Big Data in Management Research: Exploring New Avenues Defended on Friday, 11 March 2016
Digital computers entered our homes, landed on our desktops, slipped into our pockets, and have seemingly become ubiquitous. At an ever faster pace, these devices have become highly interconnected and interoperable. Consequently, our archives, our work, our actions, and our interactions are increasingly digitalized and stored in databases or made accessible via the Internet. This data, generally characterized by high volume, variety, and velocity (i.e., accumulation rate), has come to be called “Big Data”. As of yet, Big Data has seldom been utilized in management research. Not without cause, the discussion in the management literature has barely surpassed deliberation on privacy risks. Nevertheless, there are many ways in which Big Data can contribute to management science in a responsible fashion. This dissertation explores the opportunities that Big Data brings for management scholars and describes three distinct projects that show how Big Data can be utilized in management research.
The first project demonstrates how science mapping, when applied to digital repositories of academic journals, can be used to provide a systematic review of an academic field. The second project describes an innovative and powerful platform called “ReNotate”, which uses the highlights and annotations of individuals reading academic articles to make those articles machine-readable and thus highly searchable. The final project uses data from the Applicant Tracking Systems of 48 different companies (N = 441,769 applicants) to find out what determines whether an individual gets invited to a job interview.
Big Data; Knowledge Dissemination; Preselection; Personnel Selection; Bibliometrics; Science Mapping; Synthetic Validity; Relative Weight Analysis; Career Studies; Literature Search
Lee, C.I.S.G. (2016, March 11). Big Data in Management Research (No. EPS-2016-365-ORG). ERIM Ph.D. Series Research in Management. Erasmus University Rotterdam. Retrieved from hdl.handle.net/1765/79818