Employees’ Adaptation to AI Technologies: The Effects of Perceived Organizational Support and Learning Climate on AI Self-Efficacy and Adaptive Performance

Abstract

The large-scale deployment of generative AI, intelligent automation and industrial AI systems has reshaped job workflows, skill requirements and employee work experiences across manufacturing, finance, medical, digital service and consulting industries. Poor employee adaptation to AI leads to technostress, passive resistance, low task efficiency and failed digital transformation, while effective AI adaptation drives adaptive performance, innovation output and sustainable human-AI collaboration. Prior literature has separately explored perceived organizational support (POS), organizational learning climate, AI self-efficacy and adaptive performance, yet few studies adopt mixed-method design to systematically unpack the multi-layered mechanism linking dual organizational contextual factors (POS & learning climate) to employee adaptive performance via the mediating role of AI self-efficacy. Based on Job Demands-Resources (JD-R) theory and Social Learning Theory (SLT), this sequential explanatory mixed-methods research integrates quantitative questionnaire survey and semi-structured qualitative interview data to construct and verify the integrated theoretical model. In the quantitative phase, valid questionnaire data from 527 enterprise employees across five industries are collected and analyzed via SPSS 26.0 and AMOS 24.0; in the qualitative phase, 36 one-on-one semi-structured interviews are conducted, and thematic analysis is applied to extract real-world contextual mechanisms, boundary conditions and practical barriers that cannot be fully captured by quantitative statistics. Quantitative results show that both perceived organizational support and organizational learning climate exert significant positive effects on AI self-efficacy and employee adaptive performance; AI self-efficacy plays a complete mediating role between dual organizational antecedents and adaptive performance. Qualitative interview themes further supplement and interpret quantitative findings: organizational support materializes through systematic AI training, technical consultation channels and job security guarantees; a positive learning climate shapes peer knowledge sharing, trial-and-error tolerance and collective AI skill accumulation, jointly elevating employees’ confidence in mastering AI tools, which ultimately translates into proactive AI usage, fault response ability and flexible task adjustment (adaptive performance). This study forms a unified mixed-method evidence chain, enriches the theoretical framework of employee AI adaptation, and provides targeted, operable human resource management strategies for enterprises implementing AI transformation.