Configuration research on innovation performance of digital enterprises: Based on an open innovation and knowledge perspective

Introduction

Along with the rapid development of information and communication technologies such as big data, cloud computing, the Internet of Things, artificial intelligence, blockchain, and 5G, the world economy has entered a new era of the digital economy (Autio et al., 2018). By 2020, China’s digital economy will account for 38.6% of GDP and is expected to surpass the US as the world’s largest digital economy in the future (Jiang, 2020). The digital economy contains two parts: digital industrialization and industrial digitization, corresponding to the digital economic benefits of digital enterprises such as communication technology, and the innovation efficiency of traditional primary, secondary and tertiary industries based on digital technology enhancement (Papadopoulos et al., 2020). Therefore, whether it is digital industrialization or the industrial digitization of digital enterprises and traditional enterprises, the development of the digital economy has become an inevitable trend in the market economy (Teece, 2018). In this process, the vigorous development of the digital economy has formed a new engine of economic development and new momentum, but also to the traditional economy and traditional industrial development has brought the “pain” of fission and the urgent hope of transformation needs, to promote open innovation based on the digital economy driven by the inevitable choice for the future development of enterprises (Cennamo et al., 2020). Compared with the traditional industrial economy environment, the digital economy environment has digital scenarios and new features such as openness, borderlessness, strong interactivity, and uncertainty (Yuan et al., 2021). Open innovation of enterprises in the digital economy environment requires more open knowledge sharing and inclusive knowledge creation mechanism, through which knowledge resources needed for open innovation can be obtained, and through the effective utilization and integration of knowledge, thus promoting the improvement of enterprise innovation capability and innovation performance (Lee et al., 2010; Eftekhari and Bogers, 2015; Sun et al., 2020; Scaliza et al., 2022).

Compared with closed innovation within the enterprise, open innovation breaks through the closed organizational boundaries of the traditional economy. By strategically using inside-out and outside-in paths to acquire knowledge and resources from outside the organization and combine them with the enterprise’s original core competencies and organizational strategies, the enterprise can enhance its internal innovation capabilities and spread the innovation results to the external market, to further enhance its dynamic adaptive capabilities and innovation performance (Ahn et al., 2013; Sisodiya et al., 2013; Shu et al., 2016). Although open innovation may profoundly affect innovation performance (Audretsch and Belitski, 2022), existing studies have not clarified the mechanism of the effect of open innovation on innovation performance, so there is a need to further study and explore the relationship between the two (Tang et al., 2021).

Although scholars have studied the relationship between open innovation and innovation performance (Ebersberger et al., 2021; Naseer et al., 2021; Ovuakporie et al., 2021), most of these studies have focused on the independent effect of open innovation on innovation performance, while ignoring that the performance of the role of open innovation can be influenced by other organizational factors (Cheah and Ho, 2021). According to the knowledge base theory, knowledge, as the core element of open innovation, is the foundation of open innovation (Rauter et al., 2019). In the process of promoting the development of open innovation diffusion, knowledge not only needs to be shared but also needs to be integrated efficiently to enhance the degree of knowledge interaction and increase the width and thickness of knowledge, to further enhance the open innovation performance how enterprises carry out specific activities under open innovation (Gkypali et al., 2018). Therefore, knowledge, as an important part of enterprise resources, is bound to influence the role of open innovation in innovation performance (Scaliza et al., 2022). Therefore, this study argues that it is necessary to investigate the preconditions and paths of innovation performance from the perspective of knowledge management, with open innovation-knowledge management as the mainline, to supplement the “new perspective” of innovation performance research (Cuevas-Vargas et al., 2022). In practice, the premise of open innovation is the knowledge sharing among innovation subjects inside and outside the enterprises, and each innovation subject uses knowledge sharing to acquire complementary knowledge and form a knowledge integration mechanism, which eventually becomes innovation performance through systematic evolution and output.

Based on this, this paper integrates the antecedent conditions of open innovation, knowledge integration capability, and knowledge sharing, constructs a model of the driving mechanism of innovation performance of digital enterprises, and mines the role of different groups of antecedent conditions on innovation performance through the fuzzy set qualitative comparative analysis (fsQCA) method. The innovations and theoretical contributions of this paper are 1) Integrating three types of elements, namely, open innovation, knowledge integration capability, and knowledge sharing, into a holistic analysis model and analyzing the influence of the configuration of these elements on innovation performance by applying the fsQCA method, which provides a new research perspective for understanding and explaining the complex causal relationships of the factors influencing innovation performance of digital enterprises. 2) It provides new ideas to explain the contradictions in innovation performance research. Previous studies have argued the relationship between different antecedent conditions and innovation performance, but there is still disagreement on the research findings. In contrast, this paper provides a new explanation for the research disagreement from a histological perspective: innovation performance is the result of matching and linking different antecedent conditions, and the effects of these different antecedent conditions have different characteristics, and exploring the net effect of a single antecedent condition in isolation may lead to contradictory research results.

Theoretical background and hypothesis development

Innovation performance plays a crucial role in the survival and long-term development of digital firms, and understanding and explaining the driving mechanisms of innovation performance has thus become a focal issue for scholars (Yao and Huang, 2022; Li et al., 2022). This paper explores the impact of three types of factors, namely open innovation, knowledge integration capability, and knowledge sharing, on the innovation performance of digital enterprises from the perspectives of open innovation and knowledge management, and constructs a framework for analyzing the driving mechanisms of innovation performance.

