Behavioural drivers of internet of things adoption in food consumption

Purpose This study examines factors influencing consumers’ intention to use Internet of Things (IoT) technology in food practices. The adoption of the IoT by consumers has significant potential to address sustainability challenges, as IoT provides a high-frequency data layer that artificial intelligence (AI) and machine learning (ML) can translate into insights and automated processes, triggering sustainable consumer behaviours. A modified Unified Theory of Acceptance and Use of Technology (UTAUT) model is employed to assess antecedents of Behavioural Intention (BI). Design/methodology/approach Data were collected via an online research panel (n = 600). Partial Least Squares Consistent structural equation modelling (PLSc-SEM) was used to test hypotheses. To facilitate interpretation in the context of social change, we conducted a Combined Importance–Performance Map Analysis (cIPMA). Findings Performance Expectancy (PE) and Social Influence (SI) emerged as sufficient conditions for determining Behavioural Intention (BI). Attitudes (ATT) toward IoT were significantly shaped by four antecedents – PE, Effort Expectancy (EE), SI, and Trust (TR) – and ATT mediated the relationships between these variables and BI in the food-consumption context. The cIPMA further highlighted the central role of PE in shaping BI. Practical implications The findings provide practitioners with actionable guidance. Enhancing performance value, simplifying the user experience, and strengthening trust through transparent data practices can meaningfully increase consumer acceptance of IoT solutions that support sustainable food consumption. Originality/value The study contributes to refining the UTAUT model by integrating Trust and Attitudes towards IoT as mediating variables, thereby extending the understanding of the factors influencing Behavioural Intention in this domain.