NIMS - Dissertações de Mestrado em Gestão da Informação (Information Management)
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- Plano de Intervenção Prudencial: Estudo de Caso sobre a Performance e Resiliência do Banco Económico, S.A.Publication . Falcão, Sheila Jovânia Alonso; Lopes, Samuel José da RochaNos últimos anos, o Banco Económico, S.A. apresentou indicadores financeiros negativos e voláteis ao nível dos fundos próprios, qualidade dos ativos, rentabilidade e liquidez, quando comparado com o restante do setor bancário de Angola. Este cenário, reforça a importância de um plano de intervenção prudencial por parte do Banco Nacional de Angola (BNA). Esta dissertação analisa a performance e a resiliência do Banco Económico, S.A., no período de 2015 a 2024, com o objetivo de identificar os fatores internos e externos que influenciaram a evolução dos seus principais indicadores financeiros, bem como avaliar a sua capacidade de enfrentar cenários económicos previstos e assegurar a continuidade da sua atividade. A metodologia adotada combina ferramentas de visualização de dados com a aplicação de um modelo de stress testing, para avaliar a continuidade da sua atividade nos próximos cinco anos. Os resultados evidenciam fragilidades face ao setor, sobretudo ao nível da qualidade dos ativos e dos fundos próprios, com impactos diretos na rentabilidade e na liquidez, sendo que o stress testing demonstra limitações na resposta a cenários económicos sem intervenções externas e identifica a recapitalização como um fator determinante para a melhoria da sua posição financeira. Deste modo, esta dissertação contribui para o plano de intervenção prudencial do Banco Nacional de Angola (BNA), ao apresentar uma análise independente baseada em dados do Banco Económico, S.A., do Banco Nacional de Angola (BNA) e do Fundo Monetário Internacional (FMI).
- Transformation, threat and Triage: A Dynamic Cyber-Risk prioritization model for SMEs in EU Digital EnvironmentPublication . Alisah, Dozie Patrick; Martins, João Francisco Ribeiro da SilvaDigital transformation has become essential for the competitiveness of small and mediumsized enterprises (SMEs) across the European Union. However, the adoption of digital technologies also expands cyber exposure by increasing organisational dependence on interconnected information systems, cloud services, enterprise applications, and externally accessible digital platforms. Because SMEs often operate with limited cybersecurity resources, there is a need for practical approaches that support proportionate cyber risk prioritisation. This study develops a dynamic cyber risk prioritisation framework for SMEs by integrating three complementary dimensions: likelihood, impact, and digital exposure. A positivist research philosophy and secondary quantitative research design were adopted. Cyber incident data were obtained from the European Repository of Cyber Incidents (EuRepoC), restricted to expert-reviewed incidents affecting European Union Member States between January 2015 and April 2026. Enterprise digital maturity data were obtained from Eurostat using the Digital Intensity Index for enterprises employing 10–249 persons. The empirical analysis identified phishing, exploitation of public-facing applications, and valid account misuse as the dominant observable Initial Access techniques. Resilience-based impact analysis showed that ransomware-related incidents produce broader organisational consequences than other observed cyber events. A macro-level statistical analysis further indicated a moderate positive association between SME digital maturity and recorded cyber incident visibility, supporting the inclusion of digital exposure as a distinct dimension within cyber risk prioritisation while not implying direct firm-level causality. The resulting framework combines empirical attack likelihood, organisational impact derived from NIS2-based resilience characteristics, and enterprise digital exposure to provide a structured approach to cyber risk prioritisation. The study contributes to cybersecurity and information management by integrating institutional cyber evidence and digital maturity indicators into an SME-oriented decision-support framework. The framework should be understood as an analytically grounded prioritisation tool intended to support cybersecurity decision-making rather than as a predictive model of future cyber incidents.
- The effects of close relationships in privacy concerns and empowerment towards genAI applicationsPublication . Campos, Gonçalo Marques Leite Scotti; Naranjo-Zolotov, Mijail JuanovichThis thesis explores how close relationships can impact the users privacy concerns and empowerment towards genAI applications, and their intention to use them. The thesis model combines parts for the UTAUT theoretical model and the empowerment theory, also studies the links between privacy concerns and empowerment towards the use of generative AI applications and tools, and how they can be affected by the close relationships of the individuals. The results reveal that the social influence provided by family does not effect the use of genAI applications, that the users privacy concerns are not relevant to the use of genAI applications, and that the users empowerment affect significantly the users intentions to use genAI tools. The thesis contributions further confirm the privacy paradox in the users in genAI tools, and expands the relation between user empowerment and user behavior and their privacy concerns towards genAI applications.
