1 Introduction

At the macroscopic trend of modern enterprise networking development, the innovation model of business also has gradually developed to networking innovation model from the single model. Many studies have found that network embedding has generally become one of the most important factors that even determine the enterprise innovation performance. Based on the perspective of networking embedding, [37, 51, 48], Morone et al. (2004) defined that network relationship embedding is one of the most important features in modern organization relationships [31]. It has become a necessary requirement in the organization innovation process that obtaining resources through network relationship. It effectively shortens the cycle of product development, improving the development speed [4]. The communication and coordination between enterprises can promote the knowledge communication, upgrade the bilateral cooperation and enhance the relationship quality. The interaction and network links between organizations are in favor of social capital accumulation, thus advancing corporate innovation performance development [33]. Based on the network embedded structure perspective, [36, 29], Wincent et al. (2014) pointed out that the network embedded structure also has a significant impact on enterprises’ innovation performance [44]. Third, network density, closeness centrality and betweenness (number of structural holes), they all exhibit significant impact on promoting corporate innovation [26]. For example, the more structural holes are occupied by the enterprise in the network, the easier for companies to obtain scarce differential resources, such as timely information and advanced technologies [21]. Based on the perspective of embedded resource, Gulati (2009), Barne et al. (2010) noted that the resources these organizations possessed create competitive advantage, attraction and influence in the network [5, 6, 17]. They indicated that embedded resource is one of the most important features in sustainable corporate competitive advantage acquisition. The resource of enterprises is the foundation to carry out all the economic activities, the material basis of seeking social cooperation, and the value support required in corporate network relationship. It is integral in the research of network embedding dimension. This view reaches a consensus on [18] research studying the network embedding influence to service innovation performance. In the research of network embedding influencing industrial cluster innovation, Balland et al. (2014) also clearly illustrated that the business dynamic position, status and the extent of close to the center network in the cluster network directly affect enterprises’ ability of obtaining high-quality resources [3]. Thus it has an impact on the technological innovation of clustered firms and the innovation of the entire industry cluster. In a study on the influence of network relational embedding to SMEs’ technology innovation performance, Ma and He [28] selected the small and medium technology enterprises in Zhejiang Province as surveying objects. To study the influence of network relational embedding to SMEs’ technology innovation performance, the work conducts empirical research, introducing knowledge acquisition as an intermediary variable. It finally draws a basic conclusion that network relational embedding played a significantly positive role in promoting enterprise technology innovation performance, and proves the intermediation role of knowledge acquisition in the process. Choosing 132 marine engineering equipment manufacturing enterprises as surveying objects and the data source, Jia and Zhang [23] selected SEM to model. The study takes the organizational learning as an intermediate variable, building a structural equation model of network embedding influencing enterprise technological innovation performance. The analysis believes that deeper the degree of embedding among enterprises is, the more advantages enterprises have in improving technological innovation performance through exploitative learning. Selecting Chinese high-tech listed companies as empirical research objects, Zhao and Zheng [52] thoroughly studied the relationship between network embedding and enterprise innovation performance. The result is that the network density of the enterprise has a significant, positive role in promoting enterprise technological innovation performance.

Although the embedded relationship, embedded structure and embedded resource provide conditions for enterprise obtaining critical knowledge, technology and other resources, such critical knowledge, technical resources usually cannot directly be used by enterprises. However, they should be absorbed, integrated and digested before building competitive advantage, which requires companies with strong knowledge management capabilities. Through the shares, investment, cross-training, mutual exchange of workers and other forms of activities, enterprises can achieve multi-level transfer and access of information and knowledge, the knowledge will be filtered, integrated, innovated, and then be used for themselves. The knowledge management has becomes the key which affects the enterprise’s knowledge efficiency, and also determines the success of the enterprise innovation to a certain extent. Therefore, during the process of exploring the relationship between the network embedding and innovation performance, we should fully consider the impact of knowledge management in it.

Although the view of network embedding influencing the enterprise technological innovation performance has been widely recognized, there are three paradoxes [24] appear in the network embedding study. The first one is the embedded relationship paradox that strong relationship and weak ties are both positively correlated with actors’ performance in the network. The second one is the embedded structure paradox that high-density network and low-density network based on structural holes are both positively related to the performance of actors [20]. Faced with the paradox, some scholars try to introduce dynamic capability, contingency factors dynamic environment, strategic flexibility, the intensity of competition, industry characteristics and other contingent factors on the embedding paradox [43]. However, they ignore the impact of knowledge network types and enterprise knowledge management capabilities. On the one hand, the network structure, relation and resource features of different types of knowledge networks are totally different, and the impact and mechanism of them to technological innovation performance should also be different. Compared with single and dual networks, the multiple network shows the multiple structural feature of combining dense and sparse, strong links and weak links both existing, and highly heterogeneous distribution. Structural feature of multiple network equips it with the advantage of strong and weak connection meanwhile. What’s more, an effective combination of high and low density networks comes true, promoting better knowledge interaction among enterprises. However, while analyzing the influence of network embedding mechanism to technological innovation performance, most of literatures often focus on the feature of the knowledge network where the enterprise is, its embedding mode and other factors. Relatively, they neglect the impact on innovation activities that may be made by enterprise features and business activities paradigm, which are main subjects of network embedding. Secondly, previous studies mostly consider the influence to innovation performance from the perspective of single network embedded structure or single embedded relationship. They do not fully consider the interaction relationship may existing among embedded relationship, embedded structure and resource under multiple network embedding. It is difficult to carry out a systematic and thorough study on the whole network embedding. From the actual situation of enterprise operation, we can find, as competition transfers from enterprises to supply chains, companies are gradually aware of the importance to establish the supply chain with the upstream and downstream organization, but also worried about the loss of their core technology and core knowledge, the concern has become the biggest obstacle for enterprises to improve the depth and breadth of the external network embedded. In fact, the author believes that open culture is important concept for the sustainable development of enterprises. For enterprises, knowledge can fundamentally promote the performance of knowledge management, thereby promote the innovation performance of enterprise, and it needs to construct on the basis of network embedded, to form a stable long-term strategic cooperative partnership in order to be able to effectively promote the external access for knowledge, injects innovation vitality for the enterprises.

