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Performance evaluation and Army recruiting /

Designing and implementing performance metrics that support Army goals requires analysis of how different metrics would affect recruiter behavior and, in turn, recruiters' contributions toward achieving the Army's goals. The authors evaluate traditional performance metrics, such as number...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Dertouzos, James N., 1950-
Otros Autores: Garber, Steven, 1950-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Santa Monica, CA : Rand Arroyo Center, 2008.
Colección:Rand Corporation monograph series.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Dertouzos, James N.,  |d 1950- 
245 1 0 |a Performance evaluation and Army recruiting /  |c James N. Dertouzos, Steven Garber. 
260 |a Santa Monica, CA :  |b Rand Arroyo Center,  |c 2008. 
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505 0 |a Introduction -- Models of recruiter effort, market quality, and enlistment supply -- Data and econometric estimates of contract-production models -- Empirical analysis of performance measures -- Choosing performance windows and organizational units for evaluation -- Conclusions -- Appendix A: Allocation of recruiter effort: implications of a microeconomic model -- Appendix B: Recruiter behavior in the face of risk. 
520 |a Designing and implementing performance metrics that support Army goals requires analysis of how different metrics would affect recruiter behavior and, in turn, recruiters' contributions toward achieving the Army's goals. The authors evaluate traditional performance metrics, such as number of contracts signed per month per recruiter, and find that they do not adequately measure recruiter effort, skill, and productivity. They then develop a "preferred performance metric" that takes into account the difficulty of recruiting different types of youth in various markets. Using a performance metric that better reflects Army values and more accurately assesses recruiter effort and skill would have significant benefits. However, because the recruiter reward system is deeply engrained, the authors propose modest, gradual changes to the system-for example, improving mission allocation algorithms to reflect variations in market quality and differences in market segments and lengthening the performance evaluation window to at least six months to reduce emphasis on monthly station-level mission accomplishment 
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