NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)
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- An AI-Enhanced Bibliometric Analysis of Customer Feedback and Hotel Performance in HospitalityPublication . Neves, Marta Morgado; António, Nuno; Moro, Sérgio; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Universidade do Algarve, School of Management, Hospitality and TourismIn the digital era, customer feedback has evolved into a strategic asset influencing hotel performance, competitiveness, and investment decisions. This study reviews the relationship between online reviews and key performance indicators (KPIs) in the hospitality sector and proposes an AI-enhanced literature review framework. By integrating bibliometric analysis, Natural Language Processing (NLP), and Large Language Models (LLMs), the approach enables scalable, context-aware synthesis of research on customer perceptions and their financial implications. The findings show that customer feedback significantly influences hotel performance, particularly affecting pricing strategies, occupancy rates, and revenue outcomes. However, the literature remains heavily reliant on rating-based proxies and makes limited use of real operational data, thereby constraining the accuracy of financial assessments. Additionally, gaps persist in data integration, geographic representativeness, and the translation of feedback into actionable investment decisions. From a practical perspective, the results highlight the need for more advanced, data-driven frameworks that integrate textual feedback with financial metrics. The proposed methodology offers a replicable approach to support more accurate and strategically relevant analyses in hospitality management.
- What makes rural tourism tick in Portugal?Publication . Pereira, Mafalda; Tam, Carlos; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); ElsevierPositioning rural tourism as a site of informal, experiential education, this study examines how experiential learning and perceived mental health (PMH) shape rural tourists' post-visit performance in Portugal. The learning construct, with word-of-mouth (WoM), is embedded in an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, tested on 308 tourists. PMH mediated the WoM–performance link, and experiential learning emerged as the strongest driver of post-visit performance, revealing rural tourism as a learning-rich experience rather than mere leisure. The study informs tourism education, showing how rural experiences can shape curriculum design and the training of tourism professionals.
- Automating the classification of dairy cow productivity groups using interpretable machine learningPublication . Rebuli, Karina Brotto; Ozella, Laura; Masía, Fernando; Vrieze, Elisa; Vanneschi, Leonardo; Giacobini, Mario; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Elsevier BVEfficient herd management requires identifying which cows are consistently high or low performers over time. While Automatic Milking Systems (AMSs) collect rich data on milk yield and cow behavior, translating these into actionable insights for characterizing productivity remains challenging, especially in a way that is both automated and human-understandable. In this study, we present an interpretable machine learning framework to automatically classify cows in AMSs into low and high Productivity Groups (PGs), focusing on Decision Trees and Multi-Objective Genetic Programming (MOGP). The models were trained using a comprehensive dataset derived from AMSs, including production metrics and behavioral indicators. They aimed to distinguish between Low and High PGs, previously defined through a robust unsupervised multi-algorithm clustering approach applied to the same dataset and features. Specifically, the supervised models were tasked with learning to replicate the resulting classification boundaries. In addition to the interpretable models, eXtreme Gradient Boosting (XGBoost) and Support Vector Machines were included to benchmark predictive performance. To deepen the understanding of both the data and the models, we conducted feature importance analyses using model-intrinsic metrics and model-agnostic techniques, including ReliefF and Shapley values. Milking Robot Rate and Milking Frequency consistently emerged as the most critical features. MOGP yielded transparent mathematical expressions, enabling structural insights on the results of feature importance, and enhancing interpretability on the milking productivity levels distinguished by the PGs previously defined by unsupervised machine learning algorithms. Our findings suggest that interpretable models can classify PGs with competitive accuracy while offering practical insights into milk productivity of AMS cows, thereby potentially supporting trust and utility in data-driven and model-driven dairy herd management.
- Multidimensional coalition structures in the 9th European ParliamentPublication . Rosalino, Sebastião M.; Curado, António; Pinheiro, Flávio L.; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Springer NatureResearch on voting behavior in the European Parliament (EP) has long shown that legislative alignments are structured by multiple ideological dimensions whose salience varies across policy areas and political contexts. Building on this literature, this study maps coalition structures in the EP’s 9th term (2019–2024) using a co-voting network backbone approach applied to roll-call vote data. By extracting statistically significant co-support relationships among Members of the European Parliament (MEPs), we identify how coalition configurations realign across major policy areas and cannot be reduced to a single left–right divide. The results reveal pronounced issue-dependent coalition patterns: larger and governing groups, including EPP and S&D, exhibit fragmented coalition behavior, aligning with different partners depending on the policy domain. Votes on the institutional development of the Union highlight a pro- and anti-European integration dimension that cuts across traditional ideological alignments without fully replacing them. Despite facing unprecedented challenges—including Brexit, the COVID-19 pandemic, Russia’s war against Ukraine, and a period of record inflation—the EP maintained its transnational character: ideological affinity and party group membership, rather than nationality, remain the primary drivers of voting alignment. Rather than revising established accounts of the EP’s ideological space, this study contributes a complementary network-based perspective that makes coalition cohesion, fragmentation, and cross-group alignment structurally and visually explicit.