Therefore, choosing knowledge management as an intermediate variable, this work explored embedded relationship, network embedded structure and embedded resource’s impact on the enterprise technology innovation performance in the perspective of knowledge management. Through combing theories, the study built a “network embedding - knowledge management - technology innovation performance” concept model, and empirically tested SMEs in China’s Yangtze River Delta region. This research has a positively theoretical contribution to expanding the network embedding and the innovation performance studies. It also provides theoretical guidance for enterprises further improving the innovation ability and innovation performance.

2 Model building and research methods

2.1 Model building

2.1.1 Relationship between multiple network embedding and technology innovation performance

Enterprise technology innovation is a process of social interaction. The innovation subject has evolved from the mono-subject innovation model based on a single enterprise into multi-subject innovation model that is centered on innovation enterprises and supplemented by other external organizations. Since knowledge resources enable enterprises effectively integrate the internal and external of the organization through network embedding, it becomes an important choice for them to improve innovation ability and performance.

  1. (1)

    Embedded relationship and technology innovation performance

Since the enterprise’s network relation position plays an important role in knowledge acquisition, knowledge integration and knowledge communication, it has a profound effect on the enterprise innovation performance. Research shows that the strong relationship between companies is essential for enhancing mutual trust and tacit knowledge disseminating effectively. Andersson et al. [2] analyzed the two different kinds of embedded relationship—business embedding and technical embedding both had an influence on the market performance, explored the importance of relational embedding in external networks as a strategic resource for performance and competence development in multinational corporations. Moran [30] published a paper about the difference effection between embedded relationship and embedded structure on the management performance based on the survey of 120 product and sales managers in a fortune 100 pharmaceutical firms, and concluded that embedded structure played a more important role in explaining more routine, execution-oriented tasks, and embedded relationship played a more important role in explaining new, innovation-oriented tasks. Uzzi and Lancaster [38] and Capaldo [7] also believed that embedded relationship featured in strong attachment plays an important role in establishing long-term strategic partnership. The relationship can effectively promote resources, messages, business philosophies and others’ sharing and communicating among enterprises, forming long-term mutual trust cooperating mechanism. As a result, innovation and innovation performance are raised. Domestically, after investigating 264 SMEs in Zhejiang Province, Renyong [34] suggested that maintaining high joint strength among the member network companies has a significant positive effect on new product development. From related research, another scholar pointed out that open embedded relationship among enterprises can effectively promote enterprise obtaining external resources, making up resources shortage. The exchange and integration of enterprises’ inside and outside knowledge injects fresh blood for corporate development, proposing the possibility of innovation and success occurring. Capaldo [8] adopted comparative study between longitudinal cases to investigate the impact of network ties on the innovative capability of the lead firm in an alliance network. The author concluded that the ability to integrate the relationships around the company can provide competitive advantages to the knowledge-intensive alliance networks, and the sustainability primarily based on the innovative capability. Al-Laham and Souitaris [1] took the biotech industry of German as the study subject to analyze the relationships between network embeddedness and new-venture internationalization. The paper wanted to give more information about whether inter-organizational factors affected the internationalization by forming international research alliances. The results showed that if the company had already haven tight linkages with international company, and it was in the center of inter-organization, the construction of international research alliances will be more easier. Gargiulo et al. [13] researched the relationship between network closure and individual performance focus on the knowledge workers. The study showed that the performance people achieved depended on the role they played in their relationships.

On the fundamental issue of how embedded relationship affects service innovation performance, Wang [40] theoretically analyzed and empirically tested their relations from the perspective of learning ability. The study draws a mechanism that relational embedding positively acts on service innovation performance by promoting organization learning ability. Through five Chinese manufacturers within-case analyses and one inter-case analysis, Xu et al. [46] studied how the embedded relationship affects its technical innovation performance through influencing corporate exploration learning. The study built a theoretical framework on embedded relationship affecting enterprise technology innovation performance, and draws a conclusion: global manufacturing network enterprises’ positive behaviors, mutual trust, information sharing and jointly solving problems can promote enterprise new knowledge acquisition and usage, enhancing corporate technology innovation performance.

This work argued that high bonding strength maintained between the internet company, which promoted the establishment of confidence mechanism between each other. Frequent interaction facilitates the dissemination and sharing of deeper information and resources for company. Moreover, it provides an important guarantee for the promotion of collaborative innovation and innovation performance between each other. Based on the above analysis, the following assumptions are proposed:

Hypothesis H1 Embedded relationship has a significant positive impact on innovation performance.

