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Record W2510598047 · doi:10.4018/ijide.2016100101

Collaborative Business Service Modelling in Knowledge-Intensive Enterprises

2016· article· en· W2510598047 on OpenAlexaff
Thang Le Dinh, Thanh Thoa Pham Thi

Bibliographic record

VenueInternational Journal of Innovation in the Digital Economy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsKnowledge managementService (business)Collaborative networkConceptual frameworkCompetitive advantageConceptual modelFoundation (evidence)Knowledge sharingBusinessComputer scienceProcess managementMarketing

Abstract

fetched live from OpenAlex

Nowadays, knowledge-intensive enterprises, which offer knowledge-based products and services to the market, play a vital role in the knowledge-based economy. In the global networked age, collaborative business services have raised as one of the most important knowledge-intensive services that help enterprises to gain the competitive advantage. These services greatly depend on the ability to use network architectures to collaborate efficiently with business partners. This paper introduces the KB-CBSM (Knowledge-Based Collaborative Business Service Modelling) approach, which aims at providing a conceptual foundation for modelling effectively and improving incrementally collaborative business services in knowledge-intensives enterprises. The paper begins by presenting the necessity and principles of the KB-CBSM approach. Next, it presents the conceptual foundation that consists of three levels: Service value creation network, Service system and Service levels. The paper continues with a discussion and review of the relevant literature and ends with the conclusion and suggestions for further research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.000
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 head0.017
GPT teacher head0.254
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

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