Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Rethinking Upskilling and Reskilling in the Age of AI and Automation: A fsQCA Approach

Version 1 : Received: 30 August 2023 / Approved: 31 August 2023 / Online: 1 September 2023 (10:28:31 CEST)

How to cite: Ekuma, K. Rethinking Upskilling and Reskilling in the Age of AI and Automation: A fsQCA Approach. Preprints 2023, 2023090055. https://doi.org/10.20944/preprints202309.0055.v1 Ekuma, K. Rethinking Upskilling and Reskilling in the Age of AI and Automation: A fsQCA Approach. Preprints 2023, 2023090055. https://doi.org/10.20944/preprints202309.0055.v1

Abstract

In the age of artificial intelligence (AI) and automation, understanding the factors that contribute to successful upskilling and reskilling is crucial for workforce adaptability. Research on AI and automation has shown that they affect the workforce, but there is still a gap in understanding what factors contribute to successful upskilling and reskilling initiatives, especially from an organizational perspective. This study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze these factors and identify effective combinations for promoting upskilling and reskilling. By examining complex causal relationships and incorporating case studies, this article aims to enhance the understanding of upskilling initiatives in the AI and automation era. The findings provide insights into key factors driving successful upskilling and reskilling and offer implications for organizations seeking to improve their initiatives, ultimately promoting workforce adaptability and competitiveness.

Keywords

upskilling, reskilling, AI and automation, skills development, workforce adaptability, fsQCA

Subject

Business, Economics and Management, Human Resources and Organizations

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