Advertising is one of the key marketing tools managers have at their disposal to influence their customers into purchasing a new product. The overall objective of new product advertising is to inform and persuade customers. Drawing up an advertising plan for a new product that is under the influence of diffusion phenomenon is not an easy task. Hence, research in this area is very limited. In our research, we use an empirically proven diffusion demand function that explicitly incorporates the advertising component. Our results suggest that optimal advertising is determined by the advertising effectiveness, discount rate, and the ratio of advertisement to profits. Depending upon the interplay among these factors, the optimal advertising takes decrease-increase, increase-decrease, monotonically increasing or monotonically decreasing shape.

Although there has been much attention in recent years on the effects of additive background risks, the same is not true for its multiplicative counterpart. We consider random wealth of the multiplicative form x̃ỹ, where x̃ and ỹ are statistically independent random variables. We assume that x̃ is endogenous to the economic agent but that ỹ is an exogenous and nontradable background risk that represents a type of market incompleteness. Our main focus is on how the presence of the multiplicative background risk ỹ affects risk-taking behavior for decisions on the choice of x̃. We extend the results of Gollier and Pratt (1996) to characterize conditions on preferences that lead to more cautious behavior.

Short delivery time and the efficient management of component inventories are two crucial elements that determine the competitiveness of many contract assembly manufacturers, especially in the electronics industry. In this paper, we develop and analyze an optimization model to determine the optimal stocking quantities for components of an assemble-to-order product in an environment where demand is uncertain and the price for the final product and the costs of components depend on their delivery lead times. We provide an efficient solution procedure to solve the problem in which the manufacturer must deliver the full order quantity possibly in multiple shipments. We further extend our model to the situation where the manufacturer has the option of not delivering the full quantity but instead takes the penalty for a delivery shortage. We derive some analytical results that illustrate how different model parameters affect the optimal solution and provide useful insights for managing components in the assemble-to-order environment.

The resource-based view (RBV) of the firm suggests that strategic deployment of capabilities allows strategic business units (SBUs) to exploit distinctive competencies and create sustainable competitive advantage. Following the RBV, we propose a new predictive methodology for deriving typologies of SBUs that accommodates heterogeneity among SBUs with respect to their strategic capabilities, how effectively they are employed, and performance. Statistically, we devise a constrained finite-mixture structural-equation procedure that simultaneously accounts for firm capabilities, performance outcomes, and the relationships between them. The procedure allows for a comprehensive modeling and grouping of entities, and simultaneously provides a diagnosis of the sources of heterogeneity via the flexibility of estimating a series of nested models. Managerially, our proposed methodology is grounded in the strategic type and RBV literature and can capture the effects of environmental and industry-specific factors. Using data obtained from 216 SBUs in the United States for illustration, the results show that our derived four mixed-type solution dominates the four-group, Prospectors-Analyzers-Defenders-Reactors classification as well as a number of other nested model solutions in terms of objective statistical fit criteria for this data set, suggesting a more contingency-driven strategic stance adopted by these SBUs. We conclude with a discussion of the theoretical and managerial benefits of an improved methodology for empirically deriving strategic typologies.

The year 2000 (Y2K) countdown provided a uniquely visible instance of spending on information technology (IT) by U.S. companies. With public attention riveted on potential Y2K malfunctions, managers were forced to evaluate their IT and make decisions about whether to modify or replace existing systems. In the aftermath of Y2K, critics charged that the problem was overblown and that companies overspent on IT. In contrast, we posit in this paper that efforts companies made to renew and upgrade their IT may have positioned them to take advantage of new e-business applications. As Y2K approached, managers could invest opportunistically in IT, which would enable them to connect with customers and suppliers in new ways. Contrary to the alleged overspending, we find that firm value increased, on average, with Y2K spending by Fortune 1000 companies. In particular, higher firm value and subsequent earnings were associated with Y2K spending for firms in industries where IT was considered to have a transforming influence—altering traditional ways of doing business by redefining business processes and relationships. We also test whether the positive association between firm value and Y2K spending diminished with Y2K spending by industry peer firms, but we do not find support for this relative investment hypothesis.

This paper examines how established firms conduct continual entrepreneurial search for possibilities for long-term growth. Drawing on comprehensive internal documents of the DuPont Company over a 20-year period, we develop a search process that is a departure from frequent depictions of search as local or random. Longitudinal field data show that corporate entrepreneurs follow a “moving, anchored search” for growth possibilities. Employing this framework as lens, we develop propositions. We find that corporate entrepreneurs are more likely to conduct search in new domains following events that cause them to expect future performance to change significantly and lastingly. This is in contrast to the literature that has typically modeled the initiation of search as a response to poor past performance. Because new domains are unexplored territories for corporate entrepreneurs, they utilize transitional levers that they perceive will facilitate the move from existing domains to new ones. These perceived transitional levers, however, typically prove inaccurate or incomplete. Content within domains is searched using anchors whose locations and numbers change. The combination of search process and content searched influences the particular growth possibilities discovered and created. Search and pursuit of growth possibilities is accompanied by the creation of new knowledge and new capabilities.

Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, identifies clusters of future states of the world to which they are vulnerable, and then evaluates the trade-offs in hedging against these vulnerabilities. This approach can help decision makers design robust strategies while also systematically generating clusters of key futures interpretable as narrative scenarios. Our study demonstrates the approach by identifying robust, adaptive, near-term pollution-control strategies to help ensure economic growth and environmental quality throughout the 21st century.

Manufacturers and their distributors must cope with an increased flow of returned products from their customers. The value of commercial product returns, which we define as products returned for any reason within 90 days of sale, now exceeds $100 billion annually in the United States. Although the reverse supply chain of returned products represents a sizeable flow of potentially recoverable assets, only a relatively small fraction of the value is currently extracted by manufacturers; a large proportion of the product value erodes away because of long processing delays. Thus, there are significant opportunities to build competitive advantage from making the appropriate reverse supply chain design choices. In this paper, we present a network flow with delay models that includes the marginal value of time to identify the drivers of reverse supply chain design. We illustrate our approach with specific examples from two companies in different industries and then examine how industry clockspeed generally affects the choice between an efficient and a responsive returns network.

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting (i.e., not limited to geometric Brownian motion) for which European call-option prices are readily available. By approximating the value function with an appropriately chosen interpolation function, the pricing of an American-style derivative with arbitrary payoff function is converted to the pricing of a portfolio of European call options, leading to analytical expressions for those cases where analytical European call prices are available (e.g., the Merton jump-diffusion process). Furthermore, in many settings, the approach yields upper and lower analytical bounds that provably converge to the true option price. We provide computational results to illustrate the convergence and accuracy of the resulting estimators.