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The Quantitative Estimation of IT‐Related Risk Probabilities

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  • Andrea Herrmann

Abstract

How well can people estimate IT‐related risk? Although estimating risk is a fundamental activity in software management and risk is the basis for many decisions, little is known about how well IT‐related risk can be estimated at all. Therefore, we executed a risk estimation experiment with 36 participants. They estimated the probabilities of IT‐related risks and we investigated the effect of the following factors on the quality of the risk estimation: the estimator's age, work experience in computing, (self‐reported) safety awareness and previous experience with this risk, the absolute value of the risk's probability, and the effect of knowing the estimates of the other participants (see: Delphi method). Our main findings are: risk probabilities are difficult to estimate. Younger and inexperienced estimators were not significantly worse than older and more experienced estimators, but the older and more experienced subjects better used the knowledge gained by knowing the other estimators' results. Persons with higher safety awareness tend to overestimate risk probabilities, but can better estimate ordinal ranks of risk probabilities. Previous own experience with a risk leads to an overestimation of its probability (unlike in other fields like medicine or disasters, where experience with a disease leads to more realistic probability estimates and nonexperience to an underestimation).

Suggested Citation

  • Andrea Herrmann, 2013. "The Quantitative Estimation of IT‐Related Risk Probabilities," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1510-1531, August.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:8:p:1510-1531
    DOI: 10.1111/risa.12001
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    References listed on IDEAS

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    1. Lennart Sjöberg & Jana Fromm, 2001. "Information Technology Risks as Seen by the Public," Risk Analysis, John Wiley & Sons, vol. 21(3), pages 427-442, June.
    2. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    3. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    4. Oecd, 2004. "A Note on Benefit Security," Financial Market Trends, OECD Publishing, vol. 2004(1), pages 133-198.
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