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The anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort.

In addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks.

The results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver.

In future studies, the number of observations could be increased further.

The study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era.

The study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

Despite the fact that user participation (UP) has been highlighted as an important aspect in innovation, previous findings on its relationship with service innovation performance (SIP) are inconsistent. This study aims to investigate the relationships among UP, knowledge management capability (KMC) and SIP, especially in the digital age, inspired by the theories of knowledge-based and absorptive capacity.

Based on a sample of 252 Chinese e-commerce enterprises, this study adopts a hierarchical regression analysis and bootstrap method to test the theoretical framework and research hypotheses.

UP and KMC have positive effects on SIP, respectively. KMC plays a mediating role in the effect of UP on SIP. Furthermore, the intermediary role of KMC varies in different sub-paths between UP and SIP.

First, this study provides some explanations for inconsistent arguments on the relationship between UP and service innovation. Second, with the consideration of specific dimensions of UP and SIP, the mediation role of KMC varies in different sub-paths has been recognized, which provides a deeper understanding of the relationship between UP and SIP. Third, this study opens the discussion about how to realize SIP more effectively in the digital age, advancing theoretical and practical developments on service innovation.

The inconsistent results shown in previous group faultline research have created a need for investigating the underlying mechanisms of the faultline's effects. This study focuses on clarifying the competing mediating roles of information diversity and team conflict in the nonlinear relationship between board faultlines (BF) and decision quality.

This study is empirically tested with the questionnaire data from 105 Chinese listed companies.

This study finds: (1) an inverted U-shaped curve relationship between BF and board decision quality and (2) that the joint mediating effect of team conflict and information diversity leads to the inverted U-shaped curve relationship between BF and decision quality. Specifically, BF shows a U-shaped curve relationship with team conflict and an inverted U-shaped curve relationship with information diversity. Either too weak or too strong faultlines will inhibit the positive effects of information diversity and amplify the negative effects of team conflicts, leading to low-quality decisions.

This study contributes to the research on: (1) board governance as it clarifies the effect of BF on the board decision-making process and its quality, which helps to open the black box of board decision-making and (2) group faultlines as it reveals how information diversity and team conflict can play a joint mediating role in the functioning of team faultlines.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

This study investigates the influence of social trust on the attainment of corporate environmental, social and governance (ESG) objectives.

This study conducts panel regression analysis on a distinctive dataset for 2009–2017 on Chinese firms.

The analysis reveals a significant positive association between social trust and firm-level ESG practices. Moreover, the impact of social trust on shaping ESG outcomes is further amplified by factors such as economic growth, corporate governance standards and institutional quality. This relationship remains statistically positive when the authors employ alternative measures and methodologies, such as the instrumental variables, propensity score matching and difference-in-differences approaches. Notably, the results of heterogeneity tests indicate that the Trust–ESG nexus is more prominent for state-owned enterprises and firms with substantial market capitalization, superior profitability and higher leverage.

This study expands the comprehension of the determinants of ESG and underscores the influential role of social trust as an informal institution in enhancing a firm's ESG performance.

This study investigates how Danmu (danmaku) technological features (DTFs) of recommendation vlogs (rec-vlogs) impact consumer experiences and decisions.

The authors adopt the partial least squares structural equation modeling (PLS-SEM) technique with a sample of 422 viewers of Danmu-enabled rec-vlogs to examine the proposed model.

The DTFs positively affect consumer loyalty intentions through the mediation of social presence and immersion. Also, vlogger-product congruency significantly moderates the relationship between social presence and immersion.

With the increasing popularity of vlogs, retailers have adopted the vlog as a new marketing channel to connect with consumers. Although a growing body of studies focuses on this phenomenon, little is known about how DTFs help to increase consumer loyalty toward using rec-vlogs as product information sources. Anchored on the stimulus–organism–response framework, this study investigates how pseudo-synchronicity, Danmu-content congruency and parallelism, three DTFs used in rec-vlogs, impact consumer experiences of social presence and immersion, eventually influence the consumers' loyalty intentions toward rec-vlogs. The authors also examine whether the effect of social presence on immersion is contingent on vlogger-product congruency. These findings have implications for both researchers and practitioners to understand the effect of DTFs in the context of rec-vlogs.

The purpose of this paper is to demonstrate visually the knowledge structure and evolution of disruptive innovation. The paper used CiteSpace III to analyze 1,570 disruptive innovation records from the Web of Science database between 1997 and 2016.

Initially, this paper offers a comprehensive overview of papers, countries, journals, scholars and application areas. Subsequently, a time zone view of high-frequency keywords is presented, emphasizing the course of evolution of the study hotspots. Finally, a visualization map of cited references and co-citation analysis are provided to detect the knowledge base at the forefront of disruptive innovation.

The findings are as follows: the number of papers shows exponential growth. The USA has the largest contribution and the strongest center. The Netherlands shows the largest burst, followed by Japan. Journal of Production Innovation Management and Research Policy is the most important journals. Hang CC has the largest number of articles. Walsh ST is identified as a high-yielding scholar. Christensen CM is the most authoritative scholar. Engineering electrical electronic is the most widely used research category, followed by management and business. The evolutionary course of the study hotspots is divided into five stages, namely, start, burst, aggregation, dispersion and not yet formed. Eight key streams in the literature are extracted to summarize the knowledge base at the forefront of disruptive innovation.

This paper explores the whole picture of disruptive innovation research and demonstrates a visual knowledge structure and the evolution of disruptive innovation. It provides an important reference for scholars to capture the current situation and influential trends in this field.

The purpose of this study is to generate insight into the effects of entrepreneurship education in China by conducting a preliminary scoping study of the enterprising tendency of university students studying business.

This study used a self-administered questionnaire based on the General Measure of Enterprising Tendency v2 (GET2) test to measure the enterprising tendency of a group of Chinese university students. Decision trees, using the Chi-square automatic interaction detector (CHAID) approach, and multiple regression analyses were used to investigate the enterprising tendency of respondents.

The findings from this study indicate that the students have an overall medium level of enterprising tendency and strengths in some enterprising characteristics. The findings reveal that gender, family business, hometown and entrepreneurship education are significantly related to enterprising tendency but that age, household income, parents’ education and occupation are not.

Although the study is based on a relatively small sample taken from just one university in Beijing, the findings suggest that the enterprising tendency of students can be encouraged by entrepreneurship education. Combined with evidence that entrepreneurship education is at a relatively early stage of development in China, this finding suggests considerable scope to increase student’s enterprising tendency by extending, creating a more favourable environment for and improving the methods used to deliver entrepreneurship education. Enterprising tendency can be argued to naturally result in entrepreneurial intention; however, this extension is beyond the scope of this study, which is restricted to the analysis of enterprising tendency.

This study makes an original contribution to knowledge as it is one of the first studies to explore enterprising tendency among university students in China. It has value for government, policymakers and university program designers in that it provides direction for entrepreneurship education in China.