitigation strategies. First, an algorithm for mitigating UPSSs based on nudge theory was constructed, in order to determine how the nudge strategies work. Second, nudge tools, including gain disclosure, salience, and outcome notification, were integrated to construct a mitigation model for UPSSs, which synthesizes nudge theory, the model of self-regulatory processes involved in behavioral change, and system dynamics (NT-SPBC-SD theory). Finally, four scenarios of natural development, guide adjustment, balanced regulation, and enhanced change were simulated. The findings of this study are as follows: (1) The UPSS mitigation had multiple overlapping effects and critical point effects, and the nudge strategy gradually decayed or even rebounded over time. (2) Under the enhanced change scenario, the degree of UPSSs, the amount of illegal parking, and CO₂ emissions from civil vehicles decreased by 21.2%, 6.93%, and 14.54%, respectively. (3) After quantitative comparisons, the balanced regulation scenario with lower implementation costs instead demonstrated superior overall performance. The results support subsequent research and guide the enhancement of urban parking management policies to advance urban sustainability. Full article

This paper captures a qualitative review of the regulatory prescribed stress tests for UK financial services designed by the Bank of England and the Prudential Regulation Authority (PRA)/Financial Conduct Authority (FCA) after the Global Financial Crisis. It presents a critical analysis of the use of stress testing as part of supervisory practices for UK banking institutions and insurance undertakings, commenting on their qualitative characteristics, after looking at the regulatory prescribed stress tests from three key categories: the macroeconomic scenarios for banks, denoted as the bank stress tests (BST), the insurance stress tests (IST), and the biennial exploratory scenarios (BES). In this study, five trends describing regulatory prescribed stress are identified: (1) the regulatory collaboration, (2) cross-industry stress tests, (3) exploratory scenarios, (4) reporting and disclosure requirements, and (5) the underlying modelling capabilities and tools. The associated challenges of (A) governance, (B) frequency, (C) individual disclosures, (D) data and modelling, and (E) capabilities and skillset from participating institutions underpinning these stresses are highlighted, shaping the policy recommendations for future exercises. These address the gaps identified from existing stress tests towards the effective prudential supervision of UK financial services, based on each scenario category, for improvements and advances to practices. Full article

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats. Full article

Addressing climate change necessitates reducing carbon emissions, with green behavior adoption being crucial. This study examines how green consumption awareness (GCA) and carbon emission disclosures influence consumer shopping channel choices, offering a practical approach to converting awareness into actionable behavior. Using stated preference (SP) data, we investigate the impact of green awareness and information disclosure on consumers’ choices between online and offline shopping channels. The key findings include the following: (1) GCA affects shopping channel choices in certain scenarios, though not always significantly. (2) Detailed carbon information disclosure steers consumers towards lower-emission channels, especially when specific carbon data are provided. (3) The type of goods significantly influences shopping channel decisions, serving as a variable across scenarios. (4) Effective scenarios, such as a 3 km shopping trip for categories like tissue and trash bags, where the difference in channel choice under varying levels of information disclosure is statistically significant, have been identified. These insights inform recommendations for information disclosure strategies that not only enhance GCA but also drive behavioral change, thereby fostering environmentally friendly consumption behaviors that contribute to a reduction in consumers’ carbon footprint. Full article

With the frequent occurrence of online public opinion events, the problem of product stigma is becoming increasingly serious. Enterprises must use effective quality information disclosure strategies to reduce losses affecting market sales and profit. Therefore, this paper aims to address the supply chain structure composed of one product manufacturer and one component manufacturer under the influence of stigma. It constructs a decision optimization model under three scenarios: no information disclosure, the product manufacturer disclosures information, and the component manufacturer disclosures information, and uses Stackelberg game theory to solve and analyze the model. Furthermore, we use numerical examples to verify the model results, and provide management suggestions for enterprises. The research results show that enterprises suffering from product stigma should actively implement information disclosure strategies to reduce their profit losses, and the lower the stigma level, the better the effect of information disclosure will be; when the stigma level becomes more serious, enterprises should take timely steps to reduce the sales price of products, the sales price of components, and the efforts to disclose information; for industries that value confidentiality of product information, although the implementation of information disclosure by the component manufacturer can require less effort for information disclosure, the two enterprises will suffer higher economic losses. Full article

