IDEAS home Printed from https://ideas.repec.org/a/igg/jbir00/v16y2025i1p1-24.html
   My bibliography  Save this article

Visual Differentiation vs. Visual Grouping: Task Types and Visual Perception Performance

Author

Listed:
  • Fang Chen

    (University of Montana, USA)

  • Limin Zhang

    (North Dakota State University, USA)

Abstract

For business data visualization, there are two fundamental types of visual tasks: (a) differentiation tasks (identifying a data point or comparing individual data points) and (b) integration tasks (comparing sums of multiple data points and recognizing patterns). In this study, the authors propose a model of visual grouping that uses color to customize graphs for integration tasks. They conducted two experiments to investigate the effects of visual grouping on user performance across different task types. The results indicate that color grouping can enhance the outcomes of decision-making tasks, a specific type of integration task. More specifically, they found that when using graphs with visual grouping, participants spent significantly less time and achieved higher comprehension accuracy compared to those using graphs without visual grouping.

Suggested Citation

  • Fang Chen & Limin Zhang, 2025. "Visual Differentiation vs. Visual Grouping: Task Types and Visual Perception Performance," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 16(1), pages 1-24, January.
  • Handle: RePEc:igg:jbir00:v:16:y:2025:i:1:p:1-24
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBIR.380953
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jbir00:v:16:y:2025:i:1:p:1-24. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.