Numerical, secondary Big Data quality issues, quality threshold establishment, & guidelines for journal policy development

Anita Lee-Post; Ram Pakath

doi:10.1016/j.dss.2019.113135

Numerical, secondary Big Data quality issues, quality threshold establishment, & guidelines for journal policy development

Anita Lee-Post, Ram Pakath

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

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.

Original language	English
Article number	113135
Journal	Decision Support Systems
Volume	126
DOIs	https://doi.org/10.1016/j.dss.2019.113135
State	Published - Nov 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

Big data
Data quality
Numerical data
Quality threshold
Secondary data

ASJC Scopus subject areas

Management Information Systems
Information Systems
Developmental and Educational Psychology
Arts and Humanities (miscellaneous)
Information Systems and Management

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10.1016/j.dss.2019.113135

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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.",

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AB - 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.

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KW - Secondary data

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Numerical, secondary Big Data quality issues, quality threshold establishment, & guidelines for journal policy development

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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