This study explores the relationship between task-oriented behaviors, self-efficacy, and leadership emergence in women STEM students, grounded in the context of prototypical leadership theory and self-efficacy theory. Prototypical leadership theory emphasizes the alignment of leadership behaviors with group expectations, which, in STEM fields, are often task-oriented. The research examines how task-oriented behaviors, such as planning, decision-making, and supervision, influence women’s self-perception of leadership ability and their subsequent emergence as leaders. Our results show a positive relationship between task-oriented behaviors and self-efficacy and a positive relationship between self-efficacy with leader emergence, with academic experience further ngthening this link. As students’ progress through their programs, engaging in more teamwork and leadership tasks, their self-efficacy enhances, leading to stronger leadership emergence. Also, we found an indirect effect from task-oriented behavior to leader emergence via self-efficacy. These findings have significant implications for fostering leadership in women, particularly in STEM. The study calls for educational programs to enhance opportunities for women to develop these behaviors early on, ensuring their growth into leadership roles in STEM fields.

Utilizing Multi-Criteria Decision Analysis (MCDA) methods based on environmental, social, and governance (ESG) factors to rank countries according to these criteria aims to evaluate and prioritize countries based on their performance in environmental, social, and governance aspects. The contemporary world is influenced by a multitude of factors, which consequently impact our lives. Various models are devised to assess company performance, with the intention of enhancing quality of life. An exemplary case is the ESG framework, encompassing environmental, social, and governmental dimensions. Implementing this framework is intricate, and many nations are keen on understanding their global ranking and avenues for enhancement. Different statistical and mathematical methods have been employed to represent these rankings. This research endeavors to examine both types of methods to ascertain the one yielding the optimal outcome. The ESG model comprises eleven factors, each contributing to its efficacy. We employ the Performance Contribution Analysis (PCA), Clifford algebra method, and entropy weight technique to rank these factors, aiming to identify the most influential factor in countries’ ESG-based rankings. Based on prioritization results, political stability (PSAV) and the voice of accountability (VA) emerge as pivotal elements. In light of the ESG model and MCDA methods, the following countries exhibit significant societal impact: Sweden, Finland, New Zealand, Luxembourg, Switzerland, Denmark, India, Norway, Canada, Germany, Austria, and Australia. This research contributes in two distinct dimensions, considering the global context and MCDA methods employed. Undoubtedly, a research gap is identified, necessitating the development of a novel model for the comparative evaluation of countries in relation to prior studies.

En este artículo se analiza el efecto que tiene el estilo de liderazgo pasivo-evitador en el intercambio de comportamientos líder-seguidor en estudiantes universitarios, además del efecto de la naturaleza del equipo en esta relación. Para este fin, se realizó un estudio a 181 individuos de 32 equipos de estudiantes en una universidad chilena, y se utilizó un modelo de ecuaciones estructurales multiniveles para el análisis de los datos. Los hallazgos indican que el estilo de liderazgo pasivo-evitador se relaciona negativamente con el intercambio de comportamientos líder-seguidor, y que los estudiantes más avanzados en el plan de estudio propician de manera positiva la adopción de ambos comportamientos. Además, cuando los miembros de los equipos no han avanzado mucho en el plan de estudios, la relación entre el estilo de liderazgo pasivo-evitador y el intercambio de comportamientos líder-seguidor cambia y se vuelve positiva. Finalmente, se discuten las repercusiones de estos resultados en los equipos de estudiantes de educación superior y la importancia del entendimiento del liderazgo en un contexto de equipo.

Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.

We aim to implement a three-tiered healthcare supply chain model within the context of a portable blood hub during times of crisis to provide healthcare to the most vulnerable populations. Due to a lack of blood, the number of deaths related to this issue has significantly increased in recent years. A critical challenge for healthcare decision-makers is the efficient distribution of blood among different centers. This research proposes a new method to address this issue, simplifying future solutions to similar problems. As a result of our findings, we introduce a model that considers three tiers in healthcare blood supply: point one (blood collection centers), point two (blood hubs), and final destinations (hospitals and clinics) during disasters. For this study, we focused on six collection sites, five blood centers, and thirty-one medical facilities providing eight different blood types. Our model demonstrates that blood can be transported from collection sites to medical centers at a lower cost during crises. This approach requires considering the full three tiers of the healthcare supply chain rather than focusing solely on the supply chain level at blood hubs. Unlike previous studies, which focused only on individual levels of supply chain management, this study examined the entire value chain, from blood collection to delivery to the end recipient.

A company’s survival in the current competitive market hinges on its ability to meet and exceed customer expectations, as customers are invaluable assets. Patient satisfaction is crucial in healthcare, directly influencing whether patients will return to a hospital or recommend it to others. This study uses advanced data mining techniques to accurately estimate and predict patients’ likelihood of returning for future appointments by assessing their satisfaction levels. In addition to feature selection models such as Random Forest, Genetic Algorithm, and Lasso Regression, the study employs various methods, including Neural Networks, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Rule-based systems, and Naive Bayes algorithms. The analysis of the results indicates that while the Neural Network model shows superior prediction accuracy, the Lasso Regression method efficiently identifies relevant features. This research makes a significant contribution by integrating AI approaches and thoroughly examining satisfaction ratings in the Iranian healthcare industry. Moreover, the findings demonstrate that the Artificial Neural Network model best fits the predictive model and offers the highest reliability. This study aims to forecast patient satisfaction in the healthcare industry and develop a strategic roadmap for hospitals, thereby expanding the knowledge of machine learning methods for predicting customer satisfaction.