Do Twitter and Facebook Matter? Examining the Economic Impact of Social Media Marketing in Tourism Websites of Atlantic Canada
Bibliographic record
Abstract
Do Twitter and Facebook Matter? Examining the Economic Impact of Social Media Marketing in Tourism Websites of Atlantic Canada This paper quantifies the economic impacts of introducing social media as marketing tools in the tourism websites of the provinces of Atlantic Canada: New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island. The authors build on prior marketing research of tourism website design in Atlantic Canada and focus on eight forms of social media: e-newsletters, Facebook, Twitter, RSS, YouTube, Flickr, Travel Blog, and Share. They used panel techniques for data across the four provinces and the months in 2005-2010 to estimate models of tourism-related economic activity (hotel rooms available and rented, hotel occupancy, number of domestic and US visitors, etc.). As independent variables, they used measures that are standard in economic models (unemployment rates, retail sales, exchange rates, interest rates, temperatures and seasonal dummies, etc.) and various measures of the timing of the introduction of social media into provincial tourism websites. Their results highlight the strong seasonal patterns in tourism data in Atlantic Canada, the role of domestic and international economic data in predicting various measures of tourism, and provide preliminary evidence that adopting social media as marketing tools may have contributed substantially to tourism. Finally, the research seeks to highlight the role in tourism promotion of ongoing innovations in marketing, such as using social media, and the importance of tourism as an economic driver in the region.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".