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Information visualization to support management decisions

Author

Listed:
  • Jasser Al-Kassab

    (University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK;
    University of St. Gallen, Institute of Technology Management, 9000 St. Gallen, Switzerland)

  • Zied M. Ouertani

    (ABB Forschungszentrum. Wallstadter Str. 59, Ladenburg, 68526, Germany;
    University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK)

  • Giovanni Schiuma

    (University of Basilicata, Viale dell'Ateneo Lucano 10, 85100 Potenza, Italy;
    University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK;
    University of the Arts London, Innovation Insights Hub, N1C4 AA, London, UK)

  • Andy Neely

    (University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK)

Abstract

Information visualization can accelerate perception, provide insight and control, and harness this flood of valuable data to gain a competitive advantage in making business decisions. Although such a statement seems to be obvious, there is a lack in the literature of practical evidence of the benefit of information visualization. The main contribution of this paper is to illustrate how, for a major European apparel retailer, the visualization of performance information plays a critical role in improving business decisions and in extracting insights from Redio Frequency Idetification (RFID)-based performance measures. In this paper, we identify — based on a literature review — three fundamental managerial functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Then, we provide — based on real industrial case evidence — how information visualization supports business decision-making. Several examples are provided to evidence the benefit of information visualization through its three identified managerial functions. We find that — depending on the way performance information is shaped, communicated, and made interactive — it not only helps decision making, but also offers a means of knowledge creation, as well as an appropriate communication channel.

Suggested Citation

  • Jasser Al-Kassab & Zied M. Ouertani & Giovanni Schiuma & Andy Neely, 2014. "Information visualization to support management decisions," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 407-428.
    • Handle: RePEc:wsi:ijitdm:v:13:y:2014:i:02:n:s0219622014500497
    DOI: 10.1142/S0219622014500497
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    Citations

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    Cited by:

    1. Mathias Eggert & Jens Alberts, 2020. "Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 685-739, July.
    2. Sabri Erdem & Turan Tolgay Kizilelma & Ceren Altuntaş Vural, 2016. "Supporting Healthcare Executive Managers’ Decisions Through Dashboards," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-28, March.
    3. Hela Ltifi & Emna Benmohamed & Christophe Kolski & Mounir Ben Ayed, 2020. "Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 241-282, February.
    4. Ulas Akkucuk, 2020. "Visualizing big data via a mixture of PARAMAP and Isomap," International Journal of Decision Sciences & Applications (2528-956X), Center for the Strategic Studies in Business and Finance, vol. 1(1), pages 29-35, December.
    5. Cheng, Kuo-Tai, 2016. "Test of the mediating effects of regulatory decision tools in the communications regulator," Telecommunications Policy, Elsevier, vol. 40(2), pages 277-289.
    6. Sabrina Bresciani & Martin J. Eppler, 2015. "The Pitfalls of Visual Representations," SAGE Open, , vol. 5(4), pages 21582440156, October.
    7. Marian Lux & Stefanie Rinderle-Ma, 2023. "DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 106-144, April.

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