This study aims to develop and validate a strategic model tailored to the unique challenges and contexts faced by micro, small, and medium-sized enterprises (MSMEs) in Ecuador, enhancing their operational efficiency and access to financing. Employing a quantitative approach, the research utilized a non-experimental, cross-sectional design to gather data from a sample of 358 companies. The study revealed that MSMEs are significantly hindered by limited access to financing, lack of managerial skills, and technological gaps. Despite these challenges, MSMEs demonstrated considerable adaptability and resilience, underscoring their critical role in the local economy. The strategic model proposed leverages Porter’s Diamond Model to identify and address the specific competitive and operational challenges encountered by these enterprises. Key findings include the necessity for enhanced financial literacy, simplified regulatory frameworks, and the integration of digital technologies to improve competitiveness. The proposed model focuses on strategic training, fostering innovation, and creating a more supportive financing environment. The implications of this study are profound, suggesting that policymakers and practitioners should streamline regulatory processes, enhance financial and technological support frameworks, and provide tailored training programs. These strategies are intended to bolster the sustainability and growth of MSMEs, contributing to broader economic development. This research contributes to the academic literature by providing empirical evidence on the challenges faced by MSMEs in developing economies and proposing a contextually adapted strategic model to mitigate these challenges, thereby enhancing their economic impact and sustainability.

Effective harvesting strategies are crucial for maximizing annual catch and ensuring the sustainability of lobster (Homarus americanus) farming. This paper presents a nonlinear objective programming model to optimize harvesting intensity based on lobster life cycle dynamics and harvesting characteristics. We model the population dynamics of 1-4 year-old lobsters using differential equations to account for natural mortality, spawning, and harvesting effects. Solving the model with LINGO 12.0, we determine that the optimal harvesting intensity coefficient is 17.36, which maximizes annual catch to 3.88 × 10¹⁰ grams. Results indicate that maintaining harvesting intensity around this optimal value balances economic benefits and population stability, ensuring sustainable farm operations.

This article examines the factors influencing sustainable entrepreneurship (SE) in Arab countries, focusing on economic, social, and technological dimensions. Using data from various sources and structural equation modeling, the study explores the relationships between these factors and SE sustainability. The findings reveal that economic factors, such as GDP per capita and foreign direct investment (FDI), positively influence SE sustainability, emphasizing the need for a conducive economic environment. Social factors, measured by Internet usage and the Human Development Index (HDI), also significantly impact SE sustainability, highlighting the importance of access to information and education. However, technological factors like patent applications and high-tech exports did not show a significant positive relationship with SE sustainability, suggesting a minimal direct impact on SE longevity in Arab countries. These insights have implications for policymakers, stressing the importance of fostering economic growth and enhancing social infrastructure to support sustainable entrepreneurial ecosystems. Despite its robust methodology, the study has limitations, such as incomplete data for certain countries, affecting the generalizability of the findings. Future research could explore additional factors influencing SE sustainability, further investigate the role of technology, and expand the geographical scope to include more Arab countries.

This article investigates how green logistics influences Vietnam’s trade balance with Association of Southeast Asian Nations (ASEAN) countries. By using the gravity model, the article applies fixed effects (FEM) and random effects (REM) to analyze panel data on trade balance, GDP, population, trade openness, and the green logistics index of Vietnam with ASEAN countries from 2012 to 2018. The research findings indicate that green logistics has not significantly affected Vietnam’s export trade balance with ASEAN countries. The article suggests solutions for the Vietnamese government and export businesses to enhance Vietnam’s trade balance with ASEAN countries by integrating green logistics activities. By following these recommendations, Vietnam can ensure that international trade aligns with environmental conservation, laying the groundwork for sustainable and inclusive economic development in Vietnam.

Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.

Housing is one of the most significant components of sustainable development; hence, the need to come up with sustainable housing solutions. Nevertheless, the sales of houses are steadily falling due to the unaffordability of houses to many people. Based on the expanded community acceptance model, this research examines the relationships between sustainable housing and quality of life with the moderating factors of knowledge, technology, and innovation in Shenzhen. Additionally, it aims to delineate the principal dimensions influencing quality of life. The study employs purposive sampling and gathers data from residents of Shenzhen via a Tencent-distributed survey. Analysis was conducted using Smart Partial Least Squares (PLS) 4.0. Results indicate a positive correlation between economic sustainability in housing and quality of life. Contrarily, the social and environmental aspects exhibited negligible impacts on quality of life. Knowledge, technology, and innovation were identified as significant moderators in the correlation among all three sustainable housing dimensions and quality of life. The findings are anticipated to enhance understanding of the perceived impacts of sustainable housing on quality of life in Shenzhen and elucidate the role of knowledge, technology, and innovation in fostering this development.