Knowledge sharing and innovation performance

The open, borderless, digital resource flow and value co-creation features presented by the digital economy itself contain the basic elements of knowledge flow, exchange, and sharing. Under the digital economy, in the open innovation process promoted by enterprises, knowledge sharing occurs in the innovation activities such as R&D cooperation and co-creation alliance between team members within the organization, between departments, and between the organization and the outside of the organization. This enables the organization’s management system to form an inter-temporal and inter-level knowledge-sharing network that spans from consumers to supply chain members/cooperative units and then to the enterprise itself. In practice, the scenarios of knowledge sharing may go far beyond the scope of work in a narrow sense (Swap et al., 2001). These knowledge-sharing scenarios take the knowledge-sharing subject as a knowledge node, while the realization of knowledge-sharing is accomplished by the network transmission between nodes (Tsai, 2001). Tanriverdi (2005) believes that the stronger the absorptive capacity based on knowledge sharing, the more an enterprise can broaden the breadth and depth of knowledge, and the more effectively it can respond to changes in the environment. It is also more capable of screening out knowledge that is valuable for corporate innovation, thus enhancing the innovation performance of the organization (Tanriverdi, 2005). Knowledge sharing will make the sources of enterprise knowledge more diversified, and the higher the degree of diversification of knowledge sources and the higher the frequency of knowledge sharing and exchange, the faster the operation of technology development and other innovation activities of enterprises, thus shortening the product launch cycle. At the same time, when enterprises have better knowledge exchange and sharing mechanisms, they also have a stronger ability to manage new knowledge from other organizations. Successful commercialization of new products can facilitate innovation activities and improve organizational innovation performance. Zahra and George’s (2002) empirical study on knowledge sharing, knowledge absorptive capacity, and innovation performance show that knowledge sharing can have an intrinsic effect on innovation performance through the mediating effect of knowledge absorptive capacity, in addition to its direct positive effect on innovation performance (Zahra and George, 2002). Thus, a synergistic evolutionary path of knowledge sharing, knowledge absorption capacity, and innovation performance is formed. Based on this evolutionary path, enterprises continuously increase knowledge stock and enhance knowledge absorption capacity through knowledge sharing, and then internalize each other’s knowledge and innovation knowledge system to achieve innovation performance improvement and competitive advantage construction. Tripsas’s (1997) empirical study on external knowledge sharing in organizations shows that knowledge is externally shared in the process of cooperation and communication between enterprises (Tripsas, 1997). In turn, this external sharing of knowledge achieves innovation in technology development, production processes, and business models through the creative stimulation of both parties, which in turn has a significant positive impact on innovation performance (Chesbrough and Schwartz, 2007).

Open innovation and innovation performance

Open innovation is based on the strategic goal of innovation development, breaking the traditional organization’s closed boundaries, and using the inflow and outflow of knowledge from inside and outside the organization to promote innovation work such as new product development and business model reconstruction (Jin et al., 2022). Roh et al. (2022) believe that open innovation is not only a means but also a strategic business model (Roh et al., 2022). Through inside-out and outside-in open innovation, enterprises benefit from the introduction of new innovative knowledge and technology, and also profit from the export of patented technologies that are not used by enterprises. Baki and Peker (2022) reduce the risk of investment in R&D and enhance innovation performance by supporting new R&D technologies outside the organization through mutual flow or sharing of resources with consumers, suppliers, academic and research institutions, competitors, community teams, and non-profit organizations (Yildirim et al., 2022). The organization and these institutions then form an open-ended value co-creation mechanism, and this value co-creation mechanism can “divide” innovation and ultimately contribute significantly to enhancing the effectiveness of the firm’s new product development.

In the digital economy driven by big data, cloud computing and artificial intelligence, open innovation based on internal and external cooperation, intra-enterprise innovation and entrepreneurship and open innovation with the help of public knowledge have become important means for enterprises to adapt to the development of digital transformation and build dynamic competitive advantages (Benitez et al., 2022; Chaithanapat et al., 2022; Kitsios and Kamariotou, 2022). The crowdsourcing model and C2B innovation model around which open innovation is formed constitute important support for the digital development of enterprises. Yang et al. (2022) classify open innovation strategies into two dimensions: “wide external search strategy” and “deep external search strategy” to facilitate the development of new products and improve innovation performance (Yang et al., 2022). At the same time, the external search target of open innovation is divided into two dimensions: “competitors” and “non-competitors”. Based on the moderating effect of environmental competitiveness, an empirical study is conducted to investigate the relationship between open innovation strategy and new product innovation performance (Najafi-Tavani et al., 2022; Sanni and Verdolini, 2022; Santos-Vijande et al., 2022). When the external search target is an external organization other than a competitor, the competitive market environment positively moderates the relationship between “depth and width of external search strategy” and “new product innovation performance”. When the external search target is a competitor, the competitive market environment negatively adjusts the relationship between “deep external search strategy” and “new product innovation performance”.

Knowledge integration capability and innovation performance

Knowledge integration is the connection of formal or informal knowledge between individuals and business organizations that leads to new knowledge sharing and communication and can provide a basis for transforming individual knowledge into organizational knowledge. When an individual’s or organization’s knowledge is connected with another team’s knowledge and discussed, communicated, and exchanged by the individual or organization, it may further develop and integrate up-ward into the organization’s knowledge (Inkpen, 1996). Based on knowledge foundation theory and organizational learning theory, when the internal knowledge accumulation of an organization is not enough to support the development of an enterprise, seeking knowledge from outside becomes one of the important channels for the innovation and development of an enterprise. In the era of a knowledge-driven digital economy, cross-organizational cooperation enables enterprises to acquire more knowledge. However, how can the new knowledge acquired be effectively converted into useful value for the organization and make the organization more innovation-driven? Only by integrating relevant knowledge and resources more rapidly and flexibly, and coordinating the allocation of internal and external knowledge of the organization, can such effective conversion of new knowledge be successful in the open digital competitive environment.

McDonough et al. (2001) believe that enterprises create new knowledge through knowledge integration, and apply it to the business activities of the enterprise. Therefore, by the knowledge integration ability, the knowledge advantage created by the knowledge exchange and combination will be reflected in the related activities of the organization’s value creation, thus contributing to the organization’s innovation performance. Ritala et al. (2017) believe that knowledge integration is the integration of professional knowledge among members to meet the organization’s adaptation to a specific business environment. Because integrated knowledge enables organizations to efficiently plan products and markets in uncertain environments, promote new product innovation and evolution, and lay the foundation for business operations to achieve expected execution results. Therefore, the stronger the knowledge integration capability of the organization, the more knowledge will be available. The more solidly these organizations can establish their core competencies, the higher their innovation performance.

Zobel et al. (2017) empirical research based on organizational learning theory shows that if an enterprise can acquire new knowledge and integrate existing knowledge in the organization in different ways, the enterprise will have a good performance in innovation matters such as products and processes. Therefore, the stronger the organizational knowledge integration capability, the higher the level of enterprise innovation. Based on the perspective of dynamic capability theory and the empirical data of 261 enterprises participating in the Standard Alliance, Zhang et al. (2022) conducts an empirical study on the relationship between knowledge integration capabilities, partnership quality, and alliance innovation performance. The knowledge integration ability of the enterprise has a positive effect on improving the performance of the technical standards alliance, and the improvement of the quality of the partnership in the technical standards alliance will also help the knowledge integration ability and the alliance management ability to jointly play a positive role in the performance of the technical standards alliance (Qin et al., 2021; Sousa-Ginel et al., 2021; Junaid et al., 2022; Sultana et al., 2022).

