Knowledge that Transforms

We propose a method to include seasonality in any diffusion model that has a closed-form solution. The resulting diffusion model captures seasonality in a way that naturally matches the original diffusion model's pattern. The method assumes that additional sales at seasonal peaks are drawn from previous or future periods. This implies that the seasonal pattern does not influence the underlying diffusion pattern. The model is compared with alternative approaches through simulations and empirical examples. As alternatives, we consider the standard Generalized Bass Model (GBM) and the basic Bass Model, which ignores seasonality. One of the main findings is that modeling seasonality in a GBM generates good predictions but gives biased estimates. In particular, the market potential parameter is underestimated. Ignoring seasonality in cases where data of the entire diffusion period are available gives unbiased parameter estimates in most relevant scenarios. However, ignoring seasonality leads to biased parameter estimates and predictions when only part of the diffusion period is available. We demonstrate that our model gives correct estimates and predictions even if the full diffusion process is not yet available.

This series of discussions presents commentaries and a response on the impact the Nutrition Labeling and Education Act of 1990 has had on brand nutritional quality and taste as raised in Moorman et al. [Moorman C, Ferraro R, Huber J (2012) Unintended nutrition consequences: Firm responses to the Nutrition Labeling and Education Act. Marketing Sci. 31(5):717–737].

This series of discussions presents commentaries and a rejoinder on the economic perspectives on branding arising from Moorthy [Moorthy S (2012) Can brand extension signal product quality? Marketing Sci. 31(5):756–770].

Jain [Jain, S. 2012. Marketing of vice goods: A strategic analysis of the package size decision. Marketing Sci. 31(1) 36–51] examines the impact of consumers' self-control problem on the equilibrium package sizes offered by firms marketing vice goods. This series of discussions offers commentaries and a rejoinder that discuss competitive implications of firms offering small sizes and the impact of smaller sizes on the total consumer expenditure.

This paper develops and calibrates a simple yet comprehensive set of models for the evolution of binary attribute importance weights, based on a cue–goal association framework. We argue that the utility a consumer ascribes to an attribute comes from its association with the achievement of a goal. We investigate how associations may be represented and then track back the relationship of these associations to the utility function. We explain why we believe this to be an important problem before providing an overview of the extensive literature on learning models. This literature identifies key phenomena and provides a foundation for our modeling of binary attribute importance learning, which can test for three departures from “rational” learning—bias, existence of priors, and the unequal weighting of sample observations (order effects). We apply our models in a laboratory setting under a number of different relationship strengths, and we find that, in our application, consumers' learning about attribute–goal associations exhibits bias and the effects of prior beliefs when the sample realizations occur with and without noise, and order effects when the sample realizations occur with noise. We provide an example of how our models can be extended to learning about more than one attribute.

Social interaction peer effects are recognized as a potentially important factor in the diffusion of new products. In the case of environmentally friendly goods or technologies, both marketers and ...

Physicians may learn about prescription drug effectiveness directly from the firm via detailing or from patient experience. Patient-mediated learning is aided by the use of free drug samples. The effective use of samples is hampered by a lack of understanding of its exact return on investment implications. We seek to fill this gap by incorporating the physician's sample allocation behavior in the firm's decision making. We uncover the following implications for firms as well as policy makers. First, we find that the optimal sampling level for a drug category is a nonmonotonic function of patient payment ability and the price of the drug. Second, an increase in the cost of samples can lead to an increase in sampling and a decrease in detailing when the physician's propensity to provide sample subsidies is high. Third, when future market growth is expected to be high (early stage product life cycle and/or chronic drugs) and sampling efficiency is low, the use of sampling is profitable for the firm but leads to lower market coverage than when sampling is disallowed.

In many markets, a handset vendor and a service provider may enter into a tie-in for a handset to be available exclusively through the service provider. We examine when and why a service provider and a handset vendor may find this arrangement mutually profitable. We find that an exclusive handset arrangement (EHA) may serve a dual strategic purpose. By restricting its handsets to one service provider, a handset vendor may be able to induce a rival handset vendor to compete less aggressively. At the same time, the service provider may be able to essentially raise a rival service provider's handset costs by limiting the handsets available to the rival. Interestingly, the handset vendor's market share may be higher when its handset is sold exclusively than when it is not. Our results might explain why EHAs seem more attractive in some markets than in others, why some service providers have exclusive arrangements even for handset models that do not seem popular, and how some handset vendors enjoy high market shares despite having many exclusive models. Furthermore, an EHA may lower the handset vendor's incentives to improve handset quality, supporting concerns raised by proponents of wireless network neutrality.