Understanding Farmer Adoption of Artificial Intelligence in Agriculture: A Systematic Review and Socio-Technical Model Grounded in Midwest Farming Systems

Outlet Title

2026 IEEE Conference on Technologies for Sustainability (SusTech)

Document Type

Conference Proceeding

Publication Date

2026

Abstract

Artificial intelligence (AI) is transforming agricultural systems, but its implementation is still scattered at the farm scale, especially in the U.S. Midwest. This paper undertakes a “systematic literature review” (SLR) of 23 empirical studies published since 2020 to determine the main socio-technical factors affecting the introduction of AI and AI-enabled technologies among farmers. Incorporating the insights of the Unified “Theory of Acceptance and Use of Technology” (UTAUT), “Technology-Organization-Environment” (TOE), and “Socio-Technical Systems” (STS) frameworks, the review demonstrates that the factors that influence adoption are interdependent and include technological (performance expectancy, effort expectancy, task technology fit), social (trust in technology, social influence) and contextual (facilitating conditions, data privacy concern) factors. Results are compiled into the “Socio-Technical Structural Equation Model” (STS-SEM) that is used to explain the interaction of these determinants to impact the intentions of AI adoption among Midwestern farmers. The new model proposes mediating and moderating processes, namely the roles of trust, privacy, and facilitating conditions that have been under-researched in previous studies. The work has a theoretical contribution, which is that it builds on a multilevel socio-technical conceptualization of digital agriculture and a practical contribution, which is the description of policy and design implications of encouraging trustful, inclusive, and region-specific AI implementation in U.S. agriculture.

Link to Full Text

Share

COinS