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Record W2143376044 · doi:10.1080/10580531003685204

An E-Business Audit Service Model in the B2B Context

2010· article· en· W2143376044 on OpenAlexaff
Jagdish Pathak, Mary R. Lind

Bibliographic record

VenueInformation Systems Management · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAuditBusinessService (business)New business developmentBusiness processElectronic businessBusiness service providerProcess managementKnowledge managementBusiness modelAccountingService designService delivery frameworkComputer scienceMarketing

Abstract

fetched live from OpenAlex

This research studies E-business audit as a specialized service rendered in an information technology intensive environment. A service model is used to research the E-business audit context. Here auditing is viewed as a specialized technical service conducted in the context of E-business technologies, business processes, and people involved in E-business transactions. The specialized audit service is provided to the E-Business transacting firms that in most cases involve transactions between customers and suppliers. A field study of information technology auditors showed that both knowledge of the business processes and of the technologies were critical for them to render reliable and accurate E-business audit findings. The results showed the need for higher training levels in advanced IT methods and tools for technology auditors in rendering IT audit judgments for the business-to-business (B2B) context. Thus, these results provided support for the service model that calls for the appropriate knowledge and use of technologies, business processes, and people in providing service.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0060.008
Open science0.0010.004
Research integrity0.0030.002
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 head0.069
GPT teacher head0.344
Teacher spread0.275 · 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 designTheoretical or conceptual
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

Citations0
Published2010
Admission routes1
Has abstractyes

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