Intention to use AI-Based Camera Systems in German Pig Farming: An Extended Technology Acceptance Model

Abstract

This study explores the factors influencing German pig farmers' intention to use (ITU) AI-based camera systems in livestock farming. This research utilized an extended Technology Acceptance Model. Data from 185 farmers were analyzed through structural equation modeling, revealing that ease of use (β=0.276), innovation tolerance (β=0.398) and personal innovativeness (β=0.101) notably impact ITU. Concerns over data ownership and transparency showed limited effects, and perceived job relevance (β=0.355) enhanced acceptance. Expected transparency of AI camera systems had strong influence on perceived ease of use (β=0.419). A gradual integration of the factors showed that perceived usefulness has a strong influence on ITU but is superimposed by the factor job relevance in the modelling process. With an R2 of 0.749, the model has high explanatory and predictive power. These insights underscore the importance of user-centric design and transparency in AI technology deployment in agriculture. Although the ITU AI camera systems in pig farming depends on its ease of use and transparency, it also depends on the personal characteristics.