This study aims to analyze the effect of executive characteristics and family ownership as a stimulus factor for tax avoidance and to see the existence of a political relationship as a moderating variable in the effect of executive characteristics and family ownership on tax avoidance. This research was conducted on manufacturing companies listed on the Indonesia Stock Exchange during 2017-2019. In this study, the sample was determined based on the purposive sampling technique with the criteria that the sample company was manufacture listed on the IDX for three consecutive years from 2017-2019, published an annual report in the 2017-2019 period sequentially, did not experience delisting, was not a company the IPO in 2018-2019, did not experience any losses and did not have an ETR value of more than 1. The research sample was obtained from as many as 138 companies with 3 years of observation. This study uses multiple linear regression analysis (Multiple Regression Analysis) and Moderate Regression Analysis (MRA) using the Statistical Product and Service Solution (SPSS) program. The results showed (I) executive characteristics had a significant positive effect on tax avoidance (II) family ownership had a significant negative effect on tax avoidance (III) political connections were not able to strengthen the executive's positive influence on tax avoidance (IV) political connections weakened family ownership on tax avoidance. This study can also show that the sample companies tend to comply with tax rules, they avoid sanctions and fines, and consider the risk of loss that must be faced by the company when proven to do tax avoidance.

Circular economy (CE) is an interesting approach by many countries to address global environmental problems while gaining economic benefits. The implementation of CE is expected to minimize the use of resources and materials, thus contributing to sustainable development. Much literature has discussed CE assessment indicators and their evaluation, however to date, no single indicator has been proposed that can comprehensively measure the macro level of CE implementation. This study aims to develop a CE index to measure CE implementation at macro levels. In addition, it provides an overview of the impact of CE implementation on the financial, social, and environmental aspects of the economy. To this end, first, an in-depth literature review and descriptive analysis is conducted to identify existing global CE indicators and classify them into financial, social, and environmental categories based on available primary and secondary data. Then, the CE index is constructed using a mathematical equation by considering the CE framework, a single indicator of each aspect as a variable, and the variable’s weighting. Finally, the CE index is applied to evaluate the level of CE implementation of CE in Indonesia at macro level. This index is expected to be a valuable tool for measuring CE implementation and therefore improving CE performance.

Purpose: This study aims to investigate how reformed public hospitals in Indonesia design and implement their vision and mission as a management control (MC) mechanism. Theoretical framework: This study employs the Simon’s Levers of Control (LOC) framework and focuses on the belief systems lever. Design/methodology/approach: A semi-structured interview was used in this study to conduct qualitative research. The informants are 13 top management team (TMT) members from four BLUD public hospitals (PH) in Indonesia. The qualitative interview data were condensed, displayed, and analyzed. To analyze the data, the thematic deductive analysis approach was utilized. Findings: This study concluded that in Indonesia, reformed PH's vision and mission remain mostly reliant on those of the local government as well as the accreditation requirement for gaining legitimacy. It is also found that a vision and mission statement reflecting the core values of an organization can be a driver for the MC mechanism as a whole. Research, Practical & Social implications: This study has practical implications for policy making in the context of BLUD PH management. All hospital components should understand the hospital's vision and mission. It's also vital to establish a vision and mission statement that's relevant to the increasingly competitive business climate of health service providers in Indonesia, so that the strategies used can help the organization attain its goals. Originality/value: This is the first study to examine the relationships between vision and mission, as well as the overall management control system, in Indonesian public hospitals following the reform of the financial management status of public organizations. This study examines how relevant the vision and mission of public hospitals in Indonesia are, as well as how effectively they can adapt to a more competitive and business-focused organizational environment.

In medical diagnostics, brain tumor classification remains essential, as accurate and efficient models aid medical professionals in early detection and treatment planning. Deep learning methodologies for brain tumor classification have gained popularity due to their potential to deliver prompt and precise diagnostic results. This article proposes a novel classification technique that integrates the Xception model with a hybrid attention mechanism and progressive image resizing to enhance performance. The methodology is built on a combination of preprocessing techniques, transfer learning architecture reconstruction, and dynamic fine-tuning strategies. To optimize key hyper-parameters, this study employed the Dynamic Chaotic Whale Optimization Algorithm. Additionally, we developed a novel learning rate scheduler that dynamically adjusts the learning rate based on image size at each training phase, improving training efficiency and model adaptability. Batch sizes and layer freezing methods were also adjusted according to image size. We constructed an ensemble approach by preserving models trained on different image sizes and merging their results using weighted averaging, bagging, boosting, stacking, blending, and voting techniques. Our proposed method was evaluated on benchmark datasets achieving remarkable accuracies of 99.67%, 99.09%, and 99.67% compared to the classical algorithms.