Consumers’ interest in green consumption has increased rapidly in recent years with heightening concerns for environmental, social, and health risks. However, increased concerns and interest of consumers may not translate to their behavioral outcome which may be attributed to socio-economic and consumers’ internal stimuli. Furthermore, contextual differences in the marketplace may influence how consumers form their green attitudes and behavior. The purpose of this study is to assess the role of consumers’ intrinsic traits such as consumers’ personal values, their self-motivation for sustainable consumption (i.e., perceived consumer effectiveness), green skepticism, and environmental involvement in their green attitude and behavior, and to see if the country-specific contextual condition may influence consumers’ behavior. In addition, price sensitivity and environmental protection emotions are considered moderating constructs to explain the gap between green attitude and green behavior. Findings from this study provide insights into understanding Chinese and Singaporean consumers’ green behavior which is driven by their intrinsic traits and by extrinsic conditions. This understanding can help companies to develop effective green marketing communication strategies and to enhance consumer engagement in sustainable activities and consumption.

Land use changes have been demonstrated to exert a significant influence on urban planning and sustainable development, particularly in regions undergoing rapid urbanization. Tehran Province, as the political and economic capital of Iran, has undergone substantial growth in recent decades. The present study employs sophisticated Geographic Information System (GIS) instruments and the Google Earth Engine (GEE) platform to comprehensively track and analyze land use change over the past two decades. A comprehensive analysis of Landsat images of the Tehran metropolitan area from 2003 to 2023 has yielded significant insights into the patterns of land use change. The methodology encompasses the utilization of GIS, GEE, and TerrSet techniques for image classification, accuracy assessment, and change detection. The Kappa coefficients for the maps obtained for 2016 and 2023 were 0.82 and 0.87 for four classes: built-up, vegetation cover, barren land, and water bodies. The findings suggest that, over the past two decades, Tehran Province has undergone a substantial decline in ecological and vegetative areas, amounting to 2.4% (458.3 km2). Concurrently, the urban area and the barren lands have expanded by 287.5 and 125.5 km2, respectively. The increase in water bodies during this period is likely attributable to the reduction of vegetation cover and dam construction in the region. The present study demonstrates that remote sensing and GIS are excellent tools for monitoring environmental and sustainable urban development in areas experiencing rapid urbanization and land use changes.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.

This study explores the critical role of the retail sector in the global economy and the importance of working capital management within retail businesses. Recognizing retail’s influence beyond just income generation, the research examines its impact on economic stability, job creation, and national GDP, and how it links industries such as manufacturing and logistics. Employing a blended-methods approach, the study integrates quantitative analysis using AMOS software with qualitative insights from interviews with financial managers and retail experts. Key focus areas include cash flow management, market demand, and supplier relationship management in the context of working capital management. Findings highlight the necessity of effective working capital management in maintaining financial stability, optimizing shareholder wealth, and ensuring long-term business viability in the retail sector. Strategies for enhancing profitability, such as improving supplier relationships and adapting to market demands, are identified. This research contributes to understanding the economic impact of the retail sector and the intricacies of working capital management. It offers insights for policymakers, retail managers, and academics, emphasizing the need for supportive retail industry measures and effective financial management practices. The study fills a gap in literature and sets a foundation for future research in this critical area of economic studies and retail management.

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.

Industrial plastics have seen considerable progress recently, particularly in manufacturing non-lethal projectile holders for shock absorption. In this work, a variety of percentages of alumina (Al2O3) and carbon black (CB) were incorporated into high-density polyethylene (HDPE) to investigate the additive material effect on the consistency of HDPE projectile holders. The final product with the desired properties was controlled via physical, thermal, and mechanical analysis. Our research focuses on nanocomposites with a semicrystalline HDPE matrix strengthened among various nanocomposites. In the presence of compatibility, mixtures of variable compositions from 0 to 3% by weight were prepared. The reinforcement used was verified by X-ray diffraction (XRD) characterization, and thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used for thermal property investigation. Alumina particles increased the composites’ thermal system and glass transition temperature. Mechanical experiments indicate that incorporating alumina into the matrix diminishes impact resistance while augmenting static rupture stress. Scanning electron microscopy (SEM) revealed a consistent load distribution. Ultimately, we will conduct a statistical analysis to compare the experimental outcomes and translate them into mathematical answers that elucidate the impact of filler materials on the HDPE matrix.