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Keywords

regional innovation, Big Data, threshold effect, spatial durbin model

Abstract

Realizing the deep integration of Big Data development and regional innovation, and forming a healthy, orderly, and effective development model for Big Data-driven regional innovation, are important issues that urgently need to be explored. This paper mainly discusses the driving effect of Big Data development on regional innovation. Selecting the relevant data at the provincial level in China from 2011—2021, the paper uses the entropy weight method and linear weight method to measure the comprehensive indicators of Big Data development and regional innovation, establishes a two-way fixed effect model, threshold effect model and spatial Durbin model, and studies the direct effect, threshold effect and spatial spillover effect of Big Data development on regional innovation. The results of the bidirectional fixed effect model indicate that the development of Big Data has a significant promoting effect on regional innovation. The results of the threshold effect model indicate that there is a double threshold effect with increasing marginal effects in the positive impact of Big Data development on regional innovation. That is, the promoting effect of Big Data development on regional innovation will increase with the improvement of Big Data development level. The results of the spatial Durbin model indicate that the improvement of local regional innovation capability has a positive spatial spillover effect on the innovation of neighboring regions, while the development of local Big Data mainly enhances local regional innovation capability, and has a negative spatial spillover effect on the improvement of innovation capability of neighboring regions. Finally, policy recommendations are proposed in the paper. It is suggested that the innovative driving effect of Big Data should be fully leveraged; the application capabilities of regional Big Data technology need to be enhanced; an open data sharing platform need to be established; and the deep integration of Big Data and traditional industries should be promoted.

DOI

10.16315/j.stm.2023.05.001

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