This study aims to examine the influence of firm size, audit fees, audit tenure, audit committee, and auditor switching on the quality of audits. The research employs a quantitative method, using secondary data sourced from annual reports of companies in the transportation and logistics sector listed on the Indonesia Stock Exchange for the period of 2021–2023. The sample was selected using purposive sampling, resulting in 23 companies and yielding 69 observational data points. After eliminating one outlier, the final dataset comprised 68 observations. The results reveal that firm size, audit fees, audit tenure, and auditor switching do not significantly affect audit quality. However, the audit committee variable shows a significant impact on audit quality. Furthermore, collectively, all the independent variables is firm size, audit fees, audit tenure, audit committee, and auditor switching are found to influence audit quality.

This study examines the impact of interest rates on economic growth in Indonesia using a time series approach. Interest rates play a significant role in determining the levels of investment and consumer spending, both of which are key factors in economic growth. Monthly secondary data from 2016 to 2021 were used to analyze this relationship through path analysis. The results show that low interest rates can stimulate economic growth by enhancing access to funding, while high interest rates tend to reduce economic activity. The relationship between inflation, interest rates, and economic growth is highly complex, with important implications for Bank Indonesia’s monetary policy. This study emphasizes the need for a deep understanding of the interaction among these three variables to formulate policies that support sustainable growth.

This study aims to analyze the effect of Assistance Services and Transaction Security on User Emotional Experience, with Customer Satisfaction as a mediating variable in the context of using a digital wallet (e-wallet). This study uses a quantitative approach with a causal-comparative method. Data collection was carried out by distributing online questionnaires using Google Form to 150 respondents who are active e-wallet users. The research model consists of four latent variables, each of which is measured by three reflective indicators. The data analysis technique uses the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the help of SmartPLS software, and is supported by descriptive analysis using SPSS. Validity and reliability tests are carried out through outer loading tests, Average Variance Extracted (AVE), Cronbach's Alpha, and Composite Reliability. Hypothesis testing is carried out by considering the path coefficient, t-statistic, and p-value values. The results of the study indicate that Assistance Services and Transaction Security have a significant effect on Customer Satisfaction. Assistance Services also have a direct effect on Emotional Experience, while Transaction Security does not have a direct effect on this variable. Customer Satisfaction has a significant effect on Emotional Experience, and is proven to be a significant mediator in the relationship between Assistance Service and Transaction Security on Emotional Experience.

This study aims to explore consumer perceptions regarding transaction security on digital marketplaces and to evaluate the impact of community-based educational interventions. A descriptive qualitative method was employed, involving in-depth interviews with ten active marketplace users in Tasikmalaya City. The findings revealed that consumers’ sense of security is shaped by personal experience, understanding of platform security features, and digital literacy. Prior to the intervention, most participants expressed high concern over data breaches and fraud, often opting for cash-on-delivery (COD) as a form of protection. Following a series of community education sessions—comprised of discussions and safe transaction simulations—significant changes were observed in consumer attitudes and behavior. Participants became more aware of the importance of verifying sellers, reading reviews, and using secure payment methods. The study concludes that localized community education effectively enhances awareness and trust among consumers, especially in areas where digital literacy remains limited.

Entrepreneurs in the digital era face constant challenges that require adaptation and innovation. Although economic and managerial factors are often the focus, the role of psychological dimensions such as resilience and creativity in entrepreneurial success has not been widely reviewed in depth. This study aims to examine relevant theories and findings on psychological resilience and creativity in the context of entrepreneurship, and to build a conceptual framework to support entrepreneurial innovation. Using a qualitative literature study method, this study systematically searches, analyzes, and synthesizes scientific literature from various databases. The results of the synthesis indicate that psychological resilience is crucial for entrepreneurs to recover from failure and adapt to change. Characteristics such as optimism and emotional regulation are the pillars of mental resilience. Meanwhile, creativity is an innovation engine that facilitates the identification of new opportunities and the development of unique solutions. A synergistic relationship between resilience and creativity is also found: resilience provides a psychological buffer for taking creative risks, and the creative process can strengthen self-resilience. The theoretical implication is the need for a more holistic entrepreneurship model. In practice, this study recommends educational institutions, especially the Faculty of Psychology, to integrate the development of resilience and creativity into the entrepreneurship curriculum.

As global attention to sustainability increases, the implementation of Environmental, Social, and Governance (ESG) principles has become increasingly important in the business world. However, the social aspect of ESG is often overlooked compared to environmental and governance aspects. This study aims to identify and analyze the challenges faced by companies in implementing the social aspect of ESG through a literature review of various academic sources. The findings indicate that companies encounter challenges such as limited understanding, resource constraints, organizational cultural barriers, regulatory uncertainty, and stakeholder pressure. This research provides insights into the obstacles and potential strategies that companies can adopt to address them. However, the study is limited by its reliance on literature alone, highlighting the need for future research using primary quantitative or qualitative data to strengthen these findings.

This study uses a qualitative method with a literature review approach to examine the effectiveness of the two-phase method in solving linear programming problems. The two-phase method is one of the techniques in linear programming used when the objective function does not have a feasible initial solution directly. Through the exploration and analysis of various scientific literature sources from 2019 onwards, this study finds that the two-phase method can systematically provide optimal solutions, especially in complex problems with multiple constraints. Previous research has shown that this method is effective in accelerating the identification of basic feasible solutions and minimizing unnecessary iterations compared to the standard simplex method. Additionally, the two-phase method offers better numerical stability and high reliability in both industrial and educational applications. However, its effectiveness largely depends on the understanding of the algorithm and the ability to design an appropriate mathematical model. The results of this study are expected to contribute theoretically to the development of efficient solution techniques for linear optimization problems.

Object detection for sensing images is one of the promising research directions in computer vision. Applications for object detection from remote sensing images play an important role in analyzing aerial or satellite imagery. Benefits include applications in monitoring buildings and infrastructure, transportation, supporting search and rescue or responding to natural disasters, and environmental research. However, detecting objects in remote sensing images is difficult due to the diversity of shapes and sizes, viewing angles of objects, and complex background environments. In this paper, the authors present a Deep Learning (DL)-based object detection process from remotely sensed images, the main goal of which is to improve the ability to detect small objects in high-resolution aerial images. Implement and evaluate the super-slicing inference technique in the YOLOv11 model to improve the ability to detect very small and extremely small objects. Many simulation results are tested experimentally in the problem of detecting and tracking vehicles in Vietnam (Thai Nguyen). The results show that the system can accurately detect small objects such as pedestrians, motorbikes, and cars at a distance, with confidence ranging from 0.31 to 0.90. Some detection situations are successful even when the object is located at the edge of the slice. Finally, the authors discuss potential future research directions and unaddressed formulations.