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- ItemMedia Output Volatility and Reputational Stability: stock–flow dynamics in the Portuguese telecommunications sector(MDPI, 2026-04-21) Oliveira, UrielThis study assesses the elasticity between integrated media performance and corporate reputation by examining the relationship between Media Output Score (MOS) and RepScore™ in the Portuguese telecommunications sector (Altice/MEO, NOS, and Vodafone) between 2021 and 2023. Adopting a longitudinal observational design, the analysis compares inter-annual variation in communication output with corresponding changes in stakeholder-based reputation. Media performance is operationalized through MOS as a composite indicator of visibility, favorability, readership, targeting, and social amplification, while corporate reputation is measured using third-party RepScore™ data. The findings indicate directional alignment between media output and corporate reputation; however, the magnitude of reputational adjustment appears substantially lower than the amplitude of media volatility. Across heterogeneous crisis contexts, including cybersecurity incidents and governance-related events, reputational scores exhibit incremental and comparatively stable evolution despite pronounced fluctuations in media performance. These results suggest that the relationship between media output and corporate reputation is characterized by constrained responsiveness at the annual level, consistent with a stock–flow interpretation in which communication signals operate as high-variance flows and reputation evolves as a path-dependent stock. By empirically illustrating this asymmetry, the study contributes to media influence research by identifying a structural boundary condition in the translation of media exposure into stakeholder evaluation. The findings further clarify the analytical distinction between output-level communication metrics and outcome-level reputational constructs in digital media environments.
- ItemImputation of Data Missing Not at Random: artificial generationand benchmark analysis(Elsevier, 2024-09-01) Pereira, Ricardo Cardoso; Abreu, Pedro Henriques; Rodrigues, Pedro Pereira; Figueiredo, Mário A. T.Experimental assessment of different missing data imputation methods often compute error rates between the original values and the estimated ones. This experimental setup relies on complete datasets that are injected with missing values. The injection process is straightforward for the Missing Completely At Random and Missing At Random mechanisms; however, the Missing Not At Random mechanism poses a major challenge, since the available artificial generation strategies are limited. Furthermore, the studies focused on this latter mechanism tend to disregard a comprehensive baseline of state-of-the-art imputation methods. In this work, both challenges are addressed: four new Missing Not At Random generation strategies are introduced and a benchmark study is conducted to compare six imputation methods in an experimental setup that covers 10 datasets and five missingness levels (10% to 80%). The overall findings are that, for most missing rates and datasets, the best imputation method to deal with Missing Not At Random values is the Multiple Imputation by Chained Equations, whereas for higher missingness rates autoencoders show promising results.
- ItemA Perspective on the Missing at Random Problem: synthetic generation and benchmark analysis(IEEE - Institute of Electrical and Electronics Engineers, 2024-11-12) Cabrera-Sánchez, Juan Francisco; Pereira, Ricardo Cardoso; Abreu, Pedro Henriques; Silva-Ramírez, Esther LydiaProgressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to select data imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates (10%,20%,40%,60%,80%). Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods. Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at Random are presented.
- ItemO Mundo em Casa: o sucesso da televisão por cabo na era da internet(Asociación Internacional de Psicología Evolutiva y Educativa de la Infancia y de la Adolescencia, Mayores y Discapacidad (INFAD), 2015-07-15) Figueiredo, SofiaEste artigo é baseado num estudo longitudinal, entre 2005 e 2015, acerca do consumo da TV por cabo de famílias, numa área urbana do Norte de Portugal. Na última década, a televisão por cabo tem ganho dimensão e relevância, em paralelo com o crescimento do consumo da Internet, dois ícones da cultura digital de hoje. Em ambos os casos, a televisão e a Internet tendem a ser práticas crescentemente individualizadas e personalizadas, expressando identidade, preferências e diferença, e não mais rituais familiares coletivos. Ao contrário, porém, das perspetivas sobre o futuro da televisão, segundo as quais a expansão do consumo da televisão através da própria Internet, em casa e fora de casa, tende a liquidar a televisão como nós a conhecemos, a questão é que a cultura da televisão muda conforme muda a sociedade e a família, de modo que ver televisão sentado diante do aparelho continua ainda a ser uma marca distintiva do continuum cultural entre a casa e o mundo. | This article is based on a longitudinal study, between 2005 and 2015, on households cable television consumption in an urban area of northern Portugal. During the last decade, cable television has gained dimension and relevance in parallel with the growth of Internet, two icons of today’s digital culture. In both cases, television and Internet consumption at home tends to be increasingly individualized and personalized practices, expressing identity, preferences and difference, and no longer collective family rituals. However, contrary to the perspectives on the future of television, according to which the expansion of streaming television through the Internet, both at home and away from home, tends to liquidate television as we know it, the question is that television culture changes as society and family change, so that to watch TV sitting in front of the television set still continues to be, in the age of Internet, a hallmark of the cultural continuum between home and the world.
- ItemModeling Inter‑organizational Business Process Governance in the Age of Collaborative Networks(Springer, 2024-10-04) Ribeiro, Vítor; Barata, João; Cunha, Paulo Rupino daCollaborative networks require inter-organizational business process governance (IO-BPG) mechanisms to define ownership over shared resources and activities, accountability over operations, inter-organizational roles and responsibilities, and strategic partner alignment. We developed an IO-BPG modeling approach aiming to incorporate (1) IT governance activities (e.g., IT performance measurement), (2) data governance activities (e.g., data strategy management), and (3) “shadow” parallel governance-related operations. Resulting from a design science research project, our contributions include the building blocks (domain attributes, ontology, and requirements) of a novel BPMN extension, its demonstration in logistics operations, its evaluation, and design principles to guide IO-BPG modeling. Suggestions for the development and evaluation of future BPMN extensions are also highlighted based on the lessons learned in this project. For practitioners, our contribution can improve accountability reports over data assets and operations, identify dataset ownership, assist in the coordination of governance activities in networked businesses, and comply with regulations and strategic partnership agreements.