- Predicting student churn in subscription EdTechPublication . Gavrilova, Liubov; António, Nuno; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Springer BostonSubscription-based educational technology (EdTech) companies experience significant revenue loss from student churn, making retention vital. Retaining engaged learners is generally more cost-effective than acquiring new ones. This study develops a predictive churn model using anonymised student data from an EdTech platform for children’s English learning. Following a CRISP-DM process, student activity, engagement, and financial features were engineered across multiple time windows to capture evolving student behaviours. Four machine learning algorithms-logistic regression, random forest, neural networks, and XGBoost (XGB)—were trained and compared. The optimised XGB model achieved the best performance, with approximately 0.84 accuracy, 0.63 F1 score, 0.85 recall, and the area under the curve (AUC) of 0.85 on test data, effectively identifying likely churners. Shapley Additive exPlanations (SHAP) based analysis revealed that engagement metrics, particularly the number of paid classes (both current and mean over 8 weeks), total learning time, engagement at gamified features, and student tenure, were the most influential predictors, confirming that highly engaged students are less likely to churn. This interpretable model provides actionable insights for retention strategies by predicting individual churn risk and highlighting key engagement drivers. In practice, even a 1% monthly reduction in churn could translate into multi-million-dollar annual savings for subscription EdTech providers. Overall, this research extends churn prediction into the EdTech domain, demonstrates the value of long-term engagement features, and applies explainable AI to enhance model transparency, thereby supporting its practical adoption.
- What makes you trust digital data wallets?Publication . Keba, Varvara; Dhillon, Gurpreet; Oliveira, Tiago; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Associação Portuguesa para o Estudo do Quaternário (APEQ)Purpose Digital data wallets are emerging privacy-enhancing technologies that give users greater control over their personal information, yet trust remains a major barrier to adoption. This study aims to identify the factors shaping trust and how perceived risks moderate these relationships. Design/methodology/approach Using a mixed-methods approach, thirty qualitative interviews conducted in Austria, Romania and Spain identified four key antecedents of trust: trust in the Internet, trust in the community of internet users, trust in the provider and trust in the information system. A follow-up survey of 1,200 respondents tested the impact of these antecedents and the moderating role of perceived risks (identity theft and financial loss) on trust formation. Findings Results show that all antecedents, specifically performance expectancy, transparency, social influence and safety, significantly related to their respective trust targets, and that trust relationships are interdependent across targets. Risk perceptions strengthen the influence of performance expectancy and social influence but weaken the effect of transparency. Originality/value The study contributes to research on trust in digital data wallets by empirically applying and extending the application network-of-trust model to the context of decentralized personal data management. The findings demonstrate that the trust formation is differentiated across multiple trust targets and shaped by context-specific antecedents and perceived risks. The study also provides actionable insights for designers and policymakers seeking to build trustworthy digital data wallets.
- Constant Mean Curvature Surfaces with Harmonic Gauss Map in Three-Dimensional Lie GroupsPublication . Petrov, Eugene; Savchuk, Iryna; NOVA Information Management School (NOVA IMS); B.Verkin Institute for Low Temperature Physics and Engineering of the NAS of UkraineWe describe constant mean curvature surfaces with harmonic left-invariant Gauss map in three-dimensional unimodular Lie groups endowed with left-invariant metrics that are also right-invariant with respect to one-dimensional Lie subgroups, as well as in the hyperbolic space.
