Modeling the Factors of Users’ Intention to Adopt AI-Driven Livestock Recognition Systems for Transparency in G2B Transactions: An Extension of the TAM-UTAUT Integrated Model

Aigul Meirmanova, Mahadi Hasan Miraz, Ha Jin Hwang, Dinara Kaibassova

IEEE Access, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1109/access.2025.3624124
Data confidence 70 medium
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Abstract

The rapid advancement of Artificial Intelligence (AI) in agriculture has led to the emergence of livestock recognition systems designed to improve transparency in Government-to-Business (G2B) transactions. These systems utilize biometric technologies such as facial recognition to uniquely identify livestock, mitigate fraudulent activities, and strengthen trust among stakeholders. However, empirical research examining the factors and pathways that influence user adoption of such AI systems in an agricultural context remains limited, particularly in developing Information Technologies (IT) economies where digital infrastructure and user readiness may vary. To address this gap, this study proposes an extended version of the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) integrated model to examine the psychological, technological, and organizational factors influencing adoption intention. The study puts forward 5 hypotheses and tests them using survey data collected from 259 respondents from different sectors of agriculture spanning the provinces of Kazakhstan. The results indicated that the independent variables (Efficiency of Image Detection, Facilitating Conditions, IT Literacy, Fraudulent Intention) significantly influenced users’ intention to adopt AI systems. The model accounted for 70.1% and 57.2% of the variance in the key dependent variables, demonstrating robust explanatory power. Theoretically, this study contributes to the technology adoption literature by validating the extension of the TAM-UTAUT integrated model in a novel application domain — AI in agriculture. Practically, it offers actionable insights for policymakers, AI system developers, and agricultural stakeholders, aiming to promote trustworthy, efficient, and fraud-resistant livestock management solutions through AI adoption in developing IT economies.