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The profitability of a new technology is rarely known with certainty at its announcement date. Consequently, prior to making an adoption decision it behooves the firm considering the adoption of this innovation to reduce the level of uncertainty associated with its profitability. The firm accomplishes this by sequentially gathering information, updating its prior estimate of profitability in a Bayesian manner. Quantifying the uncertainty regarding the innovation permits application of dynamic programming techniques: criteria are derived which tell the firm when to stop collecting information and make the adoption decision. It will be shown that it is optimal for the firm to continue to collect information until its estimate of profitability crosses one of two thresholds: upon crossing the upper threshold the firm adopts the technology, whereas the firm rejects the technology if the lower threshold is crossed. The model predicts that even the manager who behaves optimally will occasionally adopt unprofitable technologies and reject profitable ones.
Kevin F. McCardle (Fri,) studied this question.