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How Colors in Business Dashboards Affect Users' Decision Making


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Business dashboards help users visually identify trends, patterns, and anomalies in order to make effective decisions.1 Dashboards often use a variety of colors to differentiate and identify objects.2 Although using colors might improve visualization, overuse or misuse can distract users and adversely affect decision making. This article tests this effect with the help of eye-tracking technology.

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Key Insights

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The bar charts in Figure 1 reflect sales of office-supply products. The bars in the left-hand chart are uniform in color, and the relative height is the only salient information source. However, the chart on the right uses a different color for each bar, and the variation in both height and color could be perceived as different information. As a general principle, color variation should reflect value variation.9 Use of colors can needlessly attract viewers' attention, causing them to search for meaning that is not there.3

Each dashboard in Figure 2 shows the profits by market size for geographic regions, as well as by products. Although the two dashboards are exactly the same in terms of content, they differ in the way color is used in the bars. The dashboard in the upper panel uses a blue palette that varies from zero saturation (white) to 100% saturation (deep blue), whereas the dashboard in the lower panel uses a palette that starts at 100% saturation (red), decreases to zero saturation in the middle of the scale, and then increases back to 100% saturation but in a green hue.

Contrasting colors attract viewers' attention. If the contrasting colors are not related to a viewer's task, then their use creates distraction; for example, the lower panel in Figure 2 uses two contrasting colors—dark red for less profit, dark green for higher profit. A distraction occurs if the task the viewer is performing does not focus on high or low profit; for example, if the task is to identify what product (such as coffee or tea) has the smallest difference in profit between major and small markets, then the task requires focusing on only the bottom part of the lower panel. However, contrasting colors force the viewer to also look at the contrasting areas, including the top part of the lower panel that includes information on market types (such as East and South); the dashboard in the bottom panel is an example of how colors can be misused.

To perform a decision-making task, viewers need to pay attention to specific parts of the dashboards. Viewers thus need to isolate and extract the relevant information from a diagram.5 A dashboard (related to a task) can be split into two parts—task relevant and task non-relevant.5 Using the task example in Figure 2, specific areas of the bottom part of the lower panel can be termed "task relevant," and the top part of the lower panel can be termed "task non-relevant." Misuse of colors forces viewers to look at both areas.

This article investigates how the overuse of colors, as in Figure 1, and misuse of colors, as in Figure 2, in business dashboards affects users' decision making. It uses eye-tracking technology to provide insight into how individuals read and scan displayed information, identifying how they make decisions with business dashboards.13 Eye tracking is particularly relevant in measuring a viewer's attention and effort on a visual display because it offers a window into how the viewer reads and scans the displayed information.13

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Eye Tracking

Eye tracking enables researchers to measure a subject's eye movements while reading text or viewing a picture. The involuntary and voluntary responses of eye movements reflect the internal processing of information.13 When reading, our eyes make rapid movements to shift attention from one part of a display to another, then remain almost motionless while the brain interprets the material at that location.13 The periods in which the eyes are motionless are called "fixations."14 Fixation information can be used to measure the attention individuals pay to the viewing object. Fixation is characterized by three measures:

Fixation count. Total number of fixations on a specific area of display;

Fixation duration. Total fixation time on a specific area of display; and

First fixation time. Start time of the first fixation on the display area.

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Empirical Evidence

This study involved dashboards with bar charts. Bar charts were used because they are the natural choice for displaying multiple measures3 and the most effective way to compare values across dimensions.11 It recruited 30 information systems graduate students from IS analysis and design courses at Texas A&M International University, Laredo, TX, as subjects. These students also took graduate statistics courses and were thus familiar with the elements of dashboards, including graphs and tables. Small samples are typical in eye-tracking studies due to the limited availability of equipment and the large amount of time required to collect each set of observations.6

The subjects were asked to answer questions based on two dashboards: What two subcategories of office supplies have the same sales (to test the overuse of colors in Figure 1)? and For which product type is the difference in profit between the major market and the small market the smallest (to test the misuse of colors in Figure 2)?

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Hypotheses and Design

Viewers engage in cognitive processes to perform decision-making tasks. Two such types of processes are "incidental processing" and "essential processing."10 The former does not require making sense of the presented material, whereas the latter does. Moreover, they can be related to the concepts of "System 1" and "System 2," the two basic modes of thought in the human mind.8 System 1 is the brain's fast, automatic, and intuitive approach; System 2 is the mind's slower, analytical mode, where reason dominates.8 System 1 operates involuntarily and impulsively with little effort; System 2 allocates effort to the cognitive activities demanding attention.8

Viewers of dashboards with overuse or misuse of colors show evidence of System 1 processing. When contrasting colors are used, our brains attempt to assign meaning to the colors.2 Viewers are thus directed spontaneously to the areas where the colors are present. These viewers also show use of System 2 processing because this processing is activated when they deliberately pay attention to the decision-making task. In contrast, viewers of dashboards without overuse or misuse of colors avoid System 1 processing and focus on System 2 processing instead, requiring less time to perform the task.


