Knowledge that Transforms

What the firm should say in an advertising message, the choice of content, is a critical managerial decision. Here, we focus on a particular aspect of the advertising content choice: an attribute-focused appeal versus an appeal with no direct information on product attributes. We make two assumptions that capture the reality of the advertising context. First, we assume that the bandwidth of advertising is limited: a firm can only communicate about a limited number of attributes. Second, we assume that consumers are active: they can choose to engage in a costly search to obtain additional product-related information. In this setting, we show that there exists an equilibrium where the high-quality firm chooses to produce messages devoid of any attribute information in order to invite the consumer to engage in search, which is likely to uncover positive information about the product. Whereas most of the previous literature has focused on the decision to advertise as a signal of quality, we show that message content, coupled with consumer search, can also serve as a credible signal of quality. In an extension, we show that our results are robust to endogenizing the firm's decision on the amount of advertising spending.

We investigate the evolution of online ratings over time and sequence. We first establish that there exist two distinct dynamic processes, one as a function of the amount of time a book has been available for review and another as a function of the sequence of reviews themselves. We find that, once we control for calendar date, the residual average temporal pattern is increasing. This is counter to existing findings that suggest that without this calendar-date control, the pattern is decreasing. With respect to sequential dynamics, we find that ratings decrease: the nth rating is, on average, lower than the n-1th when controlling for time, reviewer effects, and book effects. We test and find some support for existing theories for this decline based on motivation. We then offer two additional explanations for this “order effect.” We find support for the idea that one's ability to assess the diagnosticity of previous reviews decreases: when previous reviewers are very different, more reviews may thus lead to more purchase errors and lower ratings.

While millions of products are sold on its retail platform, Amazon.com itself stocks and sells only a very small fraction of them. Most of these products are sold by third-party sellers who pay Amazon a fee for each unit sold. Empirical evidence clearly suggests that Amazon tends to sell high-demand products and leave long-tail products for independent sellers to offer. We investigate how a platform owner such as Amazon, facing ex ante demand uncertainty, may strategically learn from these sellers' early sales which of the “mid-tail” products are worthwhile for its direct selling and which are best left for others to sell. The platform owner's “cherry-picking” of the successful products, however, gives an independent seller the incentive to mask any high demand by lowering his sales with a reduced service level (unobserved by the platform owner). We analyze this strategic interaction between a platform owner and an independent seller using a game-theoretic model with two types of sellers—one with high demand and one with low demand. We show that it may not always be optimal for the platform owner to identify the seller's demand. Interestingly, the platform owner may be worse off by retaining its option to sell the independent seller's product, whereas both types of sellers may benefit from the platform owner's threat of entry. The platform owner's entry option may reduce consumer surplus in the early period, although it increases consumer surplus in the later period. We also investigate how consumer reviews influence the market outcome.

Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted ratings may affect an individual's posting behavior in terms of whether to contribute (incidence) and what to contribute (evaluation), and we identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Across individuals, our results show that positive ratings environments increase posting incidence, whereas negative ratings environments discourage posting. Our results also indicate important differences across individuals in how they respond to previously posted ratings, with less frequent posters exhibiting bandwagon behavior and more active customers revealing differentiation behavior. These dynamics affect the evolution of online product opinions. Through simulations, we illustrate how the evolution of posted product opinions is shaped by the underlying customer base and show that customer bases with the same median opinion may evolve in substantially different ways because of the presence of a core group of “activists” posting increasingly negative opinions.

We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.

The failure of firms in the face of technological change has been a topic of intense research and debate, spawning the theory (among others) of disruptive technologies. However, the theory suffers from circular definitions, inadequate empirical evidence, and lack of a predictive model. We develop a new schema to address these limitations. The schema generates seven hypotheses and a testable model relating to platform technologies. We test this model and hypotheses with data on 36 technologies from seven markets. Contrary to extant theory, technologies that adopt a lower attack (“potentially disruptive technologies”) (1) are introduced as frequently by incumbents as by entrants, (2) are not cheaper than older technologies, and (3) rarely disrupt firms; and (4) both entrants and lower attacks significantly reduce the hazard of disruption. Moreover, technology disruption is not permanent because of multiple crossings in technology performance and numerous rival technologies coexisting without one disrupting the other. The proposed predictive model of disruption shows good out-of-sample predictive accuracy. We discuss the implications of these findings.

Automotive sales forecasts traditionally focus on predictors such as advertising, brand preference, life cycle position, retail price, and technological sophistication. The quality of the cars' design is, however, an often-neglected variable in such models. We show that incorporating objective measures of design prototypicality and design complexity in sales forecasting models improves their prediction by up to 19%. To this end, we professionally photographed the frontal designs of 28 popular models, morphed the images, and created objective prototypicality (car-to-morph Euclidian proximity) and complexity (size of a compressed image file) scores for each car. Results show that prototypical but complex car designs feel surprisingly fluent to process, and that this form of surprising fluency evokes positive gut reactions that become associated with the design and positively impact car sales. It is important to note that the effect holds for both economy (functionality oriented) and premium (identity oriented) cars, as well as when the above-mentioned traditional forecasting variables are considered. These findings are counter to a common intuition that consumers like unusual–complex designs that reflect their individuality or prototypical–simple designs that are functional.

The critical role of research and development (R&D) and advertising in the marketing strategy of the firm is well established. This paper conceptually and empirically examines why and how much the effectiveness of these two marketing instruments differs between times of economic expansions versus periods of economic contractions—and whether these results depend on the cyclicality of the industry in question. We consider a key marketing metric (market share) and a key financial metric (firm profit). Our empirical setting is 1,175 U.S. firms across a time period spanning over three decades. We find that R&D and advertising contribute to firm performance but that their effectiveness is not constant across the business cycle. Increasing advertising share in contractions has a stronger effect on profit and market share than increasing advertising share in expansions. Likewise, investments in R&D in contractions lead to higher gains in market share and profit than R&D investments in expansions, albeit only in subsequent years. If in contractions the firm faces tight budget constraints and has to choose between either maintaining R&D or advertising, our simulation results show that maintaining R&D is associated with better company performance. We find that advertising effectiveness, in general, and in contractions, in particular, is systematically moderated by the degree of cyclicality of the industry in which the firm operates. In relatively stable industries, advertising effects are small or even nonsignificant, and they do not go beyond the year the firm advertises. However, in highly cyclical industries, advertising effects are long-lasting, its total effect being 50% larger (market share) and 200% larger (profits) than in industries of average cyclicality. The effect of industry cyclicality on advertising effectiveness is especially pronounced in contractions. Collectively, these findings provide valuable and actionable insights into how firms should respond to contractions in order to grow profits and market share.