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The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities in contact, which may further affect the fluid-film characteristics.

Geometric morphology is integrated into the PEHL model to elucidate the fluid-film properties governed by both macro- and micromorphologies.

Results show the model, accounting for combination of elastic and plastic deformations, realistically reveals fluid film distribution affected by the significant pressure highly concentrated within surface micro roughness interaction. The designed macroscopic textured surface mitigates the fluid film rupture phenomenon and prevents accumulated wear degradation from plastic deformation.

The PEHL model takes into account both elastic and plastic deformations and realistically reveals the fluid film distribution affected by large pressures that are highly concentrated in surface micro-roughness interactions. The macro-textured surfaces are designed to mitigate fluid film rupture phenomena and prevent cumulative wear caused by plastic deformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0170/

In developing countries, construction organizations are seeking to effectively implement green innovation strategies. Thus, this study aims to assess the importance of green innovation practices and develop a measurement model for quantifying the green innovation degrees of construction firms.

A mixed-methods research approach is adopted. First, an extensive literature review is performed to identify potential green innovation items, which are then used to design a preliminary questionnaire. Next, expert interviews are conducted to pilot-test this questionnaire. Subsequently, by using a convenience non-probability sampling method, 88 valid responses are collected from construction firms in Vietnam. Then, one-sample and independent-samples t tests are employed to assess the importance of green innovation practices. Fuzzy synthetic evaluation (FSE) is also applied to quantitatively compare such practices. Finally, green innovation level (GIL) is proposed to measure the green innovation indexes and validated by a case study of seven construction firms.

This study identifies 13 green innovation variables, of which several key practices are highlighted for small/medium and large construction firms. The results of FSE analysis indicate that green process innovation is the most vital green category in construction firms, followed by green product and management innovations, respectively. As a quantitative measure, GIL could allow construction firms to frequently evaluate their green innovation indexes, thereby promoting green innovation practices comprehensively. Hence, construction firms would significantly enhance green competitive advantages and increasingly contribute to green and sustainable construction developments.

This research is one of the first attempts to integrate various green innovation practices into a comprehensive formulation. The established indexes offer detailed green innovation evaluations, which could be considered as valuable references for construction practitioners. Furthermore, a reliable and practical tool (i.e. GIL) is proposed to measure the GILs of construction firms in developing countries.

This paper examines the role of purchasing in facilitating early supplier involvement in new product development (NPD) in contexts of technological uncertainty (TU). Taking a purchasing perspective, it develops a moderate model to explain the effects of supplier involvement on NPD performance and whether and how knowledge orchestration capability (KOC) and TU affect these relationships. Additionally, KOC drivers are defined.

A total of 317 usable questionnaires from Chinese high-technology firms were collected. Moderated multiple regression (MMR) was used to test all hypotheses. Resource orchestration theory (ROT) was the adopted theoretical lens.

Two forms of supplier involvement (as knowledge source and co-creator) were found to distinctly affect NPD performance and have potential substitutive relationships. Purchasing KOC positively moderates the relationships between forms of supplier involvement on NPD performance. TU strengthens the moderating role of purchasing KOC. Furthermore, purchasing status and supply complexity are important antecedents for purchasing KOC.

These findings serve as a blueprint for involving purchasing in technologically uncertain NPD projects and improve supplier NPD integration. Additionally, management should recognize the purchasing function's role and empower it to identify ideas, knowledge and solutions within supply networks.

This research contributes to the ROT by examining the role of purchasing KOC on supplier involvement in NPD performance, especially under TU. Moreover, it demonstrates significant and positive relations between purchasing department status and external supply complexity on its KOC.

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/

This study aims to explore two main objectives. Firstly, it examines the mediating roles of green human resource management practices (GHRMPs) and green employee creativity (GEC) between green transformational leadership (GTL) and green employee behaviour. Secondly, it investigates the moderating effect of green individual values (GIVs) on the indirect relationship between GTL and green employee behaviour.

The study involved 326 employees from ECOTEL-certified hotels in India. Analysis was conducted using the Statistical Package for Social Sciences (SPSS) AMOS and MACRO.

The results indicate a direct relationship between GTL and green employee behaviour. Additionally, GHRMPs and GEC partially mediate this relationship. Furthermore, GIVs positively moderate the indirect relationship between GTL and green employee behaviour, specifically moderating the path between GHRMPs and GEC.

This study fills a significant gap in the literature by investigating the combined effects of GTL, GHRMPs, GEC and GIVs on employee pro-environmental behaviour. Understanding these relationships is crucial for organizations aiming to implement effective green initiatives and cultivate a culture of environmental responsibility among employees. This study is ground-breaking in its approach, delving into the complex network of interconnected variables through both mediation and moderation analyses. By doing so, it aims to uncover the intricate mechanisms influencing employees' inclination towards pro-environmental behaviour.

