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Managerial Decision Making in Censored Environments: Biased Judgment of Demand, Risk, and Employee Capability

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Date
2012
Author
Feiler, Daniel C.
Advisor
Larrick, Richard P
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Abstract

Individuals have the tendency to believe that they have complete information when making decisions. In many contexts this propensity allows for swift, efficient, and generally effective decision making. However, individuals cannot always see a representative picture of the world in which they operate. This paper examines judgment in censored environments where a constraint, the censorship point, systematically distorts the sample observed by a decision maker. Random instances beyond the censorship point are observed at the censorship point, while instances below the censorship point are observed at their true value. Many important managerial decisions occur in censored environments, such as inventory, risk-taking, and employee evaluation decisions. This empirical work demonstrates a censorship bias - individuals tend to rely too heavily on the observed censored sample, biasing their beliefs about the underlying population. Further, the censorship bias is exacerbated for higher rates of censorship, higher variance in the population, and higher variability in the censorship points. Evidence from four studies demonstrates how the censorship bias can cause managers to underestimate demand for their goods, over-estimate risk in their environments, and underappreciate the capabilities of their employees, which can lead to undesirable outcomes for organizations.

Type
Dissertation
Department
Business Administration
Subject
Management
biases and heuristics
Decision making
judgment
learning
risk
Permalink
https://hdl.handle.net/10161/5784
Citation
Feiler, Daniel C. (2012). Managerial Decision Making in Censored Environments: Biased Judgment of Demand, Risk, and Employee Capability. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/5784.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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