Executives' perceived environmental uncertainty shortly after 9/11

Authors:
E. S. Soofi;P. C. Nystrom;M. Yasai-Ardekani
Affiliations:
Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA and Center for Research on International Economics, University of Wisconsin-Milwaukee ...;Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA;School of Management, George Mason University, Fairfax, VA 22030, USA
Venue:
Computational Statistics & Data Analysis
Year:
2009

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Abstract

Environmental uncertainty refers to situations when decision makers experience difficulty in predicting their organizations' environments. Prediction difficulty is mapped by closeness of decision makers' probability distributions of environmental variables to the uniform distribution. A few months after the 9/11 terrorist attacks, we solicited probabilities for three environmental variables from 93 business executives by a mail survey. Each executive assigned probabilities to the future state of the economy specified as categories of growth projected for a year after the 9/11 jolt, conditional probabilities of its effect on her/his organization, and conditional probabilities of her/his organizational response capability to each economic condition. Shannon entropy maps uncertainty, but the data do not provide trivariate state-effect-response distribution. We use maximum entropy method to impute the trivariate distributions from the data on state-effect and state-response bivariate probabilities. Uncertainty about each executive's probability distribution is taken into account in two ways: using a Dirichlet model with each executive's distribution as its mode, and using a Bayesian hierarchical model for the entropy. Both models reduce the observed heterogeneity among the executives' environmental uncertainty. A Bayesian regression examines the effects of two organizational characteristics on uncertainty. Presentation of results includes uncertainty tableaux for visualizations of the joint and marginal entropies and mutual information between variables.