Firms choose strategies based on their attributes and industry conditions; therefore, strategy choice is endogenous and self-selected. Empirical models that do not account for this and regress performance measures on strategy choice variables are potentially misspecified and their conclusions incorrect. I highlight how self-selection on hard-to-measure or unobservable characteristics can bias strategy performance estimates and recommend an econometric technique that has been developed to account for this effect. Although this concern applies to a wide range of strategy questions, to demonstrate its effect I empirically examine if entry mode choice (acquisition versus greenfield) influences foreign direct investment survival. In specifications that do not account for self-selection, I find that greenfield entries have survival advantages compared to acquisitions. This confirms previous findings. However, the significance of this effect disappears once I account for self-selection of entry mode in the empirical estimates. The results confirm that estimates from models that do not account for self-selection of strategy choice can lead to incorrect or misleading conclusions.Endogenous Strategy Choice, Foreign Direct Investment, Survival, Entry Mode

The Dynamic and Stochastic Knapsack Problem (DSKP) is defined as follows. Items arrive according to a Poisson process in time. Each item has a demand (size) for a limited resource (the knapsack) and an associated reward. The resource requirements and rewards are jointly distributed according to a known probability distribution and become known at the time of the item's arrival. Items can be either accepted or rejected. If an item is accepted, the item's reward is received; and if an item is rejected, a penalty is paid. The problem can be stopped at any time, at which time a terminal value is received, which may depend on the amount of resource remaining. Given the waiting cost and the time horizon of the problem, the objective is to determine the optimal policy that maximizes the expected value (rewards minus costs) accumulated. Assuming that all items have equal sizes but random rewards, optimal solutions are derived for a variety of cost structures and time horizons, and recursive algorithms for computing them are developed. Optimal closed-form solutions are obtained for special cases. The DSKP has applications in freight transportation, in scheduling of batch processors, in selling of assets, and in selection of investment projects.

In this paper a dynamic and stochastic model of the well-known knapsack problem is developed and analyzed. The problem is motivated by a wide variety of real-world applications. Objects of random weight and reward arrive according to a stochastic process in time. The weights and rewards associated with the objects are distributed according to a known probability distribution. Each object can either be accepted to be loaded into the knapsack, of known weight capacity, or be rejected. The objective is to determine the optimal policy for loading the knapsack within a fixed time horizon so as to maximize the expected accumulated reward. The optimal decision rules are derived and are shown to exhibit surprising behavior in some cases. It is also shown that if the distribution of the weights is concave, then the decision rules behave according to intuition.dynamic programming, sequential stochastic resource allocation

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A resource allocation problem, called the dynamic and stochastic knapsack problem (DSKP), is studied. A known quantity of resource is available, and demands for the resource arrive randomly over time. Each demand requires an amount of resource and has an associated reward. The resource requirements and rewards are unknown before arrival and become known at the time of the demand's arrival. Demands can be either accepted or rejected. If a demand is accepted, the associated reward is received; if a demand is rejected, a penalty is incurred. The problem can be stopped at any time, at which time a terminal value is received that depends on the quantity of resource remaining. A holding cost that depends on the amount of resource allocated is incurred until the process is stopped. The objective is to determine an optimal policy for accepting demands and for stopping that maximizes the expected value (rewards minus costs) accumulated. The DSKP is analyzed for both the infinite horizon and the finite horizon cases. It is shown that the DSKP has an optimal policy that consists of an easily computed threshold acceptance rule and an optimal stopping rule. A number of monotonicity and convexity properties are studied. This problem is motivated by the issues facing a manager of an LTL transportation operation regarding the acceptance of loads and the dispatching of a vehicle. It also has applications in many other areas, such as the scheduling of batch processors, the selling of assets, the selection of investment projects, and yield management.