Priorização do treinamento de recursos humanos na Administração Pública por composição probabilística de preferências

Abstract

Purpose: prioritize cross-cutting training needs at the Mato Grosso State Secretariat for Public Security (SESP-MT). Method/approach: the quantitative research collected data through a questionnaire from 627 public security employees of the SESP-MT. The responses were organized on a five-point Likert scale, where "1" represented the least importance of the activity and "5" the most importance. The frequencies of responses at each value on the Likert scale were submitted to the Probabilistic Composition of Preferences for the hierarchization of transversal competencies. The results were statistically compared with SESP-MT's previous priority. Main findings: the priorities originally assigned by SESP-MT considered only the evaluations above the average value of the Likert scale (point “3”) and were compared with the results of RGO - Revista Gestão Organizacional the CPP, which considers the entire spectrum of responses on the scale. The two rankings were subjected to Spearman’s ordinal correlation, with a result of 93.3%. The literature and open data reinforced the results projected by the CPP. Theoretical, practical/social contributions: the research contributes to the advancement of academic research on human resource management and improvement of the sector at SESP- MT, presenting a methodology that considers uncertainties in human evaluation and involves the entire spectrum of Likert scale points, giving greater robustness to the results and, ultimately, confirming or reorienting public service training. Originality/relevance: the relevance of this study is to improve the processes carried out in the sector researched. The lack of studies regarding the use of methodologies for prioritizing human resources training in public administration.