Consumer Behavior

Professional house price forecast data are consistent with a rational model where agents must learn about the parameters of the house price growth process and the underlying state of the housing market. Slow learning about the long-run mean generates overreaction to forecast revisions and a modest response of forecasts to lagged realizations. Heterogeneity in signals and priors about the long-run mean helps the model account for cross-sectional dispersion in forecasts. Introducing behavioral biases helps improve the model's predictions for short-horizon overreaction and dispersion.

The relationship between social status and green consumption is pivotal in addressing the climate crisis, yet previous research reveals conflicting perspectives. We analyze a nationally representative panel survey of more than 63,000 British respondents, tracking their green consumption, attitudes, and behaviors across multiple years. The findings reveal a previously unidentified S-shaped relationship between social status and green consumption, challenging existing models that propose inverted U-shaped, negative linear, or positive linear associations.

Deciding which customer segment(s) to serve, or targeting, is a cornerstone of marketing strategy. While companies commonly target based on demographic characteristics, recent cultural movements (e.g., #MeToo, Black Lives Matter) have heightened sensitivity to the differential treatment of certain demographic groups. Yet research to date has not examined whether demographic targeting itself seems fair or appropriate.

Individuals behave differently when they know the objective probability of events and when they do not. The smooth ambiguity model accommodates both ambiguity (uncertainty) and risk. For an incomplete, competitive asset market, we develop a revealed preference test for asset demand to be consistent with the maximization of smooth ambiguity preferences; and we show that ambiguity preferences constructed fromfinite observations converge to underlying ambiguity preferences as observations become dense.

Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments.

Existing theories present a mixed account of how perceivers’ views of a target person’s antagonism relate to their perceptions of the target’s general competence and leadership effectiveness. We argue that, rather than being universal, the relationship between these perceptions varies according to perceivers’ idiosyncratic worldviews. In particular, we theorize and find across seven studies (total N = 2,065) that competitive worldview (CWV) serves as a lens through which perceivers interpret and evaluate others’ antagonistic behavior.

Consumers curate collections of items for various reasons and categorize them into subsets or categories based on different criteria as their collections grow. The items in a collection reflect a consumer's preferences, and the categories provide insights into the different contexts in which items are consumed. The authors develop a novel deep generative modeling framework that captures the network structure of consumer collections using multiple interlocked hypergraphs.

From Netflix to Spotify to TikTok, consumers’ entertainment experiences increasingly revolve around personalized content. Does consuming personalized content in a specific entertainment domain influence the likelihood of consumers discussing that domain overall, beyond the specific content they engage with? Across eleven experiments, we investigate the impact of personalized content in the music and short-form video domains (i.e., content that feels tailored to one’s tastes) on the intent to discuss domain-related topics. We find that the impact of consuming personalized content (vs.