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Although researchers have long recognized the importance of participating organizations’ (POs) relational behavior for mega construction projects (MCPs) performance, relational behavior may not be executed by POs without effective leadership from project owners. However, little is known about the mechanisms linking owners’ leadership styles to POs’ relational behavior. This study draws on full range leadership theory and role theory to examine the relationships between owners’ leadership styles (i.e. transformational and transactional) and relational behavior. POs’ role orientations (i.e. normative and economic) are considered as potential mediators.

Data were collected from 175 managers deeply involved in MCPs. Hierarchical regression model and bootstrapping methods were performed on the data to examine the direct effects of owners’ leadership on POs’ relational behavior and the mediating effects of POs’ role orientations.

The results revealed that both owners’ transformational and transactional leadership positively affect POs’ relational behavior, despite the former being higher than the latter, and indirectly influence relational behavior via POs’ normative and economic role orientation, respectively.

This study provides a clear picture of how owners’ leadership can motivate POs’ relational behavior to achieve high-quality inter-organizational relationships in MCPs. The findings can guide owners’ top manager selection by prioritizing those with transformational leadership, which is beneficial to achieving high-level relational behavior of POs. The results also imply that owners should pay greater attention to cultivating POs’ normative role orientation by encouraging teamwork and open communication to enhance their implementation of relational behavior.

Unlike previous research focusing more on intra-organizational leader–follower relationship within one PO, this study is one of the first to empirically confirm owners’ leadership as a critical antecedent of POs’ relational behavior, thus enhancing the theoretical understanding of inter-organizational relationship management in MCPs. Based on role theory, this study considers a novel organizational psychology mechanism, i.e. POs’ role orientations, as the mediator to unravel how owners’ leadership affects POs’ relational behavior, which was rarely invoked in MCP leadership literature.

This study aims to examine the empirical linkages among emotional experiences, national identity and behavioural intentions for red tourism in China.

Partial least squares structural equation modelling was applied to data from two rounds of surveys of 1,095 on-site visitors to Zunyi and Yan’an, two well-known red tourism cities in China.

Emotional experiences significantly affect national identity, with positive emotions significantly affecting national identity. Positive emotions also significantly influenced behavioural intentions, national identity positively influenced behavioural intentions and national pride mediated the relationship of “positive emotion → behavioural intentions”. In addition, previous experience played a moderating role in the relationship of “positive emotion → national pride”.

This research produced new insights on red tourism emotional experiences by clarifying its types, connotations and influences. It contributes to a better understanding of national identity antecedents and outcomes in red tourism. Findings revealed the significant role of red tourism in cultivating national identity and provide implications for academic research and practical development of red tourism.

本研究旨在考察中国红色旅游者的情绪体验、国家认同和行为意向之间的影响关系及其作用机制。

采用偏最小二乘法结构方程模型(PLS-SEM), 对来自中国红色旅游圣地——遵义和延安的1095名现场游客的问卷调查数据进行了统计分析。

情绪体验对国家认同会产生显著影响。其中, 积极情感会显著正向影响国家认同。同时, 积极情感也会对行为意向产生显著正向影响。此外, 国家认同对行为意向具有积极影响, 且国家自豪感在“积极情感→行为意图”的影响关系中充当中介作用。最后, 研究还发现旅游经历在“积极情感→国家自豪感”的影响关系中具有显著的调节作用。

本研究通过阐明红色旅游情绪体验的类型、内涵及其影响关系, 为深入理解红色旅游情绪体验提供了一个全新的视角。同时, 文章还进一步厘清了红色旅游情境中国家认同感的形成前因和影响结果的作用机理。研究结论揭示了红色旅游在培育国家认同感方面的重要价值, 并为红色旅游的学术研究和实践发展提供了重要启示。

Esta investigación examinó los vínculos empíricos entre las experiencias emocionales, la identidad nacional y las intenciones de comportamiento para del turismo rojo en China.

Se aplicó el análisis de ecuaciones estructurales basado en mínimos cuadrados parciales (PLS - SEM) a los datos recogidos mediante dos rondas de encuestas realizadasa 1.095 visitantes in situ en Zunyi y Yan'an, dos conocidas ciudades de turismo rojo de China.

