Factors Influencing Generative AI Adoption for Knowledge Management in South Africa’s Automotive Sector

Abstract

South Africa’s automotive sector is under increasing pressure to sustain competitiveness amid Fourth Industrial Revolution (4IR) transitions, persistent operational inefficiencies, and workforce ageing. Generative AI (GenAI) presents a potential pathway to strengthen knowledge management (KM) by supporting faster knowledge capture, synthesis, retrieval, and decision support. This study identifies the determinants of GenAI adoption for improving KM practices in South Africa’s automotive context. A quantitative, hypothesis-driven design was employed, integrating constructs from the PPOA, TEOG, and IEO frameworks to provide a consolidated adoption perspective. Survey data were collected from 142 industry participants and analysed using SPSS (correlation and multiple regression). The model demonstrated strong explanatory power (Adjusted R² = 0.624, p < 0.001). Results indicate that GenAI adoption is significantly and positively influenced by FATAA ethical principles, KM practices, GenAI tool capability, perceived enjoyment, perceived usefulness, compatibility, competition intensity, organisational size, mimetic pressure, and normative pressure (p < 0.05). In contrast, perceived ease of use and coercive pressure were not statistically significant in this context (p > 0.05). The study contributes a context-specific, integrated adoption model for GenAI-enabled KM in an under-researched setting and offers actionable implications for managers and policymakers focused on responsible, effective GenAI deployment.

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