  1. (2)

    Embedded structure and technological innovation performance

Embedded structure is mainly used to investigate the effect of different company locations in the network. It affects the distribution of network knowledge, and innovation behavior generated by members of the network enterprises [16]. This is mainly due to different positions of different enterprises in their networks, this difference is reflected in its different contribution to the network. On the other hand, it also reflects the difference in the quantity and quality of knowledge, information and other resources that can obtain from the network. Besides, it is also applied to investigate the degree of binding between the number of members as well as business location factors in the network. In this work, the embedded structure is measured mainly through three dimensions, namely network density, enterprise network centrality and structural holes. Network density is mainly used to measure the intensity of linkages between the internal members in the network. Zaheer and Bell [50] posited that firms with superior network structures might be better able to exploit their internal capabilities and thus enhanced their performance. Echols and Tsai [11] showed that the moderating role of network embeddedness can affect the performance of firms. Grewal et al. [15] concluded that network embeddedness had strong influence on both technical and commercial success, but the influence was different in the different systems, sometimes it was positive, and sometimes it was negative. Gay and Dousset [14] took the biotechnology industry as the research subject, studied the innovation and network structural dynamics. They thought the network was unlimited, and the power of the cooperation between companies can transmit the dynamics for the growth of the firms. Koka and Prescott [25] analyzed the influence of position on the steel company performance, and the authors thought following the change of environment, some companies which were more entrepreneurial can achieve higher performance.

Coleman [10] combined social capital with network density, and concluded that the higher the network density is, the better it will help to form trust mechanism between the actors and maintain collaborative relationships. Consequently, it shortens the flow path of in-depth information and strategic resources, with the increased depth and breadth of knowledge integration. Wu and Xu (2008) studied the network density and competitive advantage of enterprises [45]. It indicates that efficient information flow of intensive network greatly promotes the effective integration of internal network resources, and the formation of a unified behavior paradigm inside the network. Thus, it enhances service innovation. Ting Helena Chiu [35] wanted to know whether a enterprise’s network ability and network location were the key to superior innovation performance, and how the network ability and network location effect the innovation performance of the company. The results showed that network ability and central positions in the network both had positive influence on the company’s innovation performance. The firms those were in the center of the network usually had better performance than those were far away from the center. Vasudeva et al. [39] showed that structural holes and the network embeddedness had a deep influence on the innovation in the fuel cell industry. Enterprise network center is mainly used to measure the location of an individual organization in the network. Then examine the influence of their position on the acquisition and control of information, technology and other resources. The higher the centrality of the internet company, the more it shows that it is at the core of the network. Compared to other member companies, it has greater rights, which creates favorable conditions for better access to innovation resources and meeting the needs of their own innovation activities. Thus, it contributes to enhancing innovation performance. Additionally, with the help of its wide range connectivity of the outside world, companies with relative higher internet centrality establish multi-channel sources of information. As a result, it ensures comprehensiveness of information the enterprises master. So it allows companies to screen information adequately, and eliminate the false as well as more effective integration of the information. During this process, new knowledge is created, and it is applied to the development and design of services and product. In addition, it promotes the occurrence of innovation and enhances innovation performance.

From the perspective of structural holes, not all network members are connected. Companies with structural hole are bridges between companies without the connection. They gather information and knowledge, with the advantages and rights to control whether it flows outward. In economic networks, these companies can better contact idiosyncratic information and resources. By filtering and blending it effectively, these companies promote creation of new knowledge and new services, product development, resulting in enhancement of innovation performance.

Based on the above analysis, we propose the following basic assumptions:

Hypothesis H2 Embedded structure has a significant positive impact on innovation performance.

  1. (3)

    Embedded resources and technological innovation performance

Embedded resources mainly describe the influence of company’s possessed resource, focusing on competitive advantage and shaping enterprises charm. Enterprise resources include all aspects to promote the accomplishment of plan. For one thing, in traditional sense, it includes tangible human, material and financial resources. For another thing, in a rapidly changing era of knowledge, it involves the intangible time, news, information and other aspects. In the long-term development, every company will establish their own unique competitive advantage, derived from company’s possession of special resources to a large extent. Therefore, the possession of strategic resources is the prerequisite and material guarantee for the company to promote strategic transformation. Moreover, it is the foundation and the key upon which business survives and develops.

Hsueh et al. [19] believed that it is different the expected future market value of strategic resources each company possesses, which leads to incomplete markets and imperfect competition of such resources. Therefore, by making full use of its strategic resources and providing quality products and services, companies are able to enhance the competitiveness. Besides, through the implementation of appropriate management strategies, production efficiency and economic benefits is improved. Based on possession of strategic resources, it is easy to form a differentiated competitive advantage, and establishes a special attraction in the network, creating opportunities to promote cooperation with other network members. Consequently, it effectively strengthens cooperation among themselves. Differentiated possession of strategic resources promotes not only the cooperation among them, but also the collision between different knowledge and resources prone to produce spark of innovation. In addition, it facilitates the implementation of innovation, improving the possibility of successful innovation.

The foregoing analysis makes the following assumptions:

Hypothesis H3 Embedded resources have significant positive impact on innovation performance.

2.1.2 Embedded multiple network and knowledge management

Embedded multiple networks between enterprises is essential to gaining knowledge from network organizations. Competitive advantages, based on company’s own possession of resources, are indispensable in the establishment of networking relationship and taking a favorable position in the network. Studies suggest that stronger links, based on frequent communication, contribute to the establishment of the trust mechanism, which promotes the consensus between the enterprise. Excellent network relationships enhance the circulation and sharing of high-quality, in-depth information resources as well as tacit knowledge between organizations. Therefore, it opens acquisition channels of foreign knowledge for the member companies, which effectively meets the needs of innovation for new knowledge. Additionally, under conditions of companies giving full play to their own knowledge creation, it improves the throughput of internal and external knowledge resources, with enhanced innovation performance.

