The evaluation of alternatives during organizational decision making was investigated to uncover evaluation tactics used by decision makers and how these tactics and complexity influenced success. Evaluation tactics that relied upon subjective, judgmental, bargaining, and analytical inferences were uncovered from 317 strategic decisions. The complexity of these decisions was measured by the numbers of alternatives considered, number of criteria used, and perceived difficulty of the evaluation task to identify conditions under which the evaluation tactics were successful. The managerial implications of evaluating alternatives with the tactics that used judgmental, bargaining, analytical, and subjective inferences under different levels of complexity are discussed.

We provide a strategic rationale for growth options under uncertainty and imperfect competition. In a market with strategic competition, investment confers a greater capability to take advantage of future growth opportunities. This strategic advantage leads to the capture of a greater share of the market, either by dissuading entry or by inducing competitors to “make room” for the stronger competitor. As a result of this strategic effect, payoffs are in a rough sense more convex than in the case of no investment in a growth option. When the strategic advantage is strong, increased uncertainty encourages investment in growth options: higher uncertainty means more opportunity rather than simply larger risk. If the strategic effect is weak, the reverse is true. On the other hand, an increase in systematic risk discourages the acquisition of growth options. Our results contradict the view that volatility is a strong disincentive for investment.

In stochastic systems, quantiles indicate the level of system performance that can be delivered with a specified probability, while probabilities indicate the likelihood that a specified level of system performance can be achieved. We present new estimators for use in simulation experiments designed to estimate such quantiles or probabilities of system performance. All of the estimators exploit control variates to increase their precision, which is especially important when extreme quantiles (in the tails of the distribution of system performance) or extreme probabilities (near zero or one) are of interest. Control variates are auxiliary random variables with known properties—in this case, known quantiles—and a strong stochastic association with the performance measure of interest. Since transforming a control variate can increase its effectiveness, we propose both continuous and discrete approximations to the optimal (variance-minimizing) transformation for estimating probabilities, and then invert the probability estimators to obtain corresponding quantile estimators. We also propose a direct control-variate quantile estimator that is not based on inverting a probability estimator. An empirical study using queueing, inventory and project-planning examples shows that substantial reductions in mean squared error can be obtained when estimating the 0.9, 0.95, and 0.99 quantiles.

The launch of a new product or service via direct marketing is nearly always preceded by a test of that offering. Such a “live” test, conducted with a subset of the entire list of customer prospects, can sometimes be useful in a “go/no-go” decision regarding a full-scale launch of the offering. More commonly, the test is used to direct the offering more effectively toward the market segments that appear most promising. Specifically, test results are used and useful to determine whether a particular rental list of customer prospects should indeed be rented, and (for both rental and in-house lists) which specific customer segments should be contacted with the offering. This paper examines the effectiveness of managers' decisions related to designing a test and interpreting test results both conceptually—based on the literature of heuristics and biases in expert judgments—and empirically, for two new direct marketing offers. The paper describes how an interplay of management judgment and statistical models can lead to increased profits for new direct marketing offerings.

In this study, we examine how experience at the level of the organization, the population, and the related group affects the failure of Manhattan hotels. We find organizational experience has a U-shaped effect on failure; that organizations enjoy reduced failure as a function of population experience before their founding, but not after; and that related organizations provide experience that lowers failure, but it matters whether their experience is local or non-local, and if it was acquired before or after the relationship was established. These results indicate both the difficulty of applying different types of experience to reduce the risk of organizational failure, and the relevance of experience for the evolution of organizational populations.

This paper argues that it is wrong to require that regressing the outputs of a trace-driven simulation on the observed real outcomes should give a 45° (unit slope) line through the origin (zero intercept). This note proposes instead an alternative requirement: the responses of the simulated and the real systems should have the same means and the same variances. To test statistically whether this requirement is satisfied, a novel procedure is derived: regress the differences between simulated and real responses on their associated sums, and test whether the resulting intercept and slope are both zero. This novel but simple test assumes identically, independently, and normally distributed outputs of the real system and the simulated system. The old and the new procedures are investigated in extensive Monte Carlo experiments that simulate M/M/1 queueing systems. The conclusions are: (i) the naive intuitive test rejects a valid simulation model substantially more often than the novel test does; (ii) the naive test shows “perverse” behavior within a certain domain: the worse the simulation model, the higher its estimated probability of acceptance; and (iii) the novel test does not reject a valid simulation model too often (its type I error probability is correct), provided the queueing response is transformed appropriately to obtain (nearly) normally distributed responses.

Strong majority influence can potentially harm organizational decisions by causing decision makers to engage in groupthink. This study examines whether and how computer-mediated communication (CMC) can reduce majority influence and thereby enhance the quality of decisions in some situations. To measure the impact of CMC on majority influence, three settings (unsupported, face-to-face CMC, and dispersed CMC) were compared. Matching laboratory experiments were carried out in an individualistic (the US) and a collectivistic culture (Singapore) to determine how the impact of CMC might be moderated by national culture. An intellective and a preference task were used to see whether the impact of CMC might be moderated by task type. The results showed that the impact of CMC on majority influence was contingent upon national culture. In the individualistic culture, majority influence was stronger in the unsupported setting than the face-to-face CMC and dispersed CMC settings. In the collectivistic culture, there were no corresponding differences. The results also revealed that the impact of CMC on majority influence was not moderated by task type. Instead, task type had a direct impact on majority influence. Regardless of the setting involved, majority influence was stronger with the preference than the intellective task. Besides demonstrating how cultural factors may moderate the impact of CMC, this study raises the broader issue of cultural relativism in current knowledge on CMC.