Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior

• Published in: Precision agriculture Vol. 22; no. 6; pp. 1816 - 1844
• Main Authors: Mohr, Svenja, Kühl, Rainer
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Agricultural economics; Agriculture; Artificial intelligence; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; equations; Farmers; labor; Life Sciences; Physics; precision agriculture; Property rights; questionnaires; Remote Sensing/Photogrammetry; Soil compaction; Soil Science & Conservation; Statistics for Engineering; Structural equation modeling; Technology Acceptance Model; Technology utilization; Theory of planned behavior; variance; Working hours; Survey; Technology acceptance model; Artificial intelligence; Theory of planned behavior; Structural equation model
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-021-09814-x

The use of Artificial Intelligence (AI) in agriculture is expected to yield advantages such as savings in production resources, labor costs, and working hours as well as a reduction in soil compaction. However, the economic and ecological benefits of AI systems for agriculture can only be realized if farmers are willing to use them. This study applies the technology acceptance model (TAM) of Davis ( 1989 ) and the theory of planned behavior (TPB) of Ajzen ( 1991 ) to investigate which behavioral factors are influencing the acceptance of AI in agriculture. The composite model is extended by two additional factors, expectation of property rights over business data and personal innovativeness. A structural equation analysis is used to determine the importance of factors influencing the acceptance of AI systems in agriculture. For this purpose, 84 farmers were surveyed with a letter or an online questionnaire. Results show that the perceived behavioral control has the greatest influence on acceptance, followed by farmers’ personal attitude towards AI systems in agriculture. The modelled relationships explain 59% of the total variance in acceptance. Several options and implications on how to increase the acceptance of AI systems in agriculture are discussed.