In the context of establishing businesses in a new region, neglecting environmental orientation may lead to the omission of crucial motives for entrepreneurs’ migration and the subsequent course of their businesses. This present study aims to investigate the effect of green space quality (GSQ), green campaign (GC), and green attitude (GA) on green entrepreneurship pioneering intention (GEPI). Further, national pride (NP) was added as a moderator. This study utilized a cross-sectional approach using a survey method targeting small and medium-sized enterprise (SME) owners who will be relocated to the new capital city. Partial least square structural equation modeling was employed in the data analysis. The results revealed that GSQ, GC, and GA positively influence GEPI. Also, NP moderates the positive influences of GC and GA on GEPI. Entrepreneurs were motivated to pioneer green entrepreneurship in the new region due to environmental factors. Furthermore, their nationalism reinforces the connection between environmental motivations and the aspirations to undertake such pioneering endeavors. The findings present valuable insights for governments to formulate policies that encourage entrepreneurs to migrate internally and establish new economic nodes. Further, the results demonstrate how nationalism encourages green business pioneering endeavors in an untapped market.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Indonesia has ratified United Nations Convention on the Law of the Sea 1982 (UNCLOS 1982) through Law No. 17 of 1985 concerning the ratification of the 1982 Law of the Sea Convention, thus binding Indonesia to the rights and obligations to implement the provisions of the 1982 convention, including the establishment of the three Northern-Southern Indonesia’s Archipelagic Sea Lane (ALKI). The existence of the three ALKI routes, including ALKI II, has led to various potential threats. These violations not only cause material losses but, if left unchecked and unresolved, can also affect maritime security stability, both nationally and regionally. The maritime security and resilience challenges in ALKI II have increased with the relocation of the capital, which has become the center of gravity, to East Kalimantan. The research in this article aims to identify and analyze the factors influencing the success of maritime security and resilience strategies in ALKI II. The factors used in this research include conceptual components, physical components, moral components, command and control center capabilities, operational effectiveness, command and control effectiveness, and the moderating variables of resource multiplier management and risk management to achieve maritime security and resilience. This study employed a mixed-method research approach. The factors are modeled using Structural Equation Modeling (SEM) with WarpPLS 8.0 software. Qualitative data analysis used the Soft System Methodology (SSM). The results of the study indicate that the aforementioned factors significantly influence the success of achieving maritime security and resilience in ALKI II.

The growing interconnectedness of the world has led to a rise in cybersecurity risks. Although it is increasingly conventional to use technology to assist business transactions, exposure to these risks must be minimised to allow business owners to do transactions in a secure manner. While a wide range of studies have been undertaken regarding the effects of cyberattacks on several industries and sectors, However, very few studies have focused on the effects of cyberattacks on the educational sector, specifically higher educational institutions (HEIs) in West Africa. Consequently, this study developed a survey and distributed it to HEIs particularly universities in West Africa to examine the data architectures they employed, the cyberattacks they encountered during the COVID-19 pandemic period, and the role of data analysis in decision-making, as well as the countermeasures employed in identifying and preventing cyberattacks. A total of one thousand, one hundred and sixty-four (1164) responses were received from ninety-three (93) HEIs and analysed. According to the study’s findings, data-informed architecture was adopted by 71.8% of HEIs, data-driven architecture by 24.1%, and data-centric architecture by 4.1%, all of which were vulnerable to cyberattacks. In addition, there are further concerns around data analysis techniques, staff training gaps, and countermeasures for cyberattacks. The study’s conclusion includes suggestions for future research topics and recommendations for repelling cyberattacks in HEIs.

Sketching on stimulus-organism-response theory, this study aims to investigate the mediating effect of environmental passion on the relationship of the environmentally specific servant leadership with employees’ green behavior. Using purposive sampling approach, the authors adopted one month time-lagged approach to collected data from 232 academic employees in higher education institutions of China. Response rate in this study is 46.40%. The partial least-structural equation modeling (PLS-SEM) analysis was conducted in the smartpls 4.0 software to test the proposed hypotheses. The current empirical findings confirm that environmentally specific servant leadership significantly positively influence employee’s environmental passion and environmental passion significantly positively affects the employee’s workplace green behaviors. This current finding offered support in favor of mediating impact of environmental passion on the “environmentally specific servant leadership-employees workplace green behaviors” relationship. To the best of authors, this study is among pioneers’ studies to investigate the integrated relationship of environmentally specific servant leadership, environmental passion and green behavior in higher education institutions context of China. Limitations and implication have been elaborated at the end.