Large Language Models (LLMs) are proving to be a powerful toolkit for management and
organizational research. While early work has largely focused on the value of these tools for data
processing and replicating survey-based research, the potential of LLMs for theory building is yet
to be recognized. We argue that LLMs can accelerate the pace at which researchers can develop,
validate, and extend strategic management theory. We propose a novel framework called
Generative AI-Based Experimentation (GABE) that enables researchers to conduct exploratory in
silico experiments that can mirror the complexities of real-world organizational settings, featuring
multiple agents and strategic interdependencies. This approach is unique because it allows
researchers to unpack the mechanisms behind results by directly modifying agents’ roles,
preferences, and capabilities, and asking them to reveal the explanations behind decisions. We
apply this framework to a novel theory studying strategic exploration under uncertainty. We show
how our framework can not only replicate the results from experiments with human subjects at a
much lower cost, but can also be used to extend theory by clarifying boundary conditions and
uncovering mechanisms. We conclude that LLMs possess tremendous potential to complement
existing methods for theorizing in strategy and, more broadly, the social sciences.