Fukugawa [12] analyzed the determining factors during the innovation of company networks. The findings showed that the firm networks that happied to share the knowledge and implement to R&D cooperation demonstrated more intimate network relationship, easier to communicate, more effective cooperation, and higher innovation performance. Clifton et al. [9] analyzed the relationships between network structure, knowledge governance and enterprise performance. The paper showed that highly interactive, iterative, network-based processes were the ways of the innovation, knowledge governance was very important in the Dissemination framework. Liu et al. [27] believes that the closeness of the relationship between the internet company and the frequency of cooperation shows significant positive correlation with the transfer, acquisition and integration, management activities of knowledge. Strong connection has a direct the impact on cooperation and innovation with each other. It also promotes effectiveness of knowledge assessment, as well as effective integration and absorption of knowledge. Through the study, Wei et al. [42] pointed out that high frequency communication between the different levels of personnel in the internet company has a direct role in promoting technology, knowledge and information flows within the network. In addition, high-density networks often represent a larger network knowledge content, providing a huge reservoir of knowledge for the member companies. Close contacts between members of the network, in favor of companies forming a tacit understanding, promote a coherent enterprise development goals. Besides, it enhances the efficient flow of knowledge within the network, with the cost of knowledge transfer reduced. Yang (2014) also concluded that the network embed ability of the company provides a broad space for searching knowledge, thus becoming the powerful guarantee of improved innovative performance [47]. For the development of modern enterprises, its knowledge-intensive features become more prominent. In a network, a number of strategic resources-rich companies often have strong knowledge acquisition and creation skills. Their strong ability to apply and create knowledge provides fresh blood for the knowledge integration of these members. In a manner of speaking, the occupation of strategic resources provides material security for its effective knowledge management activities.

Based on the above analysis, we propose the following hypothesis:

Hypothesis H4 Embedded relationship has significant positive impact on the enterprise knowledge management.

Hypothesis H5 Embedded structure dramatically influences the enterprise knowledge management in a positive way.

Hypothesis H6 Embedded resources has prominent effect on the enterprise knowledge management.

2.1.3 Knowledge management and technology innovation performance

Gold (2001) divided a whole knowledge management process into four basic dimensions: acquisition, protection, transfer and application of knowledge. All dimensions have a major impact on innovation activities and innovation performance of the company. Based on Gold’s segmentation of knowledge management, we combined the actual situation of the service innovation performance in the context of embedded network. Furthermore, it is divided into four parts: knowledge acquisition, knowledge integration, knowledge creation and knowledge application. Effective knowledge management is first reflected as effective knowledge acquisition. Companies cannot find all the resources needed for development, so it must acquire knowledge constantly from outside to meet the needs of knowledge innovation. The acquisition of external knowledge compensates effectively for the constraints of the limited internal resources, which hinders the company’s development and innovation activities. This is essential to reducing investment of products and services as well as shortening the innovation cycle. On the one hand, knowledge acquisition is presented as deeper knowledge mining within the company. On the other hand, it represents the search, filter, identification and organization of knowledge outside the company in a large scale. As an important strategic resource for company’s development, timely and efficient access to knowledge resources directly determines its ability to advance with the times. So it is the most important issue when company faces with carrying out innovative activities.

Fig. 1
figure 1

Research framework

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Wang et al. [41] pointed out that formal, informal search width of knowledge within the company’s network directly affects the innovation performance of enterprises. Wherein, the absorptive capacity of knowledge plays a regulatory role. Additionally, different structures of hole location and extent of the close relationship between network members directly affect the breadth and depth of their knowledge of the search. From the perspective of social capital, Yu [49] explored the impact of the integration of knowledge on innovation performance. By improving organizational interests and tendency in learning, knowledge integration produces a significant impact on innovation performance. In the processes of business exchanges and cooperation, the circulation of knowledge is achieved unconsciously between the organizations. As a result, it promotes the sharing of knowledge in the network organization, the collision of creative ideas, and the generation of innovation. From the point of view of social capital theory, Hu [22] empirically analyzed the effect of knowledge integration between organizations on intellectual capital and business performance, with hotel chain as the research object. Consequently, they concluded that knowledge integration between enterprises has a significant positive effect on innovation performance of the hotel industry services.

Another key word of knowledge management is knowledge creation. After appropriate re-encoding, and a series of effective integration, the business knowledge acquired from outside will be used, which reflects the company’s ability to create knowledge. Strong knowledge transformation ability of the company can achieve effective integration of external acquire knowledge and internal knowledge, promoting the full play of knowledge performance [32]. On this basis, knowledge creation is accomplished, with new knowledge applied to the development of new products. Therefore, it promotes innovation to be carried out, and effectively improve innovation performance. That is, knowledge conversion capability is an important stage of knowledge management, with a connecting role played, bridging the gap between knowledge acquisition and knowledge creation.

