Innovation impact of adopting recent industrial revolution technologies on modernising the automotive manufacturing industry

Abstract

The automotive manufacturing industry (AMI) is undergoing rapid transformation driven by Fourth and Fifth Industrial Revolution (4IR and 5IR) technologies such as artificial intelligence (AI), the Internet of Things (IoT), cyber‑physical systems (CPS), and human–machine collaboration. While these technologies offer significant potential for productivity and competitiveness, their adoption in emerging economies such as South Africa remains uneven due to structural constraints. Existing studies have largely focused on technical and strategic adoption issues with a limited understanding of how technology adoption shapes innovation outcomes and modernisation within the South African AMI. This study addresses this gap by analysing how adoption drivers influence key modernisation outcomes – productivity, workforce upskilling, income growth, and digital commerce. Grounded in the Technology-Organisation-Environment (TOE) framework, the Resource-Based View (RBV), and Human Capital Theory (HCT), and further integrated through the Support‑Driven Capabilities and Innovation Theory (SCIT), the study adopts a quantitative, cross-sectional design. A survey was administered to 248 senior technology and operations leaders across firms listed under the National Association of Automotive Component and Allied Manufacturers (NAACAM) and the National Association of Automobile Manufacturers of South Africa (NAAMSA), yielding 221 valid responses. Measurement validity and reliability were established through pre‑testing, Cronbach’s alpha, composite reliability (CR), and discriminant validity. Structural equation modelling (SEM) with bootstrapping (5,000 resamples) was used to test 48 hypotheses covering direct and mediated relationships between technology adoption drivers – costs, labour availability, and infrastructure – and modernisation outcomes. The findings show that labour availability and technology infrastructure are the strongest enablers of modernisation, exerting significant positive direct effects on productivity (β = 0.792; β = 0.466) and organisational support for upskilling (β = 0.387; β = 0.230). Cost pressures emerge as systemic barriers, negatively affecting most modernisation outcomes. The results further show that digital modernisation depends on both financial flexibility and access to enabling technologies, as reflected in the negative indirect effects of adoption costs on digital commerce (H16, H20, H24 Infrastructure support enhances modernisation through the mediating effects of investment, education, and management, while high costs weaken these pathways and reduce the sector’s ability to absorb advanced technologies. Digital commerce consistently emerges as the least responsive outcome, indicating persistent ecosystem‑level barriers within South Africa’s digital manufacturing landscape. Theoretically, the study extends the RBV by demonstrating that modernisation outcomes are not solely technology-driven but are mediated by organisational capabilities, including investment, education, and managerial support. It further reinforces HCT by illustrating the central role of workforce upskilling in linking technology adoption to socioeconomic outcomes. Through SCIT, the study offers an integrated explanation of how support structures and organisational capabilities interact to drive innovation-led modernisation. Practically, the findings highlight the need for firms to balance cost management with sustained investments in skills and managerial capacity, while policymakers must prioritise infrastructure strengthening and education–industry partnerships to support inclusive digital modernisation in the South African AMI.