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Record W2944660148 · doi:10.1287/orsc.2018.1236

Transferring Tacit Know-How: Do Opportunism Safeguards Matter for Firm Boundary Decisions?

2019· article· en· W2944660148 on OpenAlexaff
Alex Eapen, Rekha Krishnan

Bibliographic record

VenueOrganization Science · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOpportunismSafeguardingTacit knowledgeMultinational corporationRelevance (law)BusinessIndustrial organizationEconomicsLaw and economicsKnowledge managementComputer sciencePolitical scienceLawMarket economy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.020
GPT teacher head0.245
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2019
Admission routes1
Has abstractyes

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