UNDERSTANDING THE ROLE OF AI IN ENCOURAGING ORGANIC FOOD PURCHASES: INSIGHTS FROM THE THEORY OF PLANNED BEHAVIOUR

This research paper combines the Theory of Planned Behavior (TPB) and Signaling Theory and the Elaboration Likelihood Model (ELM) to suggest a model of the processes guiding the understanding of how AI in organic food marketing may yield a purchase intention. Aligning AI applications that include personalized recommendations, predictive analytics, authenticity verification, and AI-driven storytelling to the TPB concepts of attitudes, subjective norms, and perceived behavioral control, the model proposes new theoretical insights into how technology can influence consumer-decision making in relation to sustainable consumption. Adding Signaling Theory augments how attitude is formed with the credible implication of authenticity cues, whereas ELM offers predictions of the dual paths of persuasion that moderates the normative beliefs and facilitating ease of purchase. The framework proposed provides a strong, multi-theoretical approach that will take into consideration the various challenges that are peculiar in marketing credence products such as organic food. On a practical level, this study can offer practical advice to marketers, policymakers, and supply chain players in the organic food industry in terms of practical measures that they can take. AI tools can be used by the marketers to provide more relevant, credibility-building and interactive campaigns, and the policymakers can utilise the insights gained to formulate supporting policies to promote AI adoption in favour of promoting sustainable consumption. Also, trust mechanism in terms of verification of the authenticity of goods like traceability using AI-enabled blockchain technology can reduce the lack of trust between manufacturers and consumers hence increasing market penetration. With this technological innovation and using behavioral theory, this paper highlights how AI can not only increase sales volumes but also help generate consumer commitment over the long-term to making eco-friendly purchasing decisions, part of the broader sustainability agenda.   Future studies may build on this conceptual framework to empirically test the suggested relationship with consumers in different segments and across several cultural contexts and thus confirm the adequacy of the concept to be applied to any other market. The answer is longitudinal examination of the extent to which persistence in exposure to AI-driven marketing affects changes in the attitudes, subjective norms and perceived control over behavior that occurs over time. Further insights on the incremental value of AI in sustainable consumption would be gained by comparing the use of AI-enabled marketing strategies and the traditional ones. Also, investigating possible ethics issues, i.e., data privacy and algorithm bias might be of benefit in order to gain insight into responsible AI adoption in the field of organic food.

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