This research explores the impact of employee green behavior on green transformational leadership (GTL) and green human resource management (GHRM), and their subsequent effects on sustainable performance within organizations. Utilizing a sample of 482 environmental quality promotion departments across Thailand, the study employs stratified random sampling to ensure representative data collection. Analysis was conducted using SPSS software, applying Ordinary Least Squares (OLS) regression to test the hypothesized relationships between the variables. The findings reveal a positive and significant influence of employee green behavior on both GTL and GHRM. Additionally, both GTL and GHRM are found to positively correlate with sustainable performance, indicating that enhanced leadership and management practices in the environmental domain can lead to better sustainability outcomes. This research utilizes the Ability-Motivation-Opportunity (AMO) theory as its theoretical framework, illustrating how organizations can leverage strategic HRM practices to promote environmental consciousness and action among employees, thereby enhancing their long-term sustainability success. Implications of this study underscore the importance of integrating green practices into leadership and HRM strategies, advocating for targeted training programs and energy conservation measures to boost environmental awareness and performance in the workplace. This contributes to the literature on sustainable performance by providing empirical evidence of the pathways through which green HRM and transformational leadership foster a sustainable organizational environment.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

 In the face of growing disruptions within the unconventional business environment, this study focuses on enhancing supply chain resilience through strategically reforming resources. It highlights the importance of understanding the dynamics and interactions of resources to tackle supply chain vulnerability (SCV) in the manufacturing sector. Employing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology alongside an adapted Analytic Network Process (ANP), the research investigates supply chain vulnerabilities in Pakistan’s large-scale manufacturing (LSM) public sector firms. The DANP method, through expert questionnaires, helps validate a theoretical framework by assessing the interconnectedness of supply chain readiness dimensions and criteria. Findings underscore Resource Reformation (RR) as a critical dimension, with the positive restructuring of resources identified as pivotal for public sector firms to align their operations with disruption magnitudes, advocating for a detailed analysis of resource utilization.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

Public open spaces, such as squares, parks, and sports fields, serve as crucial hubs during and after disasters, fostering a sense of normalcy and community, promoting social cohesion, and facilitating community recovery. Additionally, they offer opportunities for promoting physical and mental well-being during such crises. This study aims to enhance the responsiveness of public open spaces to disasters by prioritizing disaster resilience in their planning and design. This study consists of two main stages. Firstly, a literature review is conducted to explore the current trends in research on public open space planning and design and the incorporation of disaster resilience. Results indicate that the primary focus of the current research on planning and designing public open spaces centers around sociocultural, psychological, environmental, and economic benefits. There is limited emphasis on integrating disaster resilience into public open space planning and design, leading to a lack of clear guidance for planners and architects. The emphasis on disaster resilience in public open space planning and design mainly began after 2010, with a notable increase observed in the last six years (2017–2023). This emphasis notably centers on climate change impacts, followed by floods, and then earthquakes. Secondly, drawing on the pivotal role of public open spaces during disasters, the importance of urban planning and design, and the existing gap in incorporating disaster resilience in current research on public open space planning and design, this study develops a novel framework for enhancing public open spaces’ responsiveness to disasters through resilient urban planning and design, based on four main disaster resilience criteria: multifunctionality, efficiency, safety, and accessibility. The insights gleaned from this study offer invaluable guidance to planners, architects, and decision-makers, empowering them to develop public open spaces that can effectively respond to various circumstances, ultimately contributing to bolstering community resilience and sustainability.

This study addresses the critical issue of employee turnover intention within Malaysia’s manufacturing sector, focusing on the semiconductor industry, a pivotal component of the inclusive economy growth. The research aims to unveil the determinants of employee turnover intentions through a comprehensive analysis encompassing compensation, career development, work-life balance, and leadership style. Utilizing Herzberg’s Two-Factor Theory as a theoretical framework, the study hypothesizes that motivators (e.g., career development, recognition) and hygiene factors (e.g., compensation, working conditions) significantly influence employees’ intentions to leave. The quantitative research methodology employs a descriptive correlation design to investigate the relationships between the specified variables and turnover intention. Data was collected from executives and managers in northern Malaysia’s semiconductor industry, revealing that compensation, rewards, and work-life balance are significant predictors of turnover intention. At the same time, career development and transformational leadership style show no substantial impact. The findings suggest that manufacturing firms must reevaluate their compensation strategies, foster a conducive work-life balance, and consider a diverse workforce’s evolving needs and expectations to mitigate turnover rates. This study contributes to academic discourse by filling gaps in current literature and offers practical implications for industry stakeholders aiming to enhance employee retention and organizational competitiveness.