Scholion.ai

Research Commentary—Data-Driven Computationally Intensive Theory Development

Nicholas Berente, Stefan Seidel, Hani Safadi

Information Systems Research · 2019 · Volume 30 · Issue 1

DOI: 10.1287/isre.2018.0774

Abstract

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.

Keywords

grounded theory methodology, computational theory discovery, GTM, computational, trace data, theory development, lexicon, inductive

Enable JavaScript for citation tools and additional paper interactions.