Big Data and Data Science Methods for Management Research

By Gerard George, E. Osinga, Dovev Lavie, Brent A. Scott. 2016

355 citations recorded by the source corpus.

The recent advent of remote sensing, mobile technologies, novel transaction systems, and highperformance 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, andbusiness transactions.An earlierAcademy of Management Journal (AMJ) editorial explored the potential implications for data science inmanagement research and highlighted questions for management scholarship as well as 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 datahold.With thepromiseof bigdata comequestions about the analytical value and thus relevance of these data for theory development—including concerns over the context-specific relevance, its reliability and its validity. To address this challenge, data science is emerging as an interdisciplinary field that combines statistics, data mining, machine learning, and analytics to understand and explainhowwecan generate analytical insights and prediction models from structured and unstructured big data. Data science emphasizes the systematic study of the organization, properties, and analysis of data and their role in inference, including our confidence in the inference (Dhar, 2013).Whereas both big data and data science terms are often used interchangeably, “big data” refer to large and varied data that can be collected and managed, whereas “data science” develops models that capture, visualize, andanalyze theunderlyingpatterns in thedata. In this editorial, we address both the collection and handling of big data and the analytical tools provided by data science for management scholars. At the current time, practitioners suggest that data science applications tackle the three core elements of big data: volume, velocity, and variety (McAfee & Brynjolfsson, 2012; Zikopoulos & Eaton, 2011). “Volume” represents the sheer size of the dataset due to the aggregation of a large number of variables and an even larger set of observations for each variable. “Velocity” reflects the speed atwhich these data are collected and analyzed, whether in real time or near real time from sensors, sales transactions, social media posts, and sentiment data for breaking news and social trends. “Variety” in big data comes from the plurality of structured and unstructured data sources such as text, videos, networks, and graphics among others. The combinations of volume, velocity, and variety reveal the complex task of generating knowledge from big data, which often runs into millions of observations, and deriving theoretical contributions from such data. In this editorial, we provide a primer or a “starter kit” for potential data science applications inmanagement research. We do so with a caveat that emerging fields outdate and improve uponmethodologies while often supplanting them with new applications. Nevertheless, this primer can guide management scholars who wish to use data science techniques to reach better answers to existing questions or explore completely new research questions.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/da63f30bd5b3a1b16c261f75ca1b1daddfc5b44d

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Citations in the corpus

Deep learning in business analytics and operations research: Models, applications and managerial implications (2018, neutral): “It should also be noted that big data ( George, Haas, & Pentland, 2014; George, Osinga, Lavie, & Scott, 2016 ) is now prevalent in ﬁrms, yet traditional machine learning (e.g, support vector machines, random forests) can often not be applied to such large datasets, whereas the optimization routines…”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “(George et al. 2016).”
Editorial: Enhancing quantitative theory-testing entrepreneurship research (2019, background): “In our view, a paper is useful because it adds value—it addresses a meaningful problem in a meaningful way (George et al., 2016).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, methodology): “In our study, we have used partial least squares (PLS) for data analysis (see, Peng and Lai, 2012; Henseler et al., 2014; George et al., 2016).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “At the same time, this growth in data sources also creates new challenges for scholars who wish to use them in empirical studies (George et al., 2016; Schwab & Zhang, 2019; Wenzel & Van Quaquebeke, 2018).”
Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations (2020, background): “Because capital and lowercase words are stored with distinct computer language, making all letters lowercase ensures work is considered equivalent to Work (George et al., 2016).”