Knowledge that Transforms

In recent years there has been a growing stream of literature in marketing and economics that models consumers as Bayesian learners. Such learning behavior is often embedded within a discrete choice framework that is then calibrated on scanner panel data. At the same time, it is now accepted wisdom that disentangling preference heterogeneity and state dependence is critical in any attempt to understand either construct. We posit that this confounding between state dependence and heterogeneity often carries through to Bayesian learning models. That is, the failure to adequately account for preference heterogeneity may result in over- or underestimation of the learning process because this heterogeneity is also reflected in the initial conditions. Using a unique data set that contains stated preferences (survey) and actual purchase data (scanner panel) for the same group of consumers, we attempt to untangle the effects of preference heterogeneity and state dependence, where the latter arises from Bayesian learning. Our results are striking and suggest that measured brand beliefs can predict choices quite well and, moreover, that in the absence of such measured preference information, the Bayesian learning behavior for consumer packaged goods is vastly overstated. The inclusion of preference information significantly reduces evidence for aggregate-level learning and substantially changes the nature of individual-level learning. Using individual-level outcomes, we illustrate why the lack of preference information leads to faulty inferences.

Competition is intense among rival technologies, and success depends on predicting their future trajectory of performance. To resolve this challenge, managers often follow popular heuristics, generalizations, or “laws” such as Moore's law. We propose a model, Step And Wait (SAW), for predicting the path of technological innovation, and we compare its performance against eight models for 25 technologies and 804 technologies-years across six markets. The estimates of the model provide four important results. First, Moore's law and Kryder's law do not generalize across markets; neither holds for all technologies even in a single market. Second, SAW produces superior predictions over traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely new technology by incorporating information from other categories on time-varying covariates. Third, analysis of the model parameters suggests that (i) recent technologies improve at a faster rate than old technologies; (ii) as the number of competitors increases, performance improves in smaller steps and longer waits; (iii) later entrants and technologies that have a number of prior steps tend to have smaller steps and shorter waits; but (iv) technologies with a long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.

Recognizing that initial public offerings (IPOs) represent the debut of private firms on the public stage, this study investigates how pre-IPO customer and competitor orientations (CCOs) affect IPO outcomes. Building on information economics, we propose that CCOs influence investors' sentiments toward an IPO and that both IPO-specific variables (which influence the credibility of CCO information) and facets of the organizational institutional and task environments (which influence the appropriateness of CCO information) moderate this influence. We test the framework using data collected from computer-aided text analysis, expert coders, and secondary sources for 543 IPOs across 43 industries between 2000 and 2004. A Bayesian shrinkage model, which accounts for industry-specific effects and uses latent instrumental variables to address CCO endogeneity, shows that CCOs positively influence IPO outcomes. Furthermore, (1) underwriter reputation and venture funding positively moderate the effects of CCOs; (2) technological and market turbulence positively and institutional complexity negatively moderate the effect of customer orientation; and (3) technological turbulence, competitive intensity, and institutional complexity positively moderate the effect of competitor orientation. Also, accounting for endogeneity using latent instrumental variables substantially improves the predictive validity of the model, relative to alternative model specifications.

Social sharing of information goods—wherein a single good is purchased and shared through a network of acquaintances such as friends or coworkers—is a significant concern for the providers of these goods. The effect of social sharing on firm pricing and profits depends critically on two elements: the structure of the underlying consumer network and the mechanism used by groups to decide whether to purchase at a given price. We examine the effect of social sharing under different network structures (decentralized, centralized, and complete), which reflect a range of market conditions. Moreover, we draw from the mechanism design literature to examine several approaches to group decision making. Our results suggest that a firm can benefit from increased social sharing if the level of sharing is already high, enabling a pricing strategy targeted primarily at sharing groups rather than individuals. However, the point at which sharing becomes marginally beneficial for a firm depends on both the distribution of group sizes (which derives from the network structure) and the group decision mechanism. Additional insights are obtained when we extend the model to capture homophily in group formation and the potential that a subset of consumers will never share for ethical reasons.

This paper investigates how firms responded to standardized nutrition labels on food products required by the Nutrition Labeling and Education Act (NLEA). Using a longitudinal quasi-experimental design, we test our predictions using two large-scale samples that span 30 product categories. Results indicate that the NLEA reduced brand nutritional quality relative to a control group of products not regulated by the NLEA. At the same time, among regulated products, brand taste increased. Although this reduction in nutrition represents an unintended consequence of regulation, there were a set of category, firm, and brand conditions under which the NLEA produced a positive effect on brand nutritional quality. We find that firms were more likely to improve brand nutrition when firm risk or firm power is low. Lower risk occurs when the firm is introducing a new brand rather than changing an existing brand, and weaker power in a category is reflected by lower market share in a category. Furthermore, firms competing in low-health categories (e.g., potato chips) or small-portion categories (e.g., peanut butter) improved nutrition more than firms competing in high-health categories (e.g., bread) or large-portion categories (e.g., frozen dinners). Recommendations for firm strategy and the design of consumer information policy are examined in light of these surprising firm responses.

