MétaCan
Menu
← Back to cohort
Record W3082728243 · doi:10.1111/1911-3846.12409

Internal Control and Operational Efficiency

2018· article· en· W3082728243 on OpenAlexvenueno aff
Qiang Cheng, Beng Wee Goh, Jae Bum Kim

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsStrengths and weaknessesWelfare economicsOperational efficiencyControl (management)BusinessInternal controlPolitical scienceOperations managementHumanitiesManagementPsychologyEconomicsMarketingSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this study, we examine whether internal control over financial reporting affects firm operational efficiency. We find that operational efficiency, derived from frontier analysis, is significantly lower among firms with material weaknesses in internal control relative to firms without such weaknesses. We also find that the remediation of material weaknesses leads to an improvement in operational efficiency. Additional analyses indicate that the negative effect of material weaknesses on operational efficiency is stronger for firms with a greater demand for higher quality information for decision making, for weaknesses that are deemed to be more severe, and to a certain extent, for smaller firms. Overall, our study extends the literature by presenting systematic evidence on the effect of effective internal control on operational efficiency and informs the debate over the costs and benefits of the internal control reporting requirements under the Sarbanes‐Oxley Act of 2002.

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)0.009
metaresearch head score (Gemma)0.046
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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.294
Teacher spread0.264 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueContemporary Accounting Research→Same topicAuditing, Earnings Management, Governance→French-language works237,207→