The linkage of knowledge integration capability, open innovation, and knowledge sharing

Open innovation guides the direction and behavioral choices of digital enterprises and has a supportive role in their innovation performance. To achieve the goal of open innovation-oriented innovation performance, enterprises must have sufficient organizational resources to promote innovation actions (Acharya et al., 2022). For enterprises, the knowledge integration capability accumulated in the long-term production and operation process becomes a valuable capability. Knowledge integration capability can provide support for the realization of enterprise innovation performance (Ahlfänger et al., 2022). When the knowledge integration ability is difficult to be directly utilized, the enterprise will realize the improvement of its innovation performance by sharing knowledge with other enterprises and complementing them through knowledge sharing (Bao and Wang, 2022). Compared with enterprises with strong knowledge integration ability, enterprises with weak knowledge integration ability need to play the role of knowledge sharing more and fully release knowledge value through knowledge-sharing processes such as constructing a knowledge portfolio, improving knowledge management ability, and using knowledge to leverage opportunities (Kong et al., 2021). Meanwhile, knowledge sharing improves the open innovation environment of enterprises through optimal knowledge management, which is conducive to better innovation performance (Sheng and Hartmann, 2019; Qin et al., 2021). Therefore, the innovation performance of digital enterprises may be influenced by the linkage and complementarity of open innovation, knowledge integration capability, and knowledge sharing, which include six factors: inside-out open innovation, outside-in open innovation, knowledge systematization capability, interaction and coordination capability, socialization capability and knowledge sharing. The research framework of this paper is shown in Figure 1.

FIGURE 1

FIGURE 1. Research model.

Research design

Method

The main reasons for adopting fsQCA in this paper are as follows: 1) Traditional statistical analysis methods, such as regression analysis, can only analyze the “net effect” of individual conditions on innovation performance, which cannot solve the problem of causal complexity in innovation performance research (Ragin, 2014; Xie and Wang, 2020). The QCA method can reveal the impact of complex relationships among multiple antecedent conditions on the results based on the pooling theory (Fiss, 2011). 2) Although the group states relationships among the antecedents of innovation performance can be tested by typical correlation analysis and discriminant analysis. However, these methods are difficult to identify the interdependence and asymmetric causality among the antecedent conditions (Ragin, 2006). 3) The causal conditions in this paper are mostly continuous variables, and fsQCA can ensure the accuracy of the data after variable processing, which can fully reflect the subtle effects produced by changes in different degrees or levels of innovation performance influencing factors.

Sample selection and data collection

Regarding existing studies and the normative requirements of fsQCA methods (Fiss, 2011; Ragin, 2014). When there are 6 pre-elements in the configuration path model, more than 25 research samples are required to ensure the reliability and validity of the research results. This study combines industry planning and thinks tank research reports such as “the 2020 China Unicorn Enterprise Research Report” and “the 2020 Hurun List of China’s Top 500 Private Enterprises”. A total of 36 digital enterprises (including five provinces: Shaanxi, Henan, Jiangsu, Zhejiang, and Liaoning) provided by MBA students of Xi’an Jiaotong University are selected as research samples. The information of sample enterprises is shown in Table 1. The sample selection in this paper follows the theoretical sampling principle (Fiss, 2007), which satisfies the following three criteria: 1) the sample enterprises should be listed enterprises or industry-leading enterprises among digital enterprises to ensure the adequacy of the information. 2) The enterprises have experienced a complete innovation performance process to ensure the typicality of the sample. 3) The enterprises are involved in different industries to ensure the diversity of the sample. Meanwhile, according to the requirements of the QCA method on sample size, when the number of conditions is n, the number of samples should not be less than 2ⁿ⁻¹. Based on this, 36 digital enterprises are finally selected as samples in this paper, and the basic information of some sample enterprises is shown in Table 1.

TABLE 1

TABLE 1. Basic information of sample enterprises.

The research data in this paper were mainly collected through secondary sources. The reasons are as follows: first, the relevant public information data are abundant and have high authenticity and reliability; second, to avoid subjective influence on the collection and analysis of information when researchers conduct interviews; third, the number of sample enterprises and their scattered locations make it more difficult to conduct field research and interviews. At the same time, to try to avoid the limitations of the research caused by secondary data, this paper selects high-quality information such as annual reports of enterprises, authoritative research reports, and well-known media reports when collecting data; when processing data, members of the research team conducted several data collations and discussions and conducted coding reliability tests to ensure the validity and reliability of secondary information. The sources of information in this paper include 1) official websites of enterprises, annual reports, internal speeches, and public interviews of senior leaders; 2) industry research reports and related books; and 3) information such as mainstream media reports and comments of self-publishers. In collecting and organizing the information, the main focus is on enterprises’ innovation performance experience, open innovation, knowledge integration capability, and corresponding knowledge-sharing activities, which eventually results in a sample database of more than 900,000 words.

The coding basis for the QCA approach is derived from a holistic reflection of quantitative and qualitative data. This paper mainly draws on the concept of anchoring variables and looks for statements about open innovation, knowledge integration capability, knowledge sharing, and innovation performance from the case materials as the basis for the assignment. Referring to Fiss’s (2011) approach (Fiss, 2011), this paper adopts a quadratic assignment method, which is based on four anchors of 0 (completely unaffiliated), 0.33 (biased unaffiliated), and 0.67 (biased affiliated), and 1 (completely affiliated). The specific coding assignment process includes three steps: constructing a coding table (see Table 2), coding the information, and testing the coding reliability. Taking the coding of knowledge-sharing factors of ZGSH as an example: this paper first draws on ’s study to construct a coding table and determines six indicators in two dimensions of knowledge-sharing channels and knowledge-sharing degree as coding criteria (Daniel Sherman et al., 2005). Then two coders from the research team independently coded the sample enterprises based on the information collected from them. The coders assigned a value of 1 to the market orientation factor when they judged that the enterprise met 5 or more of the 6 indicators, and a value of 0.67 if it met 3–4 of the 6 indicators, 0.33 if it met 1–2 of the 6 indicators, and 0 if it met less than 1. Finally, the coding reliability test was conducted based on the aggregated results of the coders to ensure the reliability of the coding results.

TABLE 2

TABLE 2. Questionnaire.

