This research investigates the dynamic landscape of succession planning (SP) strategies in higher education, with a focus on synthesizing existing literature to guide improvements in presidential succession practices. The intense global competition in higher education has led to imbalances in the quantity and composition of potential successors, hindering institutions’ rapid advancement and affecting their competitiveness on the global stage. The study addresses critical challenges such as attracting, retaining, and nurturing successors in key positions beyond material incentives. Employing a literature analysis methodology, the research comprehensively examines the existing body of literature related to succession planning, offering recommendations to promote stability in leadership, foster continuous talent development, and mitigate talent crises. The study evaluates the current state of succession planning in higher education, identifying issues and their root causes. It provides a summary and analysis of ongoing research efforts related to successor quality, team formation, and cultivation models. Despite advancements through national talent cultivation policies, persistent challenges like talent scarcity, the absence of gender-inclusive succession plans, a lack of originality, and inconsistent staff flow hinder progress. The research attributes these challenges to traditional personnel systems and university administrators. Proactive measures are proposed, including creating awareness of succession planning, advocating for personnel mechanism reform, establishing a comprehensive training system, and developing a scientifically-grounded succession plan. Though the study aims to contribute to leadership development and address pressing issues faced by higher education institutions, with only a limited number utilizing mixed techniques, it restricted the comprehensive inclusion of social context knowledge and evidence regarding the motivations, beliefs, and experiences of individuals in this investigation.

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.

This research systematically reviews the relationship between populism and economic policies, analyzing their impact on state development and growth. It is the first study to comprehensively examine the interaction between these two concepts through a systematic literature review. The review process adhered to the PRISMA protocol, utilizing the Scopus, EBSCO, and Web of Science databases, covering the period from 2012 to 2024. The findings reveal a deep interconnection between populism and economic policies, with significant implications for governance and socioeconomic well-being. The review identifies that neoliberal populism combines pro-corporate elements with populist rhetoric, favoring economic elites while presenting itself as beneficial for the “people.” Additionally, it underscores that neoliberal globalization has facilitated market liberalization but also increased inequality and undermined national sovereignty. The review concludes that while populism may offer quick fixes to immediate economic issues, its simplistic and polarizing approaches can be counterproductive in the long term. Thus, there is a critical need to reevaluate and reformulate economic and governance policies to balance global economic integration with the protection of citizens’ rights and well-being.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

As a product of the integration of AI technology and media, the debate surrounding the potential replacement of human anchors by AI anchors has persisted since their inception. This paper conducts a systematic literature review of research on AI anchors in China from 2000 to 2023, grounded in theories of personalization within the field of communication studies. The analysis aims to compare the differences in personalized representation between AI anchors and human anchors, summarizing the advancements, challenges, and future directions of AI anchor communication based on personality. This contribution seeks to enhance the existing knowledge base surrounding AI anchor research.

The potential role of self-regulated learning as mediator has been deeply investigated by researchers in recent years. There is limited systematic literature review being done to investigate the role of self-regulated learning as mediator in the students’ academic learning. Therefore, searching studies in the databases WOS (Web of Science), SCOPUS, APA (American Psychological Association) PsycInfo, and ERIC (Education Resources Information Center), the present study conducted a systematic literature review on 32 studies published between 2015 and 2024 to summarize what kind of psychological factors influence students’ academic performance through self-regulated learning and assess the potential mediating role of self-regulated learning in this process. The results show that self-efficacy, emotions and motivation are significant predictors of academic achievement and self-regulated learning act as an important mediator in this relationship. An important implication was obtained that researchers can probe into the influence of specific dimensions of self-efficacy on learning performance through self-regulated learning and the influence of positive emotions such as resilience on learning outcomes with self-regulated learning as mediator.