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Online grocery retailing remains effort-intensive because consumers must coordinate many item-level decisions, making basket construction and correction a key barrier to more frequent use. This study examines consumer acceptance of an AI-pre-filled basket in Austrian online grocery retailing and links Technology Acceptance Model (TAM) mechanisms to objective interaction performance. Using a field-proximate one-group pre–post design, participants edited an AI-pre-filled basket in a standardized online shop and completed pre- and post-task surveys using 0–100 slider scales; log data captured processing time and edit actions. Analyses are based on n = 297 valid cases. Results show a substantial and statistically significant within-person increase in online grocery usage intention after the AI-basket interaction, rising from 39.17 to 59.78. The TAM results support the core mechanism: perceived ease of use is positively associated with perceived usefulness, and perceived usefulness strongly predicts behavioral intention, whereas a direct ease-of-use effect on intention is not supported. Linking perceptions to log data shows that acceptance is more strongly associated with correction demand than with processing time. AI-basket acceptance depends less on speed than on reducing rework while preserving user control; retailers should therefore design AI baskets as controllable, editable, low-rework systems rather than speed-oriented automation tools.
Lackner et al. (Mon,) studied this question.