The aim of this research was to evaluate the impact of decentralization and electronic participatory budgeting on managerial performance. Additionally, the study sought to investigate the management accounting system's role in strengthening the effect of decentralization and electronic participatory budgeting on managerial performance. This quantitative research involved 30 regional units, using primary data collected through questionnaires of 108 fund management officials. The findings of this study demonstrated that decentralization and electronic participatory budgeting both have a positive and significant effect on managerial performance. Furthermore, the management accounting system was found to act as a moderator between decentralization and performance, but not between electronic participatory budgeting and performance.

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts.

This study investigates the effect of perceived risks, i.e., financial risk, product risk, and convenience risk, as well as the COVID-19 pandemic situation on online shopping behavior among Malaysians. This study uses convenience sampling techniques and comprises 185 respondents who have experience buying online. In addition, the study setting was non-contrived, and data was gathered using a closed-ended questionnaire through an online survey. Descriptive analysis was conducted using SPSS version 25.0 software and SmartPLS version 3.2.8 to test the proposed hypotheses. This study found that perceived risk factors such as financial, product, and convenience risk did not influence online shopping behavior. In contrast, the COVID-19 pandemic positively influences online shopping behavior among consumers. It showed a new development in the theory of online shopping behavior, where users continue to make purchases despite being aware that there may be various risks due to the spread of COVID-19. The role of the ministry, business owners, and consumer associations needs to be given attention to form a sustainable electronic commerce system and protect the rights of consumers. This research can help consumers understand their rights.

The development of technology and smart mobile devices such as phones and tablets has changed the behavior of tourists when booking tourism services. Based on the technology acceptance model, this study aims to explore the factors influencing the intention to use mobile applications for accommodation booking among GENZ in Vietnam. The analysis of 218 users revealed that four factors influence the behavior of using mobile applications for booking: performance expectancy, effort expectancy, social influence, and hedonic motivation. In addition to identifying the factors affecting usage intention, this study also proposes implications to assist developers and providers in improving their applications and developing suitable product strategies for the future.

In a confirmatory study, researchers are expected to employ the covariance-based structural equation modeling (CB-SEM). One of the key presumptions when utilizing CB-SEM is that the data is multivariate normal. Nevertheless, a perfect normal distribution is rarely observed in real-life data. To resolve this, the unweighted least square (ULS) is designed to specifically deal with non-normal data in SEM. However, ULS often yields improper solutions like negative, or boundary estimates of unique variances since it considers measurement errors in observed variables. The disturbance in SEM is reflected in unique variance, which is random error due to unreliability or measurement error and reliable variation in the item that indicates unknown latent causes. Consequently, this can generate bias in indicator loadings estimates. As an action to disentangle this issue, the present study proposes the implementation of regularization parameters by adding small positive values to the variance-covariance matrix. The ratio of bias to variance in a model can be improved to obtain the best estimation performance. Pro-Active Monte Carlo simulation was used to produce multivariate non-normal data with designated sample sizes and population characteristics. The data were analyzed using R Programming Environment by employing “psych”, “MASS”, “foreign”, “mvrnonnorm”, “purr”, and “semTools” packages with 1000 replications to produce multivariate non-normal data. Next, the “lavaan” package was used for SEM and regularized SEM analyses. The outcome of this study proves the capability of regularized ULS to improve parameter estimation.

Credit card fraud poses a significant challenge for both consumers and organizations worldwide, particularly with the increasing reliance on credit cards for financial transactions. Therefore, it is crucial to establish effective mechanisms to detect credit card fraud. However, the uneven distribution of instances between the two classes in the credit card dataset hinders traditional machine learning techniques, as they tend to prioritize the majority class, leading to inaccurate fraud pre- dictions. To address this issue, this paper focuses on the use of the Elbow Fuzzy Noise Filtering SMOTE (EFN-SMOTE) technique, an oversampling approach, to handle unbalanced data. EFN-SMOTE partitions the dataset into multiple clusters using the Elbow method, applies noise filtering to each cluster, and then employs SMOTE to synthesize new minority instances based on the nearest majority instance to each minority instance, thereby improving the model’s ability to perceive the decision boundary. EFN-SMOTE’s performance was evaluated using an Artificial Neural Network model with four hidden layers, resulting in significant improvements in classification performance, achieving an accuracy of 0.999, precision of 0.998, sensitivity of 0.999, specificity of 0.998, F-measure of 0.999, and G-Mean of 0.999.

