NIMS: MagIC - Documentos de conferências internacionais
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- GenAI Usage in Higher EducationPublication . Aparicio, Manuela; Schroll, Kasper; Bernardo, Diogo Filipe Dinis; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management SchoolGenerative AI (GenAI) is rapidly reshaping higher education, yet little is known about what drives students to continue using these tools over time. This study explores the evolution of students’ perceptions through two survey-based structural models (2023 and 2025), analyzed using SEM/PLS. Results reveal a striking shift: while students initially viewed GenAI with skepticism, by 2025 they reported satisfaction and perceived academic benefits. Early adoption was influenced by educational level, effort, and performance expectancy, whereas long-term use depended on system quality, satisfaction, individual impact, and ethical concerns. Trust, though not central at first, later influenced students’ sense of fairness and ethics. These findings highlight the importance of aligning GenAI tools with students’ expectations, values, and learning needs to ensure responsible and sustained use. This research offers valuable guidance for institutions aiming to integrate GenAI effectively and ethically into academic environments.
- Institutional design thinking for entrepreneurial governancePublication . Victorino, Guilherme; Martins, João; Neto, Miguel de Castro; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Many higher education institutions (HEIs) have come to believe that successful graduates represent the pinnacle of success. The institution’s focus on graduate employability may inadvertently suppress entrepreneurial behavior in its graduates. The purpose of this paper is to investigate this paradox and argue that the institutional successes in graduate employability may inhibit entrepreneurial behavior in graduates. This paper focus on one leading European data science and information management school showcasing exceptional levels of graduate employment and partnerships with both private and public sector organizations. In spite of these very positive conditions, however, there appear to be very few start-ups formed by graduates from the school. It appears that the problem lies in the design of the institutional governance structure of the school rather than a lack of entrepreneurial initiatives. When the curriculum, partnerships, incentives, and performance metrics for students are all primarily focused on preparing students to supply talent to existing organizations, then student expectations and career development are shaped toward employment rather than starting ventures. In order to help resolve this challenge, the paper presents a design-thinking based framework for designing entrepreneurial governance that includes three phases: Inspire, Ideate, and Implement. The first phase, Inspire, involves using self-assessment and stakeholder analysis to identify the contradictions between the school's rhetorical commitments to innovation and its actual entrepreneurial outcomes. The second phase, Ideate, identifies how governance structures, curricula, and incentives can be re-designed to encourage the formation of ventures while still providing students with the opportunity to pursue employment in traditional ways. The third phase, Implement, involves transforming entrepreneurship into an embedded part of the school's operations by establishing venture pipelines, creating new performance metrics, and increasing ties to entrepreneurial ecosystems. The proposed framework represents entrepreneurship in universities as a systemic design challenge rather than solely as an educational intervention. The paper also asserts that entrepreneurship cannot be successfully taught without corresponding systemic entrepreneurial governance structures. Therefore, by synchronizing strategy, incentives, partnerships, and learning environments, universities can convert successful graduate employment outcomes into increased entrepreneurial activity in the regions in which they are located and contribute more effectively to regional innovation ecosystems.
- Machine Learning Prediction Approaches to Bike-Sharing SystemsPublication . Oliveira, Rita; Jardim, Bruno; Albuquerque, Vitoria; Neto, Miguel de Castro; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Cities are adopting innovative mobility strategies to tackle urban challenges and mitigate the effects of climate change, emphasizing the integration of shared modes, such as bike-sharing systems. This study introduces a systematic literature review focusing on predictive machine learning (ML) methods’ contributions to bike-sharing systems (BSS) to promote effective implementation, sustainability, and user adoption of such systems. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed resulting in an analysis of 27 papers published between 2018 and 2023, identifying the most frequently used ML techniques in this domain and offering an outline for future research. This study identified RF, GBT, and XGBoost models as the most used predictive modeling methods due to their robust performance in predicting station-level BSS supply and demand. The review also highlights the importance of incorporating diverse features such as weather, time, spatial factors, and socio-economic data, which significantly enhance model accuracy. This study provides a state-of-the-art about ML techniques utilized for predicting station-level BSS supply and demand to guide future research directions.
- Predicting Station Occupancy of Bike-Sharing System During EventsPublication . Oliveira, Rita; Jardim, Bruno; Albuquerque, Vitoria; Neto, Miguel de Castro; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Technological advancements have enabled the large-scale collection of bike-sharing systems (BSS), providing valuable opportunities for city planners and operators to optimize services and improve decision-making. As a soft mobility mode, BSS plays a critical role in promoting sustainable and reliable urban transportation. However, research remains limited regarding how such data can be used to evaluate the impact of city events on BSS usage and to enhance station occupancy prediction. To address this gap, this study uses Lisbon, Portugal, as a case study to investigate the influence of events on BSS usage, focusing on hourly station occupancy rates and, for the first time in the city, integrating event data into BSS prediction models. By integrating weather conditions and event data, we apply state-of-the-art machine learning algorithms, such as Random Forest, Gradient Boosting Tree, and Extreme Gradient Boosting, to predict occupancy levels. The goal is to evaluate model performance, analyze prediction error patterns, and contribute to more efficient resource allocation and management of BSS during urban events. Results show that an XGBoost model enriched with an occupancy-change feature and historical demand data delivers the most accurate station-level occupancy predictions, revealing distinct usage patterns between sports and music events and across station locations, and provides actionable insights to support targeted, time-sensitive bicycle rebalancing during high-impact event days. The study provides a benchmark methodology that can be used in other urban contexts, enabling smart cities’ advancements.
