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ERP Systems Effectiveness in Implementing Internal Controls in Global Organizations

2008· book-chapter· en· W2499574418 on OpenAlexaff
Vinod Kumar, Raili Pollanen, Bharat Maheshwari

Bibliographic record

VenueIGI Global eBooks · 2008
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsUniversity of WindsorCarleton University
Fundersnot available
KeywordsBusinessProcess managementProcess (computing)Face (sociological concept)Diversity (politics)Knowledge managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This chapter examines the effectiveness of ERP systems in implementing internal controls in global organizations, particularly controls required by the Sarbanes-Oxley Act (U.S. Congress, 2002), or SOX. It aims to understand the extent to which ERP systems are able to meet these requirements and challenges organizations face in enhancing their ERP systems for this purpose. The chapter reports the results of interviews with ERP systems managers and directors in four organizations with significant global operations. It reveals a substantial degree of completion of SOX requirements by these organizations, often facilitated by consultants, and often accomplished as part of broader systems, processes, and strategic management improvement initiatives. It also highlights some significant technical and cultural implementation challenges, such as systems inflexibility and diversity, systems security weaknesses, and resistance to change, as well as some benefits upon completion, such as improved process efficiency and systems security, and potential intangible long-term benefits.

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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