Abstract
Self-service technologies (SSTs) that replace regular checkout are widely deployed in grocery retailing to reduce customer frictions and labor needs, yet their impact on consumer purchasing behavior remains unclear. We study a specific type of these SSTs: mobile app 'scan & go' technologies. Mobile app scan & go technologies may lower customer time and effort spent during a shopping trip by eliminating queuing and double handling. However, they also shift scanning effort to customers, which may change attention and shopping patterns. We explore how customers adopt mobile app scan & go technologies in practice, and study the causal effect of adoption on their purchasing behavior. We partner with a European grocery retailer that introduced a mobile app scan & go technology in their physical stores and analyze a large transactional dataset spanning more than 7 million purchases from nearly 60,000 customers. Leveraging the staggered adoption timing and matched non-adopters, we estimate the effect of adoption using difference-in-differences designs with customer and time fixed effects and modern staggered-DiD estimators. We find that adoption increases customers' monthly purchase frequency and total monthly spending, with little change in average basket value. We also find that adoption is not 'all-or-nothing': most adopters use the scan & go technology selectively for particular shopping trips, and about one-third try it once and then discontinue using it. The observed spending and frequency gains are concentrated among customers who use the technology repeatedly. These findings suggest that mobile app scan & go technologies can strengthen customer retention, but only when they reliably reduce customer friction. Retailers should promote mobile app scan & go usage for 'major' grocery trips and design onboarding and in-store support to reduce first-use learning costs - because repeated usage, not mere adoption, is what drives performance benefits.

Abstract
We study a major cyberattack at a large omnichannel grocery retailer that triggered a one-week shutdown of its online store and a corporate data breach. Using high-frequency panel data on more than 20,000 loyalty customers over 2019 and 2022, we implement a within-customer difference-indifferences design. Over the 13-week post-attack window, online transactions and online revenue both declined by approximately 10% relative to the counterfactual, with effects persisting across short-, medium-, and long-term horizons and no evidence of recovery. This contrasts with firm-level event studies, which show only short-term market reactions. The offline channel provided limited mitigation of the effects. Customers primarily substituted for alternative retailers rather than reallocating purchases within the firm, resulting in a net revenue decline for our industry partner. Effects were heterogeneous and customers with a higher concentration of online purchases, higher patronage (greater online purchase frequency and spending), and subscription ties exhibited greater shopping resilience, whereas customers with predictable shopping patterns who likely would have shopped during the affected period (and thus directly experienced the outage) were more prone to churn. This concentration of the effect among customers most likely to have been directly affected by the outage is more consistent with a substitution mechanism triggered by the disruption itself than with a generalised trust-loss interpretation, which would predict a similar response across informed customers regardless of their individual exposure to the disruption window.