A preliminary study on constructing a competency model for special operators in our army
Objective To construct a preliminary competency model for special operators in our army.Methods The study was conducted from April 2023 to July 2023,using a combination of quantitative and qualitative analysis.Firstly,based on relative literature analysis and O*NET job analysis research,a pool of indicators of the competency of special operators was initially established.The behavioural event interview(BE1)researched paradigm and the Delphi expert consulting was used to revise and improve,to constructa preliminary competency model for special operators in our military.Results Based on the results of behavioural event interview,the excellent group was better than the average group in 45 competency indicators(all P＜0.05).After two rounds of expert consultation,the experts'opinions on 40 competency traits were more concentrated and convergent,the mean importance scores of each index were more than 4,and the coefficients of variation were less than 0.25.The competency model for special operators containsfour primary indicators of personality,ability,attitude and military-related qualities.The personality dimension included 10 secondary indicators including emotional stability,honesty and reliability,etc.The ability dimension included 16 secondary indicators including ability of handling people,ability of planning and organization,etc.The attitude dimension included 10 secondary indicators including discipline and obedience to orders,military sense of honor,etc.The military-related qualities dimension included 4 secondary indicators including political and ideological qualities,special operations professional skills qualities,etc.Conclusion The competency model of special operations personnel constructed in this study can be used as a theoretical reference for the selection of special operators in our army in the future.
Special operatorsCompetency modelLiterature analysisO*NET job analysisBehavioral event interviewDelphi expert consulting
NETL
NSTL
万方数据
