Knowledge that Transforms

Call for Nominations for a new editor-in-chief, Marketing Science. Deadline for nominations: May 15, 2007.

A sequence of bids in Internet auctions can be viewed as record-breaking events in which only those data points that break the current record are observed. We investigate stochastic versions of the classical record-breaking problem for which we apply Bayesian estimation to predict observed bids and bid times in Internet auctions. Our approach to addressing this type of data is through data augmentation in which we assume that participants (bidders) have dynamically changing valuations for the auctioned item, but the latent number of bidders “competing” in those events is unseen. We use data from notebook auctions provided by one of the largest Internet auction sites in Korea. We find significant variation in the number of latent bidders across auctions. Our other primary findings are as follows: (1) the latent bidders are significant in number relative to observed bidders, (2) the latent number of remaining bidders is considerably smaller than that of new entrants to the auction after a given bid, and (3) larger bid and time increments significantly influence the bidding participation behavior of the remaining bidders. As part of our substantive contribution, we highlight the model’s ability to understand brand equity in an Internet auction context through a brand’s ability to simultaneously bring in bidders, higher bid amounts, and faster bidding.

While marketing activities increasingly involve personalizing product offers to individually elicited preferences, these unique specifications may not be universally important for product choice. Providing evidence of the limits of treating each customer differently, three experiments show that individuals who exhibit interdependent or collectivistic tendencies tend to be more receptive to recommendations that are not personalized to their own preferences, but instead to the collective preferences of relevant in-groups. However, we find that cultural orientation affects responses to personalized recommendations for only those products whose consumption or choice decision is subject to public scrutiny. We further demonstrate that the favorability of thoughts elicited by ads offering targeted versus personalized offers mediates the effect of cultural orientation on responses to personalization. Finally, both individualistic and collectivistic consumers respond more favorably to offers of targeted recommendations when they believe relevant others share their preferences and when their level of expertise is relatively low.

Marketing literature has long recognized that brand price elasticity need not be monotonic and symmetric, but has yet to provide generalizable market-level insights on threshold-based price elasticity, asymmetric thresholds, and the sign and magnitude of elasticity transitions. This paper introduces smooth transition regression models to study threshold-based price elasticity of the top 4 brands across 20 fast-moving consumer good categories. Threshold-based price elasticity is found for 76% of all brands: 29% reflect historical benchmark prices, 16% reflect competitive benchmark prices, and 31% reflect both types of benchmarks. The authors demonstrate asymmetry for gains versus losses on three levels: the threshold size and the sign and the magnitude of the elasticity difference. Interestingly, they observe latitude of acceptance for gains compared to the historical benchmark, but saturation effects in most other cases. Moreover, category characteristics influence the extent and the nature of threshold-based price elasticity, while individual brand characteristics impact the size of the price thresholds. From a managerial perspective, the paper illustrates the sales, revenue, and margin implications for price changes typically observed in consumer markets.

User design offers tantalizing potential benefits to manufacturers and consumers, including a closer match of products to user preferences, which should result in a higher willingness to pay for goods and services. There are two fundamental approaches that can be taken to user design: parameter-based systems and needs-based systems. With parameter-based systems, users directly specify the values of design parameters of the product. With needs-based systems, users specify the relative importance of their needs, and an optimization algorithm recommends the combination of design parameters that is likely to maximize user utility. Through an experiment in the domain of consumer laptop computers, we show that for parameter-based systems, outcomes, including measures for comfort and fit, increase with the expertise of the user. We also show that for novices, the needs-based interface results in better outcomes than the parameter-based interface.

In this paper we characterize the impact of production technology on the optimal product line design. We analyze a problem in which a manufacturer segments the market on quality attributes and offers products that are partial substitutes. Because consumers self-select from the product line, product cannibalization is an issue. In addition, the manufacturer sets a production schedule in order to balance production setups with accumulation of inventories in the presence of economies of scale. We show that simultaneous optimization of the product line design and production schedule leads to insights that differ significantly from the common intuition and assertions in the literature, which omits either the demand side or the supply side of the equation. In particular, we demonstrate that more expensive production technology always leads to lower product prices and may at the same time lead to higher quality products. Further, a less efficient production technology does not necessarily increase total production costs or reduce consumer welfare. We also demonstrate that in the presence of production technology, the demand cannibalization problem may distort product quality upward or the number of products upward, which is contrary to the standard result.

In communication, information, and other industries, three-part tariffs are increasingly popular. A three-part tariff is defined by an access price, an allowance, and a marginal price for any usage in excess of the allowance. Empirical nonlinear pricing studies have focused on consumer choice under two-part tariffs. We show that consumer behavior differs under three-part tariffs and assess how consumer demand uncertainty impacts tariff choice. We develop a discrete/continuous model of choice among three-part tariffs and estimate it using consumer-level data on Internet usage. Our model extends prior work in accommodating consumer switching to competitors, thereby capturing behavior in competitive industries more accurately. Our empirical work shows that demand uncertainty is a key driver of choice among three-part tariffs. Consumers' expected bill increases with the variation in their usage, steering them toward tariffs with high allowances. Consequently, demand uncertainty decreases consumer surplus and increases provider revenue. A further analysis of consumers' responsiveness to the different elements of a three-part tariff under the provider's current pricing structure reveals that prices affect a consumer's tariff choice more than her usage quantity and that the allowance plays a strong role in consumer tariff choice. Based on our results, we derive implications for pricing with three-part tariffs.

The cross-product term in moderated regression may be collinear with its constituent parts, making it difficult to detect main, simple, and interaction effects. The literature shows that mean-centering can reduce the covariance between the linear and the interaction terms, thereby suggesting that it reduces collinearity. We analytically prove that mean-centering neither changes the computational precision of parameters, the sampling accuracy of main effects, simple effects, interaction effects, nor the R 2 . We also show that the determinants of the cross product matrix X′ X are identical for uncentered and mean-centered data, so the collinearity problem in the moderated regression is unchanged by mean-centering. Many empirical marketing researchers commonly mean-center their moderated regression data hoping that this will improve the precision of estimates from ill conditioned, collinear data, but unfortunately, this hope is futile. Therefore, researchers using moderated regression models should not mean-center in a specious attempt to mitigate collinearity between the linear and the interaction terms. Of course, researchers may wish to mean-center for interpretive purposes and other reasons.

Consumer perceptions of advertising creativity are investigated in a series of studies beginning with scale development and ending with comprehensive model testing. Results demonstrate that perceptions of ad creativity are determined by the interaction between divergence and relevance, and that overall creativity mediates their effects on consumer processing and response.