Exploring the Affordances of AI-Enabled Livestock Monitoring Systems in Rural Agricultural Communities
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Abstract
Productivity and sustainability remain persistent challenges in livestock farming across developing countries, particularly in rural contexts where digital transformation progresses unevenly. Advances in artificial intelligence (AI) offer opportunities to support livestock management; however, empirical understanding of how such technologies are perceived and utilized in rural settings remains limited. This study examines the perceived affordances of an AI-enabled livestock monitoring system in a rural community in Central Java, Indonesia. Guided by the Technology–Organization–Environment (TOE) framework, a qualitative case study approach was employed using semi-structured interviews with livestock farmers and local government officials. The findings indicate that the realization of AI-related affordances is shaped by technological conditions, including system capabilities, infrastructure limitations, and user readiness. Organizational factors—such as innovation awareness, government–community relationships, and the continuity of support programs—also influence affordance realization. Environmental conditions, particularly training adequacy, public trust, and rural geographic characteristics, further affect technology use. Overall, the study highlights that AI affordances in rural livestock systems are socio-technical and context-dependent, emphasizing the importance of context-sensitive design and implementation strategies to support sustainable livestock management.
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