Despite many investigations concerning antecedents of organizational commitment in the workplace, very few studies so far have analyzed the direct or indirect impact of HR change leadership role on organizational commitment via HR attribution. Therefore, given the reciprocal principle of social exchange theory, attribution theory and signal theory, this study formulated hypotheses and a model to test the relationships between included variables by employing the mixed-method approach. In-depth interviews were initially conducted to develop questionnaires to collect quantitative data. Employing PLS-SEM to analyze the data collected from 1058 employees working in 24 sustainable enterprises in Vietnam, the findings show that the degree of adopting HR change leadership role was positive, directly affecting organizational commitment. Also, both well-being and performance HR attribution play partially mediated roles in the relationship. The findings suggest that the organizational commitment depends on not only how the degree of adopting HR change leadership role is executed, but also how employees perceive and interpret the underlying management intent of these practices. In a sustainable context, adopting HR change leadership role plays a critical role in shaping employees’ interpretations of sustainable HR practices and their subsequent attributions. Besides, employees’ belief on why are sustainable HRM practices implemented has an influence on the organizational commitment that in turn contributes to the overall sustainable performance.

The article analyzes the process of formation of research universities as one of the elements of a strong innovation economy. The formation of a new university model is a global trend, successfully implemented in English-speaking countries. In Russia, the educational system is not yet ready to ensure the country’s effective competition in the innovation market. The Strategic Academic Leadership Program “Priority-2030” is designed to carry out the functional transformation of the entire infrastructure of human capital reproduction in a short period of time in Russia. The article presents an analysis of the main conditions for the development of a university with a research strategy, as well as an assessment of the implementation of this strategy by Moscow Polytechnic University. The methodological basis of the study was formed by qualitative methods: included observation and benchmarking of universities’ activities, which allowed to generalize the current global trends and best practices in the field of education. For the analysis we used the data of monitoring the activities of higher education organizations, data of official statistics, as well as data from reports and presentation materials of universities and online publications participating in the “5-100” and “Priority-2030” programs. The results of the study may be useful for researchers and practitioners engaged in the transformation of the Russian higher education system.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

Today it is obvious that corporate social responsibility (CSR) is more than just a volunteer activity, it is also related to the operation of the firms and to competitive advantages. Many factors influence CSR and CSR-competitiveness relations; firm size could be the most crucial one. Originally CSR is related to large companies, although smaller firms can be active in CSR mainly in different ways with different background. Based on this idea the paper aims to explore the correlation between small and medium-sized enterprises’ (SMEs) corporate social responsibility (CSR) and competitive advantages. An interview research was conducted among thirty SMEs in a Hungarian city of Győr in 2021/22 to reveal how owner-managers interpret CSR, competitiveness and their relations. As SMEs cannot provide exact data on this topic the personal perception method was used to explore the CSR-competitiveness relation. A moderate relation was observed between CSR and competitiveness and the research revealed that different methodologies have to be applied for SMEs than large companies which results from the fact that SMEs’ CSR is less formal and lacks exact data.

This paper aims to systematically analyze the current state of plastic waste legal supervision in China and to propose a vision for future governance frameworks. In recent years, along with the vigorous rise of emerging industries such as the express delivery industry and takeaway services, the consumption of plastic products has increased sharply. This trend has triggered profound reflection and high vigilance on the issue of plastic waste supervision. This trend has triggered profound reflection and acute vigilance regarding the regulation of plastic waste. Although the Chinese government has initiated multiple regulatory measures and achieved certain outcomes, from a macroscopic perspective, the issue of plastic waste pollution remains grave, and the relevant legal and regulatory system presents a complex situation with limited enforcement efficacy. Hence, it is exceptionally urgent and significant to deeply explore and formulate legislative strategies aimed at alleviating and regulating plastic waste pollution. This paper is dedicated to systematically analyzing the current state of plastic waste legal supervision from both international and domestic dimensions, and meticulously outlining the regulatory framework for plastic waste governance in China. Through the application of legal norm research methods, this paper dissects the flaws and challenges existing in the current governance mechanisms and further conducts a comparative study of the successful practices in this field in developed countries like the United States, with the intention of drawing valuable experiences. On this basis, this paper not only offers a forward-looking outlook on China’s future legislative tendencies in plastic waste pollution but also innovatively proposes a series of new insights and recommendations. These explorations aim to provide a more solid theoretical foundation and practical guidance for the governance approach to plastic waste pollution in China, promote the improvement and enhancement of the enforcement effectiveness of environmental regulations, and thereby effectively confront the global challenge of plastic pollution.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.