The authors discuss why the current conceptual base of project management research and practice continues to attract criticism since it does not adequately address the complexity that leads to software-project failure. To do so, the study explores systems thinking and artificial neural networks to shed light on complexity in software-project behavior using nonlinear functional relationships between critical success factors and project success to utilize their connectedness as an approach in order to create project-outcome prediction models. The artificial neural networks were used to create two project-outcome prediction models: one for a binary classification task to discriminate failed from successful projects using a multi-input-single-output configuration and one for a multi-task binary classification to discriminate success from failure in multiple project-success dimensions using a multi-input multi-output configuration. The results yielded high-performance values for a binary classification task, performed to predict overall project success, and slightly lower performance values for the multi-task binary classification, which was also performed to predict success in project-success dimensions. It was found that the nonlinear behavior of critical success factors may be used to create prediction models, by embedding equifinality and connectedness constructs that prove to be useful to understand projects as complex, multi-loop, and nonlinear systems. Further research is needed to investigate the causality between critical success factors in order to explore the possible propagation of critical success factors within a project system network and its implications on project success.

Background: Social services aimed at the elderly are facing great challenges caused by progressive aging of the global population but also by the constant pressure to spend funds in a rational manner. Purpose: This paper focuses on analyzing the investments into human resources aimed at enhancing home care for the elderly since many countries have recorded progress in the area over the past years. The goal of this paper is to stress the significance of performing an economic analysis of the investment. Methods: This paper combines statistical analysis methods such as correlation and regression analysis, methods of economic analysis, and scenario method. Results: The economic analysis of investing in human resources for home care service in Serbia showed that the both scenarios of investing in either additional home care hours or more beneficiaries are cost-efficient. However, the optimal solution with the positive (and the highest) value of economic net present value criterion is to invest in human resources to boost the number of home care hours from 6 to 8 hours per week and increase the number of the beneficiaries to 33%. Conclusion: This paper shows how the statistical and economic analysis results can be used to evaluate different scenarios and enable quality decision-making based on exact data in order to improve health and quality of life of the elderly and spend funds in a rational manner.

In recent years, one of the most important topics in the field of software development projects is rethinking success in context of increasing complexity and failure of software projects. Literature review shows that there is no clear line between success and failure, especially considering that a project can be both successful in one area and unsuccessful in another. In addition, the increasing complexity, context and broader conceptualization of projects, required both change in project and success definition. The aim of this paper is to perform literature review in field of software project success definition, as well as to propose a multidimensional construct that includes project management success, project success and strategic project success as three dimensions for measuring software's project success.

Young people often face uncertainty during the transition from education to work, along with high unemployment and job dissatisfaction, which is addressed in the EU Youth Strategy, highlighting the need for better career support. This study aimed to identify main factors influencing youth career decisions and to develop a decision-making model. Five core constructs were defined through literature review: Dealing with Uncertainty, Risk Preference, Adaptability and Resilience, Education and Support, and Life Satisfaction. Data were collected from 673 engineering students. Regression analysis was used to test the proposed model and hypotheses, while Mann-Whitney and Kruskal-Wallis tests examined group differences. The developed model accounts for 46.2% (R² = 0.462) of the variability in students’ career choices. Adaptability and resilience emerged as the most influential factor (β = 0.557). Certain differences, for specific constructs, were also observed in relation to different groups of family income, gender and extracurricular activity engagement. The model supports more informed career decisions and provides insights that may help improve career guidance and educational policy. The findings also may contribute to bridging theory and practice in career development research. The study is limited by its sample, which included only engineering students from the Republic of Serbia, potentially restricting the generalizability of the results.

The topic of critical success factors draws high attention of both academia and practitioners working in the software industry. Literature review shows that existing models of critical success factors for software projects are more concentrated on specific categories of success factors, neglecting the interaction that exists within these models between different groups of factors. The purpose of this paper is to present a new concept of interaction-based model for identification and evaluation of critical success factors for software projects. The approach is based on conjoint use of graph theory and Design Structure Matrices to highlight important interactions of a project system. The key result of the paper is the tool for evaluation of the interaction between critical success factors through quantification and the analysis of the possible propagation of specific factor within project success factor network, which will make possible the identification of the critical factors with the highest influence on the entire project system.