Decoding green food safety information dependency in the digital era: An intelligent validation using SEM-ANN framework

Abstract

Dependence on mobile phone information in the Internet era is a crucial issue, and psychological perception and personalized and accurate pushing significantly impact this field. This introduction of rational and planned behaviour combined with the theoretical framework of Structural Equation Modelling (SEM) and Artificial Neural Networks (ANN) are intricacy analytical tools to explore the key factors affecting the information dependence on green food safety. The key factors affecting the reliance on green food safety information were investigated. The survey covered 630 participants aged 17 to 70 in Guangdong Province, China. The study showed that perceived risk, regulation, price volatility, innovativeness, and anticipatory confirmation significantly influence the perceived usefulness of green food information, affecting satisfaction with green food. The study also highlights the significant impact of the degree of anticipatory confirmation by users on satisfaction and perceived usefulness of green food. Model path analysis revealed that anticipatory confirmation and perceived usefulness explained 61% and 74% of the variance in financial sustainability, respectively. The application of deep ANN multilayer perceptron improved the prediction accuracy of perceived usefulness with 87.54% training accuracy and 90.34% testing accuracy, further deepening the understanding of the mechanism of relying on green food safety information. This study provides strong support for a deeper understanding of the relationship between consumer behaviour and green food safety information dependence, which helps promote enterprises and market camps to more effectively formulate product or service promotion strategies and provides a solid theoretical and empirical foundation for future green food safety and personalized and accurate push thus enhancing information dependence.