Transferring Tacit Know-How: Do Opportunism Safeguards Matter for Firm Boundary Decisions?
Bibliographic record
Abstract
In recent years, scholars have demonstrated that capability theories of firm boundaries are fundamentally intertwined with contractual arguments. A productive use of capability arguments, therefore, is when they are joined with contractual ones in an integrated theory of the firm. However, contractual and capability scholars have traditionally held incommensurable views on the relevance of opportunism safeguards for a theory of the firm. Sponsors of the contractual view treat opportunism safeguards as fundamental, whereas several scholars in the knowledge-based strand of the capabilities camp consider it redundant. Moreover, in several recent integrative efforts, opportunism and safeguarding against it feature as linchpin theoretical ideas. To fully integrate contractual and capability theories, therefore, there is a need to resolve this point of incommensurability. We revisit a specific problem in the international strategy literature where the opportunism debate has been significant—the transfer of tacit know-how by multinational firms—and employ moderator-effect hypotheses to test two alternative mechanisms for why tacit know-how is transferred internally. We test whether tacit know-how is transferred internally to safeguard against opportunism or, alternatively, to avail the coordination benefits of common routines within firms. Our results indicate the former and not the latter, and thereby support a cornerstone notion in recent efforts toward an integrated theory of the firm.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".