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Employee Mobility: Leaver Vulnerability to Enforcement of Restrictive Covenants

2022· article· en· W4286622582 on OpenAlexaffabout
Sepideh Yeganegi, André O. Laplume, Bradley Bernard

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsToronto Metropolitan UniversityWilfrid Laurier University
Fundersnot available
KeywordsCovenantRanking (information retrieval)Competitor analysisEnforcementBusinessVulnerability (computing)Work (physics)TurnoverLaw enforcementLabour economicsEconomicsMarketingPolitical scienceLawManagementEngineeringComputer securityComputer science

Abstract

fetched live from OpenAlex

Prior research suggests that the turnover of higher-ranking employees poses a greater threat to parent firms. This is expected especially when high-ranking leavers start a spinout company, rather than go to work for an established competitor. In this study, we examine the enforcement of restrictive covenants as recorded in 306 Canadian court decisions involving spinouts or mobility to competitors. Our analysis results point to a higher rate of enforcement of restrictive covenants in cases involving leavers in higher-ranking roles, especially when they leave to form spinouts as opposed to going to work for a competitor. Restrictive covenants are more potent enforcement tools when leavers threaten to replicate parent firm complementary assets; that is, when higher-ranking leavers form spinouts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.029
GPT teacher head0.253
Teacher spread0.224 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes2
Has abstractyes

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