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The purpose of this study is to examine the use of digital technologies by teachers and students in teaching and learning from a multimodal layer perspective.

The article reviews 64 studies on technology use. A content analysis based on the theoretical concepts of “multimodal layers” was used to synthesise previous research.

The findings indicate that the use of technology in classroom practices by teachers and students is multifaceted and that transitions exist between technologies and sign-systems and are differently related to sign-making activities and thus constitute different uses. Between layers, traces can be made that connect the use of technology to differences in sign-making activities.

A multimodal layer perspective on technology use is fruitful to understand what happens at the intersection of technology and human activities in school practices. Moreover, more attention to multimodal layers can inform future effective technology usage and design.

The review offers comprehensive insights on how previous research has studied technology using multimodal layers as an analytical lens.

This paper aims to investigate the reliability, availability, maintenance and safety analysis method for railway network operation.

The reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of accident recovery time. The safety index of the railway network is proposed to measure the safety of railway stations and sections and the K-means method is proposed to find the safety critical stations and sections. Finally, the effectiveness of the proposed method is illustrated through a real-world case study.

The case study shows that the proposed model can produce a big-picture averaged view of the network-wide safety level and help us identify the safety critical stations and sections by considering both the expected reduction of network efficiency and capacity.

The potential application of the proposed model is to help the safety managers determine the investments in safety management of each section and station and then increase the safety and robustness of railway network operation.

The safety analysis of the railway network should consider the reliability, availability and maintenance of the railway network. In this paper, the reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of recovery time. Finally, the safety index of the railway network is proposed to analyze the safety critical stations and sections.

Based on social exchange theory and research on proactive personality, this study aims to explore the relationship between abusive supervision and unethical pro-organizational behavior (UPB), taking proactive personality as a boundary condition.

This study conducted a two-wave questionnaire survey and used data from 353 Chinese employees of a diversified company to test the research hypotheses.

The relationship between abusive supervision and UPB varied with proactive personality. Specifically, abusive supervision had an inverted U-shaped effect on UPB when proactive personality was high, while abusive supervision was negatively related to UPB when proactive personality was low.

More research are encouraged to replicate our study in different cultural contexts. Besides, future research can gather data from dyads (e.g. supervisor–subordinate dyad and coworker–subordinate dyad) so as to increase the objectivity and validity of the data.

Managers should reduce abusive supervision and elevate proactive employees' moral awareness.

This study hopes that the authors’ findings will help practitioners to devote greater attention to managing proactive personality, abusive supervision and UPB in the organization.

First, this study enriches the abusive supervision literature by identifying UPB as a consequence of abusive supervision. Second, this study provides a better understanding of the coping tactics used to combat abusive supervision. Third, this study uncovers a dark side of proactive personality by verifying the moderating effect of proactive personality.

The purpose of this study is to propose extended potentials and investigate the applicability of extended Finnis–Sinclair (FS) potential to Cr with the unit cell structure of body-centered cubic (BCC Cr).

The parameters of each potential are determined by fitting the elastic constants, cohesive energy and mono-vacancy formation energy. Furthermore, the ability of the extended FS potential to describe the crystal defect properties is tested. Finally, the applicability of reproducing the thermal properties of Cr is discussed.

The internal relationship between physical properties and potential function is revealed. The mathematical relationship between physical properties and potential function is derived in detail. The extended FS potential performs well in reproducing physical properties of BCC Cr, such as elastic constants, cohesive energy, surface energy and the properties of vacancy et al. Moreover, good agreement is obtained with the experimental data for predicting the melting point, specific heat and coefficient of thermal expansion.

In this study, new extended potentials are proposed. The extended FS potential is able to reproduce the physical and thermal properties of BCC Cr. Therefore, the new extended potential can be used to describe the crystal defect properties of BCC Cr.

Integrating appraisal theories of discrete emotions with the emotion regulation literature, this study aims to explore the relationships between exploitative leadership and certain types of counterproductive workplace behavior (CWB). Besides, this study seeks to examine the mediating roles of discrete emotions (i.e. anger and fear) and the moderating role of cognitive reappraisal within the proposed relationships.

Based on time-lagged survey data from 440 Chinese employees, this study conducted hierarchical regression analysis and bootstrapping approach to test the hypotheses.

The results revealed that exploitative leadership was positively related to approach-oriented CWB and avoidance-oriented CWB. In addition, this study found that anger mediated the relationship between exploitative leadership and approach-oriented CWB, whereas fear mediated the relationship between exploitative leadership and avoidance-oriented CWB. Further, cognitive reappraisal buffered the positive effects of exploitative leadership on anger and fear and the indirect effects of exploitative leadership on approach-oriented CWB (via anger) and avoidance-oriented CWB (via fear).

