Impact of the digital transformation of Chinese new energy vehicle enterprises on innovation performance

China's burgeoning digital economy, growing at a rate of 9.7% and accounting for a significant portion of its GDP, provides a fertile ground for enterprise digital transformation. This transformation, encompassing changes in business models, organizational structures, and customer interactions, is crucial for Chinese new energy vehicle (NEV) companies seeking to enhance efficiency, speed, and user experience. However, the precise mechanisms through which digital transformation influences innovation performance remain unclear. This research addresses this gap by investigating how digital transformation, encompassing digital technologies, products, and platforms, impacts innovation performance. The study emphasizes the crucial role of absorptive capacity—the ability to identify, assimilate, and apply external knowledge—as a mediating variable. Furthermore, it considers network embeddedness—the strength of relationships within the enterprise's network—as a moderating variable, acknowledging the influence of external resources and collaborations on the success of digital transformation initiatives. The research questions revolve around understanding the direct effect of digital transformation on innovation, the mediating role of absorptive capacity, and the moderating influence of network embeddedness. The study's significance lies in providing insights into the effective implementation of digital transformation and its contribution to the sustainable development and competitiveness of Chinese NEV enterprises. Understanding these mechanisms allows for the development of targeted strategies and policies to support the growth of this vital sector.

Existing literature highlights the positive impact of digital transformation on enterprise performance, focusing on resource optimization, cost reduction, and enhanced efficiency. However, studies diverge on the optimal approaches to digital transformation. Western research emphasizes executive-level changes and organizational restructuring, while Chinese scholarship concentrates on resource complementarity, social networks, and internal capabilities. Despite the consensus on the benefits of digital transformation, a lack of understanding regarding its precise impact on innovation performance persists. Studies focusing on social network embeddedness theory highlight its importance in facilitating communication, knowledge transfer, and resource acquisition during digital transformation. This paper builds upon this by investigating the interplay between digital transformation, absorptive capacity, and network embeddedness, extending previous research by integrating these crucial factors to better understand the impact of digital transformation on innovation performance. The study adopts Zahra and George's (2002) four-component model of absorptive capacity (knowledge acquisition, digestion, transformation, and application) and Granovetter's (1985) concept of network embeddedness to provide a more comprehensive framework. The literature review justifies the hypotheses about the positive relationship between digital transformation and innovation performance, the mediating role of absorptive capacity, and the moderating effect of network embeddedness.

The research employs a quantitative methodology based on a survey of 200 Chinese NEV enterprises, resulting in 186 valid responses. The sample includes a diverse range of enterprises, encompassing original equipment manufacturers (OEMs), tier-1 suppliers, and companies from various regions of China. Convenience sampling was utilized. The questionnaire, developed based on established scales from the literature, measures several key variables using a five-point Likert scale: digital transformation (comprising digital technology, digital products, and digital platforms), absorptive capacity (with its four dimensions: knowledge acquisition, digestion, transformation, and application), innovation performance, and network embeddedness. Control variables such as enterprise size, age, ownership nature, and industry were also included. Data analysis techniques included reliability and validity checks (Cronbach's alpha, KMO, Bartlett's test), correlation analysis (Pearson correlation), and multiple linear regression analysis (MLRA) using SPSS 23.0 to test the hypotheses. The MLRA models were constructed to assess the direct and indirect effects of digital transformation on innovation performance, mediating effect of absorptive capacity, and moderating effect of network embeddedness. Bootstrap methods were utilized to analyze the mediating effect of absorptive capacity.

The findings support the hypotheses that: (H1) Digital transformation (digital technology, digital products, and digital platforms) has a positive impact on enterprise innovation performance. (H2) Absorptive capacity has a positive effect on innovation performance. (H3) Digital transformation positively affects absorptive capacity. (H4) Absorptive capacity significantly mediates the relationship between digital transformation and innovation performance. The results of multiple linear regression analyses indicate that digital transformation significantly predicts innovation performance (β = 0.831, p < 0.01). Further, the mediating role of absorptive capacity was confirmed through analysis of the direct and indirect effects. The mediating effect of absorptive capacity is significant, accounting for a substantial portion of the relationship between digital transformation and innovation performance (a*b = 0.720, p < 0.01). However, (H5) the hypothesized moderating effect of network embeddedness on the relationship between digital transformation and innovation performance was not supported. The analysis of individual components of absorptive capacity revealed that knowledge digestion, transformation, and application all significantly and positively influenced innovation performance, while knowledge acquisition did not show a significant effect. The study also found that the different dimensions of digital transformation (technology, products, and platforms) all significantly contribute to innovation performance. The detailed regression analyses (Tables 5, 6, 7, 8, 9) provide numerical support for each hypothesis.

The findings underscore the crucial role of digital transformation in boosting innovation performance in Chinese NEV enterprises. The significant positive impact of digital transformation on innovation, mediated by absorptive capacity, highlights the importance of enterprises' ability to acquire, digest, transform, and apply new knowledge from external sources. The lack of a significant moderating effect from network embeddedness suggests that the impact of digital transformation on innovation is relatively independent of the strength of external relationships, at least within the context of this study. The results provide practical implications for NEV companies, emphasizing the importance of not only implementing digital technologies but also cultivating the absorptive capacity of their workforce. Companies should focus on creating an environment that fosters knowledge sharing, learning, and adaptation. The results provide actionable insights for managers to strategize their digital transformation efforts, focusing on building internal capabilities rather than solely relying on external collaborations. The study contributes to the literature by providing empirical evidence on the mediating and moderating mechanisms linking digital transformation to innovation in a rapidly evolving industry.

This study contributes to the literature by empirically examining the impact of digital transformation on innovation performance in Chinese NEV enterprises, considering the mediating role of absorptive capacity and the potential moderating role of network embeddedness. The findings highlight the importance of absorptive capacity in translating digital transformation into improved innovation outcomes. Future research could explore the impact of specific digital technologies and platforms on innovation performance, analyze the moderating effects of other contextual factors (e.g., government policies, regional factors), and investigate the long-term effects of digital transformation on firm sustainability. Further investigation into why network embeddedness did not show a significant moderating effect is needed.

The study's limitations include the use of a cross-sectional design which limits the ability to establish causal relationships, the reliance on self-reported data through a survey, and the specific focus on the Chinese NEV industry, potentially limiting the generalizability of the findings to other contexts. The sample size, while substantial, might not fully represent the diversity within the Chinese NEV sector. Future research could address these limitations through longitudinal studies, employing mixed methods, and broadening the scope of the research to other industries and geographical regions.