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Record W4214553635 · doi:10.1080/08874417.2022.2037476

A Semantic Model for Enterprise Digital Transformation Analysis

2022· article· en· W4214553635 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Computer Information Systems · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsSaint Mary's University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsDigital transformationComputer scienceTransformation (genetics)Enterprise modellingKnowledge managementEnterprise information systemModel transformationProcess (computing)Enterprise integrationSemantic technologyProcess managementSemantic computingEnterprise softwareInformation retrievalWorld Wide WebSemantic WebArtificial intelligenceBusinessProgramming language

Abstract

fetched live from OpenAlex

Enterprises are continuously undertaking digital transformation by implementing, maintaining, and integrating digital technologies for their information systems. However, the enterprise systems industry does not have a formal model or tool to coordinate the enterprise digital transformation process. This paper proposes a semantic model for enterprise digital transformation analysis. The proposed semantic model links all critical aspects of enterprise digital transformation and provides a multifaceted view of digital transformation for the organization. The study develops a prototype of computerized semantic model and demonstrates the usefulness of the proposed semantic model for enterprise digital transformation 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.011
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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