- Building a data analytics platform to uncover spatiotemporal patterns and improvement areas in the reliability of Carris buses in LisbonPublication . Pilao Encarnacion, Alyssa Bianca; Painho, Marco Octávio TrindadePublic transport reliability is important for commuters’ day-to-day activities, yet the Carris buses in Lisbon consistently receive complaints regarding delays and service irregularities. This thesis addresses this issue by building a data platform as a proof-ofconcept for government agencies to identify spatiotemporal patterns in Carris performance and hence guide policymaking to improve its operations. The platform uses General Transit Feed Specification (GTFS) Schedule and Real-Time data from Carris, which is then ingested, processed, and visualized using Microsoft Fabric and PowerBI to provide a view of the operator’s performance. Metrics used in the study are Delay Rate, Delay Time, Delay Variability, and Headway. Overall, results show that delays may not be as rampant, but tend to be unpredictable and severe when they do occur. Long headways between buses exacerbate these delays due to the low frequency of buses arriving. Temporal patterns indicate that delays peak during evening rush hours, on Saturdays, and during major events. Location-wise, the worst performing areas are those with major roads and junctions due to high traffic congestion. From the results, several policy recommendations are explored, such as a bonus/malus system to push for operator reactiveness and schedule & route optimization. Limitations of the thesis’ data platform itself are also presented, so that government agencies can adjust accordingly should they choose to adopt it.
- Reducing Supermarket Food Waste through Sales Forecasting: A Comparative Analysis of Time-Series Machine Learning Models for the Sales Prediction of Perishable GoodsPublication . Perlinger, Patrick; Bação, Fernando José Ferreira LucasReducing supermarket food waste is a critical global challenge, with more than one billion tons of food wasted annually. This thesis investigates the optimization of retail sales forecasting for highly perishable goods, specifically strawberries, within a single Austrian supermarket to support data-driven waste reduction. Using a structured experimental pipeline, the study evaluates the predictive accuracy of ARIMAX, LightGBM, XGBoost, LSTM, TFT and a stacking ensemble model across four distinct feature subsets including sales, weather, and calendric data. Empirical results demonstrate that the stacking ensemble model achieved the highest accuracy on the 2025 test set with a weighted absolute percentage error (WAPE) of 43.00%. Among standalone models, XGBoost emerged as the strongest performer with a WAPE of 44.22%. Furthermore, calendric data was identified as the most significant exogenous feature set to improve forecast accuracy. This research contributes to the academic discourse by providing evidence that simpler tree-based models and statistical baselines can outperform complex deep learning architectures in store-level retail environments. These findings offer analytical transferability for practitioners aiming to implement context-aware forecasting frameworks to minimize food waste.
- Agile success factors: From strategic differentiators to baseline conditions: A mixed-methods approachPublication . Martins, Vera Barata; Tam Chuem Vai, CarlosThis study seeks to identify and explain the main drivers of agile project success in the software development industry, with a particular focus on how established and emerging factors interact in contemporary, digitally enabled environments. The proposed research model includes six success factors (team capability, customer involvement, leadership style, team autonomy, digital collaboration infrastructure, and mode of collaboration). Addressing limitations in prior research that often examines these factors in isolation, the study adopts a mixed-methods approach to provide a more comprehensive perspective. Quantitative data were collected from 211 software development professionals and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships between variables. This is complemented by qualitative data from eight semi-structured interviews, offering contextual insights into how these factors operate in practice. The study contributes to theory by offering an integrated framework that combines these success factors, and to practitioners by providing actionable guidance for organizations managing agile software projects in increasingly digital and distributed work environments.
- Identifying Rental Stress Zones in Lisbon: A Spatial Rental Stress Index for Housing AffordabilityPublication . Carvalho, Diogo Filipe Ferreira de; Neves, Maria de Fátima dos Santos TrindadeThis study addresses the growing housing affordability crisis in Lisbon, where existing approaches to measuring rental stress often based on simple income-to-rent ratios, fail to reflect the influence of tourism pressure, urban amenities, and socio-spatial inequalities, limiting their usefulness for urban planning and policy-making. By developing a spatially explicit Rental Stress Index (RSI), this research seeks to provide a more comprehensive measure of rental market pressure. t In recent years, rising housing prices driven by tourism, real estate investment, and increased demand have widened the gap between rental costs and household income, intensifying intra-urban inequalities. To overcome the limitations of traditional affordability measures, this research adopts a Design Science Research Methodology (DSRM) to create a composite index integrating socioeconomic, housing, and spatial indicators. The RSI is computed at the parish level using multiple open data sources, including census data, rental prices, and geospatial data on short-term rentals and urban amenities. The results reveal a clear center–periphery pattern, with higher rental stress concentrated in central parishes, where elevated prices, tourism pressure, and urban attractiveness converge. Peripheral areas show comparatively lower stress levels. The findings demonstrate that rental stress is multidimensional, emerging from the interaction between housing market dynamics, socioeconomic vulnerability, and territorial characteristics. An interactive Power BI dashboard was developed to support the visualization and interpretation of results, enabling more informed decision-making. This study contributes a replicable, datadriven framework to support housing policy and promote more equitable urban development.
