Search results

This paper aims to investigate the effects of inhomogeneities on the rolling contact fatigue (RCF) life in elastohydrodynamically lubricated (EHL) point contacts.

A numerical model for predicting the RCF life of inhomogeneous materials in EHL contacts was established by combining the EHL model and the inclusion model through the eigen-displacement and then connecting to the RCF life model through the subsurface stresses. Effects of the type, size, location and orientation of a single inhomogeneity and the distribution of multiple inhomogeneities on the RCF life were investigated.

The RCF life of a half-space containing manganese sulfide (MnS) inhomogeneity or the mixed inhomogeneity of aluminium oxide (Al₂O₃) and calcium oxide (CaO) was longer than that for the case of Al₂O₃ inhomogeneity. For a single ellipsoidal MnS inhomogeneity, increases of its semi-axis length and decreases of its horizontal distance between the inhomogeneity and the contact center shortened the RCF life. Furthermore, the relationship between the depth of a single MnS inhomogeneity and the RCF life was found. For the half-space containing multiple inhomogeneitites, the RCF life decreased remarkably compared with the homogeneous half-space and showed discreteness.

This paper implements the prediction of the RCF life of inhomogeneous materials under EHL condition.

The current study aims to examine the antecedents and outcomes of emotional labor strategies (i.e. surface acting and deep acting) among service employees in China. The study proposed employees’ perceived closeness with customers and customers’ socioeconomic status will predict deep acting and surface acting, respectively. It further examined the mediating role of emotional labor between perceived customer attributes and employee well-being (i.e. burnout and job satisfaction).

One hundred and one employees at a jewelry store in China completed a survey regarding their perceptions of customers, use of emotional labor and well-being (e.g. job satisfaction and burnout). Correlational and regression analyses were conducted to examine the predictors and outcomes of different emotional labor strategies.

Perceived closeness with the customer group predicted employees’ use of deep acting, whereas perceived customer socioeconomic status did not predict the use of surface acting. Deep acting was negatively related with burnout, whereas surface acting did not predict burnout. Deep acting mediated the relationship between perceived closeness with customers and burnout.

To maintain employee well-being, organizations can promote a service climate to enhance employees’ perceived relationship with customers.

The study specifies the interpersonal context in which employees use different emotional labor strategies; the perceived closeness with customers predicts less burnout via the use of more deep acting. This study also supplements the existing research on emotional labor based on a Chinese sample; deep acting predicts employee well-being.

The elevator installation in old communities (EIOC) can effectively improve the public infrastructure of urban communities. However, differences in the decision-making behaviours of stakeholders lead to frequent conflicts, thereby hindering the implementation of EIOC. The purpose of this study is to explore the decision-making behavior of core stakeholders which are the government, community owners and elevator enterprises at different stages in the EIOC using the evolutionary game method.

A tripartite evolutionary game model involving the government, community owners and elevator enterprises was developed, and their evolutionary stabilisation strategies were explored in different stages. The dynamic change of the stakeholders' decision-making behaviours at different stages of the project and the influencing mechanism of the key factors on the decision-making behaviours of the three stakeholders were analysed through numerical simulation.

The results of this study showed that: Divergent interests led the government, community owners and elevator enterprises to adopt distinct decision-making behaviours at different stages, resulting in diverse attitudes and actions among stakeholders. A dynamic reward and penalty mechanism effectively motivated community owners and elevator enterprises to engage actively, fostering broad participation. However, the high regulatory cost diminished the government's regulatory effectiveness. This imbalance between penalties and incentives posed a challenge, impacting the overall effectiveness and efficiency of implementing the EIOC.

Existing research lacks exploration of the decision-making behaviours of stakeholders in community public infrastructure. This study developed a dynamic tripartite evolutionary game model in the EIOC from the gaming perspective. The results of this study provide a reference for dealing with the stakeholders' interests in the community public infrastructure and contribute to the theoretical basis for establishing an effective supervision mechanism.

