Generative grounded theory ( GGT ): Inductive theory building in the age of generative AI

Abstract

This paper introduces generative grounded theory (GGT), an AI‐enhanced method for inductive theory building that adapts grounded theory—and qualitative research procedures more broadly—to contemporary digital and algorithmic research conditions. GGT integrates generative artificial intelligence (GenAI) into grounded‐theory analysis while preserving human interpretive authority and theoretical responsibility. The method specifies a step‐by‐step process that supports systematic comparison and structured engagement with qualitative data. We demonstrate the method by outlining two new consumer‐psychology studies and reanalyzing two established grounded‐theory studies. We also address concerns surrounding GenAI use and outline future developments in the use of GenAI for qualitative analyses. GGT enables greater scale and efficiency in working with qualitative data, which are increasingly heterogeneous and multimodal, while supporting rigorous and precise theory construction and enhancing transparency and traceability. GGT can also be extended to other qualitative approaches that rely on similar analytic procedures.