In this paper, we explore a perspective on well-being in the context of digital nomads, reporting the first findings of an empirical study investigating the extent to which the nomadic lifestyle enables a healthy life and the enjoyment of well-being. Following the Gioia methodology, we collected and analyzed textual data from posts in a specific online Reddit social network discussion community to do this. Preliminary findings indicate that Fear of Missing Out (FOMO) acts as a trigger that stimulates the adoption of digital nomad lifestyles.

Software systems are critical resources for our daily lives. Due to that, computer science students need to learn software engineering knowledge and skills to develop software systems with the expected quality, under budget, and on schedule. The size and complexity of software systems and the short time to market make it necessary to have various software developers working as a team. In this scenario, the success of a project depends on coordination, communication, and collaboration. The evolution of network, communication, and computer technologies made it possible for people from different countries to work on the same project. Therefore, software engineers must collaborate remotely with others in Global Software Engineering (GSE) projects. Hence, students must also learn how to collaborate with people from different countries, with specific expectations, and different cultures and languages. This study lists the main GSE challenges the students must be ready to deal with, how researchers have addressed GSE, and how GSE education has been approached in the last five years. Researchers focused on teaching approaches, what students experienced during GSE courses, and the importance of GSE in industry. Students had the opportunity to face unsolved GSE challenges, and GSE courses still need to be improved.

The learning game “Present of the Future” has shown great potential as an effective tool for promoting futures literacy and speculative design in various educational and professional contexts. Through interactive and collaborative scenarios, participants are encouraged to explore future possibilities and develop creative solutions to contemporary challenges. Despite its promising impact, the game still lacks a structured and systematic process for evaluating the user experience on three levels: the immediate reaction to the game experience, the retention of learning in the short and long term, and collaboration in creating future artifacts. We propose a detailed experiment and an evaluative questionnaire composed of open and closed questions to fill this gap and provide a foundation for critically analyzing these dimensions. Although the experiment has not yet been conducted, the research establishes a robust theoretical and methodological framework that will allow, in future stages, the collection and analysis of quantitative and qualitative data to complement and enrich this study. Thus, we expect that the future application of these practices will contribute to a deeper understanding of the pedagogical and collaborative effects of the game.

With the advancement of Artificial Intelligence (AI) technologies, the world has been increasingly reshaped across multiple domains - healthcare, finance, education, and creative fields. This expansion transforms individual tasks and facilitates complex collaborations where humans and machines work to- gether in unprecedented ways. Through machine learning and, more specifically, large language models (LLMs), AI now plays a crucial role in enhancing productivity. Recent technological advancements have significantly boosted the text generation capabilities of LLMs, enabling them to simulate personas that can represent groups or specific well-known individuals. By simulating expert perspectives, LLMs provide insights to guide human decision-making across various collaborative frameworks in Computer-Supported Cooperative Work (CSCW). Personas, as simulated identities, have proven helpful in group brainstorming sessions, consensus-building exercises, and virtual advisory boards, which help represent diverse viewpoints and facilitate group decisions. Among these CSCW methods, the Delphi method is a well-established approach for reaching consensus among experts through iterative feedback and structured discussions. This work introduces a framework to manage and evaluate LLM-generated personas simulating expert input in Delphi studies. Our contributions include a framework designed for evaluating LLM personas in Delphi studies using a text similarity validation technique comparing real and simulated expert opinions and an evaluation of a Delphi about Brazilian higher education, assessing the LLMs' reliability in real-world scenarios. Findings show LLMs can effectively replicate expert perspectives, enhancing AI integration in specialized decision-making.

