Ge et al. (2022) believed that with the rapid development of digital technology, the digital economy has become more diverse in form and rich in connotation. It is a very difficult thing to define the digital economy only from one or several aspects. Nowadays, most scholars agree on the concept that the digital economy is an economic activity formed based on the Internet and related information and communication technology (ICT), focusing on the use of the Internet ( Li, 2017 ). Shi (2022) summarizes the digital economy as an emerging economic form based on the three elements of big data, intelligent algorithms, and computing power platforms. It is based on the computing power platform and uses intelligent algorithms to store, process, analyze, and discover knowledge about big data. It ultimately serves the optimal allocation of resources and transformation and upgrading of various industries and promotes the high-quality development of the economy. Without big data, the digital economy is “cooking without rice”. Ge et al. (2022) suggest that data is a key element in the development of the digital economy. Big data has formally changed from an asset to a means of production, reshaping the organizational and operational modes of production, exchange, and consumption, as well as the entire economic and social order and governance system. He and Cheng (2021) concluded that there is a high degree of fit between the digital economy and the high-quality development of the economy and that the digital economy can promote the high-quality development of the economy. Guo et al. (2022) also argued that digital technology applications could promote the development of the digital economy and, thus, regional high-quality development. Jiang and Sun (2020) also believed that the digital economy has a significant role in realizing the real economy’s power to turn, which can bring new kinetic energy for the high-quality development of a solid economy. And the digital economy can reduce the urban-rural income gap through technological development ( Wang, 2024 ). Cai and Ma (2021) argued that the digital economy can create multiplied value, but it may bring negative impacts on economic development due to issues such as privacy leakage.

As early as 1985, China initiated research work on the organizational forms, development paths, strategic directions, and other relevant aspects of IUR. Veronica & Fischer (2007) believed that IURI is the process of systematic optimization and cooperative innovation of the elements of each innovation subject. Chen and Yang (2012) proposed that collaborative innovation is based on co-creation, a complex way of organizing innovation. Its essence is the innovation organization mode of large-span integration carried out by multiple subjects in order to achieve major scientific and technological innovation, with dynamic and holistic characteristics. The key to the effective execution of IURC lies in the construction of the collaborative innovation platform, which can be macroscopically laid out in the context of the digital economy through the establishment of the digital platform. Zhou et al. (2013) believed that the power formation process of IURI is the process of continuous cultivation and enhancement of the core competence of each subject of IUR and ultimately complementary integration to improve the overall collaborative innovation capability and establish the competitive advantage of the alliance. Li (2023) proposed that the deep integration of IUR is conducive to the accumulation of enterprise intellectual capital. The core competence of enterprises is embodied in the insight and demand of the market. However, the core competitiveness of universities and research institutions is strong technical support. Li et al. (2022) pointed out that enterprises are both the landing point of the development of the digital economy and the main body of China’s innovation-driven development strategy. Under the influence of the digital economy and the epidemic, the establishment of a good digital environment and platform can allow industry, academia, and research to break the geographical limitations to create a good atmosphere. It can not only help enterprises overcome the difficulties in core technology but also make the scientific research results come out smoothly. Chen et al. (2020) proved through research that both formal and informal systems have a positive impact on IURI. Yuan (2022) empirical study proved that digitalization can promote the improvement of regional IURI, and is affected by regional intellectual property rights protection and the absorptive capacity of enterprises.

Through the combing of the above literature, it can be found that the digital economy can not simply be defined from a connotation, but contains many angles and levels, focusing on information technology, information platforms, and the use of knowledge. In general, the digital economy is the sharing of knowledge and technology using the construction of digital platforms. Various social subjects can acquire knowledge through the platform, and then utilize and innovate the knowledge to create new value. The digital economy has long penetrated all industries, but enterprises, universities, and research institutions have encountered more or less obstacles. If the three main bodies combine, complement each other’s strengths, and build a good digital ecosystem, it is worth pondering whether the three synergistic innovations can achieve better results. With the development of the digital economy and the impact of the epidemic, traditional IUR cooperation is no longer suitable for the current environment. Simple collaborative innovation lacks internal logic and chain cooperation, while the establishment of an IURC will make IURI more logical. Nowadays, it is worth studying whether the collaborative innovation of the IURC can burst into greater vitality if it is combined with the digital economy.

The IURC is mainly composed of three main bodies, namely, universities, scientific research institutions, enterprises, and the government also influences the construction of the IURC. Colleges and universities are the main knowledge supporters and vital forces of the cooperation of the IURC, and various types of enterprises are related to whether the results of innovation can take root. IURI is inevitably affected by the development of the digital economy.

