Delay is the leading challenge in completing Engineering, Procurement, and Construction (EPC) projects. Delay can cause excess costs, which reduces company profits. The relationship between subcontractors and the main contractor is a critical factor that can support the success of an EPC project. The problematic financial condition of the main contractor can cause delay in payments to subcontractors. This research will set a model that combines the system dynamics and earned value method to describe the impact of subcontractor advance payments on project performance. The system dynamics method is used to model and analyze the impact of interactions between variables affecting project performance, while the earned value method is applied to quantitatively evaluate project performance and forecast schedule and cost outcomes. These two methods are used complementarily to achieve a holistic understanding of project dynamics and to optimize decision-making. The designed model selects the optimum scenario for project time and costs. The developed model comprises project performance, costs, cash flow, and performance forecasting sub-models. The novelty in this research is a new model for optimizing project implementation time and costs, adding payment rate variables to subcontractors and subcontractor performance rates. The designed model can provide additional information to assist project managers in making decisions.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

The Malaysian government’s heightened focus on Technical and Vocational Education and Training (TVET) reflects a strategic move towards economic and social development, particularly in addressing youth unemployment. Recognizing the potential of TVET to contribute to these goals, there is a specific emphasis on enhancing the marketability of women in the workforce from the current 62 percent to an ambitious 95 percent. However, a notable gender gap persists in entrepreneurial pursuits within the TVET sector in Malaysia, with female representation lagging. To bridge this gap, this study aims to construct a comprehensive framework that nurtures future-ready female TVETpreneur talent. This initiative aligns with the Malaysian Higher Education Blueprint, 2021–2025, i.e., fostering a diverse and innovative workforce. An extensive literature survey was conducted to identify the factors influencing female TVET students’ entrepreneurial intention. The literature revealed that social psychological and organizational approaches are commonly used to explore and analyze the relationship between the influence of female TVET students’ talents and behavior, their exposure to entrepreneurship, mentorship and support programs, role models in TVET, curriculum design, and access to resources. A comprehensive theoretical framework was developed based on these findings, which offers significant insights related to enhancing TVET opportunities for women and advancing Malaysia’s economic and social development goals in a sustainable way.

This study investigates how digital transformation influences visitor satisfaction at 12 World Heritage Sites (WHS) across eight coastal provinces in Eastern and Southern China. Utilizing 402 valid survey responses, it explores the impact of demographic factors—education, age, and income—on visitors’ perceptions of digital services, particularly focusing on usability, quality, and overall experience. The findings reveal that younger, higher-income, and STEM-educated visitors express significantly higher satisfaction with digital services, while older, lower-income visitors report lower levels of engagement and satisfaction. This research highlights the need for tailored digital strategies that cater to diverse demographic groups, ensuring the balance between technological innovation and the preservation of cultural authenticity at heritage sites. The originality of this study lies in its focus on non-Western contexts, particularly China’s rapidly developing coastal regions, which have been largely overlooked in the global discourse on digital tourism. By applying established theoretical frameworks—such as the Technology Acceptance Model (TAM) and Expectation-Confirmation Theory (ECT)—to a non-Western setting, this research fills a crucial gap in the literature. The insights provided offer actionable recommendations for heritage site managers to enhance visitor engagement, adapt digital services to demographic variations, and promote sustainable tourism development.

The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.

This study aims to examine the entrepreneurial activities of 240 women in the districts of Konaseema, East Godavari, and Kakinada during 2021–2022, focusing on the diverse range of 286 enterprises they managed across 69 business types. These enterprises were tailored to local resources and market demands, with coconut wholesale, cattle breeding, and provision shops being the most common. The study also analyzes income distribution, noting that one-third of the women earned between ₹50,000–1,00,000 annually, while only 0.70% earned over ₹5,00,000. More than half of the enterprises served as the primary income source for their families. The research highlights the significant role these women entrepreneurs play in their communities, their job satisfaction derived from financial independence and social empowerment, and the challenges they face, such as limited capital and market access. Finally, the study offers recommendations to empower these women to seize entrepreneurial opportunities and enhance their success.

The Urabá region, known for its banana production, faces significant challenges due to seasonal droughts that affect crop productivity. The implementation of innovative technologies, such as efficient irrigation systems, is presented as a potential solution to improve the sustainability and profitability of plantations. This study validates the implementation of an irrigation system in a banana (Musa spp.) plantation located in the region of Urabá, in order to meet the water needs of the crop during periods of drought. A case study was carried out in a banana plantation in the region of Urabá, considering the maximum and minimum monthly losses due to drought, and a random sample was used to measure the weight before and after the implementation of the irrigation system, in order to carry out an economic analysis. The study shows that the implementation of a sprinkler irrigation system increases the average weight of the harvested bunches by 20%, which is reflected in an annual increase of 30.3% of exported boxes, obtaining satisfactory results in terms of internal rate of return, cost-benefit ratio and return on investment. The implementation of irrigation systems makes it possible to increase competitiveness in international markets, especially in regions such as Urabá, where the use of these technologies is still incipient.

With the rapid increase in electric bicycle (e-bikes) use, the rate of associated traffic accidents has also escalated. Prior studies have extensively examined e-bike riders’ injury risks, yet there is a limited understanding of how their behavior contributes to these accidents. This study aims to explore the relationship between e-bike riders’ risk-taking behaviors and the incidence of traffic accidents, and to propose targeted safety measures based on these insights. Utilizing a mixed-methods approach, this research integrates quantitative data from traffic accident reports and qualitative observations from naturalistic studies. The study employs a binary logistic regression model to analyze risk factors and uses observational data to substantiate the model findings. The analysis reveals that assertive driving behaviors among e-bike riders, such as running red lights and speeding, significantly contribute to the high rate of accidents. Moreover, the lack of protective gear and inadequate safety training are identified as critical factors increasing the risk of severe injuries. The study concludes that comprehensive policy interventions, including stricter enforcement of traffic laws and mandatory safety training for e-bike riders, are essential to mitigate the risks associated with e-bike use. The findings advocate for an integrated approach to urban traffic management that enhances the safety of all road users, particularly vulnerable e-bike riders.