The trilateral defense and security pact between Australia, the United Kingdom, and the United States has strong impact to the security dynamics in the Indo-Pacific area. This agreement entails a strengthened alliance between Australia and enhanced military collaboration with the United States and the United Kingdom resulting in regional volatility. This paper aims to examine the AUKUS (Australia–United Kingdom–United States Partnership) agreement and the resulting ensuing instability in the Indo-Pacific region, specifically from Indonesia’s perspective. The focus of the research is on the interplay between Indonesia’s diplomacy capability and the military functions of the Indonesian Navy as security policy. This study employs a qualitative approach to delve into in-depth insights into the evolution of AUKUS in the Indo-Pacific region, which triggered a series of responses from many countries subsequent to the announcement of the establishment of the AUKUS Defense Pact. The AUKUS establishment simply reinforces the notion that geopolitical tensions are pulling the area apart. The influence of the AUKUS-China war can jeopardize regional stability since the US and China continuously demonstrate the supremacy of their armaments in order to dissuade one another. The AUKUS-China contest has had a highly adverse impact on Indonesia. This article argues that the Indonesian Navy’s diplomatic prowess is crucial because it has the potential to play a big influence in the Indo-Pacific region’s international political dynamics concerning the South China Sea. Furthermore, the Indonesian Navy must proactively prepare for potential armed conflicts in Indonesian territorial seas by developing a comprehensive maritime policy during times of peace, leveraging its geographical advantages.

Despite the proliferation of corporate social responsibility (CSR) studies, it is accruing academic interest since there still remains a lot to be further explored. The purpose of the study is to examine whether/how CSR perception affect employee/intern thriving at work and its mediator through perceived external prestige in the hospitality industry. Data from 501 hospitality industry employees and interns in China were collected using a quantitative survey consisting of 35 questions. Statistical findings showed that CSR perception and thriving at work were positively related. Additionally, perceived external prestige partially mediated the connection between CSR perception and thriving at work. Furthermore, the study found that hotel interns generally exhibited lower levels of CSR perception and thriving at work compared with frontline or managerial staff. The study underscores the importance of collaborative efforts between hotel practitioners and university educators to enhance CSR perception and promote thriving among hotel interns. By prioritizing the improvement of CSR perception and thriving at work, the hotel sector can potentially mitigate workforce shortages and reduce high turnover rates.

The presented article focusses on the analysis of perception of the university social responsibility through the eyes of Slovak university students. The aim is to compare how the values, efficiency of the organisation (university), and the educational process influence the perception of social responsibility among university students themselves. The research is based on the application of quantitative methodology towards the evaluation of differences and similarities in perceptions using two types of tests for statistical analysis, comparative (Mann-Whitney U test) and correlational (bivariate correlation matrix of Spearman’s rho).The results of the research provide a deeper understanding of how universities can shape students’ approach to social responsibility through their values and educational processes, which has important implications for the development of university policies and practices.

Entrepreneurship education plays a crucial role in improving college students' entrepreneurial skills. With the significant momentum gained by digital entrepreneurship, there is an urgent need for digital transformation in entrepreneurship education. However, entrepreneurship education digital transformation (EEDT) is developing in a rapid but fragmented manner, which requires more systematic guidance. This study aims to assess the current research themes and formulate a framework for entrepreneurship education digital transformation. The research employs a systematic literature review and a theory triangulation method. According to the review’s outcome, which focused on 56 articles published between 2018 and 2023, the researcher constructed a conceptual framework for entrepreneurship education digital transformation. To test the construct validity of the framework, the researcher modified it twice through theory triangulation, following the guidelines of the entrepreneurship education ecosystem theory and the education digital transformation framework. This study offers recommendations for research and practice in digital transformation of entrepreneurship education, encompassing a holistic strategy, new educational approaches, novel curriculum designs, and the enhancement of digital literacy among entrepreneurship teachers.

Sustainable development has attracted widespread attention worldwide, and the circular economy has become one of the essential policies of many countries. Small and medium-sized enterprises are important drivers of world economic growth and can significantly impact the environment. Therefore, SMEs are critical players in implementing a circular economy as the basis for creating a sustainable society. Although a wealth of research on SME environmental management issues can be found in the literature, more must be known about the infusion of green practices in SMEs. The primary purpose of this study is to explore the green practice infusion of Taiwanese SMEs, a context that is particularly relevant due to Taiwan’s strong focus on environmental sustainability and its circular economy industrial development policy. Through a questionnaire survey, this study examined the factors that influence green practice infusion behavior in Taiwanese SMEs and the impact of green practice infusion on circular economy performance. The findings show that the relative advantages and compatibility of the circular economy, organizational support, human resource quality, regulatory pressure, and government support significantly impact the green practice infusion of Taiwanese SMEs. The effects of complexity, customer pressure, and environmental uncertainty on SMEs’ infusion of green practices are not statistically significant. Circular economy performance is positively correlated with green practice infusion. This study can broaden the research scope of SMEs’ environmental management and contribute to a deeper understanding of SMEs’ green practice infusion and circular economy.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.