Colour graphics and task complexity in multivariate decision making

Stella H H SO, Malcolm Smith

    Research output: Contribution to journalReview articlepeer-review

    22 Citations (Scopus)


    Advancements in information technology and graphics software mean that colour graphics are an increasingly important part of the communication of business operations and corporate reporting. Unfortunately, the research literature on the effects of colour graphics on decision performance is sparse, and lends only limited and qualified support to the claims often made for colour coded graphics. There has been no research in the accounting environment of the impact of non-redundant colour graphics (i.e. those not complemented by numerical or pattern support) on decision-making performance. The existing literature suggests that gender, task complexity, field dependence and time constraints will all impact on the effectiveness of the use of colour, so this paper reports the results of a laboratory experiment designed to assess the interaction effects of non-redundant colour coding in bar charts with information complexity, and with gender. A multivariate bankruptcy prediction decision is the task environment. Non-redundant coding, rather than redundant coding, is used in this paper, to force subjects to use the actual colour coding in their decisions and in order to evaluate the effects of colour coding more fully. The results suggest that proponents of colour graphics must qualify their claims. Colour graphics improve decision making, though their impact is significant only when information complexity is low, and then for female subjects only.

    Original languageEnglish
    Pages (from-to)565-593
    Number of pages29
    JournalAccounting, Auditing and Accountability Journal
    Issue number4
    Publication statusPublished - 1 Oct 2002

    Scopus Subject Areas

    • Accounting
    • Economics, Econometrics and Finance (miscellaneous)

    User-Defined Keywords

    • Computer graphics
    • Decision making
    • Gender
    • Information retrieval
    • Task analysis


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