Abstract
The automotive industry is an extremely important part of the global manufacturing industry, and technological innovation has been constantly updated. These innovations are not only related to the competitiveness of enterprises themselves, but also play a great role in promoting the upgrading of regional industries and economic restructuring. In this context, both academia and the industry urgently need to establish a multi-dimensional and multi-level evaluation system to fully reflect the impact of technological innovation on the automotive industry, enterprise transformation and upgrading, and regional economic development.
This dissertation constructs a technology innovation performance evaluation system combining multiple data sources, aiming at in-depth analysis of the influencing factors of technology innovation activities in the automotive industry and how these factors affect the competitiveness of enterprises and regional economic development. This dissertation adopts bibliometric methods to sort out the research context and integrates multi-source data such as annual reports of listed companies and regional macro statistics to construct a regression model, exploring the performance of technological innovation in automotive enterprises and its economic spillover effects from both theoretical and empirical perspectives.
Firstly, through bibliometric and visual analysis, this dissertation reveals the main research themes and their evolution in the field of automotive technology innovation, establishes a systematic knowledge graph, and effectively identifies the core focus of academia and industry in technology R&D, patent output and innovation diffusion. This part not only lays a solid theoretical foundation for the subsequent empirical research, but also expands the research perspective of innovation performance evaluation.
Then, based on the annual report data of listed companies, this dissertation constructs a technological innovation performance evaluation model, and deeply discusses the internal relationship between R&D input, patent output and corporate profitability. The research reveals a complex relationship between R&D activities and corporate financial performance. While the scale of R&D, such as the absolute number of R&D personnel, shows a significant positive correlation with net profit, an excessively high proportion of R&D personnel within the company structure demonstrates a negative impact, suggesting potential inefficiencies from resource imbalance. This provides a comprehensive interpretation of how enterprises realize value addition through technological innovation.
Finally, by integrating micro-innovation data of enterprises with regional macroeconomic data, this study quantitatively identified the spillover effect of technological innovation on regional economic growth. Research findings show that innovation-related activities such as the amount of R&D investment by enterprises and the government subsidies they receive have a significant positive driving effect on regional GDP growth, confirming the economic spillover value of technological innovation. However, factors such as excessively high R&D expense ratios and asset-liability ratios may have a restraining effect on regional economic growth. This conclusion reveals that when enterprise innovation empowers regional development, it must take into account both the efficiency of innovation input and the stability of the financial structure, providing an important empirical basis for the government to formulate precise industrial support policies.
This research theoretically enriches the connotation of technological innovation performance evaluation and broadens the application range of multi-source data fusion in industrial evaluation. In practice, it provides a scientific basis for the government and enterprises to formulate accurate innovation incentive policies and strategic adjustments. This dissertation has made exploration in data integration, model construction and empirical test, and has demonstrated certain academic value and application prospect.