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Record W7084155063 · doi:10.59876/a-gw5m-hyfr

Organizational Resilience and Strategic Agility: An Integrative Framework Based on Evidence During COVID-19

2025· article· en· W7084155063 on OpenAlexvenueno aff

Bibliographic record

VenueManagement international · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Leadership and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptabilityAmbidexterityResilience (materials science)Cohesion (chemistry)Phase (matter)Process (computing)LoyaltyQualitative research

Abstract

fetched live from OpenAlex

This article explores how strategic agility enables organizations to identify potential threats, mitigate risks and benefit from emerging opportunities to enhance their resilience during a crisis. Based on a qualitative study conducted from April 2020 to March 2021 among 11 French companies, the results highlight how strategic agility can be a catalyst for achieving organizational resilience. This study presents a structured three-phase process to help organizations navigate and adapt to disruptive events. Phase 1 focuses on winning back customers and leveraging digital tools to improve loyalty and efficiency. Phase 2 focuses on strengthening team cohesion and reconfiguring processes to foster collaboration and streamline operations. Phase 3 involves rethinking strategic activities and renewing business models to ensure adaptability and long-term growth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.005
Science and technology studies0.0050.020
Scholarly communication0.0070.012
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.392
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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