Abstract
An IS researcher may obtain Big Data from primary or secondary data sources. Sometimes, acquiring primary Big Data is infeasible due to availability, accessibility, cost, time, and/or complexity considerations. In this paper, we focus on Big Data-based IS research and discuss ways in which one may, post hoc, establish quality thresholds for numerical Big Data obtained from secondary sources. We also present guidelines for developing journal policies aimed at ensuring the veracity and verifiability of such data when used for research purposes.
Document Type
Article
Publication Date
11-2019
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.dss.2019.113135
Repository Citation
Lee-Post, Anita and Pakath, Ram, "Numerical, Secondary Big Data Quality Issues, Quality Threshold Establishment, & Guidelines for Journal Policy Development" (2019). Marketing & Supply Chain Faculty Publications. 8.
https://uknowledge.uky.edu/marketing_facpub/8
Notes/Citation Information
Published in Decision Support Systems, v. 126, 113135.
© 2019 Elsevier B.V. All rights reserved.
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.
The document available for download is the authors' post-peer-review final draft of the article.