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For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

This study aims to investigate the relationship between corporate tax avoidance and stock price crash risk and the moderating effects of corporate governance.

This study investigates the relationship between corporate tax avoidance and stock price crash risk using the sample consisting of listed firms in Vietnam for the period of 2011–2020 using panel regressions.

The authors find that there is a positive relationship between tax avoidance and stock price crash risk. Foreign ownership weakens the impacts of tax avoidance on stock price crash risk, while managerial ownership strengthens the impacts. Female Chief Executive Officers (CEOs) and female chairpersons weaken this relationship. Board gender diversity and state ownership have insignificant moderating impacts.

These findings could help the stock market build better internal monitoring mechanisms to reduce the impacts of tax avoidance on future stock price crash risk. Investors can recognize the characteristics of corporate governance, especially foreign ownership, managerial ownership, female CEOs and female chairpersons when making investment decisions. The policy makers should consider policies to attract foreign investment and support women entrepreneurship.

This paper contributes to the literature on the impacts of tax avoidance on stock price crash risk in emerging countries. This paper is the first to investigate the influence of corporate governance mechanisms including state ownership, foreign ownership, female CEOs and chairpersons and board gender diversity on this relationship.

This study aims to investigate the institutional, macroeconomic and firm-specific determinants of financial leverage in Vietnam and provides new evidence from the dynamic panel fractional estimator.

This study uses a panel dataset of 859 Vietnamese firms from 2008 to 2022 and employs three estimators: Feasible Generalized Least Squares (FGLS), System Generalized Method of Moments (SysGMM) and Dynamic Panel Fractional (DPF), with DPF being particularly suitable for handling fractional dependent variables and the dynamic nature of financial leverage.

The results confirm the dynamic nature of the financial leverage model, with firm-specific factors, institutional factors and macroeconomic factors playing significant roles in shaping firms' financing decisions. The DPF estimator highlights the positive impact of stock market development on leverage. This study contributes to the literature by providing new evidence on the determinants of leverage in Vietnam, using the DPF estimator for more accurate estimation and revealing the significant impact of the size of the banking sector, the size of the stock market, the stock market development index, the financial development index and the corruption perception index on leverage.

This study contributes to the literature by providing new evidence on the dynamic nature of the financial leverage model and the impact of institutional, macroeconomic and firm-specific factors on financial leverage in the context of Vietnam. The use of the DPF estimator allows for a more accurate and reliable estimation of the determinants of leverage, considering the fractional nature of the dependent variable and the persistence of capital structure decisions over time.

Financial and nonfinancial disclosures (sustainable accounting) are crucial in the annual financial reports of many firms. This study aims to explore the dynamic relationship between sustainability disclosure quality (SDQ) and financial performance (FP) within mandatory disclosure frameworks. SDQ is evaluated across six dimensions, encompassing both the quality and quantity of disclosures, aiming to understand their reciprocal influence.

Using the generalized method of moments (GMM), this research analyzes data from 2013 to 2019, focusing on 99 listed Indian firms within the S&P Bombay stock exchange (BSE) 500 index. The study uses rigorous measurement criteria to assess SDQ and uses statistical methods to unveil the causal link between SDQ and FP.

The results show a positive causal connection between SDQ and FP, where organizations with good FP make relatively higher disclosures across FP proxies than their counterparts. Additionally, the study investigates the impact of research and development (R&D) expenditure and dividend payments (DIVD) on SDQ. Notably, lower R&D spending is associated with higher quality SDs, and companies with superior SDQ exhibit increased DIVD.

The findings advocate for strengthened regulatory compliance, incentivized sustainable practices and heightened reporting standards for a transparent business environment and achieving the relevant United Nations Sustainable Development Goals.

This research contributes original insights by uncovering the intricate relationship between SDQ and FP, shedding light on the impact of R&D expenditure and DIVD on SDQ. These findings contribute to a nuanced understanding of the interplay between FP and sustainability reporting within the context of mandatory disclosure frameworks.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

This study aims to investigate the direct nexus between board characteristics, corporate social responsibility (CSR) disclosure and the cost of equity capital (CEQ). This is done by using agency theory, stakeholder theory and signalling theory, followed by an investigation into the indirect mediation impact of CSR disclosure in the board characteristics-CEQ nexus. It intends to present new experimental evidence from Jordan’s developing economy.

The study’s target population was services companies registered on the Amman Stock Exchange (ASE) between 2012 and 2020. As a result, the population and sampling of this study are represented by all services companies for whom complete data are available over the period, with a total of 43 services companies yielding 387 company-year observations. Data for our study were obtained from their annual disclosures and the ASE’s database.

The main findings demonstrated that board size, board gender variety and the number of board sessions positively affect CSR disclosure significantly. In addition, three board characteristics (i.e. board size, board independence and board gender variety) significantly negatively affect CEQ. Besides, CSR disclosure significantly negatively affects CEQ and it fully mediates the relationship between two board characteristics (i.e. board size and board gender variety) and CEQ, whereas it partially mediates the nexus between board independence, CEO/Chairman duality and the number of board sessions of board characteristics and CEQ.

