Big Data-Driven Digital Economic Industry Based on Innovation Path of Manufacturing

This work aims to investigate the impact of the development of the digital economic industry driven by big data on the innovation path of manufacturing. It seeks to uncover the key factors and trends that the manufacturing sector faces in the innovation process in the context of the digital economy era. Based on a comprehensive understanding of the current research landscape, this work first proposes the innovation path of manufacturing based on the development of the digital economic industry. Subsequently, it formulates hypotheses regarding the impact of digital economic industry development in the big data context on the innovation path of manufacturing. Finally, this work analyzes and validates the hypotheses presented. The results indicate that the fitting results of the 18 proposed development paths in the initial path show average superiority, with obvious unreasonable paths. After adjustments, various indicators of the paths show improvement, with enhanced model fit results and increased credibility. Innovation input, innovation output, and resource allocation, whether individually or in combination, affect technological innovation in manufacturing with an impact coefficient of 0.670, a direct path coefficient of 0.371. The three hypotheses are verified through baseline regression, indirect effect analysis, and the Spatial Durbin Model (SDM), ensuring the robustness of the results. This suggests that in the era of the digital economy, the manufacturing sector should actively leverage big data technology to optimize production processes, enhance product quality, and engage in deep collaboration with the digital economic industry to continuously upgrade the innovation path and achieve sustainable development.