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Record W2144238655 · doi:10.1142/s0219877012500344

HOW DO HIGH, MEDIUM, AND LOW TECH FIRMS INNOVATE? A SYSTEM OF INNOVATION (SI) APPROACH

2012· article· en· W2144238655 on OpenAlexafffund
Ziad Rotaba, Catherine Beaudry

Bibliographic record

VenueInternational Journal of Innovation and Technology Management · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversité du Québec à MontréalPolytechnique Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOrder (exchange)BusinessOutsourcingInvestment (military)Industrial organizationHigh techProduct (mathematics)Market economyEconomicsFinanceMarketing

Abstract

fetched live from OpenAlex

In the past decade, the innovation literature has mainly targeted high-tech (HT) sectors due to their higher return on investment and important role in building new societies and economies. While the HT sector is still of a leading importance, whether medium and low tech (LMT) sectors should be equivalently considered when analyzing long term economic growth, in both leading and catching up economies, is a fundamental question. This paper is our second milestone comparing HT and LMT sectors from an innovation perspective, using a National System of Innovation (NSI) approach. The general aim of this paper is to find the main principles that govern the difference between the two industrial segments (HT and LMT) while controlling for supranational boundaries. In order to measure the effect of NSI, countries are divided into two groups: leading and catching up economies. Our results suggest that, with respect to HT, leading economies can be considered as innovators, while catching up economies are the imitators. Furthermore, HT in leading economies relies on product modularity to outsource various components probably to firms in catching up economies. Catching ups are putting greater emphasis on universities to produce knowledge. In addition, firms in catching up economies benefit from high accessibility to funds in order to grow various industrial sectors, especially LMT. The role of institutions and governments with respect to regulatory policies, intellectual property protections are of high importance for firms in catching up economies, especially in LMT. As a result of those important steps, the various agents in catching up economies have achieved sustainable growth, notably in LMT. In contrast, the same growth is observed for HT for firms in leading economies. Our results suggest that catching up countries are strategizing for this sectoral evolution, renewal, and transformation process for both sectors, but with a stronger emphasis on LMT.

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)0.013
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 score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.006
Science and technology studies0.0020.005
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.017
GPT teacher head0.213
Teacher spread0.196 · 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

Citations9
Published2012
Admission routes2
Has abstractyes

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