Websites prominently display consumers' product ratings, which influence consumers' buying decisions and willingness to pay. Few insights exist regarding whether a consumer's online product rating is prone to social influence from others' online ratings. Examining this issue, the authors hypothesize that other consumers' online ratings moderate the effects of positive and regular negative features of product experience, product failure, and product recovery (to address product failure) on the reviewer's online product rating. The results from a model using 7499 consumers' online ratings of 114 hotels support the hypotheses. Other consumers' online ratings weaken the effects of positive and regular negative features of product experience but can either exacerbate or overturn the negative effect of product failure, depending on the quality of product recovery. For marketing theory, the findings indicate that consumers who influence others are themselves influenced by other consumers and that this influence is contingent on their product experience. For managerial practice, the authors offer a method to estimate the effects of product experience characteristics on online product ratings and show that social influence effects make high online product ratings a double-edged sword, exacerbating the negative effect of product failure and strengthening the benefit of product recovery.

Whenever a recession occurs, there is a heated dialog among marketing academics and practitioners about the appropriate levels of marketing spending. In this article, the authors investigate whether firms should spend more on research and development (R&D) and advertising in recessions. They propose that the effects of changes in firms' R&D and advertising spending in recessions on profits and stock returns are contingent on their market share, financial leverage, and product-market profile (i.e., business-to-consumer goods, business-to-business services, business-to-business goods, or business-to-consumer services). They estimate the model using a panel of more than 10,000 firm-years of publicly listed U.S. firms from 1969 to 2008, during which there were seven recessions. Their results support the contingency approach. The authors compute the marginal effects, which show how the effects of changes in R&D and advertising spending in recessions vary across firms. The marginal effects provide evidence of inadequate spending (e.g., 98% of business-to-consumer goods firms underspend on R&D), proactivity (e.g., 96% of business-to-business services firms are at approximately the right levels on advertising). and excess spending (e.g., 92% of business-to-consumer services firms overspend on advertising). Using the authors' approach and publicly available data, managers can estimate the effects of their firms' and competitors' R&D and advertising spending on profits and stock returns in recessions.

Firms are increasingly offering engagement initiatives to facilitate firm–customer interactions or interactions among customers, with the primary goal of fostering emotional and psychological bonds between customers and the firm. Unlike traditional marketing interventions, which are designed to prompt sales, assessing returns on engagement initiatives (RoEI) is more complex because sales are not the primary goal and, often, direct sales are not associated with such initiatives. To assess RoEI across varying institutional contexts, the authors propose and empirically implement a methodological framework to investigate a business-to-business mobile app that a tool manufacturer provides for free to engage its buyers. The data include sales by buyer firms that adopted the app over 15 months, as well as a control group of buyers that did not adopt. The results from a difference-in-differences specification, together with selection on observables and unobservables, show that the app increased the manufacturer's annual sales revenues by 19.11%–22.79%; even after accounting for development costs, it resulted in positive RoEI. This RoEI was higher when buyers created more projects using the app, so customer participation intensity appears to underlie RoEI. This article contributes to engagement literature by providing a methodological framework and empirical evidence on how the benefits of engagement initiatives materialize.

Content platforms (e.g., newspapers, magazines) post several stories daily on their dedicated social media pages and promote some of them using targeted content advertising (TCA). Posting stories enables content platforms to grow their social media audiences and generate digital advertising revenue from the impressions channeled through social media posts’ link clicks. However, optimal scheduling of social media posts and TCA is formidable, requiring content platforms to determine what to post; when to post; and whether, when, and how much to spend on TCA to maximize profits. Social media managers lament this complexity, and academic literature offers little guidance. Consequently, the authors draw from literature on circadian rhythms in information processing capabilities to build a novel theoretical framework on social media content scheduling and explain how scheduling attributes (i.e., time of day, content type, and TCA) affect the link clicks metric. They test their hypotheses using a model estimated on 366 days of Facebook post data from a top 50 U.S. newspaper. Subsequently, they build an algorithm that allows social media managers to optimally plan social media content schedules and maximize gross profits. Applying the algorithm to a holdout sample, the authors demonstrate a potential increase in gross profits by at least 8%.

This article presents a meta-analysis of prior econometric estimates of personal selling elasticity—that is, the ratio of the percentage change in an objective, ratio-scaled measure of sales output (e.g., dollar or unit purchases) to the corresponding percentage change in an objective, ratio-scaled measure of personal selling input (e.g., dollar expenditures). The authors conduct a meta-analysis of 506 personal selling elasticity estimates drawn from analyses of 88 empirical data sets across 75 previous articles. They find a mean estimate of current-period personal selling elasticity of .34. They also find that elasticity estimates are higher for early life-cycle-stage offerings, higher from studies set in Europe than from those set in the United States, and smaller in more recent years. In addition, elasticity estimates are affected significantly by analysts’ use of relative rather than absolute sales output measures, by cross-sectional rather than panel data, by omission of promotions, by lagged effects, by marketing interaction effects, and by the neglect of endogeneity in model estimation. The method bias–corrected mean personal selling elasticity is approximately .31. The authors discuss the implications of their results for sales managers and researchers.