Some US Army Recruiting, Retention, Training, and Personnel Implications of the Objective Force: The Army Enlistment Production System

Estimating elasticities with frontier and other regressions in evaluating two advertising strategies for US Army recruiting

Socio-Economic Planning Sciences ◽

10.1016/j.seps.2007.07.001 ◽

2008 ◽

Vol 42 (1) ◽

pp. 1-17 ◽

Cited By ~ 4

Author(s):

Patrick L. Brockett ◽

W.W. Cooper ◽

Linda L. Golden ◽

Subal C. Kumbhakar ◽

Michael J. Kwinn ◽

...

Keyword(s):

Us Army ◽

Advertising Strategies ◽

Army Recruiting

Download Full-text

Predicting army reserve unit manning using market demographics

Journal of Defense Analytics and Logistics ◽

10.1108/jdal-09-2017-0020 ◽

2018 ◽

Vol 2 (2) ◽

pp. 69-79

Author(s):

Nathan Parker ◽

Jonathan Alt ◽

Samuel Buttrey ◽

Jeffrey House

Keyword(s):

Local Population ◽

Decision Makers ◽

Army Reserve ◽

Content Type ◽

Us Army ◽

Staffing Levels ◽

Staffing Requirements ◽

Aid Decision ◽

Army Recruiting ◽

Reserve Unit

Purpose This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population. Design/methodology/approach This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements. Findings This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels. Originality/value This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.

Download Full-text

Exploring the Causal Relationship between Factors Affecting US Army Recruitment

Industrial and Systems Engineering Review ◽

10.37266/iser.2021v9i2.pp83-90 ◽

2022 ◽

Vol 9 (2) ◽

pp. 83-90

Author(s):

Graham Ungrady ◽

Matthew Dabkowski

Keyword(s):

United States ◽

United States Army ◽

Cognitive Mapping ◽

Armed Forces ◽

The United States ◽

Factors Affecting ◽

Us Army ◽

United States Armed Forces ◽

Future Work ◽

Army Recruiting

Every year, United States Army Recruiting Command (USAREC) dedicates considerable resources to recruiting and accessing soldiers. As the largest branch of the United States Armed Forces, the Army must meet a high recruiting quota while competing in the free-labor market for quality recruits. Over the past two decades, the Army’s success in recruiting ebbed and flowed within the broader context of society and global events. While numerous studies have examined the statistical relationship between factors associated with recruitment, these studies are observational and definitively ascribing causality in retrospect is difficult. With this in mind, we apply fuzzy cognitive mapping (FCM), a graphical method of representing uncertainty in a dynamic system, to model and explore the complex causal relationships between factors. We conclude our paper with implications for USAREC’s efforts, as well as our model’s limitations and opportunities for future work.

Download Full-text

Forecasting US Army enlistment contract production in complex geographical marketing areas

Journal of Defense Analytics and Logistics ◽

10.1108/jdal-03-2017-0001 ◽

2017 ◽

Vol 1 (1) ◽

pp. 69-87 ◽

Cited By ~ 1

Author(s):

Joshua L. McDonald ◽

Edward D. White ◽

Raymond R. Hill ◽

Christian Pardo

Keyword(s):

Design Methodology ◽

Marketing Research ◽

Recruitment Process ◽

Content Type ◽

Us Army ◽

Analytical Technique ◽

The Us ◽

Army Recruiting ◽

Zip Code ◽

Practical Implications

Purpose The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process. Practical implications Improved US Army analytical technique for forecasting recruiting goals.. Originality/value This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

Download Full-text