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Safety training can effectively facilitate workers’ safety awareness and prevent injuries and fatalities on construction sites. Traditional training methods are time-consuming, low participation, and less interaction, which is not suitable for students who are born in Generation Z (Gen Z) and expect to be positively engaged in the learning process. With the characteristic of immersive, interaction, and imagination, virtual reality (VR) has become a promising training method. The purpose of this study is to explore Gen Z students’ learning differences under VR and traditional conditions and determine whether VR technology is more suitable for Gen Z students.

This paper designed a comparison experiment that includes three training conditions: VR-based, classroom lecturing, and on-site practice. 32 sophomore students were divided into four groups and received different training methods. The eye movement data and hazard-identification index (HII) scores from four groups were collected to measure their hazard-identification ability. The differences between the participants before and after the test were tested by paired sample t-test, and the differences between the groups after the test were analyzed by one-way Welch’s analysis of variance (ANOVA) test.

The statistical findings showed that participants under VR technology condition spent less time finding and arriving at the Areas of Interest (AOIs). Both the eye movement data and HII scores indicated that VR-based safety training is an alternative approach for Gen Z students to traditional safety training methods.

These findings contribute to the theoretical implications by proving the applicability of VR technology to Gen Z students and empirical implications by guiding colleges and universities to design attractive safety training lessons.

The global business environment has brought about great innovation according to the advent of the fourth industrial revolution era. Most of the enterprises are focusing on adapting to the era of the fourth industrial revolution and trying to find appropriate strategies. Competitiveness among enterprises is changing fiercely. Such environments are threatening to the sustainability of enterprises. In this regard, it is a key issue that how enterprises can be sustainable and gain competitive advantage. Based on this background, this study emphasized the importance of environmental involvement. Enterprise can improve its image through environmental involvement. Since enterprise image is a variable that directly impacts enterprise growth and performance, it promotes the sustainability of the enterprise. Therefore, this study aims to explore the improvement factors of environmental immersion and verified its influence.

This study focused on creating shared value as a factor to improve environmental involvement. It is divided into three components, which are economic values, social values and cooperative values, respectively. The role of these three factors in enhancing environmental involvement was clearly identified and the process of enhancing enterprise image was verified.

The mediating effect of environmental involvement on the relationship between creative shared values (economic values, social values and cooperative values) and enterprise image was demonstrated.

This study emphasized the importance of environmental immersion in the era of the fourth industrial revolution and provided a way to improve enterprise image, which is directly related to the sustainability of the enterprise.

This study aims to evaluate the dry-sliding tribological properties of fine-grained tin–bronze alloy under reciprocating sliding conditions.

A fine-grained tin–bronze alloy was processed by multiaxial forging (MAF) and annealing treatment. Friction and wear experiments were conducted on a reciprocating sliding tribometer. Microstructure, tensile mechanical properties, hardness, wear rate, friction coefficient and wear morphologies of coarse-grained sample, MAF sample and MAF and annealing sample were compared.

After MAF, the strength and hardness increased distinctly, but the elongation decreased. The wear rate is increased, though friction coefficient is lower. Weaker work hardening leads up to higher sliding wear rate. After MAF and annealing, the alloy has higher strength, hardness and elongation. Lower wear rate of the alloy is correlated with the higher hardness, elongation and work hardening. The adhesion wear and abrasive wear are the primary wear mechanism.

It was found that the fine-grained alloy shows lower sliding wear rate only by combining severe plastic deformation with heat treatment. The process of MAF and annealing is useful in improving the wear resistance of tin–bronze alloy.

This paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.

This paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.

Firstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.

This is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

Underpinned by the ability–motivation–opportunity framework, this paper aims to establish a framework of employee creativity antecedents in the hospitality and tourism industries and meta-analytically examine the magnitude of effect sizes as well as the moderating effects of cultural factors.

A meta-analysis using data from 82 independent studies was conducted to explore the hypothesized relationships and verify how they were contingent on uncertainty avoidance and long-term orientation.

The results supported the majority of hypotheses about the relationships between antecedents and creativity. Furthermore, they showed that the effects of intrinsic motivation, positive affect and climate for innovation on creativity in the hospitality and tourism industries were significantly larger than those reported in previous meta-analyses. It also showed that uncertainty avoidance and long-term orientation could buffer or strengthen some associations.

This study generates some essential managerial suggestions for organizations in need of innovation. Managers can learn from the results so as to effectively promote the ability, motivation and opportunity for creativity and merge cultural elements with innovation strategy when they operate globally.

This study provides a theory-based explanation for how employee creativity can be activated. To the best of the authors’ knowledge, this study is a first attempt to meta-analytically test the underlying determinants of employee creativity in the hospitality and tourism industries. Additionally, the search for boundary conditions of the proposed relationships is likely to reconcile existing conflicts and inspire future studies.

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.

Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating machineries, this paper aims to propose a fault diagnosis method based on sensitive dimensionless parameters and particle swarm optimization (PSO)–support vector machine (SVM) for reducing the unexpected downtime and economic losses.

A relatively new hybrid intelligent fault classification approach is proposed by integrating multiple dimensionless parameters, the Fisher criterion and PSO–SVM. In terms of data pre-processing, a method based on wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and dimensionless parameters is proposed for the extraction of the vibration signal features. The Fisher criterion is applied to reduce the redundant dimensionless parameters and search for the sensitive dimensionless parameters. Then, PSO is adapted to optimize the penalty parameter and kernel parameter for SVM. Finally, the sensitive dimensionless parameters are classified with the optimized model.

As two different time–frequency analysis methods, a method based on a combination of WPD and EMD used to extract multiple dimensionless parameters is presented. More vital diagnosis information can be obtained from the vibration signals than by only using a single time–frequency analysis method. Besides, a fault classification approach combining the sensitive dimensionless parameters and PSO-SVM classifier is proposed. The comparative experiment results show that the proposed method has a high classification accuracy and efficiency.

To the best of the authors’ knowledge, very few efforts have been performed for fault classification using multiple dimensionless parameters. In this paper, eighty dimensionless parameters have been studied intensively, which provides a new strategy in fault diagnosis field.

The purpose of this paper is to examine the financing and provision of basic public services in China. The main issue addressed is how to reform the public finance system to achieve quality and fairness in the provision of basic public services.

Based on an historical analysis of the functional transformation of the public finance system in China and on an empirical analysis of the current public finance system and the public service provision system, a comprehensive understanding was gained about the relationship between the financing and provision of basic public services.

The paper argues that there is a close relationship between the provision of basic public services and the functional changes made to the public finance system. Based on a systematic retrospective study of the Chinese Government’s efforts to improve basic public services over the last three decades, this paper offers policy suggestions on further public finance restructuring that would support better service provision.

By analyzing issues in the public service provision system, this paper contributes to the debate about the efficiency improvement made to governmental functions in China.

The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control.

The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty.

This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort.

The conventional real time issues such as nonlinearity characteristics, poor controllability and stability.

It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.