In Ghana, youth unemployment remains significant challenges, with technical and vocational education and training (TVET) emerging as a potential solution to equip young people with practical skills for the job market. However, the uptake of TVET programmes among Ghanaian youth remains low, particularly among females. This study therefore explores the determinants that influence TVET choices among Ghanaian youth, with the goal of informing policy development to enhance participation in vocational education. Applying an enhanced multinomial logistic regression (MLR) model, this research examines the influence of socio-economic, demographic, and attitudinal factors on career decisions. The enhanced model accounts for class imbalances in the dataset and improves classification accuracy, making it a robust tool for understanding the drivers behind TVET choices. A sample of 1600 Ghanaian youth engaged in vocational careers was used, ensuring diverse representation of the population. Key findings reveal that males are approximately three times more likely to choose TVET programs than females, despite females making up 50.13% of Ghana’s population. Specific determinants influencing TVET choices include financial constraints, parental influence, peer influence, teacher influence, self-motivation, and vocational limitations. In regions with limited vocational options, youth often pursue careers based on availability rather than preference, which highlights a gap in vocational opportunities. Parental and teacher influences were found to play a dominant role in steering youth towards specific careers. The study concludes with recommendations for policymakers, instructors, and stakeholders to increase the accessibility, relevance, and quality of TVET programmes to meet the socio-economic needs of Ghanaian youth.

Using the unified theory of acceptance and use of technology (UTAUT), this study investigated the effect of perceived usefulness, perceived ease of use, social influence, facilitating condition, lifestyle compatibility, and perceived trust on both the intention to use and adoption of an e-wallet among adults. This quantitative study employed a cross-sectional research technique to collect data from 501 respondents via Google Form. The acquired data was assessed using partial least squares structural equation modelling (PLS-SEM). Therefore, perceived usefulness, perceived simplicity of use, social influence, lifestyle compatibility, and perceived trust all had a strong positive impact on both intentions to use and adoption of an e-wallet. This study demonstrated that the intention to use an e-wallet mediated the links between predictors and e-wallet adoption. Respondents’ age and gender moderated the effect of lifestyle compatibility on their intention to use an e-wallet. The study’s findings can assist managers and policymakers establish successful ways that capture customers’ intention to use and experience with employing an e-wallet amid a tumultuous market. Finally, such well-crafted policies may stimulate the digital platform and web-based apps, as well as raise e-wallet acceptance rates in undeveloped countries.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

The telecommunications sector is crucial in a country’s economy. The advancements in information and communication technology have had a significant impact on this sector, particularly in the electronic transactions domain. The utilization of electronic transactions in the telecommunications sector can yield substantial benefits, including increasing non-tax state revenue and economic development in Indonesia. The Covid-19 pandemic has served as a catalyst for the sharp rise in internet usage and telecommunications services, such as work-from-home, distance learning, online shopping or payments, industrial digitization, telemedicine, and others. Consequently, digital transformation has accelerated, necessitating the equitable distribution of Information and Communication Technology (ICT) in Indonesia. The utilization of electronic transactions has directly contributed to the increase in state revenue from non-tax sector, particularly in the telecommunications sector. This can be observed through the continuous growth of non-tax revenue achieved by the Ministry of Communication and Informatics, surpassing the set targets each year in terms of realized value. The objective of this research is to analyze the effectiveness, challenges, and impacts of electronic transactions in increasing non-tax state revenue, in order to generate policy recommendations in Indonesia. This study uses a normative juridical method, analyzing positive legal provisions. Data sources were obtained from three types of legal materials, and data collection was conducted both online and offline. The research results indicate that the utilization of electronic transactions can increase non-tax state revenue in the telecommunications sector through various forms such as telecommunications bill payments, e-commerce transactions, sales of prepaid credit and data packages, telecommunications devices, digital services, content delivery, and transaction monitoring and oversight.

In the era of digital disruption, the imperative development of broadband services is evident. The emergence of 5G technology represents the latest stride in commercial broadband, offering data speeds poised to drive significant societal advancement. The midst of responding to this transformative phenomenon. This pursuit unveils a landscape replete with opportunities and challenges, particularly regarding how 5G’s potential benefits can drive the government towards equitable distribution, ensuring accessibility for all. Simultaneously, there exists a legal hurdle to ensure this vision’s fruition. From a legal perspective, perceived as infrastructure for transformation, the law must seamlessly adapt to and promptly address technological progress. Utilizing normative juridical methods and analytical techniques via literature review, this research endeavors to outline the advantages of 5G and scrutinize Indonesia’s latest telecommunications regulations and policies, alongside corresponding investments. The study ultimately aims to provide a juridical analysis of 5G implementation within Indonesia’s legal framework.

This study examines the economic feasibility of the environment-friendly farmland use policy to improve water quality. Conventional highland farming, polluting the Han River basin in South Korea, can be converted into environment-friendly farming through land acquisition or application of pesticide-free or organic farming practices. We estimate the welfare measures of improvement in water quality and the costs of policy implementation for economic analysis. To estimate the economic benefit of improvement in water quality experienced by the residents residing in mid-and-downstream areas of the Han River, the choice experiment was employed with a pivot-style experimental design approach. In the empirical analysis, we converted the household perception for water quality grades into scientific water quality measures using Water Quality Standard to estimate the value of changes in water quality. To analyze the costs required to convert conventional highland farmlands into environment-friendly farmlands, we estimated the relevant cost of land acquisition and the subsidy necessary for farm income loss for organic agricultural practice. We find that the agri-environmental policy is economically viable, which suggests that converting conventional highland farming into environment-friendly farming would make the improvement in water quality visible.