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| LEADER |
01625nam a2200337Ia 4500 |
| 001 |
10.1016-j.dss.2019.113135 |
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220511s2019 CNT 000 0 und d |
| 020 |
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|a 01679236 (ISSN)
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| 245 |
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|a Numerical, secondary Big Data quality issues, quality threshold establishment, & guidelines for journal policy development
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| 260 |
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|b Elsevier B.V.
|c 2019
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.1016/j.dss.2019.113135
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|a 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. © 2019 Elsevier B.V.
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|a Big data
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|a Big data
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|a Data quality
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|a Data quality
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|a Decision support systems
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| 650 |
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|a Information systems
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| 650 |
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|a Numerical data
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| 650 |
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|a Numerical data
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|a Policy development
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|a Quality threshold
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|a Research purpose
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|a Secondary data
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|a Secondary data sources
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|a Secondary datum
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|a Secondary sources
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|a Verifiability
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|a Lee-Post, A.
|e author
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|a Pakath, R.
|e author
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| 773 |
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|t Decision Support Systems
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