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This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The deposition of particles onto a substrate during the cold spraying (CS) process relies on severe plastic deformation, so there are various micro-defects induced by insufficient deformation and severe crushing. To solve the problems, many post-treat techniques have been used to improving the quality by eliminating the micro-defects. This paper aims to help scholars and engineers in this field a better and systematic understand of CS technology by summarizing the post-treatment technologies that have been investigated recently years.

This review summarizes the types of micro-defects and introduces the effect of micro-defects on the properties of CS coating/additive manufactured, illustrates the post-treatment technologies and its effect on the microstructure and performances, and finally outlooks the future development trends of post-treatments for CS.

There are significant discoveries in post-treatment technology to change the performance of cold spray deposits. There are also many limitations for post-treatment methods, including improved performance and limitations of use. Thus, there is still a strong requirement for further improvement. Hybrid post-treatment may be a more ideal method, as it can eliminate more defects than a single method. The proposed ultrasonic impact treatment could be an alternative method, as it can densify and flatten the CS deposits.

It is the first time to reveal the influence factors on the performances of CS deposits from the perspective of microdefects, and proposed corresponding well targeted post-treatment methods, which is more instructive for improving the performances of CS deposits.

Some researchers have found that disasters may have a “blessing in disguise effect” that some disaster sites transformed into more popular tourism destinations; however, no studies have analyzed the heterogeneity of the “blessing in disguise effect”. This paper aims to explore and determine the effect of cultural distance on international inbound tourist arrivals to a post-disaster tourist destination that could explain this heterogeneous phenomenon.

This study used a threshold regression model and a differences-in-differences (DID) approach to analyze 2000–2016 international tourist arrival data from 13 main origin countries to Sichuan Province before and after the 2008 Wenchuan Earthquake in China.

The effect of cultural distance on post-disaster inbound arrivals from the various origin countries followed a non-linear U-shaped “double-edged sword” pattern rather than displaying a simple linear relationship. Most notably, the disaster appeared to have a more positive effect on arrivals from countries with larger cultural distances, while the effect on arrivals from countries with shorter cultural distances was negative.

This study found that cultural distance could explain the heterogeneous “blessing in disguise” phenomenon, and it had both positive and negative impacts on tourism destination recovery; that is, a definite “double-edged sword effect” of cultural distance was found, which could help destination marketing organizations and management departments to design appropriately targeted marketing for post-disaster tourism destination recovery.

些研究人员发现, 灾难可能会产生“因祸得福效应”, 即一些灾难现场变成了更受欢迎的旅游目的地。然而, 目前还没有研究分析这种“因祸得福效应”的异质性。本研究旨在探讨文化距离对灾后旅游目的地的国际入境旅游流的影响, 从而解释这种异质性现象。

本文采用门槛回归模型和双重差分法（DID）分析了2008年汶川地震发生前后（2000–2016年）来自13个主要来源国到中国四川省旅游的国际入境旅游流数据。

文化距离对灾后客源国的入境旅游人数的影响呈现非线性的U型“双刃剑”模式, 而不是简单的线性关系。最值得注意的是, 灾难似乎对来自文化距离较大的客源国的游客人数产生了更积极的影响, 而对来自文化距离较短的客源国的游客人数产生了消极的影响。

本研究发现, 文化距离可以解释异质性的“因祸得福”现象, 文化距离对旅游目的地恢复既有积极影响, 也有消极影响, 即文化距离具有明确的“双刃剑效应”。这可以帮助旅游目的地的灾后恢复设计合理的、有针对性的营销策略和恢复政策。

Algunos investigadores han encontrado que los desastres pueden tener la “bendición del efecto disfraz” de que algunos sitios de desastre se transformen en destinos turísticos más populares;Sin embargo, ningún estudio ha analizado la heterogeneidel “efecto de bendición disfrazado”.El objetivo de este estudio es explorar y determinar el efecto de la distancia cultural en los flujos turísticos internacionales hacia un destino turístico después del desastre, lo que podría explicar este fenómeno heterogéneo.

se empleun modelo de regresión de umbral y un enfoque de diferencias en diferencias (DID) para analizar los datos de llegada de turistas internacionales de 2000–2016 de trece países de origen principal A la provincia de Sichuan antes y después del terremoto de Wenchuan de 2008 en China.

el efecto de la distancia cultural en las llegde los diversos países de origen después del desastre siguió un patrón de “espada de doble filo” no lineal en forma de u, en lugar de mostrar una relación lineal simple.Más notablemente, el desastre pareció tener un efecto más positivo en las llegde países con distancias culturales más grandes, mientras que el efecto en las llegde países con distancias culturales más cortas fue negativo.

este estudio encontró que la distancia cultural podría explicar el heterogéneo fenómeno de la “bendición disfrazada” y que tenía impactos tanto positivos como negativos en la recuperación de un destino turístico;Es decir, se encontró un “efecto de doble filo” de la distancia cultural, que podría ayudar a las organizaciones de comercialización de destinos turísticos y a los departamentos de administración a diseñar estrategias de comercialización dirigidas adecuadamente para la recuperación de destinos turísticos después del desastre.

