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Tax Management refers to a company’s efforts to manage its tax obligations efficiently and legally in order to optimize net income. This study aims to examine the effect of Fixed Asset Intensity and Leverage on Tax Management, with Profitability as a moderating variable, in mining companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2024 period. This research is conducted because tax management practices are considered to potentially influence corporate profitability and financial performance. The study is grounded in Agency Theory and employs a quantitative approach. The sample was selected using purposive sampling, resulting in 28 companies observed over four years, with a total of 112 secondary data observations obtained from annual reports or financial statements. Data analysis was performed using EViews 13 with a Moderated Regression Analysis (MRA) approach. The findings indicate that: (1) Fixed Asset Intensity has no significant effect on Tax Management; (2) Leverage has a significant negative effect on Tax Management; (3) Profitability does not moderate the relationship between Fixed Asset Intensity and Tax Management; and (4) Profitability strengthens the effect of Leverage on Tax Management.

The rapid growth of social media has yielded vast digital traces with high potential for improving corporate forensic auditing. Their utilization, however, lags behind through technological reliability, privacy, and adherence to the law. The aim of this study is to explore effective utilization of social media digital traces in forensic auditing and develop a functional framework that lags neither behind through technological efficiency nor adherence to the law and ethics. A mixed-method design was utilized, combining quantitative machine learning analysis with qualitative document analysis and semi-structured interview insight. Quantitative data drawn from social media digital traces were processed using Random Forest algorithm with SMOTE for class balancing, while qualitative data were processed using thematic analysis. The results indicated high model performance with 91.3% accuracy and AUC-ROC of 0.94, together with three emergent themes: digital integration, ethics and privacy, and regulation and legality. The results demonstrate that digital footprints may serve as an effective early and reliable indicator for fraud detection, provided they are accompanied by clear regulatory and ethical frameworks. Its principal contribution lies in the development of an operational model that combines machine learning with legal and ethical perspectives, a new strategy which matures methodological refinement and practical application in today's forensic auditing.

Unstable financial conditions in insurance companies can serve as an early indicator of potential bankruptcy, which may have wide-ranging impacts on policyholders, shareholders, and the overall stability of the financial sector. Therefore, early detection of bankruptcy risk is critically important. This study aims to evaluate the effectiveness of the Springate model in identifying potential bankruptcy among insurance companies listed on the Indonesia Stock Exchange during the 2022–2024 period. The Springate model was chosen due to its simplicity and its ability to provide quantitative insights into a company's financial condition. Data were collected from the annual financial statements of 16 companies selected through purposive sampling based on the completeness and consistency of their financial reporting. The model applies the S-Score calculation as the basis for classifying companies into financial distress or non-financial distress categories. The analysis revealed that six companies consistently exhibited signs of financial difficulty, with three of them identified as being in a state of financial distress for three consecutive years. Meanwhile, the other ten companies demonstrated stable and healthy financial conditions throughout the observation period. These findings indicate that the Springate model is reasonably practical as an early detection tool for bankruptcy risk, particularly in the insurance sector, which is influenced by various internal factors such as risk management, as well as external factors like economic fluctuations and government regulations. Therefore, this model can be utilized as a decision-support tool for both management and investors in making strategic financial decisions.

The demand for real-time performance monitoring in the Industry 5.0 era encourages organizations to adopt integrated approaches to management control systems. This study aims to develop and validate a Balanced Scorecard (BSC)-based performance evaluation model integrated with computerized accounting systems to enhance managerial decision-making quality. The research employs a Research and Development (R&D) approach, following systematic stages of model development including needs analysis, system design, prototype construction, and expert validation. Primary data were obtained from three management accounting experts and two information system practitioners who evaluated the model using five key criteria: accuracy of key performance indicators (KPI), ease of use, indicator relevance, data visualization quality, and implementation feasibility. The findings reveal that all criteria achieved average scores above 4.0 on a five-point scale, indicating a high level of expert consensus and confirming the model’s conceptual validity. The highest scores were recorded for KPI accuracy (4.5) and implementation feasibility (4.4), demonstrating the model’s potential for real-world application. The novelty of this study lies in its integration of BSC metrics with real-time accounting data processing through the R&D approach, resulting in a systematic framework for performance monitoring. Future research is recommended to enhance dashboard interactivity and conduct field implementation testing to measure the model’s impact on decision-making quality.

This research aims to analyze the effect of Gross Regional Domestic Product (Produk Regional Domestik Bruto/PDRB), population, and tourism sector on the regional original revenue of the Special Region of Yogyakarta. This quantitative research compiled research data that were measured and tested using numerical data. This study used secondary data from the Financial and Asset Management Agency of the Special Region of Yogyakarta Province from 2014 – 2023. The data analysis of this study used SPSS 26 software. The results of this research, with significance value (sig.<0.05), show that PDRB influences the regional original revenue of sig. 0.000 (t= 7.003), the total population influences the regional original revenue of sig. 0.003 (t=4.970), and the tourism sector influences the regional original revenue of sig. 0.003 (t=4.807). The coefficient of determination shows that (R2 =0.638) which means that the effect of PDRB, population, and tourism on the regional original revenue of the Special region of Yogyakarta Province is 63.8%. In conclusion, increasing regional original revenue can be achieved and succeeded by stimulating economic growth, managing population growth, and optimally developing the potential of the tourism sector. Those efforts can be a foundation for regional governments in designing sustainable and competitive economic development policies.

Stock returns are generated by investors from buying and selling activities of the stocks they own. The generated return is determined by the increase or decrease in the stock prices. These prices are formed by the fundamental performance of the company. The purpose of this research is to examine the influence of factors such as financial distress, firm size, liquidity, and price to cash flow from operating activities on stock return. This study was conducted on transportation and logistics companies during the period of 2019-2022. A total of 22 companies were selected as samples for this research, using purposive sampling method and obtaining 88 relevant research data. The relationship between the dependent variable and independent variables was analyzed using multiple linear regression. The hypothesis test showed that the variable of financial distress, analyzed using the Zmijewski method, did not have any significant influence on stock return. Firm size, measured by total assets, was also not found to have a significant impact on stock return. The analysis of liquidity using the current ratio did not find a significant influence on stock return. However, price to cash flow from operating activities showed a significant and positive influence on stock return. This factor can be taken into consideration by investors and potential investors when analyzing the financial fundamentals of transportation and logistics companies before investing, as it has an impact on stock return.

Financial performance analysis plays a crucial role in assessing the overall health and effectiveness of a company. This research focuses on analyzing the financial performance of a retail distribution network company in Indonesia, utilizing quantitative descriptive methods. It employs financial ratio and common size analyses on primary data from financial statements to comprehend the company's financial position, profitability, liquidity, and solvency. Through a quantitative descriptive observational design, secondary data from literature and documents are collected. The analysis includes assessing liquidity via current and cash ratios, profitability through metrics like ROA, ROI, and ROE, as well as evaluating solvency using relevant ratios. The findings provide insights into the company's performance, revealing potential short-term issues due to decreasing current and cash ratios and fluctuations in profitability. These insights assist stakeholders in making informed decisions and formulating strategies, underscoring the significance of financial analysis in evaluating a company's health.