A practical implication is dashboard developers should avoid the indiscriminate use of colors in business dashboards.


Recording eye fixations can reveal the amount of information processed. A longer fixation duration might indicate difficulty extracting information from the displayed area.7 A high fixation count and longer fixation duration are thus indicative of cognitive overload. Accordingly, here is the first hypothesis regarding the overuse of colors.

Hypothesis 1. For a dashboard-related task, viewers using dashboards with overuse or misuse of colors have a higher overall fixation number and longer fixation time than viewers with dashboards with no such overuse or misuse.

Tests can be devised to determine whether viewers of dashboards with misuse of colors engage in System 1 processing first before System 2 processing. If there is evidence of this sequence, it will provide insight into the viewers' decision-making process. Such evidence can be collected by comparing task-relevant and task non-relevant areas of the dashboard with misuse of colors, as in Figure 1. It can be predicted that viewers will engage in System 1 processing because they would be immediately directed to the task non-relevant areas. Subsequently, to complete the task, the viewers must consciously engage in System 2 processing by referring to the task-relevant areas. This sequence of engagement can be identified through the eye metric known as "first fixation time."

First fixation time is used as a measure of attention to show how quickly one looks at a certain element on a dashboard.6 It is measured as the start time of the first fixation on the display area. Eye-tracking software marks a specific area of the dashboard in order to identify the viewer's eye movements in that area. If a viewer looks at a task non-relevant area at the start of the viewing time (such as the fifth second in a total viewing time of 30 seconds), then the area indeed attracted the viewer's immediate attention. Low first fixation time thus indicates the area attracted attention quickly, meaning the following hypothesis can be proposed:

Hypothesis 2. Compared to a dashboard that does not misuse colors, viewers of a dashboard that misuses colors will have a low first fixation time on task non-relevant areas and a high first fixation time on task-relevant areas.

The identification of a System 1, then System 2 activation sequence is easier for dashboards that misuse colors because viewers readily recognize the task-relevant and task non-relevant areas, as in Figure 2. This sequence identification is not possible for dashboards that overuse colors because the areas overlap, as in Figure 1.

This study followed a design in which subjects were randomly assigned to one of the variations—overuse vs. no overuse of colors and misuse vs. no misuse of colors—in dashboards. Each group included an equal number of subjects. One variation was provided to 15 subjects, and the other to the rest. The order of the dashboards was randomized; that is, some subjects received dashboards with or without overuse of colors first and some received dashboards with or without misuse of colors first. The subjects performed two tasks related to the two dashboards as their eye movements were tracked. Prior to tracking, subjects' eyes were calibrated and validated. Following calibration, the subjects were shown a task on a screen and asked to read it carefully. They then saw the dashboard and verbalized an answer. This sequence was used to avoid eye movements associated with writing down answers. The sequence was repeated for each dashboard and eye movements were tracked through EyeLink 1000 software. Verbalizations were also recorded. The tracker recorded a minimum fixation time of four milliseconds.

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Analysis of Overuse of Colors

The accuracy of the analysis between the two groups showed no statistical difference. Nearly 92% of the subjects (or 28 of the 30) answered the task correctly in both groups. But total fixation duration and fixation counts for the tasks were compared between the two groups, finding significant differences. Subjects who used the dashboard with overuse of colors took approximately 28 seconds, while those who viewed the dashboard with no such misuse took approximately 22 seconds. The independent sample t test (see Table 1) confirmed the differences in fixation durations; counts were significant in the two groups.


Use of colors can needlessly attract viewers' attention, causing them to search for meaning that is not there.


Fixation counts and durations showed the dashboard with overuse of colors induced more cognitive effort compared to the dashboard with no overuse of colors. However, this excess effort did not affect performance of the task. The sample heat map of two subjects (see Figure 3) reflects the presence of cognitive overload. A heat map uses different colors—red for the largest number of fixations, green for the fewest number of fixations—to show the number of fixations viewers make in certain areas of an image.6 The heat map on the left of the figure indicates the subject spent significant time on all bars with different colors. In contrast, the heat map on the right of the figure shows how another subject spent time on specific bars.

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Analysis of Misuse of Colors

Task performance between the two groups showed no statistical difference. Approximately 88% of the subjects (or 26 of the 30) answered the task correctly in both groups. However, a significant difference was found in the overall fixation durations and counts between the two groups. To perform the task, subjects who viewed the dashboard with misuse of colors took approximately 45 seconds, whereas those who viewed the dashboard with no such misuse took approximately 27 seconds; Table 2 shows the results of the independent sample t test, indicating a high cognitive load exists for viewers of the dashboard with misuse of colors.

