Relate-and-Discovery : exploring the confluence of UX design and electronic word-of-mouth in travel technology
Bibliographic record
Abstract
Due its costly and intangible nature, travel-related purchases involve a lot of complex decision-making, and are therefore deemed high-risk. However, since the turn of the millennium, as web-enabled technologies grew increasingly more prevalent, the manner in which people seek information has evolved, thus distorting our traditional understanding of the buyer’s journey. Using search-powered, online platforms, consumers now deploy an arsenal of means to discover new goods, services, and activities. While there are countless price comparison websites and virtual communities for obtaining travel information, all of the existing networks neglect the most effective and longstanding breed of communications—a form that regularly outperforms the standard approaches to advertising: word-of-mouth (WOM). Namely, a process in which consumers can discover information not only through search, but by relating to one another. As a result, the market is devoid of a tool for sharing personalized, experience-based, destination-specific travel recommendations with friends and family. Through Dispatch, a mobile app prototype and research proxy, the efficacy of online WOM (eWOM) will be explored further, revealing how this behaviour phenomenon coexists with the intricacies and nuances of user experience (UX) design.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".