The repository is currently being upgraded to DSpace 7. Temporarily, only admins can login. Submission of items and changes to existing items is prohibited until the completion of this upgrade process.
The US DOD budget: Can it be predicted?
Arbogast, Gordon W.
MetadataShow full item record
Arbogast, G.W. & Jadav, A. (2023). The US DOD budget: Can it be predicted? Journal of Management & Engineering Integration, 16(1), 1-10.
The Department of Defense (DOD) is part of the United States Federal Government which oversees the U.S. Military. This Department is one of the largest and most complex organizations in the world. The DOD mission is to protect and defend the United States (US) and provide national security. To achieve this, the DOD requires a major portion of the federal budget. Each year, the DOD portion is based on a variety of political and economic factors. The results of this study are noteworthy. A regression model was derived that explained 82.14% of the variation in the target ratio of the federal budget with a significance level of 0.05. Four variables were identified and listed in order of greatest impact, as determined, by their standardized coefficients. These variables may have a significant relationship with DOD's budget. The four variables are: (a) House Majority Political Party, (b) Doomsday Clock Value, (c) US President's Political Party Affiliation, and (d) US Gross Domestic Product Growth Rate. If corporations and other agencies that deal with the DOD were to be able to accurately predict year-by-year DOD budget levels, this would give them a unique, competitive advantage. The strong presence of political factors in the results may be a key indicator for DOD businesses to consider in ensuring the balance of an appropriate level of politically motivated drivers within their corporate strategy models. Further recommendations focused primarily on the factors and, ultimately, the variables that should be selected for future studies. Variables need to be selected to allow for a greater number of observations to increase the likelihood of producing accurate study results.
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, October 2023.