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Most prior studies treated human resource management (HRM) strength as a whole, while neglecting the dynamic interactions between distinct components (consensus, consistency and distinctiveness). The authors lack a deep understanding of how different components operate together to influence burnout. To address these gaps, this study aims to adopt signaling theory to investigate the interactions among different components and their impacts on employee burnout.

The authors collected time-lagged data from 231 full-time employees in manufacturing firms in Suzhou, China. The authors used the PROCESS Model 6 and hierarchical multiple regression to analyze the data.

This study found that HRM system consensus and consistency mitigate employee burnout, whereas HRM distinctiveness is not significantly related to burnout. Furthermore, the authors revealed that HRM system consistency (rather than distinctiveness) mediated the relationship between consensus and burnout. Moreover, the authors found the sequential mediating effects of HRM system distinctiveness and consistency on the association between consensus and burnout.

Considering that employees’ well-being problems may be debilitating and overwhelming during the COVID-19 pandemic, it is particularly ethical and timely for managers to direct attention to the role of HRM system strength in addressing employee burnout.

This study advances the HRM system literature by teasing out the interactions between the three pivotal components of HRM strength. Our study is among the first to empirically investigate the internal relationships between the meta-features of the HRM system and employee burnout. In doing so, the authors develop a more nuanced understanding of the collective nature of a strong HRM system that conveys a shared message about HRM to promote well-being.

The literature on information technology outsourcing (ITO) prioritises monetary considerations and overlooks human aspects. This qualitative study adopts a contextualised approach to address a research gap in understanding agency workers’ intent to stay in the ITO sector.

In contrast to previous studies that focus on intra-organisational factors and use quantitative designs, this study takes a qualitative approach. It analyses data from 85 in-depth interviews with agency workers in the Chinese ITO supply chain and project managers of supplier and client firms.

The study constructs an integrated framework covering 15 factors at three levels and shows how they interact to influence Chinese agency workers’ intent to stay in the ITO supply chain. Variations in outsourcing management styles and practices among U.S., Japanese and Chinese client firms are presented to enrich the understanding of outsourcing dynamics in a global context.

This study contributes to the ITO literature by providing new insights into the retention of highly skilled agency workers and deepening the contextual understanding of this issue, throwing light on the human aspects often overshadowed by monetary considerations.

The study’s purpose is to investigate the constraints and facilitators influencing skiing participation in Beijing. This research includes three segments based on the frequency of skiing participation (i.e. non-, low-frequency-, and high-frequency skiers). By doing so, the study offers an enhanced understanding of the Chinese skiing market and unveils insights assisting industry professionals to effectively address their customers' diverse needs and expectations.

An online survey was developed based on prior research and consisted of four sections: (1) skiing participation; (2) constraints; (3) facilitators; (4) demographics. Items in the constraint and facilitator scale were measured using a 7-point Likert scale. A total of 409 participants completed the survey. The participants included 137 non-skiers, 134 low-frequency skiers, and 138 high-frequency skiers.

Through an exploratory factor analysis, three constructs emerged: general constraints, facilitators and learning constraints. As expected, facilitators were a positive predictor of skiing participation. Importantly, the emergent construct of learning constraints was a negative predictor of skiing and yet, the construct of general constraints was insignificant. Furthermore, the three segments differ significantly in household status, income, and education level.

These results support previous research noting the relevance in skiing participation of the dimensions: facilitators and learning constraints. The findings point to the need for ski resorts in Beijing to offer instructional sessions for beginners so they may become familiar with skiing fundamentals and enhance their confidence, particularly among nonskiers and low-frequency skiers.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

As a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.

This study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.

First, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.

This paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.

The advancements in generative artificial intelligence (GenAI) encourage disruptive transformation in the hospitality industry. Previous discussions predominantly focused on the impact of AI-powered agents on the labor force. This research extends previous studies by investigating the feasibility of GenAI as an information search agent in comparison to the predominant role of search engines.

Based on the Tourist Online Information Search Behavior framework, the authors proposed that consumers’ GenAI adoption may vary upon search purpose (search type), individual differences (travel motive) and situational differences (GenAI task-oriented customization level). Four studies with a total number of 813 participants were conducted.

Taking GenAI over traditional search engines for pre-trip information search significantly increased with a non-decision-based (vs decision-based) purpose. To enhance the adoption of GenAI in its less effective but more important decision-based situations, the authors proposed and confirmed the incremental effect of utilitarian travel motives and task-oriented customization levels.

This study highlights GenAI’s potential as an information communication technology (ICT). This encourages tourism and hospitality businesses to consider integrating GenAI to strengthen ICT services. Moreover, search type, travel motive and task-oriented customization level are important in deploying GenAI for ICT improvement.

This study deepens the understanding of GenAI adoption in the tourism and hospitality sector by elaborating on the GenAI-as-ICT perspective and offers fresh insights into AI for pre-trip or pre-consumption information search.

Transporting hydrogen through natural gas pipelines in blended compositions has been proven to be a highly feasible solution in the short term. However, under hydrogen-rich environments, steel structures are prone to hydrogen-induced damage (HID). Additionally, uncertainties in various parameters can significantly impact the performance evaluation of hydrogen pipelines. Efficient reliability and sensitivity analyses of medium- to high-strength steel pipelines considering HID have become a challenge. Therefore, the primary aim of this study is to address this issue.

This study first establishes reliability analysis models for medium- to high-strength steels, represented by X65 and X80. In these models, the effect of HID is expressed by reduced stress, and its statistical parameters are calculated. Then, a highly efficient enhanced first order reliability method (FORM) is proposed for pipeline reliability analysis. This method overcomes the oscillation and convergence issues of traditional FORM when dealing with certain problems and can compute negative reliability indices. The proposed reliability analysis method is applied to solve the constructed reliability models. Finally, a reliability sensitivity analysis is conducted on the models to identify the key variables affecting the reliability of medium- to high-strength steel pipelines under HID.

First, two reliability analysis examples are used to validate the effectiveness of the proposed enhanced FORM. Then, using this method to solve the constructed reliability models for X65 and X80 steel pipelines under HID reveals that, for both types of steel, the reliability indices decrease significantly when considering HID compared to cases without HID. The decline is more pronounced for X80 steel than for X65 steel. As internal pressure increases, the reliability of both steels drops sharply, showing a concave parabolic trend. Moreover, the reliability sensitivity analysis shows that at a pressure of 10 MPa, for both X80 and X65, internal pressure, pipeline wall thickness and model error are the top three factors influencing reliability. As internal pressure increases, its influence becomes stronger, while the impact of other variables diminishes. Notably, for X80 steel, the presence of hydrogen amplifies the effect of internal pressure on pipeline reliability compared to when HID is not considered, but for X65, this trend is reversed.

Given the urgent need for safety evaluation studies on hydrogen transport through natural gas pipelines, this research provides new insights by constructing reliability models for X65 and X80 pipeline steels under HID and introducing an enhanced FORM method. The results of the reliability and sensitivity analyses of the models offer valuable insights and serve as a reference for engineering design.