Big Data and Management

By Gerard George, Martine R. Haas, Alex Pentland. 2014

1,095 citations recorded by the source corpus.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/213976a51735aaabc96eb53444b02f2c7ca04f4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

In Strategy and Management

Citations in the corpus

Big Data Research in Information Systems: Toward an Inclusive Research Agenda (2016, background): “Achieving “ consilience —that is, convergence of evidence from multiple, independent, and unrelated sources”— needs to be a matter of priority (George et al., 2014, p. 324).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “Spurred by the ongoing big data revolution, these approaches enable researchers to mine huge amounts of data (e.g., website scraped data) to develop longitudinal measures at relatively low costs (George et al., 2014; 2016).”
Business model configurations and performance: A qualitative comparative analysis in Formula One racing, 2005–2013 (2015, background): “In doing so, we answer the recent call to leverage the rich availability of sport data for better understanding competitive dynamics (George et al., 2014).”
How to improve firm performance using big data analytics capability and business strategy alignment (2016, background): “Recent literature identifies that BDAC has “the potential to transform management theory and practice”(George et al., 2014, p.325), it is the “next big thing in innovation” (Gobble, 2013, p.64); and “the fourth paradigm of science” (Strawn, 2012, p.34); or the next “management revolution” (McAfee…”
Deep learning in business analytics and operations research: Models, applications and managerial implications (2018, positive): “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…”
New games, new rules: big data and the changing context of strategy (2015, background): “…by the fact that big data is commonly presented as a context within which business opportunities or other sources of change in the environment, such as new social trends, can be identified (George et al., 2014), and these are traditional and standard concerns of prescriptive approaches to strategy.”