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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".