Exploring the Causal Relationship between Factors Affecting US Army Recruitment

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.

Predicting army reserve unit manning using market demographics

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.

Harnessing Inertia to Improve Army Enlisted Service Length: A Case for Opt-Out Enlistment Contracts

Army recruiting, initial entry training, and retention enterprises consume tremendous manpower resources and become disproportionately more expensive and challenging as the size of the Army increases. Fortunately, empirical evidence suggests that the Army could readily improve enlisted continuation rates by changing enlistment contracts from its present form, requiring soldiers to reenlist or opt-in to continue service, to open-ended enlistment contracts that require soldiers to opt-out of service upon fulfilling their service obligations. Changing enlistment contracts to an opt-out paradigm—similar to how officer populations are currently managed—could greatly increase the number of soldiers who continue service past their initial enlistment obligation. Improved continuation rates could save the Army hundreds of millions in recruiting and reenlistment incentives, as well as freeing thousands of Non-Commissioned Officers serving as recruiters, drill sergeants, and retention specialist to support other operational requirements.

Forecasting US Army enlistment contract production in complex geographical marketing areas

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.