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This paper aims to introduce a wheeled vehicle robot for adapting to the surface terrain of the 500-m diameter reflector of the FAST radio telescope in China.

By analyzing vehicles applied for different off-road environments, a six-wheeled architecture with a passive “triple-bogie” suspension is selected. A subscale model of the vehicle robot is designed, along with statics modeling and multibody simulations of the dynamics on simulated reflector panel surfaces. The slope- and step-climbing abilities of the subscale vehicle are discussed in accordance with numerical and experimental tests. An engineering scale vehicle is subsequently manufactured and tested on surface terrains of lateral as well as vertical gaps, and is finally validated on the FAST reflector.

This model of vehicle robot exhibits strong structure stability under desired payload. It can stably cross lateral gaps for maximum surface slope 28° and can traverse vertical gap for maximum surface slope 23°. The traversing abilities satisfy the mobility requirements subjected to surface terrains of FAST reflector.

The engineering vehicle robot negotiates the lateral as well as vertical gaps between triangle panels and has been successfully applied to the FAST reflector serving for inspection and maintenance work.

This paper aims to present a modular multimodal flexible electronic skin that can be used for robot collision detection in human–robot interactions. This type of electronic skin will meet the requirements of performance indicators such as sensing mode, sensing domain coverage and dynamic data update rate in human–robot interactions.

The electronic skin uses a modular architecture, and the sensing module is designed to be adjustable in size so that it can be easily deployed on complex robot surfaces, increasing area coverage, reducing power consumption, and improving data update rates.

The authors evaluated electronic skin through experiments using a UR5 robot. Electronic skin has high static scene perception differentiation and dynamic scene perception abilities. Moreover, the robot realizes a high-speed collision response based on the electronic skin proposed in this study.

The proposed electronic skin provides crucial technical support for advancing robotic technologies, and holds promising prospects for industrial applications.

While the importance of supply chain learning (SCL) is widely recognized by both academia and industry, the mechanisms through which SCL is developed and its effects on environmental performance remain insufficiently understood. This study conceptualizes and empirically investigates the role of relational capital and information technology (IT) in enhancing SCL and improving environmental performance.

Drawing on socio-technical system theory (STS) and a knowledge-based view, this research employs structural equation modeling to test the proposed model, utilizing survey data collected from Chinese manufacturing companies.

The results demonstrate that relational capital with supplier and customer significantly enhances both supplier and customer learning. While IT does not directly impact supplier and customer learning, it exerts an indirect influence through its positive effect on relational capital. In other words, relational capital mediates the relationship between IT and supplier and customer learning. Additionally, this study finds that both supplier and customer learning enhance environmental performance.

This study provides actionable insights for managers, emphasizing the importance of relational capital in fostering SCL and improving environmental performance. By understanding these relationships, managers can develop more effective strategies for leveraging SCL as a tool for sustainability.

This study adds to the existing body of knowledge in supply chain management by offering a more nuanced and holistic model to explain how relational capital, IT and SCL interact to influence environmental performance, particularly within the context of Chinese manufacturing companies.

The online healthcare platform (OHP) has become an essential element of the healthcare system, representing a technological shift in the job responsibilities of medical professionals. Drawing on a technology-based job demands–resources (JD-R) model, this study aims to examine how the technological characteristics of OHP affect doctors’ OHP use psychology and behavior.

This empirical study was based on a survey conducted among 423 doctors with OHP use experience. The proposed model underwent assessment through partial least squares structural equation modeling (PLS-SEM) to reveal the effects of technology-based job demands (i.e. technology-based work overload and technology-based work monitoring) and resources (i.e. perceived usefulness, facilitating conditions and IT mindfulness) on doctors’ OHP fatigue and continuance use intention.

Results suggest that technology-based work monitoring, perceived usefulness and facilitation conditions have significant impacts on doctors’ psychological and behavioral responses to using OHP, whereas technology-based work overload and IT mindfulness have a single impact on continuance use intention and fatigue of OHP.

It assists doctors, healthcare administrators, policymakers and technology developers in understanding OHPs’ technological characteristics, enabling them to harness its benefits and mitigate potential challenges. Additionally, given the self-reported cross-sectional data from China, future studies can improve generalizability and adopt experimental methods or longitudinal designs with objective data.

It extends the research on OHP by employing a technology-based JD-R model to explore work attributes and dual effects associated with OHP’s technological characteristics. It also enriches existing research by examining the role of OHP’s technological characteristics in doctors’ psychological and behavioral responses.

This study aims to theoretically hypothesize and empirically examine the impact of economic policy uncertainty (EPU) on firms' innovation performance as well as the contingency conditions of this relationship.

This study collects and combines secondary longitudinal data from multiple sources to test for a direct impact of EPU on firms' innovation performance. It further examines the moderating effects of firms' operational and marketing capabilities. A series of robustness checks are performed to ensure the consistency of the findings.

In contrast to the common belief that EPU reduces the innovativeness of firms, the authors find an inverted-U relationship between EPU and innovation performance, indicating that a moderate level of EPU actually promotes innovation. Further analysis suggests that firms' operational and marketing capabilities make the inverted-U relationship steeper, further enhancing firms' innovation performance at a moderate level of EPU.

This study adds to the emerging literature that investigates the operational implications of EPU, which enhances our understanding of the potential bright side of EPU and broadens the scope of operational risk management.

