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.

As Saudi Arabia embarks upon a transformative economic journey under the umbrella of its Vision 2030 and National Transformation Plan, the Saudi government plans to implement various initiatives to engage the private sector in meeting new national development goals, including the provision of 1600 schools through the public-private partnership (PPP) route. This article provides an international outlook and review of the use of PPPs to deliver school infrastructure and analyzes Saudi Arabia’s potential to implement this promising program. Effective use of the PPP model can guarantee the timely provision of schools and other infrastructure projects that could fulfill the vision of Saudi Arabia’s political leadership, potentially serving as a catalyst and blueprint for other Gulf states. The case study argues that, while Saudi Arabia’s schools’ program enjoys significant political support, its government needs simultaneously to pursue the parallel objective of developing the necessary institutional, legal, regulatory, and supervisory frameworks essential for successful PPP projects globally. The article concludes with recommendations to mitigate existing challenges and foster the involvement of the private sector in education sector development.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

This study presents a simple yet informative bibliometric analysis of servant leadership literature, aiming to provide a basic overview of its scholarly landscape and identify general trends. We conducted this analysis in September 2023. We focused solely on the Scopus database to understand the current state of servant leadership research. Despite extensive search efforts, we found no similar bibliometric analyses within the servant leadership domain during our study period. Therefore, our focus is to present a brief and straightforward analysis of current research in this field based on identification trends over time, connection between co-occurrence of author keywords, most and less discussed keyword, and areas of high and low concentration. Our findings show an increase in scholarly publications, reflecting a growing acknowledgment of servant leadership’s relevance in management practices. Interconnected keywords and themes such as leadership, transformational leadership, job satisfaction, work engagement, authentic leadership, ethical leadership, organizational citizenship behavior, trust, and leadership development emerge prominently. Additionally, less-discussed keywords such as accountability, core self-evaluations, educational leadership, stewardship, customer orientation, and psychological well-being provide alternative perspectives on these research results. While acknowledging limitations inherent in our bibliometric research, such as potential publication bias and language restrictions, our study offers valuable insights for scholars and practitioners interested in this area.