Coding reliability test

Coding reliability refers to the degree of consistency in the coders’ assignment of factors. The higher the degree of consistency, the higher the coding reliability. Drawing on the studies of Bhatt et al. (2010) and Greckhamer et al. (2018) (Bhatt et al., 2010), this paper uses the average mutual agreement reliability index to measure coding reliability. This paper contains 252-factor coding assignments (36 enterprises, 7-factor coding assignments per enterprise), and a total of 43-factor coding assignments were initially inconsistent between the two coders in the coding process, so the value of the mutual agreement index (MAI) obtained using the Holsti formula is 82.94%, which is calculated as follows:

$MAI=\frac{2M}{N_1+N_2}=\frac{2\times \left(252-43\right)}{252+252}\times 100\%=82.94\%(1)$

where M is the number of factors assigned identically by both coders, N₁ is the number of factors assigned by the first coder, and N₂ is the number of factors assigned by the second coder.

Since this paper is assigned by two coders, the mutual agreement is the average mutual agreement, and the reliability coefficient is calculated as follows:

$\mathrm{r}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}=\frac{n\times MAI}{1+\left(n-1\right)\times MAI}=\frac{2\times 0.8294}{1+\left(2-1\right)\times 0.8294}\times 100\%=90.67\%(2)$

From the above calculation results, it can be seen that this paper has good coding confidence. In addition, for the different results appearing in the process of coding assignment, the members of the group discuss together and finalize the corresponding assignment results.

Data analysis and results

Analysis of the necessity of individual conditions

The necessity of the antecedent conditions was first analyzed to test whether any single antecedent condition constitutes a necessary condition for innovation performance. fsQCA3.0 test results are shown in Table 3. It can be seen that the consistency values of the influence of each antecedent condition on innovation performance are below 0.9, indicating that there is no single antecedent condition that has a dominant influence on innovation performance, and a configuration analysis of each antecedent condition is required.

TABLE 3

TABLE 3. Analysis of necessary conditions.

Analysis of sufficient conditions

The adequacy of conditional configuration is measured using conformance, but its calculation method and minimum acceptance criteria differ from necessity analysis. According to Schneider and Wagemann, the frequency threshold should be set according to the size of the research sample, and the frequency threshold for small and medium-sized studies is usually set to 1 (Schneider et al., 2010). Since the number of samples in this paper is 36, the frequency threshold is set to 1 in the adequacy analysis. Meanwhile, according to the suggestion of Ragin and Fiss, this paper sets the original consistency threshold to 0.8 and the PRI consistency threshold to 0.75. In subsequent normalization operations, complex, parsimonious, and intermediate solutions are obtained. Following the existing research practice, this paper selects the intermediate solution as the main reference for the explanatory sufficiency analysis and distinguishes the core and auxiliary elements of the group state based on the parsimonious solution and the intermediate solution. If the antecedent condition appears in both the parsimonious solution and the intermediate solution, the condition is the core condition; if the antecedent condition appears only in the intermediate solution, the condition is the auxiliary condition, and the results are shown in Table 4.

TABLE 4

TABLE 4. Sufficiency analysis of conditional configuration.

As can be seen from Table 4, the consistency level of both individual solutions (configuration) and the overall solution is above the minimum acceptable standard of 0.75, where the consistency of the overall solution is 0.844 and the coverage of the overall solution is 0.780. The six configurations in Table 4 can be considered a sufficient combination of conditions for digital firms to achieve innovative performance.

Configuration 1 (KSC*IOI*KS→IP): This configuration is a sufficient condition for innovation performance consisting of high knowledge systematization capability, high inside-out open innovation, and high knowledge sharing capability. When the knowledge systematization capability is strong, to meet market demand, achieve competitive advantage in the market and achieve high innovation performance, enterprises need knowledge sharing and knowledge systematization capability to break organizational boundaries. At the same time, enterprises can further improve the effectiveness of knowledge utilization through knowledge sharing, creatively integrate and allocate knowledge to market innovation activities, and improve the ability of enterprises to launch new products and develop new technologies, thus promoting innovation performance.

Configuration 2 (KSC*ICC*OOI*KS→IP): This configuration is a sufficient condition for innovation performance consisting of high knowledge systematization capability, high interaction and coordination capability, high outside-in open innovation, and high knowledge sharing capability. Unlike configuration 5, although the knowledge systematization capability and interaction and coordination capability are high in configuration 2, it only has high outward-inward open innovation. In this case, knowledge sharing becomes a key factor to ensure the successful development and use of new products and technologies.

Configuration 3 (ICC*SC*IOI*∼OOI*KS→IP): This configuration is a sufficient condition configuration for innovation performance consisting of high interactive co-ordination capability, high socialization capability, high inside-out open innovation, low outside-in open innovation, and high knowledge sharing capability. The results of this configuration show that firms with high interaction and coordination capabilities and high socialization capabilities are willing to invest in knowledge acquisition actions inside and outside the organization and influence innovation performance by exploring new technologies, products, and business markets. In contrast, more inside-out open innovation and strong knowledge-sharing capabilities provide firms with the knowledge and capabilities to undertake technological innovation and new product development, which in turn leads to innovation performance.

Configuration 1, Configuration 2, and Configuration 3 are all manifested by the combined effect of open innovation, knowledge integration capability, and knowledge sharing, so this paper names them as the balanced driving model of “knowledge integration capability-open innovation-knowledge sharing”.

Configuration 4 (KSC*∼ICC*∼SC*IOI*∼OOI→IP): This configuration is a sufficient condition for innovation performance consisting of high knowledge systematization capability, low interaction, and coordination capability, low socialization capability, high inside-out open innovation, low outside-in open innovation. In configuration 2, the firm has high knowledge systematization capability as the main knowledge integration capability, and high inward-looking open innovation provides a suitable environment for the firm’s innovation and innovation performance. At the same time, the low external-inward open innovation environment reduces the external coordination pressure on innovation and allows firms to focus on improving their innovation performance in technology innovation and new product development due to the low number of collaborators and the difficulty of transforming existing knowledge and technology into market-compatible value innovation.

Configuration 5 (KSC*ICC*IOI*OOI→IP): This configuration is composed of high knowledge systemization ability, high interaction, and coordination ability, high in-side-out open innovation, and high outside-in open innovation configuration of sufficient conditions for innovation performance. Enterprises with dual capabilities of high knowledge systemization ability and high interaction and coordination ability need to improve their innovation performance in the case of a highly open innovation environment. Because enterprises need an open innovation environment, to achieve breakthroughs in both market development and technological innovation. The inside-out open innovation guarantees the internal environment that enterprises need to carry out risk-taking activities and free innovation, while the outside-in open innovation promotes enterprises to improve their innovation performance based on external knowledge.

