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Record W4379356295 · doi:10.1108/ijebr-07-2022-0681

Strategic factors conferring organizational resilience in SMEs during economic crises: a measurement scale

2023· article· en· W4379356295 on OpenAlexaff
Martie‐Louise Verreynne, Jerad A. Ford, John Steen

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConceptualizationOperationalizationResilience (materials science)Scale (ratio)Social connectednessProactivityAdaptation (eye)BusinessStrategic planningFlexibility (engineering)OriginalityProcess managementKnowledge managementMarketingEconomicsComputer sciencePsychologyManagementCreativitySocial psychology

Abstract

fetched live from OpenAlex

Purpose The paper aims to develop a strategic conceptualization and measurement scale of organizational resilience to support researchers examining how small firms prepare and respond deliberately to general disruptions in the operating environment over more extended time frames. Design/methodology/approach The paper uses a four-step process to develop, present and test (for predictive validity) a scale of strategic organizational resilience for frequent events or those needing long-term responses. Findings The resulting seven-factor measurement scale of organizational resilience consists of readiness, slack, problem-solving, flexibility, connectedness, adaptiveness and proactiveness. Originality/value The literature on organizational resilience explains how organizations recover from rare but catastrophic events by focusing on adaptation principles and short-term survival. The broader conceptualization presented here enables the study of organizational resilience in small-medium size enterprises (SMEs) across more frequent and pervasive events, such as financial crises, industry downturns and other forms of structural change and technological disruption. This is operationalized in a measure that includes new strategic factors associated with forward-planning and more traditional operationally focused elements.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.090
GPT teacher head0.343
Teacher spread0.253 · 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
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

Citations32
Published2023
Admission routes1
Has abstractyes

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