The assessment of managerial competencies in information technology (IT) organizations requires robust and validated instruments capable of predicting performance in volatile, uncertain, complex, and ambiguous environments. This study presents a preliminary validation of the MLPD (Machine Learning Predictive Development) model, which integrates 360° multidimensional evaluation, situational awareness, and exploratory analytics. Conceived as a pilot application and proof-of-concept, the research was conducted within the IT organization of a Chilean Defense Institution responsible for the management and administration of information and communication technologies. This study aims to determine how the three most commonly cited managerial competency domains (Transformational Leadership, Situational Awareness, and Collaborative Management) are weighted in additive models of 360° performance evaluation in a defense IT context, and also seeks to determine whether these weightings differ between civilian and military evaluators. Although the study focuses on a specialized case study with a limited sample of 9 IT leaders, the robustness of the preliminary findings is supported by the analysis of 165 rating records from 360° evaluations clustered within 9 leaders. Through this granular data set, multiple linear regression models were developed to examine the predictive relationships among three core competency domains—Transformational Leadership, Situational Awareness, and Collaborative Management—and their impact on overall managerial performance. The results identify Collaborative Management as the strongest predictor of performance, and highlight significant differences between civilian and military evaluators. This finding challenges conventional assumptions about leadership effectiveness in IT contexts and suggests that horizontal coordination capabilities are more critical than vertical authority. These preliminary results validate the model’s internal structure within a highly hierarchical environment, establishing a foundational benchmark for future large-scale applications of the MLPD model in diverse organizational contexts.
Saldaña et al. (Thu,) studied this question.