This work makes a case for legitimacy as a framework with which to examine how educators made decisions about implementing entrepreneurship education (EE) in higher education institutions (HEIs) to better understand the educator within the educational ecosystem. It then uses a new legitimacy framework that includes self-legitimacy to examine the issues a group of educators in China encountered when implementing new constructivist entrepreneurship modules in their non-entrepreneurship curricula.

The researchers utilized focus groups to collect data from 24 groups of educators at HEIs in 4 regions of China. The researchers used a bottom-up thematic analysis process to identify themes and used legitimacy as a lens to analyze the data.

The results are presented in three main categories: theorization, or how the practice aligns with existing practice; diffusion, or how the practice is perceived by stakeholders; and self-legitimacy, or how the practice impacts the educator’s image of the self. The data show that legitimization of their constructivist EE practice has not occurred at each of these stages, leaving educators struggling to rationalize how the new practice fits into their existing ecosystem.

Using legitimacy as an approach, the research adds to an understanding of how and why entrepreneurship educators adopt practice and how they are empowered to change practice within their existing institutional structures. It brings different legitimacy theories into one framework to examine changes to EE practice and it applies self-legitimacy to education, an area previously only examined in high power distance situations like law enforcement, but which is appropriate for high power distance educational cultures like China.

This study intends to explore whether green transformational leadership is effective in promoting employee green performance. What role do moral reflectiveness and green crafting play in the impact of green transformational leadership on employee green performance?

This study collected research data from a series of questionnaire surveys using a multisource and time-lagged design. We collected 582 completed questionnaires from 97 groups in chemical firms.

The analysis showed that (1) green transformational leadership positively affected employee green performance and (2) moral reflectiveness and green crafting sequentially mediated the relationship between green transformational leadership and employees’ green performance.

The 2-1-1 multilevel mediation model clarified how the perspectives of leaders and employees are associated, confirming that green transformational leadership successfully promotes the green performance of subordinates through value communication and resource provision. Chemical companies need green transformational leaders passionate about environmental issues to encourage employee engagement in sustainability initiatives, ultimately enhancing employees’ green performance and achieving sustainable development of the chemical organization.

Flooding is China’s most frequent and catastrophic natural hazard, causing extensive damage. The aim of this study is to develop a comprehensive assessment of urban flood risk in the Hubei Province of China, focusing on the following three issues: (1) What are the factors that cause floods? (2) To what extent do these factors affect flood risk management? (3) How to build an effective comprehensive assessment system that can be used to reduce flood risk?

This study combines expert opinion and evidence from the extent literature to identify flood risk indicators across four dimensions: disaster risk, susceptibility, exposure and prevention and mitigation. The Criteria Importance Through Intercriteria Correlation (CRITIC) and the Grey Relational Analysis (RA)-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach were applied to calculate the weighting of factors and develop a model of urban flood risk. Then, ArcGIS software visualizes risk levels and spatial distribution in the cities of Hubei Province; uncertainty analysis verified method accuracy.

The results show that there are significant differences in the level of urban flood risk in Hubei Province, with cities such as Tianmen, Qianjiang, Xiantao and Ezhou being at high risk, while cities such as Shiyan, Xiangyang, Shennongjia, Yichang, Wuhan and Huanggang are at lower flood risk.

The innovative method of combining CRITIC-GRA-TOPSIS reduces the presence of subjective bias found in many other flood risk assessment frameworks. Regional data extraction and uncertainty analysis enhance result reliability, supporting long-term decision-making and urban planning. Overall, the methodological approach developed provides an advanced, highly effective and efficient analysis and visualization of flood risk. This study deepens the understanding of flood risk assessment mechanisms and more broadly supports the development of resilient cities.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

This research paper investigates the utilization of artificial intelligence (AI) among teachers in higher education (universities and colleges) in India and its impact on teaching activities. The study explores teachers’ perceptions, attitudes and the factors influencing the integration of AI in their teaching practices.

A questionnaire-based survey was conducted involving 500 teachers in higher education (university and college) in India. Data analysis included descriptive statistics, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structure equation modeling.

The study addresses teachers’ expectations and attitudes toward AI integration in teaching practices. Results suggest that AI can potentially enhance teaching practices among teachers in higher education in India. The findings contribute to understanding AI adoption in teaching, providing insights for educational institutions and policymakers. Further research is recommended to validate the results across different regions and academic settings, leading to the development of strategies and support systems for successful AI implementation in teaching practices.

The originality of this research lies in its investigation of the integration of AI in college teaching practices among teachers in India. It contributes to the existing literature by exploring teachers’ perceptions, attitudes and the factors influencing the adoption of AI, providing valuable insights for educational institutions and policymakers in the Indian context.