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Record W2925452770 · doi:10.15353/rea.v11i1.1518

Management Quality and Innovation in Emerging Countries

2019· article· en· W2925452770 on OpenAlexvenueno aff
Oleg Sidorkin

Bibliographic record

VenueReview of Economic Analysis · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersGrantová Agentura České Republiky
KeywordsIncentiveQuality managementQuality (philosophy)BusinessInnovation managementQuality policyIndustrial organizationProduct (mathematics)Product innovationSurvey data collectionMarketingEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

I study the relationship between management quality and innovation input, and output of firms in ten emerging countries using data from the Management, Organization and Innovation (MOI) Survey. I find that management quality is tightly connected to the decisions of firms to invest in R&D. An improvement in management quality from the 25th percentile to the median is associated with a 3.3 percentage point increase in the propensity to invest in R&D. Furthermore, there are positive but weak association between management quality and product innovation. The empirical results for individual management practices show that the quality of incentive management is intimately connected to innovation performance. The quality of monitoring management is related to higher inputs into innovation, but not to innovation output. The quality of incentive management is related to higher input into innovation, but not to innovation output. All results hold after controlling for differences in management quality by industries. Additional analysis of management quality asymmetry shows that the results are driven mainly by firms with low quality management.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.446

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 head0.023
GPT teacher head0.279
Teacher spread0.255 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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