Artificial Intelligence, Job Displacement, and Gender-Specific Training Pathways: A Multi-Group Analysis
Artificial Intelligence, Job Displacement, and Gender-Specific Training Pathways: A Multi-Group Analysis

Summary/Abstract: This study investigates gender-based differences in how AI-Based Performance Analytics (AIPAN) and Women’s Engagement in Technology (WET) influence Skill Gap Identification using AI (SGIAI) and its subsequent effect on Training Program Alignment (TPA). The researchers studied female and male participants independently through SEM multi-group analysis of their data. AIPAN served as a strong predictor of SGIAI for women alongside WET, which contributed β = 0.712 and β = 0.261, respectively (p < 0.001). Additionally, SGIAI demonstrated a strong β relationship of β = 0.810 to TPA (p < 0.001). The analysis indicates that AIPAN (β = 0.577, p < 0.001), together with WET (β = 0.211, p < 0.001), transmitted significant indirect effects to TPA through SGIAI. SGIAI received considerable direct impact from AIPAN (β = 0.943, p < 0.001) when studying males, yet WET demonstrated no significant relationship (β = -0.036, p = 0.361). AIPAN demonstrated a major indirect relationship with TPA through its β = 0.790, considerable effect (p < 0.001). However, WET did not produce a meaningful impact on TPA measurement. The combination of WET and AIPAN variables generated no meaningful interaction effects within both the male and female populations. Numerous factors point to predictive analytics as a vital element in aligning AI skills, but reveal that women demonstrate increased WET influence, which requires gender-responsive digital transformations.

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