This study aims to investigate the effectiveness of community involvement in waste management through participatory research. Its objective is to bridge the theoretical underpinnings of participatory research with its practical implementation, particularly within the realm of waste management. The review systematically analyzes global instances where community engagement has been incorporated into waste management initiatives. Its principal aim is to evaluate the efficacy of participatory strategies by scrutinizing methodologies and assessing outcomes. To achieve this, the study identified 74 studies that met rigorous criteria through meticulous search efforts, encompassing various geographical locations, cultural contexts, and waste management challenges. In examining the outcomes of participatory research in waste management, the study explores successful practices, shortcomings, and potential opportunities. Moving beyond theoretical discourse, it provides a detailed analysis of real-world applications across various settings. The evaluation not only highlights successful engagement strategies and indicators but also critically assesses challenges and opportunities. By conducting a comprehensive review of existing research, this study establishes a foundation for future studies, policy development, and the implementation of sustainable waste management practices through community engagement. The overarching goal is to derive meaningful insights that contribute to a more inclusive, effective, and globally sustainable approach to waste management. This study seeks to inform policymaking and guide future research initiatives, emphasizing the importance of community involvement in addressing the complexities of waste management on a global scale.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.

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.

Business model innovation (BMI) has garnered substantial academic and corporate attention in recent decades. Researchers have not yet agreed on the most complicated BMI practices in the high-tech startups (HTS). Despite being the second-biggest economy in the world today, China has done little research on the practice of business model innovation in China’s high-tech startups. This study addresses the factors that impact the business model innovation of high-tech startups in China. Our study aims to fill the research gap by visualising and analysing, using systematic literature review (SLR) analyses and reviewing 36 in-depth articles, from 688 academic literature sources. Relevant publications from Scopus, Springer, ScienceDirect, Web of Science, IEEE Xplore, and the JDM e-library expose the current research status from 2013 to December 2023 without bias. We conducted a literature-based investigation to identify essential insights on the BMI factors in the literature and derived a high-tech startup’s BMI critical factor. Our study shows that three main factors affect the innovation of business models in high-tech startups in China. The findings raise managers’, entrepreneurs’, and executives’ knowledge of corporate resource bricolage and cognitive style constraints in business model innovation and their pros and cons. The findings will help Chinese academics understand enterprises’ institutional environment and resource bricolage as final suggestions and proposals for corporates, regulators, and policymakers are presented.

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.