Nonaka (1994) suggested that only on the condition that knowledge acquired outside is integrated with internal resources, the company can achieve the effectiveness of external knowledge resources. Furthermore, it boosts economic effect within the company; otherwise, it is invalid knowledge acquisition. Foreign knowledge can be used to put forward practical requirements in knowledge transformation ability. The capacity of knowledge application reflects the ability of the organization to apply knowledge to solve practical problems [45]. For the enterprise, the ultimate goal of knowledge management is to maximum devote their own knowledge resources to business operations, which enhances operating efficiency and innovation performance.

Based on the above analysis, the present study proposes the following hypothesis:

Hypothesis H7 Knowledge management has a significant positive impact on innovation performance.

2.1.4 Research framework

Multiple-network embeddedness mainly reflect different dimensions of network embedding. The existing literature primarily focuses on three types, namely embedded relationship, embedded structure and embedded resources. For embedded relationship, the strength, quality and durability of relationship is investigated. In terms of embedded structure, we focus on network density, enterprise network centrality and structural holes. Meanwhile, embedded resources mainly investigate the impact of the strategy resources owned by the enterprises as well as its cooperative enterprises on knowledge management and innovation performance. Knowledge management is divided into four factors: knowledge acquisition, knowledge integration, knowledge creation and knowledge application. Through the intermediary role of knowledge management, these three important dimensions of embedded network have a crucial impact on innovation performance. Based on the above hypothesis analysis, we built a basic relational model, representing the effect of embedded networks on innovative performance by influencing knowledge management activities. Additionally, it reflects the relationship between embedded relationship, embedded structure, embedded resources knowledge management and innovation performance. The theoretical framework of this paper are shown in Fig. 1.

2.2 Research methods

2.2.1 Sample resource and selection

In order to fully test the significance of the above theoretical model and research hypothesis, to expand the amount of data, Yangtze River Delta Region was chosen as the research object, with 190 questionnaires distributed. Wherein, 38 questionnaires were distributed in Shanghai, while 32 parts, 27 parts, 58 parts in Suzhou, Hangzhou, Ningbo regions, respectively. In addition, 35 questionnaires were delivered through the electronic mail. Among them, all the questionnaires were received, with 11 questionnaires invalid according to statics, and the recovery rate was 94.2%.

2.2.2 Variables measured

To ensure the reliability and validity of the scale the study used, the measurement of the variable fully referred to pertinent literatures about embedded networks, knowledge management, and innovation performance studies. Based on the reference, we consulted the metrics basic scale involved with embedded structure, embedded resources knowledge management, and technology innovation performance. Combined with the characteristics of this research, the preliminary study scale was developed and designed. On this basis, we conducted full consultation with the relevant experts in the field as well as business executives. After being discussed and revised repeatedly, the final scale in this research was ascertained, with the scientificity and feasibility of this study.

The questionnaire used in this work adopted internationally accepted rating scale. Likert five-point scale with a scoring method was used to measure the specific impact of the multi-network embeddedness on innovation performance and the corresponding factor. And with the help of structural equation model, it was validated for the theoretical model about the effect of embedded networks on technology innovation performance. Additionally, we systematically explored the internal relations and the degree of effect between dimensions of the embedded networks, knowledge management and technology innovation performance. As for the measurement of the five basic variables in this theoretical model, this study was mainly based on theoretical analysis about knowledge management. Moreover, basic influence factors of embedded networks, technology innovation performance were concerned to design measurement for each variable. The relative weak clarity of the seven-point scale makes it difficult for the respondents to distinguish and identify accurately, resulting in negative impact on the accuracy of the research. Having considered that, this study used Likert five-point scale to score. The higher the score is, the more it conforms to the description of the scale.

3 Analysis of the results of empirical research

3.1 Reliability and validity analysis of variables

To test the internal consistency of the scale, this work analyzed Cronbach’s \(\alpha \) proposed by Cronbach, Lee in 1951. The reliability coefficient is represented by \(\alpha .\) Generally, the higher the coefficient \(\alpha \) is, the better the reliability of the questionnaire. It is widely believed when \(\alpha \) coefficient> 0.9, it indicates that the reliability of this questionnaire is excellent. When 0.7 \(<\, \alpha \) coefficient < 0.9, the questionnaire is good, while 0.35 \(<\, \alpha \) coefficient < 0.7 indicates that the questionnaire has medium reliability. However, if the coefficient \(\alpha <0.35,\) it reflects that the questionnaire is in low reliability level, and should be rejected.

In this work, the dimensions of embedded relationship contain three factors: the quality, strength, and durability of relationship. The \(\alpha \) values are 0.792, 0.776, and 0.753, respectively, with the explained amount of factors exceeding 0.6. The dimensions of embedded structure include network density, enterprise network centrality, and structural holes. The \(\alpha \) values of the three factors are 0.775, 0.778, and 0.763, respectively, with the explained amount of factors higher than 0.4. The dimensions of embedded resources consist of strategic resources possessed by enterprises and its cooperative. \(\alpha \) of these two factors are 0.752 and 0.747, respectively, with the explained amount of factors surpassing 0.5. Financial performance, market access, market share, and customer satisfaction are included in technological innovation performance. \(\alpha \) of these four factors are 0.801, 0.785, 0.746, and 0.882, respectively. Moreover, the explained amounts of factors are over 0.5. Knowledge management is composed of four explained amounts, and \(\alpha \) are 0.749, 0.765, 0.812, and 0.763, respectively, with the explained amounts of factors greater than 0.5. It is generally believed that the explained amount of factors greater than or equal to 0.4 is more acceptable to the study of humanities and social sciences. In addition, the value of reliability \(\alpha \) is higher than 0.745. Therefore, the variables in the present work (namely the embedded network, the reliability of knowledge management and technological innovation performance, and the effect of validity) show better performance, meeting research needs Table 1.

