Resilience-building in small island family-owned accommodation sector
Bibliographic record
Abstract
This study aims to understand the crises experienced by family-owned accommodation businesses in small island contexts and how resilience is built alongside the development of their dynamic capabilities (sensing, seizing, and transforming) as they navigate through crises. The study uses a qualitative method and focuses on the experiences of tourism businesses operating in the islands of Boracay, Negros, and Siargao, which are among the top island destinations in the Philippines. Semi-structured interviews with 18 participants were conducted through 1-hour online video calls, phone calls, and written 2 interviews. Crises that affect tourism businesses are mostly due to external factors. Small islands are at high risk of natural hazards, but businesses do not consider typhoons and earthquakes as crises per se since they frequently experience these hazards. Family businesses have distinct strategies, such as knowledge transfer, maintenance of stable financial resources, infrastructure development, employee training, better marketing strategies, and a focus on sustainability to enhance their dynamic capabilities and build their resilience, thus making them more adaptive to future crises. However, government support for the local tourism industry is still needed to ensure a sustainable tourism industry. Dynamic capabilities and resilience are often linked with each other yet there is limited knowledge on how resilience is built specifically in the context of family-owned businesses in small island context. This study addresses this gap in the literature by using dynamic capabilities as a framework to understand resilience development. Resilience and dynamic capabilities are then adaptive strategies in crisis management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".