SHS Web of Conf.
Volume 92, 2021The 20th International Scientific Conference Globalization and its Socio-Economic Consequences 2020
|Number of page(s)||8|
|Section||Regions and Economic Resilience|
|Published online||13 January 2021|
- Peters, M.A. (2020). Beyond Technological Unemployment: The Future of Work. Educational Philosophy and Theory, 52(5), 485-491. [CrossRef] [Google Scholar]
- Boyd, J.A., Huettinger, M. (2019). Smithian Insights on Automation and the Future of Work. Futures Journal, 111, 104–115. [CrossRef] [Google Scholar]
- Flemisch, F., Abbink, D. A., Itoh, M., Pacaux-Lemoine, M.P., Weßel, G. (2019). Joining the Blunt and the Pointy End of the Spear: Towards a Common Framework of Joint Action, Human–machine Cooperation, Cooperative Guidance and Control, Shared, Traded and Supervisory Control. Cognition, Technology & Work, 21(4), 555–568. [CrossRef] [Google Scholar]
- McKinsey Global Institute (2016). Digital Globalization: The New Era of Global Flows, available at https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20globalization%20The%20new%20era%20of%20global%20flows/MGI-Digital-globalization. [Google Scholar]
- European Commission (2019). The Changing Nature of Work and Skills in the Digital Age, Publications Office of the European Union, available at https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/changing-nature-work-and-skills-digital-age. [Google Scholar]
- Hofmann, P., Samp, C., Urbach, N. (2020). Robotic Process Automation. Electronic Markets, 30(1), 99–106. [CrossRef] [Google Scholar]
- Cherry, M. A. (2020). Back to the Future: A Continuity of Dialogue on Work and Technology at the ILO. International Labour Review, 159(1), 1-23. [CrossRef] [Google Scholar]
- Jarrahi, M. H. (2019). In the Age of the Smart Artificial Intelligence: AI’s Dual Capacities for Automating and Informating Work. Business Information Review, 36(4), 178-187. [CrossRef] [Google Scholar]
- Wojtak, W., Ferreira, F., Vicente, P., Louro, L., Bicho, E., Erlhagen, W. (2020). A Neural Integrator Model for Planning and Value-Based Decision Making of a Robotics Assistant. Neural Computing and Applications. [Google Scholar]
- Ionescu, L. (2019). Big data, Blockchain, and Artificial Intelligence in Cloud-Based Accounting Information Systems. Analysis and Metaphysics, 18, 44-49. [CrossRef] [Google Scholar]
- Ionescu, L. (2019). Would Taxing the Robots Curtail Technological Advancement or Mitigate the Risks of Automation? Contemporary Readings in Law and Social Justice, 11(1), 33-38. [CrossRef] [Google Scholar]
- Ram, J., Zhang, C., Koronios, A. (2016). The Implication of Big Data Analytics on Business Intelligence: A Qualitative Study in China. Forth International Conference on Recent Trends in Computers Science & Engineering, Elsevier, Procedia Computer Science, 87, 221-226. [Google Scholar]
- Naastepad, W. M., Budd, C. H. (2019). Preventing Technological Unemployment by Widening our Understanding of Capital and Progress: Making Robots Work for Us. Ethics and Social Welfare, 13(2), 115-132. [CrossRef] [Google Scholar]
- Di Nardo, M., Florentino, D., Murino, T. (2020). The Evolution on Man-machine Interaction: The Role of Human in Industry 4.0 Paradigm. Production & Manufacturing Research, 8(1), 20-34. [CrossRef] [Google Scholar]
- Jung, J. (2019). The Fourth Industrial Revolution, Knowledge Production and Higher Education in South Korea. Journal of Higher Education Policy and Management, 42(2), 134-156. [CrossRef] [Google Scholar]
- WEF (2019). The Future of Jobs Report. Retrieved from: https://www.weforum.org/reports/the-future-of-jobs-report-2018. [Google Scholar]
- Coatney, K. (2019). Cyber-Physical Smart Manufacturing Systems: Sustainable Industrial Networks, Cognitive Automation, and Big Data-driven Innovation. Economics, Management, and Financial Markets, 14(4), 23–29. [Google Scholar]
- Drennan-Stevenson, K. (2019). Real-World Implementation of Cyber-Physical Production Systems in Smart Manufacturing: Cognitive Automation, Industrial Processes Assisted by Data Analytics, and Sustainable Value Creation Networks. Journal of Self-Governance and Management Economics, 7(3), 14–20. [Google Scholar]
- Tuffnell, C., Kral, P., Siekelova, A., and Horak, J. (2019). Cyber-Physical Smart Manufacturing Systems: Sustainable Industrial Networks, Cognitive Automation, and Data-Centric Business Models. Economics, Management, and Financial Markets, 14(2), 58–63. [CrossRef] [Google Scholar]
- Nica, E. (2019). Cyber-Physical Production Networks and Advanced Digitalization in Industry 4.0 Manufacturing Systems: Sustainable Supply Chain Management, Organizational Resilience, and Data-driven Innovation. Journal of Self-Governance and Management Economics, 7(3), 27–33. [CrossRef] [Google Scholar]
- Kral, P., Janoskova, K., Podhorska, I., Pera, A., and Neguriță, O. (2019). The Automatability of Male and Female Jobs: Technological Unemployment, Skill Shift, and Precarious Work. Journal of Research in Gender Studies, 9(1), 146–152. [CrossRef] [Google Scholar]
- Kovacova, M., Kliestikova, J., Grupac, M., Grecu, I., and Grecu, G. (2019). Automating Gender Roles at Work: How Digital Disruption and Artificial Intelligence Alter Industry Structures and Sex-based Divisions of Labor. Journal of Research in Gender Studies, 9(1), 153–159. [CrossRef] [Google Scholar]
- Groener, M. (2019). Automated Robotic and Network Connectivity Systems for Self-Driving Vehicle Technology. Contemporary Readings in Law and Social Justice, 11(2), 36–42. [CrossRef] [Google Scholar]
- Tooby, C. (2019). Governance Mechanisms of Analytical Algorithms: The Inherent Regulatory Capacity of Data-driven Automated Decision-Making. Contemporary Readings in Law and Social Justice, 11(1), 39–44. [CrossRef] [Google Scholar]
- Gutschow, E. (2019). Big Data-driven Smart Cities: Computationally Networked Urbanism, Real-Time Decision-Making, and the Cognitive Internet of Things. Geopolitics, History, and International Relations, 11(2), 48–54. [CrossRef] [Google Scholar]
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