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.

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.

This study evaluated the efficiency and productivity of the manufacturing industries of Singapore. Singapore is one of the world’s most competitive countries and manufacturing giants. All 21 manufacturing industries as classified by Singapore’s Department of Statistics were included in the study as decision-making units (DMUs). Using the Malmquist DEA on data spanning 2015–2021, we found that excerpt for the Paper and Paper product industry, all industries recorded positive total factor productivity (TFP). TFP ranged from 0.977 to 1.481. In terms of technical efficiency, 14 out of 21 industries showed positive efficiency change. The highest TFP was recorded in 2020 and the lowest in 2016. By measuring and improving efficiency, industries in Singapore can achieve cost savings, increase output, and enhance their competitiveness in the global marketplace. In addition, efficiency measurement can help policymakers identify potential areas for improvement and develop targeted policies to promote sustainable economic growth. Given these benefits, performance measurement is inevitable for industries and policymakers in Singapore to achieve economic objectives. Manufacturing industries need to find ways to manage the size and scale of operations as we flag this as an area for improvement.

Providing and using energy efficiently is hampered by concerns about the environment and the unpredictability of fossil fuel prices and quantities. To address these issues, energy planning is a crucial tool. The aim of the study was to prioritize renewable energy options for use in Mae Sariang’s microgrid using an analytical hierarchy process (AHP) to produce electricity. A prioritization exercise involved the use of questionnaire surveys to involve five expert groups with varying backgrounds in Thailand’s renewable energy sector. We looked at five primary criteria. The following four combinations were suggested: (1) Grid + Battery Energy Storage System (BESS); (2) Grid + BESS + Solar Photovoltaic (PV); (3) Grid + Diesel Generator (DG) + PV; and (4) Grid + DG + Hydro + PV. To meet demand for electricity, each option has the capacity to produce at least 6 MW of power. The findings indicated that production (24.7%) is the most significant criterion, closely followed by economics (24.2%), technology (18.5%), social and environmental (18.1%), and structure (14.5%). Option II is strongly advised in terms of economic and structural criteria, while option I has a considerable advantage in terms of production criteria and the impact on society and the environment. The preferences of options I, IV, and III were ranked, with option II being the most preferred choice out of the four.

Participation in the implementation of green values that are becoming a global norm often experiences challenges. In response with trends of social media use, a study of barriers to green product purchase intention among social media users is conducted. By descriptive qualitative approach, three keywords are employed, namely: (1) “barriers to green consumption”; (2) “barriers of purchase intention; and (3) “social media use and barriers to green consumption”. The findings reveal: (1) the study of barriers to green product purchase intention among social media users has been gaining importance for future research; (2) the potential future research area includes: (a) the level of belief in green products purchase intention that explains the rationalization of green consumption (green knowledge); and (b) the use of digital media through the role of social media in promoting green consumption (green promotion). The theoretical implication emphasizes contribution to the theory of sustainable marketing, namely barriers as dynamics of market interactivity that are capable of generating responsiveness leading to business competitiveness. While practical implication is shown in business efforts to transform challenges into opportunity.

This study investigates the impact of toll road construction on 59 micro, small, and medium enterprises in Kampar, Pekanbaru, and Dumai cities. The research aims to analyze the economic and environmental effects of infrastructure expansion on businesses’ profitability and sustainability, providing insights for policymakers and stakeholders to develop mitigation strategies to support MSMEs amidst ongoing infrastructure development. Structural equation modeling, spatial environmental impact analysis, and qualitative data analysis using five-level qualitative data analysis (FL-QDA) were all used together in a mixed-methods approach. Data collection involved observations, interviews, questionnaires, and geospatial analysis, including the use of a Geo-Information System (GIS) supported by drone reconnaissance to map affected areas. The study revealed that the toll roads significantly enhanced connectivity and economic growth but also negatively impacted local economies (β = 0.32, R² = 0.60, P-value ≤ 0.05). and the environment (β = 0.34, P-value ≤ 0.05), as 49% of respondents experienced a 50% decrease in profitability. To mitigate the risk of impact, policymakers should prioritize the principle of prudence to evaluate the significance of mitigation policy implementation (β = 0.144, P-value ≥ 0.05). In a nutshell, toll road construction significantly impacts MSMEs’ business continuity, necessitating an innovative strategy involving monitoring and participatory approaches to mitigate risk.