This article examines the study on placebo effects, conducted by Baba Shiv, Ziv Carmon, and Dan Ariely. The idea behind the researchers' experiment is simple. The more a person pays for a beverage advertised to increase mental acuity, the bigger the performance enhancing effect should be. However, paying more for the beverage did not improve mental performance so much as paying discounted prices impaired performance. Such a result suggests that the product marketing can establish a placebo effect in terms of its efficacy that precedes the price effect

In Shiv, Carmon, and Ariely (2005), the authors demonstrate that marketing actions such as price promotions and advertising evoke consumer expectations, which can alter the actual efficacy of the marketed product, a phenomenon they call “placebo effects of marketing actions.” In this rejoinder, they build on the preceding commentaries and refine their framework to account more fully for factors that may influence this placebo effect, and they describe directions for further research in this new topic area

As technology advances, it becomes more feasible to load products with a large number of features, each of which individually might be perceived as useful. However, too many features can make a product overwhelming for consumers and difficult to use. Three studies examine how consumers balance their desires for capability and usability when they evaluate products and how these desires shift over time. Because consumers give more weight to capability and less weight to usability before use than after use, they tend to choose overly complex products that do not maximize their satisfaction when they use them, resulting in “feature fatigue.” An analytical model based on these results provides additional insights into the feature fatigue effect. This model shows that choosing the number of features that maximizes initial choice results in the inclusion of too many features, potentially decreasing customer lifetime value. As the emphasis on future sales increases, the optimal number of features decreases