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This research is to determine scenarios and perspectives for improving the cluster strategy of business integration in the post-COVID-19 era with the help of the methodology of the game theory.

The methodology of this research includes the complex method, statistical method, correlation analysis and the game theory of decision-making.

Based on the analysis of scientific approaches, we formulate the authors' treatment of the essence of the notion of clustering, which characteristics are evaluated in this work. In this treatment, we distinguish factors that influence the development of clustering of business structures of the state, which level is assessed within the analysis. The components of the competitiveness of business structures are among such factors. Cluster structures of certain countries successfully functioned during the COVID-19 pandemic, using effective strategies created independently (United States) and based on the strategies of non-market regulation (China).

The scientific novelty of this research consists in the identification of the types and characteristics of the strategies of clustering of business structures formed during the COVID-19 and post-COVID-19 eras.

Despite the relevance of financial information relating to logistics service providers (LSPs), recent research has paid little attention to the financial analysis of LSPs. The aim of this paper is to examine the balance sheet structure of LSPs in order to find out if there are differences between single providers or defined LSP groups (clusters), respectively. Furthermore, the dependency of asset, capital and liquidity structures on LSPs specific characteristics is pointed out. Finally, we show which financial indicators positively influence profitability.

A total of 150 quoted LSPs from all over the world, allocated to six different clusters depending on scope of service were examined. A detailed balance sheet analysis using contingency theory, complemented by a correlation analysis, provides information about the financial structure, similarities and differences within and in-between the LSP clusters.

It was found that there are many differences regarding the financial structures of LSPs. The asset and liquidity structure of LSPs show significant differences, while the capital structure is mostly homogeneous. Profitability is achieved in various ways: Focusing on high net profit margin or asset turnover rates.

Only quoted LSPs are analyzed. With this broad research approach the authors point out the range of possibilities for financial statement analysis of LSPs and demonstrate the potential for future research.

Financial analysis yields information for making strategic decisions including organic growth, outsourcing, mergers and acquisitions or cooperation between LSPs.

This paper contributes to further performance examinations of LSPs by providing a profound financial statement analysis with potential benefits for logistics executives, analysts and researchers.

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.

The purpose of this paper is to study how local government policy influences the structure of Chinese pharmaceutical clusters during their industrial catch-up.

This paper applies a case study method by targeting pharmaceutical clusters in Tonghua, Taizhou, and Tianjin.

The varied structures of pharmaceutical clusters in China demonstrate local governments' efforts to utilize local resources accordingly. While the local governments in China introduce different policies to firms with different ownership in the process of constructing different cluster composition, all the local governments emphasize motivating the development of small- and middle-sized enterprises for cluster dynamics.

The local governments should try to reach a balance between short-term foundation and long-term competitiveness for industrial cluster development.

This paper provides the detailed analysis of local governments' influences on the formation of pharmaceutical clusters in China and helps to enrich the knowledge about how local government promotes industrial clusters to realize industrial catch-up through sectoral innovation system.

Due to the close connection between urban cluster and carbon emissions (CEs) but a lack of study on it of the construction industry, this paper aims to explore the relationship between the polycentric spatial structure (PSS) of the urban clusters and CEs of the construction industry (CECI).

This research uses panel data of 10 Chinese urban clusters from 2006–2021, calculates their PSSs in the aspects of economy and employment and adopts a panel regression model to explore the effect of the spatiotemporal characteristics of the PSSs on the CECI.

First, the CECI in 10 Chinese urban clusters showed a rising trend in general, and the CECI in the Yangtze River Delta (YRD) was much higher than those in the rest of urban clusters. Second, both Shandong Peninsula (SP) and Guangdong-Fujian-Zhejiang (GFZ) exhibited high degrees of polycentric characteristics, while Beijing-Tianjin-Hebei (BTH) showed weaker degrees. Third, the results demonstrated that the polycentric development of urban clusters did not help reduce the CECI but rather promote the CE. The polycentric index, considering the linear distance from the main center to sub center, had a more significant impact on the CECI.

Previous studies have investigated the impact of urban spatial structure (USS) on CEs; however, few of them have studied in the field of construction industry. Moreover, most research of CEs have concentrated at the national and provincial levels, with fewer studies on urban clusters. This paper contributes to this knowledge by investigating how the PSS of urban cluster influence the CECI.

In the network strategy view, relative competitive advantage stems not merely from opportunity structures embedded in networks but also from the distribution of ability and motivation among firms. Thus, there is a need to “bring the firm back in” to the network strategy narrative. We demonstrate that a mixed-methods design, blending large-sample data with micro-data on specific firms and their networks, can increase our understanding of the interplay of network structure and actor mechanisms, thus bridging the chasm between theory and practice in network strategy. We believe this is a critical step toward the “strategic design of networks.”

The purpose of this paper is to propose an efficient finite element formulation for nonlinear analysis of clustered tensegrity that consists of classical cables, clustered cables and bars.

The derivation of the finite element formulation is based on the co-rotational approach, which decomposes a geometrically nonlinear deformation into a large rigid body motion and a small-strain deformation. A tangent stiffness matrix of a clustered cable is proposed and the Newton-Raphson scheme is employed to solve the nonlinear equation.

The derived tangent stiffness matrix, including an additional stiffness terms that describes the slide effect of pulleys, can regress to the stiffness matrix of a classical cable, which is convenient for the implementation of finite element procedure. Two typical numerical examples show that the proposed formulation is accurate and requires less iteration than the force density method.

The co-rotational formulation of a clustered cable is originally proposed, although some mature methods, such as the TL, Force Density and Dynamic Relaxation method, have been applied to nonlinear analysis of clustered tensegrity. The proposed co-rotational formulation proved efficient.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

The purpose of this paper is to analyze the network structure of technology in and between different fields, as well as the evolution of their relations.

Using the patent data in Derwent Innovation Index (DII) from 1991 to 2010, this paper analyzes the co-classification of Derwent Manual Code (DMC) of patents in all technology fields. Large-scaled co-classification matrices are employed to generate the DMC co-classification networks. In addition, analyses are pursued at different levels of aggregation in four five-year windows: 1991-1995, 1996-2000, 2001-2005 and 2006-2010. Using Girvan-Newman algorithm in the clustering process, the structure transformations over time are detected.

The paper identifies the key technological knowledge in certain fields and finds out how different technological fields are connected and integrated. What is more, the dynamic evolution between networks in different time periods reveals the trend of generic technology development in the macroscopic level.

The paper investigates a large quantity of data – all the patent data in DII from 1991 to 2010 in this paper. The paper applies Girvan-Newman algorithm in the co-classification analysis and uses co-classification networks to reveal technology network structures. Evolution coincident with the realistic technological shifts can be observed.

Destinations have in the scholarly literature been labeled as communities of interdependent organizations that collectively coproduce a variety of products and services. The paradigm comes close to describing destinations as firms which are embedded in interfirm networks. Recent studies provide crucial insights into an understanding of destinations' orchestration and structuration as coproducing interfirm networks. However, systematic knowledge about how these systems evolve and develop is lacking. This chapter addresses this issue and elaborates how the concepts of scale-free and small-world networks together can explain the process of destination evolution. The discussion also suggests how such theorizing can spur avenues for future research.