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing. Full article

This study examines the impact of corporate characteristics on climate change governance among 100 of the world’s largest companies, with 1400 observations in the fiscal year 2020. We consider variables such as company location, size, profitability, female board representation, years of reporting using Task Force on Climate-Related Financial Disclosures (TCFD) guidelines, the inclusion of UN Global Compact and Global Reporting Initiative (GRI) information, Dow Jones Sustainability Index (DJSI) membership, MSCI ESG ratings, and the presence of a climate transition plan, a sustainability executive, and a sustainability board committee. Applying a multi-theoretical framework, we employ correlation analysis and univariate and multiple linear regressions to assess the relationships. Our findings reveal positive correlations between climate governance and the presence of a climate transition plan, MSCI ratings, DJSI membership, and the existence of a sustainability executive. Additionally, companies located in developed countries exhibit significantly higher levels of climate change governance. These results hold across various scenarios, offering valuable insights for researchers, academics, business leaders, practitioners, and regulators. With the growing importance of climate change reporting, understanding the key contributing factors for effective climate governance is crucial for organizations seeking to address this critical issue. Full article

Recently, as the non-face-to-face society persists due to the coronavirus (COVID-19), the Internet usage rate continues to increase, and input devices, such as keyboards and mice, are mainly used to authenticate users in non-face-to-face environments. Due to the nature of the non-face-to-face environment, important personal data are processed, and since these personal data include authentication information, it is very important to protect them. As such, personal information, including authentication information, is entered mainly from the keyboard, and attackers use attack tools, such as keyloggers, to steal keyboard data in order to grab sensitive user information. Therefore, to prevent disclosure of sensitive keyboard input, various image-based user authentication technologies have emerged that allow sensitive information, such as authentication information, to be entered via mouse. To address mouse data stealing vulnerabilities via GetCursorPos() function or WM_INPUT message, which are representative mouse data attack techniques, a mouse data defense technique has emerged that prevents attackers from classifying real mouse data and fake mouse data by the defender generating fake mouse data. In this paper, we propose a mouse data attack technique using machine learning against a mouse data defense technique using the WM_INPUT message. The proposed technique uses machine learning models to classify fake mouse data and real mouse data in a scenario where the mouse data defense technique, utilizing the WM_INPUT message in image-based user authentication, is deployed. This approach is verified through experiments designed to assess its effectiveness in preventing the theft of real mouse data, which constitute the user’s authentication information. For verification purposes, a mouse data attack system was configured, and datasets for machine learning were established by collecting mouse data from the configured attack system. To enhance the performance of machine learning classification, evaluations were conducted based on data organized according to various machine learning models, datasets, features, and generation cycles. The results, highlighting the highest performance in terms of features and datasets were derived. If the mouse data attack technique proposed in this paper is used, attackers can potentially steal the user’s authentication information from various websites or services, including software, systems, and servers that rely on authentication information. It is anticipated that attackers may exploit the stolen authentication information for additional damages, such as voice phishing. In the future, we plan to conduct research on defense techniques aimed at securely protecting mouse data, even if the mouse data attack technique proposed in this paper is attempted. Full article

The development of subterranean non-motorized traffic infrastructure, commonly referred to as the underground pedestrian system (UPS), has become increasingly necessary in densely populated megacities worldwide as a means of advancing the sustainable development goal 11, which aims to promote sustainable cities and communities. To improve the overall spatial performance, it is imperative to decipher the fundamental formation mechanism of sustainable underground pedestrian systems (SUPSs) that is simultaneously influenced by spatial morphology and pedestrian behaviors. Thereby, two representative case studies, namely the Wujiaochang UPS and the Loushanguanlu UPS located in Shanghai, were selected for an in-depth investigation. This study employed correlation and regression analysis to examine the impact of spatial configuration variables and spatial attribute factors on pedestrian flow distributions in distinct SUPSs. The findings indicate that the variables of betweenness, as measured by both Euclidean and Angular metrics, along with the presence of metro station locations and commercial space connected by the UPS, are the three most significant factors influencing pedestrian behaviors in both scenarios. The disclosure has been made that the Wujiaochang UPS is seamlessly integrated into a comprehensive three-dimensional pedestrian network both above and below ground. By contrast, it appears that the Loushanguanlu UPS exhibits a greater degree of self-sufficiency as an underground system. This study aims to elucidate the mechanism underlying the development of SUPSs, thus offering effective guidance for the implementation of three-dimensional walking systems in cities that prioritize sustainability. Full article

