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The bridge expansion joint (BEJ) is a key device for accommodating spatial displacement at the beam end, and for providing vertical support for running trains passing over the gap between the main bridge and the approach bridge. For long-span railway bridges, it must also be coordinated with rail expansion joint (REJ), which is necessary to accommodate the expansion and contraction of, and reducing longitudinal stress in, the rails. The main aim of this study is to present analysis of recent developments in the research and application of BEJs in high-speed railway (HSR) long-span bridges in China, and to propose a performance-based integral design method for BEJs used with REJs, from both theoretical and engineering perspectives.

The study first presents a summary on the application and maintenance of BEJs in HSR long-span bridges in China representing an overview of their state of development. Results of a survey of typical BEJ faults were analyzed, and field testing was conducted on a railway cable-stayed bridge in order to obtain information on the major mechanical characteristics of its BEJ under train load. Based on the above, a performance-based integral design method for BEJs with maximum expansion range 1600 mm (±800 mm), was proposed, covering all stages from overall conceptual design to consideration of detailed structural design issues. The performance of the novel BEJ design thus derived was then verified via theoretical analysis under different scenarios, full-scale model testing, and field testing and commissioning.

Two major types of BEJs, deck-type and through-type, are used in HSR long-span bridges in China. Typical BEJ faults were found to mainly include skewness of steel sleepers at the bridge gap, abnormally large longitudinal frictional resistance, and flexural deformation of the scissor mechanisms. These faults influence BEJ functioning, and thus adversely affect track quality and train running performance at the beam end. Due to their simple and integral structure, deck-type BEJs with expansion range 1200 mm (± 600 mm) or less have been favored as a solution offering improved operational conditions, and have emerged as a standard design. However, when the expansion range exceeds the above-mentioned value, special design work becomes necessary. Therefore, based on engineering practice, a performance-based integral design method for BEJs used with REJs was proposed, taking into account four major categories of performance requirements, i.e., mechanical characteristics, train running quality, durability and insulation performance. Overall BEJ design must mainly consider component strength and the overall stiffness of BEJ; the latter factor in particular has a decisive influence on train running performance at the beam end. Detailed BEJ structural design must stress minimization of the frictional resistance of its sliding surface. The static and dynamic performance of the newly-designed BEJ with expansion range 1600 mm have been confirmed to be satisfactory, via numerical simulation, full-scale model testing, and field testing and commissioning.

This research provides a broad overview of the status of BEJs with large expansion range in HSR long-span bridges in China, along with novel insights into their design.

The China-plus-one strategy is an increasingly attractive supply chain risk management strategy and has been adopted by many multinational enterprises. However, relevant research remains limited and warrants empirical validation. This paper addresses this research gap by investigating the determinants of emerging multinational enterprises’ adoption of a China-plus-one strategy.

The China-plus-one adoption decision of 1,527 firms was predicted using a logistic model based on Taiwan’s official data – Investigation of Foreign Sales and Manufacturing, for the years 2020–2022. This database is administered by the Department of Statistics of the Ministry of Economic Affairs (DSMEA) in Taiwan. Analysis was conducted using SPSS 25.0.

It is suggested that customer pressure and supplier relocation, functioning as push forces, lead to the China-plus-one strategy adopted by Taiwanese firms. Regarding pull forces, lower production cost has a positive association with China-plus-one adoption, whereas sufficient local workforce supply does not have a significant effect. Finally, in terms of the mooring forces, local sales performance is negatively related to the strategy adoption, whereas local access to components shows a positive association with such strategy.

This research uniquely adopts the push–pull–mooring framework to examine factors affecting supply chain restructuring in international business, representing a novel domain for this framework.

This study aims to widen the understanding of how remote work shapes the feedback environment by examining the perceptions of leaders and subordinates of daily, dyadic feedback interactions. The emphasis is on understanding how reciprocity within leader-member exchange (LMX) relationships manifests and how it influences the feedback dynamics.

Template analysis of a qualitative data set consisting of 81 semi-structured interviews with leaders (n = 29) and remote working subordinates (n = 52) was performed.

Drawing on the theoretical frameworks of the feedback environment and the leader-member exchange, the findings demonstrate the imbalance between the efforts of leaders and subordinates in building and maintaining a favourable feedback environment in the remote work context. The results of this study highlight the importance of the dyadic nature of feedback interactions, calling for a more proactive role from subordinates.

Given the estimation that the COVID-19 pandemic has permanently changed the way organizations work, leaders, subordinates and HR practitioners will benefit from advancing their understanding of the characteristics of dyadic, daily feedback interaction in remote work.

Qualitative research on feedback and leader-member exchange interactions in remote work that combines the perceptions of leaders and subordinates is sparse.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

The purpose of the paper is to understand whether, why and when role overload predicts an organization-directed CWB: cyberloafing. To do so, we utilize the Stressor-Emotion–CWB theory and social exchange theory.

