Investigating Online Social Media Network Acceptance in the Tourism and Hospitality Industry in Oman
Bibliographic record
Abstract
In the emerging tourism and hospitality industries such as that of Oman, companies can market their services and products using the Social Media Networks (hereafter SMNs) and engage customers to identify their requirements online. Oman recognizes the benefits of SMNs in the tourism and hospitality industry and it has made major efforts to ensure the success of this newly introduced industry like its neighboring country the United Arab Emirates (hereafter UAE). Even though, the hospitality industry is vital to the economy of Oman, the Omani hospitality industry continues employing the conventional approach while conducting transactions. Understanding the influence of accepting such an innovation in the hospitality industry in Oman raises a fruitful research question to investigate. Therefore, it is this study’s objective to examine the influence of SMNs Acceptance in the tourism and hospitality industry in Oman. For the attainment of the study’s objective, the study uses a survey questionnaire to 200 respondents that have visited Oman recently, where 182 responses were properly filled and returned. The structural equation modeling (hereafter SEM) had been utilized to analyze the collected data. The results reveal that the respondents had high degree of satisfaction with their travel experience and they intended to continue using SMNs for tourism purposes. Nonetheless, it was found that the major factors influencing their decisions are: perceived usefulness, perceived ease of use, subjective norms and reliability.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".