Navigating The AI-Augmented Workplace: How Digital Leadership and The AMO Framework Impact Job Insecurity and Human-Algorithm Symbiosis
This study investigated the mechanisms of artificial intelligence (AI) integration among knowledge workers by testing a dual-stage moderated mediation model. Based on the Ability-Motivation-Opportunity (AMO) framework and Job Demands-Resources (JD-R) theory, this study examines whether digital leadership and algorithmic transparency moderate the mediating effects of AI-induced job insecurity on human-algorithm symbiosis. This study used a cross-sectional quantitative design. Data were collected using purposive sampling from an expert niche consisting of 71 academic publishing managers, quality auditors, and academic staff in the higher education sector. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 with 5,000 bootstrap samples. Contrary to mainstream narratives, the hypothesized dual-stage moderated mediation was not supported. Interestingly, AI-oriented AMO practices and digital leadership positively predicted AI-induced job insecurity, contradicting the expected mitigating effect. However, this heightened insecurity failed to mediate or impede collaborative performance. The Phase 1 model yielded an acceptable R2 = 0.638, while the Phase 2 model (R2 = 0.562) revealed a robust and highly significant direct effect of AMO practices on the establishment of human-algorithm symbiosis. This study challenges conventional assumptions about the "dark side" of AI integration by revealing the phenomenon of "instrumental pragmatism" in highly autonomous knowledge workers. These findings demonstrate that in specific academic settings, professionals bypass psychological trauma (job insecurity) and structural rhetoric (leadership vision), directly translating practical HR interventions (AMO) into effective human-AI collaborations. This research urges a shift in HR strategy from complex change management to direct capability building