Data science: developing theoretical contributions in information systems via text analytics

Scholars have been increasingly calling for innovative research in the organizational sciences in general, and the information systems (IS) field in specific, one that breaks from the dominance of gap-spotting and specific methodical confinements. Hence, pushing the boundaries of information systems is needed, and one way to do so is by relying more on data and less on a priori theory. Data, being considered one of the most important resources in research, and society at large, requires the application of scientific methods to extract valuable knowledge towards theoretical development. However, the nature of knowledge varies from a scientific discipline to another, and the views on data science (DS) studies are substantially diverse. These views vary from being seen as a new scientific (fourth) paradigm, to an extension of existing paradigms with new tools and methods, to a phenomenon or object of study. In this paper, we review these perspectives and expand on the view of data science as a methodology for scientific inquiry. Motivated by the IS discipline’s history and accumulated knowledge in using DS methods for understanding organizational and societal phenomena, IS theory and theoretical contributions are given particular attention as the key outcome of adopting such methodology. Exemplar studies are analyzed to show how rigor can be achieved, and an illustrative example using text analytics to study digital innovation is provided to guide researchers.