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The Impact of Experience and Time on the Use of Data Quality Information in Decision Making

Author

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  • Craig W. Fisher

    (Information Systems, Marist College, Poughkeepsie, New York 12601)

  • InduShobha Chengalur-Smith

    (Management Science and Information Systems, SUNY, University at Albany, Albany, New York 12222)

  • Donald P. Ballou

    (Management Science and Information Systems, SUNY, University at Albany, Albany, New York 12222)

Abstract

Data Quality Information (DQI) is metadata that can be included with data to provide the user with information regarding the quality of that data. As users are increasingly removed from any personal experience with data, knowledge that would be beneficial in judging the appropriateness of the data for the decision to be made has been lost. Data tags could provide this missing information. However, it would be expensive in general to generate and maintain such information. Doing so would be worthwhile only if DQI is used and affects the decision made.This work focuses on how the experience of the decision maker and the available processing time influence the use of DQI in decision making. It also explores other potential issues regarding use of DQI, such as task complexity and demographic characteristics. Our results indicate increasing use of DQI when experience levels progress through the stages from novice to professional. The overall conclusion is that DQI should be made available to managers without domain-specific experience. From this it would follow that DQI should be incorporated into data warehouses used on an ad hoc basis by managers.

Suggested Citation

  • Craig W. Fisher & InduShobha Chengalur-Smith & Donald P. Ballou, 2003. "The Impact of Experience and Time on the Use of Data Quality Information in Decision Making," Information Systems Research, INFORMS, vol. 14(2), pages 170-188, June.
    • Handle: RePEc:inm:orisre:v:14:y:2003:i:2:p:170-188
    DOI: 10.1287/isre.14.2.170.16017
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    References listed on IDEAS

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    3. Janvrin, Diane & Mascha, Maureen Francis, 2014. "The financial close process: Implications for future research," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 381-399.
    4. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    5. Ann-Frances Cameron & Jane Webster, 2013. "Multicommunicating: Juggling Multiple Conversations in the Workplace," Information Systems Research, INFORMS, vol. 24(2), pages 352-371, June.
    6. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2009. "Impact of the Union and Difference Operations on the Quality of Information Products," Information Systems Research, INFORMS, vol. 20(1), pages 99-120, March.
    7. Jeremiah Mwendwa Mumo (Bsc. HRIM), 2023. "Use of Routine Health Information among Health Management Teams in Makueni County, Kenya," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(8), pages 73-109, August.
    8. Marco Haid & Sabine Graschitz & Peter Heimerl, 2019. "Error Reports in the Light of Error Management Climate, Task Complexity and Personnel Composition," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 12(3), pages 14-23, December.
    9. Dunk, Alan S., 2007. "Innovation budget pressure, quality of IS information, and departmental performance," The British Accounting Review, Elsevier, vol. 39(2), pages 115-124.
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