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Record W2124019974 · doi:10.1111/jems.12024

Do Higher Costs Spur Process Innovations and Managerial Incentives? Evidence from a Natural Experiment

2013· article· en· W2124019974 on OpenAlexaffabout
Benoît Dostie, Rajshri Jayaraman

Bibliographic record

VenueJournal of Economics & Management Strategy · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsIncentiveBankruptcyNatural experimentSpurPanel dataShock (circulatory)Difference in differencesProcess (computing)Industrial organizationIdentification (biology)BusinessSet (abstract data type)EconomicsMicroeconomicsEconometricsFinanceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This paper asks whether firms respond to cost shocks by introducing process innovations and increasing the use of managerial incentives. Using a large panel data set of workplaces in Canada, our identification strategy relies on exogenous variation in costs arising from increased border security along the 49 th parallel following 9/11. Our longitudinal difference‐in‐differences estimates indicate that firms responded to the cost shock by introducing new or improved processes, but did not change their use of managerial incentives. These results suggest that the threat of bankruptcy may provide impetus for improving efficiency.

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)0.000
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 score0.716
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.004
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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

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