Economic policy uncertainty has increased around the world since the financial crisis of 2007–2008. While scholars have devoted a lot of time and energy to investigating the impact of economic policy uncertainty (EPU) on firm innovation, they have not reached consistent research conclusions. This study aimed to clarify the above research differences by exploring the impact of EPU on firms' relative exploitative innovation emphasis, so as to provide a more comprehensive and granular understanding of the relationship between EPU and firm innovation.

This study obtained 17,165 firm-year data points from 3,107 listed companies in China. It analyzed the above data with a fixed effects model. In addition, this study used an instrumental variables method to solve potential endogeneity problems.

Based on real options theory and contingency theory, the authors proposed and found that EPU has a significant positive effect on relative exploitative innovation emphasis. In addition, the authors proposed and found that this effect is more pronounced in industries with high technological uncertainty, low competitive intensity, and low state monopolization.

This study is the first to explore why firms prefer exploitative innovation over exploratory innovation from the perspective of EPU. In doing so, this study expands and enriches the EPU literature and the innovation literature. Furthermore, by introducing the moderating role of industry environment, this study deepens the authors' understanding of how complex interactions between industry and institutional environments work together to shape firm strategic choices, and especially firm innovation. Finally, the conclusions of this study have important practical implications for shareholders to take measures to balance exploitative innovation and exploratory innovation to achieve better development.

The profound impact of the COVID-19 pandemic on the film industry has underscored the growing significance of online movies. However, there is limited research available on the factors that influence the viewership of online films. Therefore, this study aims to use the signaling theory to investigate how signals of varying qualities affect online movie viewership, considering both signal transmission costs and prices.

This study uses a sample of 1,071 online movies released on the iQiyi from July 2020 to July 2022. It uses OLS regression and instrumental variable method to examine the impact of various quality indicators on the viewership of online movies, as well as the moderating effect of price.

After conducting a thorough analysis of this study, it can be deduced that the varying impacts on online movie viewership are attributed to disparities in signal transmission costs. Specifically, star influence and rating exhibit a positive effect on the viewership of online movies, whereas the number of raters has a detrimental impact. Furthermore, there exists an “inverted U-shaped” relationship between the number of reviews and online movie viewership. Additionally, within the consumer decision-making process, both price-cost and price-quality relationships coexist. This is evident as prices negatively affect online movie viewership but positively moderate the relationship between rating, number of reviews and online movie viewership.

The research findings of this study offer valuable insights for online film producers to effectively leverage quality signals and pricing, thereby capturing market attention and enhancing film profitability.

Based on upper echelon theory and signaling theory, we aim to examine the impact of returnee executives on firms’ relative exploratory innovation focus and the moderating effect of economic policy uncertainty on this relationship.

Using panel data of Chinese listed companies from 2009 to 2020, we obtained empirical evidence to support our arguments.

Returnee executives positively influence firms’ relative exploratory innovation focus. This means that firms with returnee executives will shift the focus of their innovation activities toward exploratory innovation more than exploitative innovation. In addition, we find that economic policy uncertainty strengthens this relationship.

First, by showing how returnee executives positively influence firms’ shift in focus to exploratory rather than exploitative innovation, we expand our understanding of firms’ trade-offs between exploratory and exploitative innovation. Second, this study examines how returnee executives influence the relative importance that firms place on exploratory and exploitative innovation, allowing us to build a realistic and nuanced view of how returnee executives influence firms’ strategic choices. Finally, this study expands the strategic leadership literature and responds directly to the call for studies focusing on how institutional environmental conditions and executive characteristics work together to shape firm outcomes.

Owing to the limitations of traditional infectious disease dynamic systems in accurately encapsulating the nuances of China’s dynamic epidemic prevention policies and considering the varying sensitivity of local governments to the unfolding of public health emergencies (PHEs), this paper introduces a novel infectious disease dynamic system.

This system, rooted in the distinct characteristics of infectious diseases and nuanced prevention and control measures, leverages a learning model for enhanced precision. It intricately incorporates factors such as the infectivity in sealed and controlled areas and the role of asymptomatic patients, thereby refining the dynamics of isolation, sealing, control and the transition from asymptomatic to confirmed cases. Employing the Markov Chain Monte Carlo (MCMC) parameter estimation approach significantly augments the accuracy in pinpointing the valid parameters of disease spread. Empirical analysis was meticulously carried out, using data from the Shanghai epidemic from 1 Mar 2022 to 1 Jul 2022.

This analysis not only illuminates the profound impact of control efforts on the trajectory of the epidemic but also underscores the pivotal role of social distancing in curbing the rapid transmission of infectious diseases. Furthermore, it reveals that an accelerated detection rate during the swift spread and peak of the epidemic paradoxically leads to a surge in confirmed cases and a consequent strain on medical resources, thereby impeding the pace of medical intervention.

A stage-wise dissection of the Shanghai epidemic and comparative analyses against the evolution profiles in ASEAN countries elucidates the five stages of PHE risk evolution in alignment with the crisis lifecycle theory. These stages encompass hidden transmission, multi-point dissemination, multi-chain parallelism, rapid spread, fluctuation rebound and multi-community spread, each presenting unique challenges and dynamics in the control and management of the epidemic.