Generative AI-Augmented Thematic Analysis

This study explores the use of generative AI tools in qualitative research, specifically to enhance thematic analysis (TA). Traditional TA, while widely adopted in marketing, is inherently labor-intensive and prone to researcher biases. The method proposed here, generative AI-augmented thematic analysis (GAATA), addresses these limitations by using gen AI tools to automate and augment the data analysis process. GAATA comprises three phases: prompt designing, code generation/validation, and theme generation/validation. The method leverages the capabilities of multiple gen AI tools to generate initial codes and themes. These codes/themes are then validated and refined by human researchers, thereby increasing reliability and reflexivity in the TA process. A set of generic prompt templates are provided to enable researchers with less technical expertise to use GAATA. An empirical comparison between GAATA and traditional thematic analysis (TA) is then undertaken. The emergent themes are validated across four dimensions: conceptual mapping, thematic specificity, theoretical alignment, and time efficiency. GAATA, augmenting gen AI with structured human oversight, offers a scalable, robust alternative that accelerates and enriches thematic analysis in market research.