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Quantitative fretting wear prediction is of practical significance for industrial components. This study aims to establish a fretting wear model considering the formation of tribolayers and provide better fretting wear prediction.

Based on the characteristics for the formation of tribolayers, the ratio of fretting amplitude to nominal contact area length in the fretting direction is used to characterize their formation and contribution to the wear volume. The wear volume is then associated with the product of the friction energy and the ratio of fretting amplitude to nominal contact area length.

Better prediction in the wear volume can be achieved with the proposed fretting wear model by taking the formation of tribolayers into consideration.

The contribution of the formation of tribolayers to the wear volume is considered in the model and better prediction can be achieved.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2023-0004/

A finite element method (FEM) model of the frictional behavior of two rough surfaces with a group of third-body particles confined by the surface asperities is established. By monitoring the stress distribution, friction force and the displacement of the surfaces, how the frictional instability is induced by these particles is studied. This modeling job aims to explore the relation between the meso-scale behavior and the macro-scale frictional behavior of these particles.

By using FEM, a 2D model of two frictional rough surfaces with a group of elastic or elasto-plastic particles confined by surface asperities is established. The Mises stress, macro friction force and displacements of elements are monitored during compressing and shearing steps.

The macro friction coefficient is more stable under higher pressure and smaller under higher shearing speed. The dilatancy of the interface is caused by the elevation effect of the particles sheared on the peak of the lower surface, particles collision and third body supporting. The combined effect of particles motion and surface–surface contact will induce high-frequency displacements of surface units in restricted direction.

Previous studies about third-body tribology are mainly concentrated on the frictional behavior with large number of particles distributed homogeneously across the interface, but this paper focuses on the behavior of third-body particles confined by surface asperities.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2019-0544/

This paper aims to improve the tribological performance of grease-lubricated spherical plain bearings (SPBs) under heavy load, dimple-type textures were prepared by laser on the outer surface of the inner ring. The influence of roughness parameters of a textured surface on reducing friction coefficient and temperature rise was also explored.

This study adopts a laser processing method to fabricate dimple-type textures. Three-dimensional roughness parameters were used to characterize the textured surfaces. The friction coefficients of five SPBs with surface texture and one original commercially available SPB without surface texture under different nominal loads were measured on a self-established test rig. The data of temperature rise were obtained by nine embedded thermal couples.

The results indicate that SPBs with textures generally exhibit lower friction coefficients than the original SPB without textures. The dimple depth has a significant influence on improving the tribological performance, which coincides with the analysis by surface roughness parameters. A textured surface with negative Ssk and high Vvc has the minimum temperature rise.

As it is too difficult to arrange sensors into heavy-load SPBs, there are few reports about the temperature characteristics. Through nine embedded thermal couples, the distribution of temperature rise on the inner ring of SPBs was given in this study. The positive effect of surface texture on reducing temperature rise and friction coefficient was verified, which is beneficial for the design of heavy-load SPBs.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

The purpose of this study is to explore the effect of two types of presentation formats (textual and imagery) involving online restaurant reviews (ORRs). The study examines ORRs on the basis of review usefulness and review enjoyment; the study investigates the effects of the two presentation formats on review usefulness and review enjoyment.

Data from 1,776 reviews were collected from Yelp.com using a Web data-harvesting technique. Hypothesized relations in the model were tested by Tobit regression analysis.

Empirical results show that different formats of ORRs affect review usefulness and review enjoyment in different manners; whereas both attributes of textual format (review length and readability) affect review usefulness, both aspects of imagery format (physical environment images and food and beverage images) are positively related to review enjoyment. However, review length and food and beverage images are the most important factors affecting both review usefulness and review enjoyment. In particular, the relation between the two formats of ORRs and review usefulness is mediated by review enjoyment.

The focus of this study is the two formats of ORRs in terms of dual coding theory and the mediating role of review enjoyment. The findings help online review website organizers manage the operation of various review formats in a proper manner. Managers can effectively select those formats that would achieve the desired effect.

Unlike previous studies on the relation of attributes to online reviews, this study examines the perceptions of ORRs’ usefulness and enjoyment. In addition, this study encompasses diverse ORR factors (review length, review readability, physical environment images, food and beverage images) for a more comprehensive interpretation of ORRs.

This study investigates the convergence-of-interest and entrenchment effects on the relationship between managerial ownership and financial distress using evidence from the Chinese stock market. It also analyzes whether the relationship is mediated by research and development (R&D) investment.

Using a dataset consisting of 19,059 firm-year observations of Chinese listed companies in the Shanghai and Shenzhen Stock Exchanges between 2010 and 2020, this study employs both piecewise and curvilinear models.

