Abstract
ABSTRACT The reconstruction of the economic history of Brazil since independence from Portugal (1822) may lead to a new understanding of its economic growth. The deep-rooted idea that Brazil could have done better means there is a need to delve into each phase of its development. In this paper, we provide a very long-run perspective (1822-2019) of Brazil’s economic growth and process of real convergence. On the one hand, this review indicates that structural changes observed in the middle of the 20th century were crucial in promoting the country’s growth and real convergence with technologically advanced countries. On the other hand, poor institutional conditions and deficient human capital formation have emerged since colonial times as critical factors underlying Brazil’s inability to establish robust and sustainable economic growth.

Abstract
Technological breakthroughs, such as the Internet of Things, Big Data repositories, artificial intelligence or additive manufacturing, are triggering a Fourth Industrial Revolution. This new revolution, also known as Industry 4.0, is characterized by the combination of physical and digital worlds in digital ecosystems that connect the different members in the value chain from clients to suppliers and distributors. Companies are redefining their strategies based on this new paradigm to obtain a competitive advantage. They aim to achieve more efficient and flexible productive processes that can produce high-quality products at low costs, investing on mass customization to satisfy their clients. Accordingly, governments are implementing support programs that create a suitable environment for the adoption of technological innovation strategies by the companies. Although some programs may diverge in some objectives, they all aim to promote workers' skills adaptation, technological supply development, and business modernization. The Portuguese Government also released its program for Industry 4.0 support, known as Portugal i4.0, which is intended to stimulate Portuguese economy digitalization. Furthermore, in latest years, it has been supporting projects through European funds mobilizations from Portugal 2020 program. The present study analyses whether companies that received financial support from Portuguese government to implement innovative projects, within the Industry 4.0 paradigm, were able to improve economic and financial performance and competitivity gains. For such purpose, it was applied an inference statistical method to analyse the differences verified in economics and financial indicators between the periods before and after projects implementation in a selected group of companies.

Abstract
The increasing need to innovate to keep or create competitive advantage and the difficulty in retaining resources that enable innovation, force companies to look for partnerships. Partnering with the appropriate and leading R&D (Research and Development) institutions is critical to be able to innovate. One of the first challenges that emerges when building a collaboration is the selection of organizations to form the consortium. Technology foresight might be an interesting process to start building such partnership, as it helps identify technology opportunities and the entities that lead technology development. This paper uses case research to study the creation and use of a technology foresight process to build new R&D collaborations, while identifying of technology development opportunities. Findings from the case study led to the identification of how the technology foresight process was used to create a new R&D collaboration in the company under study, while identifying what motivated such collaboration and how it was managed. Furthermore, important characteristics of the team responsible for that process and of the process management are highlighted.

Abstract
Technological evolution has continuously driven the development of industries and consequently of society. The fourth industrial revolution consists in the combination of a set of physical and digital technologies that has been changing systems' operations within industries. The pharmaceutical industry has a considerable impact on well-being and has been strongly challenged with this new reality, not only by those that are transversal to all industries but also due to the fact that it is a highly regulated sector, which creates additional barriers for industry 4.0 (I4.0) initiative's implementation. However, it is due to the fact that this revolution provides high growth opportunities to the industry, and consequently for the improvement of population's quality of life, that this topic has been subject to so much research at a global level. This study's main purpose is to understand the impact of I4.0 paradigm implementation in the pharmaceutical industry (mainly in the production area), to analyze the technological readiness of Portuguese pharmaceutical companies to implement I4.0 technologies and to understand the role of the I4.0 paradigm to fight the pandemic situation caused by the COVID-19. To achieve this purpose, an exploratory multiple-case study based on semi-structured interviews was conducted in two Portuguese pharmaceutical companies. It is expected that the results of this work lead to recommendations that help the Portuguese pharmaceutical industry to be better prepared to face the challenges that are coming with this revolution.

Abstract
Despite the wide spread of gamification as a means of influencing behavior, we do not yet fully understand its effectiveness in promoting sustainable behaviors among young people. This question becomes all the more relevant when it comes to influencing their mobility habits, considering the negative impact of motorized transportation on urban livability. As a consequence, the promotion of soft mobility has been on the policy agenda in many countries. In this study, we explore the potential of gamification and the use of rewards as a way to incentivize young citizens to adopt soft mobility over motorized transports. Our goal was to understand how a gamified app with a built-in reward system can influence the promotion of soft mobility among young people in cities, focusing particularly on walking and cycling. To achieve this, we adopted a quantitative research methodology, carrying out a structured survey in three schools enrolled in the Sharing Lisboa project. We used statistical tools based on partial least squares structural equation modeling (PLS-SEM) to analyze the data. We found that an app influences the users' perception of its usefulness, leading to a positive attitude towards its use. Contrary to what was initially assumed, the reward system only influences the perceived usefulness, suggesting that it is important to convince potential users to try the system but that it does not influence their attitude. Moreover, the instrumental attitude, which is related to the benefits and functions of an app, together with the subjective (injunctive/descriptive) norms and perceived behavioral control, have a positive influence on walking/cycling travel intention. Therefore, social pressure, especially from family and friends, is important for building the intention to travel by bicycle/on foot.

Abstract
Our lives have been increasingly filled with technologies that use Artificial Intelligence (AI), whether at home, in public spaces, in social organizations, or in services. Like other technologies, adopting this emerging technology also requires society's attention to the challenges that may arise from it. The media brought to the public some unexpected results from using these technologies, for example, the unfairness case in the COMPAS system. It became more evident that these technologies can have unintended consequences. In particular, in the public interest domain, these unintended consequences and their origin are a challenge for public policies, governance, and responsible AI. This work aims to identify the technological and ethical risks in data-driven decision systems based on AI and conduct a diagnosis of these risks and their perception. To do that, we use a triangulation of methods. In the first stage, a search on Web of Science has been performed. We consider all the 412 papers. The second stage corresponds to a analysis of experts. The papers have been classified according to the relevance to the topic by the experts. In the third stage, we use the survey method and include risk insights from stage two in our questions. We found 24 concerns which arise from the perspective of the ethical and technological risk perspective. The perception of participants regarding the level of concern they have with the risks of a data-driven system based on AI is high than their perception of society's concern. Fairness is considered the risk whose perception is more severe. Fairness, Bias, Accountability, Interpretability, and Explainability are considered the most relevant concepts for a responsible AI. Consequently, also the most relevant for responsible governance of AI. © 2022 ACM.