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Supervisor consultation is conducive to promoting employee voice. This research examines an underlying mediating mechanism and a boundary condition that strengthens the influence of supervisor consultation.

Two-wave survey data were collected from 302 full-time employees. Path analysis was adopted to test the hypotheses.

Supervisor consultation is positively related to employee voice. Employee felt obligation for constructive change (FOCC) serves as a mediating mechanism. Supervisor consultation is more positively related to employee FOCC and subsequent voice when managerial openness is higher than lower.

This study highlights the incremental mediating effect of FOCC beyond that of two alternative mediators between supervisor consultation and employee voice and the moderating role of managerial openness in affecting the influence of supervisor consultation.

The purpose of this study is uncovering the connotative and symbolic meaning of “luxury-lite brands” [轻奢].

Applying mixed methods, this study conducted two studies: (1) a semiotic analysis of a focus group discussion and 10 interviews on luxury-lite brands and (2) a content analysis of 248 Weiblog posts from 10 luxury-lite brands in a two-month period.

Study 1 showed that luxury-lite brands are interpreted as foreign brands that serve people's needs for social presence, and symbolize youthfulness, tastefulness, and aspirations. Other descriptors of luxury-lite brands included unique design, and less than the best quality offered by luxury brand. Study 2 suggested brands are missing out on a wide range of stories that resonate with their core segments in their social media advertising.

Based on the definition of luxury-lite brands in the context of China proposed by the researchers, we recommend that managers broaden topics of stories, make more effort to create desirable symbolic brand meaning, and use social media to excite these young crowds.

Luxury-lite brands have been a cultural sign in the Chinese market projected to grow into an over 90 billion USD business by 2025. Therefore, an insightful understanding of the masstige market of China is a must for any Western masstige brand to be successful and competitive.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

Social network sites are becoming more visual-centric. The purpose of this paper is to examine the effectiveness of visual social network sites as a marketing platform.

This paper comprises two experiments. The first experiment sought to examine the effect of social influence; while the second experiment examined the effect of image quality on the perception of quality and purchase intention.

The first experiment showed that social influence did not affect perceived quality or purchase intention. In the second experiment, a one-way between-groups multivariate analysis of covariance (MANCOVA) adjusting for sport involvement showed a statistically significant difference between the two groups. Respondents in the experimental group reported higher levels of perceived quality (adjusted M=3.68) and purchase intention (adjusted M=3.23) when compared to the control group’s perceived quality (adjusted M=3.12) and purchase intention (adjusted M=2.17).

One of the limitations of this study is that it only examined the effects through a single social network site, i.e. Instagram. As there are other visual-centric social network sites, such as Tumblr and Pinterest, which operate slightly different from Instagram, it remains to be established how such effects vary across these social network sites.

The results suggest that social influence on Instagram is limited and marketers should invest in images of high quality when marketing on visual social network sites.

While there are many studies examining the effectiveness of marketing on social network sites, these studies have primarily focussed on earlier social network sites, such as Facebook. Newer social network sites that are more visual-centric, such as Instagram, are different from earlier social network sites and studied to a lesser extent. This study adds insights on the marketing effectiveness of visual-centric social network sites and deepens the understanding on marketing in general on social media.

To solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction (PSR) theory and intelligent diagnosis techniques to extend the one-dimensional vibration signal to the high-dimensional phase space to reveal the system information implied in the univariate time series of the vibration signal.

In this paper, a new method based on the PSR technique and convolutional neural network (CNN) is proposed. First, the delay time and the embedding dimension are determined by the C-C method and the false nearest neighbors method, respectively. Through the coordinate delay reconstruction method, the two-dimensional signal is constructed, and this information is saved in a set of gray images. Then, a simple and efficient convolutional network is proposed. Finally, the phase diagrams of different states are used as samples and input into a two-dimensional CNN for learning modeling to construct a PSR-CNN fault diagnosis model.

The proposed PSR-CNN model is tested on two data sets and compared with support vector machine (SVM), k-nearest neighbor (KNN) and Markov transition field methods, and the comparison results showed that the method proposed in this paper has higher accuracy and better generalization performance.