The authors collected data from 506 employees working in five Information Technology (IT) firms in India using a multiwave survey design. Results revealed support for all the hypotheses.

This study found that negative affect partly carries the influence of role overload to cyberloafing. Secondly, the results indicate that role overload has a direct and positive relationship with cyberloafing. Third and finally, we found that narcissism moderates the positive relationship between role overload and negative affect, followed by cyberloafing.

This paper showcases that role overload can evoke cyberloafing both as a coping mechanism and a retaliatory response directed at the organization. This paper further demonstrates that employees high in narcissism are vulnerable to stressors like role overload, and as a result, they experience more negative emotions and engage in more cyberloafing.

Digital platform work monitored by algorithms is increasingly supplementing or substituting standard employment. Though gig workers are faced with the vulnerable, fragile and precarious digital platform work environment, the reason why gig workers remain highly willing to show good task performance has been so far unexamined. Building upon the reciprocity of the social exchange theory, this study aims to explore the antecedents and boundary condition of facilitating gig workers’ task performance.

First, to minimize common method variance, decline spurious mood effects and ensure data robustness, we conducted a two-wave time-lagged survey and collected 269 survey responses from gig workers on different gig platforms in China (e.g. Meituan, Eleme, Didi, Credamo, Zaihang) at two time nodes. Second, abiding by two stage procedures of the PLS-SEM (partial least square structural equation model) approach, we analyzed a moderated mediation model in the digital platform work context.

Results present that both platform work remuneration and flexibility help gig platforms develop an affective trust relationship with gig workers, thus encouraging them to repay the platform by performing platform tasks well. Algorithmic monitoring shows a “double-edged sword” moderating role since it weakens the indirectly positive relationship between platform work remuneration and task performance via affective trust but enhances the indirectly positive relationship between platform work flexibility and task performance via affective trust.

Understanding the importance of remuneration and flexibility in developing affective trust can help platforms design effective human resource management (HRM) strategies that enhance worker motivation of maintaining high engagement and performance under precarious working conditions. Additionally, optimizing the “double-edged sword” moderating role of algorithmic monitoring makes it more humanized, enhancing the efficiency with these HRM strategies and making both workers and platforms beneficial.

These findings offer an affective trust-based explanation for the mechanism of maintaining high work performance motivation in the nonstandard and precarious employment from the social exchange perspective, while understanding the (de)humanized aspect of algorithmic monitoring by revealing its “double-edged sword” moderating role.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

In an era marked by growing environmental concerns, businesses are increasingly recognizing the importance of Corporate Social Responsibility (CSR) in influencing customer behavior, particularly in the context of sustainability and green practices. This paper aims to examine the impact of Corporate Social Responsibility (CSR) on Customer Green Behavior (CGB) through the mediating role of corporate image, service quality, customer trust and customer satisfaction.

A convenient sampling technique was employed to collect the data sample. A total of 741 questionnaires were distributed across four different hotel sectors in China. By using Structural Equation Modeling, the results suggest that CSR significantly influences CGB. Moreover, corporate image, service quality, customer trust and customer satisfaction show a partial mediating effect in the relationship between CSR and CGB.

The study findings suggest that the hotel industry should invest in CSR initiatives to enhance CGB by conducting pro-environmental activities. This study emphasizes how important CSR initiatives are in encouraging customers to adopt eco-friendly behavior. Overall, the results of this study extend the understanding of CSR, CGB, corporate image, service quality, customer trust and customer satisfaction in the context of the hotel industry and offer theoretical and managerial implications for developing and developed economies.

The originality value of this research lies in its comprehensive examination of the mediating effects of corporate image, service quality, customer trust and customer satisfaction on the relationship between CSR and CGB in the hotel industry. Furthermore, the study’s focus on the specific context of China adds novel insights to the existing literature on CSR and CGB. Discussions, limitations and research suggestions for future study are also provided.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

This paper aims to study whether rural collective construction land marketization (RCCLM) affects pension industry and analyze its impact mechanism.

Based on the theory of planned behavior (TPB) and social cognition theory (SCT), this paper constructs a theoretical framework for the purchase behavior of rural retirement housing with the influence of RCCLM. A mixed-methods investigation combining qualitative and quantitative study is adopted in this paper.

The research results indicate that the purchase intention of rural retirement housing has a significant positive impact on the purchase behavior. However, RCCLM has a significant negative impact on the purchase intention of rural retirement housing. A logical framework of “land system participant behavior” has been constructed from three main bodies: government, developers and elderly urban and rural residents.

This paper provides suggestions for the three entities from the perspectives of macro, medium and micro-level to improve transaction system for rural collective construction land use right (TSRCCLUR), providing references for the collective construction land marketization policy and the development of the pension industry.

This paper deepens the study of behavior intention in planned behavior, enriches TPB model in the study of rural retirement housing security and clarifies the influence mechanism of the rural retirement housing purchase intention with the theoretical and empirical test of the model.