Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
권호 표지
DOI QR코드

DOI QR Code

The Effectiveness of Interactive Visualizations for Multi-Attribute Decision Making with Contextual Data

  • Received : 2018.01.02
  • Accepted : 2018.01.15
  • Published : 2018.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Objective The aim of this study is to investigate whether visual aids can enhance decision outcomes for multi-attribute choice making problems with practical datasets. Background: Information visualization techniques have been used to develop visual aids to help consumers process the given information, thus improving decision outcomes by increasing decision quality. However, due to the fact that decision quality is difficult to measure, several studies used context-free data to minimize the impact of participants' preference structures to reduce subjective factors. To better understand whether these visual aids are effective in a more practical setting, we need to conduct studies using data with context. Method: The experiment was conducted as a between-subject study with two interfaces, SimulSort for the interactive visualization interface and Typical Sorting for the non-visual traditional interface. Each participant had three decision making trials and the decision-making quality was measured by selecting the nondominated option that is considered as the best choice. A total of 127 participants participated through an online experiment platform. Results: Using the SimulSort interface, the odds of selecting a nondominated option, compared to not selecting it, increased and has a significant effect. Both the interface and type of selected option had significant main effects on the time spent. A posthoc analysis revealed that the type of selected option had significant effect for the participants who used SimulSort by spending more time to make a decision, however, this effect was not present for the participants who used Typical Sorting. The participants were generally confident on their decisions while using both interfaces. Conclusion: The results revealed that SimulSort could enhance the probability of making a better decision than compared to Typical Sorting. The study also shows an effective way to conduct a controlled study that has an objective measure for decision making using data with context. Application: The results of the experiment can help implement visual aids to help consumers make better decisions in everyday life.

Keywords

References

  1. Bautista, J. and Carenini, G., An empirical evaluation of interactive visualizations for preferential choice, Proceedings of the Working Conference on Advanced Visual Interfaces (pp. 207-214). Napoli, Italy, 2008.
  2. Carenini, G. and Loyd, J., ValueCharts: analyzing linear models expressing preferences and evaluations, Proceedings of the Working Conference on Advanced Visual Interfaces (pp. 150-157). Gallipoli, Italy, 2004.
  3. Chen, Y.C., Shang, R.A. and Kao, C.Y., The effects of information overload on consumers' subjective state towards buying decision in the internet shopping environment, Electronic Commerce Research and Applications, 8(1), 48-58, 2009. https://doi.org/10.1016/j.elerap.2008.09.001
  4. Grant, A.M. and Schwartz, B., Too much of a good thing the challenge and opportunity of the inverted utility, Perspectives on Psychological Science, 6(1), 61-76, 2011. https://doi.org/10.1177/1745691610393523
  5. Hahn, M., Lawson, R. and Lee, Y.G., The effects of time pressure and information load on decision quality, Psychology and Marketing, 9(5), 365-378, 1992. https://doi.org/10.1002/mar.4220090503
  6. Haubl, G. and Trifts, V., Consumer decision making in online shopping environments: The effects of interactive decision aids, Marketing Science, 19(1), 4-21, 2000. https://doi.org/10.1287/mksc.19.1.4.15178
  7. Hauser, J.R. and Wernerfelt, B., An evaluation cost model of consideration sets. Journal of Consumer Research, 393-408, 1990.
  8. Hur, I. and Yi, J.S., SimulSort: multivariate data exploration through an enhanced sorting technique, Human-Computer Interaction: Novel Interaction Methods and Techniques, 5611, 684-693, 2009.
  9. Hur, I., Kim, S.H., Samak, A. and Yi, J.S., A comparative study of three sorting techniques in performing cognitive tasks on a tabular representation, International Journal of Human-Computer Interaction, 29(6), 379-390, 2013. https://doi.org/10.1080/10447318.2012.713802
  10. Kittur, A., Chi, E.H. and Suh, B., Crowdsourcing user studies with mechanical turk, Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems (pp. 453-456). Florence, Italy, 2008.
  11. Lapersonne, E., Laurent, G. and Le Goff, J.J., Consideration sets of size one: An empirical investigation of automobile purchases. International Journal of Research in Marketing, 12(1), 55-66, 1995. https://doi.org/10.1016/0167-8116(95)00005-M
  12. Lurie, N.H., Decision making in Information Rich environments: The role of information structure, Journal of Consumer Research, 30(4), 473-486, 2004. https://doi.org/10.1086/380283
  13. Payne, J.W., Bettman, J.R. and Johnson, E.J., he adaptive decision maker. Cambridge University Press, 1993.
  14. Rao, R. and Card, S.K., The table lens: Merging graphical and symbolic representations in an interactive focus+ context visualization for tabular information. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Systems: Celebrating Interdependence (pp. 318-322), 1994.
  15. Savikhin, A., Maciejewski, R. and Ebert, D., Applied visual analytics for economic decision-making, IEEE Symposium on Visual Analytics Science and Technology (pp. 107-114), 2008.
  16. Shah, A. and Oppenheimer, D., Heuristics made easy: An effort-reduction framework. Psychological Bulletin, 134(2), 207-222, 2008. https://doi.org/10.1037/0033-2909.134.2.207
  17. Spenke, M. and Beilken, C., InfoZoom - analysing formula one racing results with an interactive data mining and visualisation tool. Ebecken, N. Data mining II (pp. 455-464), 2000.
  18. Spenke, M., Beilken, C. and Berlage, T., FOCUS: the interactive table for product comparison and selection. In Proceedings of the 9th annual ACM symposium on user interface software and technology (pp. 41-50). New York, USA, 1996.
  19. Wang, J., Peng, W., Ward, M.O. and Rundensteiner, E.A., nteractive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. IEEE Symposium on Information Visualization 2003 (pp. 105-112), 2003.
  20. Williamson, C. and Shneiderman, B., The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system, Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 338-346), 1992.
  21. Wittenburg, K., Lanning, T., Heinrichs, M. and Stanton, M., Parallel bargrams for consumer-based information exploration and choice, Proceedings of the 14th annual ACM symposium on user interface software and technology (pp. 51-60). New York, NY, USA, 2001.
  22. Yoon, K. and Hwang, C., Multiple attribute decision making: An introduction. SAGE, 1995.