Since July 2022, Indonesia's Electronic Customs Declaration (ECD) system has replaced traditional paper-based forms for international arrivals. Although intended to enhance efficiency and user convenience, the system has generated considerable dissatisfaction, with 83.14% of recorded complaints citing technical or usability challenges. This study examines the determinants of passenger satisfaction with the ECD system through an integrated framework that combines the Expectation-Disconfirmation Model (EDM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the DeLone & McLean Information Systems Success Model. A cross-sectional survey of 207 Indonesian international passengers at Soekarno-Hatta International Airport was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), suitable for evaluating latent constructs and mediation effects in complex models. The results indicate that effort expectancy, drawn from UTAUT, significantly influences perceived system quality and Disconfirmation, both of which serve as critical mediators of user satisfaction. System Quality, based on usability, reliability, and interface design, exerts an indirect effect through Disconfirmation, as conceptualized in EDM and DeLone and McLean's framework. Collectively, the variables explain 86.3% of the variance in satisfaction, with all key paths statistically significant (p < 0.001). These findings underscore the importance of expectation alignment, ease of use, and perceived system quality in shaping satisfaction with digital public services and provide practical insights for user-centered design and implementation of government technologies.

The increasing number of automobiles on the highway has led to a major difficulty in municipal traffic management. Intelligent Transportation Systems (ITS) require dependable traffic prediction algorithms capable of providing accurate forecasts at numerous time steps. This research proposes an Enhanced-Graph Neural Network (E-GNN) technique for traffic prediction and has been explored to augment the traditional GNN and temporal dependencies in traffic networks. A multimodal input was deployed for the preprocessing of the input data with GNN-Layer. An additional data stream was integrated to influence the traffic flow. The approach leverages strategically positioned loop detector sensors on the road network as a means of harvesting real-world traffic data. The suggested E-GNN technique for the estimation of real-time traffic speed was developed using two separate actual traffic datasets, such as PeMS-BAY and METR-LA. The result obtained over time shows a significant improvement, as seen in the 15-minute ahead prediction; the RMSE of EGNN reduced by 26.25% when compared with the existing state-of-the-art techniques.

Traffic congestion in Bogor City has led to significant fuel waste and severely impeded the flow of vehicles in this densely populated urban area. To solve this issue and help individuals plan their travels more effectively, creative solutions that offer real-time traffic information are needed. The goal of this research is to create an Android-based traffic monitoring information system that makes it easier to access data on traffic conditions. The waterfall approach utilizes the React Native Maps package for digital map creation and the React Native framework for Android application development. The Android-based information system technology then serves as a useful traffic monitoring tool. Digital maps connected with CCTV and Google Traffic enable the visualization of current traffic conditions, allowing individuals to readily determine the traffic situation, including information on public transportation routes. Test findings utilizing the black-box method reveal that the system works well on Android devices running 7.0 Nougat or higher. The system's key functions, such as real-time traffic monitoring, public transit route information, and the locations of traffic service facilities, were successfully deployed as designed. Even with basic data from CCTV connection, the Waterfall-based Android Traffic Information System has the advantage of being easy to use and flexible enough to adjust to local demands. Although 78% of respondents were satisfied with the system, 22% of users reported delayed system access during peak hours. Alternative routes from SIJAB using Google Maps or Waze led to a reduction in the average time taken to travel by 12.5%, and in some cases, up to 30% compared to traditional paths during peak hours.

The Jakarta–Bandung high-speed train (HST) project aims to enhance intercity connectivity between two major metropolitan regions in Indonesia, with anticipated benefits in mobility, economic development, and tourism. This infrastructure initiative is projected to create employment during both construction and operation phases. However, achieving the targeted ridership of 30,000 daily passengers remains a key challenge, particularly when compared to the current usage of conventional trains, which fluctuates between 10,000 and 14,000 passengers per day. This gap raises concerns regarding the long-term sustainability of the HST system. This study applies a system dynamics modelling approach to analyze various factors influencing passenger demand, including fare structures, service frequencies, travel time, and station accessibility. The model integrates feedback loops to capture the dynamic interrelations between policy interventions, transportation infrastructure, and land use. Simulation results reveal that demand is highly sensitive to seasonal variations, with peak travel periods occurring during mid-year and year-end holidays. Among the evaluated scenarios, the highest ridership is achieved when strategies such as integrated multimodal connectivity, optimized pricing, and tourism promotion are implemented. These findings highlight the importance of coordinated policy measures in enhancing system performance. The study concludes that a comprehensive, multi-sectoral approach is essential for achieving operational viability and long-term sustainability in high-speed train systems, particularly in emerging economies seeking to maximize infrastructure investments.

