Work has been continuously changing throughout history. The most severe changes to work occurred because of the industrial revolutions, and we are living in one of these moments. To allow us to address these changes as early as possible, mitigating important problems before they occur, we need to explore the future of work. As such, our purpose in this paper is to discuss the main global trends and provide a likely scenario for work in 2050 that takes into consideration the recent changes caused by the COVID-19 pandemic. The study was performed by thirteen researchers with different backgrounds divided into five topics that were analyzed individually using four future studies methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios. As the study was done before COVID-19, seven researchers of the original group later updated the most likely scenario with new Bibliometrics and Brainstorming. Our findings include that computerization advances will further reduce the demand for low-skill and low-wage jobs; non-standard employment tends to be better regulated; new technologies will allow a transition to a personalized education process; workers will receive knowledge-intensive training, making them more adaptable to new types of jobs; self-employment and entrepreneurship will grow in the global labor market; and universal basic income would not reach its full potential, but income transfer programs will be implemented for the most vulnerable population. Finally, we highlight that this study explores the future of work in 2050 while considering the impact of the COVID-19 pandemic.
The Fourth Industrial Revolution is causing considerable changes to the world of work. The interaction between technology and work, which takes many forms such as digitalization, automation, and augmentation, is happening quickly and broadly, impacting economic sectors left almost untouched by previous industrial revolutions. In this scenario, Higher Education Institutions (HEIs) must be able to foresee changes to prepare future professionals to match the needs of this new digital age. The professionals that are being prepared today need to learn a new set of skills related to emerging and disruptive technologies, such as Artificial Intelligence, the Internet of Things, and Big Data. In this paper, we propose a Knowledge Management model to help manage the HEIs’ teaching staff knowledge about the future of work, specifically, the expected skills and competencies to be highly demanded from professionals in the future. Therefore, we performed a brief review of related work about Knowledge Management in the context of HEIs, Management of Future-oriented Knowledge, and the application of the Delphi method to studies concerning the Future of Work. Considering this previous work, we propose a Knowledge Management model that combines the European Foundation for Quality Management (EFQM) Excellence Model framework with a Delphi process that is used during the Knowledge Generation step of the Knowledge Management process. The proposed model considers that professors are experts in their areas of concentration and, as such, are capable of helping their HEIs with their knowledge that can be used to improve the courses’ curricula. The model also considers that HEIs can help professors make this knowledge explicit, then store, transfer, and apply it. We provide detailed information about how to apply the model, how to deal with potential application problems and the model limitations. The proposed Knowledge Management model can help HEIs to keep up with the trends of demands of the labor market.
During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs.
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