The method proposed in this paper provides a reliable solution in the field of rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0113/

The purpose of this paper is to demonstrate how an organized framework for risk management called Risk Navigator SRM^© can be practically applied to common risk management problems.

The program makes complex risk principles easier to understand and access by linking together disparate and difficult risk management concepts into a single strategic risk management (SRM) framework. The strategic framework is organized into ten steps in order to organize and develop a practical and applied risk management plan. This paper demonstrates the SRM process in a crop insurance example. A simple version of the program, called Risk Navigator Lite^©, is also applied to crop insurance to demonstrate how robust the framework is for adaptation to field settings, where data may be limited or where decision makers might have limited capacity to understand complicated principles necessary for risk management.

This manuscript elaborates upon how the SRM process may be effectively implemented by agricultural producers. The information herein should also help students of risk management better comprehend how to apply what they learn.

The contribution of Risk Navigator is to make underused, sophisticated risk management concepts and tools more available to farm and ranch managers, and others, by putting them into a framework that is both easy to use and comprehensive.

This study aims to prepare thymol-based deep eutectic solvents (DESs) and use them as lubricates for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricants were investigated and the anti-wear mechanism of thymol-based DESs was discussed.

The findings demonstrate that because of the formation of a fluid lubrication film and excellent kinematic viscosity, the lubrication effect of the prepared DES is improved by about 50% compared to the base lubricating oil. The prepared [Ch]Cl-thymol DES has a better anti-friction and lubrication effect than citric-thymol, EG-thymol and urea-thymol DESs, with an average friction coefficient of about 0.04.

In this work, the friction reduction properties of thymol-based DESs were studied as lubricants for the first time, and the lubrication mechanism of sample materials was investigated.

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.

The purpose of this study is to prepare ZnFe₂O₄ nanospheres, sheet MoS₂ and three ZnFe₂O₄@MoS₂ core-shell composites with various shell thicknesses, and add them to the base oil for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricant additives were investigated and the effects of shell thickness and sample concentration on the tribological properties of core–shell composite lubricant additives were discussed.

The findings demonstrate that each of the five sample materials can, to varying degrees, enhance the lubricating qualities of the base oil and that the core–shell nanocomposite sample lubricant additive has superior lubricating properties to those of ZnFe₂O₄ and MoS₂ alone, among them ZnFe₂O₄@MoS₂-2 core–shell composites with moderate shell thickness performed most ideally. In addition, the optimal concentration of the ZnFe₂O₄@MoS₂ lubricant additive was 0.5 Wt.%, and a concentration that was too high led to particle deposition and affected the friction effect.

In this work, ZnFe₂O₄@MoS₂ core–shell composites were synthesized for the first time using ZnFe₂O₄ as the carrier and the lubrication mechanism of core–shell composites and single materials were compared and studied, which illustrated the advantages of core–shell composite lubricant additives. At the same time, the influence of different shell thicknesses on the lubricant additives of core–shell composites was studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2022-0367/

The purpose of this research is to achieve automatic and accurate book purchase forecasts for the university libraries and improve efficiency of manual book purchase.

The authors presented a Book Purchase Forecast model with A Lite BERT(ALBERT-BPF) to achieve their goals. First, the authors process all the book data to unify format of books' features, such as ISBN, title, authors, brief introduction and so on. Second, they exploit the book order data to label all books supplied by booksellers with “purchased” or “non-purchased”. The labelled data will be used for model training. Last, the authors regard the book purchase task as a text classification problem and present a model named ALBERT-BPF, which applies ALBERT to extract text features of books and BPF classification layer to forecast purchased books, to solve the problem.

The application of deep learning in book purchase task is effective. The data the authors exploited are the historical book purchase data from their university library. The authors’ experiments on the data show that ALBERT-BPF can seek out the books that need to be purchased with an accuracy of over 82%. And the highest accuracy reached is 88.06%. These indicate that the deep learning model is sufficient to assist the traditional manual book purchase way.

This research applies ALBERT, which is based on the latest Natural Language Processing (NLP) architecture Transformer, to library book purchase task.