Table 1 Test results of reliability and validity
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Table 2 Multiple linear regression analysis of the relationship between multi-network embeddedness and enterprise knowledge management
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3.2 Regression analysis

In order to better examine scientificity of the hypothesis above, this work carries out systematic regression analysis for each variable. The detailed results of the analysis are shown in Table 2. We choose the three factors of embedded relationship (the strength, quality and durability of relations), the three factors of embedded structure (network density, enterprise network centrality and structural holes), and the two factors of embedded resources (the strategic resources possessed by the company and its cooperative) as independent variables. The four factors of company’s knowledge management—the acquisition, integration, and creation, application of knowledge—are selected as the dependent variable. We conduct multiple linear regression analysis of it, finding that all factors of independent variables show a significant positive impact on factors of company’s knowledge management. That is, the basic assumptions H4–H6 that embedded relationship, structure, and resources have a positive significant effect on knowledge management. It is verified by regression analysis and pass hypothesis testing.

Similarly, the hypothesis that multiple network embeddedness influences technological innovation performance is analyzed by multiple linear regression analysis (see Table 3). The results show that the embedded relationship, embedded resources play a significant role in promoting company’s technological innovation performance. In other words, hypotheses H1 and H3 are verified by regression analysis. However, embedded structure does not promote the innovation performance prominently, and hypothesis H2 is not proved by regression analysis.

Table 3 Multiple linear regression analysis of the relationship between multiple network embeddedness and enterprise technological innovation performance
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In a similar way, we conduct multiple linear regression analysis on the relationship between enterprise knowledge management and technological innovation performance (see Table 4). According to the results, four factors of knowledge management—knowledge acquisition, knowledge integration, knowledge creation, and knowledge application—have a significant positive effect on technological innovation performance of enterprises. In other words, the hypothesis H7 is verified.

Table 4 Multiple linear regression analysis of the relationship between enterprise knowledge management and technological innovation performance
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Based on the results of multivariate linear regression analysis, Table 5 shows the basic conditions that the seven theoretical hypotheses pass the test.

Table 5 Test results of regression analysis
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Fig. 2
figure 2

Impact mechanism model of the multi-network embeddedness on innovation performance under the view of knowledge management

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Table 6 Analysis of fitting degree of structural equation model about the impact mechanism of multi-network embeddedness on innovation performance
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3.3 Model validation

3.3.1 Setting structural equation model

To effectively understand and analyze the internal relationship and interaction strength between the variables, we design structural equation model, as shown in Fig. 2. Specific decomposition is as follows. Firstly, three main dimensions related to multiple network embeddedness—embedded relationship (\(\xi _1 ),\) embedded structure (\(\xi _2 ),\) and embedded resources (\(\xi _3 )\)—are selected as an exogenous latent variables of the model. Secondly, we choose the quality (\(x_1 ),\) strength (\(x_2 ),\) and the durability(\(x_3 )\) of the relationship as exogenous indicators of embedded relations (\(\xi _1 ).\) Similarly, network density (\(x_4 ),\) enterprise network centrality (\(x_5 ),\) and structural holes (\(x_6 )\) are set as the exogenous indicators of the embedded structure (\(\xi _2 ).\) Furthermore, the exogenous indicators of the embedded resources (\(\xi _3 )\) consist of strategic resources possessed by the enterprise (\(x_7 )\) and its cooperative (\(x_8 ).\) Additionally, the endogenous latent variables of the structural equation model are composed of knowledge management (\(\eta _1 )\) and technological innovation performance (\(\eta _2 ).\) Wherein, the acquisition (\(y_1 ),\) integration (\(y_2 ),\) creation (\(y_3 ),\) and application (\(y_4 )\) of knowledge are selected as the exogenous indicators of knowledge management (\(\eta _1 ).\) Finally, oval is the latent variable, and rectangle the observed variables.

Fig. 3
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Result of the impact mechanism model of the multi-network embeddedness on innovation performance under the view of knowledge management

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3.3.2 Fitting degree of structural equation model

Test indicators of impact mechanism model of innovation performance include chi-square index (\(\chi ^2\)/df), goodness of model fit index (CFI, GFI, TLI, NFI, IFI) and the root mean square error of approximation (RMSEA). In this work, Table 6 shows the fitting situation of the overall theoretical models. Wherein, \(\chi ^2\)/df is 2.993 (between 1 and 3). The index values of IFI, CFI, and NFI are at levels above 0.9. Though indicators of GFI, TLI do not reach the level of 0.9 (closes to 0.9), indicating that all the fitting indicators reach acceptable levels. Moreover, RMSEA = 0.068 < 0.080. Therefore, all relevant indicators values are within the acceptable range. This illustrates the impact mechanism model of the multi-network embeddedness on innovation performance has relative high fitting degree under the view of knowledge management.

3.3.3 Validation results of structural equation model

After the calculation of relevant data, Fig.  3 shows the structural equation model, concerning the impact mechanism of multi-network embeddedness on innovation performance.