Forestry infrastructure plays a critical role in promoting tree growth to achieve carbon-neutral targets. However, as a quasi-public good, it faces challenges because of its non-excludability, meaning that everyone can use it whether they pay for it or not, which results in a phenomenon known as ‘free-riding’ and poor supply. In China, the government can regulate the supply behaviour by adjusting information feedback approaches, such as disclosing the supply value and revenue. This study examined three information feedback approaches: full feedback (disclosing supply and revenue), half feedback (disclosing only supply), and no feedback (no disclosure). It then combined these three information feedback methods with other three groups of variables, namely whether there was a reward or punishment mechanism, whether the return rate of forestry infrastructure was certain, and whether the foresters could communicate with each other, and 20 policy scenarios were designed. Using experimental economics, foresters’ supply behaviours in these policy scenarios were simulated. The results revealed that: (1) The scenario yielded the highest supply, which is with half feedback, certain return rate of forestry infrastructure, with a reward or punishment mechanism, and no communication. (2) When there is no reward or punishment mechanism, no communication, and the return rate of forestry infrastructure is certain, no feedback increases the supply. In the presence of rewards and punishments, half feedback leads to the highest supply. If there are no rewards or punishments but with a certain return rate and communication, full feedback results in the highest supply. (3) Implementing a reward or punishment mechanism and information feedback simultaneously increases the supply more effectively. The theoretical analysis and policy recommendations of this study aim to improve the supply status of forestry infrastructure. Full article

Transparency and disclosure (T&D) of information trigger the interest of all stakeholders, including investors in a company. Cognizance of the company’s financial health before investing is very necessary. Disclosure of information in the firm’s financial reports reflects the firm’s financial performance. A firm’s financial health protects investors’ and other stakeholders’ interests and the firm’s long-term sustainability. Owing to the importance of T&D and a firm’s financial health, this paper investigates the impact of T&D on the financial distress (FD) of non-financial firms (NFFs) listed in India. This study examines both linear and nonlinear connectivity of T&D and financial distress (FD). Their association is also investigated in a competitive scenario (under the moderating effect of competition). The panel data analysis is incorporated into the study having 78 NFFs as cross-sectional units with a timeframe from 2016 to 2020. Altman Z-score measures a firm’s FD (higher Z-score means low FD). BOS (Berger, Ofek and Swary) and AC (Almeida and Campello) scores are taken to consider investors’ perspectives of the firm’s FD. The T&D and Lerner indexes are used to assess the level of T&D and competition. The findings reveal that a higher T&D level decreases a firm’s financial stability or increases a firm’s FD. In nonlinear association, it is found that T&D has an inverted U-curved connection with financial stability or U-curved association with FD. It indicates that initially, higher T&D reduces FD, and after a threshold, it increases FD. However, under competition, T&D is not found to be significantly impactful for FD. The study is novel as no previous study has focused on such association under competition and taking investors’ perspective of a firm’s FD. Full article

Emerging adulthood is identified as a time of personal growth wherein emerging adults engage in sexual exploration and risky behaviors, potentially resulting in the contraction of a sexually transmitted infection (STI). Due to the continued reliance on parents for support during this developmental period, emerging adults (EAs) may need to disclose their STI status to their parents. This study applies the health disclosure decision-making model (DD-MM) to extend our understanding of EA disclosures of sensitive health information such as STIs to parents. Data were collected from 204 college students. The results of mediational analyses provided some support for the mediating effects of family communication patterns on the relationship between relational quality and illness assessment (i.e., stigma) and willingness to disclose in a given scenario. The theoretical and practical implications of this are discussed. Full article

Extraordinary economic conditions during the COVID-19 pandemic caused many IFRS 9 impairment models to produce unreliable results. Severe market reactions, resulting from unprecedented events, prompted swift action from the regulatory authorities to maintain the financial system’s stability. Banks managed the uncertainty and volatility in the models with expert overlays, increasing the risk of biased outcomes. This study examines new ways of enhancing the governance and transparency of the IFRS 9 economic scenarios within banks and suggests additional financial disclosures. Benchmarking is proposed as a useful tool to evaluate the IFRS 9 economic scenarios and ensure effective challenge as part of a model risk governance framework. Archimedean copulas are used to generate objective economic benchmarks. Ideas around benchmarking are illustrated for a set of South African economic variables, and the outcomes are compared to the IFRS 9 scenarios published by the six biggest South African banks in their annual financial statements during the pandemic. Full article

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results. Full article