- Evaluating No-Code AI Knowledge Assistants: Vector Database-Driven Chatbots for Digital Transformation in EnterprisesPublication . Nienaltowski, Maciej; Lopes, Nuno Alexandre Moura PintoThis thesis examines whether no-code platforms can support the development of effective knowledge assistants for enterprise environments by designing and evaluating a Retrieval-Augmented Generation (RAG) system. The study addresses the problem of information fragmentation, where employees struggle to find reliable guidance across dispersed documents. The prototype integrates workflow orchestration, cloud document monitoring, vector database storage, and language models to automate document ingestion, semantic retrieval while excluding archived content, and response generation with safeguards against unrelated queries. The system was evaluated using 24 UK government policy documents and 21 structured queries covering factual recall, synthesis, negative testing, and document lifecycle scenarios, achieving an average accuracy score of 4.67 out of 5.0 and a relevance score of 5.0 out of 5.0 via LLM-as-judge evaluation. The design includes continuous synchronization between cloud storage and the vector database, metadata-driven document management, a two-stage retrieval process combining vector search with semantic prioritization, and structured error logging, all implemented through visual workflows without custom code. The results indicate that such systems can be implemented using no-code tools under controlled conditions, although limitations remain regarding optical character recognition for scanned documents, role-based access control, and validation in multi-user settings. The study suggests that design choices related to orchestration, metadata structure, and document lifecycle management play an important role in system performance alongside the underlying language models, and it provides a reproducible approach for developing enterprise knowledge systems using visual development tools.
- Determinants of Consumer Satisfaction in E-Commerce: The Roles of Perceived Usefulness and Behavioral IntentionPublication . Pereira, Maria Carlota Martins; Neves, Maria de Fátima dos Santos TrindadeThe rapid growth of e-commerce has increased academic interest in identifying the factors that affect customer satisfaction in online shopping environments. This study investigates the principal factors influencing consumer satisfaction on e-commerce platforms, highlighting the importance of information quality, system quality, service quality, trust, perceived usefulness, and behavioral intention. A conceptual framework, based on the DeLone and McLean Information Systems Success Model, the Technology Acceptance Model, and the Theory of Planned Behavior, was developed and empirically validated using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 219 participants. The findings reveal that perceived usefulness is the strongest predictor of both behavioral intention and consumer satisfaction. Trust significantly enhances perceived usefulness, while service quality and behavioral intention directly influence satisfaction. In contrast, information quality, system quality, and trust do not exhibit a significant direct effect on satisfaction. These results highlight the central role of perceived usefulness as a key mechanism through which trust and service quality translate into satisfaction in data-driven e-commerce environments. The study contributes to the literature by providing empirical evidence from the Portuguese context and by clarifying the relative importance of technological and behavioral factors in shaping consumer satisfaction.
- How can an Interactive Business Intelligence Report Enhance the Communication and Analysis of Humanitarian Security Incident Data?Publication . Wiatr, Maria; Neves, Maria de Fátima dos Santos TrindadeThis thesis explores how an interactive business intelligence (BI) report can enhance the communication and analysis of humanitarian security incident data, using the Aid Worker Security Database (AWSD) as its case study. The research addresses the limitations of existing AWSD visualizations, which are largely static and offer limited analytical depth, by designing and implementing a dynamic BI solution through a Design Science Research Methodology. The solution combines a star-schema data model developed according to Kimball’s dimensional modeling principles, structured data transformation guided by Medallion Architecture, and report development in Microsoft Fabric and Power BI. The study includes a literature review, data architecture design, interactive report development, and evaluation through a structured questionnaire completed by the data owner. The resulting artifact provides a BI report for exploring humanitarian security incidents and demonstrates the potential of interactive visualization to support research, advocacy, and communication in humanitarian contexts.