Efficient Strategies for Improving Health Functional Food Advertising in the AI Era: A Q Methodology Approach

Abstract

This study aims to identify the subjective perception types of general consumers toward AI-based health functional food (HFF) advertising amid the rapid expansion of artificial intelligence (AI) in marketing.To achieve this objective, Q methodology, which is well suited for exploring human subjectivity, was applied to analyze the perception structures of research participants.The results revealed three distinct perception types regarding AI-recommended HFF advertising.Type 1, the "Privacy Concerns & Algorithmic Bias Watchdog," expressed strong concerns about ethical issues such as personal data usage and algorithmic bias.Type 2, the "Tech-Utility & Efficiency Seeker," showed a relatively positive attitude toward AI, recognizing the usefulness of data-driven personalized information and analytical capabilities while emphasizing efficiency in information search.Type 3, the "Ambivalent Realist," acknowledged the convenience and efficiency of AI but simultaneously perceived limitations related to consumer trust, psychological resistance, and reduced human interaction.Despite these differences, all types agreed that AI's marketing performance is widely recognized and that its role is expanding beyond data analysis into the creative domain of advertising.They also shared the view that AI-driven improvements in advertising efficiency represent a structural and sustainable transformation, while emphasizing that the intrinsic quality of the product remains the fundamental determinant of consumer choice.Based on these findings, this study proposes five strategies for improving HFF advertising in the AI era: data-driven personalization, enhanced scientific credibility, multi-channel optimization, interactive consumer engagement, and strengthened ethical and social responsibility.These findings provide practical guidelines for companies developing AIbased marketing strategies.

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