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The impact of expected data transparency, misuse, and ownership on the perceived ease of use of AI-camera systems in animal husbandry
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2025
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Gesellschaft für Informatik e.V.
Zusammenfassung
This study investigates factors influencing the perceived ease of use (PEOU) of AI-camera systems among German pig farmers. AI-based surveillance systems support tasks such as animal detection, tracking, behavior analysis, and disease diagnosis, but their adoption is hindered by concerns over data privacy and usability. Using the Technology Acceptance Model (TAM) as a foundation, the study explores three factors: perceived risk of data abuse (RI), perceived property rights of data (PR), and perceived transparency (TR). Survey data from 185 pig farmers were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that TR significantly enhances PEOU, while RI negatively impacts it, aligning with prior studies linking trust and usability. Higher PR also boosts PEOU, suggesting that clearer data ownership rights could improve AI adoption. These findings highlight the importance of transparent systems and defined data ownership to foster AI integration in agriculture.