Las experiencias emocionales afectan significativamente a la identidad nacional, y las emociones positivas influyen significativamente en la identidad nacional. Las emociones positivas también influyeron significativamente en las intenciones de comportamiento; la identidad nacional influyó positivamente en las intenciones de comportamiento y el orgullo nacional medió la relación “emoción positiva → intenciones de comportamiento”. Además, la experiencia previa desempeñó un papel moderador en la relación “emoción positiva → orgullo nacional”.

Esta investigación aportó nuevos hallazgos sobre las experiencias emocionales del turismo rojo al aclarar sus tipos, connotaciones e influencias. Contribuye a una mejor comprensión de los antecedentes y resultados de la identidad nacional en el turismo rojo. Los resultados de la investigación revelan el importante papel del turismo rojo en el cultivo de la identidad nacional y ofrecen implicaciones para la investigación académica y el desarrollo práctico del turismo rojo.

The aims of this research include (1) to identify the scores of reading literacy in 34 provinces and (2) to determine the impact of ICT literacy with other variables on reading literacy in Indonesia.

Thematic maps and Spatial Autoregressive Regression were applied to 2019 AKSI Survey data.

The results showed that only D.I. Yogyakarta, DKI Jakarta and Kepulauan Riau have a high percentage of reading literacy scores in the excellent category. The ICT literacy and teachers' competency scores significantly affect the percentage of reading literacy. Meanwhile, the percentage of lack of learning materials and GRDP per capita has no significant effect.

Previously, the national exam has been used to determine the quality of education in Indonesia, but it is ineffective because it only measures cognitive aspects. In 2015, the Ministry of Education initiated the AKSI survey, which measures cognitive (reading, math and science literacy) and non-cognitive aspects, as an effort to improve the quality of education in Indonesia. Some literature states that reading literacy is the most basic indicator for determining the quality of education, but in Indonesia, it is the lowest achievement. To improve reading literacy scores, the government has to utilize technological advances through School Digitization. However, this should be supported by the ICT literacy of students. Presently, there is no study to evaluate the impact of ICT literacy on reading literacy, which is also affected by regional value differences.

This study aims to examine the effects of dialect connectedness between the chairman and the chief executive officer (CEO) (DCCC) on the tunneling activities of controlling shareholders.

This study uses abnormal related-party transactions (ARPT) as a proxy for tunneling activities and traces dialects of chairmen and CEOs based on the respective birthplace information. Baseline results are examined using a fixed-effects model. The results remain robust when using the instrumental variable approach, propensity score matching (PSM) technique, changing the measurement of tunneling and Heckman two-step selection model.

The results show that DCCC reduces tunneling activities. This negative association is more pronounced for non-state-owned enterprises and firms whose chairmen and CEOs work in the respective hometowns. DCCC restrains tunneling activities through mechanisms by establishing an informal supervisory effect on CEOs because the CEOs fear reputational damage and strengthening cooperation between chairmen and CEOs. Further analyses suggest that this negative association is more significant when chairmen and CEOs are non-controlling shareholders, but the association is weakened during the coronavirus disease 2019 (COVID-19) crisis.

As dialect is a carrier of culture, this study's results imply that cultural proximity can replace formal mechanisms to enhance corporate governance.

This study holistically and systematically consolidates the available research on digital reading to reveal the research trends of the past 20 years. Moreover, it explores the thematic evolution, hotspots and developmental characteristics of digital reading. This study, therefore, has the potential to serve as a research guide to researchers and educators in relevant fields.

The authors applied a bibliometric approach using Derwent Data Analyzer and VOSviewer to retrieve 2,456 publications for 2003–2022 from the Web of Science (WoS) database.

The results revealed that most studies' participants were university students and the experimental methods and questionnaires were preferred in digital reading researches. Among the influential countries or regions, institutions, journals and authors, the United States of America, University of London, Electronic Library and Chen, respectively, accounted for the greatest number of publications. Moreover, the authors identified the developmental characteristics and research trends in the field of digital reading by analyzing the evolution of keywords from 2003–2017 to 2018–2022 and the most frequently cited papers by year. “E-books,” “reading comprehension” and “literacy” were the primary research topics. In addition, “attention,” “motivation,” “cognitive load,” “dyslexia,” “engagement,” “eye-tracking,” “eye movement,” “systematic analysis,” “meta-analysis,” “smartphone” and “mobile reading/learning” were potential new research hotspots.

