Marketing effectiveness of hotel Twitter accounts: the case of Saudi Arabia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of the study is to investigate the relationship between consumers’ attitudes toward hotel Twitter accounts and attitudes toward hotel brands, booking intentions and electronic word-of-mouth. The study focuses on Saudi Arabia owing to the widespread use of Twitter in that market. Design/methodology/approach Modifying a previously developed theoretical model on marketing effectiveness of social media, this study empirically tests consumers’ attitudes toward hotel Twitter accounts. Data have been collected via a structured online survey. A confirmatory factor analysis and a structural equation model fit have been used to test the model. Findings When consumers have positive attitudes toward hotel tweets, they have positive attitudes toward the hotel’s Twitter account, which, in turn, improves their attitudes toward the hotel’s brand and results in intent to book and spread electronic word of mouth. Originality/value The study contributes to the body of knowledge about social media marketing effectiveness in the hospitality industry.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it