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The purpose of this paper is to explore how internal and external factors simultaneously drive firms to adopt green supply chain (GSC) initiatives and to construct a comprehensive research model by drawing upon institutional theory, stewardship theory, and view of performance.

The data collected from 380 manufacturers in the electrical and electronics industries in Taiwan were analyzed via structural equation modeling and bootstrapping.

First, institutional pressures affect the GSC initiatives of firms. Second, institutional pressures influence the environmental stewardship behaviors (ESBs) of managers. Third, the ESBs of managers affect the GSC initiatives of firms. Fourth, the GSC initiatives of firms influence their environmental performance, economic performance, and competitiveness. Fifth, the bootstrapping results reveal that institutional pressures indirectly affect the GSC initiatives of firms through the ESBs of managers.

Environmental sustainability has intensified the need for firms to develop a corporate culture. Future research can investigate the relationship among the institutional pressures, greening corporate culture, and GSC initiatives of firms.

Those managers facing institutional pressures must continually focus on the effects of external factors on the GSC initiatives of their firms. They must also increase their commitment and support to such initiatives to attain favorable levels of environmental performance, economic performance, and competitiveness.

This study integrates four streams of literature on institutional theory, stewardship theory, GSC initiatives, and view of performance. Apart from analyzing field- and organization-level data simultaneously, this paper is also the first to demonstrate the relationships among institutional pressures, ESBs of managers, GSC initiatives, and firm performance.

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.

To investigate the relation between the Poisson's ratio of a re‐entrant honeycomb structure by varying the micropolar material constants such as micropolar Young's modulus E_m, micropolar Poisson's ratio V_m, characteristic length l, coupling factor N, and micropolar elastic constants in accordance with the micropolar elastic restrictions, a 2‐D triangular finite element formulation including an extra degree of freedom was derived on the basis of Eringen's micropolar elasticity theory by using a linear triangular element. The effects on the structural Poisson's ratio of the honeycomb structure are studied in detail.

This paper aims to propose and test a moderated mediation model examining the relationships among ethical work climate, organizational identification, leader-member-exchange (LMX) and organizational citizenship behavior (OCB).

Numerous regression analyses were performed using PROCESS (version 2.13), a macro for SPSS developed by Hayes (2017) to test this moderated mediation model.

The analytical results showed that organizational identification mediates the positive relationship between an ethical work climate and OCB. The analytical results also showed that LMX moderates the direct effect of ethical work climate on organizational identification and that LMX also moderates the indirect effect of ethical work climate on OCB via organizational identification.

This study provides numerous valuable implications for hotels to develop effective strategies to promote employees’ OCB and improve their organizational identification.

This study was the first attempt to propose and test a moderated mediation model that explores the relationships among ethical work climate, organizational identification, leader-member-exchange (LMX) and OCB.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.