The Gaussian Markov Random Field (GMRF) is widely acknowledged for its remarkable flexibility, especially in the realm of high-resolution prediction, when compared to conventional Kriging methods. Rooted in the fundamental principles of Bayesian estimation, this methodology underscores the importance of a meticulous examination of prior and hyperprior distributions, along with their corresponding parameter values. Sensitivity analyses are crucial for evaluating the potential impact of these distributions and parameter values on prediction results. To determine the most effective values for hyperprior parameters, an iterative trial-and-error approach is commonly employed. In our research, we systematically assessed a variety of parameter values through exhaustive cross-validation. Our study is focused on optimizing hyperprior parameter values, with a particular emphasis on Penalized Complexity (PC). We applied our method to conduct spatiotemporal high-resolution predictions of PM2.5 concentrations in Jakarta province, Indonesia. Achieving accurate predictive modeling of PM2.5 concentrations in Jakarta is contingent upon this optimization. We identified that the optimal values for PC hyperprior parameters, with a range of less than 2,000 and a hyperprior standard deviation greater than 1 with a 0.1 probability, yield the most accurate predictions. These parameter values result in the minimum mean absolute percentage error (MAPE) of 19.35393, along with a deviation information criterion (DIC) of -154.23. Our findings highlight that the standard deviation parameter significantly influences model fit compared to the relatively insignificant impact of the range parameter. When coupled with high-resolution mapping, these optimized parameters facilitate a comprehensive understanding of distribution patterns. This process aids in detecting areas particularly susceptible to risks, thereby enhancing decision-making efficacy regarding air quality management.

This paper aims to investigate if trust, perceived usefulness, and perceived ease of use affect the intention to use e-payment. Also, the study explores if attitudes towards the use of e-payment influence consumer's intentions to use the e-payment system which is supported by testing the moderation effect of Self-Efficacy, and Computer Anxiety on the attitude to use such systems in higher education institutes. The study found that there are a variety of effects of the Electronic Payment Acceptance Model in the United Arab Emirates that pertain to sociological, legal, and economic aspects. The United Arab Emirates can benefit from a more robust and inclusive digital payment ecosystem by comprehending and implementing the lessons gained from such research. Among the lessons learned from this study is that using electronic payment leads to many benefits, it is not possible to benefit from all these benefits if the acceptance rate of technology, especially electronic payment, is low. For this reason, this research came to provide solutions to the possibility of increasing the acceptance of technology among individuals and organizations through a complete model and studying the impact of its factors and the factors that moderate the relationship in it.

To ensure that national development objectives in rural areas are achieved evenly and sustainably, the Government of Indonesia applies the principles of Village Sustainable Development Goals (SDGs), which are derivative programs of SDGs. One of the indicators in measuring the progress and independence of villages in Indonesia is the availability of cellular phone signal access. Cellular phone signals have a vital role because most internet users in Indonesia rely on mobile data connections from cellular operators. However, the signal emitted by a provider tower has a limited range. According to the data of the Developing Villages Index in 2022, Tasikmalaya Regency is one of the regencies with the highest number of villages that have weak signal strength in West Java Province, Indonesia. To examine the effect of distance and height difference between the placement of the nearest provider tower and the location of the Village Office on the internet signal strength category in Tasikmalaya Regency, Logistic Spatial Autoregressive modeling is needed. In this study, the Bayesian Markov-Chain Monte Carlo estimation method was used, because it has advantages in flexibility and computational efficiency. In spatial modeling, there is a spatial weight matrix determined by the researcher’s understanding of the observed phenomenon. The variable observed in this study is signal strength, which has an orientation at a distance. However, there are several types of distance-based spatial weight matrices, such as K-nearest neighbor, radial distance, power distance, and exponential distance. To determine the most suitable distance-based spatial weight matrix in internet signal strength modeling, the four (4) weight matrices were compared based on the goodness of fit measure models, calculated from the confusion matrix. The results of the analysis showed that the radial distance weight matrix with a threshold distance of d = 1.7km is the most suitable use of distance-based spatial weight matrix in internet signal modeling in Tasikmalaya Regency. The weight matrix exerted a positive spatial autocorrelation effect of 57.141%. In addition, the height difference factor between the location of the provider tower with the location of the village office has a greater effect than the horizontal distance.

As time passes, social media, which was formerly used as a means of communication between users, is experiencing a transition as a means for broadcasting information, conducting business, advertising, and even political campaigning. In elections, social media is also used to discredit political opponents to reduce the electability of opposing candidate. Spreading hate speech and fake news to undermine the electability of opposing candidate is a common violation of the law committed by supporters of one candidate over another. Considering that the number of social media users increases annually at a very rapid rate, the hazard of social media abuse has the potential to grow. In 2022, Indonesia had 191 million social media users in January 2022. Obviously, this will make the election situation more tumultuous and has the potential to cause societal divisions. The government must have a control system in place to screen social media content that can be considered illegal. In this study, fake news and hate speech are classified using the Bidirectional Gated Recurrent Unit (BiGRU). Lastly, K-Prototype was used to do clustering based on categorization dimensions and probable distribution to identify which clusters had the greatest risk of breaking the law, creating confusion, and dispersing broadly throughout society. It is hoped that the clusters that are created will represent the levels of priority of tweet data that requires prompt attention from the government to prevent it from spreading and inciting social unrest. Based on the results of the analysis, the BiGRU fake news model yields a F1-score of 95%, while the BiGRU hate speech model yields a F1-score of 90%. Clustering data using K-Prototype in this research can reduce the number of tweet data from 13,183 to 1,791 data. These new data are considered as a priority that must be pursued in preventing social media disputes.

