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This study explores how the high-end equipment manufacturing industry (HEMI) achieves intelligent development through the digital innovation ecosystem. While this industry urgently needs to achieve intelligent development through innovation breakthroughs, existing research lacks a deep analysis in conjunction with the digital innovation ecosystem. Considering the sophisticated nature of HEMI and the unique characteristics of the digital innovation ecosystem, this paper aims to uncover the innovation potential and synergetic development opportunities that arise from their integration.

This study uses Dynamic Qualitative Comparative Analysis (QCA) to explore the evolving relationship between the digital innovation ecosystem and intelligent development in HEMI enterprises. Data from 60 HEMI enterprises were collected from 2015 to 2022, and the study window was divided into two-year intervals for analysis. Compared to traditional QCA methods, this approach overcomes the limitations of cross-sectional analysis, fully accounting for time’s influence on causal relationships for more accurate results.

The study reveals that the digital innovation ecosystem of HEMI drives intelligent development through the coordinated interactions of its elements within each time window. Configuration paths and key driving factors evolve dynamically, reflecting the complexity of the ecosystem’s role in driving intelligent development. The study suggests that enterprises dynamically adjust their strategies to different stages, enhancing the effectiveness of intelligent transformation.

The paper proposes and validates a digital innovation ecosystem framework for HEMI, systematically exploring its role in driving intelligent development. The study fills a research gap and extends innovation ecosystem theory by identifying core driving factors and their evolutionary trends through Dynamic QCA. It offers a new perspective on the dynamic role of digital innovation ecosystems in intelligent transformation.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

The purpose of this article is to conduct a main path analysis of 627 articles on the theme of Pythagorean fuzzy sets (PFSs) in the Web of Science (WoS) from 2013 to 2020, to provide a conclusive and comprehensive analysis for researchers in this field, and to provide a study on preliminary understanding of PFSs.

The research topic of Pythagorean fuzzy fields, through keyword extraction and describing the changes in characteristic themes over the past eight years, are firstly examined. Main path analysis, including local and global main paths and key route paths, is then used to reveal the most influential relationships between papers and to explore the trajectory and structure of knowledge transmission.

The application of Pythagorean fuzzy theory to the field of decision-making has been popular, and combinations of the traditional Pythagorean fuzzy decision-making method with other fuzzy sets have attracted widespread attention in recent years. In addition, over the past eight years, research interest has shifted to different types of PFSs, such as interval-valued PFSs.

This paper implicates to investigate the growth in certain trends in the literature and to explore the main paths of knowledge dissemination in the domain of PFSs in recent years.

This paper aims to identify the topics in which researchers are currently interested, to help scholars to keep abreast of the latest research on PFSs.

The stringent requirements for environmental protection have induced the extensive applications of water-lubricated journal bearings in marine propulsion. The nonlinear dynamic analysis of multiple grooved water-lubricated bearings (MGWJBs) has not been fully covered so far in the literature. This study aims to conduct the nonlinear dynamic analysis of the instability for MGWJBs.

An attenuation rate interpolation method is proposed for the determination of the critical instability speed. Based on a structured mesh movement algorithm, the transient hydrodynamic force model of MGWJBs is set up. Furthermore, the parameters’ analysis of nonlinear instability for MGWJBs is conducted. The minimum water film thickness, side leakage, friction torque and power loss of friction are fully analyzed.

With the increase of speed, the journal orbits come across the steady state equilibrium motion, sub-harmonic motion and limit circle motion successively. At the limit circle motion stage, the orbits are much larger than that of steady state equilibrium and sub-harmonic motion. The critical instability speed increases when the spiral angle decreases or the groove angle increases. The minimum water film thickness peak is at the rotor speed of 4,000 r/min for the MGWJB with Sa = 0°. As rotor speed increases, the side leakage decreases slightly while the friction torque and the power loss of friction increase gradually.

Present research provides a beneficial reference for the dynamic mechanism analysis and design of MGWJBs.

