Impatience when choosing for oneself has been widely documented, but are people also impatient when making prosocial decisions about benefits to unknown others? The authors study whether and why people care about a donation’s “impact timing,” the time when the benefits of the donation would reach the beneficiaries. In seven pre-registered experiments (N = 2,462) and an analysis of GoFundMe secondary data, they show that people consistently prefer to donate to a charitable program with an earlier impact timing, across different causes, donation amounts, timeframes, charity sizes and tradeoffs between impact timing and other considerations (impact magnitude, number of prior donors, remaining cost to complete, quality rating, and overhead). They find that decreased perceived benefits of delayed donation impact provides an incomplete explanation of the impact timing preference. Beyond less discounting of less delayed benefits, people also infer greater donation efficacy-based trust from earlier impact timing, increasing donation preferences. Together, the strong preference for earlier impact timing is an important and largely overlooked aspect of prosocial decision-making, with implications for building trust in prosocial organizations.
Subscription programs are increasingly central to platform strategy, but their effects on competitors and category demand remain unclear. The authors study restaurant delivery subscriptions using individual-level transaction data on Postmates Unlimited, the first widely available U.S. subscription program in the category, and DoorDash's later DashPass program. Using a matched, staggered difference-in-differences design, they find that signing up for Postmates Unlimited is associated with a 90.4% increase in spending at the focal firm, with no significant average change in competing-firm spending and no offsetting decline in adjacent food categories, consistent with category expansion. Competitive effects depend on customers' prior platform usage. For “singlehomers” (who only used Postmates prior to subscribing), subscribing is associated with increased spending at both the focal firm and competing platforms, even after cancellation. For “multihomers” (who had also previously used competing platforms), the focal-firm effect is larger, and competing-firm spending falls while customers remain subscribed; after cancellation, the focal-firm lift persists, but the decrease in competitor spending does not. DashPass results are broadly similar, but customers with prior Postmates subscription experience show little increase in category spending. Together, these findings suggest subscriptions can both expand markets and capture share, depending on prior platform usage and subscription experience.
This paper proposes an instrument-free correction for omitted-variable bias and overcomes a key limitation of Gaussian copula control function (GCCF) approaches. Omitted-variable bias is the most common source of endogeneity in marketing, and many articles apply GCCF to address it. Yet GCCF require a nonlinear regressor-error relation, whereas textbooks assume linear, additive relationships between regressors and omitted variables. The authors prove that GCCF is biased in classical omitted-variable settings. Extant articles that address omitted-variables with GCCF also show language cues implicitly assuming linear relationships, which invalidate the use of GCCF. The proposed endogeneity correction is tailored to this prominent case, utilizing independent component analysis (ICA). It assumes a latent normally distributed component that additively influences both the endogenous regressor and the error. This can be interpreted as the sum of omitted variables, which, according to the Central Limit Theorem, add up to a normal distribution. Through ICA, this component is identified and extracted from the endogenous regressor, rendering the regression model free of endogeneity and enabling causal inference. The paper proves that the model is point-identified despite endogeneity due to omitted variables, that the proposed estimator is consistent and asymptotically normal, and that Jackknife resampling yields valid standard errors.
To guide consumers’ decisions regarding when to replace products such as water filters, printer ink cartridges, and engine oil, companies often provide visualizations of the product’s lifespan. We document a lifestage bias in which representing a product’s lifespan with more (vs. fewer) visually distinct stages increases replacement intentions even when the product’s actual usage is the same. The effect emerges because a greater number of visually distinct stages leads consumers to mentally simulate previous use, thus enhancing the perception that a product has progressed through its lifespan and encouraging product replacement. Ten studies document lifestage bias and provide support (via mediation and moderation) for the underlying usage simulation mechanism. Notably, lifestage bias is eliminated when stages are represented non-visually or when stages are not visually distinct. Furthermore, lifestage bias is attenuated when product deterioration can be observed directly. These findings provide important insights into how consumers make product replacement timing decisions. Adjusting the number of distinct stages in product replacement guides offers firms and policymakers a practical tool to influence replacement timing—accelerating it when timely replacement is necessary for health, safety, or regulatory reasons (thus improving consumer welfare), or delaying it when items can be safely used for longer (thus reducing waste).