This chapter provides a comprehensive review of research and developments relating to the use of Web 2.0 technologies in education. As opposed to early educational uses of the Internet involving publication of static information on web pages, Web 2.0 tools offer a host of opportunities for educators to provide more interactive, collaborative, and creative online learning experiences for students. The chapter starts by defining Web 2.0 tools in terms of their ability to facilitate online creation, editing, and sharing of web content. A typology of Web 2.0 technologies is presented to illustrate the wide variety of tools at teachers’ disposal. Educational uses of Web 2.0 technologies such as wikis, blogs, and microblogging are explored, in order to showcase the variety of designs that can be utilized. Based on a review of the research literature the educational benefits of using Web 2.0 technologies are outlined, including their ability to facilitate communication, collaborative knowledge building, student-centered activity, and vicarious learning. Similarly, issues surrounding the use of Web 2.0 tools are distilled from the literature and discussed, such as the possibility of technical problems, collaboration difficulties, and plagiarism. Two case studies involving the use Web 2.0 tools to support personalized learning and small group collaboration are detailed to exemplify design possibilities in greater detail. Finally, design recommendations for learning and teaching using Web 2.0 are presented, again based on findings from the research literature.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

This chapter seeks to analyse neighbouring in times of rapid change in the Chinese metropolis of Shanghai. Using the local neighbourhood of Tianzifang as a case study, this chapter shows how the pace of change in Shanghai since the 1950s has reshaped the social fabric of the area, including traditional neighbour relations. As Tianzifang has shifted from an unknown neighbourhood comprised of traditional lilong dwellings, where social relations played out in private communal settings, to one of Shanghai’s most famous tourist spots, the relationships between residents, as well as with ‘outsiders’ and the familiar local environment, have also changed. Traditional neighbourly ties have been broken, rebuilt and transformed again through waves of new residents coming into the area and the displacement of others who can no longer afford to live there. For those who remain, the question of who is, and thus who is not, a ‘real’ neighbour has been narrowed to include only ‘insiders’ with a deep sense of belonging and connection to the area – i.e. the urban working class who have always lived in Tianzifang and own rather than rent their homes. Yet even this is no longer sufficient. The disruption that Tianzifang’s physical and social transformation has brought to residents’ lives has also imposed new social norms about how neighbours should behave in this new, intermediary space of public and private. ‘Real’ neighbours are those who conform to changing social norms while neighbours who breach them can be ostracised and excluded by others who once saw them as an insider.

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

Rapid development of IT and communication technologies resulted in the “Smart” concept, which of late has become quite popular. The smart concept signifies the integration of organizational networks and smart features that enrich the ecosystem, facilitate daily activities for all stakeholders, and for automatization. Smart concept was discussed as a complicated technological infrastructure in urban areas intended to promote economic, social and environmental welfare. The latest technological developments gave rise to the concepts of smart planet, smart city and smart destination that have become important in recent years. Technological innovations have had a big influence on the development of the tourism industry. Smart concept is conceptualized as smart tourism for tourism sector. Smart Tourism generally has a positive effect on the rapid change of information and technology and on the tourism sector, tourism activities and increase in customer satisfaction. In this chapter, the concept of Smart and its smart technologies are explained and information about its reflections on the tourism sector and smart tourism destinations are discussed.

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.

The extant literature provides much-needed support to understand marketing accountability and how marketing actions are related to financial performance (FP). However, we have limited understanding of the relationships between marketing actions and firms' social performance (SP) and environmental performance (EP). Understanding these links is critical to enhancing sustainable FP, SP, and EP. Moreover, the literature provides limited understanding of the measures by which SP and EP may be operationalized, or the data necessary to reach a conclusion. This study bridges these gaps by extensively reviewing the extant literature to offer a set of measures and data sources to operationalize SP and EP, and empirically show their relationships with marketing actions. We find that greenhouse gas (GHG) emission, environmental disclosure score, waste reduction, energy consumption, and recycling are prominent measures of EP, and that social disclosure score, philanthropy or community spending, and diversity of gender and race are prominent measures of SP. The KLD, ASSET4, and Bloomberg are prominent sources of data that can be used to operationalize SP, to which CDP may be added for EP. We also show that marketing actions positively affect EP and SP. This study contributes to the extant literature on SP and EP by identifying measures and data sources and linking marketing actions to both performance types. It contributes to policy development by identifying the importance of EP and SP and how marketing actions can help achieve such performance.