Knowledge that Transforms

Store brand entry has become a key issue in marketing as it may structurally change the performance of and the interactions among all market players. Based on their multivariate time-series analysis, the authors demonstrate permanent performance effects of store brand entry, typically benefiting the retailer, the consumers, and premium-brand manufacturers, while harming second-tier brand manufacturers. For the retailer, they consistently find two beneficial effects of store brand entry: high unit margins on the store brand itself and higher unit margins on the national brands. This increase in unit margins implies that the retailer strengthens its bargaining position vis-à-vis national brand manufacturers. However, store brand entry only rarely yields category expansion and does not create store traffic or revenue benefits. Second, consumers do not obtain lower prices on all national brands, only on some second-tier brands. However, they benefit from enlarged product assortment and intensified promotional activity that lowers average price paid for two out of four categories. For the manufacturers, store brand entry is typically beneficial for premium-price national brands, but not for second-tier national brands. Often, premium brands experience lower long-term price sensitivity and higher revenues, whereas second-tier brands experience higher long-term price sensitivity and lower revenues.

Standard diffusion models capture social contagion only coarsely and do not allow one to operationalize different contagion mechanisms. Moreover, there is increasing skepticism about the importance of contagion and, as has long been known, S-shaped diffusion curves can also result from heterogeneity in the propensity to adopt. We present hypotheses about conditions under which specific contagion mechanisms and income heterogeneity are more pronounced, and test these hypotheses using a meta-analysis of the q/p ratio in applications of the Bass diffusion model. The ratio is positively associated with the Gini index of income inequality in a country, supporting the heterogeneity-in-thresholds interpretation. The ratio also varies as predicted by the Gamma-Shifted Gompertz diffusion model, but the evidence vanishes after controlling for national culture. As to contagion, the q/p ratio varies with the four Hofstede dimensions of national culture—for three of them in a direction consistent with the social contagion interpretation. Furthermore, products with competing standards have a higher q/p ratio, which is again consistent with the social contagion interpretation. Finally, we find effects of national culture only for products without competing standards, suggesting that technological effects and culturally moderated social contagion effects might not operate independently from each other.

Many theories of consumer behavior involve thresholds and discontinuities. In this paper, we investigate consumers' use of screening rules as part of a discrete-choice model. Alternatives that pass the screen are evaluated in a manner consistent with random utility theory; alternatives that do not pass the screen have a zero probability of being chosen. The proposed model accommodates conjunctive, disjunctive, and compensatory screening rules. We estimate a model that reflects a discontinuous decision process by employing the Bayesian technique of data augmentation and using Markov-chain Monte Carlo methods to integrate over the parameter space. The approach has minimal information requirements and can handle a large number of choice alternatives. The method is illustrated using a conjoint study of cameras. The results indicate that 92% of respondents screen alternatives on one or more attributes.

Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path.

The literature on structural equation models is unclear on whether and when multicollinearity may pose problems in theory testing (Type II errors). Two Monte Carlo simulation experiments show that multicollinearity can cause problems under certain conditions, specifically: (1) when multicollinearity is extreme, Type II error rates are generally unacceptably high (over 80%), (2) when multicollinearity is between 0.6 and 0.8, Type II error rates can be substantial (greater than 50% and frequently above 80%) if composite reliability is weak, explained variance (R 2 ) is low, and sample size is relatively small. However, as reliability improves (0.80 or higher), explained variance R 2 reaches 0.75, and sample becomes relatively large, Type II error rates become negligible. (3) When multicollinearity is between 0.4 and 0.5, Type II error rates tend to be quite small, except when reliability is weak, R 2 is low, and sample size is small, in which case error rates can still be high (greater than 50%). Methods for detecting and correcting multicollinearity are briefly discussed. However, since multicollinearity is difficult to manage after the fact, researchers should avoid problems by carefully managing the factors known to mitigate multicollinearity problems (particularly measurement error).

