Abstract
The United States government forecasts a shortage of 1,000,000 Science, Technology, Engineering, and Mathematics (STEM) workers over the next 10 years, putting STEM workforce sustainability at risk. The U.S. federal government has launched a range of programs, initiatives, and commissions to address this critical shortage. Past research has shown that organizational group-oriented culture, smaller work team groups, and worker homogeneity relate to lower attrition rates and higher levels of reported worker satisfaction. Applying Complex Adaptive Systems, this study takes a systems approach to examine worker attrition rates and worker group sizes in relation to STEM density across high-density STEM organizations. The base-case organization for this study is the National Aeronautics and Space Administration (NASA) because it maintains the highest density of STEM workers across all U.S. federal organizations and consistently ranks first in teamwork, innovation, and worker satisfaction in the annual Office of Personnel Management (OPM) Federal Employee Viewpoint Survey. The methodology for this study comprises an empirically validated NETLOGO agent-based model of worker attrition using OPM data sets for high-density STEM organizations. Model initialization parameters are represented and validated by historical attrition data for NASA and two control group organizations. The control groups include a lower-limit high-density STEM model and a medium high-density STEM model for validation with historical worker attrition data for the Environmental Protection Agency and Federal Communications Commission. The findings for this study confirm that STEM worker density is negatively related to attrition rate across all high-density organization types. The model output observations further show the emergence of a negative relationship between organizational STEM density and average worker group size, yet the opposite association is observed for STEM workers. The agent-based modeling approach is an important addition to the current line of academic focused research on STEM workers because it provides a bottom-up insight which helps inform theories and policy effects on ways to mitigate forecasted STEM shortages. Future research could be extended to apply Tipping Point Theory to STEM density and attrition rate variability to better understand STEM threshold ranges across high-density STEM organizations. Future models could also investigate specific STEM and Non-STEM worker characteristics to include age, gender, salary, or education level.
Highlights
The United States (U.S.) government forecasts a shortage of 1,000,000 Science, Technology, Engineering, and Mathematics (STEM) workers over the 10 years, putting STEM workforce sustainability at risk [1]
The units for the y-axis, Attrition Rate, is based on the ratio of workers that separate from an organization to workers that remain in an organization during a fiscal year
The units for the y axis, Attrition Rate, is based on the ratio of workers that separate from an organization to workers that remain in an organization during a fiscal year
Summary
The United States (U.S.) government forecasts a shortage of 1,000,000 Science, Technology, Engineering, and Mathematics (STEM) workers over the 10 years, putting STEM workforce sustainability at risk [1]. A substantial body of systems and agentbased modeling research on worker attrition and grouporiented culture has focused on linkages to organizational performance and employee satisfaction at large [3–8]. These studies have shown that worker homogeneity and smaller work groups generally relate to improved worker performance, satisfaction, and lower attrition rates across all worker-types [6–8]. Worker propensity for attrition has been shown to be most significantly influenced by those most similar in terms of culture, values, and frequency of interaction [6–8]
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