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The impact of decision support system features on user overconfidence and risky behavior

  • Empirical Research
  • Published:
European Journal of Information Systems

Abstract

There is considerable research on how Decision Support Systems (DSSs) enable users to make better decisions. However, there is less focus on the possibility that some of their features may introduce biases and encourage suboptimal user behavior. We report on an experimental study that examines three DSS features that are generally considered beneficial to the user: the degree of choice the system provides to the user, the presence of competition among concurrent users, and the use of training to increase system familiarity. We hypothesize that the three DSS features may increase risky behavior (measured as the amount invested in a stock investment task with random outcomes) and overconfidence, conceptualized as the illusion of control, the phenomenon whereby people believe their chances of success at a task are greater than would be warranted by objective analysis. Our results confirm the effects of the three DSS features on risky behavior but only degree of choice impacts overconfidence. Moreover, overconfidence does not appear to mediate the impact of the DSS features on risky behavior. Finally, we hypothesize and confirm that, controlling for the effect of actual performance, overconfidence increases user satisfaction with the decision-making process and outcome.

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Authors and Affiliations

  1. Zicklin School of Business, Baruch College, City University of New York, New York, U.S.A.

    Chi-Wen Chen & Marios Koufaris

Authors
  1. Chi-Wen Chen
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  2. Marios Koufaris
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Corresponding author

Correspondence to Marios Koufaris.

Appendices

Appendix A

Instructions

First, in order to help you make the best investment decisions in this study, we would like to provide you with some information on financial theories on how stock prices change in financial markets.

The random walk hypothesis is a financial theory that states that stock market prices evolve according to a random walk, which means that they increase or decrease randomly. According to this theory, past stock prices are of no value in forecasting future prices because past, current, and future prices merely reflect market responses to information that comes into the market at random. In short, price movements are no more predictable than the pattern of the walk of a drunk.

There are also other theories that state that stock prices may be predicted to some degree, based on knowing specific information from the past, such as the changes in stock prices in the prior 5 years, earnings announcements over the prior 6 months, or market volatility. However, there is no single accepted theory on how stock prices evolve in markets.

Now that you understand a little more about stock price behavior in markets, please click on Next’ to start your investment task.

Appendix B

IOC (Adapted from Kahai et al (1998))

  1. 1

    Please tell us how confident you feel about the investment decision that you just made (7-point scale ranging from ‘Not at all confident’ to ‘Absolutely confident’)

  2. 2

    How certain are you that your stock price prediction is accurate? (7-point scale ranging from ‘Not at all certain’ to ‘Absolutely certain’)

  3. 3

    How likely do you think that your investment decision will result in maximizing your earnings? (7-point scale ranging from ‘Not at all likely’ to ‘Absolutely likely’)

  4. 4

    What do you think the price of the stock you invested in will be in the next month?

Satisfaction (Adapted from Kahai et al (1998)) (7-point scale ranging from ‘Strongly disagree’ to ‘Strongly agree’)

  1. 1

    I was satisfied with the decisions I made in this investment task

  2. 2

    I was satisfied with the decision-making process during this investment task

  3. 3

    Overall, I was satisfied with my performance in this investment task

Appendix C

Figure C1

Figure C1
figure 4

PLS model results.Note: Only significant relationships are shown. Interaction effects were tested and none were significant.

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Chen, CW., Koufaris, M. The impact of decision support system features on user overconfidence and risky behavior. Eur J Inf Syst 24, 607–623 (2015). https://doi.org/10.1057/ejis.2014.30

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