In Indonesia, two-wheeled vehicles, or motorbikes, make up over 80% of all motorized vehicles. These bikes are popular due to their affordability, fuel efficiency, and ability to navigate through traffic quickly. However, the high volume of motorbikes leads to complicated traffic patterns and poses safety risks. To enhance the safety of motorcyclists, lane separation is the most effective engineering solution. This research aims to develop a design model for an exclusive motorcycle lane (EML) on arterial roads in urban areas through microsimulation. The EML design model is created using traffic simulations conducted with PTV VISSIM 2020. The inputs for the simulation are EML width and motorcycle (MC) volume. The EML width ranges from 3.0 meters to 4.0 meters, while the MC volume varies from 910 MC/hr to 4800 MC/hr. The output of the simulation is analyzed to establish the relationship between EML width, motorcycle volume, and the volume-to-capacity ratio (VCR). The modeling will yield the maximum motorcycle volume for each EML width, as well as the relationships between volume, speed, and VCR. According to the EML design model, the maximum MC volume for an EML width of 3.0 m is 3,889 MC/hr, a 3.25 m width, 4,175 MC/hr, a 3.5 m width, 4,565 MC/hr, a 3.75 m width, 5,000 MC/hr, and a 4.0 m width, 5,140 MC/hr.

The determinants of travel mode choice among university students and staff were examined to address a gap in campus mobility research, particularly within tropical environments. Data were obtained from 923 respondents at Mahidol University, Thailand, and analyzed through the application of the Nested Logit Model (NLM), which accounts for hierarchical decision structures across six travel modes: trams, bicycles, motorcycle taxis, private motorcycles, private cars, and walking. Exploratory factor analysis was employed to identify latent constructs influencing satisfaction, including comfort, built environment, and flexibility. The analysis indicated that active and shared modes, particularly trams and walking, were generally preferred. Travel time, cost, and scheduling flexibility were found to be key determinants of mode selection, with flexibility exerting a positive influence and travel time and cost acting as constraints. Weather-related factors were not statistically significant, suggesting that infrastructural conditions may mitigate climatic impacts on active travel. Elasticity analysis further demonstrated that changes in service attributes can prompt modal shifts between motorized and active travel. It is concluded that integrating attitudinal and contextual variables into discrete choice modeling offers a deeper understanding of mode choice behavior in campus environments. Policy implications include the enhancement of shaded pathways, the improvement of service reliability, and the adoption of flexible scheduling strategies to promote sustainable and health-supportive mobility. These findings provide a framework for the development of targeted campus transport policies in climate-sensitive settings.

A bibliometric analysis was conducted in this study covering the period between 2015 and 2024 to establish a roadmap for big data analytics in auditing. Excel, RStudio, and R software were employed to analyse the performance, co-occurrences, citations, and authorship of 91 articles selected from the Scopus database. According to the acquired results and pertinent observations, 2022 was the most productive year with 21 publications. The Journal of Emerging Technologies in Accounting was the most prolific journal, with ten publications. Besides, a total of 40 articles originated from the United States, significantly surpassing the number of publications issued by other countries. These findings indicated a growing attention on research related to audit quality and big data analytics in auditing. This thorough review provided insights into the historical background and current status of data analytics and auditing, while identifying gaps that necessitated further academic inquiry. The study provided a performance analysis and described the evolution of a profession, functioning as a vital resource for researchers and professionals who aim to understand emerging research trends in the pursuit of future studies.

The rapid emergence of the sharing economy has introduced significant changes to the structure of urban transport systems, particularly in terms of labour dynamics and social interaction. This paper investigates the impact of ridesharing platforms on social perspectives, new labour market structures, and evolving value systems within urban transport. The study aims to understand how these platforms influence drivers' perceptions of life, social connectedness, and work-related satisfaction by examining the role of new labour characteristics and platform-induced values. To address these aims, data were collected from over 2,000 Grab driver-partners across Vietnam through a nationwide questionnaire survey. Responses were analysed using Covariance-Based Structural Equation Modelling (CB-SEM) to explore the interrelation between latent constructs representing labour market dynamics, emerging values, and social perspectives. Findings indicate that ridesharing platforms enable higher autonomy in work scheduling, foster stronger connections between drivers and customers, and contribute positively to drivers’ income, well-being, and social engagement. These transformations suggest a paradigm shift in urban transport labour and social integration, driven by digital mobility platforms. The results provide insights for policymakers and transport planners regarding the governance of platform-based labour and its integration with smart mobility strategies in urban areas.