Both Configuration 4 and Configuration 5 show that the innovation performance of digital enterprises is driven by knowledge integration capability and open innovation, so this paper names them as a dual drive model of “knowledge integration capability-open innovation”.

Configuration 6 (KSC*∼ICC*SC*∼OOI*KS→IP): This configuration is a sufficient condition for innovation performance consisting of high knowledge systematization capability, low interaction and coordination capability, and high socialization capability, low outside-in open innovation, and high knowledge sharing. This configuration reflects the tendency of enterprises to develop the knowledge integration capability of internalizing knowledge systematization and turning knowledge into value. Under this knowledge integration capability, enterprises are good at seizing opportunities and being brave in innovation, and continuously integrating and developing existing knowledge, which can significantly affect innovation performance. However, due to the limited innovation environment within the enterprise, it is necessary to promote the cultivation of open innovation culture through knowledge sharing, reduce the cost of market development and innovation actions, and then ensure that corporate activities can be supported by innovation culture.

Configuration 6 shows the linkage effect of knowledge integration capability and knowledge sharing on innovation performance, so this paper names it a dual drive model of “knowledge integration capability-knowledge sharing”.

Robustness test

We used standard methods to conduct a robust analysis of QCA results. The commonly used methods are: Adjusting the calibration threshold, changing the consistency threshold, adding or deleting the shell, changing the frequency threshold, and adding other conditions. This paper draws on Greckhamer’s practice and increases the PRI consistency threshold from 0.75 to 0.80 to carry out an adequacy analysis (Greckhamer et al., 2018), and finds that the test results are almost completely consistent with the original research results (see Table 4). In addition, in this paper, the original consistency threshold is increased from 0.80 to 0.85, the robustness test is performed again, and the obtained results are consistent with the original consistency threshold of 0.80.

Discussion and implications

Research implication

From the perspective of configuration matching, this paper uses fuzzy set qualitative comparative analysis (fsQCA) to explore the configuration effects of antecedent conditions, such as knowledge systematization ability, interaction coordination ability, socialization ability, open innovation from inside out, open innovation from outside in and knowledge sharing, on innovation performance of digital firms. Three models to improve innovation performance are summarized: the balanced driving mode of “knowledge integration ability-open innovation-knowledge sharing”, the dual driving mode of “knowledge integration ability-open innovation” and the dual driving mode of “knowledge integration ability-knowledge sharing”.

Firstly, innovation performance has the characteristics of “multiple concurrent” and “all paths lead to the same destination”. The innovation performance of digital enterprises is the result of the interaction of multiple antecedents, that is, multiple concurrencies. In addition, the interaction between antecedent conditions will form different configurations, that is, all paths lead to the same destination. The results show that there are six different configurations of innovation performance, and each configuration is composed of multiple antecedent conditions. In this paper, the fsOCA method is adopted to reveal the matching effect of the above antecedent conditions on innovation performance, explain the influencing mechanism of innovation performance from a holistic perspective, and enrich and supplement the previous research on innovation performance based on the contingency perspective.

Secondly, knowledge integration ability is an important foundation for digital enterprises to improve innovation performance. It can be seen from the configuration results that among the six configurations to improve innovation performance, any configuration has the condition of knowledge integration ability, that is, innovation performance can be improved under the guidance of knowledge systematization ability, interaction coordination ability, or socialization ability. Under different knowledge integration capabilities, enterprises will present different choices for their organizational knowledge management and utilization methods. No matter what kind of knowledge integration capability is based on, enterprises’ innovation actions are formulated and implemented according to the internal and external conditions of enterprises (Sun et al., 2022). Therefore, different knowledge integration capabilities can promote innovation performance to a certain extent.

Thirdly, knowledge sharing is important for enterprises to improve innovation performance. According to the configuration results of innovation performance, knowledge sharing plays a core role in multiple configurations. This indicates that no matter whether there is a good open innovation environment or not (Fan et al., 2021), enterprises need to integrate and utilize knowledge effectively. When the enterprise has a good open innovation environment, only by using an effective knowledge-sharing strategy can the open innovation environment be converted into real performance gains. In the conservative open innovation environment, enterprises can fully cultivate the open innovation environment by using knowledge sharing, excavating the innovation value of the environment, and avoiding the low innovation performance caused by the environment.

Management implications

This paper provides the following management implications for digital enterprise innovation performance.

Firstly, the environment in which digital enterprises are located is characterized by uncertainty, interactivity, and borderlessness. It is increasingly difficult for enterprises to improve innovation performance in the actual development process. Therefore, enterprises should pay attention to the guiding role of knowledge integration ability in innovation performance (Hu et al., 2020). Based on selecting knowledge integration ability suitable for their development, enterprises should make corresponding adjustments to innovation activities according to knowledge integration ability, and promote the formation of technology or product innovation matching knowledge integration ability.

Open innovation can improve the risk tolerance of enterprises in pursuing innovation performance and provide them with necessary environmental support. This internal policy for the enterprise offers a new way to improve innovation performance: according to the needs of innovation performance, enterprises in the process of production and management necessary to have a good open innovation environment, namely through the analysis of the unfavorable situation of innovation may face and the deep understanding of the effect on different types of open innovation. Enterprises can achieve a dynamic balance between innovation performance and knowledge sharing to avoid hindering high innovation performance due to too high or too low open innovation levels.

The findings of this paper can inspire digital firms to consider and improve innovation performance from a knowledge-sharing perspective. The purpose of knowledge sharing is to manage the knowledge owned by an enterprise and to generate “new knowledge or capabilities”. The improvement of innovation performance depends, to a certain extent, on acquiring and using knowledge. Therefore, enterprises should explore and create new uses for their existing knowledge through knowledge sharing, try to construct new knowledge combinations, and apply the new combinations to innovation performance practices.

Limitations and further research

This paper has the following shortcomings, and also provides a direction for future research: Firstly, this paper only considers antecedent conditions such as knowledge integration ability, open innovation, and knowledge sharing, but many factors affect innovation performance. Future research can include factors such as strategic orientation, resources, and senior management team, to study the influencing factors of innovation performance more comprehensively and improve the explanatory power of research results. Secondly, 36 enterprises were selected as the analysis samples in this paper. Limited by the number of samples, the results of qualitative comparative analysis are limited in the universality of application. In the future, more data on industries and enterprises can be collected for further analysis.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

JG made significant contributions to the conception or design of the work, and made major adjustments to the theoretical framework in the process of repair. TC and RM critically drafted or modified important knowledge contents, and made major adjustments and modifications to research hypotheses in the revision stage to sort out the structure of the full text.

