Search results

This study examines the relationships between herding behaviour, market overreaction and financial stability in developed and Brazil, Russia, India and China (BRICS) markets from 1 January 2017 to 31 December 2023. It identifies the significant differences in these phenomena across different market types and their implications for financial stability.

This study employs panel data regression, quantile regression, Granger causality tests and the Baron and Kenny mediation model to analyse the data. These methods are used to explore the extent to which herding behaviour exacerbates market overreaction and affects financial stability.

The results reveal that herding behaviour exacerbates short-term market overreaction, leading to increased financial instability, particularly in BRICS markets. In contrast, herding behaviour does not significantly impact intermediate-term overreactions in developed markets. The study also finds that market overreaction significantly mediates the relationship between herding behaviour and financial stability.

These findings have practical implications for policymakers. Understanding how herding behaviour and market overreaction impact financial stability can help formulate strategies to enhance market stability and mitigate systemic risks, particularly in more volatile BRICS markets.

Enhanced financial stability has broad social implications, including improved investor confidence and economic growth. Policymakers can use these insights to create more stable financial environments, which can lead to more robust economic development and reduced vulnerability to financial crises.

This study provides new insights into the differential impact of herding behaviour and market overreaction on financial stability in developed and BRICS markets. By confirming the mediating role of market overreaction, this study enhances our understanding of financial market anomalies and contributes to the literature on financial stability.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

Technological innovations have affected and modified practically every element of human life and work. Education is one of the areas that has been greatly influenced by its dynamism. Technology has created an ecosystem in which education can be delivered via e-modes. The objective of this chapter is to study the impact of personalized learning on the student’s concentration level. The research methodology is top-down and includes both primary and secondary data sources. The sample was drawn using a probability-random sampling procedure with a response distribution of 50%, a margin of error of 10%, and a confidence level of 90%. The inquiry was carried out in various Indian states throughout the academic year 2020–2023. A 419-student sample was used in the study, and they were evaluated in two rounds using a pre-tested questionnaire. The association was determined for the experiencing group utilizing SPSS Version 25 and the analytic methods of factor analysis. This chapter presents options for higher education institutions, governments, and regulators to embrace and adapt. We are convinced that the study’s various recommendations will contribute to the development of a long-term strategy and plan to achieve the aim of ‘education for all, work for all’. The study investigates how the existing education model might adapt in the near future to keep up with the shifting paradigm and the arrival of Education 5.0.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.

We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.

Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.

Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.

The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

This study aims to investigate the causes of leakage in radial oil seals under dynamic eccentricity, elucidate the influence of operating parameters on leakage failure and develop methods for predicting and preventing such leakage.

Based on the principle of cam motion and considering viscoelasticity, develops a motion model of the compression and release of the shaft seal and proposes a method to determine its failure. In addition, this study quantifies the leakage gap and formulates a quantitative calculation model to accurately determine the location and shape parameters of the leakage gap.

Leakage gaps predominantly occur during the release phase of the shaft seal. Their presence can be identified by comparing the descending times of the seal and the shaft during this phase. An increase in rotation speed and eccentricity heightens the likelihood of gap formation, with both the dimensions and leakage rate of the gap increasing as these factors escalate. Eccentricity, in particular, has a more pronounced effect on gap formation.

This study clarifies the failure mechanisms of radial oil seals under dynamic eccentricity and introduces a criterion for identifying leakage gaps, providing valuable theoretical guidance for the design and optimization of radial oil seals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0192/.

In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance.

Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module.

The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz^1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa.

This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.

This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface.

The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects.

The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface.

Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces.

The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools.

Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration.

Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

The purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).

This study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.

The logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.

Previous researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

In this chapter, we describe the importance of good governance in the metaverse. It offers unlimited opportunities and presents unique governance challenges. First, we describe the concept of good governance and its relevance to the metaverse. We emphasize that the speed of metaverse adoption depends upon the presence or absence of effective governance. Recognizing the metaverse as the next iteration of the internet, we present significant governance issues. Some issues such as interoperability, security, safety, privacy, law, and digital inequality are critical governance issues in the metaverse. Next, we explore the diverse governance frameworks to ensure the implementation of policies and regulations. These frameworks include decentralized governance, cross-sector collaboration, and standards-based governance. We also describe the best practices which are essential for good governance. To materialize the concepts and principles discussed, we present a compelling case study centered on Decentraland. This insightful exploration dissects a decentralized autonomous organization (DAO)-based governance structure, offering valuable insights into the intricacies and stages of governance proposals. We acknowledge both the merits and potential drawbacks inherent to this approach. This chapter aims to offer an all-encompassing view of metaverse governance, essentially serving as a comprehensive roadmap for traversing the multifaceted landscape of this digital frontier.

Current developments in the FinTech payment industry have shown a rapid revolution in Industry 4.0, and understanding the factors affecting individual acceptance of facial recognition payment (FRP) is crucial. Hence, this study aims to evaluate the benefits and risks of FRP system adoption in Malaysia.

The perceived risks and benefits framework is adopted as the foundation in this study to examine the various risks and benefits that users perceive, along with the trust factor, to study the relationships between these variables. Data were collected via an online questionnaire, and the hypotheses were tested using Partial Least Squares analysis on 277 responses.

The results revealed that perceived risk is a significant predictor of users' intention to use the FRP system. Privacy risk and financial risk significantly influence perceived risks, while security risk does not. Although convenience, perceived ease of use and perceived trust positively influence perceived benefits, perceived benefits do not significantly influence adoption intention. Moreover, perceived trust negatively affects perceived risks while positively affecting both perceived benefits and adoption intention. Additionally, personal innovativeness moderates the relationship between perceived risks and the intention to use the FRP system.

This study helps policymakers and service providers understand individuals’ concerns and expectations regarding FRP systems. It aids practitioners in developing strategies to build trust, address innovativeness differences and mitigate risks, serving as a roadmap for integrating these systems into Malaysia's financial landscape.

This study distinguishes itself from prior research by evaluating FRP system adoption in Malaysia through the lens of perceived risks and benefits framework. It also explores personal innovativeness as a moderator, examining its impact on the relationship between usage intention and perceived risks and benefits. Additionally, it highlights perceived trust as a crucial factor influencing individuals' intention to adopt FRPs.