Does Gender, Age and Usage Matter in Big Data’s Perception Applied in Online Tourism?
Bibliographic record
Abstract
It is often asserted that big data can potentially help firms make data-oriented decisions. The rise of big data completely revolutionized the tourism industry, explaining partly why this research intends to explore the relationship between big data applied in online tourism and online tourists’ behavior. This study will tackle the question in relation to big data’s perception based on gender, age and usage’s interaction, factors identified throughout the literature as important. It is found that both advantages and disadvantages of big data positively impact online consumer behavior. Although surprising, this result is justified in the fact that consumers always consider the trade-off between advantages and disadvantages which explains their willingness to still use online services. It is also found that usage is a determinant factor influencing big data’s perception. One major take away is that disadvantages of big data do not necessarily translate into a negative behavior. Moreover, online tourism website designers can tailor their products in a way to appeal to light users of online tourism services, considering heavy users are likely to buy in no matter what.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".