Strategic factors conferring organizational resilience in SMEs during economic crises: a measurement scale
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".