This study presents a comprehensive bibliometric analysis of the literature on public financial management (PFM), aiming to identify key trends, influential publications, and emerging themes. Using data from Web of Science and Scopus, the study examines the evolution of PFM research from 1977 to 2024. The findings reveal a significant increase in PFM research output, particularly after 2010, with countries like the United States, the United Kingdom, and China contributing the most publications. Central themes such as financial management, transparency, and accountability remain prominent while emerging topics like gender budgeting, health insurance, and blockchain technology reflect shifting priorities in the field. The study employed performance analysis and science mapping techniques to assess the structure and dynamics of PFM research. The analysis highlights key focus areas, including fiscal decentralization and sector-specific management, and identifies gaps in the existing literature, particularly regarding interdisciplinary and international collaboration. The results suggest that while PFM remains rooted in traditional governance and financial control, there is a growing emphasis on modern, innovative solutions to address contemporary challenges. This study’s insights provide a roadmap for future research, emphasizing the importance of transparency, technological integration, and inclusive financial policies. In conclusion, this bibliometric analysis contributes to understanding PFM’s evolving landscape, offering scholars and policymakers a clearer perspective on current trends and future directions in the field. Future research should focus on expanding interdisciplinary approaches and exploring the practical impacts of emerging PFM trends across different regions.

Extensive research on pro-environmental behaviour (PEB) reveals a significant knowledge gap in understanding the influence of social class, perceived status and the middling tendency on pro-environmental behaviour. Using the International Social Survey Programme Environment dataset, and conducting multilevel mixed-effects linear regressions, we find that the middling tendency and biased status perceptions significantly influences pro-environmental behaviour. Those who deflate their social position have higher pro-environmental behavior and this reinforces the idea that pro-environmental behaviour is driven by a post-materialist effect rather than a status enhancement effect. Moreover, the objective middle class is still a stronger contributor to higher PEB levels compared to subjective middle class. We also find the relation between class, status and PEB vary by country. These findings provide vital insights into the intricate and heterogenous dynamics between class, status and pro-environmental behaviour among different countries and shed light on class and status as driving forces behind pro-environmental behaviour.

Government performance means the results of government work. Its use is to evaluate government accountability, decision-making, efficiency, effectiveness, transparency, and achievement of goals. Purpose: This paper aims to explore the understanding of performance measurement tools commonly used in government, the reasons for using them, and the implementation of performance measurement in Indonesia. Method: This study uses a meta-synthesis method, an integrative review approach from 2000–2021, in the Scopus database using the keywords measurement system, performance measurement, performance measurement government, measurement system government. Results and Discussion: The final sample consisted of 23 studies, and the results showed that the most commonly used performance measurement was the balanced scorecard. This is because the balanced scorecard is able to explain the vision, mission, strategy, results, and operational actions, so that it can achieve local government goals. Research implications: Insight into government performance measurement can be used to determine the strengths and weaknesses of various performance measurement tools so that the government can implement performance measurement tools that are more appropriate for its government. Originality/Value: This study offers an adaptation of existing methods to measure government performance more effectively. In addition, this study focuses on the context of developing countries, which can provide new contributions to the literature.

This study investigates the influence of service quality, destination facilities, destination image, and tourist satisfaction on tourist loyalty in the Pasar Lama Chinatown area of Tangerang City. Utilizing data from 400 respondents, the study employed structured questionnaires analyzed through descriptive statistics, reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM). The results reveal that service quality (β = 0.47, p < 0.001), destination facilities (β = 0.33, p < 0.001), and destination image (β = 0.4, p < 0.001) all significantly enhance tourist satisfaction, which in turn has a strong positive effect on loyalty (β = 0.58, p < 0.001). Direct paths also show that service quality, destination facilities, and destination image independently contribute to tourist loyalty. Bootstrapping confirms satisfaction’s mediating role between these factors and loyalty. Practical recommendations suggest prioritizing service quality improvements, facility enhancements, and a positive destination image to foster loyalty and promote tourism sustainability in Pasar Lama, China. These insights assist tourism managers in developing strategies to enhance long-term visitor retention and engagement in the area.

The study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.