Digital Policy, Green Innovation, and Digital-Intelligent Transformation of Companies

Xin Tan, Jinfang Jiao (), Ming Jiang, Ming Chen, Wenpeng Wang and Yijun Sun
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Xin Tan: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China
Jinfang Jiao: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China
Ming Jiang: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China
Ming Chen: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China
Wenpeng Wang: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China
Yijun Sun: School of Economics and Management, Qingdao University of Science and Technology, 99 Songling Road, Qingdao 266061, China

Sustainability, 2024, vol. 16, issue 16, 1-23

Abstract: In the midst of rigorous market rivalry, enhancing a company’s competitiveness and operational efficiency in an era of rapid IT advancement is a pressing concern for business leaders. The National Big Data Comprehensive Zone (BDCZ) pilot scheme, instituted by the Chinese government, systematically addresses seven core objectives, encompassing data resource management, sharing and disclosure, data center consolidation, application of data resources, and the circulation of data elements. This policy initiative aims to bolster the establishment of information infrastructure through big data applications, facilitate the influx and movement of talent, and propel corporate sustainable growth. Utilizing a quasi-natural experiment approach, we assess the pilot policy’s influence on the digital-intelligent transformation (DIT) of manufacturing companies from a green innovation ecosystem perspective, employing datasets from 2010 to 2022, and methodologies such as Difference-in-Differences (DID), Synthetic Differences-in-Differences (SDID), and Propensity Score Matching-DID (PSM-DID). The findings indicate that the BDCZ initiative significantly fosters DIT in manufacturing companies. The policy’s establishment confers benefits, including access to increased government support and innovation capital, thereby enhancing the sustainability of green innovation efforts. It also strengthens corporate collaboration, engendering synergistic benefits that improve regional economic progression and establish a conducive environment for digital development, ultimately enhancing the regional innovation ecosystem. The pilot policy’s impact varies across entities, with more profound effects observed in developed financial markets compared to underdeveloped ones. Additionally, non-state-owned companies exhibit a greater response to BDCZ policy interventions than their state-owned counterparts. Moreover, manufacturing bussiness with a higher proportion of executive shareholding are more substantially influenced by the BDCZ. This article fills the research gap by using the quasi-natural experiment of BDCZ to test the impact on DIT of companies and provides inspiration for local governments to mobilize the enthusiasm of manufacturing companies for DIT.

Keywords: digital economy; digital ecology; digital intelligence transformation; DID; SDID (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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