On the Genesis of Interfirm Relational Contracts
Bibliographic record
Abstract
In a wide range of circumstances, relational contracts can enable strategies that might not otherwise be possible. While most studies focus on the maintenance and performance of existing relational contracts, this paper explores the origins of interfirm relational contracts, focusing on how firms go from no contract to a relational contract. Relying on a microanalytic investigation of the birth of the desktop laser printer industry, we identify a combination of four emergent and deliberate steps that enhance the probability that a relational contract will arise: preexisting personal relationships, capability complementarity, cultural similarity, and pursuit of noncompeting but mutually reinforcing revenue models. We consider how each of these factors helps to undergird the parties’ clarity and credibility to increase the probability of the genesis of a relational contract, which in turn enables collaborating firms to undertake successful, difficult-to-imitate strategies.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".