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Research on the Impact of Open Government Data on Innovation of Listed Companies

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posted on 2025-02-07, 03:37 authored by Jingsong Fan

In the context of the digital economy, open government data is a pivotal resource in advancing innovation and economic development. This study employs a multi-period difference-in- differences (DID) methodology to rigorously investigate the influence of open government data on corporate innovation and growth. The empirical analysis, drawing on data from A- share listed companies on China’s Shanghai and Shenzhen stock exchanges between 2012 and 2022, demonstrates that open government data has a statistically significant and positive impact on corporate innovation. This conclusion is further substantiated by a series of robustness checks. The mechanism analysis reveals that open government data fosters corporate innovation by enhancing the information environment and mitigating financing constraints. Further disaggregated analysis indicates that the innovation-enhancing effects of open government data are particularly pronounced in state-owned companies and firms with a lower proportion of independent directors. Crucially, open government data not only directly catalyzes corporate innovation but also contributes to increased productivity and economic efficiency, thereby generating significant indirect economic impacts. This study highlights the critical role of open government data as a catalyst for company innovation in the digital economy era. The findings suggest that policymakers should promote interdepartmental and cross-regional data sharing and reduce barriers to data access for companies. Moreover, it is recommended that differentiated policies be formulated in accordance with the specific characteristics of different types of companies to optimize the economic benefits derived from data openness.

History

Table of Contents

1. Introduction -- 2. Institutional background and literature review -- 3. Research hypotheses -- 4. Research methodology and design -- 5. Empirical tests -- 6. Research conclusions -- References

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Master of Research

Department, Centre or School

Department of Applied Finance

Year of Award

2024

Principal Supervisor

Zheyao Pan

Additional Supervisor 1

Jianlei Han

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

43 pages

Former Identifiers

AMIS ID: 399133

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