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Record W2552499238 · doi:10.1109/es.2015.14

Enterprise Capability Modeling: Concepts, Method, and Application

2015· article· en· W2552499238 on OpenAlexaff
Pericles Loucopoulos, Christina Stratigaki, Mohammad Hossein Danesh, George Bravos, Dimosthenis Anagnostopoulos, George Dimitrakopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Toronto
FundersMinistry of Economy, Trade and Industry
KeywordsEnterprise architectureEnterprise integrationComputer scienceIntegrated enterprise modelingEnterprise modellingEnterprise systems engineeringProcess managementViewpointsEnterprise architecture managementEnterprise life cycleKnowledge managementEnterprise softwareEnterprise information systemService-oriented modelingSystems engineeringEnterprise architecture frameworkEngineeringArchitectureSoftware architecture

Abstract

fetched live from OpenAlex

Strategic alignment among digital services and organizational objectives is crucial for IT, if the enterprise is to use it for competitive advantage. The motivation for the work presented in this paper is based on the need for the design of services that meet the challenges of alignment, agility and sustainability in relation to dynamically changing enterprise requirements. To this end, the paper presents an approach to enterprise modeling that historically has its roots in strategic management and more recently has been considered within the broader spectrum of enterprise architecture, business process management and service-oriented development. We refer to this approach as a capability-centric modeling approach. The paper establishes a framework within which capability modeling would be used in collaboration with other modeling viewpoints and focuses on the specific concepts and techniques that relate to enterprise capability. These concepts and techniques are elaborated upon using a scenario from a leading digital services enterprise.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.303
Teacher spread0.283 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations24
Published2015
Admission routes1
Has abstractyes

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