This study varies from earlier studies, in that it builds a new research model by looking at the mediating role of CSR disclosure in the nexus among board characteristics and the CEQ.

Climate change negatively affects agriculture and food security, and jeopardizes Myanmar's agriculture, which is vital to ensure food security, rural livelihoods, and the economy. This study explores the asymmetric impacts of climate change on Myanmar's agricultural sector.

We utilize the nonlinear autoregressive distributed lag (NARDL) approach for the years 1991–2020, the Wald test to validate the asymmetric relationship between climate change and agriculture, and the FMOLS and DOLS approaches to confirm the validity of the outcomes.

Our findings reveal that temperature has a positive impact on Myanmar's agriculture, whereas rainfall and CO2 have negative effects over the long and short terms. Evidently, decreasing temperatures more favorably impact agriculture than increasing temperatures, while increasing rainfall more negatively impacts agriculture than decreasing rainfall. Increasing carbon emissions have a more detrimental effect on agriculture than decreasing them.

We gathered data over periods longer than 30 years to provide more robust findings. However, owing to data limitations, such as missing values or unavailability, the study period spans from 1991 to 2020.

This study contributes to the existing literature on the asymmetric effects of climatic and non-climatic factors on agriculture. It is the first study in Myanmar to use the NARDL approach to measuring the effects of climate change on both the agricultural gross production index and value, providing robust findings.

This study explored students' perspectives on designing and implementing the new clinical interprofessional education (IPE) module for chronic disease management at the primary care level in the Vietnamese context.

Students from seven different university-level healthcare programmes participated in a cross-sectional survey Course Experience Questionnaire-based study. Additionally, two open-ended questions were presented to gather qualitative data, mapping student perspectives. Statistical analyses and thematic analyses were performed.

The results show that students agree with quality statements about IPE design features, such as good teaching, clear goals and standards, appropriate workload, appropriate assessment and a blended learning approach. Answers to the open-ended comments pointed out the strengths of the IPE module in providing opportunities for interprofessional learning, improving interprofessional collaboration competencies, real-life interprofessional collaboration practice and continuous feedback from tutors. Students also reported weaknesses in the IPE module, including a lack of feedback on the care plan and logistics of the IPE module.

Students positively recognise key design features of the IPE module, combining classroom activities with standardised patient simulations, clinical practice and home visits. Chronic disease management at the primary care level is a suitable context for training students to work interprofessionally. The strengths and weaknesses identified by students could help in the redesign and future implementation of the IPE module in the Vietnamese context. They could inspire practices in Southeast Asian medical education and beyond.

A shortage of investment capital is one of the main issues facing most developing economies, including Tanzania. Policymakers, economists, and governments in these countries have been debating how to overcome this obstacle and improve economic growth through foreign direct investments (FDI). The present study endeavors to examine the relationship between FDI and economic growth in Tanzania during the period of 1991–2021. The analysis considers financial development, capital formation, and labor force involvement as intermediation variables.

The study uses time series data to estimate an autoregressive distributed lag (ARDL) model, as well as Granger causality tests to investigate the relationship between the variables under study.

The findings show a long-run co-integration between the studied variables. However, only FDI and labor input have a significant impact on gross domestic product (GDP) growth in both the short and long run. Bidirectional causality exists between FDI, labor input, and GDP growth in the short and long run.

This study contributes to an understanding of the relationship between inbound FDI, labor input, and economic growth in Tanzania, as well as updating the government to improve its financial system to gain more from FDI inflows and hence boost growth-enhancing effects.

This study provides insight into the relationship between inbound FDI and economic growth in Tanzania, which is a critical policy concern in developing economies. Extant studies’ results remain controversial, and this is the first study in Tanzania to investigate the FDI-growth nexus, by considering financial development, capital formation, and labor input simultaneously.

This study aims to examine the influence of geopolitical risk (GPR) on corporate cash holdings in an emerging market, India. It also investigates whether the effect of GPR on cash holdings varies across financially constrained and unconstrained firms, and across the different sectors.

This study uses the fixed-effect regression model to examine the effect of GPR on the corporate cash holdings of 2090 Indian firms from 2003 to 2021. To correct the potential endogeneity issue and ensure the robustness of the results, this study uses two-stage least squares regression, alternative cash holdings proxies, GPR measures and across the different periods (Global financial crisis and COVID-19).

The paper finds that GPR has a positive impact on the cash holdings of Indian firms. The authors also find that the positive relationship between GPR and cash holdings is consistent for financially constrained and unconstrained firms. Furthermore, the results also show that firms in the construction sector maintain higher cash reserves than other sectors.

To the best of the authors’ knowledge, this study is one of the first-ever studies which examines the effect of GPR on corporate cash holding for an emerging economy like India. The use of alternative measures of cash holding, GPR, and estimation methods make this study more robust.