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Webrooming (i.e. searching information online and making the final purchase in a physical store) has become a popular shopping practice, but remains insufficiently studied. To address this, a research framework encompassing online and offline channel attributes (i.e. online review diagnosticity, online search convenience, expected price loss, offline purchase effort and offline after-sales service convenience), consumer traits (i.e. anticipated regret) and shopping experience (i.e. smart-shopping perception) as determinants of webrooming continuance intention is proposed.

The proposed model was validated by conducting a questionnaire-based survey that yielded 354 useable responses. The data was subjected to partial least squares structural equation modelling and importance-performance map analysis.

According to the obtained results, online review diagnosticity, offline after-sales service convenience and anticipated regret are the vital antecedents of webrooming continuance intention, while smart-shopping perception acts as the mediator.

The current study adds significantly to the body of knowledge about webrooming by validating the inter-relationships between online review diagnosticity, after-sales service convenience, anticipated regret, smart-shopping perception and webrooming continuance intention.

The purpose of this paper is to assesses whether supervisor justice is linked to COVID-19 negative and positive impacts directly and indirectly through the mechanisms of stress and resiliency among auxiliary police in China.

This study utilized survey data from more than 300 auxiliary police in a large Chinese provincial capital city in 2020. Structural equation modeling was conducted to analyze the direct and indirect relationships between supervisor justice and COIVD-19 impacts.

Results indicate that supervisor justice connects to COVID-19 negative impacts indirectly through stress. Supervisor justice is also indirectly related to positive impact through resiliency.

The findings' generalizability is limited due to using a nonrandom sample of officers. Officers' emotional states in the forms of stress and resiliency are important in mediating the association between supervisory justice and COVID-19 impacts.

The present study represents one of the first attempts to empirically investigate the occupational experiences of a vital group of frontline workers in Chinese policing. This study also generates evidence to support the importance of officers' emotional conditions in reducing negative COVID-19 impacts in an authoritarian country.

The purpose of this paper is to assess the influences of social trust and neighborhood cohesion on public trust in the police in China.

This study used survey data collected from roughly 5,600 respondents by the 2012 Chinese General Social Survey (CGSS). Multivariate regression was employed to analyze the effects of two forms of social trust, generalized trust and particularized trust, and three types of neighborhood cohesion, neighbor solidary, support and interaction, on public trust in the Chinese police, controlling for personal background characteristics.

Both generalized trust and particularized trust exerted a significant positive effect on trust in the Chinese police. Greater neighborhood cohesion also enhanced public trust in the police. Elderly, women, less educated and people with rural hukou and higher perceived social class were more likely to trust the police.

The CGSS data contained only a single item that could be used to measure public trust in the police. Future studies should consider using multiple survey items to reflect Chinese people’s trust from different conceptual dimensions, such as procedural- and outcome-based trust and police legitimacy. The CGSS data also did not contain information on some relevant predictors, such as victimization and fear of crime, personal and vicarious contact experiences with the police, and news and social media usage and exposure. Future studies, if possible, should incorporate these theoretically relevant and empirically proven variables into the analysis.

Improving neighborhood cohesion is a clear path to cultivate stronger public trust in the police. Policy-makers and officials must bring the neighborhood-centered approach back to local governance by working closely with police leaders and other private and parochial social institutions to launch programs that can effectively stabilize and strengthen local communities and actively promoting positive interactions and social bonds among residents. Policies and programs aimed at enhancing public trust in the police should target at younger, better educated and urban Chinese who are more likely to be critical of the police.

Despite their high relevance, social trust and neighborhood cohesion have received only limited attention in past research on Chinese attitudes toward the police. This study represents one of the first attempts to examine different forms of social trust and neighborhood cohesion on public trust in the police in China.

This study aims to investigate the relationship between COVID-19 impacts and auxiliary police officers’ mental health as well as the moderating role of supervisor procedural justice.

Based on the role theory and a police officer survey from China, this quantitative study investigates the relationship between COVID-19 impacts and mental health status among auxiliary police, a rarely examined police type. We also examine the moderating role of supervisor procedural justice.

