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.

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Abstract
Researchers rely on confidential and sensitive microdata provided by national statistical institutes and other organizations, making disclosure control a critical challenge. Manual output checking processes are time-consuming and require expert knowledge, limiting scalability. This paper presents an automated framework that integrates large language models (LLMs), prompt engineering, and workflow automation (n8n) for statistical disclosure control (SDC). The system introduces an AI-driven output validation, including code generation, data processing, and risk assessment, allowing researchers to pre-check their outputs via a Seamless Within-Activity Review (SWAR) approach. Key challenges such as computational costs, confidentiality concerns, and the need for human oversight are addressed, and reinforcement learning is proposed to enhance future risk evaluation. The framework marks a step toward scalable, privacy-compliant, AI-assisted disclosure control in official statistics.

Abstract
Deep learning and large language models (LLMs) have recently enabled studies in state-of-the-art technologies that enhance recommender systems. This research focuses on solving the next-item recommendation problem using these challenging technologies in Web applications, specifically focusing on a case study in the wine domain. This paper presents the characterization of the framework developed for the object of study: adaptive recommendation based on new modeling of the initial data to explore the user's dynamic taste profile. Following the design science research methodology, the following contributions are presented: (i) a novel dataset of wines called X-Wines; (ii) an updated recommender model called X-Model4Rec-eXtensible Model for Recommendation supported in attention and transformer mechanisms which constitute the core of the LLMs; and (iii) a collaborative Web platform to support adaptive wine recommendation to users in an online environment. The results indicate that the solutions proposed in this research can improve recommendations in online environments and promote further scientific work on specific topics.

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