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Record W2112345763 · doi:10.1002/gsj.1093

Accumulative and Assimilative Learning, Institutional Infrastructure, and Innovation Orientation of Developing Economy Firms

2015· article· en· W2112345763 on OpenAlexafffund
Raveendra Chittoor, Preet S. Aulakh, Sougata Ray

Bibliographic record

VenueGlobal Strategy Journal · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInternationalizationBusinessContext (archaeology)IndigenousIndustrial organizationProduct (mathematics)Panel dataMarket orientationEmerging marketsResource (disambiguation)MarketingInternational tradeEconomics

Abstract

fetched live from OpenAlex

We examine the role of internationally acquired knowledge and supra‐firm institutional infrastructure on developing firms' innovation orientation. Empirical results, based on a panel of 11,048 Indian manufacturing firms during the period 1990 to 2009, show that the macro‐ and micro‐institutional context in which firms are embedded condition the effect of global resource and product market participation on indigenous innovation efforts. In particular, technology imports (accumulative learning) have a stronger effect on inducing investments in innovation when the macro‐institutional development is weak and for firms that are affiliated to business groups. However, product market internationalization (assimilative learning) plays a more important role in facilitating innovation efforts as the institutional environment becomes stronger and for independent firms that do not possess the network advantages inherent in business groups.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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

Citations79
Published2015
Admission routes2
Has abstractyes

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