With the recent technological advancements, empowered by the self-learning
capabilities of algorithms, and the increasing power of machines computation, artificial intelligence (AI)-driven technologies have become more pervasive and performant, less costly, and more effective at addressing and solving prevailing business problems. In this respect, firms operating in the AgriTech sector make no exception and are indeed being significantly impacted by AI-driven technologies and systems. We argue in this paper that given the unique characteristics of AI technologies and emerging challenges and aspirations
of the AgriTech sector, there is a need for re-examining traditional theorizations of technology adoption and readiness within AgriTech firms. Specifically, we develop a comprehensive AI readiness and adoption empirical framework that delineates the determinants of AI readiness and uncovers a set of key strategic components that can help AgriTech firms better manage their readiness process for AI adoption. We employ a mixed methods approach and collect through 236 e-surveys and 25 interviews from one of the most influential conferences in the AgriTech field. Our findings have implications for research and practice.
|Accepted - 2022