Purpose: The level of the environment is gradually declining, especially with regard to the serious problem of solid waste. Solid waste segregation-at-source is seen as the most essential approach to helping the natural environment minimize the amount of waste generated before being transferred to waste disposal sites and landfills in many rapidly growing towns and cities in developing countries. However, a number of previous environmental-based research have focused only on the general scope of recycling, sustainable development, and the purchase intention for sustainable food products. This situation has led to useful and relevant information on the research scope of households’ intention to segregate solid waste at source, which remains largely unanswered. The aim of this paper is, therefore, to provide a literature review to develop a novel theoretical framework in understanding the determinants of households’ intention to practise solid waste segregation-at-source. Theoretical framework: The study provides a detailed explanation of the application of the Theory of Reasoned Action, the Fietkau-Kessel Model, the Focus Theory of Normative Conduct, and the Value-Basis Theory to predict the relationship between attitude, subjective norms, environmental concerns, and environmental knowledge of households on intention to practise solid waste segregation-at-source. Design/methodology/approach: This research is descriptive in nature. Findings: A better understanding of the potential mediator and moderator is needed to contribute to the body of knowledge on the causal relationship between the studied variables. In conclusion, the researchers discuss how the framework can be used to address future research implications as more evidence emerges. Research, practical and social implications: The current study is expected to broaden previous research in order to improve general understanding of attitudes and subjective norms towards the specific research scope of solid waste segregation-at-source.

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.

This paper aims to research the impact of psychological contract fulfilment on employee innovative work behaviour, and the mediating role of work engagement and the moderating role of social support. A quantitative analysis was adopted to address in research. Two-wave data were collected from 332 respondents working in China. Hierarchical regression analyses were conducted to assess the proposed hypotheses. Results revealed that psychological contract fulfilment positively impacted innovative work behaviour. In addition, engagement partially mediated the relationship between psychological contract fulfilment and innovative work behaviour. Furthermore, the findings suggest that social support moderates the relationship between work engagement and innovative work behaviour, and, in turn, moderates the indirect effect of psychological contract fulfilment on innovative work behaviour through work engagement. This research extends the generalizability of findings in the psychological contract literature. The results bear significant implications for the management of employees’ innovative work behaviour.

Air pollution in Jakarta has become a severe concern in the last four months. IQAir, in August 2023, revealed that the level of air pollution had reached 161 points on the Air Pollution Standard Index (APSI). The negative impact on society has placed air pollution as a concern for environmental safety and survival in danger. This condition will encourage the development of a national policy agenda to integrate environmental welfare through various energy efficiency channels. This research analyzes the relationship between air pollutant elements that can reduce air quality. The analysis includes pollutant intensity measured by APSI per unit of pollutant as a measure of efficiency. The aim is to observe energy use, which causes an increase in pollutant levels. This research utilizes dynamic system modeling to produce relationships between parameters to produce factors that cause pollution. The parameters used are motorized vehicles, waste burning in landfills, industry, and power plants. The results of historical behavioral tests and statistical suitability tests show that the behavior is suitable for the short and long term. The simulation results show that the pollution level will worsen by the end of 2027, a hazardous condition for society. The optimistic scenario simulation model proposes immediate counter-measures to reduce pollution to 45.01, the ideal condition. To accelerate improvements in air quality, the Government can plan policies to reduce the use of coal by power plants and industry, as well as the use of electric motorized vehicles, resulting in an ideal reduction in pollution by 2024. In conclusion, pollution can be reduced effectively if the Government firmly implements policies to maintain that air quality remains stable below 50 points.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

This study is aimed at exploring the degree of association between workforce diversity dimensions and the academic performance of four universities in Ethiopia. The diversity management attributes were diversity, climate, values, and organizational justice; identity, schemas, and communication adapted to the contexts of higher education institutions. The universities were selected purposively, and stratified and systematic sampling techniques were further used to identify respondents. Quantitative and qualitative data were collected to achieve the purpose of the study. Correlation and regression analyses were used to analyze the data. Results from correlation analysis revealed that there are statistically significant positive relations between the dimensions of workforce diversity and academic performance. This implies that the organizational performance of higher education institutions can be significantly influenced by existing diversity. The freedom to express one’s own identity in the university workforce landscape was also observed to be limited in the universities studied, and this has to be improved. A democratic work environment is critical for the productivity of the staff, and an effort has to be geared towards the goal of creating such an environment. The regression analysis indicated that diversity, climate, organizational justice, identity, schema, and communication have statistically significant effects on the academic performance of higher educational institutions in Ethiopia. Finally, academic leaders are advised to apply the transformational leadership style, as it moderates the relationship between diversity management and academic performance.