We measure the value of promotional activities and referrals by content creators to an online platform of user-generated content. To do so, we develop a modeling approach that explains individual-level choices of visiting the platform, creating, and purchasing content as a function of consumer characteristics and marketing activities, allowing for the possibility of interdependence of decisions within and across users. Empirically, we apply our model to Hewlett-Packard's (HP) print-on-demand service of user-created magazines, named MagCloud. We use two distinct data sets to show the applicability of our approach: an aggregate-level data set from Google Analytics, which is a widely available source of data to managers, and an individual-level data set from HP. Our results compare content creator activities, which include referrals and word-of-mouth efforts, with firm-based actions, such as price promotions and public relations. We show that price promotions have strong effects but are limited to the purchase decisions, whereas content creator referrals and public relations efforts have broader effects that impact all consumer decisions at the platform. We provide recommendations as to the level of a firm's investments when “free” promotional activities by content creators exist. These free marketing campaigns are likely to have a substantial presence in most online services of user-generated content.

Until recently, brand identities were built by firms via brand image advertising. However, the flourishing consumer communication weakened the firms' grip on their brands. The interaction between advertising and consumer communications and their joint impact on brand identity is the focal point of this paper. We present a model in which consumer preference for functional attributes may correlate with the identity they desire to project of themselves. This correlation is known to the firm but not to the consumers. Both the firm and the consumers can communicate their desired brand identity, although the actual brand identity is determined endogeneously by the composition of consumers who purchase it (i.e., what types of people consume the brand). We find that sometimes the firm can strengthen the identity of its brand by refraining from advertising. This result is based on the following intermediate finding: advertising can diminish the endogeneous informativeness of consumer communications by making it one-sided. Furthermore, it turns out that refraining from brand image advertising may be optimal for the firm when the product is especially well positioned to create a strong identity—i.e., when consumer preferences for functional and self-expressive attributes are highly correlated.

Word-of-mouth (WOM) plays an increasingly important role in shaping consumers' attitudes and buying behaviors. Prior work in marketing has mainly focused on the aggregate impact of WOM on product sales as well as the generation of WOM. Very little attention has been paid to the consumption or usage of WOM. In this paper, utilizing a unique data set that collects information from the automobile category on whether a consumer generates WOM to others and uses WOM for making purchase decisions, we build a discrete-choice model to study consumer WOM generation and WOM consumption decisions simultaneously and empirically answer questions that have not been explored previously. We are particularly interested in studying the key drivers of WOM generation/consumption and the synergy effect between the two WOM-related activities. We apply the proposed model to survey data collected on the automobile category. We find a strong synergy between WOM generation and WOM consumption. Although some consumers view WOM generation and WOM consumption as complementary to each other, others tend to perceive the two activities as competing with each other. We also find that consumer product experience and media exposure are positively correlated with their propensity to generate WOM. However, their effect on WOM consumption is mixed. Our empirical analysis also provides evidence of unobserved heterogeneity in the way consumer WOM activities are related to consumer product experience. Overall, these findings lead to important managerial implications on targeting for effective use of WOM as a marketing tool.

The extensive adoption of uniform pricing for branded variants is a puzzling phenomenon, considering that firms may improve profitability through price discrimination. In this paper, we incorporate consumers' concerns of peer-induced price fairness into a model of price competition and show that a uniform price for branded variants may emerge in equilibrium. Interestingly, we find that uniform pricing induced by consumers' concerns of fairness can actually help mitigate price competition and hence increase firms' profits if the demand of the product category is expandable. Furthermore, an individual firm may not have an incentive to unilaterally mitigate consumers' concerns of price fairness to its own branded variants, which suggests the long-run sustainability of the uniform pricing strategy. As a result, fairness concerns from consumers provide a natural mechanism for firms to commit to uniform pricing and enhance their profits.

Although the assumption of utility-maximizing consumers has been challenged for decades, empirical applications of alternative choice rules are still very new. We add to this growing body of literature by proposing a model based on the idea of a “satisficing” decision maker. In contrast to previous models (including recent models implementing alternative choice rules), satisficing depends on the order in which alternatives are evaluated. We therefore conduct a visual conjoint experiment to collect search and choice data. We model search and product evaluation jointly and allow for interdependence between them. The choice rule incorporates a conjunctive rule for the evaluations and, contrary to most previous models, does not rely on compensatory trade-offs at all. The results strongly support the proposed model. For instance, we find that search is indeed influenced by product evaluations. More importantly, the model results strongly support the satisficing stopping rule. Finally, we perform a holdout prediction task and find that the proposed model outperforms a standard multinomial logit model.