Managers should reduce leaders' exploitation and enhance employees' skills on emotional management and cognitive reappraisal.

First, by verifying the effects of exploitative leadership on both approach-oriented and avoidance-oriented CWB, this study adds to the literature on exploitive leadership and provides a more complete understating of the relationship between exploitative leadership and workplace deviance. Second, this study enriches the understanding of the process through which exploitative leadership affects employees by demonstrating the novel mediating roles of discrete emotions (i.e. anger and fear) through the lens of appraisal theories of discrete emotions. Third, by verifying the moderating role of cognitive reappraisal, this study provides insights into the boundary conditions of the influences of exploitive leadership.

The purpose of this paper is to explore user engagement (UE) within the Metaverse (MV) environment, emphasising the crucial role of immersive experiences (IEs). This study aims to understand how IEs influence UE and the mediating effects of hedonic value (HV) and utilitarian value (UV) on this relationship. Additionally, the authors examine the moderating impacts of user perceptions (UPs) such as headset comfort, simulation sickness, prior knowledge and ease of use on the utilisation of the MV. This study seeks to elucidate the dynamics of virtual travel at a pre-experience stage, enhancing the comprehension of how digital platforms can revolutionise UE in travel and tourism.

This study used a triangulation methodology to provide a thorough investigation into the factors influencing UE in the MV. A systematic literature review (SLR) was conducted to frame the research context and identify relevant variables. To gather empirical data, 25 interviews were performed with active MV users, supplemented by a survey distributed to 118 participants. The data collected was analysed using structural equation modelling (SEM) to test the hypothesised relationships between IEs, UPs, HV and UV and their combined effect on UE within the MV.

The findings from the SEM indicate that engaging in the MV leads to a positive IE, which significantly enhances UE. Additionally, it was discovered that HV and UV play a mediating role in strengthening the link between IEs and UE. Furthermore, UPs, including headset comfort, simulation sickness, prior knowledge and ease of use, are significant moderators in the relationship between IEs and MV usage. These insights provide a nuanced understanding of the variables that contribute to and enhance UE in virtual environments.

This research contributes original insights into the burgeoning field of digital tourism by focusing on the MV, a rapidly evolving platform. It addresses the gap in the existing literature by delineating the complex interplay between IEs, UPs and value constructs within the MV. By using a mixed-method approach and advanced statistical analysis, this study provides a comprehensive model of UE specific to virtual travel platforms. The findings are particularly valuable for developers and marketers in the hospitality and tourism sectors seeking to capitalise on digital transformation and enhance UE through immersive technologies.

This paper aims to systematically expound the theory and development background of supply chain finance and blockchain, design a railway freight supply chain financial platform based on blockchain, determine the risk management system and business support system of supply chain finance business and analyze the value generated by the combination of supply chain finance business and blockchain.

Investigation and research method; Prototype method; Model method; Value analysis.

The business model integrating supply chain finance and blockchain technology will bring great changes to freight industry. The development of supply chain finance is beneficial to the healthy development of the core participants of railway freight transport business and its upstream and downstream ecosystems. It links commerce, logistics, warehousing and financial services together and builds an industry-integrated ecological service platform through information technology platform and supporting system, taking data as the basis and combining information technology such as blockchain as innovative means.

This paper will provide important reference value for related research. This paper innovatively designs the supply chain financial platform of freight transportation industry-integrating blockchain technology and analyzes its business model, technical system, risk management and control system and value system in detail, which will provide technical support for the innovative reform of freight information technology and realize the stable and high-speed development of freight logistics informationization.

The purpose of this study is to explore the association between eating patterns, social identity and the well-being of adolescents via a mixed methods study of Chinese teenagers. The specific research questions presented in this study are as follows: What is the relationship between social eating and well-being? How is the relationship between social eating and well-being mediated by social identity?

This study is based on a sequential mixed methods study, including interviews with 16 teenage–parent dyads, and a large-scale survey of over 1,000 teenagers on their eating patterns, conducted with the support of public schools. A model that tests relationships among social eating, social identity and subjective well-being is developed and tested.

The results show that dining with family members leads to improved subjective well-being for teenagers, through a partial mediator of stronger family identity. However, dining with peers is not found to influence subjective well-being.

The privileged position of family meals demonstrated through this study may be an artifact of the location of this study in one Chinese city. Further research is needed related to the connections among social identity, objective well-being and the social patterns of teenagers’ food consumption behavior.

To improve the subjective well-being of teenagers, families, public policy-makers and food marketers should support food consumption patterns that promote family meals.

While many food-related consumer studies focus on the individual, social and environmental influences of food choices of adolescents, few studies address how eating patterns affect overall well-being. These results reinforce the importance of understanding the effect of the social context of teenagers’ eating patterns on health and well-being.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.