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Based on resource dependence theory (RDT) and transaction cost theory (TCT), we aim to investigate the relationship between supply chain concentration and firm performance. Based on the resource-based perspective, we further investigate the moderating effect of marketing and operational capabilities on the relationship between supply chain concentration and firm performance.

Based on data from 2,082 firms with 8,371 observations from 2008 to 2020 in China, we use stochastic frontier analysis to calculate marketing capability and operational capability and use multinational regressions to test our research model.

We find a U-shaped relationship between supplier concentration and firm performance; there is also a U-shaped relationship between customer concentration and firm performance. In addition, the relationship between supplier concentration and firm financial performance is strengthened by the firm’s marketing capability, and the relationship between customer concentration and firm financial performance is weakened by the firm’s operational capability.

Drawing from RDT and TCT, this study extends the research on the impact of supply chain concentration on firm performance. The study finds that supply chain concentration and firm performance have a nonlinear relationship, and it is further moderated by marketing capability and operational capability, providing insights for managers.

The purpose of this paper is to study friction and wear properties of three types of steels against paper-based friction disc, including 65Mn, 20#steel and 30CrAl, so as to obtain the appropriate working conditions for different friction materials in the transmission system.

Based on actual working conditions, pin-on-disc tests are conducted on a universal material tester. The two evaluation indexes, including average friction coefficient and variation coefficient, are introduced to analyze the different friction properties among three types of steel. Furthermore, the temperature-dependent wear pattern and wear depth are subsequently studied.

The results show that 65Mn is more suitable for working under heavy load and low velocity, but 30CrAl and 20#steel are suitable for working under light load and high velocity. Moreover, wear primarily occurs on paper-based material and peaks at about 325.

This research of different materials and friction property for friction pairs is helpful to improve the performance and prolong the service life of transmission systems.

Suitable working conditions of different friction materials are obtained, and the correlation between wear and decomposition in high temperature is verified.

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.