Recommendations often play a positive role in the decision process by reducing the difficulty associated with choosing between options. However, in certain circumstances recommendations play a less positive and more undesirable role from the perspectives of both the recommending agent or agency and the person receiving the recommendation. Across a series of four studies, we explore consumer response when recommendations by experts and intelligent agents contradict the consumer's initial impressions of choice options. We find that unsolicited advice that contradicts initial impressions leads to the activation of a reactant state on the part of the decision maker. This reactance, in turn, leads to a behavioral backlash that results not only in consumers ignoring the agents' recommendations but in intentionally contradicting them.

In several markets, consumers can gain further information regarding how well a product fits their preferences only by experiencing it after purchase. This could then generate loyalty for the products tried first. This paper considers a model in which consumers learn in the first period about the product they buy and then make choices in the second period about the competing products, given what they learned in the first period. The paper finds that if the distribution of valuations for each product is negatively (positively) skewed, a firm benefits (is hurt) in the future from having a greater market share today—the brand loyalty characteristic. With negative skewness, two effects are identified: On one hand, marginal forward-looking consumers are less price sensitive than myopic consumers, and this is a force toward higher prices. On the other hand, forward-looking firms realize that they gain in the future from having a higher market share in the current period and compete more aggressively in prices. For similar discount factors for consumers and firms, the latter effect dominates. The paper also characterizes the importance of consumer learning effects on the market outcome.

Managers are very interested in word-of-mouth communication because they believe that a product's success is related to the word of mouth that it generates. However, there are at least three significant challenges associated with measuring word of mouth. First, how does one gather the data? Because the information is exchanged in private conversations, direct observation traditionally has been difficult. Second, what aspect of these conversations should one measure? The third challenge comes from the fact that word of mouth is not exogenous. While the mapping from word of mouth to future sales is of great interest to the firm, we must also recognize that word of mouth is an outcome of past sales. Our primary objective is to address these challenges. As a context for our study, we have chosen new television (TV) shows during the 1999–2000 seasons. Our source of word-of-mouth conversations is Usenet, a collection of thousands of newsgroups with diverse topics. We find that online conversations may offer an easy and cost-effective opportunity to measure word of mouth. We show that a measure of the dispersion of conversations across communities has explanatory power in a dynamic model of TV ratings.

Franchisees within large branded chains loudly complain of a form of channel conflict known as “encroachment” or “impact.” Encroachment occurs when franchisors add new units of their brand proximately to their franchisees' existing units. Franchisees claim that their revenues have substantially decreased as a result of encroaching same-brand entry. The topic of encroachment has not only dominated franchisee association agendas and trade journal headlines but has also become a hot topic for politicians and policymakers. Yet, until now, evidence of encroachment has been strictly anecdotal. This paper provides the first systematic evidence of encroachment. Using revenue data from the Texas lodging industry in the 1990s, I find that when franchisors approve new same-brand units in the vicinity of incumbent units, these new units cannibalize the incumbents' revenues. In contrast to the result for franchisors, the addition of a new unit by company-owned brands in the vicinity of same-brand units is associated with an increase in the incumbents' revenues. This contrast suggests that encroaching behavior is caused by incentives that result from the governance form of franchising and is not simply an outcome that accompanies all expansion. This finding informs theory on governance forms and exclusive territories. Implications for practitioners and policy are also discussed.

Prior work in marketing has suggested that advertising threshold effects—levels beneath which there is essentially no sales response—are rarely encountered in practice. Because advertising policies settle into effective ranges through early trial and error, thresholds cannot be observed directly, and arguments for their existence must be based primarily on a "statistical footprint," that is, on relative fits of a range of model types. To detect possible threshold effects, we formulate a switching regression model with two "regimes," in only one of which advertising is effective. Mediating the switch between the two regimes is a logistic function of category-specific dynamic variables (e.g., order of entry, time in market, number of competitors) and advertising levels, nesting a variety of alternative formulations, among them both standard concave and S-shaped responses. A sequence of comparisons among parametrically related models strongly suggests: that threshold effects exist; that market share response to advertising is not necessarily globally concave; that superior fit cannot be attributed to model flexibility alone; and that dynamic, environmental, competitive, and brand-specific factors can influence advertising effectiveness. These effects are evident in two evolving durables categories (SUVs and minivans), although not in the one mature, nondurable category (liquid detergent) studied.