Factor Theory: Bridging Talent Management Ends With Data Science Means
Abstract
This research explains and predicts Airman turnover as a function of multiple influence factors and presents methods to predict separation with high accuracy. A concept called Factor Theory develops multidimensional Airman behavioral models to bridge the gap between the ends of talent management and the means of data science. The results demonstrate that Airmen act as predictable rational actors, weighing the costs and benefits of continued service as a value maximizing proposition. Further, distinct retention behaviors manifest by Air Force Specialty Codes (AFSC) as a function of subculture dynamics and differing valuations of service experiences. By combining all theorized factors from the 2015 Air Force survey in a single dataset, machine learning and classical analytics indicate that Airman separation is predictable for Pilots, Combat Systems Operators, Physicians, and all other Air Force Officers with greater than 97% accuracy. The results refute the notion of Airmen as decisional "black boxes" and expendable assets. Rather, human capital is a perishable strategic resource that creates distinct advantages over the enemy, requiring contextualized management by Air Force Specialty Code(AFSC) in order to attain maximum returns. This research proposes two holistic solutions. First, a comprehensive Air Force Talent Management System; second, a new system of officer career progression called Career Multipath derived from the 16th Chief of Staff of the Air Force General Michael Ryan's Force Development Initiative Program. This research provides rapid, evidence based solutions to the Air Force Future Operating Concept, Strategic Master Plan, and wicked problem sets to remain competitive in future war.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2018
- Accession Number
- AD1055456
Entities
People
- Sean O. Siddiqui
Organizations
- Air Command and Staff College