- Centro Nacional de Dados Oceanográficos (NODC-PT)Publication . Dias, Telmo; Fortes, Isabel; Alves, Margarida; Esteves, Rita; Dias, Elisabete; Barata, Paulo; Costa, Ana Cristina; Baptista, Márcia L.; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management SchoolThe Portuguese National Oceanographic Data Centre (NODC-PT) was established to improve the management and FAIRness of marine data produced by Portuguese institutions. Developed in coordination with the Portuguese Committee for the Intergovernmental Oceanographic Commission (IOC), NODC-PT adopts a federated architecture that promotes data sharing while preserving the autonomy of data providers. The infrastructure supports metadata harmonization, interoperable access services, and data stewardship practices aligned with FAIR principles (Findable, Accessible, Interoperable and Reusable). Integrated within the IOC International Oceanographic Data and Information Exchange (IODE) network, NODC-PT contributes to national and international initiatives related to ocean digitalization and the United Nations Decade of Ocean Science for Sustainable Development. This paper presents the main architectural principles, integration models, and governance approaches adopted by NODC-PT, highlighting its contribution to marine data reuse, marine research strengthening, and long-term preservation of oceanographic information.
- From Six to TwoPublication . Vanneschi, Leonardo; Rosenfeld, Liah; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Geometric Semantic Genetic Programming (GSGP) is an extension of Genetic Programming (GP) that captured the interest of researchers because of its ability to induce a unimodal error surface for any supervised learning problem. Although still a recent development, the Semantic Learning with Inflate and Deflate Mutations (SLIM-GSGP) extension of GSGP has already attracted significant attention due to its novel ability to generate offspring that are smaller than their parents, effectively addressing the problem of steady model growth in GSGP. This paper presents SLIM-DUO, an extension of SLIM-GSGP that integrates the six existing SLIMGSGP variants into solely two unified formulations: DUO-MUL and DUO-SUM. This integration streamlines benchmarking and hyperparameter exploration while aiming to retain comparable predictive performance at approximately one third of the computational cost. Across five test problems, SLIM-DUO achieves predictive performance comparable to that of SLIM-GSGP, with model size differences that remain within previously observed dataset-dependent variability among SLIM variants. Overall, SLIM-DUO substantially reduces the computational effort required during both the configuration and benchmarking phases, highlighting a favorable balance between model size and computational complexity and preserving solution quality.
- Advancing the Academic Discourse on Algorithmic BiasPublication . Bandeira, Pâmella Elis; Limongi, Ricardo; Rohden , Simoni F.; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Academy of ManagementAlgorithmic bias remains a challenge in artificial intelligence (AI), which has implications for technological development and societal equity. Despite substantial progress in identifying sources of bias and developing mitigation strategies, the literature exhibits fragmentation, with technical and social perspectives often treated in isolation. Our study addresses this gap by proposing an interdisciplinary theoretical framework integrating computational sciences, social theory, and ethics constructs to examine the interplay between bias sources and mitigation strategies. By introducing constructs such as “normative data influence” and “adaptive fairness metrics,” the framework highlights the co-evolution of technical solutions and social norms. The findings emphasize the importance of participatory design and inclusive governance to ensure the fairness and accountability of AI systems. This research offers a holistic perspective on algorithmic fairness, advances theoretical insights, and provides a practical roadmap for designing transparent and inclusive AI-based decision-making systems, thereby contributing to the academic discourse.
- Spatiotemporal Statistical Analysis of Octopus Fishing Activity in the Algarve Based on Geographically Weighted Regression [poster]Publication . Oliveira, Beatriz; Costa, Ana Cristina; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)
- Assessing BERTopic stability on a specialized scientific corpusPublication . Vasconcelos, Carolina; Mendonça, Sandro; Damásio, Bruno; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Neural topic models such as BERTopic have gained popularity for analyzing large text corpora, yet their stability properties remain underexplored. We evaluate BERTopic robustness along three dimensions using a corpus of 20,565 peer-reviewed abstracts published by banking institutions between 1980 and 2023. First, we measure seed sensitivity by fitting the same model specification across 11 random seeds and comparing document assignments, topic-level consistency, and word-level overlap. Document-level agreement ranges from 51% to 94%, while top-word Jaccard similarity is more stable (57–86%). Second, we compare five outlier reduction strategies and document a coherence–coverage trade-off: the baseline model achieves cv = 0:72 with 43% outliers, whereas the best post-hoc strategy reduces outliers to near zero at cv =0:66. Third, we assess temporal semantic stability by re-estimating topic representations within four decade windows and computing pairwise cosine similarities, which range from 0.76 to 0.99. These results show that BERTopic produces topics whose semantic content is more robust than their document assignments suggest, and that outlier handling and seed choice are first-order methodological decisions.
- Examining the Contradictions Between Centrality Measures and Self-Identified Influencers in Online Social NetworksPublication . Garcia, Ângela; António, Nuno; Rita, Paulo; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management SchoolThe rapid growth of social media has significantly impacted how brands promote their products and interact with consumers. Consumers increasingly use the internet to gather information about products and brands. This fact has led brands to invest heavily in influencer marketing to boost brand awareness. Therefore, identifying influential figures who can help spread brand messages is crucial, and one effective way to achieve this is by calculating social network analysis’ centrality measures. This study explores the alignment between those identified through centrality measures in online social networks (OSNs) and self-proclaimed influencers. To validate the proposed methodology, this exploratory study uses Instagram data from the Portuguese brand Oliva Store as a case study. The analysis revealed a significant misalignment between self-identified influencers and those identified through network centrality measures. Among the various centrality measures, PageRank Centrality was the most effective, accurately identifying around 23% of self-proclaimed influencers. These findings challenge the notion that self-proclaimed influencers hold the highest influence. They highlight the complex dynamics of OSNs, where organizational entities can also play significant roles. This study provides critical insights for marketers and social media professionals, emphasizing the need for a nuanced approach to identifying and leveraging influencers to optimize marketing strategies.