From the existing literature, Xue et al. (2022) combed the research hotspots and development vein of IURI research. It is believed that the guiding role of government agencies will be strengthened, the support capacity of knowledge innovation will be improved, and the ecological environment of collaborative innovation will be more optimized. Yuan (2022) empirically found that the current deepening digitalization process has deepened the integration between industry, academia, and research and enhanced the value-added knowledge in the innovation system. Zhang et al. (2022) argued that IURI is a process that involves the interaction of three core innovation subsystems, namely, enterprises, universities, and research institutions.

The ultimate goal of this study is to improve the collaborative innovation system of IUR. To promote high-quality development and improve the matching of technology demand. According to the current situation, literature combing and the research objectives of this paper, the first-level indicators are determined as four dimensions: the degree of digitization, the degree of participation of the main body of collaborative innovation, the operation mechanism of the digital ecosystem, and the degree of collaborative innovation between industry, university, and research ( Fan & Wu, 2020 ).

Determining the number of levels of evaluation indicators is the first task in designing the indicator system of IURC from the perspective of the digital economy. By the general method of indicator system design, and based on the theory of systematic evaluation. The overall goal of collaborative innovation is hierarchically decomposed, and a three-tier “pyramid” structure of the indicator system is constructed. The first-level indicators are the four dimensions of the degree of digitization, the degree of participation of the main body of collaborative innovation, the operation mechanism of the digital ecosystem, and the degree of collaborative innovation between industry, university, and research, while the second-level indicators (guideline level) and the third-level indicators (implementation level) are formulated according to the existing literature and the actual situation, as shown in Table 1 .

A total of 218 questionnaires were collected through Wenjuanxing (WJX) formal research. Eliminating the questionnaires with short answer time and answers showing obvious regularity, a total of 199 valid questionnaires were obtained, with an effective rate of 91%. According to the analysis results, it can be seen that the numerical characteristics of the statistical variables reflect the distribution of the respondents in this survey. According to Table 2 , the percentage of respondents by occupation, type of business and type of institution can be found. The study mean and standard deviation can also be seen.

Cronbach’s alpha coefficient is an important reference index to determine the reliability of the scale, when the coefficient is greater than 0.7. It indicates that the reliability of the scale is credible and further data analysis can be conducted. As shown in Table 3 , each value of Cronbach’s for the efficient system of collaborative innovation of IURC (X1 X2 X3 X4 X5 X7 X8 X9), and the performance of collaborative innovation of IURC (Y1 Y2) in the digital economy is higher than 0.7. It shows that the scale used in this study has a high degree of reliability and the data quality is reliable.

When the validity of the data is tested using factor analysis, the KMO value and Bartlett’s Spherical Test value are first tested. In this case, when the KMO value is greater than 0.6 and at the same time the Bartlett’s Spherical Test value is less than 0.05 the conditions for doing factor analysis are met. After testing, it is known that the KMO value of this study is 0.938 and the Bartlett value is less than 0.01. Table 4 shows that the questionnaire satisfies the prerequisites for factor analysis.

Table 5 shows the correlation between the variables. It can be seen that the positive correlation between the degree of digitization and the collaborative innovation performance of IURC (β = 0.288, p < 0.01). Positive correlation between the operation mechanism of the digital ecosystem and the performance of IURI (β = 0.173, p < 0.05). Positive correlation between the participation degree of the main body of the collaborative innovation and the collaborative innovation performance of IURC (β = 0.474, p < 0.01)

**The correlation is significant at the 0.01 level (two-tailed). *At the 0.05 level (two-tailed), the correlation is significant.

The questionnaire was measured using a five-point Likert scale with 1 - 5 indicating very dissatisfied, dissatisfied, neutral, satisfied, and very satisfied, respectively.

Table 6 shows the degree of digitization of each subject. The mean value of the degree of digitization of colleges and universities is lower than the mean value of digitization of all subjects, which indicates that the degree of digitization development of colleges and universities is relatively slow. At the same time, the mean value of the degree of digitization of government digitization, enterprise digitization, and research institutions digitization are all around 3.5. Among them, the questionnaire shows that enterprises have the best degree of digitization. But overall, the subjects are not satisfied with the degree of digitalization and the current situation, none of the dimensions has a mean value of more than 4 (satisfied). The development of digitalization needs to be further improved.

In summary, the analysis of the questionnaire reveals that the digitization level of each participating subject needs to be further improved. The cooperation between each subject needs to be deeper and more effective. The integration and development of the IURC is still in the primary stage. The next research on how to promote the collaborative development of the IURC under digitization and improve the performance of collaborative innovation is particularly important. It lays the foundation for further research below.

