Journal of Marketing Research

In a range of studies across platforms, researchers have shown that online ratings are characterized by distributions with disproportionately heavy tails. The authors of this study focus on understanding the underlying process that yields such “J-shaped” or “extreme” distributions. They propose a novel theoretical mechanism behind the emergence of J-shaped distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. The authors present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. They show that although all three mechanisms can give rise to extreme distributions, only the utility-based and attrition-based mechanisms can explain the authors’ empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility.

Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers’ reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers’ predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: it is easier for consumers to internalize a favorable decision outcome that is rendered by a human than by an algorithm, but it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.

Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.

Drawing from a content analysis of publicly traded companies’ privacy notices, a survey of managers, a field study, and five online experiments, this research investigates how consumers respond to privacy notices. A privacy notice, by placing legally enforceable limits on a firm's data practices, communicating safeguards, and signaling transparency, might be expected to promote confidence that personal data will not be misused. Indeed, most managers expected a privacy notice to make customers feel more secure (Study 1). Yet, consistent with the analogy that bulletproof glass can increase feelings of vulnerability despite the protection offered, formal privacy notices undermined consumer trust and decreased purchase interest even when they emphasized objective protection (Studies 2, 3, and 5) or omitted any mention of potentially concerning data practices (Study 6). These unintended consequences did not occur, however, when consumers had an a priori reason to be distrustful (Study 4) or when benevolence cues were added to privacy notices (Studies 5 and 6). Finally, Study 7 showed that both the presence and conspicuous absence of privacy information are sufficient to trigger decreased purchase intent. Together, these results provide actionable guidance to managers on how to effectively convey privacy information (without hurting purchase interest).

Firms use aggregate data from data brokers (e.g., Acxiom, Experian) and external data sources (e.g., Census) to infer the likely characteristics of consumers in a target list and thus better predict consumers’ profiles and needs unobtrusively. The authors demonstrate that the simple count method most commonly used in this effort relies implicitly on an assumption of conditional independence that fails to hold in many settings of managerial interest. They develop a Bayesian profiling introducing different conditional independence assumptions. They also show how to introduce additional observed covariates into this model. They use simulations to demonstrate that in managerially relevant settings, the Bayesian method will outperform the simple count method, often by an order of magnitude. The authors then compare different conditional independence assumptions in two case studies. The first example estimates customers’ age on the basis of their first names; prediction errors decrease substantially. In the second example, the authors infer the income, occupation, and education of online visitors of a marketing analytic software company based exclusively on their IP addresses. The face validity of the predictions improves dramatically and reveals an interesting (and more complex) endogenous list-selection mechanism than the one suggested by the simple count method.

Credit card minimum payments are designed to ensure that individuals pay down their debt over time, and scheduling minimum automatic repayments helps people avoid forgetting to repay. Yet minimum payments have additional, unintended psychological default effects by drawing attention away from the card balance due. First, once individuals set the minimum automatic repayment as the default, they then neglect to make the occasional larger repayments they made previously. As a result, individuals incur considerably more credit card interest than late payment fees avoided. Using detailed transaction data, the authors show that approximately 8% of all of the interest ever paid is due to this effect. Second, manual credit card payments are lower when individuals are prompted with minimum payment information. In an experiment, the authors test two new interventions to mitigate this effect—a prompt for full repayment and a prompt asking those repaying little to pay more—yielding large counter effects. Thus, shrouding the minimum payment option for automatic and manual payments and directing attention to the full balance may remedy the unintended effects of default minimum payments.

In many sectors of the entertainment industry, a few employees attract the public spotlight when performing the core service. For example, in professional team sports, a team of players competes in games, and in TV shows, a cast of artists acts in different episodes. These employees, coined “spotlight personnel,” are an essential but expensive element of ongoing service delivery. Despite their importance and cost, very little is known about how changes in spotlight personnel affect service performance and demand. To address this gap, this article uses unique data on professional German soccer teams, tracking the quantity (number of players) and quality (average transfer price) of spotlight personnel hiring (incoming transfers) and turnover (outgoing transfers), objective service performance (winning percentage), and demand (ticket sales) across four decades, using both traditional and novel time series methods. The results show that service performance and demand are primarily affected by spotlight personnel hiring rather than by turnover. Hiring quantity decreases service performance yet increases demand, whereas hiring quality benefits both service performance and demand. The analysis further uncovers that these effects are subject to dynamic interactions and nonlinearities. Investment scenarios showcase how understanding these effects can substantially improve managerial decision making.

Seven experiments (total N = 3,509) and a large field data set (N = 1,820,671) demonstrate that time periods of equal duration are not always perceived as equivalent. The authors find that periods feel longer when they span more time categories (e.g., hour, month). For example, periods like 1:45 p.m.–3:15 p.m. and March 31–April 6 (boundary-expanded) feel longer than, say, 1:15 p.m.–2:45 p.m. and April 2–April 8 (boundary-compressed). Reflecting this, participants anticipated completing more work during boundary-expanded periods than during equivalent boundary-compressed periods. This effect appears to result from the salience and placement of time boundaries. Consequently, participants preferred scheduling pleasant activities for boundary-expanded periods and unpleasant activities for boundary-compressed periods. Moreover, participants were willing to pay more to avoid—and required more money to endure—a long wait when that wait was presented as boundary-expanded. Finally, data from more than 1.8 million rideshare trips suggest that consumers are more likely to choose independent rides (e.g., UberX) when they are boundary-compressed when the alternative shared option (e.g., UberPool) is boundary-expanded. Together, our studies reveal that time periods feel longer when they span more boundaries and that this phenomenon shapes consumers’ scheduling and purchasing decisions.

A trend reported by both academics and practitioners is that advertising on TV has become increasingly energetic. This study investigates the association between the energy level in ad content and consumers’ ad-tuning tendency. Using a data set of over 27,000 TV commercials delivered to U.S. homes between 2015 and 2018, the authors first present a framework to algorithmically measure the energy level in ad content from the video of ads. This algorithm-based measure is then compared with human-perceived energy levels showing that the measure is related to the level of arousal stimulated by ad content. By relating the energy levels in ad content with the ad-tuning tendency using two empirical procedures, the authors document the following: overall, more energetic commercials are more likely to be tuned in or less likely to be avoided by viewers. The positive association between energy levels in ad content and ad tuning is statistically significant after controlling for placement and other aspects of commercials. However, the association varies across product categories and program genres. The main implication of this study is that advertisers should pay attention to components of ad content other than loudness, which has been regulated by law.

This article proposes a panel data generalization for a recently suggested instrumental variable‐free estimation method that builds on joint estimation. The author shows how the method can be extended to linear panel models by combining fixed-effects transformations with the common generalized least squares transformation to allow for heterogeneous intercepts. To account for between-regressor dependence, the author proposes determining the joint distribution of the error term and all explanatory variables using a Gaussian copula function, with the distinction that some variables are endogenous and the others are exogenous. The identification does not require any instrumental variables if the regressor–error relation is nonlinear. With a normally distributed error, nonnormally distributed endogenous regressors are therefore required. Monte Carlo simulations assess the finite sample performance of the proposed estimator and demonstrate its superiority to conventional instrumental variable estimation. A specific advantage of the proposed method is that the estimator is unbiased in dynamic panel models with small time dimensions and serially correlated errors; therefore, it is a useful alternative to generalized-method-of-moments-style instrumentation. The practical applicability of the proposed method is demonstrated via an empirical example.