MétaCan
Menu
Back to cohort
Record W4405280886 · doi:10.1111/1911-3846.13003

Flexible or rigid? Evidence on managerial ability and cost structure

2024· article· en· W4405280886 on OpenAlexvenueno aff
Rajiv D. Banker, Rong Huang, Yuxuan Wang, Yan Yan

Bibliographic record

VenueContemporary Accounting Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsRigidity (electromagnetism)Downside riskBusinessCost structureFixed costPropensity score matchingOperations managementEconomicsMicroeconomicsFinanceMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Abstract This study investigates the association between managerial ability and cost rigidity. Cost rigidity refers to the relative proportion of fixed and variable costs. We expect that high‐ability managers will assess the potential upside congestion and downside default risks and choose an appropriate level of cost rigidity accordingly. Our results show that, on average, high‐ability managers tend to adopt a more rigid cost structure because they are more likely to realize favorable demand, and therefore, they retain higher capacity with more fixed inputs to alleviate potential congestion risk. We further document that firms with high‐ability managers will exhibit a higher (lower) level of cost rigidity when facing higher congestion risk (default risk). Our results are robust to using a propensity score matching method, a CEO turnover subsample, and alternative measures of cost rigidity and managerial ability. Taken together, this study suggests that firms' capacity management choices vary with the level of managerial ability.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.136
GPT teacher head0.359
Teacher spread0.222 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueContemporary Accounting ResearchSame topicCorporate Finance and GovernanceFrench-language works237,207