Funding

This research was funded by the Humanities and Social Sciences Project of the Ministry of Education in 2019: A comparative research on Sino-German vocational education integration of industry and education and inheritance of craftsman spirit (19YJC880010, Ministry of Education, China). Research on intelligent technology consultation service system driven by big data (2020ZDLGY09-05)

Conflict of interest

HD, TC ,and RM were employed by Shaanxi Provincial Land Engineering Construction Group Co., Ltd.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Acharya, C., Ojha, D., Gokhale, R., and Patel, P. C. (2022). Managing information for innovation using knowledge integration capability: The role of boundary spanning objects. Int. J. Inf. Manag. 62, 102438. doi:10.1016/j.ijinfomgt.2021.102438

CrossRef Full Text | Google Scholar

Ahlfänger, M., Gemünden, H. G., and Leker, J. (2022). Balancing knowledge sharing with protecting: The efficacy of formal control in open innovation projects. Int. J. Proj. Manag. 40, 105–119. doi:10.1016/j.ijproman.2021.09.007

CrossRef Full Text | Google Scholar

Ahn, J. M., Mortara, L., and Minshall, T. (2013). The effects of open innovation on firm performance: A capacity approach. STI Policy Rev. 4, 79–93.

Google Scholar

Audretsch, B. D., and Belitski, M. (2022). The limits to open innovation and its impact on innovation performance. Technovation 152, 245–259. doi:10.1016/j.technovation.2022.102519

CrossRef Full Text | Google Scholar

Autio, E., Nambisan, S., Thomas, L. D. W., and Wright, M. (2018). Digital affordances, spatial affordances, and the Genesis of entrepreneurial ecosystems: AUTIO et al. Strategic Entrepreneursh. J. 12, 72–95. doi:10.1002/sej.1266

CrossRef Full Text | Google Scholar

Bao, Z., and Wang, C. (2022). A multi-agent knowledge integration process for enterprise management innovation from the perspective of neural network. Inf. Process. Manag. 59, 102873. doi:10.1016/j.ipm.2022.102873

CrossRef Full Text | Google Scholar

Benitez, J., Arenas, A., Castillo, A., and Esteves, J. (2022). Impact of digital leadership capability on innovation performance: The role of platform digitization capability. Inf. Manag. 59, 103590. doi:10.1016/j.im.2022.103590

CrossRef Full Text | Google Scholar

Bhatt, G., Emdad, A., Roberts, N., and Grover, V. (2010). Building and leveraging information in dynamic environments: The role of IT infrastructure flexibility as enabler of organizational responsiveness and competitive advantage. Inf. Manag. 47, 341–349. doi:10.1016/j.im.2010.08.001

CrossRef Full Text | Google Scholar

Cennamo, C., Dagnino, G. B., Di Minin, A., and Lanzolla, G. (2020). Managing digital transformation: Scope of transformation and modalities of value Co-generation and delivery. Calif. Manag. Rev. 62, 5–16. doi:10.1177/0008125620942136

CrossRef Full Text | Google Scholar

Chaithanapat, P., Punnakitikashem, P., Khin Khin Oo, N. C., and Rakthin, S. (2022). Relationships among knowledge-oriented leadership, customer knowledge management, innovation quality and firm performance in SMEs. J. Innovation Knowl. 7, 100162. doi:10.1016/j.jik.2022.100162

CrossRef Full Text | Google Scholar

Cheah, S. L.-Y., and Ho, Y.-P. (2021). Commercialization performance of outbound open innovation projects in public research organizations: The roles of innovation potential and organizational capabilities. Ind. Mark. Manag. 94, 229–241. doi:10.1016/j.indmarman.2021.02.012

CrossRef Full Text | Google Scholar

Chesbrough, H., and Schwartz, K. (2007). Innovating business models with Co-development partnerships. Research-Technology Manag. 50, 55–59. doi:10.1080/08956308.2007.11657419

CrossRef Full Text | Google Scholar

Cuevas-Vargas, H., Aguirre, J., and Parga-Montoya, N. (2022). Impact of ICT adoption on absorptive capacity and open innovation for greater firm performance. The mediating role of ACAP. J. Bus. Res. 140, 11–24. doi:10.1016/j.jbusres.2021.11.058

CrossRef Full Text | Google Scholar

Daniel Sherman, J., Berkowitz, D., and Souder, W. E. (2005). New product development performance and the interaction of cross-functional integration and knowledge management. J. Prod. Innov. Manage. 22, 399–411. doi:10.1111/j.1540-5885.2005.00137.x

CrossRef Full Text | Google Scholar

De Boer, M., Van Den Bosch, F. A. J., and Volberda, H. W. (1999). Managing organizational knowledge integration in the emerging multimedia complex. J. Manag. Studs. 36, 379–398. doi:10.1111/1467-6486.00141

CrossRef Full Text | Google Scholar

Ebersberger, B., Galia, F., Laursen, K., and Salter, A. (2021). Inbound open innovation and innovation performance: A robustness study. Res. Policy 50, 104271. doi:10.1016/j.respol.2021.104271

CrossRef Full Text | Google Scholar

Eftekhari, N., and Bogers, M. (2015). Open for entrepreneurship: How open innovation can foster new venture creation: Open innovation and new venture creation. Creativity Innovation Manag. 24, 574–584. doi:10.1111/caim.12136

CrossRef Full Text | Google Scholar

Fan, Y.-J., Liu, S.-F., Luh, D.-B., and Teng, P.-S. (2021). Corporate sustainability: Impact factors on organizational innovation in the industrial area. Sustainability 13, 1979. doi:10.3390/su13041979

CrossRef Full Text | Google Scholar

Fiss, P. C. (2007). A set-theoretic approach to organizational configurations. Acad. Manage. Rev. 32, 1180–1198. doi:10.5465/amr.2007.26586092

CrossRef Full Text | Google Scholar

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Acad. Manage. J. 54, 393–420. doi:10.5465/amj.2011.60263120

