IDEAS home Printed from https://ideas.repec.org/a/aic/revebs/y2008i2andonei.html
   My bibliography  Save this article

Principles of Information Visualization for Business Research

Author

Listed:
  • Ioan I. ANDONE

    (Faculty of Economics and Business Administration, "Al.I.Cuza" University of Iaºi)

Abstract

In the era of data-centric-science, a large number of visualization tools have been created to help researchers understand increasingly rich business databases. Information visualization is a process of constructing a visual presentation of business quantitative data, especially prepared for managerial use. Interactive information visualization provide researchers with remarkable tools for discovery and innovation. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional spaces. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100 msec updates even for million-record databases. Visualizations of business information are therefore widely used in actually business decision support systems, and by business researchers also. Visual user interfaces called dashboards are tools for reporting the status of a company and its business environment to facilitate business intelligence and performance management activities. In this study, we examine the research on concepts, and the principles of business information visualization, because we hope to be using correctly by business Ph.D. students in their researches. Visual representations are likely to improve business managers, and business researchers efficiency, offer new insights, and encouraging comparisons.

Suggested Citation

  • Ioan I. ANDONE, 2008. "Principles of Information Visualization for Business Research," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 2, pages 161-178, November.
  • Handle: RePEc:aic:revebs:y:2008:i:2:andonei
    as

    Download full text from publisher

    File URL: http://rebs.ro/resource/REBS_2/Case%20Studies/Andone_I_-_Principles_of_information_visualization_for_business_research.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friendly, Michael & Kwan, Ernest, 2003. "Effect ordering for data displays," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 509-539, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Parsons, Linda M. & Tinkelman, Daniel, 2013. "Testing the feasibility of small multiples of sparklines to display semimonthly income statement data," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 58-76.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthew Ward & Zaixian Xie & Di Yang & Elke Rundensteiner, 2011. "Quality-aware visual data analysis," Computational Statistics, Springer, vol. 26(4), pages 567-584, December.
    2. Tanguiane, Andranick S., 2022. "Analysis of the 2021 Bundestag elections. 2/4. Political spectrum," Working Paper Series in Economics 152, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. C. Hurley & R. Oldford, 2011. "Eulerian tour algorithms for data visualization and the PairViz package," Computational Statistics, Springer, vol. 26(4), pages 613-633, December.
    4. Dietsch, Michel & Petey, Joël, 2015. "The credit-risk implications of home ownership promotion: The effects of public subsidies and adjustable-rate loans," Journal of Housing Economics, Elsevier, vol. 28(C), pages 103-120.
    5. John Fox & Michael Friendly & Georges Monette, 2009. "Visualizing hypothesis tests in multivariate linear models: the heplots package for R," Computational Statistics, Springer, vol. 24(2), pages 233-246, May.
    6. Ana Muñoz-Mazón & Laura Fuentes-Moraleda & Angela Chantre-Astaiza & Marlon-Felipe Burbano-Fernandez, 2019. "The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    7. Valero-Mora, Pedro M. & Young, Forrest W. & Friendly, Michael, 2003. "Visualizing categorical data in ViSta," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 495-508, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aic:revebs:y:2008:i:2:andonei. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sireteanu Napoleon-Alexandru (email available below). General contact details of provider: https://edirc.repec.org/data/feaicro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.