The casual effect of data production factor adoption on company performance: Empirical evidence from Chinese listed companies with PSM-DID

Abstract

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The usage of data production factor (DPF) has been extensively studied in academic research and industry. The purpose of this study is to examine the causal effects of DPF adoption on company performance. We firstly provide a measurement of DPF adoption by text mining, which is superior to previous studies that use only single metric. Then, based on PSM-DID method, we use the data of China’s listed companies from 2011 to 2019 to identify the causal relationship between data elements adoption and company’s performance. We find that the adoption of DPF can significantly increase companies’ performance. Further heterogeneity tests show that companies from the service industry and state-owned companies achieve a significant improvement in the performance after adopting DPFs in production. Altogether, this study provides the micro evidence on the relationship between the adoption of DPFs and company performance, providing significant implications for the development of digitalization and intelligence production.

Keywords

• data production factor
• performance
• text mining
• PSM-DID
• causal effect