CrossRef Full Text | Google Scholar

Gkypali, A., Arvanitis, S., and Tsekouras, K. (2018). Absorptive capacity, exporting activities, innovation openness and innovation performance: A sem approach towards a unifying framework. Technol. Forecast. Soc. Change 132, 143–155. doi:10.1016/j.techfore.2018.01.025

CrossRef Full Text | Google Scholar

Greckhamer, T., Furnari, S., Fiss, P. C., and Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strateg. Organ. 16, 482–495. doi:10.1177/1476127018786487

CrossRef Full Text | Google Scholar

Hu, X., Danso, B. A., Mensah, I. A., and Addai, M. (2020). Does innovation type influence firm performance? A dilemma of star-rated hotels in Ghana. Sustainability 12, 9912. doi:10.3390/su12239912

CrossRef Full Text | Google Scholar

Inkpen, A. C. (1996). Creating knowledge through collaboration. Calif. Manag. Rev. 39, 123–140. doi:10.2307/41165879

CrossRef Full Text | Google Scholar

Jiang, X. (2020). Digital economy in the post-pandemic era. J. Chin. Econ. Bus. Stud. 18, 333–339. doi:10.1080/14765284.2020.1855066

CrossRef Full Text | Google Scholar

Jin, C., Liu, A., Liu, H., Gu, J., and Shao, M. (2022). How business model design drives innovation performance: The roles of product innovation capabilities and technological turbulence. Technol. Forecast. Soc. Change 178, 121591. doi:10.1016/j.techfore.2022.121591

CrossRef Full Text | Google Scholar

Junaid, M., Zhang, Q., and Syed, M. W. (2022). Effects of sustainable supply chain integration on green innovation and firm performance. Sustain. Prod. Consum. 30, 145–157. doi:10.1016/j.spc.2021.11.031

CrossRef Full Text | Google Scholar

Kitsios, F., and Kamariotou, M. (2022). Digital innovation and entrepreneurship transformation through open data hackathons: Design strategies for successful start-up settings. Int. J. Inf. Manag., 102472. doi:10.1016/j.ijinfomgt.2022.102472

CrossRef Full Text | Google Scholar

Kong, Q., Li, R., Peng, D., and Wong, Z. (2021). High-technology development zones and innovation in knowledge-intensive service firms: Evidence from Chinese A-share listed firms. Int. Rev. Financial Analysis 78, 101883. doi:10.1016/j.irfa.2021.101883

CrossRef Full Text | Google Scholar

Lee, S., Park, G., Yoon, B., and Park, J. (2010). Open innovation in SMEs—an intermediated network model. Res. Policy 39, 290–300. doi:10.1016/j.respol.2009.12.009

CrossRef Full Text | Google Scholar

Li, L., Li, M., Ma, S., Zheng, Y., and Pan, C. (2022). Does the construction of innovative cities promote urban green innovation?. J. Environ. Qual. 318, 115605. doi:10.1016/j.jenvman.2022.115605

PubMed Abstract | CrossRef Full Text | Google Scholar

McDonough, E. F., Kahnb, K. B., and Barczaka, G. (2001). An investigation of the use of global, virtual, and colocated new product development teams. J. Prod. Innovation Manag. 18, 110–120. doi:10.1111/1540-5885.1820110

CrossRef Full Text | Google Scholar

Najafi-Tavani, S., Zaefarian, G., Robson, M. J., Naudé, P., and Abbasi, F. (2022). When customer involvement hinders/promotes product innovation performance: The concurrent effect of relationship quality and role ambiguity. J. Bus. Res. 145, 130–143. doi:10.1016/j.jbusres.2022.03.001

CrossRef Full Text | Google Scholar

Naseer, S., Khawaja, K. F., Qazi, S., Syed, F., and Shamim, F. (2021). How and when information proactiveness leads to operational firm performance in the banking sector of Pakistan? The roles of open innovation, creative cognitive style, and climate for innovation. Int. J. Inf. Manag. 56, 102260. doi:10.1016/j.ijinfomgt.2020.102260

CrossRef Full Text | Google Scholar

Ovuakporie, O. D., Pillai, K. G., Wang, C., and Wei, Y. (2021). Differential moderating effects of strategic and operational reconfiguration on the relationship between open innovation practices and innovation performance. Res. Policy 50, 104146. doi:10.1016/j.respol.2020.104146

CrossRef Full Text | Google Scholar

Papadopoulos, T., Baltas, K. N., and Balta, M. E. (2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. Int. J. Inf. Manag. 55, 102192. doi:10.1016/j.ijinfomgt.2020.102192

PubMed Abstract | CrossRef Full Text | Google Scholar

Qin, J., van der Rhee, B., Venkataraman, V., and Ahmadi, T. (2021). The impact of IT infrastructure capability on NPD performance: The roles of market knowledge and innovation process formality. J. Bus. Res. 133, 252–264. doi:10.1016/j.jbusres.2021.04.072

CrossRef Full Text | Google Scholar

Ragin, C. C. (2006). Set relations in social research: Evaluating their consistency and coverage. Polit. Anal. 14, 291–310. doi:10.1093/pan/mpj019

CrossRef Full Text | Google Scholar

Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. La Jolla, CA: University of California Press. doi:10.1525/9780520957350

CrossRef Full Text | Google Scholar

Rauter, R., Globocnik, D., Perl-Vorbach, E., and Baumgartner, R. J. (2019). Open innovation and its effects on economic and sustainability innovation performance. J. Innovation Knowl. 4, 226–233. doi:10.1016/j.jik.2018.03.004

CrossRef Full Text | Google Scholar

Ritala, P., Huizingh, E., Almpanopoulou, A., and Wijbenga, P. (2017). Tensions in R&D networks: Implications for knowledge search and integration. Technol. Forecast. Soc. Change 120, 311–322. doi:10.1016/j.techfore.2016.12.020

CrossRef Full Text | Google Scholar

Roh, T., Noh, J., Oh, Y., and Park, K.-S. (2022). Structural relationships of a firm’s green strategies for environmental performance: The roles of green supply chain management and green marketing innovation. J. Clean. Prod. 356, 131877. doi:10.1016/j.jclepro.2022.131877

CrossRef Full Text | Google Scholar

Sanni, M., and Verdolini, E. (2022). Eco-innovation and openness: Mapping the growth trajectories and the knowledge structure of open eco-innovation. Sustain. Futur. 4, 100067. doi:10.1016/j.sftr.2022.100067

