This study examines how access to different types of financial services influences household welfare in Indonesia. Using data from a large sample of 331,068 households, the research applies Ordinary Least Squares (OLS) regression to evaluate the impact of financial institutions on household income, which serves as a proxy for welfare. The findings reveal that access to microfinance and commercial banks significantly improves household income, highlighting the critical role these institutions play in enhancing welfare. Conversely, households relying on informal financial institutions tend to have lower incomes, indicating a negative effect on welfare. Further analysis reveals important variations based on gender and geographic location. Microfinance and cooperatives are particularly beneficial for female-headed households and those in rural areas, underscoring their importance in supporting underserved populations. On the other hand, access to commercial banks benefits both male and female headed households but has a stronger impact in urban areas where formal banking services are more readily available. While informal financial institutions negatively affect urban households, they provide modest advantages for female-headed and rural households, serving as an alternative in areas lacking formal financial services. These findings underscore the need for targeted financial inclusion policies that address gender and regional disparities. Such policies should prioritize expanding access to microfinance and cooperatives for rural and female-headed households while also improving access to formal banking services in urban areas to enhance overall welfare.

This study investigates the impact of green innovation strategy on project management performance in the food sector of Saudi Arabia, examining the mediating roles of strategic quality planning and strategic leadership. Using a cross-sectional quantitative approach, data were collected in January 2025 from 341 employees through a convenience sampling technique. Structural equation modeling via SmartPLS was employed to analyze the data. Results indicate that green innovation strategy positively influences strategic quality planning (β = 0.655) and strategic leadership (β = 0.541), which in turn significantly enhance project management performance (β = 0.566 and β = 0.657, respectively). Additionally, strategic quality planning (β = 0.456) and strategic leadership (β = 0.505) partially mediate the relationship between green innovation strategy and project management performance. These findings highlight the critical role of green innovation and strategic management practices in improving project outcomes within Saudi Arabia’s food industry.

East Timor is one of the youngest nations in the world, having been established in 2002. The majority of East Timor's population works in the agricultural sector, yet the mining sector (oil and gas) plays a dominant role in contributing to the country's GDP. The management of oil revenue in East Timor is adapted from the model implemented in Norway, known as Norway Plus. Aware that oil and gas resources must be supported by other economic sectors in the long term, this study aims to analyze potential scenarios for developing East Timor's key economic sectors, as well as to propose potential policy pathways. The research approach uses prospective methods (SMIC-Prob analysis and MULTIPOL), which are suitable for policy formulation due to their future-oriented nature. Data collection was conducted through Focus Group Discussions (FGD) involving experts from both the government and academia. The results from the SMIC-Prob analysis show that the oil and gas sector indeed has the highest probability. However, in the long term, the agricultural sector emerges as a crucial alternative. Furthermore, the MULTIPOL analysis indicates that in scenarios focusing on the development of non-oil and gas sectors, policies should prioritize the development of local economic sectors. The government of East Timor is recommended to seriously develop non-oil and gas sectors by utilizing revenue from oil and gas extraction. Additionally, environmental governance is also a critical consideration to achieve sustainable, inclusive development.

This study investigates the determinants of Long-Term Care Insurance (LTCI) adoption intention using the Unified Theory of Acceptance and Use of Technology (UTAUT), with Technology Readiness (TR) as a moderating variable. A quantitative approach was applied, utilizing self-administered questionnaires from 180 participants across health service institutions in Guangxi Province, China. Data were measured on a seven-point Likert scale and analyzed using Structural Equation Modeling (SEM) via SmartPLS. Results confirm that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence LTCI adoption intention. Additionally, TR moderates these relationships, strengthening their effects. The findings underscore TR's critical role in enhancing LTCI adoption and offer practical insights for policymakers and practitioners seeking to promote LTCI uptake.

This study examines the adoption intention of the Long-Term Care (LTC) regulatory system in Guangxi, China, emphasizing the influence of innovation attributes and perceived risk. It analyzes how relative advantage, compatibility, complexity, trialability, and observability positively affect healthcare providers' and elderly care institutions' willingness to adopt the system. The study further explores the moderating role of perceived risk in strengthening the relationship between these innovation attributes and adoption intention. Data were collected through a survey of 370 professionals from hospitals, rural health centers, and elderly care institutions and analyzed using SPSS and structural equation modeling (SEM). Results indicate that all five innovation attributes significantly enhance adoption intention, with perceived risk amplifying these effects. The findings underscore the need for supportive policies, technological advancement, and coordinated stakeholder engagement to ensure successful LTC system implementation. This research provides actionable insights for policymakers and industry leaders to support the expansion of LTC insurance systems amid China’s aging population.

