Explore the vast range of titles available.

This article examines the effects of collective layoff announcements on sales and marketing-mix elasticities, accounting for supplyside constraints. The authors study 205 announcements in the automotive industry using a difference-in-differences model. They find that, following collective layoff announcements, layoff firms experience adverse changes in sales, advertising elasticity, and price elasticity. They explore the moderating role of announcement characteristics on these changes and find that collective layoff announcements by domestic firms and announcements that do not mention a decline in demand as a motive are more likely to be followed by adverse marketing-mix elasticity changes. On average, sales for the layoff firm in the layoff country are 8.7% lower following a collective layoff announcement than their predicted levels absent the announcement. Similarly, advertising elasticity is 9.8% lower and price elasticity is 19.2% higher than absent the announcement. Conversely, layoff firms typically decrease advertising spending in the country where collective layoffs have occurred, yet they do not change prices. These findings are relevant to marketing managers of firms undergoing collective layoffs and to analysts of collective layoff decisions.

Two-sided markets are composed of platform owners and two distinct user networks that either buy or sell applications for the platform. The authors focus on multihoming—the choice of an agent in a user network to use more than one platform. In the context of the video game console industry, they examine the conditions affecting seller-level multihoming decisions on a given platform. Furthermore, they investigate how platform-level multihoming of applications affects the sales of the platform. The authors show that increased platform-level multihoming of applications hurts platform sales, a finding consistent with literature on brand differentiation, but they also show that this effect vanishes as platforms mature or gain market share. The authors find that platform-level multihoming of applications affects platform sales more strongly than the number of applications. Furthermore, among mature platforms, an increasing market share leads to more seller-level multihoming, while among nascent platforms, seller-level multihoming decreases as platform market share increases. These findings prompt scholars to look beyond network size in analyzing two-sided markets and provide guidance to both (application) sellers and platform owners.

Analyzing panel data of 32,650 checking-account holders facing a menu of three-part tariff contracts, we document several findings that indicate that subscribers use simple heuristics to learn about the desirability of the contracts they have chosen. Our main findings are: subscribers change contracts in a direction that diminishes the probability of re–experiencing the trigger for switching; subscribers exhibit recency effects in switching; and after switching the majority of switchers systematically pay higher fees than they did before. We argue that directional learning theory best explains why consumers behave in a manner that yields suboptimal economic outcomes.

We study customers' adoption and subsequent switching decisions with regard to a menu of three-part tariff plans offered by a commercial bank. Using a rich panel data set covering 70,510 fee-based checking accounts over 30 months, before and after the introduction of the plans, we find that most customers adopt non-cost-minimizing plans, preferring plans with large monthly allowances and high fixed payments. Furthermore, after adoption, customers who exceed their allowances and consequently pay overage fees are more likely to switch to plans with larger allowances than customers who do not experience such fees. Notably, after switching, these overage-paying customers pay higher monthly payments than before. In contrast, switching customers who did not pay overage payments before switching pay less after switching. Our findings, unlike those of previous research on experience-based learning, suggest that the behavior of experienced customers does not converge to the predictions of neoclassical models. We propose that “overage aversion,” which is closely related to loss aversion and mental accounting, is the most plausible explanation for our findings. This paper was accepted by John List, behavioral economics.

Patients increasingly request their physicians to prescribe specific brands of pharmaceutical drugs. A popular belief is that requests are triggered by direct-to-consumer advertising (DTCA). We examine the relationship between DTCA, patient requests, and prescriptions for statins. We find that although the effect of requests on prescriptions is significantly positive, the mean effect of DTCA on patient requests is negative, yet very small. More interestingly, both effects show substantial heterogeneity across physicians, which we uncover using a hierarchical Bayes estimation procedure. We find that specialists receive more requests than primary care physicians but translate them less into prescriptions. In addition, we find that the sociodemographic profile of the area a physician practices in moderates the effects of DTCA on requests and of requests on prescriptions. For instance, physicians from areas with a higher proportion of minorities (i.e., blacks and Hispanics) receive more requests that are less triggered by DTCA and are accomodated less frequently than physicians from areas with a lower proportion of minorities. Our results challenge managers to revisit the role of DTCA in stimulating patient requests. At the same time, they may trigger public policy concerns regarding physicians' accommodation of patient requests and the inequalities they may induce.

We propose an individual-level model of a two-stage service diffusion process. In the first stage, customers decide whether to “consider” joining the service. This (Consideration) stage is modeled by a hazard model. Customers who decide to consider the service move on to the Choice stage, wherein they choose among the service alternatives and an outside No Choice option. This stage is modeled by a conditional Multinomial Logit model. The service provider does not observe the transition in the first stage of potential customers who have yet to choose a brand. Such potential customers may have started to consider joining the service, yet chose the outside alternative in each period thereafter. One of the main contributions of the model is its ability to distinguish between these two non-adopter types. We estimated the model using data on the adoption process of newly introduced service plans offered by a commercial bank. We employed the hierarchical Bayes Monte Carlo Markov Chain procedure to estimate individual as well as population parameters. The empirical results indicate that the model outperforms competing models in breadth of analysis, model fit, and prediction accuracy.

The life sciences industryLife sciences industry forms the innovative producer side of therapies in the healthcare industry. The industry has several unique features and is an important part of the economy. The life sciences industry gives rise to interesting research questions, as well as enables new model development to support managerial decision making. The academic marketing literature has produced a sizeable array of decision-support tools for the life science marketers. In this chapter, we present to researchers and managers in the life sciences industry a broad overview of these analytical tools, categorized according to subject areas, and the key managerial insights that have been derived from them. We first present the typical models employed in the following modeling traditions: choice model, count model, learning model, modeling key opinion leaders, diffusion model, sales growth model, and launch model. We then discuss the findings on the role of marketing categorized according to the following decision areas: direct-to-physician promotion, direct-to-consumer advertising, pricing, and product usage adherence. We conclude with a number of areas that we think need more research.