Determinants of AI Adoption Intention among Young Prospective Livestock Farmers in Indonesia
Muhammad Helmi (a,b), Budi Hartono (a), Siti Azizah (a), Marji (c), Muhammad Halim Natsir (a), Herly Evanuarini (a), Anie Eka Kusumastuti (a), Eka Nurwahyuni (a,b), Putu Wahyu Kusuma Wardhana (c), Nabila Nur Hidayati (a), Nanang Febrianto (a*)

a) Faculty of Animal Science and Technology, Universitas Brawijaya
Jalan Veteran, Malang 65145, Indonesia
b) Universitas Brawijaya Kediri Campus (PSDKU Kediri)
Kediri, East Java, Indonesia
c) Faculty of Computer Science, Universitas Brawijaya
Jalan Veteran, Malang 65145, Indonesia

Abstract

Artificial intelligence (AI) is increasingly promoted as a means of modernising livestock production, yet little is known about how the next generation of farmers views it. This study examined the factors that encourage and hinder AI adoption intention among young prospective livestock farmers in Indonesia. A survey was completed by 91 animal science students and young farmers aged 18-21 years. Most of them were involved in livestock activities through internships or small-scale enterprises. The research model drew on UTAUT, TAM and the Theory of Planned Behaviour and was tested with PLS-SEM using 5,000 bootstrap resamples. After one self-efficacy item was removed, the measurement model met the reliability and validity criteria. Attitude toward AI was the strongest predictor of behavioural intention (&#946- = 0.65), and the model explained 77.8% of the variance in intention. Personal innovativeness was the only significant antecedent of attitude (&#946- = 0.33). It also affected intention indirectly through attitude (&#946- = 0.22). Self-efficacy had a direct positive effect on intention (&#946- = 0.29). Respondents rated cost and technical barriers as moderate, but neither barrier significantly lowered attitude or intention. Effort expectancy showed a negative net effect once attitude was taken into account. Generative chatbots were by far the most familiar AI application. In the open-ended answers, respondents most often named limited capital, limited knowledge and lack of training as the practical obstacles. Young farmers^ readiness for AI appears to depend more on personal disposition and confidence than on external conditions. Extension programmes and livestock curricula should therefore build hands-on AI skills while widening access to affordable tools.

Keywords: Artificial intelligence- Technology adoption- Young farmers- Livestock agribusiness- Personal innovativeness- PLS-SEM

Topic: Agribusiness and related subjects

Plain Format | Corresponding Author (Nanang Febrianto)

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