Gold is one of the most attractive commodities and popular investments. Investment experts often recommend investing in gold because gold is one of the safest investments. It is a stable classic hedge, although the conditions of currency volatility or global markets are depreciated. However, the gold price fluctuations can be influenced by some other factors, such as the USD Index, which reflect and measure the strength of the US Dollar currency, and the Index of Dow Jones Industrial Average (DJIA) or a reflection of the political and economic conditions of the stock market. In this study, we conduct a global gold price forecast (USD) based on the USD Index, the DJIA Index, and the influence of time trends. Based on the data's characteristics, we face the fact that the data is nonlinear, contains outliers, and its pattern is not easy to specify parametrically. Due to the complexity of the model, we then propose a more flexible, robust modeling technique called the Bayesian Nonparametric Quantile Generalized Additive Model method. According to the results for the median case, the proposed method shows an accurate forecasting category due to the value of the Mean Absolute Percentage Error, MAPE less than 10 percent.

Companies today strive to integrate manufacturing processes with the environment to strike a balance. This study aims to examine the impact of green implementation for companies on the performance of manufacturing companies. Data was collected using Google Forms distributed online to the manufacturing companies domiciled in East Java. The criterion for the companies is that they have been committed to implementing a green approach to the production process, procurement of environmentally friendly raw materials, and green products. The partial least square technique analyzed data from as many as 115 respondents, with the position as senior staff level and higher, who have worked for at least two years and are permanent employees. The results showed that the implementation of eco-design has an impact on green purchasing and green manufacturing. Eco-design and green information systems implemented by the company can improve manufacturing performance by producing adequate overall product quality, and the number of products produced varies according to market demand. Therefore, the green information system affects green manufacturing and purchasing in manufacturing companies. Therefore, green manufacturing and green purchasing can impact manufacturing performance. The research results contribute to practitioners, especially top management, in committing to implementing Green which affects the performance of manufacturing companies. The theoretical contribution of the research is to enrich green supply chain management and sustainable performance for manufacturing companies.

The aim of this study is to examine the impact of digital marketing on consumer behavior in the fast fashion industry in the Kingdom of Saudi Arabia (KSA). The fast fashion industry has been growing rapidly in the KSA, and digital marketing has played a significant role in changing consumer behavior in this industry. The study adopted a quantitative research design and used online surveys as the primary data collection method. The sample consisted of a convenient sample of participants who had purchased fast fashion products in the KSA. The data was analyzed using various statistical methods, including descriptive statistics, correlation analysis, and regression analysis. This study's findings shed new light on how digital marketing has affected customer behavior in the fast fashion sector in the KSA. Consumers in the KSA have a good reaction to digital marketing methods in the fast fashion industry, and this effect is significant.

The aim of the current study is to examine the impact of accounting information systems (AIS) on enhancing financial information security in Jordanian banks by the moderating role information technology (IT) governance. The sample of the study includes 149 administrative employees in banks operating in Jordan and a questionnaire is used as the tool of the study. The results indicate that there is an effect for accounting information systems on enhancing financial information at operating banks in Jordan on information systems' operating, inputs, and outputs. The results also indicated in effect the level for information technology governance on the relationship between accounting information systems in achieving information security at the banks operating in Jordan. Considering the result obtained, the study concluded with a group of recommendations the most important among which was to work on establishing departments to protect and secure accounting information, as well as securing qualified cadres to monitor the systems.