National identity has a profound impact on building a modern state, maintaining social stability and promoting economic development. Based on three waves of data collected from the World Values Survey (WVS) in Hong Kong between 2005 and 2018, this study aims to examine the changes in the national identity awareness of Hong Kongese over time.

The data used in this paper originate from the WVS. The WVS is a cross-country time-series survey that has been carried out in seven waves in 85 countries around the world, since 1981. There are three waves of data involving Hong Kong, which were obtained from the surveys in 2005, 2014 and 2018.

This study examined the changes in the national identity awareness of Hong Kongese over time and found that this has shown both continuity and rupture. Extreme groups lacking national identity have emerged and become more common over the decades and the elites’ national identity is much stronger than that of the lower and middle classes. It also shows that political trust, social capital, subjective well-being and possession of authoritarian personality have strong explanatory power for the changes in Hong Kongese national identity over time, but their explanatory strength varies across eras.

Based on three waves of surveys conducted by the WVS in Hong Kong in 2005, 2014 and 2018, respectively, this paper charts these changes over time and explores the differences in how they are influenced by political trust, social capital, subjective well-being and authoritarian personality.

This paper aims to investigate the preparation of AlN and Al₂O₃, as well as the effect of nano-AlN and nano-Al₂O₃, on friction and wear properties of copper-steel clad plate immersed in the lubricants.

Nano-AlN or nano-Al₂O₃ (0.1, 0.2, 0.3, 0.4 and 0.5 Wt.%) functional fluids were prepared. Their tribological properties were tested by an MRS-10A four-ball friction tester and a ball-on-plate configuration, and scanning electron microscope observed the worn surface of the plate.

An increase in nano-AlN and Al₂O₃ content enhances the extreme pressure and anti-wear performance of the lubricant. The best performance is achieved at 0.5 Wt.% of nano-AlN and 0.3 Wt.% of nano-Al₂O₃ with PB of 834 N and 883 N, a coefficient of friction (COF) of approximately 0.07 and 0.06, respectively. Furthermore, the inclusion of nano-AlN and nano-Al₂O₃ particles in the lubricant enhances its extreme pressure performance and reduces wear, leading to decreased wear spot depth. The lubricating effect of the nano-Al₂O₃ lubricant on the surface of the copper-steel composite plate is slightly superior to that of the nano-AlN lubricant, with a COF reaching 0.07. Both lubricants effectively fill and lubricate the holes on the surface of the copper-steel composite plate.

AlN and Al₂O₃ as water-based lubricants have excellent lubrication performance and can reduce the COF. It can provide some reference for the practical application of nano-water-based lubricants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0255/

This study aims to explore the antecedents of bootlegging from the perspective of paradoxical leadership. Based on the theory of planned behavior (TPB), it examines a multiple mediation model with harmonious innovation passion, role breadth self-efficacy and perceived error management culture as mediators, to interpret why paradoxical leadership influences employee bootlegging.

To test the theoretical model, data were collected from 218 full-time employees from enterprises in Chinese cities using a three-wave time-lagged design. Path-analysis and a bootstrapping approach in Mplus7 were used to examine the hypotheses of the theoretical model.

The results show that paradoxical leadership has a positive influence on bootlegging. In the multiple mediation model, the effect paths of harmonious innovation passion and role breadth self-efficacy are significant but there is an insignificant difference in their power, while the effect path of perceived error management culture is insignificant, although it has a significant simple mediating effect and sequential mediating effect.

This study is among the first to show the influence of paradoxical leadership on bootlegging, responding to the research call to use the paradoxical factors to capture the antecedents of innovative behaviors. Second, this study enriches the outcomes of paradoxical leadership, that of bootlegging. Third, this study provides a TPB-based mechanism of how paradoxical leadership promotes bootlegging by increasing employees’ harmonious innovation passion, role breadth self-efficacy and perceived error management culture. This provides a new theoretical perspective to explain the relationship between paradoxical leadership and employee bootlegging. It also responds to the call for exploration of the multiple pathways of leadership.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model.

The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated.

The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations.

This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm.