Data-Driven Computationally Intensive Theory Development

Increasingly abundant trace data provide an opportunity for information systems researchers to generate new theory. In this research commentary, we draw on the largely manual tradition of the grounded theory methodology and the highly automated process of computational theory discovery in the sciences to develop a general approach to computationally intensive theory development from trace data. This approach involves the iterative application of four general processes: sampling, synchronic analysis, lexical framing, and diachronic analysis. We provide examples from recent research in information systems.