Assessing the Speed of Human Resource Adaptation in Responding to Generative AI Disruption: A Three-Wave Longitudinal Study of the Academic Community at Universitas Sulawesi Barat
Keywords:
Adaptation Velocity, Generative AI, Learning Culture, Technostress, Longitudinal Study, Academic PerformanceAbstract
This study measures the speed of human resource (HR) adaptation in responding to generative artificial intelligence (AI) disruption. Using a three-wave longitudinal panel design, the study surveyed 250 members of the academic community at Universitas Sulawesi Barat over twelve months. It drew on dynamic capabilities theory, social cognitive theory, and conservation of resources theory. Adaptation velocity was treated as a dynamic construct, with learning culture as a positive moderator and technostress as a negative moderator. Data were analyzed using Latent Growth Curve Modeling and Structural Equation Modeling. The results show a significant increase in adaptation velocity over time (slope = 0.28, p < 0.001), with substantial inter-individual variation. Generative AI exposure positively affected adaptation velocity (β = 0.35, p < 0.001); this effect was strengthened by learning culture (β = 0.18, p = 0.010) and weakened by technostress (β = −0.15, p = 0.012). Adaptation velocity, in turn, positively affected academic performance (β = 0.32, p < 0.001) and mediated the effect of AI exposure on performance (β = 0.11, p = 0.006). These findings indicate that adaptation to AI is not a static event but a dynamic, time-dependent process whose pace is largely determined by organizational support and individuals’ capacity to manage technological stress. The study contributes a temporal dimension to dynamic capabilities theory and offers practical guidance for managing AI adoption in higher education
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