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Record W2314195236 · doi:10.1177/1548051816633066

The Internationalization of Small and Medium-Sized Family Enterprises

2016· article· en· W2314195236 on OpenAlexaff
Paloma Almodóvar, Alain Verbeke, Óscar Rodríguez‐Ruiz

Bibliographic record

VenueJournal of Leadership & Organizational Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInternationalizationTobit modelAsset (computer security)BusinessIndustrial organizationQuality (philosophy)Sample (material)Panel dataAsset specificityWork (physics)MainstreamMarketingEconomicsEconometricsFinanceInternational tradeCorporate governanceComputer science

Abstract

fetched live from OpenAlex

This article assesses the role of human asset quality in the internationalization of small and medium-sized family enterprises. Building on mainstream international business theory, we propose a model with three “states” of human asset quality (low, medium, and high) available to the firm that can be linked to particular levels of export intensity. Importantly, achieving higher export intensity is not always associated with higher human asset quality across the board: There is a key difference between generic (generally available) and specialized (highly firm-specific) human asset quality. We empirically test our model through Tobit panel data analyses with random effects, whereby we study a sample of 610 Spanish firms for the period 2006 to 2010. This research represents the first-ever work conceptualizing and empirically testing a nonlinear, cubic relationship between human asset quality and small and medium-sized family enterprises’ internationalization levels. We find an S-shaped relationship between both general and specialized human assets and the level of export intensity.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.260
Teacher spread0.190 · 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 teacher head, 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

Citations27
Published2016
Admission routes1
Has abstractyes

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