TY - JOUR
T1 - The Promise and Perils of Using Big Data in the Study of Corporate Networks
T2 - Problems, Diagnostics and Fixes
AU - Heemskerk, Eelke
AU - Young, Kevin
AU - Takes, Frank W.
AU - Cronin, Bruce
AU - Garcia-Bernardo, Javier
AU - Henriksen, Lasse F.
AU - Winecoff, William Kindred
AU - Popov, Vladimir
AU - Laurin-Lamothe, Audrey
N1 - Published online: 4 December 2017
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Big corporate network data
KW - Big data
KW - Corporate networks
KW - Diagnostics
KW - Network data quality
KW - Big corporate network data
KW - Big data
KW - Corporate networks
KW - Diagnostics
KW - Network data quality
UR - https://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=110980865608954&rft.object_portfolio_id=&svc.holdings=yes&svc.fulltext=yes
U2 - 10.1111/glob.12183
DO - 10.1111/glob.12183
M3 - Journal article
VL - 18
SP - 3
EP - 32
JO - Global Networks: A Journal of Transnational Affairs
JF - Global Networks: A Journal of Transnational Affairs
SN - 1470-2266
IS - 1
ER -