Adaptive practices in SMEs: leveraging dynamic capabilities for strategic adaptation
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.
Bibliographic record
Abstract
Purpose A global pandemic, broken supply chains, workforce constraints, technological advancements in artificial intelligence, etc. illustrate the continual threats that SMEs face. Extending the dynamic capability concepts of sensing, seizing and transforming, this research investigates practices by which SMEs successfully adapt over time. Design/methodology/approach A comparative case study method was employed using a purposive sample of SMEs, consisting of three American firms and one Canadian firm. Findings Three sets of organizational practices, termed adaptive practices, that underlie dynamic capabilities for successful adaptation were identified: (1) continuous learning and process improvement, (2) leveraging reciprocal relationships and (3) communicating effectively. Research limitations/implications The selected cases are from two countries in North America. Using a qualitative, inductive process, the authors are able to identify patterns of actions within various organizations; however, they are not able to establish causality. Practical implications This study provides practical guidance for leaders to take action to improve their SME's dynamic capabilities for adaptation through creating coherent bundles of specified adaptive practices. Social implications Better understanding of how SMEs successfully adapt to high uncertainty and business viability threats can result in multidimensional (e.g. financial, emotional) and multi-level (individual, family, community), positive outcomes for societal stakeholders. Originality/value The findings of this study build on the literature of dynamic capabilities and organizational practices and provide a practical foundation for effective adaptation, labeled as adaptive practices.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it