- Social networks as pathways of political mobility in Portuguese governmentsPublication . Borges, Rafael; Shaul, Carolina; Pinheiro, Flávio L.; Damásio, Bruno; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); PLOS - Public Library of SciencePolitical mobility in executive appointments is often explained through party affiliation and career trajectories, yet relational embeddedness across institutional arenas may also characterise those who reach ministerial office. We examine, retrospectively and in an exploratory manner, whether the social networks formed by co-participation in Portuguese governmental and parliamentary venues are associated with ministerial appointments. Using web-scraped data on the composition of the Constitutional Governments and the Portuguese Parliament between 2015 and 2024, complemented with the Registers of Interests of government and parliament officials, we construct an individual-venue bipartite network and its projection onto individuals. We estimate the cumulative network of Governments XXI-XXII and, within each policy-domain, retrospectively screen potential candidates for the XXIII Government by ranking actors in the top five positions of at least one centrality measure (betweenness, reach, closeness, weighted closeness, or eigenvector), computed on portfolio-specific subgraphs and restricted to nodes reachable from the Prime Minister. With the identification of 9 out of 16 in-network ministerial appointments, this procedure outperforms both a random selection baseline (mean recall 0.6%) and a career-ladder heuristic based on prior secretarial experience in the same domain (recall 12.5%), consistent with network centrality being informative about the appointment process in stable, well-populated policy-domains. We assess whether Ministers exhibit systematically higher institutional reach than non-ministerial actors using bipartite degree centrality, visualised via a log-scaled KDE, and validated with non-parametric tests (10,000-iteration permutation tests and Mann-Whitney U tests). We decompose connectivity by venue type (e.g., Government, Parliament, Political Party) using a categorical profile comparison and cosine similarity. Ministers display higher bipartite degree centrality than non-ministerial actors (mean 0.0200 vs. 0.0105; difference 0.0096; permutation p-value = 0.021; Mann-Whitney p-value < 0.001). Categorical profiles diverge substantially (cosine similarity 0.494), with significant gaps concentrated in Government and Parliament venues, while Parliamentary Bodies show no meaningful difference (p-value = 0.919). Venue-specific projected network analysis further reveals that the minister-non-minister centrality gap is largest within the political party network, where ministers score nearly double on eigenvector centrality (+96.0%; permutation p-value = 0.003), pointing to elite positioning within partisan structures.
- Gaussian mixture modeling layer and its application to end-to-end generative high-dimensional clusteringPublication . Marques, Alexandre; Henriques, Roberto; Castelli, Mauro; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Elsevier Science B.V., Amsterdam.Learning multivariate Gaussian mixture models featuring full covariance matrices through backpropagation alone remains a challenge. In this work, we introduce a Gaussian mixture modeling framework that learns general Gaussian mixture distributions over input data through gradient-based backpropagation, enabling local probabilistic modeling within deep neural networks. The forward and backward passes of the proposed implementation exhibit quadratic computational complexity with respect to input dimensionality. The proposed Gaussian mixture modeling layer is differentiable with respect to both the input and its parameters, allowing seamless integration into deep learning architectures. We apply our formulation in an end-to-end generative variational autoencoding approach to high-dimensional image clustering. The empirical results demonstrate the applicability of the Gaussian mixture modeling layer to unsupervised learning through generative modeling, and establish its viability as a building block for deep probabilistic models operating on high-dimensional data.
- Celestial NavigationPublication . Lampreia, Suzana; Policarpo, Hugo; Almeida, Pedro P. de; Roboredo, Nuno R.; Ruivo, Jorge M.; Henriques, Rafael B.; Lobo, Victor; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); MDPI - Multidisciplinary Digital Publishing InstituteGlobal Navigation Satellite Systems (GNSS) are incompatible with the underwater domain as radiofrequency signals cannot penetrate the water column, leaving Autonomous Underwater Vehicles (AUVs) reliant on dead-reckoning systems that accumulate positional errors over time. When AUVs surface to reset their navigation, they face another challenge: GNSS itself is increasingly vulnerable to jamming and spoofing in contested environments. Automated Celestial Navigation (CN) has emerged as a promising alternative to other navigation methods, making it possible to derive the absolute position from observations of celestial bodies, entirely independent of human-made signals. This work provides a state-of-the-art review of automated CN technologies, focusing on the literature from 2020 onwards. The review covers Solar Tracking Sensors (STSs), star trackers and horizon detection algorithms and assesses their suitability for AUV integration through a structured SWOT analysis. Following this, a conceptual CN system based on Sunto’s STS is developed for the Light Autonomous Underwater Vehicle platform employing a proposed ten-step integration methodology. Computer-Aided Design models illustrate the conceptual setup, though hydrodynamic/structural verification remains subject to future work. Results suggest that solar-based CN can serve as a periodic absolute position corrector within a hybrid AUV navigation architecture, without requiring satellite infrastructure, which contributes towards a more resilient AUV navigation.