How does digital technology administrative penalty affect big data technology innovation: evidence from China

The rapid growth of the information technology industry has led to an unprecedented surge in data generation, making big data the cornerstone of the digital economy. Big data technology, with its ability to process and analyze massive datasets, offers immense potential for boosting productivity and efficiency, facilitating the integration of digital and real economies, and promoting long-term economic growth. However, the widespread adoption of big data technology also presents significant risks such as data misuse and privacy violations, potentially hindering digital technology innovation. Effective governance is crucial to mitigate these risks and create a supportive ecosystem for digital innovation. China, a major player in the global digital economy, employs administrative penalties as a key regulatory tool to manage big data technology innovation. This study aims to empirically investigate whether these administrative penalties, specifically digital technology administrative penalties (DTAP), serve as an effective institutional safeguard for promoting big data technology innovation (BDTI) in China, considering regional disparities and the mechanisms at play.

Existing literature on digital technology innovation primarily focuses on two perspectives: the intrinsic qualities of enterprises (human capital, executive expertise, communication, alliances, mergers and acquisitions) and the external environment (market demand, digital infrastructure, industrial structure, foreign direct investment). Government involvement is often analyzed through industrial policy, infrastructure development, financial incentives, pilot schemes, intellectual property rights protection, and environmental regulations. However, few studies explore the institutional perspective, particularly the role of administrative penalties, as a key driver of innovation. This study contributes to this gap by examining the impact of administrative penalties on technological innovation from an institutional enforcement perspective, extending the empirical evidence to non-listed firms in contrast to previous studies that have primarily focused on listed companies.

This study employs a panel data approach using data from 281 cities in China from 2008 to 2020. The dependent variable is BDTI, measured by the number of big data technology patent applications or granted patents per capita. The independent variable is DTAP intensity, calculated as the number or monetary value of administrative penalties per capita. The study incorporates city and year fixed effects to control for unobserved heterogeneity and time-invariant factors. Control variables include economic development (per capita GDP, GDP growth rate), foreign investment, industrial structure, financial technology expenditure, urbanization rate, population density, financial development level, and financial efficiency. The study tests three hypotheses: H1: DTAP facilitates BDTI; H2: DTAP contributes to the development of new business forms, thus promoting BDTI; H3: DTAP contributes to the digitalization of industries, thus promoting BDTI. To analyze the mechanisms, a mediation analysis is conducted using the development of new business forms (NBUS) and the level of industrial digitization (IDIG) as mediating variables. Instrumental variable (IV) estimation is used to address potential endogeneity issues. Heterogeneity analysis is conducted to examine differences in the effects of DTAP across first-tier and non-first-tier cities and cities with varying levels of digital factor-driven industry development.

The study finds a significantly positive correlation between DTAP intensity and BDTI, supporting Hypothesis 1. This relationship remains robust after controlling for various factors, addressing endogeneity concerns through instrumental variable estimation, and employing different estimation models. The mediation analysis reveals that DTAP promotes BDTI through two key channels: (1) fostering the development of new business forms (supporting Hypothesis 2) and (2) accelerating industrial digitization (supporting Hypothesis 3). The impact of DTAP on BDTI is heterogeneous. The positive effect of DTAP is significantly greater in first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen) and in cities with relatively low levels of digital factor-driven industry development. The study further presents scatter plots and regression lines visualizing the relationship between DTAP and BDTI, demonstrating a clear positive linear relationship. The R-squared values in the regressions range from 10.2% to 16.3%, indicating that DTAP explains a substantial portion of the variation in BDTI.

The findings suggest that administrative penalties, when implemented effectively, can serve as a powerful tool for promoting big data technology innovation. The positive impact of DTAP can be attributed to its ability to establish clear institutional norms, incentivize long-term innovation investment by firms, and ensure a regulated competitive market. The results also highlight the importance of considering regional heterogeneity when designing and implementing innovation policies. The stronger impact of DTAP in first-tier cities might be due to the already robust innovation ecosystem and resource availability in these areas, while the greater effect in less-developed regions could be attributed to the larger potential for improvement and the greater need for regulatory intervention to prevent unregulated behavior. The study's findings contribute to the literature by demonstrating the positive role of administrative penalties, particularly within a rapidly developing digital economy, providing insights into the interaction between institutional arrangements and technological innovation.

This study provides compelling evidence that DTAP significantly promotes BDTI in China, operating through mechanisms of normative impact, incentives, and deterrence. The heterogeneous effects across regions suggest that policymakers should tailor their strategies to specific contexts. Future research could explore the differential impacts of DTAP across various digital technologies and across different countries, considering diverse administrative penalty systems and levels of economic and social development. Further research should also investigate the long-term effects of DTAP on BDTI and the potential unintended consequences.

The study primarily relies on patent data as a proxy for BDTI, which may not fully capture the breadth of innovation activities. The data on administrative penalties are sourced from a specific data provider, potentially introducing some bias. The study focuses on China, and the findings may not be directly generalizable to other countries with different institutional contexts and regulatory frameworks. Future research should consider more comprehensive measures of BDTI and explore the impact of different types of administrative penalties.