The Smart City as a Field of Innovation: Effects of Public-Private Data Collaboration on the Innovative Performance of Small and Medium-Sized Enterprises in China

Data is increasingly considered to be a key component in stimulating innovation (Cockburn, Henderson, and Stern, 2019). Numerous promising possibilities have been opened up by rapidly emerging, data-intensive technologies, including the Internet of Things (IoT) and artificial intelligence. The analysis and interpretation of big data are critical in the growth of technology firms in terms of AI training and computing capabilities (Allam and Dhunny, 2019). Small and medium-sized enterprises (SMEs), with their limited resources internally, particularly face a serious challenge of implementing innovation that depends upon data.

The smart city provides an important opportunity for creating data-driven innovation. Significant amounts of data are increasingly available from various sources through sophisticated devices and equipment scattered in smart cities. Many smart city projects across the globe provide rich opportunities for SMEs to explore data-driven innovation (Bresciani, Ferraris, and Del Giudice, 2018). China, in particular, has recently been active in collecting and utilizing various kinds of data in smart cities. The availability of and access to data help to improve the software development of firms in China, where massive amounts of data resources are held by the government (Beraja, Yang, and Yuchtman, 2022).

In the process of smart city development, there are also many tasks that are complementary to each other, including connecting databases, building online platforms that connect different data coming from different data sources, operating online platforms, and providing products and services to citizens. These diverse kinds of tasks involved in smart city projects initiated by local governments have brought about new business opportunities for innovative SMEs in China. To implement the policies of encouraging the development of SMEs by the central government, municipal governments have introduced policies that give priority to SMEs in participating in smart city projects (Ministry of Industry and Information Technology, 2016). Those companies that have access to the data held by government agencies are expected to benefit from utilizing the rich data for creating innovative products and services.

There were few empirical studies conducted, however, to examine how data are actually managed and provided in smart cities and how they affect companies’ innovative activities. It remains unclear how public agencies and private enterprises collaborate on data and how that influences the innovation performance of SMEs in China. In smart cities, different types of public-private collaboration are involved, including hardware purchase, platform building, platform operation, and data analysis. It is not yet well-understood how these different types of collaboration influence the innovative performance of SMEs.

In this study, we intend to address how data are managed through collaboration between the government and companies in smart cities and how the mode of collaboration influences firms’ performance on innovation. By focusing on the case of SMEs in China, this research aims to shed light on what kinds of data are available and used in smart cities and how the government and enterprises collaborate on data to facilitate innovation.

The analysis of this study utilizes data on more than eight million contracts extracted from the official procurement database of the government. Data on companies are assembled with regard to the registered capital, industry, software products, and patents in 1990-2021 from the Tianyancha website. A panel data is established with key characteristics of SMEs, software and patents outputs, and their record on obtaining different government contracts annually. The government contracts are divided into three categories, namely, data analysis, platform building, and equipment supply, based on keyword identifications. To deal with the unbalance between the treatment group (the companies that obtained government contracts) and the control group, we use propensity score matching (one-to-one nearest neighbor matching) to narrow down the sample size of the control group to that of the treatment group. Then we apply the traditional difference-in-difference (DID) and  DID with multiple time periods (Callaway & Sant’Anna, 2021) methods to examine whether there are significant differences in innovative outputs of software products and patents before and after the companies receive government contracts. We also compare how the innovation performance of companies differs based on the types of contracts these companies obtain. That makes it possible to identify what kinds of data collaboration would be effective in improving the innovative performance of SMEs.

Our preliminary analysis of average treatment effect suggests that obtaining the government contracts, especially research contracts and platform building type of contracts, can effectively help improve the innovation performance when comparing between the control and treatment group. After checking the common support graph and doing a balance test, the outcome is solid. In general, based on the average treatment effect on the treated group, innovation performance, as measured by the number of patents and software productions, has shown a significant increase for those companies that obtain a research type of contract, whereas the increase is smaller for the platform building contractor and slight for the equipment purchase contractor. Government purchase of equipment that contains data-intensive technologies promotes the use of these products in smart cities. That, however, does not involve any substantive exchange or transfer of data possessed by the government and would not significantly contribute to stimulating innovation at firms.

This study will have useful implications for establishing public-private collaboration on data for facilitating innovation in smart cities. Specifically, government procurement should also be viewed as a policy tool for improving China's technological innovation. According to our findings, research and platform development can successfully assist firms in improving their innovation performance. As a result, the government should grant enterprises access to additional data resources and data management related tasks if they want to see more industry structural transformation, as stated in the government documents.