This research takes the transfer behavior of users from Tencent QQ to WeChat as an example to discuss the wider transfer behavior of social media users on the Internet.

This paper collects data through a combination of offline interviews and online questionnaire surveys, and utilizes data analysis tools to construct structural equation modeling (SEM). Using Statistical Product and Service Solutions (SPSS) Statistics 22.0 and Analysis of Moment Structures (AMOS) 22.0 software with SEM, this study was carried out to provide reasonable statistical support for relevant proposed hypotheses based on 368 effective samples acquired through the questionnaire.

The findings of this study show that subjective norm, transfer experience, social communication, and knowledge acquisition all have significant associations with transfer intention and switching behavior. To be specific, transfer intention exerts a positive association on switching behavior; function setting, privacy protection and personal innovation have a favorable association with transfer intention; transfer cost has a significantly negative relationship with transfer intention and switching behavior; function setting has no important relationship on switching behavior.

The research results provide a reference for improving the viscosity and loyalty of social media users in the new era and resolving the problem of user churn.

There are two opposite views about whether the Antitrust Law is conducive to the development of the economy. One view is that the Antitrust Law can restrain monopoly, maintain market competition and benefit economic growth. The other view is that the Antitrust Law inhibits innovation by monopolistic firms and fosters rent-seeking, which is bad for economic growth. To provide a possible perspective for clarifying the controversy, this paper aims to answer the following two questions: first, will the Antitrust Law inhibit corporate innovation? Second, does the antitrust enforcement agency discriminate against private enterprises?

Based on the samples of A-share listed companies from 2003 to 2013, the authors use the implementation of China’s Antitrust Law in 2008 as a policy shock, take the monopoly enterprises in each industry as the treatment group and competitive enterprises as the control group, using the difference-in-differences method to test the impact of the implementation of the Antitrust Law on corporate innovation activities.

The results show that compared with competitive enterprises, the patent output of monopolistic enterprises was significantly reduced after the implementation of the Antitrust Law, which indicates that the Antitrust Law does inhibit the innovation activities of monopolistic enterprises. Further research finds that the innovation suppression effect of the Antitrust Law is more prominent in state-owned enterprises, which means that the government does not have “selective law enforcement” against private enterprises in the process of law enforcement. Therefore, the results provide evidence for the idea that government intervention is neutral.

First, the paper enriches and expands the research on the factors affecting corporate innovation from the perspective of market structure. Second, it enriches and expands relevant research on the consequences of implementing the Antitrust Law from the perspective of corporate innovation. Third, it not only provides the relevant empirical evidence for clarifying the dispute about the Antitrust Law but also is helpful to clarify whether the Chinese Government has “selective law enforcement” against private enterprises.

To develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets.

The research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance.

The EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method.

The originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.

From a knowledge-management perspective, this paper aims to analyze the digital transformation of the business models of traditional Chinese sporting goods companies in the context of the pandemic crisis and to explore the role of their digital transformation in coping with the crisis.

Using theoretical sampling, typical sporting goods companies are selected for case studies. We provide an in-depth analysis of how these companies achieve high performance levels through the digital transformation of their business models in the post-COVID-19 era and discuss the key role of knowledge management in this achievement.

Focusing on the challenges faced by Chinese sporting goods enterprises during the pandemic crisis from the knowledge-management perspective, we find that through the digital transformation of their business models, enterprises can improve their knowledge-management capabilities, enhance their flexibility to respond to sudden crises and maintain a higher level of corporate performance.

This paper has significant implications for sporting goods companies wishing to achieve high corporate performance through the digital transformation of their business models in the post-COVID-19 era. Future research should address the dynamic mechanism of the digital transformation of business models to improve enterprise knowledge-management capabilities and the impact mechanism of knowledge-management capabilities on interenterprise organizational resilience.

This paper proposes specific strategies in the process of the digital transformation of business models that are essential for improving enterprises’ internal and external knowledge-management capabilities.

Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing.

It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience.

The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics.

The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences.

This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.