Intergenerational Dynamics of Work Attitudes and Performance in Hybrid AI Work

Abstrak

This study examines how generational cohorts differ in translating workplace transformation into performance outcomes in AI-enabled hybrid systems within AI-enabled and hybrid work systems. Using an extended Job Demands–Resources framework, this study analyzes key predictors of employee performance, we examine the effects of attitudes toward change, technological adaptability, job loyalty, and social interaction on employee performance and compare Millennials and Baby Boomers using Multi-Group Analysis (MGA). Data were collected through a cross-sectional survey and analyzed using PLS-SEM with multi-group analysis to assess measurement robustness and structural path differences across cohorts. The findings reveal that social interaction is the most powerful predictor of performance across generations, with a significantly stronger effect among Baby Boomers. Technological adaptability demonstrates a universally positive impact, yet its magnitude varies by cohort. In contrast, job loyalty shows a comparatively modest contribution to performance, suggesting a shift from traditional commitment paradigms toward more conditional forms of allegiance. MGA results confirm significant generational heterogeneity in key structural paths. This study offers a contextualized model of generational performance in AI-driven work environments, this study advances a contextualized model of generational performance, offering novel insights for global scholarship on multigenerational workforce management.