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Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine
Abeer Abdullah Alaql1, Fahad Alqurashi1, Rashid Mehmood2
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Science Progress
|November 29, 2023
Summary
This study uses big data and machine learning to identify 28 key parameters for understanding multi-generational labor markets. These insights are crucial for developing sustainable economies and societies amidst global challenges.
Area of Science:
- Economics
- Sociology
- Computer Science
- Data Science
Background:
- Aggressive capitalism impacts social, economic, and planetary sustainability.
- Persistent economic issues (inflation, energy costs, wars, pandemics) and workforce changes (Great Attrition, diversity) necessitate transformative approaches.
- Existing economic models struggle with current global challenges.
Purpose of the Study:
- To discover multi-perspective parameters for multi-generational labor markets using big data and machine learning.
- To develop a data-driven software tool for parameter discovery.
- To enhance the theory and practice of AI-based knowledge discovery for autonomous systems and novel labor economics.
Main Methods:
- Utilized big data and machine learning for parameter discovery.
- Analyzed 35,000 academic article abstracts (Web of Science, 1958-2022) and 57,000 LinkedIn posts (2022).
- Applied quantitative and visualization methods, extracting multiple taxonomies for multi-generational labor market exploration.
Main Results:
- Discovered 28 parameters categorized into five macro-parameters: Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues, and Generations-specific Issues.
- Developed a complete machine learning software tool for data-driven parameter discovery.
- Provided a knowledge structure and literature review of multi-generational labor markets.
Conclusions:
- The findings enhance AI-based knowledge and system parameter discovery for autonomous capabilities.
- Promotes novel approaches to labor economics and markets, fostering sustainable societies and economies.
- Offers a data-driven framework for understanding and navigating complex multi-generational labor markets.
Keywords:
Machine learningbig data analyticsgeneration Xgeneration Ygeneration Zgeneration alphalabour economicslabour marketslatent Dirichlet allocationnatural language processingsmart citiesMore Related Videos
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