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Record W1981051799 · doi:10.1080/13662711003633371

Patterns of Innovation Capabilities in KIBS Firms: Evidence from the 2003 Statistics Canada Innovation Survey on Services

2010· article· en· W1981051799 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIndustry and Innovation · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsStatistics CanadaUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConsolidation (business)BusinessMarketingIndustrial organizationKnowledge acquisitionService (business)Survey data collectionKnowledge managementStatisticsComputer science

Abstract

fetched live from OpenAlex

The aim of this paper is to shed light on complementarities and substitutions between various types of innovation capabilities in knowledge-intensive-based service (KIBS) firms. The data used in this study are the responses of 2,625 innovative firms to the 2003 Statistics Canada Innovation Survey on services. The empirical results suggest the presence of three patterns of complementary innovation capabilities, one pattern of substitute activities and finally, four patterns of innovation capabilities that are independent from each other. Hence, the results suggest the presence of complementarities: first, between internal R&D, external R&D, acquisition of equipment and machinery, and marketing activities; second, between external R&D, acquisition of equipment and machinery, acquisition of external knowledge and marketing activities; third, between acquisition of equipment and machinery, acquisition of external knowledge and marketing activities. Such complementarities lead to the conclusion that, in practice, managers of KIBS firms consider the consolidation of these capabilities jointly instead of separately. The paper also discusses issues related to patterns of capabilities that are substitutes and independent from each other. The results of this study also show significant heterogeneity in the determinants of the different patterns of innovation capabilities.

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.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.311
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.251
Teacher spread0.195 · 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