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Using the multifunctional friction and wear testing machine independently developed by the research group, the friction and wear tests of different friction conditions (contact pressure and sliding speed) are conducted on the brake materials of high-speed trains with the ambient humidity of 95% and the initial temperature of the disk of 200°C.

Friction and wear.

The test results show that changing the friction conditions has a significant effect on the braking performance of high-speed trains.

YES.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0171/

The purpose of this study is to investigate the influence of IP (Intellectual Property) on the intention for premium consumption of Generation Z, and to construct a theoretical model of IP on the premium consumption of Generation Z. Based on the results of the study, it provides better marketing suggestions to merchants, and is an expansion of previous research on the consumption behavior of Generation Z.

This paper contains two empirical tests and one experimental analysis. First, this study crawl over 5,000 pieces of Generation Z’s consumption data from Poizon, an e-commerce platform and exclusive trending community for Generation Z. Second, this study designs a two-group online experiment to collect 292 valid data from members of the Generation Z. The authors use Stata software for multiple linear regression, t-tests, and ANOVA to test the hypotheses.

The results of the data analysis show that IP has a significant positive effect on the premium consumption intention of Generation Z, and the limited release strategy positively moderates the effect. Self-image congruence and social identification play mediating role in the influence of IP on Generation Z’s premium consumption.

First, this study finds a link between IP and commodity premiums, which is the first study to explore the effect of IP on commodity price changes. Second, this study is the first to examine the marketing science value of IP using a combination of empirical tests and experimental analysis. These fill research gaps. Finally, the mechanism of IP’s influence on Generation Z’s premium consumption is revealed, enriching the literature on Generation Z’s consumption behavior.

This paper divides the dyadic supply chain into three power structures according to the relative channel power of the supply chain members and consequently examines the optimal supply chain pricing decisions when both suppliers and retailers are concerned with fairness issues.

Three models are constructed, namely the Stackelberg game model with the supplier as the leader, the Nash game model with the balance of power and another Stackelberg game model with the retailer as the leader. The equilibrium solutions are solved, and their results are analyzed.

The retail price of a product increases with an increase in the fairness concerns of the leader in a supply chain in which the supplier or retailer is the leader, while the fairness concerns of the member with less channel power have no effect on the retail price. In a power-balanced supply chain, both suppliers and retailers increase their retail prices as their fairness concerns increase. The relative size of the members’ fairness concerns affects member profits and total supply chain profits.

The main contributions are as follows: First, this paper proposes a new approach to studying supply chain pricing strategy, considering fairness concerns and power structure. Secondly, three game models are constructed. The Nash equilibrium solution is introduced to study the fairness of supply chain participants in pricing decisions and overall supply chain profitability. Finally, the supply chain management theory is expanded by this study on pricing decisions and supply chain performance.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The mounting health misinformation on social media triggers heated discussions about how to address it. Anchored by the influence of presumed influence (IPI) model, this study investigates the underlying process of intentions to combat health misinformation. Specifically, we analyzed how presumed exposure of others and presumed influence on others affect intentions to practice pre-emptive and reactive misinformation countering strategies.

Covariance-based structural equation modeling based on survey data from 690 Chinese participants was performed using the “lavaan” package in R to examine the proposed mechanism.

Personal attention to health information on social media is positively associated with presumed others’ attention to the same information, which, in turn, is related to an increased perception of health misinformation’s influence on others. The presumed influence is further positively tied to two pre-emptive countermeasures (i.e. support for media literacy interventions and institutional verification intention) and one reactive countermeasure (i.e. misinformation correction intention). However, the relationship between presumed influence and support for governmental restrictions, as another reactive countering method, is not significant.

This study supplements the misinformation countering literature by examining IPI’s tenability in explaining why individuals engage in combating misinformation. Both pre-emptive and reactive strategies were considered, enabling a panoramic view of the motivators of misinformation countering compared to previous studies. Our findings also inform the necessity of adopting a context-specific perspective and crafting other-oriented messages to motivate users’ initiative in implementing corrective actions.

A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution.

A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI.

Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality.

