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Record W4402771677 · doi:10.1504/ijkms.2024.141563

Knowledge management through the lens of business process management: a bibliometric analysis

2024· article· en· W4402771677 on OpenAlexaff
d Nélia, Elaine Resende, Darli Rodrigues Vieira, Eduardo Amadeu Dutra Moresi, Alencar Bravo, Helga Cristina Hedler

Bibliographic record

VenueInternational Journal of Knowledge Management Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsKnowledge managementProcess (computing)BusinessBusiness process managementProcess managementComputer scienceBusiness processMarketingWork in process

Abstract

fetched live from OpenAlex

Given that process management is useful for registering and organising knowledge, business process management (BPM) can be perceived as a form of knowledge management. Through BPM practices, it is possible to analyse and redesign processes to represent and translate organisational objectives and strategies that aim to add value to customers. This exploratory article seeks to map the scientific production on this topic in the Scopus database and presents a literature review on BPM and knowledge management. The keyword co-occurrence network made it possible to identify the main concepts and those that emerged. The co-citation and bibliographic coupling network methods made it possible to carry out a relational analysis of citations, obtaining results that showed the interrelationship between documents and researchers on the subject within the scientific community. This article highlights the knowledge gaps and broadness of this topic, opening possibilities for further research. This article concludes that the interaction between process management and knowledge management influences the success of organisations worldwide.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Bibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0440.092
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.044
GPT teacher head0.351
Teacher spread0.307 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Knowledge Management StudiesSame topicCollaboration in agile enterprisesCategoryBibliometricsFrench-language works237,207