Study on the influencing factors of digital transformation of construction enterprises from the perspective of dual effects—a hybrid approach based on PLS-SEM and fsQCA

The digital transformation of Chinese construction enterprises is crucial for achieving sustainable and high-quality development in the construction industry. However, there is still a lack of in-depth research on the impact mechanism of digital transformation in construction enterprises. The purpose of this study is to explore the multiple influencing factors and complex causal relationships of digital transformation in construction enterprises and promote the deep integration of digitalization and construction enterprises. To this end, based on the dual-effect perspective (net effect perspective of a single influencing factor and configuration effect perspective of multiple influencing factors), using the “technology–organization–environment” framework (TOE framework) to construct a research model of influencing factors for digital transformation in construction enterprises. A sample of 236 construction enterprise managers was surveyed, and partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) methods were used to empirically analyze the dual effects of influencing factors for digital transformation in construction enterprises. The results show that: (1) from the net effect perspective, there are seven factors that significantly impact digital transformation in construction enterprises; (2) from the configuration effect perspective, there are three paths that can achieve high-level digital transformation in construction enterprises, and one path that leads to low-level digital transformation; (3) from the dual-effect perspective, top management support and policy support are key factors for digital transformation in Chinese construction enterprises. The research results enrich the relevant research on digital transformation in construction enterprises and provide a reference basis for promoting digital transformation in construction enterprises

System Identification of Enterprise Innovation Factor Combinations—A Fuzzy-Set Qualitative Comparative Analysis Method

High-tech manufacturing enterprises, as innovative entities, are a key focus of national attention. Currently, such enterprises are facing both internal governance pressure and external institutional pressure. Unlike traditional studies that mostly use regression equations, this article uses the fuzzy-set qualitative comparative analysis method to examine how high-tech manufacturing enterprises can coordinate their internal governance mechanisms and external institutional pressures to achieve optimal innovation. This improves the complex mechanism of the multiple factors jointly explaining corporate innovation, and also helps to elucidate the nonlinear relationship between internal governance factors, external institutional factors, and corporate innovation, effectively enriching research methods and results. However, there has not been any research on the issue of enterprise innovation from the perspective of coordinating the two, which urgently needs to be addressed. This article examines how high-tech manufacturing enterprises can reconcile their internal governance mechanisms with external institutional pressures to achieve optimal innovation. The results showed that (1) a single factor cannot constitute the necessary conditions for innovation in high-tech manufacturing enterprises, but executive and shareholder governance have universality in the innovation in high-tech manufacturing enterprises; (2) in the absence of political advantages, high-tech manufacturing enterprises should focus on the coordinated development of internal governance, making board, executive, and shareholder governance the core conditions for innovative development; (3) with political advantages as the main focus and market attention as a supplement, high-tech manufacturing enterprises promote innovative development by combining executive and shareholder governance. This finding indicates a significant substitution effect between government legitimacy and board governance, and confirms that the importance of obtaining government legitimacy for high-tech manufacturing innovation is higher than market legitimacy. This article enriches the research on enterprise innovation by linking internal corporate governance with external institutional pressure, expands the research on the coordination relationship between institutional pressure and corporate governance, and has enlightening significance in revealing the collaborative path for innovation in high-tech manufacturing enterprises

The Evaluation Prediction System for Urban Advanced Manufacturing Development

With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM

Spatiotemporal distribution and dynamics evolution of artificial intelligence development in China

The quantified measurement and comprehensive analysis of artificial intelligence development (AIDEV) are vital for countries to form AI industrial ecology and promote the long-term development of regional AI technology. Based on the innovation ecosystems (IE) theory, this paper constructs an evaluation system to measure and analyze the spatiotemporal distribution and dynamic evolution of the AIDEV in China from 2011 to 2020. The results show that the AIDEV of China presents an overall upward trend and an obvious unbalance in the spatial distribution which is “eastern > central > western”. Meanwhile, the provinces of low-level AIDEV are catching up with the high-level provinces, which leads to the regional difference of AIDEV narrowing. Moreover, the concentration and polarization phenomenon of AIDEV in China has been weakening and the AIDEV will continue to increase in the next three years. Further, there is a significantly positive spatial autocorrelation of AIDEV. Finally, high AIDEV provinces will increase the probability of surrounding provinces’ AIDEV to develop. This paper expands the research stream in the field of AI research, extends the application scenarios of IE theory, and puts forward some relevant policy recommendations

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems

Extracellular vesicle‐packaged ILK from mesothelial cells promotes fibroblast activation in peritoneal fibrosis

Abstract Progressive peritoneal fibrosis and the loss of peritoneal function often emerged in patients undergoing long‐term peritoneal dialysis (PD), resulting in PD therapy failure. Varieties of cell‐cell communications among peritoneal cells play a significant role in peritoneal fibrogenesis. Extracellular vesicles (EVs) have been confirmed to involve in intercellular communication by transmitting proteins, nucleic acids or lipids. However, their roles and functional mechanisms in peritoneal fibrosis remain to be determined. Using integrative analysis of EV proteomics and single‐cell RNA sequencing, we characterized the EVs isolated from PD patient's effluent and revealed that mesothelial cells are the main source of EVs in PD effluent. We demonstrated that transforming growth factor‐β1 (TGF‐β1) can substitute for PD fluid to stimulate mesothelial cells releasing EVs, which in turn promoted fibroblast activation and peritoneal fibrogenesis. Blockade of EVs secretion by GW4869 or Rab27a knockdown markedly suppressed PD‐induced fibroblast activation and peritoneal fibrosis. Mechanistically, injured mesothelial cells produced EVs containing high level of integrin‐linked kinase (ILK), which was delivered to fibroblast and activated them via p38 MAPK signalling pathway. Clinically, the expression of ILK was up‐regulated in fibrotic peritoneum of patients undergoing long‐term PD. The percentage of ILK positive EVs in PD effluent correlated with peritoneal dysfunction and the degree of peritoneal damage. Our study highlights that peritoneal EVs mediate communications between mesothelial cells and fibroblasts to initiate peritoneal fibrogenesis. Targeting EVs or ILK could provide a novel therapeutic strategy to combat peritoneal fibrosis