The study aims to explain the relationship between the effectiveness of a business and its management through the analysis of working capital. The findings prove the complementary relationship. The analysis of working capital will always have a significant impact on the effectiveness of business management. The main objective of any corporation is to be effective in business, which can be achieved by analyzing the working capital. The result shows that analysis of working capital based on factors like operational efficiency, the company’s earnings and profitability, cash management, corporate receivable management, and corporate inventory management creates room for improvement and effectiveness in business management. Firms might enhance finances for business expansion by lowering their working capital requirements. It has also been revealed that there is a considerable difference in industries across time. It was observed that there is a high association between working capital efficiency and firm profitability. A highly efficient corporation is less vulnerable to liquidity risk and is also self-sufficient in terms of external finance. Numerous studies have been done to regulate the true rapport between working capital investments and their impact on financial presentation. It demonstrates that effective investment in working capital management may boost profitability and business value. The relationship between accounting and finance was explained by measuring working capital management in demand to illustrate the status of profitability. It was suggested that accountants take a more professional approach to updating their accounting and finance skills in their organization through effective working capital management.

High-quality implementation of cross-border mergers and acquisitions (cross-border M&As) is an important pathway for emerging-market multinational enterprises (EMNEs) to enhance their international competitiveness. However, in comparison to developed countries, cross-border M&As by EMNEs are often prohibited by the liability of origin caused by negative political coverage. How and why negative political coverage affect the completion of cross-border M&As by EMNEs? What are the contextual constraints that moderate the impact of negative political coverage on cross-border M&As completion? Based on the “liability of origin” theory, this paper addresses these questions using data from the Zephyr database on cross-border M&As by EMNEs in the United States from 2016 to June 2021 and employing a logit model for estimation. The research findings are as follows: (1) Negative political coverage leads to negative perceptions of emerging market countries by host country stakeholders, creating the liability of origin and stigmatizing the corporate nationality, thereby reducing the success rate of cross-border M&As by EMNEs. (2) Increasing geographical distance leads to information asymmetry, reinforcing the negative impact of negative political coverage on the completion of cross-border M&As by EMNEs. (3) Relevant mergers and acquisitions exacerbate the negative effect of negative political coverage on the success rate of cross-border M&As by EMNEs. (4) Being a publicly traded firm and having successful experience in cross-border M&As both intensify the negative impact of negative political coverage on the success rate of cross-border M&As by EMNEs.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).