Diffusion of innovation in controlled environment agriculture: A mixed-methods study of digital decision support tool adoption
Lauren Lindow, Catherine Campbell, Coleman Longwater, Ying Zhang, Ana Martin‐Ryals, Ziynet Boz
Abstract
Controlled environment agriculture (CEA) enables farmers to manage all aspects of crop growing environments. However, the complexity of operations necessitates decision-support tools (DSTs) that integrate and analyze large datasets for optimized management. Despite their benefits, the adoption of DSTs is influenced by factors beyond technical effectiveness, such as cost, usability, and perceived value. This study aimed to evaluate the experiences and perceptions of CEA operators regarding DSTs, identify barriers to adoption, and determine the characteristics necessary for widespread acceptance, using the Diffusion of Innovation Theory as a framework. A mixed-methods approach was employed, consisting of a survey of 44 CEA operators across the United States by in-depth interviews with 14 respondents. The survey and interviews explored DST experiences, concerns, and desired features, with data analyzed using thematic analysis. Farmers desired general farm management tools that could be easily customized to their specific needs and operations. Key preferences included seamless data integration across tools, automation, and Artificial Intelligence (AI) integration for predictive modeling and decision suggestions, while maintaining human oversight. Cost and trialability were major barriers, with farmers requiring financial benefits that outweigh costs. Complexity of use and incompatibility with existing workflows were significant deterrents to adoption. The findings underscore the importance of user-centered design, financial feasibility, and demonstrable tool performance. This study highlights critical factors influencing DST adoption in CEA and provides actionable insights for developers to design tools that are cost-effective, user-friendly, and customizable. Addressing these barriers can enhance adoption rates and optimize farm operations, ultimately advancing the CEA industry.