In light of the increasing recognition of environmental and social challenges, technology-driven corporations often face significant barriers in aligning their innovation-driven goals with sustainable practices. Despite their capacity for rapid technological advancements, these companies struggle to integrate sustainability objectives into their core strategies due to competing priorities and a lack of cohesive frameworks. This study explores Knowledge Management’s (KM) capacity to facilitate and implement sustainable practices. We use the Rapid Review methodology to systematically review the literature and achieve this objective. A total of 35 articles were analyzed, examining factors related to organizational knowledge, the adoption of KM within technology-driven companies, and effective sustainable practices. The results highlight the importance of KM in facilitating and implementing sustainable practices, showing that technology-driven companies that invest in organizing their knowledge can improve their effectiveness in implementing sustainable initiatives, increase their resilience and competitiveness, and strengthen trust with stakeholders.

In recent years, artificial intelligence (AI) has advanced rapidly, transforming various sectors of society and delivering value to governments, businesses, and end users. However, these advances have also increased computational costs and training times, resulting in significant energy consumption by AI systems. This situation highlights the urgent need for regulatory discussions and proposals that guide AI development and innovation toward energy sustainability. This work reviews, organizes, and categorizes recent regulatory policies for AI energy sustainability into four dimensions for trend analysis. The results show that most identified policies concentrate on transparency and energy efficiency, indicating a higher maturity of these measures within regulatory discussions. This work seeks to raise awareness among stakeholders involved with AI and sustainability, encouraging reflections on energy consumption and efficiency to build a more sustainable future.

Traditional methods for Technology Foresight often struggle to keep up with the fast changes in technology. This work looks at how crowdsourcing can improve Technology Foresight by using collective intelligence to spot trends and tackle complex problems. The goal of this research is to overcome the limits of traditional approaches by using online platforms and new technologies like big data and machine learning to make innovation more accessible. We examine how to apply crowdsourcing effectively in Technology Foresight, discussing its benefits, challenges, and real-world uses. We used a Rapid Review (RR) approach, combined with Large Language Models (LLMs), to refine how we select and analyze research. This method helped us find important insights and categorize applications in different areas. The findings show that crowdsourcing can speed up technology forecasting by bringing in diverse viewpoints and encouraging collaboration across fields. Examples include its use in urban planning, disaster management, and open innovation platforms. Furthermore, combining crowdsourcing with advanced technologies enhances its effectiveness in foresight processes. This work offers practical advice for organizations that want to use crowdsourcing in their innovation strategies and suggests future ways to integrate new technologies into Technology Foresight.

Future-related studies are key in identifying and predicting emerging technologies, fostering innovation, and driving scientific progress. Games have increasingly served as a tool to engage researchers in Technology Forecasting and Foresight activities, presenting a gamified approach to gather, analyze, and synthesize information to allow future foresight. In this work, we use the Rapid Review methodology to analyze the scientific literature and explore the use of games as tools for Technology Forecasting and Foresight. We analyzed and categorized the articles by theme and type of study, identifying the areas where games can be most beneficial to guide professionals performing Technology Forecasting and Foresight, helping companies and governments better understand the future. The empirical evidence found provides a comprehensive understanding of how games can enhance foresight activities and contribute to the success of Technology Forecasting and Foresight initiatives. Our analysis also provides insights for future research about gamified strategies in Foresight.

Since 2016, the demand for remote work has grown by nearly 400%, with 3.5 million remote job vacancies posted, and it is expected to continue to grow in the coming years. Therefore, one of the challenges in advancing remote work is fostering innovation, particularly serendipity, in a remote workforce. This fortunate discovery can pave the way for technological advancements, new business strategies, or even scientific revolutions. However, creating moments of serendipity in a remote work environment is a significant challenge. Thus, finding the right approach to stimulate serendipity in a remote work environment is an ever-evolving challenge. Therefore, this work aims to understand the possibilities of serendipity that collaboration tools used in remote work can support. The methodology used for this work was a Rapid Review. First, we explore the factors related to serendipity in physical offices, for which we identified twenty-three elements. Next, we compiled 38 strategies for remote work that were found to promote serendipity, which we organized in a framework for better observation. Our findings can serve as a starting point for designing new tools and identifying existing software and tools that already play a supportive role.

