Consumer Behavior

In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of  GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an

As concern with climate change increases, people seek to behave and consume sustainably. This requires understanding which behaviours, firms and industries have the greatest impact on emissions. Here we ask if people are knowledgeable enough to make choices that align with growing sustainability intentions.

Using publicly available data from 299 pre-registered replications from the social sciences, we find that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the paper characteristics, study design and results, author information, and replication effort. To understand why, we analyze the textual differences between replicable and nonreplicable studies.

Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines — which we define as repeated behaviors with recurring, temporal structures — for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. We propose a new approach for doing so, which we apply in the context of ridesharing.

Making good health insurance decisions is important for health outcomes and longevity, but consumers’ errors are well documented. The authors examine whether targeted choice architecture interventions can reduce these mistakes. The article examines the interaction of two choice architecture tools on improved consumer insurance decisions in online health care exchanges: (1) ordering the options from best to worst based on a high-quality user model and (2) partitioning the total set of options.

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.

How can firms encourage consumers to adopt smartphone apps? The authors show that several inexpensive choice architecture techniques can make users more likely to enable important app features and complete app onboarding. In six preregistered experiments (n = 5,968) and a field experiment (n = 594,997), choice architecture interventions manipulating choice sequence, color, and wording of app adoption decisions dramatically increased app adoption. Across experiments, integrating multiple feature decisions into a single choice increased adoption.