Abstract
During the final 18 months that we were serving as Editors at Organizational Research Methods (ORM), we noticed an unusual number of submissions centered around “big data” issues. So many, in fact, that it made sense to bundle these papers into a set as an “Ad Hoc” Feature Topic on issues related to Big Data and Modern Data Analytics. Most of these papers were edited by James M. LeBreton and Adam W. Meade, during our time as Editor and Associate Editor at ORM. All papers underwent the traditional peer-review, but we augmented the set of papers with a few invited submissions. The result was the eight papers comprising the current issue of ORM.
The first two papers (Tonidandel, King, & Cortina; Wenzel & Van Quaquebeke) provide a general introduction to Big Data, including a discussion of the challenges and opportunities inherent in the collection and analysis of Big Data. The third paper (Luciano, Mathieu, Park, & Tannenbaum) discusses how Big Data may be leveraged to better articulate and test organizational theory. The fourth paper (Braun, Kuljanin, & DeShon) discusses special issues related to the acquisition and management of Big Data. The fifth paper (Tay, Ng, Malik, Zhang, Chae, Ebert, Ding, Zhao, & Kern) emphasizes the importance of data visualization and how it differs from the visualization of data in traditional contexts. The sixth paper (Putka, Beatty, & Reeder) illustrates how more recent methods of statistical prediction (developed largely in response to the Big Data revolution) may be used to address prediction problems in both traditional and Big Data contexts. Finally, the last two papers (Kobayashi, Mol, Berkers, Kismihok, & Den Hartog) are focused on issues related to text mining and include both a general overview and an illustrative example/tutorial.
Overall, we hope that this collection of papers provides organizational researchers with a useful starting point for learning more about Big Data and Modern Data Analytics.