Augmented inference making: organizing the chain of thought trials from perception to conception

With the abundance of data and digital traces of human behavior that are currently available, and the accessible algorithmic analyses to analyze them, management scholars have the opportunity to expand their scope of theoretical discovery and tackle increasingly complex management. However, we need additional elaboration on how algorithmic analysis can augment humans inference making, and how to best integrate algorithmic analyses into inductive theory building processes. Addressing this need, we propose in this paper Augmented Inference Making as a distinct method to improve inductive theory building from large sets of textual data.