Abstract This study addresses customer churn prediction in contractual utility services by applying survival analysis models, which provide time-to-event insights beyond traditional machine learning approaches. Churn is defined as subscription cancellation within a given period. Unlike classification models that only indicate whether churn will occur, survival models estimate hazard rates, capturing how churn risk evolves over time. Our objectives are twofold: (1) to compare two survival models—the Cox Proportional Hazards (CPH) model and the Aalen Additive (AA) model—in identifying key drivers of churn, and (2) to demonstrate their interpretability in predicting churn timing for more effective customer intervention strategies. Experiments with data from a gas utility company show that survival models can successfully predict customer churn across products and contract types. By estimating individual-level risk profiles, these models highlight customers most likely to leave, enabling segmentation based on churn likelihood and timing. This provides actionable insights for designing targeted retention efforts. Overall, the study demonstrates the added value of survival analysis in churn prediction: it not only forecasts whether customers are at risk but also when churn is likely, supporting timely, tailored strategies that reduce attrition and strengthen customer retention.

Abstract Online grocery shopping and the use of digital voice assistants (DVAs) have become increasingly common. Despite this development, research on DVAs in the context of grocery purchasing remains limited. This study examines DVAs as frontline service technologies in grocery shopping. It advances understanding of how privacy concerns, technology anxiety, and consumers’ price and brand consciousness influence the acceptance of DVAs. The results show that technology anxiety negatively affects individuals’ perceptions of DVAs, regardless of whether consumers are brand- or price-conscious. Moreover, privacy concerns reduce the behavioral intention of brand-conscious but not price-conscious consumers. The findings provide important implications for researchers and practitioners concerning consumers’ acceptance of DVAs for online grocery shopping.