Low-cost housing homeownership funding for junior staffers is challenging in private sector organisations, especially in developing countries. Motivating private sector investment in junior staffers’ homeownership via a developed expanded corporate social responsibility (ECSR) may promote achieving Sustainable Development Goal 11 (SDG 11). Therefore, the study investigates the role of the ECSR framework in improving Nigeria’s private sector junior staffers’ homeownership and achieving SDG 11. Data were collected via face-to-face interviews with selected participants in six of Nigeria’s geo-political zones. The study adopted thematic analysis to analyse the collected data. Six variables emerged from the 18 re-clustered sub-variables. This includes institutionalising ECSR in low-income homeownership, housing finance for junior staffers’ homeownership, and housing incentives and stakeholders’ participation for low-income earners. The research employed six variables and 18 sub-variables to develop the improved private sector’s junior staffers’ homeownership via ECSR and achieving SDG 11 (sustainable cities and communities) and their targets. The research presents a novel approach by attempting to integrate SDG 11 with Corporate Social Housing, an extension of corporate social responsibility, especially to align the SDGs with evolving perspectives on Expanded Corporate Social Responsibility in Nigeria.

This research reviews the environmental, social, and governance (ESG) performance of corporate social responsibility (CSR) and technology innovation development, and analyzes the impact of technology innovation on ESG performance and its influencing mechanism. In additional, the main purpose of this study is to gain an understanding the relationships of ESG performance, CSR and technology innovation in Art industry. We found that technology innovation impact CSR of art firm, and ESG performance with the moderating variable of technology innovation has a significant and positive impact on CSR. Likewise, the study is based on primary panel data collected from 161 consumer, product and service manufacturing companies through an electronic questionnaire (Google, Microsoft online survey) with five-point Likert measurement scale. The exploratory factor analysis is proposed to be carried out using IBM SPSS 27.0 and the confirmatory factor analysis (CFA analysis) is proposed to be carried out using SmartPLS.4.0 analysis software, and this study investigate the measurement factors and the reliability of the construct items and to validate the factorial structure of the research variables. Moreover, digital technology and CSR has the potential to contribute to this impact. Based on these findings, we propose relevant ESG performance recommendations to improve technology innovation and CSR. Our findings offer an excited knowing and learning of the impact of ESG performance, CSR and technology innovation in Chinese art industry. Furthermore, this study extends stakeholders theory and Schumpeter’s Innovation Theory by proving their utility in the perspective of CSR, ESG performance.

In engineering, a design is best described based on its alternative performance operation. In this paper, an electric power plant is analysed based on its effective operational performance even during critical situation or crisis. Data is generated and analysed using both quantitative and qualitative research approach. During maintenance operation of an electric power plant, some components are susceptible to wide range of issues or crises. These includes natural disasters, supply chain disruptions, cyberattacks, and economic downturns. These crises significantly impact power plant operations and its maintenance strategies. Also, the reliable operation of power plants is often challenged by various technical, operational, and environmental issues. In this research, an investigation is conducted on the problems associated with electric power plants by proposing a comprehensive and novel framework to maintenance the power plant during crises. Based on the achieved results discussed, the framework impact and contribution are the integration of proactive maintenance planning, resilient maintenance strategies, advanced technologies, and adaptive measures to ensure the reliability and resilience of electric power plant during power generation operations in the face of unforeseen challenges/crisis. Hypothetical inferences are used ranging from mechanical failures to environmental constraints. The research also presents a structured approach to ensure continuous operation and effective maintenance in the electric power plant, particularly during crisis (such as environmental issues and COVID-19 pandemic issues).

With the progress of the times, the development of information technologies such as big data, artificial intelligence, and cloud computing is also in full swing. These new information technologies continue to impact China's existing traditional industries, enabling them to integrate and innovate, while also promoting China's entry into the digital economy era. For the accounting industry, this has also led to higher demands from society for accounting professionals, and most basic accounting personnel will face the dilemma of job changes as a result. This requires vocational colleges to consider more comprehensive training strategies when cultivating accounting professionals, so that accounting professionals can adapt to the new accounting functional requirements of contemporary society.

Leadership behavior is a critical component of effective management, significantly influencing organizational success. While extensive research has examined key success factors in road management, the specific role of leadership behaviors in road usage charging (RUC) management remains underexplored. This study addresses this gap by identifying and analyzing leadership behavior dimensions and their impact on management performance within the RUC context. Using a mixed-methods approach, focus group discussions with industry practitioners were conducted to define eight leadership behavior dimensions: Central-Level Leadership Guidance (LE1), Local-Level Leadership Guidance (LE2), Central-Level Leadership Commitment (LE3), Local-Level Leadership Commitment (LE4), Subordinate Understanding from Central-Level Leadership (LE5), Subordinate Understanding from Local-Level Leadership (LE6), Work Motivation (LE7), and Understanding Rights and Obligations (LE8). These dimensions were further validated through a quantitative survey distributed to 138 professionals involved in RUC management in Vietnam, with the data analyzed using structural equation modeling (SEM) and partial least squares (PLS) estimation. The findings revealed that LE3 (Central-Level Leadership Commitment) had the strongest direct impact on management performance (MP) and mediated the relationships between other leadership dimensions and management outcomes. This study contributes to the theoretical understanding of leadership in RUC management by highlighting the centrality of leadership commitment and offering practical insights for improving leadership practices to enhance organizational performance in infrastructure management.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.