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The purpose of this study is to explore the moderating effect of Confucian values and the mediating effect of relationship quality on the relationship between organizational justice and employee service orientation in the tourism and hospitality industry.

Structural equation modeling was applied to a sample of 421 responses in a questionnaire survey from employees of tourism and hospitality firms in mainland China.

Employee relationship quality fully mediated the relationship between organizational justice and service orientation. Confucian values negatively moderated the direct effect of organizational justice on employee relationship quality and the indirect effect of organizational justice on service orientation.

This study offers insights for hospitality managers how to improve employee service orientation and establish Confucian values in the practice of organizational justice. Tourism and hospitality organizations should equally treat all employees as internal customers and use distinct strategies to manage employees with high and low Confucian values in employee selection and management of training and development.

This study highlights the contributions of organizational justice and relationship quality to employee service orientation. It also demonstrates that Confucian values explain why many Chinese employees are less sensitive to low fairness: these values negatively moderate the organizational justice–relationship quality–service orientation relations. By linking organizational justice to relationship quality and employee service orientation, the findings enrich our understanding of the applications of internal marketing and social exchange theories under Confucian values.

This study aims to empirically examine how blockchain, one of the emerging Industry 4.0 technologies, can combat climate change by improving their green innovation performance, particularly under conditions of policy uncertainty.

This study utilizes the difference-in-difference-in-difference (DDD) method to explore the effect of blockchain on enterprises' green innovation performance. The analysis is based on data from Chinese-listed enterprises spanning the period from 2013 to 2021.

First, the adoption of blockchain in enterprises registered in areas designated as low-carbon pilot cities can significantly improve their green innovation performance. Second, the enhancement of green innovation efficiency emerges as the primary driving force behind the adoption of blockchain, thereby leading to improved green innovation performance. Lastly, it is observed that blockchain adoption has a greater positive impact on improving green efficiency in private enterprises compared to state-owned enterprises in China.

For managers, the findings can provide valuable insights to help them better prepare for the challenges and opportunities presented by the era of Industry 4.0. For policymakers, this study offers valuable insights into the interaction between new technologies in Industry 4.0 and the performance of green innovation, thereby aiding in the formulation of effective policies.

This study contributes to bridging the existing gap between the adoption of new technologies, such as blockchain, and their potential impact on climate change. Moreover, this research enriches practitioners' understanding of how new technologies in the era of Industry 4.0 can be applied to address significant challenges like climate change.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

With the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research to combine with the perceived helpfulness study needs urgent solution. Furthermore, deviations exist in the three common methods of perceived helpfulness research. Finally, the traditional information fusion analysis only analyzes the advantages and disadvantages of products in competitiveness analysis without taking account of the competitive environment.

This study puts forward a novel prediction model of perceived helpfulness in conjunction of unsupervised learning and sentiment analysis techniques, to conduct the comparison with pros and cons of congeneric products.

This paper adopts Wilcoxon test to demonstrate the significant rectification of our competitiveness analysis to the traditional methods. It is noted that the positive reviews of the products in this study impact more on product word of mouth and competitiveness than negative ones.

To sum up, the results of this study benefit businesses in locating their dynamic market position with competitors in practice and exploring new method for long-term development strategic planning.