The promise and perils of using big data in the study of corporate networks

Problems, Diagnostics and Fixes

Datos Bibliográficos

ID4262857
AutoresEelke M Heemskerk (0000-0001-7356-5318, University of Amsterdam, autor de correspondencia), Eelke Heemskerk (University of Amsterdam, CORPNET PO Box 15578 Amsterdam 1001 NB Netherlands), Kevin L Young (0000-0003-4783-0266, Amherst College, autor de correspondencia), Kevin Young (0000-0002-6369-5108, University of Massachusetts Amherst, Political Science Amherst Massachusetts 01003 USA), Frank W Takes (0000-0001-5468-1030, University of Amsterdam, CORPNET PO Box 15578 Amsterdam 1001 NB Netherlands, autor de correspondencia), Bruce Cronin (0000-0002-3776-8924, University of Greenwich London UK, autor de correspondencia), Javier Garcia‐bernardo (0000-0002-6119-1790, University of Amsterdam, CORPNET PO Box 15578 Amsterdam 1001 NB Netherlands, autor de correspondencia), L F Henriksen (0000-0003-4271-6528, Copenhagen Business School, autor de correspondencia), William Kindred Winecoff (0000-0003-3415-537X, Indiana University Bloomington Bloomington Indiana USA, autor de correspondencia), Vladimir Popov (0000-0002-6565-8730), Владимир Попов (0000-0003-0428-3725, University of Greenwich London UK, autor de correspondencia), Audrey Laurin‐lamothe (Université du Quebec en Outaouais Gatineau (Québec) Canada, autor de correspondencia)
Año2018
Volumen18
Número1
Páginas3-32
Fecha de publicación2018-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaGlobal Networks (JOURNAL)
Identificadores de la revistaISSN: 1470-2266 • E-ISSN: 1471-0374
EditorialWiley (PUBLISHER • GB)
DOI10.1111/glob.12183
OpenAlexW2528289494
IdiomaEN
Citas recibidas23
Referencias citadas68

Network data on connections between corporate actors and entities - for instance through co-ownership ties or elite social networks - are increasingly available to researchers interested in probing the many important questions related to the study of modern capitalism. Given the analytical challenges associated with the nature of the subject matter, variable data quality and other problems associated with currently available data on this scale, we discuss the promise and perils of using big corporate network data (BCND). We propose a standard procedure for helping researchers 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 through a specific work-flow procedure. For each step in this procedure, we provide a set of best practices for how to identify, resolve and minimize the BCND problems that arise

Big data · Data mining · Data science · Computer Science · Elite Sociology and Global Capitalism · Social and Cultural Dynamics · Social Capital and Networks