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    Conference on ENTERprise Information Systems

    Conference on ENTERprise Information Systems

    Conference

    年发文量

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    论文(1207)

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    1The Impact of Artificial Intelligence on Project Manager’s Skills
    Irina Gonçalves, Luísa Domingues

    Project management is a critical driver of organizational success, requiring professionals to integrate diverse technical expertise with strong interpersonal skills. The introduction of Artificial Intelligence (AI) has presented new opportunities for project managers, enabling the automation of tasks and the analysis of large volumes of data. However, AI serves as a complement to the manager’s role, rather than a replacement for the soft skills essential to leadership and decision-making. This study, based on recent literature and interviews with project managers, concludes that AI is perceived as a strategic asset. Tools such as ChatGPT and Copilot are widely used to streamline processes, manager’s role continues to rely on human capabilities, including critical thinking and technological curiosity. The interaction between technology and human skills will be crucial for the future of project management, ensuring efficiency without compromising the quality of decisions or interpersonal dynamics.

    2025
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    2Analysis and Improvement of an Industrial Company’s Evacuation Process Using Simulation
    André Rodrigues,Luís Pinto Ferreira,Elga Costa,Ana Luísa Ramos, Gorete Senra,Paulo Ávila

    Fire safety in industrial environments is essential to ensure quick and safe evacuations. This study used computer simulation with Pathfinder software to improve the evacuation of buildings in an industrial company. A total of 10 strategies were analysed. The results showed that the simulation is effective for dynamic what-if studies, with the best scenario showing an evacuation time of 132.3 seconds and the worst scenario, 558.0 seconds. The average distances covered varied from 31.5 meters in the best scenario to 134.6 meters in the worst scenario. It was concluded that multiple exits and additional doors are vital. It is recommended to strategically place the doors, carry out evacuation drills and develop an automated door release system to increase safety. In this study, people movement velocity was defined by a normal distribution, presenting an average velocity of 1.4 m/s and a standard deviation of 0.2m/s.

    2025
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    3Predictive Models for Planning Seasonality: the Algarve’s Case
    Ema Baptista, Paula V. Martins, Sílvia Fernandes

    During the summer, a higher influx of tourists overwhelms local healthcare services. This pressure exacerbates the shortage of professionals and hinders the efficient management of hospitals and health centers. The overall objective of this study is to investigate the relationship between tourism seasonality and the demand for healthcare in Algarve (Portugal).The specific objectives are: to identify seasonal challenges in the demand for healthcare services and discuss solutions to optimize resource allocation. A selected machine learning model shows that the impact of overnight stays in tourist accommodations outweighs the impact of the resident population on visits to emergency services in the public healthcare sector. This behavior highlights the importance of tourism as a structural factor in the demand for emergency services. Future planning should consider this kind of models and algorithms in incorporating these issues for better service quality and productivity.

    2025
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    4Cultural Diversity and Team Performance: A Computational Exploration of Nonlinear Effects
    Jamie Bridel, Carlos J. Costa

    This study investigates the complex and often contradictory relationship between cultural diversity and team performance. It addresses the theoretical hypothesis that the relationship is nonlinear — specifically, moderate diversity levels enhance performance, whereas very low or very high levels may negatively influence it. A computational simulation model defines cultural diversity as the average pairwise Euclidean distance between team members’ cultural vectors in a multidimensional space. Team performance is modeled as a quadratic function of diversity. Monte Carlo simulations generate synthetic team compositions and evaluate resulting performance outcomes. The model also incorporates motivational factors — derived from gamification theory — and contextual support as additional performance determinants. Simulation results reveal an inverted U-shaped relationship between cultural diversity and team performance, affirming the moderation hypothesis found in diversity research. Teams with moderate diversity consistently outperform those with either low or excessive diversity. Additionally, the results show that higher motivation and contextual support levels significantly boost team performance, even when diversity levels vary. These findings suggest that motivation and environment amplify diversity’s positive potential.This research introduces a novel, extensible computational model that enables the exploration of diversity-performance dynamics using synthetic data. Unlike prior work, the model integrates cultural distance, motivational mechanisms (e.g., gamification elements), and contextual support into a single analytical framework. It also emphasizes interpretability by modeling diversity effects with a quadratic function, allowing for theoretical clarity and empirical alignment. The model offers a data-generating framework for organizational decision- makers aiming to optimize team composition in culturally diverse contexts. It can support strategic decisions related to diversity management, leadership interventions, and gamification design to enhance team outcomes.

    2025引用:1
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    5Artificial Intelligence in Support of Teaching: Predictive Models and Adaptive Systems for Personalising Learning
    Jorge Duque, José Moreira,Firmino Silva, Helena Gonçalves

    Artificial Intelligence (AI) is now a success factor in various business sectors and in the education sector in particular, it can contribute innovative models for personalising higher education. The use of predictive models makes it possible to identify learning difficulties and anticipate student performance with more effective pedagogical actions. AI-based adaptive systems also allow content and teaching methodologies to be aligned with the needs of each student, promoting a more efficient and motivating learning path. This article explores the impact of AI in supporting teaching, highlighting the involvement of teachers and students in the use of intelligent technologies. In particular, it analyses the application of algorithms, natural language processing, robotics and digital systems to support academic difficulties and the implementation of adaptive systems for personalising learning. The study contributes to understanding the potential of AI and the importance of integrating it into the school and academic context, promoting a highly reactive and student-centred learning environment, adjusted to the needs and opportunities of the 21st century.

    2025引用:1
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    高被引作者

    作者引用发文
    Moutaz Haddara22413
    Ahmed Elragal1898
    Erik Proper1475
    Geert Poels1386
    Agnes Nakakawa862
    Tiago Oliveira778
    Ruivo, P.737
    Manu De Backer723
    Joao Barata644
    Stijn Hoppenbrouwers613

    高产作者

    作者引用发文
    António Cunha2427
    Domingos Alves3524
    Gabriela Fernandes2817
    Emanuel Peres2116
    Agnar Johansen214
    Joaquim João Moreira De Sousa3913
    Moutaz Haddara22413
    Telmo Adao1212
    Ricardo Gomes411
    Petra Schubert811

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