This study examines the factors influencing e-government adoption in the Tangerang city government from 2010 to 2022. We gathered statistics from multiple sources to reduce joint source prejudice, resulting in a preliminary illustration of 1670 annotations from 333 regions or cities. These regions included major urban centers such as Jakarta, Surabaya, Bandung, Medan, Makassar, and Denpasar, as well as other significant municipalities across Indonesia. After removing anomalous values, we retained a final illustration of 1656 annotations. Results indicate that higher-quality digital infrastructure significantly boosts e-government adoption, underscoring the necessity for resilient digital platforms. Contrary to expectations, increased budget allocation for digital initiatives negatively correlates with adoption levels, suggesting the need for efficient spending policies. IT training for staff showed mixed results, highlighting the importance of identifying optimal training environments. The study also finds that policy adaptability and organizational complexity moderate the relationships between digital infrastructure, budget, IT training, and e-government adoption. These findings emphasize the importance of a holistic approach integrating technological, organizational, and policy aspects to enhance e-government implementation. The insights provided are valuable for policymakers and practitioners aiming to improve digital governance and service delivery. This study reveals the unexpected negative correlation between budget allocation and e-government adoption and introduces policy adaptability and organizational complexity as critical moderating factors, offering new insights for optimizing digital governance.

Despite the current craze for e-commerce live streaming, its specific impact on consumer repurchase intentions and the underlying mechanisms remain insufficiently explored, creating a notable gap in existing research. The purpose of this study is to investigate the precise impact of e-commerce live streaming on consumers’ repurchase intentions and to uncover the path through which this influence occurs. Drawing on behavioral cognitive theory, this paper employs a contextual experimental method to examine how e-commerce live streaming affects consumer repurchase behavior. The experimental results show that e-commerce live can significantly improve consumer repurchase intention, consumer loyalty and market order can positively regulate the effect of e-commerce live. This paper not only verifies the effectiveness of e-commerce live broadcasting, but also provides new ideas for brands and governments to strengthen the ability of e-commerce live broadcasting to “bring goods”.

Tourism is one of the important sectors that support Indonesia’s economic growth. The tourism sector itself plays a strategic role in increasing the country’s foreign exchange. However, during the Covid-19 pandemic, tourism became one of the most affected sectors. Electronic visa on arrival (e-VOA) is a form of digital transformation in immigration services offered by the Indonesian government to increase the number of tourist arrivals during the recovery of the national economy, especially in the tourism sector, after the Covid-19 pandemic. This study provides an in-depth insight into how e-VOA functions as a digital transformation tool in the immigration and tourism sectors. By exploring the impact of e-VOA implementation, this article contributes to the understanding of how digitalisation can improve the efficiency of administrative processes and support the recovery of the tourism sector in post-pandemic Bali. This study uses qualitative approaches and methods with descriptive analysis techniques to create an objective description of a situation through numbers or statistical data. The results of this study show that e-VOA services effectively contribute to an increase in the number of foreign tourists in Bali. It also has a positive impact on the economic growth of tourism-related businesses in Bali.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

The problem of stunting is not only related to children’s short height, but also has an impact on high morbidity rates, due to long-term nutritional deficiencies. which hinders motor and mental development in children. The objectives of this research are: 1) to understand household food security, 2) to understand the eating habits of pregnant women and toddlers regarding existing belief systems and traditions, and 3) to understand resilience mechanisms in overcoming food emergencies to prevent stunting. The data collection process uses a mixed methods approach by combining qualitative and quantitative research. The research results show that the determining factor for the incidence of stunting in coastal areas of Indonesia is the lack of household food availability due to subsistence economic life which then has an impact on eating behavior in the household, namely the lack of quality and quantity of the types of food consumed. daily. Apart from that, there is still a lack of understanding by pregnant women regarding the importance of providing complementary breast milk food to toddlers, low literacy of food diversity among toddlers, and low public trust in the importance of immunization. Furthermore, the high rate of early marriage in society and the limited awareness of using clean water is caused by a philosophy that still considers rivers as a source of life, so the water is used for consumption. Apart from that, socio-cultural mechanisms as a strategy to resolve the problem of food shortages have not yet been implemented.

This study examines how Artificial Intelligence (AI) enhances Sharia compliance within Islamic Financial Institutions (IFIs) by improving operational efficiency, ensuring transparency, and addressing ethical and technical challenges. A quantitative survey across five Saudi regions resulted in 450 validated responses, analyzed using descriptive statistics, ANOVA, and regression models. The findings reveal that while AI significantly enhances transparency and compliance processes, its impact on operational efficiency is limited. Key barriers include high implementation costs, insufficient structured Sharia datasets, and integration complexities. Regional and professional differences further underscore the need for tailored adoption strategies. It introduces a novel framework integrating ethical governance, Sharia compliance, and operational scalability, addressing critical gaps in the literature. It offers actionable recommendations for AI adoption in Islamic finance and contributes to the global discourse on ethical AI practices. However, the Saudi-specific focus highlights regional dynamics that may limit broader applicability. Future research could extend these findings through cross-regional comparisons to validate and refine the proposed framework. By fostering transparency and ethical governance, AI integration aligns Islamic finance with socio-economic goals, enhancing stakeholder trust and financial inclusivity. The study emphasizes the need for targeted AI training, the development of structured Sharia datasets, and scalable solutions to overcome adoption challenges.