Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty.

With the introduction of Industry 5.0, which emphasizes greater social responsibility and sustainable development for companies, it is imperative to explore the intricate relationship between digital transformation (DT) and environmental, social, and governance (ESG) rating. This study aims to understand how DT impacts ESG rating, considering the evolving competitive advantage of firms from the perspectives of resource-based view theory (RBV) and knowledge management (KM) theory. This study examines the relationship between DT and ESG rating, as well as the mediating effect of green innovation (GI) in this relationship. Utilizing data from 360 Chinese manufacturing firms listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2010 to 2022, the findings reveal that with the exception of digital technology and data analysis, DT was shown to have significant impacts on corporate ESG rating. Specifically, digital business strategy and leadership, and human resource capabilities and culture showed positive relationships, while transformative organization and operations exhibited a negative relationship. Furthermore, GI was found to mediate the relationship between DT and ESG rating. By uncovering the internal mechanisms through which DT affects ESG rating, this study provides valuable insights for companies looking to enhance their sustainable practices within the framework of Industry 5.0.

This paper uses data from Chinese listed companies (2013–2023) to investigate how financial shared service centres (FSSCs) affect ESG performance. The results indicate that through strengthening internal controls, environmental protection, and employment standards, FSSCs considerably improve ESG performance. After controlling for endogeneity and employing different variable measures, these impacts are still strong. Additionally, this relationship is positively moderated by corporate digital transformation; FSSCs have a greater ESG impact on companies with greater digital maturity. The impact is most noticeable for the East-Central Chinese businesses, state-owned enterprises, and those with limited funding. These findings provide important direction for policymakers seeking to advance sustainable business practices through organisational change and demonstrate how internal management innovations, such as FSSCs, can help achieve the Sustainable Development Goals (SDGs).

Thailand’s advantageous location, strong infrastructure, and uptake of digital technologies all contribute to its standing as a Southeast Asian centre for logistics and supply chains. The impact of internal skills on performance during interruptions and how Thai-listed firms handle the risk of supply chain disruptions are poorly understood. By combining the resource-based view, contingency theory, and dynamic capabilities theory, this research fills this knowledge gap by investigating how industry reacts to particular threats, including those involving infrastructure, planning, workforce, and security, and how their mitigation strategies—such as internal risk management and collaboration—affect firm performance. The research investigates 14 hypotheses that relate disruption threats and mitigation techniques to firm performance using data from 167 listed firms on the Stock Exchange of Thailand (SET), survey data, and statistical analysis. The findings show that while well-managed employees and security concerns may have beneficial effects, infrastructure hazards significantly impair corporate performance. Performance is improved by internal resources and cooperative partnerships with supply chain partners, although collaboration with government organisations might be less successful. The results also founded on three recognised ideas, the research contributes to theory and in action by giving governments and businesses advice on how to prioritise resilience investments. To increase Thailand's total supply chain resilience in accordance with national plans like Thailand 4.0, recommendations include enhancing infrastructure, customising regional responses, and fostering digital capabilities. Future studies on sector-specific hazards, resilience indicators, and cross-country comparisons in an ASEAN context are suggested by the findings.

Technology Acceptance Model (TAM) was used to look at the factors that affect people's willingness to use new technologies. The study focused on how TAM can be used in smart electric vehicle applications. The key variables examined comprised perceived performance, interface usability, and user awareness. The sample consisted of 249 owners of electric vehicles in Thailand. The results confirmed that the perceived usability of the application—which includes features such as real time charging status monitoring and the convenience of locating charging stations—positively influenced users' attitudes. Moreover, a user centric interface enhanced customer satisfaction and acceptance, thus affecting their intention to persist in using the application. It was found that user experience is very important for making new technologies work well with existing ones. The study also suggested ways to make apps that work better with users' tastes in the future. Henceforth, developers should prioritize intuitive design principles and incorporate user feedback throughout the development process to ensure that applications not only satisfy functional requirements but also elevate overall user engagement. Through this approach, they can develop methods that enhance lasting allegiance and stimulate greater uptake rates within the market.