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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 summary is machine-generated.

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.

Keywords:

Machine learning big data analytics generation X generation Y generation Z generation alpha labour economics labour markets latent Dirichlet allocation natural language processing smart cities

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Published on: June 10, 2021

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.