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The promise and perils of using big data in the study of corporate networks: Problems, diagnostics and fixes

The promise and perils of using big data in the study of corporate networks: Problems, diagnostics and fixes

Heemskerk, Eeelke, Young, Kevin, Takes, Frank W., Cronin, Bruce, Garcia-Bernardo, Javier, Henriksen, Lasse F., Winecoff, William Kindred, Popov, Vladimir and Laurin-Lamonthe, Audrey (2017) The promise and perils of using big data in the study of corporate networks: Problems, diagnostics and fixes. Global Networks: A journal of transnational affairs, 18 (1). pp. 3-32. ISSN 1470-2266 (Print), 1471-0374 (Online) (doi:10.1111/glob.12183)

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Abstract

Network data on connections among corporate actors and entities – for instance through co-ownership ties or elite social networks – is increasingly available to researchers interested in probing many important questions related to the study of modern capitalism. We discuss the promise and perils of using Big Corporate Network Data (BCND) given the analytical challenges associated with the nature of the subject matter, variable data quality, and other problems associated with currently available data at this scale. We propose a standard process for how researchers can deal with BCND problems. While acknowledging that different research questions require different approaches to data quality, we offer a schematic platform that researchers can follow to make informed and intelligent decisions about BCND issues and address these issues through a specific work-flow procedure. Within each step in this procedure, we provide a set of best practices for how to identify, resolve, and minimize BCND problems that arise.

Item Type: Article
Uncontrolled Keywords: Corporate Networks; Big Data; Network Data Quality; Diagnostics; Big Corporate Network Data
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HF Commerce
H Social Sciences > HT Communities. Classes. Races
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Centre for Business Network Analysis
Faculty of Business > Department of International Business & Economics
Last Modified: 02 Jan 2018 14:49
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT b
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/16717

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