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This study aims to provide insights into Airbnb’s exit from the China market, specifically from the perspective of platform competition.

Based on the understanding of the evolution and competition of peer-to-peer (P2P) accommodation market in China and the double-edged network effects, this viewpoint article provides an in-depth discussion on Airbnb’s withdrawal from the China market.

This viewpoint paper contends that Airbnb faced increasing challenges in establishing unique competitive advantages against local rivals, who demonstrate a capacity for innovation and maintaining loyal customers in adapting to the market evolution. Consequently, this may inhibit Airbnb from attracting the mainstream market and lead Airbnb into a negative spiral, exacerbated by indirect network effects.

This study offers insights into the failure of an internationalisation process, drawing from an analysis of the competitive environment and the competition strategies of P2P accommodation platforms in China. These findings have practical implications for the implementation of international business strategies in P2P accommodation.

本观点文章旨在基于平台竞争的视角对爱彼迎退出中国的现象进行阐释

基于对中国共享住宿/民宿市场的演变和竞争、双边网络效应的理解, 本文对爱彼迎退出中国进行了深入探讨。

本文提出, 爱彼迎在建立独特竞争优势以应对本地竞争对手方面面临日益严峻的挑战, 因为本地竞争者在适应市场变化方面展现出突出的创新和留客的能力。这可能会阻碍爱彼迎吸引主流市场, 并导致其跌入由间接网络效应加剧的恶性循环中。

基于对中国共享住宿平台的竞争环境和策略的分析, 本文洞悉了一个国际化失败的案例。本文的发现对于共享住宿的国际化策略实施具有实践意义。

Este artículo de opinión pretende aportar ideas sobre la salida de Airbnb del mercado chino, concretamente desde la perspectiva de la competencia entre plataformas.

Basándonos en la comprensión de la evolución y la competencia del mercado de alojamientos P2P en China, así como en los “efectos de red de doble filo”, este artículo de opinión ofrece una discusión en profundidad sobre la retirada de Airbnb del mercado chino.

Este documento sostiene que Airbnb enfrentó desafíos crecientes para establecer ventajas competitivas únicas frente a sus rivales locales, demostrando estos últimos una capacidad de innovación y del mantenimiento de la lealtad de sus clientes al adaptarse a la evolución del mercado. En consecuencia, esto puede inhibir a Airbnb para atraer al mercado principal y llevar a la empresa a una espiral negativa, exacerbada por efectos de red indirectos.

Este estudio ofrece una visión del fracaso de un proceso de internacionalización, a partir de un análisis del entorno competitivo y de las estrategias de competencia de las plataformas de alojamiento P2P en China. Estos resultados tienen implicaciones prácticas para la aplicación de estrategias empresariales internacionales en los alojamientos P2P.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

This study aims to provide insights and guidance for future research on service robot acceptance in the tourism and hospitality (T&H) domain through a comprehensive literature review.

A total of 249 journal articles, retrieved from the Scopus and Web of Science databases, were reviewed using an inductive analysis method.

Since its emergence in 2008, the topic of robot acceptance has gained significant attention, especially after 2019. The research assesses the current state of research, methodologies used, theoretical foundations and antecedents influencing acceptance. Five categories of antecedents affecting acceptance were systematically identified: robot design factors, individual/organisational factors, situational factors, appraisal factors and affective factors.

This study provides a comprehensive overview of the current landscape of service robot acceptance research. It offers insights and theoretical directions to advance research in the T&H domain.

本研究旨在通过全面的文献综述, 为未来旅游与酒店(T&H)领域服务机器人接受的研究提供见解与指导。

本文采用归纳分析法对检索自Scopus和Web of Science数据库的249篇期刊文章进行分析。

自2008年出现第一篇研究机器人接受的文章以来, 机器人接受话题得到了极大关注, 尤其是在2019年之后。本研究评估了研究现状、方法、理论基础及影响接受的前因。系统性地归纳出影响机器人接受的五类前因：机器人设计、个体与组织、情境、评价及情感因素。

本研究全面概述了服务机器人接受研究的现状, 为推进旅游与酒店领域的研究进展提供了深刻见解与理论指导。

Este estudio tiene como objetivo proporcionar ideas y orientación para futuras investigaciones sobre la aceptación de robots de servicio en el ámbito del turismo y la hospitalidad (T&H) a través de una extensa revisión de la literatura.

