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This study aims to explore the influence of corporate sustainability on organizational value, specifically focusing on companies ranked in the Just Capital Market ranking. The aim is to establish whether higher sustainability rankings are associated with increased firm value and to investigate how corporate social responsibility (CSR) activities affect both financial and non-financial outcomes.

This study uses the Ohlson model to assess the value-generation potential of the top and bottom ten companies in the Just Capital Market ranking from 2013 to 2018. The analysis involves evaluating stock prices and other financial metrics and incorporating non-financial indicators related to CSR activities to gain a comprehensive understanding of their impact on firm valuation.

The results indicate a strong connection between high sustainability rankings and increased market value. Companies such as Microsoft, Intel and Alphabet, which have robust CSR initiatives, have shown significant improvements in market performance due to greater stakeholder engagement and detailed non-financial disclosures. On the other hand, companies with low sustainability ratings have demonstrated weaker market performance, which indicates the financial risks associated with neglecting CSR activities. This study underscores the critical importance of integrating CSR into fundamental business strategies to create sustainable value.

This study addresses the limitations of traditional financial indicators by incorporating non-financial factors into the valuation process. The study offers a more comprehensive assessment of firm value, reflecting modern business practices and the evolving global economy landscape. Integrating nonfinancial indicators enhances valuation accuracy and provides a holistic view of company performance, enabling stakeholders to make informed decisions based on a broader range of factors. This innovative method may reshape firm valuations, leading to more accurate and reliable assessments in contemporary business contexts.

This study aims to assess the impact of information and communication technologies (ICT) and financial inclusion on economic growth. This study also examines whether ICT can be a determinant of financial inclusion. Moreover, this study provides new evidence concerning whether ICT can reduce the financial inclusion gap.

This study uses the country-level data over the period 2005–2019 and estimate using the dynamic and the static panel model.

The results show that the ICT and financial inclusion interaction variable substantially and positively impacts economic growth for only certain interaction variables. ICT is an essential determinant of financial inclusion and reduces some gaps.

This study contributes to the literature by considering whether ICT and financial inclusion impact economic growth in high- and low-income countries. The other contribution of this study is that ICT represents a determinant in promoting financial inclusion. The final contribution of this study is providing new evidence concerning whether ICT can reduce the financial inclusion gap so that financial access can increase, financial inclusion can develop and simultaneously encourage economic growth.

This research focuses on the controlling irreversibilities in a radiative, chemically reactive electromagnetohydrodynamics (EMHD) flow of a nanofluid toward a stagnation point. Key considerations include the presence of Ohmic dissipation, linear thermal radiation, second-order chemical reaction with the multiple slips. With these factors, this study aims to provide insights for practical applications where thermal management and energy efficiency are paramount.

Lie group transformation is used to revert the leading partial differential equations into nonlinear ODE form. Hence, the solutions are attained analytically through differential transformation method-Padé and numerically using the Runge–Kutta–Fehlberg method with shooting procedure, to ensure the precise and reliable determination of the solution. This dual approach highlights the robustness and versatility of the methods.

The system’s entropy generation is enhanced by incrementing the magnetic field parameter (M), while the electric field (E) and velocity slip parameters (ξ) control its growth. Mass transportation irreversibility and the Bejan number (Be) are significantly increased by the chemical reaction rate (C_r). In addition, there is a boost in the rate of heat transportation by 3.66% while 0.05⩽ξ⩽0.2; meanwhile for 0.2⩽ξ⩽1.1, the rate of mass transportation gets enhanced by 12.87%.

This paper presents a novel approach to analyzing the entropy optimization in a radiative, chemically reactive EMHD nanofluid flow near a stagnation point. Moreover, this research represents a significant advancement in the application of analytical techniques, complemented by numerical approaches to study boundary layer equations.

The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

In large-scale environments, LIO-SAM (Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping) exhibits poor robustness due to the accumulation of errors caused by factors such as the prevalence of similar surroundings and the lack of features in certain open areas. Therefore, the purpose of this study is to optimize the loop detection module of LIO-SAM to reduce error accumulation and enhance mapping and localization performance.

