How Digital Transformation Affects Small Auto Manufacturers in China’s Supply Chain

Abstract

This dissertation examines how digital transformation (DT) shapes the performance and positioning of small and medium-sized enterprises (SMEs) in China’s automotive supply chain. While large assemblers and tier-1 suppliers rapidly adopt ERP, IoT, cloud platforms, and data analytics, many SMEs face structural disadvantages that slow or fragment adoption. Building on the Resource-Based View (RBV) and Supply Chain Power Theory, the study uses a mixed-methods design: a survey of 72 SMEs and 30 semi-structured interviews. Quantitative analysis shows a strong positive association between digital readiness and reported performance benefits (r = 0.88) (Jia, Shafie & Kasim, 2025). Size and age moderate readiness: larger and older SMEs exhibit higher scores and more extensive integration. However, capability gaps are widespread. Fifty-eight percent of firms report no clear digital roadmap; 67 percent cite skills shortages, and 72 percent cite investment cost as a major barrier (Restrepo-Morales et al., 2024). End-to-end integration is uncommon: only 39 percent link finance, sales, and logistics systems (Wamba et al., 2017). Qualitative findings enrich these patterns. Firms that sequence implementation, budget for training, and involve staff in process redesign report faster decision cycles, lower operating costs, and improved flexibility (Teece, 2007). Where tools are installed without governance or change management, benefits are partial and brittle. Externally, collaborative buyer governance and policy support (subsidies, training hubs) help SMEs cross adoption thresholds, while coercive mandates without onboarding exacerbate dependence (Cox, 2001). Empirically, this dissertation contributes one of the first sector-focused portraits of digital adoption among Chinese automotive SMEs. Theoretically, it integrates RBV and power-based perspectives to show that internal capabilities and external dependence jointly condition DT outcomes (Barney, 1991; Vial, 2019).