Growing environmental concerns have led to increased demand for ‘green’ or environmentally friendly business. This has resulted in growing interest in the research of Green Information Technology (GIT). However, to date, such research has had a disproportionate emphasis on organisational antecedents while often overlooking outcomes. The current study aims at giving a better insight into the state of GIT adoption among oil companies in Sudan. If these companies were to adopt a green business model, it would significantly impact the environment given that they typically contribute significantly to environmental degradation. To this end, this study a) determines the level of awareness of GIT adoption among employees of oil companies in Sudan, b) identifies the key factors affecting the GIT adoption, c) examines the effect of training, top management support, perceived ease of use, perceived usefulness, relative advantages, and GIT behaviour on GIT adoption, and (d) examines the effect of GIT adoption on outcomes, namely business performance, competitive advantage, and process innovation. From a sample of 292 respondents, the result revealed that top management and GIT behaviour were two of the four antecedents not supported by data, thereby rendering them insignificant. Surprisingly, the survey data supports all three hypotheses that recognise a positive relationship between GIT adoption and the outcomes. This study provides important empirical evidence from oil companies that lack a green adoption policy that encourages them to consider joining the green bandwagon. The study concludes that most respondents are aware of GIT.

The primary objective of this study was to assess the challenges associated with the digital transformation process in the accounting systems of companies listed on the Khartoum Stock Exchange, focusing on the perspectives of senior management, financial management, and technology management. These departments were considered integral due to their profound understanding of digital transformation processes, requirements, and existing as well as anticipated challenges. Employing a descriptive-analytical approach, the study utilized a questionnaire to gather data from 315 individuals within the study population. The results revealed a favorable inclination among Khartoum Stock Exchange-listed companies towards digital transformation, manifested through a notable automation of accounting procedures, the cultivation of a digital culture, and the adoption of strategic approaches for digital transformation. However, the study identified several challenges, including insufficient technological infrastructure at the national level, the absence of a clear national strategic plan for digital transformation, and limited financial resources allocated to digital transformation in accounting systems. The study also proposed a set of solutions to address these challenges, categorized into national and corporate levels.

This study presents a structural equation model to describe the interrelationships between Digital Marketing, Brand Articulating Capability, Brand Resonance Capability, and Marketing Performance. It employs a cross-sectional study to examine the relationships among those research variables. This study utilized a survey methodology, which involved gathering data from 292 participants who were representatives of small and medium enterprises (SMEs) in South Sulawesi, Indonesia. Out of the 292 questionnaires distributed, 270 were considered appropriate for analysis. Four hypotheses were formulated and examined by employing structural equation modeling (SEM) analysis, which revealed several noteworthy findings. The results suggest that SMEs with a stronger focus on digital marketing are more likely to enhance their capacity to articulate their brand, leading to improved marketing effectiveness. In contrast, those with a higher level of Digital Marketing tend to acquire brand resonance capabilities. The findings of this study highlight the significance of the Brand Articulating Capability and Brand Resonance factors in improving the impact of Digital Marketing on Marketing Performance, hence facilitating the growth of SMEs.

This study on the moderating effect of electronic quality in mobile marketing aims to examine the factors that influence how Saudi commercial banks are viewed by their customers. A research framework that sheds light on the state of the research was developed after a comprehensive analysis of the accessible literature. The theoretical foundation of this study is the idea of perceived characteristics, which identifies five critical factors that influence adoption rates. The empirical results of this study are presented based on a sample of 300 respondents (n = 300). The research was conducted using the statistical technique of least squares structural equation modeling (PLS-SEM). The reporting format conforms to accepted PLS-SEM analysis standards. The results reveal a significant association between mobile marketing and customer perceptions in the context of Saudi commercial banks, especially when electronic quality is used as a mediating variable. Based on these findings, we suggest that Saudi commercial banks should strategically include e-quality in their digital marketing campaigns, paying special attention to mobile marketing.

Heading towards a cashless society, consumers have undergone a significant shift toward mobile payment services after COVID-19. The proliferation of various mobile payment applications has resulted in low consumer loyalty to mobile payment providers. Thus, continuance intention of mobile payments becomes crucial for mobile payment providers. The integration of UTAUT2 and the dedication-constraint-based mechanism are adopted to elaborate approach in retaining customers. The dedication mechanism is built by examining antecedents of satisfaction. Meanwhile, the constraint mechanism is driven by switching costs, preceded by habit and economic incentives. A total of 297 mobile payment users participated by filling out questionnaires in a field survey. The results show that the dedication mechanism dominates in creating satisfaction by increasing perceived usefulness, while the constraint mechanism is more influenced by habit than economic incentives. This research provides insights for mobile payment providers to enhance satisfaction by understanding consumers' needs in using mobile payments and to increase switching costs by fostering habit, thereby encouraging continuance intention of mobile payments in the future.

The rapid advancement of technology has had a significant impact on a wide range of industries, including the auditing industry. It is now obvious that employing Computer Assisted Auditing Techniques (CAATs) is a possible tactic for enhancing the effectiveness and efficiency of the audit process. This study evaluates how CAATs affect auditors' expectations for performance and effort in Jordan. Through a comprehensive survey of Jordanian auditors, this research provides insights into the factors that drive CAATs adoption. Utilizing structural equation modeling, the study confirms that both Effort Expectancy and Performance Expectancy positively influence CAATs adoption. These relationships are supported by robust path coefficients and low P-values, indicating statistical significance. The results of this study should clarify the possible advantages of including CAATs in the audit process and point out any difficulties auditors could encounter. Companies and professionals may choose wisely whether to embrace and use CAATs by comprehending Performance Expectancy and Effort Expectancy.