Understanding the adoption of smartphone apps in crop protection: an extended replication study

Abstract Context Smartphone apps are becoming increasingly important in crop protection decision-making, yet understanding how adoption patterns and determinants evolve over time remains limited. Furthermore, replication studies to understand farmers’ decision-making in this context are scarce. Aims This study replicates and extends previous research on German farmers’ adoption of crop protection smartphone apps to examine temporal changes in technology acceptance factors and enhance theoretical frameworks. Methods An online survey of 195 German farmers conducted in 2025 provided data from a non-random sample for structural equation modeling to test the Unified Theory of Acceptance and Use of Technology (UTAUT), the Task-Technology-Fit (TTF) model, and an integrated UTAUT–TTF framework. Key Results Effort Expectancy now has stronger correlation with Behavioral Intention to adopt a crop protection app than Performance Expectancy does. Agricultural app usage grew by 42%, with farmers employing more crop protection apps (2.87 vs. 2.21) and showing greater willingness to purchase premium applications. The integration of TTF with UTAUT improved explanatory power and predictive accuracy, confirming that alignment between app functionality and specific farm tasks correlates with adoption intentions. Conclusion Successful replication validates the UTAUT framework’s temporal stability while also revealing shifts in adoption determinants as agricultural app markets mature beyond early adopters. Implications As one of the first replication studies in precision agriculture adoption research, these findings provide critical guidance for agricultural app developers to prioritize intuitive interfaces alongside functionality, while the validated UTAUT-TTF integration offers researchers an enhanced framework for examining technology adoption. Understanding these evolving adoption patterns is essential for developing economically viable precision agriculture solutions that achieve widespread implementation. Impact This study enhances the scientific understanding and practical implementation of digital tools in Precision Agriculture by revealing how farmers’ adoption patterns of crop protection apps have evolved over time. By replicating a validated Unified Theory of Acceptance and Use model and integrating the Task-Technology-Fit model, it identifies intuitive usability as well as alignment between app functionality and specific farm task requirements as critical drivers of adoption. These findings help developers design more targeted and effective applications that match farmers’ needs, thereby supporting the management of spatial and temporal variability through more effective digital technology adoption. The research contributes to improved agricultural decision-making by demonstrating how digital tools can enhance farmers’ ability to make timely, evidence-based crop protection decisions. Furthermore, this framework supports environmental sustainability by promoting digital tools that enable more precise pesticide applications through site-specific information and optimized timing, directly contributing to reduced environmental impact when farmers adopt intuitive apps that align with their operational requirements. Lastly, this research demonstrates the value of temporal replication in technology acceptance research in Precision Agriculture, offering a robust, transferable framework for evaluating digital innovations in agriculture. Highlights Successful replication of Michels et al. (2020a) confirms UTAUT’s theoretical stability over time while revealing a shift in adoption drivers: Effort Expectancy now has stronger correlation with Behavioral Intention to adopt a crop protection app than Performance Expectancy. Agricultural app usage has increased (42% more apps per farmer) with growing willingness to purchase premium applications. Task-Technology-Fit directly correlates with adoption intentions, confirming the importance of aligning app functionality with farmers’ specific requirements. Field documentation functionality has experienced the greatest adoption growth (57% vs. 39%). Persistent gaps between perceived usefulness and actual adoption highlight the continued need and opportunities for better design and support to improve implementation.