The Impact of Guidance Information on Checkout Efficiency and User Experience in Supermarket Self-Checkout Machines
Year: 2025
Type: article
Abstract: In recent years, numerous retail conglomerates have adopted self-checkout technologies to enhance accuracy, streamline in-store processes, and mitigate labor expenses, thereby catalyzing the expansion of the self-checkout system market. Nonetheless, due to consumer apprehensions regarding the utilization of self-checkout services, additional personnel are still required on-site, indicating that the usability and user experience of self-checkout systems necessitate further refinement. Consequently, this investigation delves into the usability and user experience of supermarket self-checkout services. Through scenario-based tasks in the preliminary research, which includes usability assessments, cognitive load evaluations, and semi-structured interviews, it was discovered that users, particularly during their initial interaction with the machine and amidst queueing crowds, displayed heightened cognitive strain and error rates. Based on these findings, we optimized the self-checkout interface and operational procedures, proposing designs for 2 × 2 interface styles and guidance cues, which underwent testing in a subsequent experiment. The experimental results indicated: (a) Photo-based guidance cues proved more efficacious compared to illustrated cues. (b) Users primarily rely on visual cues when seeking nearby equipment, with other interface cues serving a secondary function. (c) Clearly distinguishing button menus and frequently used options in the checkout information enabled users to promptly discern key points and seamlessly finalize their transactions. Drawing from the findings of this study, we propose design recommendations. Future machine designs should prioritize swift, accurate operations, and an enhanced interactive experience.
Source: SAGE Open
Authors: Po-Jen Yang, Szu-Chi Chen, Meng-Cong Zheng
Institution: National Taipei University of Technology
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W4417186295