Centre for Research in Digital Services

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Publications

Corporate ownership concentration drivers in a context dominated by private SME's

Publication . Reis, Pedro; Pinto, António

This paper aims to ascertain how company-specific factors influence the corporate ownership concentration of Portuguese firms. The paper employs several different regression techniques: Generalized Linear Model, Ordered Logit, 2 Stage Least Squares, Ordinary Least Squares, Truncated and Constrained regression. Additionally, to test the model's prediction power, it conducts an in and out-of-sample analysis and used joint-rolling window re- gressions and dependent variables intervals partition to test the robustness of the model under different sample restrictions. Firm size, profitability, the number of subsidiaries, and bank concentration are positive determinants of ownership concentration, while an opposite influence is found concerning auditor qualification and the board of directors' size. Significant implications are provided for the policymaking in countries where capital markets are underdeveloped, and concentrated ownership is common to help the regulator determining the power of controlling shareholders. This study enriches the literature on the determinants of corporate ownership, being the first study to approach non-public companies. It adds novelty by incorporating new company factors which are scarce in ownership studies.

2021-10Journal article

Open access

Does Tax, Financial, and Government Incentives Impact Long-Term Portuguese SMEs’ Sustainable Company Performance?

Publication . Picas, Sara; Reis, Pedro; Pinto, António; Abrantes, José Luís

This article aims to assess how fiscal and financial incentives and government support conditioned the profitability of Portuguese SMEs between 2010 and 2019. The high tax and financial burdens on SMEs have consequences for sustainability and business development. Thus, the study analyzes different incentives provided by the Portuguese government to ease this burden and improve business profitability. The study uses panel data with fixed effects using five different sources of information from five internal tax grant types, three different European Union program financial subventions, and three national budget-specific expenses. The results obtained suggest that tax incentives influence the profitability of SMEs; however, government incentives do not have any impact. The QREN (financial) incentives positively decide the ROA and negatively impact the ROE, contributing to sustainable performance. Portugal 2020 incentives have a weak effect on the first years, improving in the following years. However, the incentive related to R&D is not relevant. This work aims to contribute to decision making for managers, shareholders, and government entities, allowing them to choose those measures that could increase the company’s added value, and for governments, as a tool to select incentives that will most benefit SMEs” profitability. This work identifies the key incentives that impact companies’ profitability.

2021-10-27Journal article

Open access

Technological Innovations in Decarbonisation Strategies: A Text-Mining Approach to Technological Readiness and Potential

Publication . Costa, Paulo Moisés; Duarte, Antonio; Tomé, Paulo; Bento, Nuno; Fontes, Margarida

This study presents a novel, multifaceted approach to evaluating decarbonisation technologies by integrating advanced text-mining tools with comprehensive data analysis. The analysis of scientific documents (2011–2021) and mapping 368 technologies from the IEA’s Energy Technology Perspectives identified 41 technology domains, including 20 with the highest relevance and occurrence. Domain readiness was assessed using mean Technology Readiness Levels (TRLs) and linked to six decarbonisation pathways. The “Electrification of uses” pathway ranked highest, demonstrating significant CO2 mitigation potential and high readiness (mean TRL 7.4, with two-thirds of technologies scoring over 7) despite challenges in hard-to-electrify sectors. The findings provide actionable insights for policymakers, highlighting the need for pathway-specific strategies, a deeper understanding of synergies between pathways, and balancing innovation with deployment to accelerate decarbonisation.

2024-12-23Journal article

Open access

Playfulness and communication for children with autism spectrum disorder: guidelines for a videogame

Publication . Alves, Valter; P. Duarte, Rui; Fonseca, F.; Bernardo, Marco V.; Barreto, Pedro; Silva, C.E.; Felizardo, Sara; Videira, I.; Matos, A.; Henriques, C.

Autism Spectrum Disorder (ASD) affects sensory processing and conditions the development of communication skills and social interaction. Literature shows that children with ASD are fond of technologies and videogames in particular. The predictable and constant behaviour of technological components, the visual appeal, and the challenges are often highly appreciated (Zakari et al., 2014). Besides, videogames typically allow users to play alone, which is adequate to the profile of such an audience. The use of videogames by autistic children has shown to be relevant, and studies are evidencing gains in several areas (Malinverni et al., 2017; Hedges et al., 2018; Ng & Pera, 2018; Valencia et al., 2019; Baldassarri et al., 2020). Even so, existing solutions that were specifically developed for this audience have assumedly pedagogical goals, which systematically compromises their ludic dimension (Hirsh-Pasek et al., 2015). A study is being developed to design and implement a videogame that focuses on pure playfulness and provides an advantage to players who adopt specific strategies that rely on communicating with other players. This videogame is conceived for both intervention and research. The game mechanics explores the flow theory (Csikszentmihalyi, 2011), in order to dynamically adapt the challenges to the skills shown by the players, trying not to let them reach states of anxiety (due to lack of skills) or boredom (due to lack of challenge). This reasoning is extended to motor skills, as autistic people may have difficulties. In this context, it is important to clarify that the study is limited to children with ASD without associated intellectual development disorders that compromise the viability of the very act of playing. Also instrumental to the project, different scenarios are designed so that researchers can observe and collect scientific data, aiming at better understanding the related issues. Such scenarios support the analysis of the influence of physical proximity between the players, their prior level of familiarity, and their relative communicational abilities. Also under analysis is the impact of repeating the experience, both in terms of in-game performance and regarding a possible contribution to the relationship between participants and, eventually, with third parties. The core of this paper is the presentation of the design guidelines that were created to support the videogame. The guidelines result from the contributions of experts, organised according to a Delphi technique (Green, 2014). The set of experts cover the fields of ASD, game design, special education, occupational therapy, rehabilitation, and educational research. Also included is the description of the videogame development, which resorts to agile methodologies, allowing for an incremental and iterative production, supported by recurrent tests and consistently validated according to the intended objectives.

2021-07Journal article

Restricted access

Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial Attacks

Publication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, Pedro

The rise of deepfakes—synthetic media generated using artificial intelli gence—threatens digital content authenticity, facilitating misinformation and manipu lation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures—XCeption, ResNet, and VGG16—for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, XCeption achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These find ings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deep fake threats

2025-01-25Text

Open access