The mining sector faces a complex dilemma as an economic development agent through social upliftment in places where mining corporations operate. Resource extraction is destructive and non-renewable, making it dirty and unsustainable. To ensure corporate sustainability, this paper examines the effects of knowledge management (KM), organizational learning (OL), and innovation capability (IC) on Indonesian coal mining’s organizational performance (OP). We used factor and path analysis to examine the relationships between the above constructs. After forming a conceptual model, principal component analysis validated the factor structure of a collection of observed variables. Path analysis examined the theories. The hypothesized framework was confirmed, indicating a positive association between constructs. However, due to mining industry peculiarities, IC does not affect organizational performance (OP). This study supports the importance of utilizing people and their relevant skills to improve operational performance. The findings have implications for managers of coal mining enterprises, as they suggest that KM and OL are critical drivers of OP. Managers should focus on creating an environment that facilitates knowledge sharing and learning, as this will help improve their organizations’ performance.

The improper disposal of litter by tourists poses a significant threat to tourism destinations worldwide, including in Indonesia. To mitigate marine litter, promoting eco-friendly behavior (EFB) among tourists is essential. This study applies the extended Theory of Planned Behavior (TPB), which posits that an individual’s behavior is driven by their attitudes, subjective norms, and perceived behavioral control, to better understand the factors influencing eco-friendly behavioral intentions. In this research, ecological consciousness and ecological knowledge were added to the traditional TPB framework to gain deeper insights into tourist behavior. Data were collected through a structured questionnaire from 876 visitors to Lake Singkarak, Indonesia. The findings demonstrate that the inclusion of ecological consciousness and ecological knowledge significantly enhances the predictive power of the TPB model in explaining eco-friendly behavioral intentions. Based on these results, raising public awareness, improving government management, and enhancing the quality of lake attractions are recommended to encourage responsible tourism. These measures can reduce litter and conserve lake habitats, ultimately contributing to the sustainability of tourism in the region.

 The main goal of the article is to formalize the key business models of marketing of modern companies and substantiate the key stages, types and trends of development. The relevance and need to pay significant attention to the marketing digital business model when organizing a business is substantiated. Using structural and logical analysis and criticism of scientific research, the essence, advantages and disadvantages are determined, the main blocks, stages and key elements of the structure of business models of modern companies are argued. It has been proven that marketing digital business models serve as a logical and visual plan for organizing all business processes of companies from production, marketing, sales and logistics to building a hierarchy of profitability. The key development trends are substantiated and the most popular business models of business organization in modern conditions are structured on the basis of scientific generalization, structural and logical analysis and mathematical modeling. Practical significance is characterized by the fact that the marketing business models of world-class companies are generalized and structured, taking into account their specifics and characteristics. Practical recommendations and key stages of building a company’s business model and its implementation into reality have been formed to achieve strategic business goals.

Based on the resource-based view and institutional theory, this study investigates the impact of their environmental management capabilities and environmental, social, and governance (ESG) pressure on the non-financial performance of small and medium-sized enterprises (SMEs). In particular, it examines the interaction effect of ESG pressures on the relationship between SMEs’ environmental management capabilities and non-financial performance. For this study, a total of 1865 SME lists were obtained through Jeonnam Techno Park and Jeonnam Small Business Job and Economy Promotion Agency. Based on this, a total of 127 questionnaires were returned as a result of a telephone, e-mail, and online survey, and finally, an empirical analysis was conducted based on 120 questionnaires. We conducted an empirical analysis of Korean SMEs and obtained the following results: First, environmental management capabilities have a significant, positive effect on SMEs’ non-financial performance. Second, ESG pressure has a significant, negative effect on the non-financial performance of SMEs. Next, we analyzed the moderating effect of ESG pressures and observed that ESG pressures strengthen the positive effect of environmental management capabilities on non-financial performance. Based on the resource-based perspective and institutional theory, this study provides meaningful academic implications by examining environmental management capabilities and ESG pressures, which have not been identified in previous studies, as factors of non-financial performance that are becoming important under the new management paradigm, such as climate change and ESG. Furthermore, while ESG pressure has a significant negative effect on non-financial performance, we find that it is a moderating variable that strengthens the relationship between SMEs’ environmental management capabilities and non-financial performance, which has useful academic and practical implications for ESG and strategic management.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.