Recent Artificial Intelligence advancements raise concerns about the future of work, particularly technological unemployment. Studies show automation's impact, but tools like ChatGPT disrupt even traditionally secure professions like programming. In this study, we reevaluate AI's effects using a method to assess the impact of Generative AI technologies on occupations to understand the potential effects of Generative AI systems on software development work. Valuable insights were obtained by gathering the view of a group of workers, primarily composed of developers who are starting their careers, regarding the impact of these technologies on the tasks they perform to provide a comprehensive understanding of the implications of Generative AI for software development. Results show that all programming tasks performed by these workers would experience some impact by Generative AI -65% of the tasks being considerably impacted, 12% moderately impacted, and 18% minimally impacted. This analysis highlights the substantial influence of Generative AI technologies on software development, mainly affecting those in the early stages of their career. The results of this work contribute to the academic community with valuable information. Policymakers can also use this information, as this work provides a comprehensive view of the impacts of Generative AI on software developers, considering their direct impact on job tasks.

Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.

The military has developed wargames to explore their doctrines and train operational concepts. Wargames are often designed to teach educational objectives and share and produce knowledge. The success of a wargame design depends on how it achieves these objectives. Players are the main actors in wargames and often group into teams. The final result of the wargame depends on the collaboration and interaction between the members of each team. Therefore, this work aims to understand how collaboration occurs in wargames. Moreover, we observed the activities and processes during a large-scale wargame to gather how the players collaborate. A better understanding of collaboration in wargames may provide insights for game designers to improve the wargames design process to consider how the players collaborate.

Shopping tools are widely used by millions of users to find the best deals in different marketplaces worldwide. In this work, we introduce the concept of crowdsourced deals, which refers to deals found and shared by a crowd of potential consumers. We analyze various crowdsourced deal tools and highlight their features. By formalizing collaboration in certain types of bargain-hunting tools, we can develop new techniques to improve the user experience and attract more contributors to maintain an active and stable community. Understanding the concept of crowdsourced deals can also benefit organizations looking to increase their sales and analyze their online reputation. Bargain-hunting tools provide a form of marketing that we can refer to as online word-of-mouth advertising, which is crucial in today’s consumer society, similar to well-known word-of-mouth advertising.

In the process of creating a Technological Roadmap, a large amount of data must be analyzed, and future events may be spread across several document databases, making it harder to researchers retrieve all future events described in these publications in a timely fashion. Technology Roadmapping is a time-consuming and error-prone method that usually requires a group of experts to be properly executed. This work presents NERMAP, a collaborative system capable of semiautomating the Technology Roadmapping process, through the Named Entity Recognition method. The evaluation showed that NERMAP is capable of retrieving up to 83% of future events in documents. Furthermore, the system was also able to identify some future events that our validation team missed. By using the system, researchers can perform the analysis in considerably less time, thus reducing costs. Therefore, the proposed system enables a small group of researchers to analyze a large number of documents in a short time, streamlining their ability to identify, process, and analyze information in the form of roadmaps.

The effective allocation of personnel avoids the waste of resources and enables the organization to achieve the maximum possible results. In this work, we present a decision aid for Navy personnel allocation based on the Coppe-Cosenza Model and the Hungarian Algorithm. We implemented the model in R, using existing packages, and can be operated by non-experts. The proposed model extends the potentialities of the Coppe-Cosenza Model, presenting an optimal cost solution and presenting a feasible transformation to use the aggregation matrix as a basis for a loss/cost function. Finally, we build a feasible and meaningful integration of both techniques to adapt them to the problem domain peculiarities. Since the optimization algorithm is polynomial, the model can be used for considerable large problems.

In recent decades, technological advances have exponentially accelerated the search for digital transformation. The use of technologies based on Artificial Intelligence (AI) is essential to achieve these goals. In this work, we investigate the future trends in the field. We conducted a systematic review and analyzed their content with a methodology based on Future-Oriented Technology Analysis (FTA) techniques. We have identified 30 future events related to digital transformation and AI and their respective feasible years. Our findings are categorized into industry fields: business process management, construction industry, digital economy, human resources, healthcare, industry, social innovation, tourism, and education. Our work allows decision-makers to draw up strategies, map the paths to be taken, and better prepare for their future needs, using AI as a tool for value aggregation.

