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.

Abstract
In the pharmaceutical industry, noncompliance with any good manufacturing practice (GMP) leads to deviation, resulting in potential retention of finished product batches, reprocessing, or rejection-consequently increasing lead time and cost. This study aimed to outline a strategy to define, classify, and mitigate recurrent deviations occurring more than once within 12 months. This research followed an action research methodology, carried out within a Portuguese pharmaceutical company. A transversal analysis of the deviation management process was conducted across three phases: recording, investigation, and conclusion. The intervention included defining objective recurrence criteria, developing investigation models based on structured problem-solving, and redesigning the deviation management information system. The implementation decreased recurrent deviations by 78 percent, and a new process was established, facilitated by the participation and involvement of everyone in the organization. This article introduces pioneering contributions to the pharmaceutical industry by presenting novel criteria for assigning recurrence to recorded deviations and integrating Good Manufacturing Practices (GMP) with big data and analytics. Our approach enhances decision-making and manufacturing processes by structurally incorporating all types of causes beyond the human factor, emphasizing recurring deviations over extended periods. It defines conditions for correct deviation classification and constructs a decision matrix for investigation models. Additionally, it presents workshop management, providing analysis templates and a prototype information system, and outlines key steps to mitigate deviations, highlighting research limitations and future directions.

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.

Abstract
This paper focuses on the comprehensive assessment of regional performance in attaining the 2030 Strategic Framework for Education and Training (ET2030) established by the European Union. To this end, we propose a composite indicator framework based on robust Benefit-of-the-doubt models empirically validated through an extensive analysis of data spanning 32 countries and 101 NUTS-I level regions for 2019. We integrate contextual variables into a robust conditional model to ensure an equitable evaluation among regions grappling with distinct circumstances. Specifically, the unemployment rate and the percentage of the population holding national citizenship are considered. Moreover, the research identifies best practices from high-performing regions that can serve as benchmarks for underperforming areas. Analyzing regional-level data is crucial for understanding disparities between European regions and within countries.

Abstract
This paper investigates the relationship between school principals' managerial practices and two key mensions of school performance: students' cognitive outcomes and school climate. School performance assessed using a classical Data Envelopment Analysis (DEA) framework, complemented by both unconditional robust and conditional robust models to evaluate the influence of managerial practices on school efficiency. We introduce a methodological innovation that allows for a nuanced analysis of how contextual variables-specifically, principals' managerial practices-affect performance, both individually and through their interactions. The analysis is based on 2019 INVALSI data from a nationally representative sample of 8th grade students in Italian schools. The findings show that principals' practices, as well as the ways in which these practices interact, play a significant role in shaping school efficiency, particularly by promoting a positive supportive school climate.