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B everage consumption occurs many times a day in response to short-run needs that fluctuate. We develop a model in which consumers are heterogeneous in self-regulating consumption by balancing short-run needs (e.g., hydration and mood) with long-term goals (e.g., health). The model has two novel features: (1) utility depends on the match between occasion-specific needs and product attributes, and (2) dynamics of consumption and stockpiling are at the level of product attributes. We estimate the model using unique intraday beverage consumption, activity, and psychological needs data. We find that only a third of individuals do not self-regulate. Of the two-thirds who self-regulate, over 40% self-regulate adaptively based on past choice, whereas 25% selfregulate both adaptively and anticipating future needs. Our attribute-need match model enables us to assess unmet demand for new products with attributes that match co-occurring occasion-specific needs. Specifically, we find that a product satisfying a combination of "health-hydrating" needs expands overall beverage consumption by as much as 5%. Our framework of modeling heterogeneity in self-regulation by balancing short-run needs with long-term goals is more broadly applicable in contexts where situational needs vary, and long-term effects are gradual and hard to discern (e.g., nutrition, smoking, and preventive health care).
We propose an instrumental-variable (IV) approach to estimate the causal effect of service satisfaction on customer loyalty by exploiting a common source of randomness in the assignment of service employees to customers in service queues. Our approach can be applied at no incremental cost by using routine repeated cross-sectional customer survey data collected by firms. The IV approach addresses multiple sources of biases that pose challenges in estimating the causal effect using cross-sectional data: (1) the upward bias from common-methods variance resulting from the joint measurement of service satisfaction and loyalty intent in surveys, (2) the attenuation bias caused by measurement errors in service satisfaction, and (3) the omitted variable bias that may be in either direction. In contrast to the common concern about the upward common-methods bias in estimates using cross-sectional survey data, we find that ordinary-least-squares substantially underestimates the casual effect, suggesting that the downward bias resulting from measurement errors and/or omitted variables is dominant. The underestimation is even more significant with a behavioral measure of loyalty, where there is no common-methods bias. This downward bias leads to significant underestimation of the positive profit impact from improving service satisfaction and can lead to underinvestment by firms in service satisfaction. Finally, we find that the causal effect of service satisfaction on loyalty is greater for more difficult types of services. This paper was accepted by Juanjuan Zhang, marketing.
Pricing products such as used cars, houses, and artwork is often challenging, because each item is unique, and the seller, ex ante, lacks information about the demand for individual items. This paper develops a dynamic pricing model for products with significant item-specific demand uncertainty, in which a forward-looking seller learns about the item-specific demand through an initial assessment, as well as during the selling process. The model demonstrates how seller learning, through several mechanisms, can lead to the commonly observed downward trend in the prices of individual items. These mechanisms include the seller’s optimal adjustment of prices over time to account for the dynamic adverse selection of unsold items and the diminishing option value in future learning. The model is estimated using novel panel data of a leading used-car dealership. Counterfactual experiments show that the value of learning in the selling process is $203 per car. Conditional on subsequent learning in the selling process, the initial assessment further improves profit per car by $139. With the dealer’s net profit per car being about $1,150, these estimates suggest a potentially high return to taking an information-based approach toward pricing products with item-specific demand uncertainty. This paper was accepted by Juanjuan Zhang, marketing.
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