Abstract
Generative artificial intelligence (GenAI) promises substantial productivity gains for organisations, yet unresolved questions about data management and privacy continue to shape managers' and employees' confidence. This study examines workplace adoption of GenAI and shows how trust, conditioned by perceptions of data-management integrity, information transparency, and privacy risk, influences acceptance. This mixed-method study tests, using a survey-based structural equation model plus interviews focused on managerial practices among daily GenAI practitioners, two core insights: (i) trust is the strongest predictor of intention to use GenAI, and (ii) trust depends chiefly on manager's and employees' belief that organisational data are handled reliably and objectively through management routines. Perceptions of transparency or privacy risk exert no direct influence on either trust or usage. Building on these results, the study delineates four managerial domains: data-management process, information transparency, privacy risk, and trust, alongside twenty future research questions designed to understand how GenAI is linked to managerial practices. For practice, the findings recommend monitoring, calibrated disclosure, and adaptive privacy protocols as concrete managerial levers to strengthen GenAI acceptance. The evidence highlights trustworthy data governance, not abstract explainability, as the foundation of sustainable GenAI adoption. The study also provides a roadmap of actionable management practices to guide its implementation in modern workplaces.

Abstract
Purpose Grounded in source credibility and the elaboration likelihood model (ELM), the current study examines the impact of influencers' credibility and consumer involvement on consumer attitudes and purchase intention, exploring potential changes in consumer behaviour before and after the COVID-19 pandemic. In addition, the mediating role of consumer involvement was examined. Design/methodology/approach A repeated cross-sectional research design was applied to compare consumer behaviour at two different time points. Two sets of data (pre-pandemic, consisting of 297 participants and post-pandemic, consisting of 307 participants) were collected through an online survey among female consumers of beauty products. Findings The findings confirm the positive effect of influencers' credibility on attitudes and consumer involvement. Moreover, the findings highlight the direct impact of consumer involvement on consumer attitudes and purchase intention. In addition, the mediating role of consumer involvement is supported. The comparison between pre- and post-pandemic periods revealed that influencers' credibility demonstrated a weaker effect on attitudes towards influencer-endorsed products after the pandemic. Conversely, consumer involvement had a strong influence on attitudes toward influencer-endorsed products after the pandemic. Practical implications The findings, along with the comparison of the two data sets, provide theoretical and practical implications regarding the relationship between influencers' credibility and consumer involvement. Originality/value This study provides empirical evidence on consumer behaviour before and after the COVID-19 pandemic, using a repeated cross-sectional design to identify and compare changes in habits and attitudes across two distinct time periods.

Abstract
Aquaculture is the world's fastest-growing food production sector, yet it lags behind other industries in adopting upcoming digital technologies. Challenges, such as integrating multimodal data and maintaining reliable network connectivity, have hindered the development of digital twins for monitoring aquaculture systems. This paper addresses these challenges through two main contributions: (i) a novel edge-based architecture for digital twinning that enables distributed, localized monitoring and actuation, reducing dependence on centralized systems and robust networks; and (ii) a three-stage algorithmic approach for mortality monitoring tailored to edge computing environments. This approach enables early detection of rising mortality rates using data fused from diverse sources, including directly monitored environmental parameters (e.g. pH and temperature), and novel optical biosensors that make use of lightweight computer vision and machine learning techniques for the estimation of bacterial concentrations within edge devices. The algorithmic strategy was tested in a real-world recirculating aquaculture system for Solea senegalensis, where bacterial concentration was estimated with an F1-score of 0.83 across five concentration levels using biosensor imagery. Moreover, a multimodal drift detection algorithm successfully identified abnormal data trends aligned with significant changes in input distributions, with preemptive drift signals preceding critical 7-day mortality spikes.

Abstract
Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time-series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one-class and binary classification algorithms. This study aims to classify time-series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time-series data to train both one-class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade-off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one-class and binary classification ultimately relies on dataset's features and task objectives.

Abstract
Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset's distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Abstract
The Eighth International Workshop on Narrative Extraction from Texts (Text2Story'25) was held on April 10 th , 2025, in conjunction with the 47 th European Conference on Information Retrieval (ECIR 2025) in Lucca, Italy. During this half-day event, more than 30 attendees engaged in discussions and presentations focused on recent advancements in narrative representation, extraction, and generation. The workshop featured a keynote address and a mix of oral presentations and poster sessions covering nineteen papers. The workshop proceedings are available online 1 . Date: 10 April 2025. Website: https://text2story25.inesctec.pt/.