Qualitative Comparative Analysis (QCA) is a method that began to emerge in the 1980s. It focuses on the complex causal relationship between conditions and outcomes. Since the causes of social phenomena are often interdependent rather than independent. Explaining the causes of social phenomena cannot be limited to focusing on the effects of individual conditions on the outcomes. The QCA approach to analyzing outcomes using histograms combines the strengths of both qualitative and quantitative analyses.

According to the empirical research, it is known that the evaluation index of collaborative innovation of IURC is feasible. But what kind of situation will promote the performance of collaborative innovation of IURC needs to be further explored, and whether its performance is affected by multiple paths? On the basis of the previous study, this chapter adopts the fsQCA method to further explore the formation path of collaborative innovation performance of the IURC. Not only can we find the cause paths that trigger the same outcome variables, but also explore the complementarity and substitutability between different paths. It is conducive to further deepening the understanding of collaborative innovation in the IURC. This paper uses fsQCA3.0 software to analyze this.

Step 1: Data calibration. fsQCA needs to determine the full affiliation point, crossover point and full unaffiliated point, and calibrate each antecedent condition, and result into a fuzzy set of 0 - 1. According to Pappas and Woodside’s study, this paper sets the criteria of fully unaffiliated point, intersection point, and fully unaffiliated point to 0.05, 0.5, and 0.95, respectively. The calibration was performed using the direct calibration method and the calibration results are shown in Table 7 .

Step 2: Necessity Condition Analysis ( Table 8 ). The necessity condition is to determine whether the existence or non-existence of a single condition is necessary for the outcome variable. The results show that there are X2 X4 X5 higher than 0.9. Indicating that these three conditions are necessary to achieve high collaborative innovation performance in the IURC.

Step 3: Sufficiency analysis of conditional path. The measure of the adequacy of the path is also analyzed according to the consistency of the different conditions, with the consistency threshold set at 0.75 and the case threshold set at 1. First, the high IURC co-innovation performance (scientific and technological transformation and scientific and technological output) is analyzed as an outcome variable for adequacy analysis, which can derive the path of the managerial practices constituting the high IURC co-innovation performance. Then also analyzed as an outcome variable for path, to further explore which IURC collaborative practice deficiencies negatively affect organizational resilience, and to make this study more comprehensive. Conditional variable occurrences are denoted by and , where denotes the core condition, denotes the edge condition, ⊗ denotes the absence of the core causal variable, and ⊗ denotes the absence of the edge causal variable. A blank indicates that the conditioning variable had a negligible effect on the outcome.

After the counterfactual analysis to obtain the intermediate solution, it is concluded that eight groupings produce the output of scientific and technological achievements of high IURC collaborative innovation performance. As can be seen in Table 9 , the coverage of high IURC collaborative innovation performance scientific and technological achievements output is 0.967430, respectively, which is more than half of the overall sample. The consistency index of the solution presented in the table is 0.854817, which means that the different combinations of IURI in the 8 groupings can be used as sufficient conditions to

explain the output of the scientific and technological achievements of the IURC. The comprehensive analysis of the eight paths shows the output of scientific and technological achievements of IURC collaborative innovation performance under the complex effect of nine conditions. It can be summarized into four realization configurations, namely, configuration 1 (H1a, H1b, H1c, H1d), configuration 2 (H2), configuration 3 (H3), and configuration 4 (H4a, H4b). The mechanisms of the four different groupings leading to the output of scientific and technological achievements of high IURC collaborative innovation performance are detailed below.

As a result, four effective paths of collaborative innovation performance of the IURC to improve the output of scientific and technological achievements can be summarized:

The analysis is done in the same way as coming up to obtain the intermediate solution. It can be concluded that there are eight paths that generate high IURC innovation performance of scientific and technological achievements transformation. As can be seen in Table 10 , the coverage of the high IURC collaborative innovation performance of scientific and technological achievements transformation is 0.985381, which is more than half of the overall sample. And the consistency index of the solution presented in the table is 0.773702. Which means that the different combinations of IURI in the 8 paths can be used as sufficient conditions to explain the transformation of scientific and technological achievements of the IURC. Comprehensive analysis of the eight groupings to see, the complex role of the nine conditions. The IURC collaborative innovation performance of scientific and technological achievements of the transformation the more complex. It is more difficult to unify the generalization, which can be summarized as seven kinds of realization of the path. The mechanisms of the seven different paths leading to the transformation of scientific and technological achievements of high IURC collaborative innovation performance are detailed below.