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The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.

Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.

With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.

This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.

Although the importance of network embeddedness attracted much attention in recent years, the interpretations of the underlying mechanism almost focus on the positive effects and neglect the potential negative aspects. This paper aims to use Chinese listed manufacturing enterprises, in the perspective of network embeddedness, to analyze whether resource curse effects exist in strategic networks of enterprises.

This paper examines the resource curse effect that exists in enterprise networks through analysis of enterprise’s total factors productivity compared with those of its industry peers with different degrees of embeddedness. This paper then uses several different methods, such as clustered fixed and random effect model, core and peripheral model, and generalized method of moments estimation in endogenous checks, to detect network curse effects and reveal three potential mechanisms, including overinvestment, extra maintaining cost and innovation extrusion.

This paper finds that excessive network embeddedness hinders continuous improvement of productivity, which is derived from three mechanisms: excessive network embeddedness makes information redundancy among enterprises, and the imitate-follow effect makes excessive investment seriously; enterprises have to encounter more problems and difficulties, such as information coordination and benefit negotiation, to maintain network relationship, which leads to extra cost; enterprises with abundant network resources may tend to more emphasis on marketable operational abilities, resulting in resource decentralization and less investment on innovation, which is finally not beneficial to productivity improvement in the long run.

Compared with previous literatures, this paper not only enriches the understanding of negative mechanisms in enterprise`s network embeddedness, but also unfolds the inconsistency of network embeddedness effects in practice and finds new evidence that the two types of network embeddedness are consistent in certain circumstances.

The purpose of this research paper is to present a performance measurement system for enterprise networks (PMS‐EN) developed within the context of a European project's context.

This research follows a constructivist approach, based on the following activities: recompilation, analysis and study of scientific knowledge, acquisition of main postulates and construction of initial framework.

This paper describes a performance measurement system called PMS‐EN that can be used for managing performance in the enterprise networks context. After analysing different frameworks relating to such a thematic, and once their main weaknesses were identified, it was decided to develop a PMS that will overcome these difficulties and, at the same time, provide enterprises operating within these environments, with a simple, efficient, robust and useful framework.

This PMS has been built upon the framework called PMS IE‐GIP, but thought could be given to building it upon other frameworks existing within the literature.

Even though this proposal has been successfully applied to an enterprises network, it should be tested further by implementing it to other enterprises networks, with the main objective of enriching and extending it to other sectors.

This paper has described a new performance measurement system called PMS‐EN that can be used for managing performance in the enterprise networks context. It provides enterprises operating in this context a simple, efficient, robust and useful framework. Additionally, it is an efficient and effective tool for creating, managing and monitoring performance measures, in an analytical and graphical way, at both global and individual levels of the enterprise networks, which is lacking in most of the existing frameworks.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

Collective reputation cognition is an enterprise's perception of the general rules of reputation evaluation, jointly formed by a network's collective members. It affects the choice of enterprises' innovation behavior and guides enterprises to occupy a dominant position in the innovation network, thus achieving high innovation performance. In this process, it is inseparable from the enterprise's good network competence. This study attempts to bring collective reputation cognition, network competence and innovation performance into the same framework and aims to explore the relationship among them and determine the influential roles of collective reputation perception and network capability on innovation performance.

This study uses 227 Chinese enterprises in the innovation network as samples and applies partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to explore the questions mentioned above.

The results verify the relationship among collective reputation cognition, network competence and innovation performance. Furthermore, the results yield five paths that lead to high innovation performance, such as “putting ability first” and “both fame and competence”, which are different combinations of collective reputation cognition and network competence.

Based on institutional theory, this study considers the network context and identifies “collective reputation cognition” as a key variable. Meanwhile, it opens the “black box” of the mechanism of reputation's influence on innovation performance and finds that the combined paths of collective reputation cognition and network competence achieve high performance in terms of innovation.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

The behavior of the entities in a small and medium-sized enterprise (SME) cooperation network is influenced by the core enterprise. Addressing the problem of how the network vulnerability changes when the core enterprise is attacked is a challenging topic. The purpose of this paper is to reveal the failure process of SME cooperation networks caused by the failure of the core SME from the perspective of cascading failure.

According to the Torch High Technology Industry Development Center, Ministry of Science & Technology in China, 296 SMEs in Jiangsu province were used to construct an SME cooperation network of technology-based SMEs and an under-loading cascading failure model. The weight-based attack strategy was selected to mimic a deliberate node attack and was used to analyze the vulnerability of the SME cooperation network.

Some important conclusions are obtained from the simulation analysis: (1) The minimum boundary of node enterprises has a negative relationship with networks' invulnerability, while the breakdown probability has an inverted-U relationship with networks' invulnerability. (2) The combined effect of minimum boundary and breakdown probability indicates that the vulnerability of networks is mainly determined by the breakdown probability; while, minimum boundary helps prevent cascading failure occur. Furthermore, according to the case study, adapting capital needs and resilience in the cooperation network is the core problem in improving the robustness of SME cooperation networks.

This research proposed an under-loading cascading failure model to investigate the under-loading failure process caused by the shortage of resources when the core enterprise fails or withdraws from the SME cooperation network. Two key parameters in the proposed model—minimum capacity and breakdown probability—could serve as a guide for research on the vulnerability of SME cooperation networks. Additionally, practical meanings for each parameter in the proposed model are given, to suggest novel insights regarding network protection to facilitate the robustness and vulnerability in real SME cooperation networks.

