Exploring the Causal Relationship between Factors Affecting US Army Recruitment
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Abstract
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.
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How to Cite
Ungrady, G., & Dabkowski, M. (2022). Exploring the Causal Relationship between Factors Affecting US Army Recruitment. Industrial and Systems Engineering Review, 9(2), 83-90. https://doi.org/10.37266/ISER.2021v9i2.pp83-90
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References
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U.S. Army Recruiting Command (USAREC). (n.d.). About U.S. Army Recruiting. (n.d.). Retrieved January 9, 2021, from https://recruiting.army.mil/aboutUSAREC/
Asch, B. J., Heaton, P., & Savych, B. (2009). Recruiting Minorities: What Explains Recent Trends in the Army and Navy? Santa Monica, CA: RAND.
Asoni, A., Gilli, A., Gilli, M., & Sanandaji, T. (2020). A mercenary army of the poor? Technological change and the demographic composition of the post-9/11 U.S. military. Journal of Strategic Studies, 1-47.
Bailey, B. (2007). The Army in the marketplace: Recruiting an all-volunteer force. The Journal of American History, 94(1), 47-74.
Burger, E. C. (2000). A multivariate times series analysis of U.S. Army recruiting [Masters Thesis, Naval Postgraduate School]. NPS Archive: Calhoun.
Kok, K. (2009). The potential of Fuzzy Cognitive Maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19, 122-133.
Kosko, B. (1986). Fuzzy Cognitive Maps. International Journal of Man-Machine Studies, 24, 65-75.
LaRue, S., Dabkowski, M., & Furfaro, R. (2017). Exploring Unforeseen Causal Relationships in Fuzzy Cognitive Maps. Proceedings of the 2017 Annual General Donald R. Keith Memorial Capstone Conference, West Point, NY.
Papageorgiou, E. I. (2014). Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms. Berlin: Springer-Verlag.
Papageorgiou, E. I., & Salmeron, J. L. (2014). Methods and Algorithms for Fuzzy Cognitive Map-based Modeling. In E. I. Papageorgiou, Fuzzy Cognitive Maps for Applied Sciences and Engineering (pp. 1-28). Berlin: Springer-Verlag.
Penn, A. S., Knight, C. J. K., Lloyd, D. J. B., Avitabile, D., Kok, K., Schiller, F., Woodward, A., Druckman, A., & Basson, L. (2013). Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region. PLOS ONE, 8(11), e78319.
Philipps, D., & Arango, T. (2020, January 10). Who Signs Up to Fight? Makeup of US Recruits Shows Glaring Disparity. The New York Times.
Rostker, B., Klerman, J. A., & Zander-Cotugno, M. (2014). Recruiting older youths: Insights from a new survey of army recruits. Santa Monica, CA: RAND.
Shields, P. M. (2020). Dynamic intersection of military and society. In A. Sookermany, Handbook of military sciences (pp. 1-23). Basingstoke: Springer.
Turney, S. & Bachhofer, M. (2016). FCMapper: Fuzzy Cognitive Mapping. R package version 1.1. Retrieved March 19, 2021, from: https://cran.r-project.org/web/packages/FCMapper/index.html
U.S. Army Recruiting Command (USAREC). (n.d.). About U.S. Army Recruiting. (n.d.). Retrieved January 9, 2021, from https://recruiting.army.mil/aboutUSAREC/