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Record W4226157802 · doi:10.5539/ibr.v14n12p57

Business Model Construction from Dynamic Capabilities Perspective

2021· article· en· W4226157802 on OpenAlexvenueno aff
Xiong Wang, Sarana Photchanachan

Bibliographic record

VenueInternational Business Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProcess managementBusiness architectureArtifact-centric business process modelComputer scienceTask (project management)Dynamic capabilitiesBusiness process modelingBusiness domainBusiness modelAdaptation (eye)Business ruleBusiness Process Model and NotationBusiness processKnowledge managementBusinessSystems engineeringMarketingEngineering

Abstract

fetched live from OpenAlex

The construction of the business model is to shape the structure of various activities in the plate according to the task attributes and task objectives of each section in the business model. The Dynamic Capabilities of the enterprise determine the result of the construction of the business model, and its strength also affects the adaptation and operation of the business model institutions to the new business model. Enterprises with strong Dynamic Capabilities have a good execution ability in the business model architecture, and can also quickly adapt to the structural framework of changing their business model, and improve the feasibility of the actual operation of the business model. This paper will be in the framework of the Dynamic Capabilities theory, the construction of business model, through the case from model construction, design innovation to the coordinated operation of the new model, concluded that the two have a strong interaction, including the enterprise higher-order Dynamic Capabilities has a strong positive influence on the choice of business model, construction and application.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.335
Teacher spread0.288 · 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.

Study designSimulation or modeling
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

Citations1
Published2021
Admission routes1
Has abstractyes

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