This study covers the lack of literature analyzing the empirical relationship between leadership styles and self-management in agile frameworks. We have conducted a comprehensive survey in agile teams in order to analyze the relation between the self-management concept and the different types of leadership that occur in such teams. To perform the study we have followed the Goleman’s leadership classification and the Marquet’s and Kniberg’s self-management models. Besides the previous concepts, we also investigate how different leadership styles exert alignment and autonomy within agile teams. The statistical evidence of these results has been validated for the sample at hand using statistical hypothesis tests. The results of the study show that respondents’ perception of self-management in their teams remains similar and significant for all leadership patterns. Moreover, we study the effect of the alignment dimension within the concept of self-management, showing that authoritarian leadership styles, when present in agile teams, compensate low levels of autonomy with high levels of alignment.

The use of management by objectives (MBO) methodologies, particularly the Objectives and Key Results (OKR) framework, has gained widespread attention in recent years as a means of improving organizational performance. This research aims to show that OKRs, a key MBO method, can enhance company performance and employee satisfaction. We conducted a comparative and retroactive analysis of MBO methods used in the 20th and 21st centuries, and surveyed 238 people in nine countries and various companies and positions. We analyzed the results of the survey and compared the variables associated with best practices in the OKR methodology. Our study examined a diverse range of companies, positions, qualifications, and ages, which provides an analysis of whether or not OKR are being used in the organization and a comprehensive understanding of how OKRs can be implemented across in different contexts. By identifying the best variables associated with optimal performance and employee satisfaction, our research provides practical guidance for organizations seeking to adopt OKR methodology. Furthermore, our study can contribute to the development of best practices in the area of management by objectives, particularly in the context of OKRs, leading to more effective and efficient goal-setting practices in organizations.

The objective of this study is to explore how various personality types correlate with enhanced work performance. The Enneagram type of the participants in the experiment was established by using the simplest version of the Riso–Hudson test. A two-way ANOVA was performed under the principles of the Design of Experiments, which allowed the identification of main effects and interactions in the response, i.e., the marks of the university teams. We found that the interactions between certain Enneagram types seem to increase the average performance marks as a primary effect. Conversely, when certain Enneagram types coincided within a team, the marks significantly decreased, posing a risk to project success. According to our results, the Enneagram framework may be used as a preliminary stage for identifying potential team members for future projects.

Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context, the emergence of eXplainable Artificial Intelligence (XAI) has helped bridge the gap between professionals in the aeronautical and aerospace sectors and the AI systems and models they work with. For this reason, this paper provides a review of the concept of XAI is carried out defining the term and the objectives it aims to achieve. Additionally, the paper discusses the types of models defined within it and the properties these models must fulfill to be considered transparent, as well as the post-hoc techniques used to understand AI systems and models after their training. Finally, various application areas within the aeronautical and aerospace sectors will be presented, highlighting how XAI is used in these fields to help professionals understand the functioning of AI systems and models.