Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning.

Abstract

The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.