The results indicate that managerial ownership has a negative association with firm financial distress in both the low (below 12%) and high (above 18%) convergence-of-interest regions of managerial ownership, suggesting that managerial ownership in this region may contribute to improve firm financial status. Meanwhile, managerial ownership has a positive association with firm financial distress in the entrenchment region (12–18%), implying that managerial ownership in the entrenchment region may contribute to impair firm financial status. Furthermore, the results show that R&D investment mediates the association between managerial ownership and financial distress.

This study is the first to provide evidence of a nonlinear relationship between managerial ownership and financial distress, and identify the entrenchment region in the Chinese setting.

The purpose of this paper is to explore the key success factors behind Bangtan Boys’ (BTS) popularity, and how they can contribute to sustaining it, along with detailed strategies for the success of global pop.

This study adopts a mixed-methods approach that uses text mining and interviews and uses the success of BTS to find the key factors accounting for its sustained popularity. For use in text mining, we collected data related to BTS from social network sites (SNS) and analyzed this data using latent Dirichlet allocation (LDA) topic modeling, term frequency analysis and keyword extraction. In addition, we conducted interviews to explore the key factors accounting for the sustained popularity of BTS.

We found ten key success factors—active global fandom, SNS communication, fans' loyalty, empathy through music, storytelling and world view, performance quality, music video quality, overseas expansion at an early stage, efforts for self-development and teamwork among members— for a global pop group's success and sustained popularity.

This study contributes to the literature by finding key factors for success and sustained popularity of a global group through using a mixed-methods approach.

Our results suggest strategies to sustain the popularity of global groups and its potential to benefit across the entertainment industry.

This study is among the first to comprehensively examine the key factors for Korean pop’s (K-pop) sustained popularity by using a mixed-methods approach of text mining and interviews.

Information and communication technologies (ICTs) are a key player in the food services and restaurants sector; thus, the aim of this work consists in studying the previous research on ICTs in food services and restaurants in the context of tourism and hospitality through a systematic literature review.

The systematic literature review is performed on full papers published in journals included in the Journal Citation Report of the WoS in the category of Hospitality, Leisure, Sport and Tourism. A total of 165 articles from 28 journals are analyzed, following different criteria, such as the research methods, perspectives, statistical techniques, geographical focus, topics, technologies, authors and universities.

The restaurant sector is more and more based on the creation of experiences and ICTs, through their multiple possibilities, can undoubtedly contribute to adding value to the simple meal and create and recreate experiences to attract and retain customers who are increasingly sophisticated and hooked on ICTs. ICTs are basic for managers taking decision at the highest level in food services and restaurants, so ICTs should not be seen as a technical tool but as an essential element for top management.

This paper examined articles from very well-known tourism and hospitality journals, leaving aside others as well as different publication formats such as books or papers presented at conferences.

A significant contribution made with this paper is the availability of a list of topics in the context of ICTs in food services and restaurants. These topics are classified into three areas (Consumers, Suppliers and Environment and Tendencies) that can serve as a future research framework. The paper also provides useful information to restaurant managers about ICTs, to researchers for their future projects and to academics for their courses.

Using the resource-based and the resource dependence theoretical approaches of the firm, the paper explores firm responses to supply chain disruptions during COVID-19. The paper explores how firms develop localization, agility and digitization (L-A-D) capabilities by applying (or not applying) their critical circular economy (CE) and blockchain technology (BCT)-related resources and capabilities that they either already possess or acquire from external agents.

An abductive approach, applying exploratory qualitative research was conducted over a sample of 24 firms. The sample represented different industries to study their critical BCT and CE resources and capabilities and the L-A-D capabilities. Firm resources and capabilities were classified using the technology, organization and environment (TOE) framework.

Findings show significant patterns on adoption levels of the blockchain-enabled circular economy system (BCES) and L-A-D capability development. The greater the BCES adoption capabilities, the greater the L-A-D capabilities. Organizational size and industry both influence the relationship between BCES and L-A-D. Accordingly, research propositions and a research framework are proposed.

Given the limited sample size, the generalizability of the findings is limited. Our findings extend supply chain resiliency research. A series of propositions provide opportunities for future research. The resource-based view and resource-dependency theories are useful frameworks to better understanding the relationship between firm resources and supply chain resilience.

The results and discussion of this study serve as useful guidance for practitioners to create CE and BCT resources and capabilities for improving supply chain resiliency.

The study shows the socio-economic and socio-environmental importance of BCES in the COVID-19 or similar crises.

The study is one of the initial attempts that highlights the possibilities of BCES across multiple industries and their value during pandemics and disruptions.