Thriving Through Adversity: A Survival Learning Framework for Entrepreneurial Transformation During the COVID-19 Crisis in Indonesia
Bibliographic record
Abstract
This study examines the challenges faced by entrepreneurs during the COVID-19 pandemic, with an emphasis on their learning processes, the strategies they implemented, and the insights they earned. The research employs Interpretative Phenomenological Analysis (IPA) to provide insights into entrepreneurs’ experiences during the crisis. We used purposeful sampling to select small and medium-sized business owners on Java Island who survived the pandemic. We conducted semi-structured interviews and analyzed the data using NVIVO 12 software. This study introduces the Survival Learning Framework (SLF), a structured approach enabling entrepreneurs to navigate crises like COVID-19. SLF consists of three phases—knowing, understanding, and execution—emphasizing resilience, strategic awareness, and holistic transformation to foster adaptability and long-term sustainability. The Survival Learning Framework (SLF) offers a structured, multidisciplinary approach to entrepreneurial resilience, prioritizing transformation and long-term growth over mere recovery. By integrating adaptive resilience, experiential learning, and effectuation theories, it fills a critical literature gap, providing both theoretical advancements and practical strategies for navigating crises like COVID-19.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".