Se revisaron un total de 249 artículos académicos, obtenidos de las bases de datos Scopus y Web of Science, utilizando un método de análisis inductivo.

Desde su aparición en 2008, el tema de la aceptación de robots ha recibido una atención significativa, especialmente después de 2019. La investigación evalúa el estado actual de la investigación, las metodologías empleadas, las bases teóricas y los factores que influyen en la aceptación. Se identificaron de manera sistemática cinco categorías de antecedentes que afectan la aceptación: factores de diseño del robot, factores individuales/organizacionales, factores situacionales, factores de evaluación y factores afectivos.

Este estudio proporciona una visión integral del panorama actual del panorama actual de la investigación sobre la aceptación de robots de servicio. Ofrece ideas y direcciones teóricas para avanzar en la investigación en el ámbito de T&H.

This study aims to understand Chinese consumers’ perceived barriers to using peer-to-peer (P2P) accommodation before and after the outbreak of COVID-19 and the negotiation strategies they applied in overcoming the barriers and enabling consumption.

A qualitative research design with 28 semi-structured interviews was used. Data were analysed by content analysis.

Five psychological barriers and four functional barriers were found to inhibit consumers from using P2P accommodation both before and after the COVID-19 outbreak. In overcoming the perceived barriers, consumers applied both behavioural negotiation strategies, including seeking information, behavioural adaptation, selective choice and seeking social support, and cognitive negotiation strategies, including cognitive adaptation and trusting agents. COVID-19 was found to serve as both a barrier and a facilitator for using P2P accommodation. A barriers–negotiation framework was developed in the context.

Theoretically, this study advances consumer resistance and perceived barriers literature by integrating negotiation and developing a barriers–negotiation framework of P2P accommodation usage. This study also offers insights for practitioners in the P2P accommodation industry.

This study showcases the role of negotiation in understanding barriers to using P2P accommodation, paving the way to extend relevant knowledge to advance consumer resistance research, which is an emerging topic in the broader management domain.

To better understand what inhibits people from participating in collaborative consumption, this paper aims to develop research models of users and non-users to examine the relationship between perceived risks and intentions to use peer-to-peer (P2P) accommodation. Moreover, differences of risk perceptions and behavioral intentions between P2P accommodation users and non-users were identified.

The method of convenience sampling was used to collect data. Excluding the invalid questionnaires, 520 were kept for further analysis. In this paper, SPSS and partial least squares (PLS) were used to analyze the data.

The findings highlighted the important role of perceived risks in determining tourists’ intentions to use P2P accommodation. The results showed that non-users have higher perceived risks in regards to all four risk dimensions (psychological risk, physical risk, performance risk and social risk), and they also have significantly lower behavioral intentions to use P2P accommodation than the users. For users, only the psychological risk has significant negative effects on behavioral intentions, while for non-users, both psychological and physical risks are important inhibitors.

This paper contributes to a better understanding of the antecedents that influence consumers’ participation in collaborative consumption in the P2P accommodation domain. Second, it extends the literature on perceived risk by discussing users and non-users in this market. Finally, this research provides insights into the P2P accommodation market in China, which enables online travel platforms to develop targeted marketing strategies.