In this work, the analysis of the data results of hypothesis H1 (embedded relationship has a significant positive effect on technological innovation performance) and hypothesis H3 (embedded relationship has a significant positive impact on knowledge management) are as follows. As Fig. 3 shows, the path coefficient of the impact of embedded relationship on enterprise knowledge management is 0.602, with a significant level of 0.001. It indicates that hypothesis H1 obtains support from the operation of result equation model. Similarly, the path coefficient of the impact of embedded relationship on innovation performance is 0.502, with the same significant level. Namely hypotheses H1 and H4 acquire data analysis support. The results further show that company’s embedded relationship significantly promotes knowledge management and innovation performance. In other words, it is of great importance to build high-quality, multi-level, long-term and stable relationship between companies and members of the network, enhancing their knowledge management and innovation performance.

In this work, the analysis of the data results of hypothesis H2 (embedded structure has a significant positive effect on technological innovation performance) and hypothesis H5 (embedded structure has a significant positive impact on knowledge management) are as follows. According to Fig. 3, the path coefficient of the impact of embedded structure on enterprise knowledge management is 0.583, with a significant level of 0.001. It indicates that hypothesis H5 obtains support. However, the counterpart of embedded structure on innovation performance is 0.067, not reaching a significant level. Therefore, hypothesis H2 (embedded structure has a significant positive effect on technological innovation performance) is not verified by structural equation, which is consistent with the conclusion of regression analysis earlier. From another level, in terms of the impact of embedded structure on innovation performance, this conclusion indicates that knowledge management plays a mediating role in this perspective fully.

Similarly, the analysis of the data results of the hypotheses H3 (embedded resources have a significant positive impact on innovation performance) and H6 (embedded resources have a significant positive impact on technological innovation performance) are shown below. From Fig. 3, the path coefficient of the impact of embedded resources on enterprise knowledge management is 0.578, with a significant level of 0.001. It indicates that hypothesis H6 obtains support. Furthermore, the counterpart of the impact of embedded resources on technological innovation performance is 0.237, and the significant level is 0.05, illustrating that H3 wins the support. Consequently, it is essential to possess differentiated resources for the company as well as strategic resources for its cooperative, for the reason that it boosts the improvement of knowledge management and technological performance.

In terms of hypothesis H7 (knowledge management has a significant positive effect on technological innovation performance), Fig. 3 shows that the path coefficient of the impact of enterprise knowledge management on innovation performance is 0.453. Additionally, it reaches the significant level of 0.01, namely H7 acquires the support. The higher the level of knowledge management is, the better it enhances technological innovation performance.

4 Analysis and discussion

Under the view of knowledge management, we conducted empirical research on the impact of multi-network embeddedness on technological innovation performance. The results show that hypotheses we proposed in assumed part are basically verified.

Overall, from the perspective of knowledge management, we built the theoretical model of the impact mechanism of multi-network embeddedness on technological innovation performance. In this model, the enterprise knowledge management is a central part. Furthermore, the level of knowledge management directly determines the success of innovation as well as the level of technological innovation performance. Meanwhile, in the four factor structure model of knowledge management, knowledge acquisition is the most core part. Four factors are related to this model: knowledge acquisition, knowledge integration, knowledge creation, and knowledge application. Based on it, the factor loading coefficient of knowledge acquisition is the highest, reaching 0.87. That is, knowledge acquisition is the most important among the four factors, which is consistent with the actual situation of enterprise knowledge management in fact. Knowledge is the most intensive input elements of technological innovation. As a result, the multi-channel knowledge acquisition ensures knowledge reserve, providing the basis to guarantee smooth implementation of knowledge integration, knowledge creation and knowledge application for the subsequent enterprises. It also indicates that, to improve the competitiveness in the era of knowledge economy, enterprises must establish multi-level and comprehensive knowledge acquisition channels, providing effective protection for the input of knowledge and innovation resource of enterprises. In the second important place is knowledge application, which is the final output product of knowledge acquisition, knowledge integration as well as knowledge creation. Therefore, as an important input feature of enterprise innovation, the effectiveness of knowledge application has a direct impact on the construction of core competitiveness. Moreover, it is the key to the success of enterprise innovation and the continuous improvement of innovation performance.

In the theoretical model of the impact mechanism of enterprise technological innovation performance, each dimension of embedded networks was analyzed separately. On the one hand, embedded relationship directly influences the technological innovation performance. On the other hand, it plays an indirect role on technological innovation performance through knowledge management. According to the results of Fig. 3, the direct effect of embedded relationship on enterprise technological innovation performance is 0.502 while the indirect effect is 0.273 (0.602 \(\times \) 0.453). By adding the direct and indirect effect, the overall effect of embedded relationship on enterprise technological innovation performance is 0.775. Consequently, it indicates that, while keeping the other conditions remain unchanged, the variable (embedded relationship) increases a unit, and technological innovation performance will increase 0.775 unit in total.

Based on the regression analysis of relationship between embedded relationship and knowledge management, the strength, quality and durability of relationships related to embedded relationship promote enterprise knowledge management and technological innovation performance. All of them pass the significance verification. According to correlated results of analysis, it is crucial for the enterprise and network members to maintain high-quality, deep-seated, and multi-faceted long-term cooperative relationship. For the reason that it expands the source of knowledge and improves the quality of knowledge acquisition. Additionally, it achieves large-scale range of knowledge integration and enhances application capabilities of knowledge. Consequently, new knowledge is created, applied to the development of new products, promoting technological innovation.