CrossRef Full Text | Google Scholar

Santos-Vijande, M. L., López-Sánchez, J. Á., Loredo, E., Rudd, J., and López-Mielgo, N. (2022). Role of innovation and architectural marketing capabilities in channelling entrepreneurship into performance. J. Innovation Knowl. 7, 100174. doi:10.1016/j.jik.2022.100174

CrossRef Full Text | Google Scholar

Scaliza, J. A. A., Jugend, D., Chiappetta Jabbour, C. J., Latan, H., Armellini, F., Twigg, D., et al. (2022). Relationships among organizational culture, open innovation, innovative ecosystems, and performance of firms: Evidence from an emerging economy context. J. Bus. Res. 140, 264–279. doi:10.1016/j.jbusres.2021.10.065

CrossRef Full Text | Google Scholar

Schneider, M. R., Schulze-Bentrop, C., and Paunescu, M. (2010). Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance. J. Int. Bus. Stud. 41, 246–266. doi:10.1057/jibs.2009.36

CrossRef Full Text | Google Scholar

Sheng, M. L., and Hartmann, N. N. (2019). Impact of subsidiaries’ cross-border knowledge tacitness shared and social capital on MNCs’ explorative and exploitative innovation capability. J. Int. Manag. 25, 100705. doi:10.1016/j.intman.2019.100705

CrossRef Full Text | Google Scholar

Shu, C., Zhou, K. Z., Xiao, Y., and Gao, S. (2016). How green management influences product innovation in China: The role of institutional benefits. J. Bus. Ethics 133, 471–485. doi:10.1007/s10551-014-2401-7

CrossRef Full Text | Google Scholar

Sisodiya, S. R., Johnson, J. L., and Grégoire, Y. (2013). Inbound open innovation for enhanced performance: Enablers and opportunities. Ind. Mark. Manag. 42, 836–849. doi:10.1016/j.indmarman.2013.02.018

CrossRef Full Text | Google Scholar

Sousa-Ginel, E., Franco-Leal, N., and Camelo-Ordaz, C. (2021). Knowledge conversion capability and networks as drivers of innovation in Academic Spin-Offs. J. Eng. Technol. Manag. 59, 101615. doi:10.1016/j.jengtecman.2021.101615

CrossRef Full Text | Google Scholar

Sultana, S., Akter, S., and Kyriazis, E. (2022). How data-driven innovation capability is shaping the future of market agility and competitive performance? Technol. Forecast. Soc. Change 174, 121260. doi:10.1016/j.techfore.2021.121260

CrossRef Full Text | Google Scholar

Sun, Y., Liu, J., and Ding, Y. (2020). Analysis of the relationship between open innovation, knowledge management capability and dual innovation. Technol. Anal. Strateg. Manag. 32, 15–28. doi:10.1080/09537325.2019.1632431

CrossRef Full Text | Google Scholar

Sun, Y., Sun, Y., and Liu, J. (2022). Does strategic alliance knowledge heterogeneity truly promote innovation performance? Sustainability 14, 3443. doi:10.3390/su14063443

CrossRef Full Text | Google Scholar

Swap, W., Leonard, D., Shields, M., and Abrams, L. (2001). Using mentoring and storytelling to transfer knowledge in the workplace. J. Manag. Inf. Syst. 18, 95–114. doi:10.1080/07421222.2001.11045668

CrossRef Full Text | Google Scholar

Tang, T., Fisher, G. J., and Qualls, W. J. (2021). The effects of inbound open innovation, outbound open innovation, and team role diversity on open source software project performance. Ind. Mark. Manag. 94, 216–228. doi:10.1016/j.indmarman.2021.02.013

CrossRef Full Text | Google Scholar

Tanriverdi, H. (2005). Information technology relatedness, knowledge management capability, and performance of multibusiness firms. MIS Q. 29, 311–334. doi:10.2307/25148681

CrossRef Full Text | Google Scholar

Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Res. Policy 47, 1367–1387. doi:10.1016/j.respol.2017.01.015

CrossRef Full Text | Google Scholar

Tripsas, M. (1997). Surviving radical technological change through dynamic capability: Evidence from the typesetter industry. Industrial Corp. Change 6, 341–377. doi:10.1093/icc/6.2.341

CrossRef Full Text | Google Scholar

Tsai, W. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Acad. Manage. J. 44, 996–1004. doi:10.5465/3069443

CrossRef Full Text | Google Scholar

Xie, X., and Wang, H. (2020). How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis. J. Bus. Res. 108, 29–41. doi:10.1016/j.jbusres.2019.10.011

CrossRef Full Text | Google Scholar

Yao, H., and Huang, W. (2022). Effect of R&D Subsidies on External Collaborative Networks and the Sustainable Innovation Performance of Strategic Emerging Enterprises: Evidence from China. Sustainability 14 (8). doi:10.3390/su14084722

CrossRef Full Text | Google Scholar

Yang, H., Li, L., and Liu, Y. (2022). The effect of manufacturing intelligence on green innovation performance in China. Technol. Forecast. Soc. Change 178, 121569. doi:10.1016/j.techfore.2022.121569

CrossRef Full Text | Google Scholar

Yildirim, E., Ar, I. M., Dabić, M., Baki, B., and Peker, I. (2022). A multi-stage decision making model for determining a suitable innovation structure using an open innovation approach. J. Bus. Res. 147, 379–391. doi:10.1016/j.jbusres.2022.03.063

CrossRef Full Text | Google Scholar

Yuan, S., Musibau, H. O., Genç, S. Y., Shaheen, R., Ameen, A., and Tan, Z. (2021). Digitalization of economy is the key factor behind fourth industrial revolution: How G7 countries are overcoming with the financing issues? Technol. Forecast. Soc. Change 165, 120533. doi:10.1016/j.techfore.2020.120533

CrossRef Full Text | Google Scholar

Zahra, S. A., and George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Acad. Manage. Rev. 27, 185–203. doi:10.5465/amr.2002.6587995

CrossRef Full Text | Google Scholar

Zhang, L., Yang, W., Yang, L., and Gao, L. (2022). How does the value co-creation of e-commerce platform and manufacturers affect channel performance? The moderating role of innovation capability and market turbulence. Electron. Commer. Res. Appl. 52, 101120. doi:10.1016/j.elerap.2022.101120

CrossRef Full Text | Google Scholar

Zobel, A.-K., Lokshin, B., and Hagedoorn, J. (2017). Formal and informal appropriation mechanisms: The role of openness and innovativeness. Technovation 59, 44–54. doi:10.1016/j.technovation.2016.10.001

CrossRef Full Text | Google Scholar