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(1) The shear lag effect and its additional deflection contribution to composite beams based on spatial grid elements were presented. (2) A refined spatial grid element analysis method that can simultaneously obtain the internal forces, displacements and stresses of various parts of a composite beam.

A refined spatial grid element analysis method.

The proposed method can directly obtain the internal forces and displacements of the joints of the composite beam roof, floor and web.

To comprehensively comprehend the mechanical behavior of double-girder steel plate composite girder bridge structures and facilitate refined analysis, this paper introduces a refined spatial grid element analysis model applicable to both the global and local domains.

Based on the collective agentic perspective of social cognitive theory and supplemented by social support theory, this study aims to investigate how the entrepreneurial collective efficacy of entrepreneurial teams impacts team effectiveness. Specifically, this study hypothesizes that entrepreneurial collective efficacy is a critical factor affecting effectiveness through the mechanism of instrumental support to varying extents, depending on team size.

The hypotheses were tested on a Chinese sample of new venture teams (N teams = 81; N individuals = 335). A multisource questionnaire that included questions for team leaders and other team decision makers was designed. The data on entrepreneurial collective efficacy (six items) and instrumental support (four items) comprised the aggregate responses from all team members; the data on team effectiveness (seven items) was reported by team leaders.

The findings indicate that entrepreneurial collective efficacy is positively associated with team effectiveness through instrumental support, especially in small teams.

This study makes important contributions to the research related to exploring in uncertain environments (entrepreneurship) how (instrumental support) entrepreneurial collective efficacy impacts team effectiveness, as well as more particularly under what conditions (team size), all within the specific context of collectivistic cultures (China).

This study aims to understand how Gen Z consumers perceive fashion brands’ corporate social responsibility (CSR) communication through emotional appeals on Instagram and how such perception affects their overall behaviour towards the brand.

The study adopts a qualitative research approach through photo-elicitation and 14 semi-structured in-depth interviews with members of Gen Z, using one of the world’s largest fast fashion brands, H&M, as the study context.

It is increasingly difficult to capture the attention of Gen Z as they have become immune to the typical CSR messages despite attempting to appeal to their emotions. This makes CSR communication alone challenging in influencing brand perception. However, behaviour towards the brand, such as purchase intention, is not necessarily dependent on whether consumers are convinced of the brand’s CSR activities, as greater value is placed on fast fashion’s price and availability. For Gen Z, such elements surpass their concern for sustainable fashion. Since more emphasis is placed on neutral endorsers due to their trustworthiness, CSR efforts may be disseminated through such third parties to achieve desired outcomes.

Understanding consumers’ perceptions of the current CSR effort allows brand managers to reevaluate their CSR communication strategies to appeal to Gen Z and encourage positive brand behaviour.

Contrary to previous studies, which have focused on organisational outcomes, the study has in-depth explored consumers’ perception of CSR efforts on Instagram and the implications of such perceptions for long-term brand building.

This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.

Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.

The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.

This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/

The study explores the mechanism between employee demand–ability fit and promotability ratings. Moreover, we examine whether political skill moderates the relationship above.

The present study received 260 effective supervisor-rated questionnaires from various industries in Taiwan.

The results reveal that employee demand–ability fit positively relates to promotability ratings, and task performance mediates the above relationship. In addition, political skill positively moderates the relationship between employee demand–ability fit and task performance.

We used the supervisor rating for our variables to test the hypotheses.

This study was aimed at clarifying the post-fire shear strength of self-drilling screws and the load-bearing capacity of single overlapped screwed connections using steel sheets and self-drilling screws. The self-drilling screws for shear tests were made of high-strength, martensitic-stainless and austenitic stainless-steel bars.

Shear loading tests were conducted on self-drilling screws to obtain basic information on post-fire shear strength. Tensile tests were conducted on the screwed connections to examine the transition of failure modes depending on the test temperature after experiencing the heating and cooling procedures.

The post-fire shear strengths and reduction factors of self-drilling screws of each steel grade were quantified. Furthermore, heated temperature-dependent sheet bearing failure, net sheet failure and screw shear failure modes were observed for the screwed connections.

The transition of the failure modes of the screwed connection could be explained using the equations of the post-fire shear strength proposed in this study. The basic experimental data required to evaluate the post-fire shear strength of screws were obtained.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

The structure and protective effect of Al-coated Nd-Fe-B magnets before and after grain boundary diffusion were studied to explore the feasibility of improving the corrosion resistance of Nd-Fe-B magnets by Al coating and Al grain boundary diffusion.

The Al coating was deposited on sintered Nd-Fe-B magnets by magnetron sputtering, and then the Al-coated Nd-Fe-B magnets were put into the vacuum tube furnace for grain boundary diffusion process. The influence of Al coating and grain boundary diffusion process on the corrosion resistance of Nd-Fe-B magnets was investigated using electrochemical tests.

Results showed that the Al coating thickness increases, the corrosion current density of Al-coated magnets first increases and then decreases with increasing coating time. The Al coating particles transform from small millet shaped particles to equiaxed polygonal particles, and finally to big millet shaped particles with increasing coating time. The diffusion temperature has little effect on the corrosion potential, and the corrosion current density of Al-diffused magnets after grain boundary diffusion is much higher than that of Al-coated magnets before grain boundary diffusion. The corrosion potential and corrosion current density of magnets first increase, and then decrease with increasing tempering temperature.

As for high intrinsic coercivity Nd-Fe-B magnets, Poor preparation of Al coatings can result in Al coatings lacking protective properties, and the Al coating should be used cautiously as the surface protective coating of magnets. Grain boundary diffusion leads to the disappearance of Al coating, and reduces the corrosion resistance of Nd-Fe-B magnets, while tempering treatment can improve the corrosion resistance of Al-diffused magnets.