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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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 languageEnglish
Article number113135
JournalDecision Support Systems
StatePublished - Nov 2019

Bibliographical note

Funding Information:
We thank Dr. Simon Sheather, Ph.D., University of Kentucky, for referring us to the Big Data sets used in this study. Anita Lee-Post is an Associate Professor of the Department of Marketing and Supply Chain at the University of Kentucky. She received her Ph.D. in Business Administration from the University of Iowa. Her research interests include sustainability, business analytics, e-learning, and knowledge management. She has published in journals such as Decision Support Systems , OMEGA , Decision Sciences , Journal of Innovative Education , Computers and Industrial Engineering , International Journal of Production Research , and Information and Management . She is the author of Knowledge-based FMS Scheduling : An Artificial Intelligence Perspective . She is the associate editor of the Journal of Organizational Computing and Electronic Commerce . She serves on the editorial review boards of Production Planning and Control , International Journal of Business Information Systems , International Journal of Data Mining , Modeling and Management , and Journal of Managerial Issues . She is the recipient of the Fulbright U.S. Scholar Grant. Ram Pakath is Professor at the C. M. Gatton College of Business & Economics, University of Kentucky, USA, with teaching and research interests in Analytics and Adaptive Systems. He holds a bachelor's degree in Mechanical Engineering from Bangalore University (India), a master's degree in Business Administration from University of Madras (India), a master's degree in Operations Research and Industrial Engineering from The University of Texas at Austin, and a doctorate in Management (MIS) from Purdue University. Ram's research articles have appeared in the following forums: Behaviour and Information Technology , Computational Economics , Decision Sciences , Decision Support Systems , European J. of Operational Research , IEEE Transactions on Systems , Man , and Cybernetics , Information and Management , Information Systems Research , J. of Computer Information Systems , J. of Electronic Commerce Research , and J. of Organizational Computing and Electronic Commerce . Ram has contributed refereed material to several books, including, Cases on Information Technology Management in Modern Organizations, Decision Support Systems: A Knowledge-based Approach, Handbook on Decision Support Systems 1 — Basic Themes, Handbook of Industrial Engineering, Security, Trust, and Regulatory Aspects of Cloud Computing in Business Environments, and Operations Research and Artificial Intelligence. He also has several invited/contributed conference presentations and proceedings to his credit. He is a Senior Editor at Decision Support Systems and is an Editorial Board Member at J. of Organizational Computing and Electronic Commerce . Ashland Oil, the C. M. Gatton College of Business and Economics, IBM, the Kentucky Science and Engineering Foundation, and the University of Kentucky have funded his prior academic endeavors.

Publisher Copyright:
© 2019 Elsevier B.V.


  • 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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