This study aims to analyze social networking and network centrality in the case of community enterprises, which the existing literature has not addressed. To do so, the authors use the survey of community enterprises from the One Tambon One Product (OTOP) entrepreneurship program of Nong Khai border province in Thailand as a case study.

Social network analysis (SNA) is a tool to study and understand the relationship patterns of units of analysis, which can be individual, household, community or production units, and how those units interact through social or economic activities. Network positions are important when identifying the centralization of links in a particular network. If a representative is close to the center of a network, it is possible that the production unit will be able to contact or coordinate with a greater number of other members of the cluster, create knowledge sharing, promote collaboration and then typically demonstrate greater performance.

The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Enterprises in partnerships with external agencies are mostly cooperating with branding and marketing dimensions, followed by the design dimension. There is no cooperation regarding production and distribution. Most community enterprises have established at least a one-dimensional network of cooperation with external agencies; only five community enterprises have isolated nodes with no partnerships having been created within this group.

The study was limited by the surveys having been conducted in a single area and, therefore, can be used only as a case study for this area. Surveys in larger group sizes and in a wider range of areas would lead to results with greater applicability and reliability.

These results bring to mind policy proposals to increase the competitiveness of community enterprises through the development of social networks as follows: firstly, knowledge should be created with community enterprise operators to understand their supply chains and analyze the strengths and weaknesses and core competencies of their enterprises; and secondly, enterprises should be educated about which agencies can assist businesses at each stage in the value chain system and encouraged to ask for help in adding value at each stage of production.

A “OTOP to Business Networking” platform for community and private enterprises should be created with projects/activities that offer venues to exchange business learning and opportunities. Holding meetings among people in a variety of business sectors may help inspire entrepreneurs to innovate to further their businesses, which can lead to networking businesses conducting activities across community enterprises and the private sector for the mutual benefit of producing merchandise for large customers and markets. Learning systems, production techniques and cooperation should be created, as well as opportunities to increase market share and profitability, if this platform is successful.

This paper is the first study, to the best of the authors’ knowledge, to utilize SNA to examine the use of social networking among community enterprises participating in Thailand’s OTOP entrepreneurship program in Nong Khai province. The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Therefore, the government can play an important role in helping to develop a network of community enterprises with external entities at each stage of the value chain to enhance the competitiveness of each enterprise.

For the basic problems on platform innovation, such as platform innovation connotation and characteristics, the driving mechanism and the influence mechanism are less been studied. This study aims to explore how to achieve platform innovation in traditional service enterprises.

Based on the theory of enterprise network and binary learning, respectively, this paper discusses the behavior of binary learning based on network structure and network impact on efficiency and innovative platform innovation, and analyzed the realization of the platform innovation path.

The research draws the following conclusions: the network structure-based exploitative learning can promote the efficiency platform innovation, while the network behavior-based exploratory learning can promote the novelty platform innovation. The interaction between network structure and network behavior embedded in traditional services is more conducive to exploratory learning so as to promote novelty platform innovation, and the platform innovation of traditional service enterprises is a process from efficiency-oriented to novelty-oriented. The innovation effect generated by exploratory learning based on network behavior is much higher than that generated by exploitative learning based on network structure. The theoretical contributions of this study are as follows: first, this study compares the similarities and differences between service innovation of platform-oriented enterprises and platform innovation of service enterprises. On this basis, it clearly defines the concept of platform innovation and divides it into two categories: efficiency platform innovation and novelty platform innovation. Second, it reveals the two paths for traditional service enterprises to realize platform innovation, and the interaction between these two paths are also explored, which promotes the scenario-based and dynamic study of platform innovation in traditional service enterprise. The conclusion of this study provides theoretical reference for traditional service enterprises to carry out platform innovation.

Theoretical contribution of this paper lies in: first, the concept of platform innovation is clearly defined. Current research about platform innovation is mainly around the innovation of platform enterprise and the platform innovation of traditional enterprise, but there is no document that makes clear distinction; some literature even equates innovation of platform enterprise with platform innovation of traditional enterprise. In this paper, through a detailed literature review and analysis, clearly define the concept of platform innovation and divided into efficiency platform innovation and novel platform innovation, which has made theoretical contribution to the depth of the research. Second, expand the platform innovation research of traditional service industry. In recent years, the platform innovation research of traditional enterprise has become a hot spot, but they focus on the attention of the platform transformation of traditional manufacturing industry, such as Haier; the traditional service industries seem to be “empty,” but, in fact, the traditional service industry platform innovation is of great significance and more worth looking forward to. In this paper, the longitudinal case studies can promote academic concerns focus on the traditional service industry, and also provides the theory instruction practice. Third, it promotes the platform innovation research of traditional enterprise and dynamic analysis. Based on the theory of enterprise network and binary learning, respectively, it discusses the behavior of binary learning based on network structure and network impact on efficiency and innovative platform innovation, and analyzed the realization of the platform innovation path. On the one hand, it enriches the research type of platform innovation; on the other hand, the dynamic evolution mechanism of platform innovation research can make up for the deficiency of the existing literature.