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Record W4382987196 · doi:10.55905/oelv21n6-135

Agile governance theory: a multi-scenario empirical assessment

2023· article· en· W4382987196 on OpenAlexfundno aff
Alexandre Luna, Marcelo Marinho

Bibliographic record

VenueOBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
FundersUniversidade Federal de PernambucoConselho Nacional de Desenvolvimento Científico e TecnológicoCiência sem FronteirasUniversidade de PernambucoUniversity of British ColumbiaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsOperationalizationAgile software developmentStructural equation modelingCorporate governanceMediationContext (archaeology)Knowledge managementProcess managementConfirmatory factor analysisEmpirical researchBusinessManagement sciencePsychologyComputer scienceManagementEngineeringPolitical scienceMathematicsEconomicsEpistemology

Abstract

fetched live from OpenAlex

Context: Agile Governance Theory (AGT) has emerged as a potential model for organizational chains of responsibility across business units and teams. Objective: This study aims to assess how AGT is reflected in practice. Method: AGT was operationalized down into 16 testable hypotheses. All hypotheses were tested by arranging eight theoretical scenarios with 118 practitioners from 86 organizations and 19 countries who completed an in-depth explanatory scenario-based survey. The feedback results were analyzed using Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA). Results: The analyses supported key theory components and hypotheses, such as mediation between agile capabilities and business operations, through governance capabilities. Conclusion: This study supports the theory and suggests that AGT can assist teams in gaining a better understanding of their organization’s governance in an agile context. A better understanding can help remove delays and misunderstandings that can come about with unclear decision-making channels, which can jeopardize the fulfillment of the overall strategy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.393
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.344
Teacher spread0.306 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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