This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

This study aims to provide insights into wine consumers in Hong Kong and Mainland China, and the diversity of their behavior, by investigating their motivations, consumption and purchasing behavior and segmenting the sample based on wine knowledge and frequency of consumption to identify three distinct clusters that are then profiled.

Data was collected through an online survey of wine consumers of ethnic Chinese origin residing in Mainland China and Hong Kong, using two methods of recruitment (a convenience sample from wine-related databases, a panel survey undertaken by an international market research firm). The final sample comprised 477 respondents, about equally divided among Mainland Chinese and Hong Kong wine consumers. Analysis of the data using the Statistical Package for Social Sciences for Windows (SPSS) 29 included a series of descriptive analyses, followed by a two-step cluster analysis using an Euclidean distance method.

Wine knowledge and frequency of wine consumption were relatively higher within the sample as a whole than previous studies, but three distinct consumer segments are evident. There is evidence of a democratization of wine consumption, and a greater focus on wine consumption for enjoyment in relaxed and informal settings. rather than consumption driven by health and status, occurring primarily in commercial or business settings. The dominance of red wine in this market is also declining, and intrinsic cues – specifically, prior experience of the wine – are becoming important in wine purchase decisions.

This study uses a broad sample of wine consumers to identify trends in wine consumption patterns and motivations, and purchase decision-making behavior, of Mainland Chinese and Hong Kong wine consumers. The identification of three clusters of wine consumers offers both points of comparison with previous segmentation research globally and within China, whilst also identifying scope for further research.

This paper aims to unveil the greenwashing intention of green bonds issuing in Chinese enterprises through the lens of stock pricing efficiency.

Drawing on data of Chinese listed companies during 2012–2021, this study uses a difference-in-differences method to study how and through what mechanisms issuing green bonds impacts stock pricing efficiency.

Issuing green bonds lowers stock pricing efficiency, verifying the greenwashing intention of green bonds in China. Potential mechanisms underlie the increased investor attention and sentiment resulting from the information disclosures about corporate green and low-carbon development. This greenwashing issue is more pronounced in firms facing lower financing constraints, having stronger relations with the government, and located in highly marketized regions. In the context of uncertainty surrounding economic policies, especially trade policies, issuing green bonds can signal a weakening of the greenwashing effect.

The quality of information disclosure should be emphasized to ensure a substantive commitment to environmental responsibility signaled by green bond issuance, thereby mitigating greenwashing concerns.

Regulators and standard-setters should improve the issuance system for green bonds and promote the sustainable development of the green bond market through formulating unified certification criteria for green bonds and implementing a stringently periodic reporting system.

First, to the best of the authors’ knowledge, it is the first study to draw on the quality of information disclosure and the perspective of stock pricing efficiency to identify whether firms issuing green bonds engage in greenwashing. Second, the study uncovers the black-box underlying this greenwashing issue through investor attention and sentiment and examines further the moderating role of economic policy uncertainties.

Among the topmost challenges, limiting the transformation of conventional public–private partnership (PPP) projects to meet net-zero targets is financial risk. This challenge is more prevalent in PPP projects in developing economies like Ghana, where financial investments have dwindled due to the recent COVID-19 recession. This paper aims to assess the key financial challenges in transitioning to net-zero PPP projects in Ghana.

The research method process was set as follows. First, a review of the literature to identify the major financial risks from journal articles, project reports and documents was undertaken, followed by questionnaire development and collection of data. Finally, the analysis of 134 questionnaire data was examined with the fuzzy synthetic evaluation.

The results indicate that the following financial challenges could hinder the transition to net-zero PPP projects in the country: increasing borrowing charges to build net-zero PPP projects due to the global covid-economic recession, poor project financial management, unstable local capital market and excessive labour, health and safety costs.

Although, the study was conducted in Ghana, a country in the Sub-Saharan African region, the outcomes have significant impacts for similar developing countries in research investigations into the problem.

Assistance is provided in this study for PPP project practitioners in identifying the key financial challenges and possible strategies to mitigate them.

Towards net-zero sustainability, this study highlights the crucial financial barriers to overcome in the rapid transition to climate change and zero carbon solutions in PPP projects.

The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.

Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.

Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.

The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.