Auxiliary police officers reported both negative and positive impacts from COVID-19, while the negative impacts were mainly in the work domain, but the positive impacts were primarily in the life area. OLS regression results indicate that negative impacts, especially work-related negative impacts, are significantly related to depression and anxiety, and supervisor procedural justice moderates the relationship between positive impacts and depression and anxiety.

Firstly, we adopted the role theory to examine how public health emergencies affect police officers in their work and life domains. Secondly, we advance the organizational justice literature by assessing whether supervisor procedural justice can moderate the relationship between COVID-19 impacts and their mental health. Thirdly, this research extends the literature on depression and anxiety of auxiliary police officers in China, who attracted less attention in current literature and policies.

The purpose of this paper is to improve the mechanical and tribological properties of Si₃N₄ ceramics and to make the application of Si₃N₄ ceramics as tribological materials more extensive.

Si₃N₄-based composite ceramics (SN-2L) containing nitrogen-doped graphene quantum dots (N-GQDs) were prepared by hot press sintering process through adding 2 Wt.% nanolignin as precursor to the Si₃N₄ matrix, and the dry friction and wear behaviors of Si₃N₄-based composite against TC4 disc were performed at the different loads by using pin-on-disc tester.

The friction coefficients and wear rates of SN-2L composite against TC4 were significantly lower than those of the single-phase Si₃N₄ against TC4 at the load range from 15 to 45 N. At higher load of 45 N, SN-2L/TC4 pair presented the lowest friction coefficient of 0.25, and the wear rates of the pins and discs were as low as 1.76 × 10⁻⁶ and 2.59 × 10⁻⁴mm³/N·m. The low friction and wear behavior could be attributed to the detachment of N-GQDs from the ceramic matrix to the worn surface at the load of 30 N or higher, and then an effective lubricating film containing N-GQDs, SiO₂, TiO₂ and Al₂SiO₅ formed in the worn surface. While, at the same test condition, the friction coefficient of the single-phase Si₃N₄ against TC4 was at a range from 0.45 to 0.58. The spalling and cracking morphology formed on the worn surface of single-phase Si3N4, and the wear mechanism was mainly dominated by adhesive and abrasive wear.

Overall, a high-performance green ceramic composite was prepared, and the composite had a good potential for application in engineering tribology fields (such as aerospace bearings).

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0161/

Carbon reduction in the construction supply chain can critically affect the construction industry’s transition to an environmentally sustainable one. However, implementing carbon reduction in all parties is restricted because of the poor understanding of the drivers influencing the low-carbon construction supply chain (LCCSC). The purpose of this paper is to systematically identify the drivers of LCCSC, analyze their causality, and prioritize the importance of their management.

A decision-making analysis process was developed using an integrated decision-making trial and evaluation laboratory (DEMATEL)–analytical network process (ANP). First, the hierarchical drivers of the LCCSC were identified through a literature review. The DEMATEL method was subsequently applied to analyze the interactions between the drivers, including the direction and strength of impact. Finally, the ANP analysis was used to obtain the drivers’ weights; consequently, their priorities were established.

Various factors with complex interactions drive LCCSC. With respect to their influence relationships, incentive policy, regulatory policy, consumers’ low-carbon preference, market competition, supply chain performance, and managers’ low-carbon awareness have more significant center degrees and are cause drivers. Their strong correlations and influence on other drivers should be noticed. In terms of weights in the driver system, regulatory policy, consumers’ low-carbon preference, supply chain performance, and incentive policy are the key drivers of LCCSC and require primary attention. Other drivers, such as supply chain collaboration, employee motivation, and public participation, play a minor driving role with less management priority.

Despite some contributing studies with localized perspectives, the systematic analysis of LCCSC drivers is limited, especially considering their intricate interactions. This paper establishes the LCCSC driver system, explores the influence relationships among the drivers, and determines the key drivers. Hence, it contributes to the sustainable construction supply chain domain by enabling decision-makers and practitioners to systematically understand the drivers of LCCSC and gain management implications on priority issues with limited resources.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.

In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.

Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.

This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.