Abstract
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.
Highlights
Just like in business and society, data in research is increasing in volume, velocity and variety, and requires new ways of extracting value from it
The organizational science has been criticized for the lack of innovative research due to the dominance of gap-spotting type of research [14]
Data science has been considered as a wave that brings a plethora of opportunities to scientific research [3, 36], and information systems (IS) discipline is no exception
Summary
Just like in business and society, data in research is increasing in volume, velocity and variety, and requires new ways of extracting value from it. In IS, it has been difficult to describe the structure of IS theories since the discipline deals with phenomena arising at the intersection of the natural, social, and artificial (design) sciences [16, 17]. This key structural and ontological question has some answers though. In the simplest form, a theory is comprised of a set of statements These statements are language-bound, capture specific concepts—including constructs, units, factors and variables, and make a claim or a proposition about relationships between those concepts [16, 18]. In addition to structural elements, theories constitute assumptions about their underlying logic, temporal and contextual factors that specify their range of coverage, or boundaries of generalizability [19]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
