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Record W3010923023 · doi:10.1108/jfc-01-2020-0002

Corruption at Rolls-Royce: can it happen again?

2020· article· en· W3010923023 on OpenAlexaff
Dominic Peltier‐Rivest

Bibliographic record

VenueJournal of Financial Crime · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsLanguage changeDue diligenceIndictmentAccountingBusinessForeign Corrupt Practices ActBusiness ethicsEnforcementLawPolitical scienceFinance

Abstract

fetched live from OpenAlex

Purpose This study aims to analyse Rolls-Royce’s (RR) recent corruption case, its 2017 global anti-bribery and corruption (ABC) manual, and its 2017 annual report to assess whether it has put the best corruption prevention strategies into place. Design/methodology/approach This is a legal case study based on RR’s 2017 deferred prosecution agreement (DPA) with the UK serious fraud office. It uses the new ISO 37001 standard as a theoretical framework. Findings RR’s DPA suspends an indictment covering 12 counts of conspiracy to corrupt, false accounting and failure to prevent bribery. RR’s ABC manual exhibits significant shortcomings as compared to ISO 37001’s requirements. RR’s ABC manual does not provide any reference to the setting, reviewing and achievement of measurable anti-bribery objectives; does not state that anti-bribery training is provided at planned intervals to employees and external business associates that pose more than a low risk of bribery; does not explain the authority and independence of its head of ethics and compliance; does not state any maximum for gifts and hospitality given or received; does not provide clear assurances that reports made through its main internal channels will be treated confidentially and that complaints about senior management will be investigated by an outside firm; and does not subject its advisers to a formal due diligence process. RR’s annual report notes that it operates in an industry prone to corruption. Finally, internal control failure and compliance fatigue mean that no anti-bribery management system can be completely effective. Research limitations/implications This paper extends previous research by analysing the best corruption prevention strategies that organizations can implement. It does not endeavour to certify whether RR is ISO 37001 compliant, and it analyses only publicly available documents. Practical implications This study’s prevention strategies will help deter corruption and improve internal controls within organizations. Originality/value No previous study has used the new ISO 37001 standard as a framework for such corruption case analysis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.303
Teacher spread0.249 · 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.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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