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.

 Purpose: This research paper aims to assess the proficiency of tertiary education providers in engaging with online learning environments, especially in the context of the post-COVID-19 transition. The COVID-19 pandemic accelerated the adoption of online learning platforms, it is essential to understand how educational institutions have adapted and evolved in their approach to virtual education. The central research question explores how Continuous Professional Development (CPD), Technological Infrastructure (TI), and Support Systems (SS) collectively influence educators’ proficiency in online teaching (POT). Study design/methodology/approach: A comparative study was performed, comparing data collected during the COVID-19 pandemic with post-pandemic data from higher education institutions in Uzbekistan. In-depth interviews were conducted with 15 education facilitators representing both public and international educational institutions. This purposive sampling approach allows for a holistic exploration of the experiences, challenges, strategies, and preparedness of these facilitators during the transition to online learning. Manual qualitative data classification and content analysis were employed to understand themes in respondent experiences and identified actions. Findings: The study reveals the significant role of CPD, robust TI, and effective SS in enhancing the Proficiency of tertiary education providers in engaging with Online Teaching. These elements were found to be significant determinants of how well institutions and educators adapted to the shift to virtual education. The research offers valuable insights for educators, policymakers, and students, aiding in decision-making processes within academia and guiding the development and implementation of effective online teaching strategies. Originality/value: This study contributes to the existing literature by providing an in-depth understanding of the adjustments education facilitators make in response to the pandemic. It emphasizes the importance of ongoing preparation for online learning and highlights the role of digital workplace capabilities in ensuring successful interaction in virtual educational environments.

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.

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.

This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.