Risk Sharing in Government Contracting: Strategic Alliances as Safeguards in Government Supplier Relationships
Bibliographic record
Abstract
Interorganizational alliances have been extensively studied as strategic arrangements that enable firms to manage risks arising from their embeddedness in external relationships. However, the unique dynamics of business-to-government (B2G) relationships, where firms often face regulatory and political risks, remain underexplored. In this study, we examine how firms reliant on U.S. Department of Defense (DoD) contracts use strategic alliances to mitigate these challenges. Drawing on resource dependence theory and the resource-based view of the firm, we theorize that firms with higher government contract value are more likely to form alliances with other government contractors to help share risk and navigate government contracting challenges. We further identify two boundary conditions—an internal buffer (whether a firm operates as a generalist or specialist contractor) and an external buffer (the level of market-demand risk)—that moderate this relationship. Our analysis of 339 U.S. publicly traded DoD contractors from 2001 to 2019 provides robust support for our hypotheses. A post hoc mediation analysis further shows that alliances partially mediate the relationship between government contract value and market performance. Our study contributes to interorganizational relationships and business-government interface literatures by articulating the unique dynamics of B2G alliances and offering nuanced insights into how firms manage their relationships with powerful government buyers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".