Mechanisms of employability development for applied talents in the artificial intelligence era

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Authors: Liguo Wu, Chanti Wu, Jinzhou Geng, Haitao Zhao, Zongtuan Liu
Cited by: 1
References: 52

Abstract

Rapid AI integration is transforming global labor markets while widening the digital skills gap. Despite existing efforts, research remains limited by ambiguous AI-driven dimensions, a lack of systematic exploration into factor hierarchies, and methodological constraints that leave deep-seated causal mechanisms underexplored. Integrating the COTE (Customer-Organization-Technology-Environment) value co-creation framework with TOE and UTAUT models, this study employs a multi-stage mixed-methods design centered on a pivotal cross-border e-commerce enterprise. Utilizing a tripartite research design comprising Grounded Theory (n = 15), DEMATEL (n = 6), and Interpretive Structural Modeling (ISM), we develop a holistic Macro-Micro framework to identify and deconstruct 15 critical factors influencing the employability of applied talents. The findings reveal a multi-layered Environment-Organization-Competency (E-O-C) transmission path where corporate culture acts as the foundational driver, triggering a recursive feedback loop among workplace mindset, communication, and career development, which ultimately facilitates the top-level outcome of AI technology acceptance. This systematic framework provides actionable guidance for stakeholders to optimize talent cultivation models in the era of AI.

Fields of Study & Subjects

Higher Education and Employability Educational Leadership and Innovation Human Resource and Talent Management

Publication details

Journal / source
Scientific Reports
Publisher
Springer Science and Business Media LLC
Volume
16
Issue
1
Publication date
25 April 2026
Language
EN