Abstract

Abstract With the global population expected to rise to over 10 billion by 2050, there is growing pressure on sustainable food systems to meet food demand while maintaining environmental sustainability (United Nations, 2024). The study explores factors affecting smallholder farmers' adoption of robots in sustainable food systems. The United Nations has established Sustainable Development Goals (SDGs), including SDG 2, which focuses on eradicating hunger, ensuring food security and better nutrition, and fostering sustainable agriculture (United Nations, 2024). Smallholder farmers are vital to maintaining global food security, despite facing several obstacles, such as resource scarcity, lack of access to modern technologies, and climate change. In many regions, smallholder farms are a cornerstone of food security, contributing significantly to food production and the sustenance of rural communities (Carelsen, Ncube, & Fanadzo, 2021). Around 500 million smallholder farmers worldwide contribute approximately 70–80% of the food production in Asia and sub-Saharan Africa (Lowder, Skoet & Singh, 2014). Furthermore, smallholder farming supports livelihoods, reduces poverty, and ensures cultural heritage in farming practices (United Nations, 2024). Despite their importance, smallholder farmers face numerous challenges that impede their productivity and sustainability, such as resource scarcity, climate change, and lack of access to modern technologies (Nath, 2023). Robots offer promising solutions to many of the challenges faced by smallholder farmers in sustainable food systems. The adoption of robots holds the potential for transforming smallholder farming to contribute to sustainable farming practices by minimising soil disturbance and optimising input usage, thus reducing environmental impacts (Khan et al., 2021). In sustainability, robots with Artificial Intelligence can tackle issues like targeted weed control and minimising reliance on chemical pesticides (Balaska et al., 2023). This aligns with the UN Sustainable Development Goals (SDGs) for sustainable food systems (United Nations, 2024). However, robots' adoption in sustainable food systems also presents several challenges, such as affordability, skills, and infrastructure (Nath, 2023). In addressing these challenges, robots can contribute more to sustainable food systems. There is still a knowledge gap regarding factors affecting smallholder farmers' ability to adopt robotics in sustainable food systems (Bahn, Yehya & Zurayk, 2021). The study will adopt the Technology-Organizational-Environmental (TOE) framework to explore factors affecting smallholder farmers' adoption of robots. The TOE framework identifies three contexts (technology, organizational, and environmental) that influence the adoption of technology innovations in organizations (Oliveira & Martins, 2011). The study will adopt a systematic literature review based on a quantitative content analysis approach. Content analysis is useful for research involving large amounts of text and documents (Mouton, 2001). The study will analyze secondary data from peer-reviewed articles to help answer the research question. The sampling method will consist of searching for keywords relevant to the research study in various databases such as Google Scholar, ScienceDirect, Taylor & Francis Online, Elsevier, and Springer. The review will follow Siddaway’s (2014) five-phase model: scoping, planning, searching, screening, and applying eligibility criteria. The findings aim to guide future research, inform policy, and support smallholder integration into digitally empowered sustainable food systems. The research will identify trends, gaps, and influential factors shaping robots' adoption in sustainable food systems. The study will provide a foundation for research that promotes inclusive digital transformation in sustainable food systems. It underscores the importance of addressing multidimensional challenges to foster robots’ adoption in sustainable food systems.

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