Organizations often use one or more knowledge-based approaches in order to structure and promote their Intellectual Capital (IC), ensuring flexibility and resilience in an environment of constant change, competitiveness and innovation capabilities. In this context, Knowledge Management (KM) and Future-oriented Technology Analysis (FTA) are two research topics that have been used by organizations. However, organizations usually do not use both KM and FTA. Moreover, when they use both, they apply them as separate initiatives. Therefore, applying KM and FTA together produce many challenges. In this direction, this work presents a framework that uses FTA to improve KM processes through the use of the IC, as well the use of KM processes to improve FTA Strategies. Moreover, the proposed framework forms a virtuous circle, in which KM processes, Intellectual Capital and FTA strategies interact producing a mutual benefit.

The practice of Knowledge Management within the scope of the Brazilian Federal Government advocates achieving the strategic objectives of organizations, aiming the provision of better services to society. Such improvements can be achieved by acquiring new knowledge or transforming existing knowledge, that although belong to the organization could be equally useful to other organizations. However, internal rules of organizational confidentiality, the lack of motivating and integrative policies make these organizations closed in the aspect of the transfer of acquired knowledge to other organizations, leading to efforts and redundant spending to acquire new knowledge. In this paper, we propose the creation of a Knowledge Management service bus for Federal Government organizations, aiming the transfer of knowledge among their organizations thought software interoperability. Therefore, our proposal enables, beyond the software interoperability among the participating organizations, the creation of Knowledge Management Systems' standards.

The content creation process on the Internet changed with the emergence of Social Networks, with the volume of information overgrowing. Unfortunately, social networks have shown a high potential for disseminating hoaxes and, more recently, fake news. This fact encouraged the emergence of fact-checking organizations, usually carried out by journalism organizations. However, they have limitations to meet the enormous demand arising from the immense volume of fake news currently on the Internet. Thus, this study proposes a crowd-based solution as a way to meet this high demand for fact-checking. The proposed fact-checking process uses crowdsourcing to performing the filtering, analysis and classification of news. In this way, we can use a checking mechanism that may scale proportionally to the creation of fake news.

The labor market is becoming increasingly competitive, with new technologies and products being launched at all times.Companies that have the privilege of creating the products at the beginning of innovation can serve more customers and thus generate more profits.In this search for the market, companies are increasingly willing to please their customers, trying to understand what they seek.There are several concepts, ways, strategies, and technologies that help companies better understand what people are searching for, interfering with this market.These strategies include Futureoriented Technology Analysis (FTA) and others, such as Knowledge Management (KM), from which we focus on Technological and Participatory practices.These strategies are directly related bringing a fundamental advantage to the company that knows how to use these concepts effectively.

The Joint Planning Process of the Brazilian Ministry of Defense comprises the elaboration of possible courses of action (COA) to be adopted in a joint operation. The Commander, advised by his General Staff, assesses each COA and selects subjectively the one that best meets the mission statement. Such decision-making process has no simulation tool to assist the Commander and his General Staff in confronting their own COA with possible enemy COAs. We analyzed how the Brazilian Ministry of Defense (MD) codifies knowledge in such simulated confrontation, their structures and the knowledge flow. Starting with the information from MD doctrines, we structured the explicit knowledge in models and diagrams, which describe business processes, taxonomies, conceptual maps, and mental maps. Understanding the knowledge creation process increases the efficiency of an operation, while reduces confrontation errors, minimizing revision efforts and optimizing the use of the operational capabilities of the Armed Forces.

The advance of technology has influenced the relationship between government and citizen. In this scenario, the convergence between digital democracy, crowd computing, information visualization, mining of social media and collaborative information systems can increase citizen participation in solution for social problems. The aim of this paper is to propose an application that provides a collaborative environment and allows citizens to help with the solutions of social problems. Expect that the application encourage the participation increase of citizens in decision-making of public sector. In proposal is described some features and the basic application architecture. Furthermore, in order to facilitate understanding was generated a prototype and a conceptual model of the application.

