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.

Dredging and reclamation operations are pivotal aspects of coastal engineering and land development. Within these tasks lie potential hazards for personnel operating dredging machinery and working within reclamation zones. Due to the specialized nature of the work environment, which deviates from conventional workplace settings, the risk of workplace accidents is significantly heightened. The aim of this study is to conduct a comprehensive risk analysis of the safety aspects related to dredging and reclamation activities, with the goal of enhancing safety and minimizing the frequency and severity of potential dangers. This research comprises a thorough risk analysis, integrating meticulous hazard identification from sample projects and literature reviews. It involves risk assessment by gathering insights from experts with direct working experience and aims to assess potential risks. The study focuses on defining effective risk management strategies, exemplified through a case study of a nearshore construction project in Thailand. The study identified numerous high and very high-risk factors in the assessment and analysis of occupational safety in dredging and reclamation work. Consequently, a targeted response was implemented to control and mitigate these risks to an acceptable level. The outcome of this study will provide a significant contribution to the advancement of guidelines and best practices for improving the safety of dredging and reclamation operations.

The operational performance of container ports is crucial for efficient logistics and trade. However, there is limited understanding of how external integration through Customer and Supplier Integration (SCI-CI and SCI-SI) impacts port operational performance (POP), particularly in emerging markets like Oman. This study addresses this gap by examining the relationship between SCI-CI, SCI-SI, and POP, and explores the mediating role of supply chain management (SCM) practices in this context. Using the Resource-Based View (RBV) as the theoretical framework, the study employed a quantitative cross-sectional survey method. A total of 377 questionnaires were distributed to managers at Sohar and Salalah ports, with 331 usable responses obtained, representing an 88 percent response rate. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that SCI-CI and SCI-SI have significant direct and indirect positive effects on POP, and they directly influence SCM practices. SCM practices, in turn, significantly enhance POP. Notably, SCM practices partially mediate the relationship between SCI-CI and SCI-SI with POP. These findings underscore the strategic importance of external integration and SCM practices as internal resources for improving port performance. This research provides valuable insights for decision-makers and policymakers in optimizing port operations.

Current study examines the intervening role of team creativity for the relationship of four kinds of KM practice with innovation and the moderating effect of proactiveness in IT companies based on a Knowledge-Based View (KBV). Data was collected from 316 employees of IT companies who engage in software development in teams with the help of a simple random sampling method. Results indicate that KM practices have a positive impact on innovation. Also, team creativity plays mediating role in the relation of two KM practices i.e., knowledge sharing and knowledge application with innovation. Whereas proactiveness plays a positive moderating role in the relation of knowledge application and knowledge generation with innovation. Moreover, it plays a negative moderating role in relation of Knowledge sharing with innovation. This research adds to the body of literature by suggesting a framework of knowledge diffusion, knowledge storage, knowledge generation, knowledge application, team creativity, proactiveness, and innovation in a single model. This research also adds to the body of literature by proposing the intervening role of team creativity in the relationships of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. The results of this research help the managers to use the team creativity concept to intervene in relation of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. The results of the current study also give valuable insights to managers into why they can use the proactiveness to moderate the relations of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. Current study adds in the body of literature by proposing the entire manuscript on the basis of two theories i.e., Knowledge-Based View (KBV) builds on and expands the RBV.

This study aims to underscore the relevance of pre-existing resilience experiences within communities affected by socio-political violence in Colombia, particularly in the context of developing effective risk management practices and enriching the CBDM model. This research employs a qualitative design, incorporating a multiple case study approach, which integrates a comprehensive literature review, in-depth interviews, and focus groups conducted in two Colombian communities, namely Salgar and La Primavera. The community of La Primavera effectively harnessed community empowerment and social support practices to confront socio-political violence, which evolved into a form of social capital that could be leveraged to address disaster risks. Conversely, in Salgar, individual and familial coping strategies took precedence. It is concluded that bolstering citizen participation in disaster risk management in both communities and governmental support for community projects aimed at reducing vulnerability is imperative. This study reveals that capabilities developed through coping with the humanitarian consequences of armed conflict, such as community empowerment and practices of solidarity and social support, can enhance community resilience in the face of disasters.