This study provides valuable insights into the current status, research direction, thematic evolution and developmental characteristics in the field of digital reading. Therefore, it has implications for publishers, researchers, librarians, educators and teachers in the digital reading field.

The advantages of blockchain technology are being widely discussed by academics, the business community and government, because blockchain can promote data sharing, optimise business processes, reduce operation costs, improve collaborative efficiency and build credible systems. The supply chain is becoming a key area for the application of blockchain technology. However, few studies have discussed the effect of such emerging technologies on the supply chain in depth. Therefore, this paper aims to analyse how blockchain empowers supply chain and promotes supply chain management.

Based on a review of relevant literature and blockchain applications in practice, this paper analyses the development and research status of blockchain technologies. In addition, considering the different operational processes within the supply chain, the authors discuss the opportunities and challenges of blockchain technologies, such as the transparency of supply, intelligent manufacturing, the security of logistics, the platformisation of sales and the ecology of governance.

The authors find that information sharing, information traceability and trust establishment are the key categories of research achievements and applications of supply chain management. The central issues for blockchain researchers are the authenticity of transaction data, the traceability of long supply chains and the establishment of trust for all participants.

From the practical and theoretical perspectives, this paper shows the development of blockchain technologies to clarify the challenges, opportunities and prospects. This paper elucidates and facilitates the development of emerging interdisciplinary research and the practice of supply chain.

This study aims to explore image-based visual price determinants (image features and visual aesthetic perception) and how image features affect Airbnb listing price on a sharing accommodation platform.

The study uses an SOR model and a hedonic price model to examine the connections between the characteristics of image features, visual aesthetic perception and Airbnb listing prices. The model is then examined by an econometric model using data from Insideairbnb.com.

Empirical results revealed that image features have a significant positive effect on visual aesthetic perception, visual aesthetic perception has a significant positive effect on Airbnb listing price and visual aesthetic perception has a significant mediating effect between image features and Airbnb listing price.

This study contributes to the relationship and effect mechanism among image features, visual aesthetic perception and Airbnb listing price and has some implications for both property operators and the sharing accommodation platform.

本研究探讨了基于图像的视觉价格决定因素（图像特征和视觉美学感知）以及图像特征如何影响共享住宿平台Airbnb价格。

本研究采用SOR模型和hedonic价格模型来检验图像特征特征、视觉美感与Airbnb房源价格之间的关系。然后使用Insideairbnb.com上的数据, 通过计量经济学模型对该模型进行检验。

实证结果显示：1）图像特征对视觉美学感知有显著的正向影响; 2）视觉美学感知对Airbnb价格有显著的正向影响; 3）视觉美学感知在图像特征和Airbnb价格之间有显著的中介效应。

本研究有助于探讨图像特征、视觉美学感知和Airbnb价格之间的关系和影响机制, 对房源经营者和共享住宿平台都有一定的借鉴意义。

Este estudio explora los determinantes visuales del precio basados en las imágenes (características de las imágenes y percepción estética visual) y cómo afectan las características de las imágenes al precio de los anuncios de Airbnb en una plataforma de alojamiento compartido.

El estudio emplea un modelo SOR y un modelo de precios hedónicos para examinar las conexiones entre las características de los rasgos de la imagen, la percepción estética visual y los precios de Airbnb. A continuación, se examina el modelo mediante un modelo econométrico utilizando datos de Insideairbnb.com.

Los resultados empíricos revelan que 1) las características de la imagen tienen un efecto positivo significativo sobre la percepción estética visual, 2) la percepción estética visual tiene un efecto positivo significativo sobre el precio de los anuncios de Airbnb, y 3) la percepción estética visual tiene un efecto mediador significativo entre las características de la imagen y el precio de los anuncios de Airbnb.

Este estudio contribuye al mecanismo de relación y efecto entre las características de la imagen, la percepción estética visual y el precio del anuncio de Airbnb, y tiene algunas implicaciones tanto para los operadores inmobiliarios como para la plataforma de alojamiento compartido.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.