Emergency situations are unpredictable and happen in many ways around the world. Natural disasters or similar events of major proportions require governmental intervention. In order to do so, the public administration must keep resources ready to attend these events. In this work, we propose a framework to analyze economic factor that may influence the government supply chain. We evaluate the proposed framework, analyzing medical supplies in the Brazilian Navy, focusing in the surgical gloves — a representative supply. In our analysis we found out that Price Registration bids tend to be the most effective policy in economic crisis scenarios.

The Future-oriented Technology Analysis (FTA) literature recommends to use diversified FTA methods to improve the quality of the results. As the FTA methods are performed, the output of one method is used as an input of the next one. We propose that FTA methods rely on collaboration in order to produce the final result of the FTA. In this work, we analyze the collaboration applied to the FTA, from the collaboration among the researches when performing a method to the collaboration between the FTA methods. Finally, we also discuss the awareness of the collaborative work and how the FTA methods may support them.

People make decisions every day. If the wrong decisions are made in governments and companies, this may cause huge financial losses or other negative consequences. There are several methods to help decision-makers with these hard decisions. Delphi is one of those methodologies. It relies on the wisdom of crowds to extract the best solution from a multidisciplinary group of experts. Long-term decisions may take more than a decade to be proven right or wrong, and as the time passes, the reasons for the decision may be forgotten. There is a lack of methods which enable storage of the reasons for a decision made using the Delphi methodology. We propose an ontology which stores the whole process, thereby allowing the decision-maker to validate the reasons for the decision. If the experts' opinions are still valid, it is likely that the decision is still valid. We also prove that our ontology is minimal and sufficient to satisfy our objective.

Software development projects with geographically disperse teams, especially when use UML models for code generation, may gain performance by using tools with collaborative capabilities. This study reviews the distributed collaborative UML editors available in the literature. The UML Editors were compared using a Workstyle Model. Then, we discuss the fundamental challenges which these kind of UML Editors face to assist distributed developers and stakeholders across disperse locations.

Crowdsourcing tools have improved e-learning over the last few years. However, these tools vary in many aspects. This work discusses and analyzes the basic e-learning processes based on these tools, defining ways to classify them through these processes. Finally, we define conceptual models tailored to each type of process, using a standard notation for software development. These conceptual models may be used to build new e-learning crowdsourcing tools.

Delphi is a traditional method for supporting the decision-making process. It relies on a panel of experts who collaboratively reach a consensus about a given topic. This method may be empowered with new communication technologies which can gather a larger and more geographically distant expert group. In this work, we discuss in depth the gathering of a panel of experts for Delphi, which may be constrained by geographic location and competence levels, using methods to find experts in social networks. The Delphi process was also adapted to use social network analysis methods to find experts, thus allowing faster expert selection from small local groups to huge worldwide groups.

Recent crowd sourcing tools are changing the Education. However, each tool has a specific approach, business model and e-Learning goals. Due the increasing contribution of crowd sourcing in the e-Learning process, it has become important to study it and map the challenges intrinsic to this activity. This work discusses and classifies several crowd sourcing e-Learning tools that can be found in the Internet. We defined and used eleven dimensions to classify the 22 tools found, ranging from online Universities to marketplaces for online courses. The work contributions are: provide the first map the crowd sourcing tools and a framework for classify the future tools.

In scientific and engineering scenarios we can notice the predominance of explicit knowledge being manipulated and distributed, which makes recommender systems very useful in this environment. But along with a knowledge management approach this kind of system can support the organization in better identifying competences, help engage users in a continuous and dynamic knowledge exchange, and customize knowledge dissemination as much as possible. In this work we detail a collaborative recommender system which is used in a Knowledge Management Environment for Scientific and Engineering contexts; we show how this approach can be aimed at a KM process and how this approach can deal with other kinds of knowledge used in research centers and universities.