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

Arpan Kumar Kar and Yogesh K. Dwivedi

International Journal of Information Management, 2020, vol. 54, issue C

Abstract: 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.

Keywords: Big data analytics; Image mining; Network mining; Sentiment analysis; Text mining; Inductive theory building; Machine learning; Information management; Data science; Review (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:54:y:2020:i:c:s0268401220311257

DOI: 10.1016/j.ijinfomgt.2020.102205

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