Black Swan Theory of Events and the Impact to Hospitality Operators: Literature Review and Proposed Analysis
Bibliographic record
Abstract
The Black Swan theory of events is an incident that deviates beyond what is normally expected of a situation and is extremely difficult to predict (Talib, 2007). Black Swan events impact the hospitality industry in many ways. Weather, widespread illness, civil unrest, terrorism, and loss of utilities are out of the immediate control of the industry’s participants but impact their results. Occurrences at the property/corporate level can also impact performance in ways such as: food contaminants impacting restaurants, cruise ships losing power, running a ground, or sinking, experiencing a widespread Nora virus, hotels face fires, strikes, data breaches, boycotts, as well as events such as amusement parks having ride fatalities and animal welfare protests, etc. All of these events are quickly spread by multiple news sources, and consumers are able to search quickly using Google or other search engines to get more details.Previous studies have looked at the aftereffects of exogenous—or self-inflicted— events, and how management teams reacted or did not react and the impact to operating results (Kosova & Enz, 2012). However, there is a paucity of academic analysis of Black Swan events to determine whether there is a way to reduce the impact.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".