Establishing model parameters is fast becoming more complex especially with generalized linear mixed models (GLMMs); which comprises of generalized linear models and classical linear mixed models. Evaluating generalized linear mixed models (GLMMs) parameters with maximum likelihood techniques involves some levels of complexity, to proffer solutions to this challenge, techniques involving approximation of integrals were considered in this paper. Some approximation methods for parameter estimation were considered to establish the most effective and adaptive model using a good number of model performance metrics/criteria. Penalized quasi-likelihood, adaptive gauss-Hermite quadrature, and Laplace approximation estimation techniques were considered to fit the real clinical data set with binary outcomes. Real-life data analysis showed some better fitness and superiority of an adaptive gauss-Hermit quadrature technique over some other existing estimation techniques using a set of model performance metrics. Data users at various levels of analysis may now consider adaptive gauss-Hermite quadrature technique over other estimation techniques in fitting GLMMs with binary responses. Coefficients of the model with good performance metrics were also considered in establishing effects of clinical follow-up on medical diagnoses of individual patients.

This study develops an integrated model to explain e-commerce adoption among agricultural producers by combining the Technology–Organisation–Environment (TOE) framework with social identity theory (SIT). Drawing on cross-sectional data from 585 farmers in Shanxi Province, China, and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the research assesses both the direct and mediating effects of technological readiness, organisational barriers, and environmental enablers. Results reveal that social identity acts as a critical mediator, transforming indirect contextual drivers into adoption behaviour. Notably, factors such as complexity and digital environmental change influence adoption exclusively through social identity rather than direct paths. These findings advance existing literature by embedding sociopsychological mechanisms into digital adoption models, and offer practical guidance for promoting inclusive e-commerce development in rural and agricultural contexts, particularly in developing regions like rural China.

Recently, the food industry has faced numerous challenges such as rising demand, climate change, and the imperative to improve the quality and safety of food products. This research investigated the role of Artificial intelligence (AI) and the Internet of Things (IoT) in managing food supply and distribution projects. The main objective of this study was to analyze how these technologies could be implemented to optimize the process of supply chain and enhance the efficiency and effectiveness in food distribution. Successful cases of technological implementation in the food industry highlighted the associated benefits and challenges of adopting AI and IoT. Ten critical factors influencing the roles of AI and IoT in food supply and distribution were identified and considered in the current study. Following a systematic coding process through meta-synthesis, concepts related to each factor were extracted from previous studies. Finally, expert opinions were gathered by a questionnaire survey whereas the Kappa index was calculated using SPSS software. The obtained value of 0.78 indicated a desirable agreement in the perspectives between researchers and experts. By leveraging AI, organizations are able to analyze big data, predict demand, optimize inventory, and reduce resource waste. Likewise, IoT, through connecting devices and sensors to the network, enables the collection of real-time data, which assists managers in making better decisions regarding the timing and location of food distribution.

As the global tourism industry becomes increasingly shaped by digital transformation, the strategic role of Information and Communication Technologies (ICTs) has gained particular significance, especially in emerging economies. Albania, with its growing appeal as a tourist destination and its economic reliance on tourism, presents a compelling context in which to examine the digital maturity of tourism enterprises. This study investigates the adoption of ICT tools—particularly websites and e-marketing practices—among Albanian tourism businesses and assesses the impact of managerial attitudes and digital infrastructure on perceived business performance. Grounded in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the research employs a quantitative survey methodology, collecting responses from 208 enterprises across five key tourism regions. The findings reveal that 81% of these enterprises maintain an active website, with adoption rates significantly higher in coastal areas such as Durrës and Sarandë. Hierarchical regression analysis demonstrates that a favorable perception of ICTs is positively associated with ICT-based marketing strategies (r = 0.243, p < 0.01) and reported profit growth (R² = 0.291). Although high initial website development costs are acknowledged as a barrier, they are also correlated with long-term profitability, reflecting a growing recognition of ICT as a strategic asset rather than a cost burden. The results support the dual theoretical lens: TAM explains the behavioral inclination toward digital tools, while RBV underscores their value as inimitable resources for sustained competitive advantage. The study highlights the need for targeted government policies, including digital upskilling programs, infrastructure investment, and support for small enterprises lagging in digital readiness. Future research could expand this inquiry through longitudinal and cross-country analyses, exploring how emerging technologies—such as AI-driven personalization, immersive media, and data analytics—reshape destination competitiveness in the digital age.