Table 2 indicate overuse and misuse
of colors in dashboards create distractions and thus viewers’ cognitive
overload. Second, the areas affected
by overuse and misuse of colors attract
viewers’ attention and delay performance of a task. Although such distraction increases a viewer’s cognitive load,
that increase is not great enough to affect task performance. It can be argued
viewers engaged in System 2 processing, ensuring task performance is not
affected. Third, use of colors affects
the decision-making process when using dashboards. The first fixation times
(in Table 3) and the fixation sequence
analysis (in Figure 5) indicate color
variations in dashboards affect viewers’ decision-making processes. Finally, the decision performance is not
negatively affected in all groups (see
the cells in Table 4).
Specific suggestions can thus be
made to dashboard developers concerning use of colors in business dashboards. Although cognitive overload
does not necessarily affect a decision
maker’s performance, overload is undesirable. A practical implication is
dashboard developers should avoid the
indiscriminate use of colors in business
dashboards. Using the concepts of task-relevant and task non-relevant areas,
they need to think in advance about how
a dashboard will be used. They should
first identify the task-relevant and task
non-relevant areas of the dashboard for
possible decision-making tasks. Note
these areas could change based on tasks
users intend to perform with the dashboards. Following such identification,
dashboard developers should avoid
highlighting task non-relevant areas, as
doing so causes distraction. Instead, the
task-relevant areas should be highlighted to attract viewers’ attention. Figure 6
reflects the effect of highlighting task-relevant (blue) and task non-relevant
(brown) areas. If a task relates to decision making with small markets, then
areas related to small markets are task
relevant. This example shows highlighting specific areas of visualization can
This research shows dashboards
with misuse and overuse of colors do not
lead to poorer decision performance but
rather decision makers using such dash-
boards taking longer to make a decision.
One notable practical finding is organi-
zations do not need to redevelop their
dashboards unless the cost of redevel-
opment is less than the cost of the extra
decision time. It is likely existing dash-
boards do not need to be altered, though
new dashboard development should
avoid overuse and misuse of colors.
These results also apply to the use
of colors in dashboards. Bar charts are
used more frequently in dashboards
than in any other aspect of information visualization.
12 Dashboards are designed for users to see how various indicators are performing15 and are thus
used primarily to identify trends and
patterns for decision making.
3 Here, bar
charts within dashboards were used to
identify patterns. Future studies can investigate the effect of colors on new generations of complex dashboards (such
as those providing interactivity through
a drill-down feature) and on ways to
measure task performance (such as
1. Brath, R. and Peters, M. Dashboard design: Why design
is important. DM Direct Newsletter (Oct. 2004).
2. Few, S. Dashboard confusion. Information Week
3. Few, S. Information Dashboard Design: Displaying
Data for At-A-Glance Monitoring. Analytics Press,
Burlingame, CA, 2012.
4. Goldstein, E.B. Sensation and Perception. Thomson
5. Hegarty, M., Canham, M., and Fabrikant, S. Thinking
about the weather: How display salience and knowledge
affect performance in a graphic inference task. Journal
of Experimental Psychology 36, 1 (2010), 37–53.
6. Jacob, R.J.K. and Karn, K.S. Eye tracking in human-computer interaction and usability research: Ready
to deliver the promises. Chapter 4 in The Mind’s Eye:
Cognitive and Applied Aspects of Eye Movement,
R. Radach, J. Hyona, and H. Deubel, Eds. Elsevier
Sciences, Oxford, U.K., 2003, 573–605.
7. Just, M.A. and Carpenter, P.A. Eye fixations and cognitive
processes. Cognitive Psychology 8, 1 (1976), 441–480.
8. Kahneman, D. Thinking, Fast and Slow. Farrar, Straus
and Giroux, New York, 2011.
9. Kosslyn, S.M. Graph Design for the Eye and Mind.
Oxford University Press, 2006.
10. Mayer, R.E. and Moreno, R. Nine ways to reduce
cognitive load in multimedia learning. Educational
Psychologist 38, 1 (2003), 43–52.
11. Murray, D. Tableau Your Data!: Fast and Easy Visual
Analysis with Tableau Software. John Wiley and Sons,
Inc., New York, 2013.
12. Peck, G. Tableau 8: The Official Guide. McGraw Hill
Education, New York, 2014.
13. Rayner, K. Eye movements in reading and information
processing: 20 years of research. Psychological
Bulletin 124, 3 (1998), 372–422.
14. Sharif, B. and Maletic, J. An eye-tracking study on the
effects of layout in understanding the role of design
patterns. In Proceedings of the IEEE International
Conference on Software Maintenance ( Timişoara,
Romania, Sept. 12–18). IEEE Press, 2010, 41–48.
15. Yigitbasioglu, O. and Velcu, O. A review of dashboards
in performance management: Implications for design
and research. International Journal of Accounting
Information Systems 13, 1 (2012), 41–59.
Palash Bera ( firstname.lastname@example.org) is an assistant professor
in the John Cook School of Business at Saint Louis
University, Saint Louis, MO.
© 2016 ACM 0001-0782/16/04 $15.00
is organizations do
unless the cost
is less than
the cost of the
extra decision time.