Abstract.A common precept of decision analysis under uncertainty is the choice of an action which maximizes the expected value of a utility function. Savage's (1954) axioms for subjective expected utility provide a normative foundation for this principle of choice. This paper shows that the same set of axioms implies that one should select an action which maximizes the probability of meeting an uncertain target. This suggests a new perspective and an alternate target-based language for decision analysis. We explore the implications and the advantages of this target-based approach for both individual and group decision-making. (2000): 91B06, 91B10, 90B50 Mathematics Subject Classification

R&D intensity and manufacturing performance were evaluated in this study of 600 durable goods firms in 20 countries. In a path-analytic model, R&D intensity was significantly associated with improvements in market share (R 2 = 34%), controlling for firm size, previous market share, and regardless of industry or region of the world. Market share increases were also significantly correlated with improvements in manufacturing agility (R 2 = 4%). Agility improvement was significantly correlated with R&D intensity, and computerization in manufacturing, controlling for firm size and region, and did exhibit industry effects, with electronic equipment firms elevated on this measure. Computerization exhibited regional (not industry) differences, with South American firms depressed on this measure. The role of computerization in manufacturing and agility in firm innovativeness are discussed.R&D, Agility, Process Innovation, Product Innovation

This paper develops an approach based on performance targets to assess a preference function for a multiobjective decision under uncertainty. This approach yields preference functions that are strategically equivalent to conventional multiattribute utility functions, but the target-oriented approach is more natural for some classes of decisions. In some situations, the target-oriented preference conditions are analogous to reliability theory conditions for series or parallel failure modes in a system. In such cases, reinterpreting the conditions using reliability concepts can be useful in assessing the preference function. The target-oriented approach is also a generalization of common forms of goal programming. The approach has particular applicability for resource allocation decisions where the outcome of the decision is significantly determined by the actions of other stakeholders to the decision, such as new product development or decision making in a controversial regulated environment.

Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally, which banner advertisements to serve to consumers in order to maximize click-through rates, brand consideration, and purchase likelihood. Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified from consumers' clickstreams. This paper describes the first large-sample random-assignment field test of banner morphing-over 100,000 consumers viewing over 450,000 banners on CNET.com. On relevant webpages, CNET's click-through rates almost doubled relative to control banners. We supplement the CNET field test with an experiment on an automotive information-andrecommendation website. The automotive experiment replaces automated learning with a longitudinal design that implements morph-to-segment matching. Banners matched to cognitive styles, as well as the stage of the consumer's buying process and body-type preference, significantly increase click-through rates, brand consideration, and purchase likelihood relative to a control. The CNET field test and automotive experiment demonstrate that matching banners to cognitive-style segments is feasible and provides significant benefits above and beyond traditional targeting. Improved banner effectiveness has strategic implications for allocations of budgets among media.

Some firms have broad product lines; others have lean product lines. To determine the appropriate number of entries in a specific firm's product line, the author develops a model that balances the benefits of increased revenue from a broad product line against production and engineering costs. Two innovations were central in the development of the model: (1) redefining how products are scored on various product attributes so that attribute scores vary normally across the population of products and (2) redefining how the number of entries in a product portfolio is calculated in order to discount the significance of entries that are highly similar to existing products. The author also introduces the notion of a centroid time to more easily adjust sales and total development costs for product life cycle and investment life cycle effects. These redefinitions enable a firm's profit to be modeled as a simple function of its effective number of product entries, the effective number of competitors entries, the total sales in the segment, variable profits adjusted for capacity constraints, and product development costs. This leads to a simple expression for the profit-maximizing number of effective entries, both when competitor portfolios are fixed and when competitors dynamically adjust their portfolios. The author illustrates how to estimate and apply the model on a realistic example.