为更好了解协同消费的顾客参与，本论文构建了使用者和非使用者模型来检验其感知风险和使用P2P住宿行为意向的关系。此外，本论文也试图探讨使用者和非使用者在感知风险和行为意向方面的差异。

研究样本采取方便抽样方式，共回收520份有效问卷。本论文采用SPSS和偏最小二乘法（PLS）分析数据。

研究结果凸显了感知风险对影响游客使用P2P住宿的重要作用。P2P 住宿非使用者不仅有更高的感知风险（表现在四个风险维度上：心理风险，身体风险，性能风险和社会风险），而且比使用者有更低的使用意向。对于使用者来说，只有心理风险对行为意向有显著地影响作用；对于非使用者，心理风险和身体风险都是影响行为意向的因素。

本论文对于阻碍P2P住宿的参与因素进行了深入探讨。其次，本论文通过讨论使用者和非使用者的差异拓展了感知风险相关研究。最后，本论文研究了P2P住宿业在中国的市场情况，为在线旅行平台开发目标市场策略提供了启示。

P2P 住宿，Airbnb, 感知风险，使用者和非使用者，行为意向

By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding PDEs and constraints as soft penalties in the loss function can cause gradient imbalances, leading to training and accuracy issues. This study aims to introduce the augmented Lagrangian method (ALM) and transfer learning to address these challenges and enhance the effectiveness of PINNs for hydrodynamic lubrication analysis.

The loss function was reformatted by ALM, adaptively adjusting the loss weights during training. Transfer learning was used to accelerate the convergence of PINNs under similar conditions. Additionally, the iterative process for load balancing was reframed as an inverse problem by extending film thickness as a trainable variable.

ALM-PINNs significantly reduced the maximum absolute boundary error by almost 80%. Transfer learning accelerated PINNs for solving the Reynolds equation, reducing training epochs by an order of magnitude. The iterative process for load balancing was effectively eliminated by extending the thickness as a trainable parameter, achieving a maximum percentage error of 2.31%. These outcomes demonstrated strong agreement with FDM results, analytical solutions and experimental data.

This study proposes a PINN-based approach for hydrodynamic lubrication analysis that significantly improves boundary accuracy and the training process. Additionally, it effectively replaces the load balancing procedure. This methodology demonstrates considerable potential for broader applications across various boundary value problems and iterative processes.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0277/

In high-pressure pumps, due to the interaction of asperities on the upper and lower surfaces, the piston–cylinder interface suffers severe lubrication and sealing problems during mixed lubrication. This study aims to establish a mixed thermo-elastohydrodynamic (EHD) model for the lubrication gap to determine how working conditions affect the lubricating characteristics and sealing performance of the interface.

A mixed thermo-EHD lubrication model is established to investigate the lubricating characteristics and sealing performance of the interface between the piston and cylinder. The model considers piston tilting, thermal effect, surface roughness and bushing deformation. The interface lubricating characteristics and sealing performance under different working conditions are calculated by the proposed numerical model.

A higher inlet pressure contributes to an increase in the minimum film thickness. Increased shaft speed can significantly reduce the minimum film thickness, resulting in severe wear. Compared to roughness, the impact of the thermal effect on the interface sealing performance is more significant.

The proposed lubrication model in this study offers a theoretical framework to evaluate the lubricating characteristics and sealing performance at the lubrication gap. Furthermore, the results provide references for properly selecting piston-cylinder surface processing parameters.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0072/

Multitilting-pad journal bearings (MTPJBs) used in large-scale hydraulic turbines often suffer from complex operating conditions, which greatly influence the overall performance of the rotating machine. The purpose of this study is to establish a thermal-elastic-hydrodynamic lubrication model for MTPJBs that can predict the static and dynamic characteristics of high-speed and heavy-load MTPJBs under different operating conditions.

A thermo-elasto-hydrodynamic lubrication model considering the turbulence effect is proposed for high-speed and heavy-load TPJBs, which is solved using the coupled finite difference method and finite element method. The model considered the turbulence effect, thermal energy diffusion, viscosity–temperature–pressure relationship and elastic deformation of the pads. The influences of the operating conditions on static and dynamic characteristics of tilting pad journal bearings were analyzed in depth.

The operating conditions have a strong effect on the static properties of the bearings. The dynamic characteristics of the TPJB were the most influenced by the shaft speed. The effects of the load direction on the dynamic properties of the TPJB were much stronger than those of the static characteristics.

This study used analytical methods and models to provide theoretical guidance for evaluating lubricating characteristics, assembling conditions and overall health.

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.