Theory building with big data-driven research – Moving away from the “What” towards the “Why”

•Data driven research based on big data is creating an existential challenge in IS research.•There is a need to move beyond “what the big data represents” to the “why it is so”.•Methodological adaptations have been proposed in this opinion article for theory building.•Directions are provided for grounding findings to contribute to IS discipline through theory building.•Gaps in existing approaches with guidelines for future research is proposed. Data availability and access to various platforms, is changing the nature of Information Systems (IS) studies. Such studies often use large datasets, which may incorporate structured and unstructured data, from various platforms. The questions that such papers address, in turn, may attempt to use methods from computational science like sentiment mining, text mining, network science and image analytics to derive insights. However, there is often a weak theoretical contribution in many of these studies. We point out the need for such studies to contribute back to the IS discipline, whereby findings can explain more about the phenomenon surrounding the interaction of people with technology artefacts and the ecosystem within which these contextual usage is situated. Our opinion paper attempts to address this gap and provide insights on the methodological adaptations required in “big data studies” to be converted into “IS research” and contribute to theory building in information systems.