To determine whether there was a System 1, then System 2 sequence, the study identified task-relevant and task-non-relevant areas. First, a specific task non-relevant area was identified from the dashboard that did not have to be viewed to perform the task. This area is the bar that indicates the small market in the East zone (dark red) for the chart "market type by market size" that appears in the top part of the lower panel of Figure 2. The time of the first fixation was obtained when a viewer would look at this dark red bar. This time was compared with the first fixation time of the same area (light blue) in the top part of the top panel in Figure 2. On average, the subjects using the dashboard with misuse of colors looked at this area within 6.2 seconds of their average viewing time of 45.2 seconds. On the other hand, subjects using the dashboard with no such misuse looked at this specific area within 18.2 seconds of their average viewing time of 26.8 seconds. Three of the subjects in the latter group did not look at this area at all.

The first fixation times of the two groups were compared for the task-relevant areas in the dashboard. The bar chart labeled "Product Type By Market Size" was chosen as a task-relevant area because viewers needed to see this area to complete the task. The results (see Table 3) show viewers using the dashboard with no misuse of colors viewed this area much more quickly than the other group, indicating the contrasted areas indeed distracted the viewers who engaged in System 1 and System 2 processing, respectively.

The sample heat maps of two subjects (see Figure 4) reflect the analysis mentioned in Table 2 and Table 3. The bottom-left heat map in Figure 4 shows areas with contrasting colors attracted viewer's attention, whereas the bottom-right heat map shows the focus was on the task-relevant areas (such as the chart title).

A fixation-sequence analysis was conducted to determine whether a viewer's decision-making process involving the dashboard with misuse of colors was different from those viewing the dashboard with no such misuse. To do it, the dashboard with misuse of colors, as in Figure 2, was divided into 13 zones (see Figure 5), and the eye-fixation sequences of all subjects were mapped with these zones. Zone 9 was the most relevant because it contained the answer to the task. The mapping results were ranked in the order in which the zones were visited first by the subjects, as in Figure 5. The task-relevant areas are highlighted in the table within Figure 5, indicating the viewers of the dashboard with misuse of colors visited the task non-relevant areas (such as Zone 3 and Zone 7) first, followed by the task-relevant areas (such as Zone 9). This sequence demonstrates viewers engaged in System 1 processing first, then System 2 processing. In contrast, viewers of the dashboard with no such misuse visited the task-relevant areas (such as Zone 6) first. The viewers thus engaged System 2 processing directly. Together with the heat maps and the statistical analysis in Table 3, this analysis provides evidence the misuse of colors affects the pattern of a viewers' eye movements and decision-making processes.

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Conclusion

This article has reported the effects of overuse and misuse of colors in dashboards on decision making, as summarized in Table 4.

The study made several interesting observations. First, the high fixation counts and durations in Table 1 and 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,5 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 cause distraction.

This research shows dashboards with misuse and overuse of colors do not lead to poorer decision performance but rather decision makers using such dashboards taking longer to make a decision. One notable practical finding is organizations do not need to redevelop their dashboards unless the cost of redevelopment is less than the cost of the extra decision time. It is likely existing dashboards do not need to be altered, though new dashboard development should avoid overuse and misuse of colors.


One notable practical finding is organizations do not need to redevelop their dashboards unless the cost of redevelopment is less than the cost of the extra decision time.


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 memory retention).

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References

1. Brath, R. and Peters, M. Dashboard design: Why design is important. DM Direct Newsletter (Oct. 2004).

2. Few, S. Dashboard confusion. InformationWeek (Mar. 2004).

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 Wadsworth, 2007.

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 (Timioara, 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.

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Author

Palash Bera (pbera@slu.edu) is an assistant professor in the John Cook School of Business at Saint Louis University, Saint Louis, MO.

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Figures

F1Figure 1. Overuse of colors in bar charts.

F2Figure 2. Colors can attract unnecessary attention and viewer distraction.

F3Figure 3. Heat maps of a subject performing a task with overuse vs. no overuse of colors in dashboards.

F4Figure 4. Heat maps of two subjects performing tasks with dashboards.

F5Figure 5. First fixation-time-sequence analysis.

F6Figure 6. Effect of highlighting task-relevant and non-relevant areas.

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Tables

T1Table 1. Analysis of fixation durations and counts for dashboards with overuse of colors.

T2Table 2. Analysis of fixation durations and counts for dashboards with misuse of colors.

T3Table 3. Analysis of the first fixation times for dashboards that misuse colors; similar results were obtained for the other task-non-relevant and -relevant areas between the two groups.

T4Table 4. Summary of eye-tracking study.

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