Under the view of knowledge management, we built theoretical model about impact mechanism of multi-network embeddedness on technological innovation performance. From the perspective of embedded structure, it does not play a direct role in innovation performance, but indirectly influences innovation performance, with knowledge management playing an intermediary role. Figure 3 shows the direct effect of embedded structure on technological innovation performance is not significant, but the indirect effect is 0.264 (0.583 \(\times \) 0.453). Therefore, it signifies that knowledge management completely plays an intermediary role in the effect of embedded structure on technological innovation performance, with their own conduction mechanisms. This also shows that, while maintaining the other conditions remain unchanged, the variable of embedded structure ascends a unit, and technological innovation performance will increase 0.264 units totally.

This work suggests that dense network reduces the development and living space of enterprises, resulting in a depressing feeling within the enterprise. Moreover, it stifles innovation vigor and desire. With a long history, enterprises at the network center position tend to form a step-by-step development model and operation mechanism, increasing barriers to the implementation of effective innovation. Therefore, its impact on innovation performance can only be achieved by their knowledge management activities.

From the point of view of embedded resources in the theoretical model, it not only has a direct effect on the innovative performance, but also influences technological innovation performance indirectly through knowledge management. Based on Fig. 3, the direct effect of embedded resources on technological innovation performance is 0.237 while the indirect effect is 0.262 (0.578 \(\times \) 0.453). That is, the variable of embedded resources produces 0.499 overall impact effect through the impact of it on technological innovation performance and the conduction mechanism of knowledge management. Namely keeping the other conditions unchanged, the variable of embedded resources increases a unit, and the technological innovation performance will increase 0.499 units in total.

According to the regression analysis of relationship between embedded resources and knowledge management, we conducted the analysis of the impact of embedded resources (related to strategic resources possessed by the enterprises and its cooperative) on knowledge management and technological innovation performance. All pass the significance test with data support. Therefore, from the results of the entire empirical analysis, it can be concluded that the differentiated strategic resources owned by the company is crucial to the survival and development. Besides, it is a source of competitive advantage, and the stepping stone of establishing their position in the network as well as cooperation with other company. Meanwhile, strategic resources owned by the cooperative enterprises enable enterprises to become more powerful and effective, creating more room for development.

5 Conclusions

During the process of modern enterprise innovation activities, as its most critical core elements, knowledge has a profound impact on its innovation performance level in terms of control of the amount and improvement of the quality. This makes knowledge management activity (such as foreign knowledge acquisition, knowledge integration, etc.) particularly important. As a result, in the long process of development, enterprises should continuously develop its extensive and effective social networks. Additionally, it is supposed to establish their own important role in the network, and promote the effective knowledge management. Thus, it enhances enterprise innovation performance and builds core competitiveness of the long-term development.

To better understand the impact mechanism of embedded networks on the enterprise technological innovation performance under different situations, this work used the basic method of regression analysis and structural equation. We built basic conceptual framework of the relationship between embedded network and knowledge management as well as technological innovation performance. On the basis of it, seven major basic hypotheses were proposed. According to the systematic analysis of related data conducted by SPSS software, the obtained basic conclusions are as follows.

Firstly, by influencing enterprise knowledge management activities, three dimensions of embedded network (embedded relationship, embedded structure and embedded resources) positively enhance technological innovation performance of enterprises in different levels. Wherein, embedded relationship and embedded resources on the one hand, contribute directly to innovation performance. On the other hand, through the promotion of enterprise knowledge management activities, they indirectly boost the upgrading of enterprise technological innovation performance. However, as for embedded structure, its facilitation of enterprise technology innovation performance is achieved mainly through the intermediary role played by knowledge management.

Secondly, in terms of the degree of action, embedded relationship better reflects the mutual trust mechanism between the network members. As a result, excellent, durable, and stable cooperative relationships are more likely to achieve effective sharing of differentiated resources. Thus, the effective input of heterogeneous knowledge is accomplished, which remarkably enhances innovation activities and technological innovation performance. From the perspective of embedded resources resources possessed by enterprises are important basis of building the core competitiveness of enterprises. Additionally, it is also an important guarantee of the external business. The processes of the formation of enterprise network often are that of resources complementing to each other. As for embedded structure, the enterprise network centrality it reflects usually has two opposite effects on enterprises. On the one hand, it broadens the channel of knowledge. On the other hand, its serious homogeneity problems increase cost of screening effective knowledge. The presence of structural holes can improve the possibility of heterogeneous knowledge acquisition. However, due to indirect relationship with the enterprise, it limits the transmission channels of knowledge. Thus, the effective implementation of knowledge management activities is restricted, reducing the enhancement of enterprise technological innovation performance.

In fact, due to the complexity and diversity knowledge involved in the innovation process, any organization can not exhaust all the knowledge needed for innovation, which limitation of resources greatly restrict the innovation ability of enterprise, innovation success rate decreased, it makes the companies lonely to practice service innovation behaviors increasingly rare. As a result, more and more companies begin to cross organizational boundaries, through the advantages on differentiation resources occupy, to establish different levels partnerships with market members, and seek a favorable position in business partnerships, to achieve the addition of innovative knowledge and other resource, and conform the innovation composition forces, promote the upgrading of innovation performance. The conclusions of this study can help enterprises to understand how network embedding affects enterprise’s innovation performance through knowledge management activities, which can improve the effective cooperation with network members and promote innovation performance of enterprises and the whole network. To analyze the influence of network embedding on technology innovation performance of enterprises from the perspective of knowledge, we achieve a new research perspective about the relationship between the network embedding and technology innovation performance.