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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.

The purpose of this study is to examine the effect of trust on user adoption of artificial intelligence-generated content (AIGC) based on the stimulus–organism–response.

The authors conducted an online survey in China, which is a highly competitive AI market, and obtained 504 valid responses. Both structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis.

The results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform. Both cognitive trust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively moderates the effect of cognitive trust in platform on trust in AIGC. The fsQCA identified three configurations leading to adoption intention.

The main limitation is that more factors such as culture need to be included to examine their possible effects on trust. The implication is that generative AI platforms need to improve the intelligence, transparency and empathy, and mitigate knowledge hallucination to engender users’ trust in AIGC and facilitate their adoption.

Existing research has mainly used technology adoption theories such as unified theory of acceptance and use of technology to examine AIGC user behaviour and has seldom examined user trust development in the AIGC context. This research tries to fill the gap by disclosing the mechanism underlying AIGC user trust formation.

Although previous research has recognized the pivotal role of mobile social media in knowledge sharing among project members, little is known about what factors affect knowledge sharing in mobile social media groups (MSMGs). Against this background, using normative social influence theory, this study attempts to explore factors influencing knowledge sharing in MSMGs.

Data from 205 Chinese construction project members are collected and used for analysis. Ordinary least squares regression by Stata 16 is used to test the proposed hypotheses.

Concerning role norms, gender difference in knowledge sharing behavior (KSB) is found, while it is not the case for knowledge quality (KQ). Work experience is found to positively affect KQ, but shows no influence on KSB. As for group norms, the inverted-U relationship between number of members and KSB is partially supported. In addition, organizational norms generally exhibit the greatest influence on both KSB and KQ among the three forms of norms.

This study deepens the understanding of knowledge sharing factors in mobile social media environments and affords practical implications for how to make full use of social media for knowledge management within construction project teams.

This study aims to investigate the effect of top management team (TMT) stability on outward foreign direct investment (OFDI) of Chinese firms and the moderating effects of state ownership and managerial ownership on this relationship.

An empirical analysis based on the ordinary least square regression model is conducted using Chinese A-share listed firms that engaged in OFDI from 2008 to 2021.

TMT stability has a positive effect on firms’ OFDI. Moreover, state ownership significantly strengthens the positive relationship between TMT stability and OFDI, while managerial ownership weakens this positive relationship.

The findings help firms to effectively retain TMT talents and promote the smooth internationalization of firms, thereby enhancing their long-term development capabilities and competitive advantages.

This study expands the investigation of the factors influencing OFDI at the micro level of the TMT, providing valuable decision-making insights for firms.

This paper explores the linkage of digital infrastructure to the cost of debt.

This study uses the implementation of the “Broadband China” policy that improves digital infrastructure as an exogenous shock and exploits the difference-in-differences method (DID).

Empirical analyses show that digital infrastructure leads to increased firms’ borrowing costs, which is robust to several robustness checks. In addition, we find that this unfavourable effect can be attributed to intensified market competition led by digital infrastructure construction. Cross-sectional analysis shows that this effect is greater for non-SOEs and smaller firms. Finally, we offer additional evidence of the unfavourable effect by showing that digital infrastructure construction leads to decreased fundamentals.

Our paper unveils how digital infrastructure construction affects firms’ business strategy in using private debts and extends the determinants of firms’ borrowing costs.

This study aims to investigate the corrosion behavior of Cu-containing 3Ni steel in simulated marine environments and to provide basic guidance for improving the corrosion resistance of marine high-strength steels.

The corrosion properties of Cu-containing 3Ni steel were evaluated in five different NaCl concentrations by alternating wet and dry cycling method. The corrosion behavior was investigated by electrochemical impedance spectroscopy, scanning electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. The mechanism of the influence of Cl ion concentrations on the corrosion behavior of Cu-containing 3Ni steel in marine environments was analyzed.

The results showed that the corrosion resistance of Cu-containing 3Ni steel decreased with NaCl concentration increasing. With the increase of NaCl concentration, the number of FeOOH particles decreased and their size increased, resulting in an increase in the porosity and a decrease in the density of corrosion products. High NaCl concentration could inhibit the formation of NiFe₂O₄ and disrupt the electronegativity of the inner film of corrosion products, which further weakened the enrichment of Ni and Cu, and enhanced the permeability of Cl ions.

The influence of NaCl concentrations on the corrosion behavior of Cu-containing 3Ni steel was systematically studied and the influence laws of corrosion behavior were obtained in this paper, providing basic data for the optimal design of Cu-containing 3Ni steels.

This article explores leadership as a philosophical attitude. The purpose is to get a deeper understanding of leadership in everyday working life.

The article is based on a thematic analysis of 16 qualitative research interviews with leaders of different levels within the Norwegian Labour and Welfare Administration (NAV).

Leadership is not merely a set of skills or a functional role. The study has identified the broader implications of leadership as a philosophical attitude for personal development, motivation and social responsibility; and the relational side of leader’s social mission.

The study has examined what the interviewees say; it has not observed what they actually do.

Leadership as a philosophical attitude deals with personal dimensions and shows how leaders can adjust their behaviour based on their character traits and the situational demands.

This article bridges theory to practice through the empirical application of philosophical attitudes in leadership practice.

This study aims to apply deep convolutional neural network Mask-R-CNN algorithm based on transfer learning to realize the segmentation of online wear fragments.

Wear debris analysis is considered to be one of the most effective methods to maintain the condition of mechanical equipment. In this paper, the friction and wear testing machine was used to design pin-disk rotation, pin-disk reciprocation and four-ball test to produce cutting, sliding, laminar and fatigue debris. A semi-online sampling system was designed to collect ferrographic images containing various fragments. The images were rotated and flipped to augment the data and enhance the generalization ability of the model. The data set required for data analysis is established. Using COCO pre-trained Mask R-CNN data set as a benchmark, the region proposal network (RPN) is trained with labeled wear debris images to enhance the ability of RPN to recognize background and wear debris. Two transfer learning scenarios are tested in the network head of the Mask R-CNN.

The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.

The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0182/

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.

This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation.

Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation.

Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control.

The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece.

This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry.

This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.