In real-world markets, supply chain costs often fluctuate over time due to the contango and backwardation effects, making multi-period supply chain planning complex and critical. This paper presents a multi-period supply chain optimization model that explicitly incorporates these effects into cost forecasting and decision-making. A multi-period supply chain model is developed, considering the cost uncertainty introduced by contango and backwardation. An integrated polynomial regression fuzzy method is proposed to address this problem by predicting future fluctuations in purchasing, ordering, and logistics costs. A mixed-integer linear programming (MILP) model is formulated to minimize the total supply chain cost across multiple periods. Moreover, improving the hybrid genetic algorithm (IHGA) is proposed to solve this problem. The performance of the proposed IHGA is triggered by integrating trust region, quasi-Newton, and pattern search methods. Response Surface Methodology (RSM) determines the optimal parameter settings and hybridization structure. A real-world case study involving surgical instrument manufacturing companies validates the proposed approach. The results highlight optimal supplier selection and order allocations for each period, and performance comparisons reveal that the IHGA outperforms traditional algorithms in terms of cost efficiency, computational time, and convergence behavior.

The decision to comply with taxes by a business depends on the business's choice of behavior, especially the nature of its operations. The research on mega data to find relationships in the economy is urgent, including the logistics industry. Improving logistics efficiency in countries is a necessary goal in countries to increase their ability to contribute to the economy. Researching the impact of tax compliance of logistics enterprises through a survey of 404 logistics enterprises in Vietnam and through quantitative analysis using SPSS software, the research results show that enterprises with high financial efficiency have a higher level of tax compliance, at the same time, enterprises with high quality human resources are likely to have a higher level of tax compliance. The research results also show that highly efficient logistics activities of enterprises are likely to have a higher level of tax compliance, at the same time, appropriate national tax policies are likely to help enterprises have a higher level of tax compliance.

The rising usage of E-Auditing and its effect on businesses through new technology developments and rules demonstrates how audit systems can maximize operational efficiency and business decision quality. Researchers are undergoing a study to determine E-Auditing acceptance rates. The TOE model represents “Technological, Organizational, and Environmental” variables that function as key examination areas in organizational analysis and management practices when researchers study technological implementation and adoption patterns in industrial environments. The research proposes that these three aspects (technical aspects with both Relative advantage (RA) and Technology Compatibility (TC) and organizational aspects including top management support (TMS) and readiness (R)) along with environmental aspects such as competitive pressure (CP) contribute to e-auditing adoption. Auditor trust appeared in this study as the suggested moderating factor. A total of 235 participants provided info outside random sampling while the analysis used SPSS software. The experimental results proved that factors associated with TOE provide legitimate grounds for E-auditing acceptance. Evidence demonstrates that TOE variables provide justification for why organizations would accept E-auditing technology. Data show that trust functions as a supportive variable for the relationship between TOE and e-auditing but provides minimal strength. E-Auditing adoption research needs further investigation within emerging economies to understand better how users adopt this tool. The objective for decision-makers should focus on expanding user understanding of E-Auditing adoption along with educating decision-makers about the benefits of implementing this system.

Metaheuristic algorithms are a class of optimization techniques that have revolutionized problem-solving across various domains. These algorithms provide a versatile and powerful approach to finding near-optimal solutions for complex, combinatorial, and computationally intensive problems. They draw inspiration from natural processes, such as evolution, swarm behavior, or annealing, to iteratively refine solutions by intelligently navigating the problem space. Metaheuristics have become indispensable tools in both academia and industry, helping researchers and practitioners address real-world problems efficiently and effectively. The Pelican optimization algorithm (POA) is a recently developed metaheuristic algorithm that simulates the hunting behavior of pelicans. In complex optimization problems, an POA may have slow convergence or fall in sub-optimal regions, especially in high complex ones. In this paper, Levy flight is integrated into the exploration phase to enhance its search capabilities. Furthermore, a novel exponential parameter has been introduced to enhance the algorithm's overall performance by facilitating a smoother shift between exploration and exploitation phases. These modifications are intended to keep the algorithm from being locked in local optima. The developed algorithm named as IPOA was tested using widely recognized twenty-three benchmark functions with a variety of characteristics, a set of CEC2022 test suites, and five different engineering constrained problems. The results demonstrate the superiority and effectiveness of IPOA in tackling function optimization and constrained design engineering problems.

The research objective is to determine the impact of social media on tourists' intention to return: research in Can Tho city, Vietnam, on that basis, proposes some managerial implications to enhance tourists' intention to return. The article uses qualitative research methods combined with quantitative research. Qualitative research conducted by consulting 03 experts to determine the electronic word-of-mouth factor affecting tourists' intention to return, determining the research model, and adjusting the scales to form a questionnaire for use in official research. Quantitative research assessed scale reliability using Cronbach's Alpha, evaluating the measurement model and structural equation modeling. The data analysis results have identified three independent factors affecting tourists' intention to return: Electronic Word of Mouth, Destination Image, and Attitudes Toward Destination. At the same time, destination image and attitude towards the destination impact tourists' intention to return. Based on the research, some solutions are proposed to improve tourists' intention to return.