Management and efficiency have a fundamental impact on the performance of public hospitals, as well as on their philanthropic mission. Various studies have shown that the financial weaknesses of these entities affect the planning, setting of goals and objectives, monitoring, evaluation and feedback necessary to improve health systems and guarantee accessibility as an inalienable right. This study aims to analyze the management and efficiency of third-level and/or high-complexity hospitals in Colombia, through a statistical model that uses financial analysis and key performance indicators (KPIs) such as ROA, ROE and EBITDA. A non-experimental cross-sectional design is used, with an analytical-synthetic, documentary, exploratory and descriptive approach. The results show financial deficiencies in the hospitals evaluated; hence it is recommended to make adjustments in the operating cycle to increase efficiency rates. In addition, the use of the KPIs ROA and ROE under adjusted models is suggested for a more precise analysis of the financial ratios, since these adequately explain the variability of each indicator and are appropriate to evaluate hospital management and efficiency, but not in EBITDA ratio, hence the latter is not recommended to evaluate hospital efficiency reliably. This study provides relevant information for public health policy makers, hospital managers and researchers, in order to promote the efficiency and improvement of health services.

Increasing the environmental friendliness of production systems is largely dependent on the effective organization of waste logistics within a single enterprise or a system of interconnected market participants. The purpose of this article is to develop and test a methodology for evaluating a data-based waste logistics model, followed by solutions to reduce the level of waste in production. The methodology is based on the principle of balance between the generation and beneficial use of waste. The information base is data from mandatory state reporting, which determines the applicability of the methodology at the level of enterprises and management departments. The methodology is presented step by step, indicating data processing algorithms, their convolution into waste turnover efficiency coefficients, classification of coefficient values and subsequent interpretation, typology of waste logistics models with access to targeted solutions to improve the environmental sustainability of production. The practical implementation results of the proposed approach are presented using the production example of chemical products. Plastics production in primary forms has been determined, characterized by the interorganizational use of waste and the return of waste to the production cycle. Production of finished plastic products, characterized by a priority for the sale of waste to other enterprises. The proposed methodology can be used by enterprises to diagnose existing models for organizing waste circulation and design their own economically feasible model of waste processing and disposal.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

There is insufficient consideration of Generation Z’s cultural and generational needs in the implementation of biometric attendance systems in Arabic educational settings. This study delves into Generation Z’s discipline, exploring their perspectives on attendance systems and aligning commitment with their interests. The primary aim is to gauge biometric systems’ impact on productivity. Google Form questionnaires collected data from young employees, ages 25 to 35, who belong to Generation Z’s working in the higher education system. Structural equation modeling and descriptive analysis assessed the data. While biometric systems enhance discipline, they may dampen morale. Implementing systems fairly and maintaining flexibility is vital. The study underscores the importance of evaluating employees based on achievements. It sheds light on biometric systems’ role in attendance management and organizational performance, aiding HR practices. The results showed no significant effect of Employee Management Practices (EMP) on organization performance through Biometric Attendance Technology (BAT) (B = 0.049, t = 1.330, p = 0.184). Nor significant effects of Organizational Performance Metrics (OPM) (B = 0.019, t = 0.608, p = 0.543). Technological Infrastructure (TI) (B = 0.019, t = 0.2461, p = 0.645), or Satisfaction and Engagement (ESE) (B = 0.057, t = 1.381, p = 0.167) on organization performance through Biometric Attendance Technology. The mediator impact was also found to be not significant (P > 0.05). Therefore, both direct and specific indirect effects were not significant. Indicating that Biometric Attendance Technology does not mediate the relationship between these variables and organizational performance.

The objective of this study is to examine the impact of decentralization on disaster management in North Sumatra Province. Specifically, it will analyze the intergovernmental networks, local government resilience, leadership, and communication within disaster management agencies. The study used a hybrid research approach, integrating qualitative and quantitative methodologies to investigate the connections between these factors and their influence on disaster response and mitigation. The study encompassed 144 personnel from diverse government tiers in North Sumatra and performed a meta-analysis on the implementation of disaster management. Intergovernmental networks were discovered to enhance collaboration in disaster management by eliminating regulatory gaps and efficiently allocating logistics. Nevertheless, local governments have obstacles as a result of limited resources and inadequate expertise, notwithstanding the progress made in infrastructure technology. The F test results reveal that leadership and communication have a substantial impact on the performance of BPBD personnel. The meta-assessment classifies its impact as extraordinarily high, suggesting comprehensive evaluation and successful achievement of goals in disaster management planning. Efficient cooperation among relevant parties is essential in handling calamities in North Sumatra. The government, commercial sector, NGOs, universities, and society have unique responsibilities. To improve effectiveness, governments should encourage private sector involvement, while institutions can increase their research contributions.

Performance Management is a major concern to various stakeholders in Education System, it is considered to be key driver to improve school effectiveness and learning quality. However, the complexity of education Systems, has made it challenging to apply an effective PM model. This study paper introduces a maturity model with six dimensions, fifteen Capability Areas and forty-two Best-Practices to assess education systems’ organizational capacity for performance management. It provides deep insights into their structural and functional characteristics and serves as a framework for decision-makers to identify and implement missing practices while enhancing existing ones. The maturity model was developed following the Design Science Research methodology to ensure both rigor and relevance. A bottom-up approach guided its design, integrating insights from extensive literature reviews and lessons learned from benchmark countries. The evaluation process employed a qualitative approach, using focus groups with a carefully selected cohort of academics, experts, and practitioners. The Moroccan case study serves as part of the “Reflection and Learning” phase, providing an initial test for the model and paving the way for further empirical research. Future studies will aim to test, refine, and extend the model, facilitating its application across diverse educational contexts.