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Record W2891947042 · doi:10.1177/0021886318797597

Inside the Onion: Understanding What Enhances and Inhibits Organizational Resilience

2018· article· en· W2891947042 on OpenAlexafffund
Laura Gover, Linda Duxbury

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsCarleton UniversityVancouver Island University
FundersCanadian Institutes of Health Research
KeywordsConceptualizationResilience (materials science)PerceptionOrganizational changePsychologyConceptual modelOrganizational learningOrganizational commitmentOrganizational studiesKnowledge managementSociologySocial psychologyPublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Organizational resilience refers to an organizations’ capacity to withstand changes over time. Most existing models of organizational resilience have not been empirically tested and/or tend to focus on “what resilience is” and little attempt has been made to investigate factors that enhance and/or diminish an organization’s resilience. This qualitative research study, therefore, seeks to advance theorizing about organizational resilience by identifying and exploring both the enablers and inhibitors of organizational resilience. Longitudinal interview data are analyzed to explore employees’ perceptions about what has impacted their organization’s ability to cope with change. A conceptual model of organizational resilience is proposed. The contributions of this model are that it is the first, to our knowledge, to (1) propose a multilevel conceptualization of organizational resilience, and (2) include within the model the idea that earlier changes can both enhance and inhibit the organizations’ current ability to cope with change.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0060.011
Open science0.0010.007
Research integrity0.0020.002
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 head0.033
GPT teacher head0.270
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

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 designQualitative
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

Citations35
Published2018
Admission routes2
Has abstractyes

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