MétaCan
Menu
Back to cohort
Record W4386699061 · doi:10.32920/24134937.v1

The Impact of Emerging Market Competition on Innovation and Business Strategy: Evidence from Canada

2023· preprint· en· W4386699061 on OpenAlexaffabout
Mu-Jeung Yang, Nicholas Li, Lorenz Kueng

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIncentiveInnovatorCompetition (biology)Product innovationIndustrial organizationEx-anteProduct marketProduct (mathematics)EconomicsEmerging marketsEmpirical evidenceNew product developmentBusinessMicroeconomicsMarketingEntrepreneurship

Abstract

fetched live from OpenAlex

<p>Does intensifying emerging market competition boost or inhibit innovation? We estimate how a representative panel of Canadian firms adjusts innovation activities, business strategies, and exit in response to large increases in Chinese import competition. Our analysis shows that the innovation response of firms depends on the type of innovation: on average, product innovation incentives are stimulated by competition while process innovation incentives decline. We develop a theory that combines these different innovation types with partially irreversible innovation strategy choices to derive novel performance implications in response to competition. Consistent with this theory, we find that firms that initially pursue process innovation strategies and survive have higher profits ex-post, but are ex-ante more likely to exit. In contrast, firms that initially pursue product innovation strategies have higher profits if they survive, without significant impact on exit. Both empirical patterns are consistent with our theory, which suggests that innovator performance depends on the balance of innovation incentive effects and competitive failure risk. </p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.079
GPT teacher head0.275
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 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicFirm Innovation and GrowthFrench-language works237,207