MARKET-DRIVEN MANAGEMENT STRATEGIES USING ARTIFICIAL INTELLIGENCE TO STRENGTHEN FOOD SAFETY AND ADVANCE ONE HEALTH INITIATIVES

Abstract

This quantitative cross sectional, case-based study addresses the practical gap in understanding how artificial intelligence enabled, market driven management strategies improve food safety performance and contribute to One Health outcomes in contemporary food enterprises. Data were collected through a structured Likert five-point questionnaire from 212 managers and professionals in case organizations operating certified food safety management systems and AI or cloud-based analytics for monitoring, traceability, and risk assessment. Key latent variables included AI enabled market driven strategies, food safety performance, One Health aligned outcomes, food safety standards adoption, and external regulatory or market pressure. Reliability and factor analysis confirmed internally consistent scales (α = 0.86–0.93). Correlation and multiple regression models, complemented by bootstrapped mediation and moderated regression, were used to test five hypotheses. AI enabled strategies showed a strong positive effect on food safety performance (β = 0.49, R² = 0.485, p < 0.001), while food safety performance and AI enabled strategies together explained over half of the variance in One Health aligned outcomes (R² = 0.567). Mediation analysis demonstrated that food safety performance significantly transmitted the impact of AI enabled strategies to One Health outcomes (standardized indirect effect = 0.23). External regulatory and market pressure strengthened the AI–performance link, with the slope of AI strategies on safety performance increasing from 0.36 at low pressure to 0.61 at high pressure. Overall, the findings indicate that embedding AI within market driven management systems can simultaneously enhance food safety performance and support integrated human, animal, and environmental health objectives.