@misc{10481/107735,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107735},
abstract = {This article advances empirical understanding of subcontracting behaviour in US defence procurement by integrating financial, contractual, and governance variables within an interpretable machine-learning framework. Using decision tree models trained on a decade of contract and accounting data for 32 defence firms, it predicts annual subcontracting intensity and identifies the most influential predictors—cost of goods sold, operating margin, D&A, and common stock. The analysis reveals that cost structure dominates, while liquidity and governance indicators refine predictive accuracy, confirming multi-channel interactions among incentives, transparency, and capacity. Methodologically, the study demonstrates how rule-based predictive models can translate financial records into oversight benchmarks aligned with principal–agent and transaction-cost theories. Substantively, it links financial discipline, governance quality, and accountability, offering a transparent, replicable approach for future research on predictive regulation in high-risk contracting environments.},
publisher = {Public Money & Management},
keywords = {accountability},
keywords = {defence contracting},
keywords = {environmental, social and governance (ESG) indicators},
keywords = {financial governance},
keywords = {public procurement},
title = {Financial governance and subcontracting in US defence firms: a predictive model for public accountability},
doi = {https://doi.org/10.1080/09540962.2025.2579076},
author = {Teruel-Gutiérrez, Ricardo and Carreres-Prieto, Daniel and Molina-Moreno, Valentín and Gálvez-Sánchez, Francisco Jesús},
}