The Promise and Perils of Using Big Data in the Study of Corporate Networks

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 -