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Record W2619922184 · doi:10.5267/j.msl.2017.5.001

Does organizational agility affect organizational learning capability? Evidence from commercial banking

2017· article· en· W2619922184 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
Fundersnot available
KeywordsAgile software developmentOrganizational learningKnowledge managementStructural equation modelingSample (material)Affect (linguistics)Reliability (semiconductor)BusinessOrganizational performanceOrganizational commitmentPsychologyProcess managementComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Both organizational agility and learning capability are prerequisites for organizational survival and success. This study explores the contribution of agility practices to organizational learning capabilities at the commercial banks in Jordan. To examine the proposed model, a sample of 158 employees within top and middle managements was used. Structural Equation Modeling was conducted for assessing validity and reliability of measurement instrument, evaluating model fit, and testing hypotheses. This study recognizes agility as a key element of learning facilitators. Findings affirm the strategic value of agility and conclude that administrators working within agile organizations would be able to acquire conditions that foster learning.

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.

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 categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0040.004
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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