Locus of Uncertainty and the Relationship Between Contractual and Relational Governance in Cross-Border Interfirm Relationships
Bibliographic record
Abstract
The relationship between contractual and relational arrangements in interorganizational relationships has been subject to an ongoing debate. We propose that in the context of cross-border partnerships, the governance mechanisms can be both substitutes and complements depending upon contingencies posed by uncertainties of two different origins: environmental and behavioral. We argue that environmental uncertainty (i.e., instability and unpredictability of the external environment) drives the formal and relational arrangements into a more substitutive relationship by elevating the adaptation complications in which increasing reliance on either form of governance inhibits the effective operation of the other. Contrastingly, behavioral uncertainty (in the form of inadequate common grounds and shared frameworks among collaborating firms) encumbers the understanding of partner behavior and conduct and drives the governance mechanisms into a more complementary relationship in which contractual and relational mechanisms facilitate the effective operation of each other. Empirical results from 205 cross-border partnerships of large U.S. firms support our theorized relationships.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".