Based on the LIO-SAM framework, the LinK3D (Linear Keypoints Representation for 3D LiDAR Point Cloud) feature extraction algorithm is integrated in the front end, while the BoW3D (Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM) loop detection algorithm is integrated in the back end. The features extracted by LinK3D serve as the range factors for the LiDAR, the BoW3D generates loop closure factors and these, along with inertial measurement unit (IMU) preintegration factors and global positioning system (GPS) factors, are added to the factor graph of LIO-SAM. This addition of constraints enhances the mapping and localization effects, optimizing the overall mapping and localization performance.

Based on the electrically controlled car, experiments were conducted in the experimental scenario proposed in this paper. Compared to LIO-SAM, the method presented in this paper significantly reduces cumulative errors. While ensuring real-time performance, it demonstrates superior mapping and localization effects.

This paper proposes and validates a method that integrates LinK3D, BoW3D and LIO-SAM, named LB-LIOSAM, which enhances the accuracy of feature extraction, optimizes the loop detection module of LIO-SAM and improves its mapping and localization performance in specific environmental scenarios.

This study aims to provide a new perspective on the factors determining a country’s tourism performance, understand the interrelationships among these factors and explore their implications for the future of tourism in high-income countries.

The study employs a fuzzy-set qualitative comparative analysis (fsQCA) using five variables from the World Economic Forum’s Travel and Tourism Development Index (TTDI). The focus is on identifying seven configurations of antecedents of Travel and Tourism Industry Gross Domestic Product (T&T Industry GDP).

The study identifies seven configurations of antecedents influencing T&T Industry GDP, revealing how these factors operate in different scenarios, specifically in countries with high and low T&T GDP. These configurations offer insights into potential future pathways for tourism development.

The study implies that tourism is a complex phenomenon influenced by multiple interacting factors. It provides a framework for understanding how different combinations of factors can lead to high or low tourism performance, offering valuable insights for anticipating and shaping the future of tourism.

This study adds value by providing a more nuanced understanding of the tourism industry, challenging the notion of singular effects of variables and highlighting the importance of analyzing multiple, interacting factors in understanding and predicting tourism performance. It contributes to the field of futures studies by offering a tool for anticipating potential future scenarios and their impact on the tourism industry.

It is desired to provide a diversified iterative scheme for solving the constrained solutions of the generalized coupled discrete-time periodic (GCDTP) matrix equations from the perspective of optimization.

The paper considers generalized reflexive solutions of the GCDTP matrix equations by applying the Jacobi gradient-based iterative (JGI) algorithm, which is an extended variant of the gradient-based iterative (GI) algorithm.

Through numerical simulation, it is verified that the efficiency and accuracy of the JGI algorithm are better than some existing algorithms, such as the GI algorithm in Hajarian, the RGI algorithm in Sheng and the AGI algorithm in Xie and Ma.

It is the first instance in which the GCDTP matrix equations are solved applying the JGI algorithm.

The study investigates the influence of managerial discretion over accruals on banks' financial reporting quality. Furthermore, it examines the role of ownership in shaping managerial incentives to manipulate banks’ reporting quality in a developing economy.

The sample includes 37 Indian public- and private-sector banks from the fiscal year 2001–2022. The discretionary LLP (DLLP) is used to examine various managerial incentives and accounting quality. The models are estimated using panel fixed-effect regression and the system generalized method of moments. The results survive several sensitivity checks.

The results exhibit a low quality of financial reporting in public-sector banks, which is evident through the higher use of DLLP for income smoothing and signaling. In contrast, the low-capitalized private-sector banks employ DLLP to manage capital.

The study’s sample size is relatively small and focuses on a single country. Future researchers can investigate other emerging economies to better generalize the findings of this study.

The study highlights the influential role of ownership in shaping managerial incentives in the banking industry. Moreover, the study is of utmost importance for governments, regulators and policymakers in devising policies that reduce agency conflicts and improve financial stability in emerging economies.

The study subscribes to the growing literature on the role of ownership in influencing the banks’ financial reporting quality. To the best of the author’s knowledge, this is one of the limited studies in the context of government-owned vs private-owned banks in an emerging economy.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

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.