Big data and data science methods for management research: From the Editors

• Main Authors: GEORGE, Gerard, Ernst C. OSINGA, LAVIE, Dovev, SCOTT, Brent A.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Management Sciences and Quantitative Methods; Strategic Management Policy
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/4964; https://ink.library.smu.edu.sg/context/lkcsb_research/article/5963/viewcontent/BigDataMethodsFTE_2016_afv.pdf
• Institution: Singapore Management University

The recent advent of remote sensing, mobile technologies, novel transaction systems, and high performance computing offers opportunities to understand trends, behaviors, and actions in a manner that has not been previously possible. Researchers can thus leverage 'big data' that are generated from a plurality of sources including mobile transactions, wearable technologies, social media, ambient networks, and business transactions. An earlier AMJ editorial explored the potential implications for data science in management research and highlighted questions for management scholarship, and the attendant challenges of data sharing and privacy (George, Haas & Pentland, 2014). This nascent field is evolving rapidly and at a speed that leaves scholars and practitioners alike attempting to make sense of the emergent opportunities that big data holds. With the promise of big data come questions about the analytical value and thus relevance of this data for theory development -- including concerns over the context-specific relevance, its reliability and its validity.