Technological Readiness Index (TRI) and the intention to use smartphone apps for tourism: A focus on inDubai mobile tourism app
Bibliographic record
Abstract
The study sought to find out the effect of Technological Readiness Index (TRI) on the adoption of the inDubai application by potential tourists to Dubai.The finding showed a distinct relationship between TRI dimensions and the intention to make use of the inDubai mobile application.The findings of the study further proved that the TRI model as developed by Parasuraman can indeed prove the intention of individuals to adopt a new technology.The study showed that if the first two dimensions (Optimism and Innovation) are present, then a traveler will most likely see the perceived benefits of using the product or technology which in turn will lead to a positive intention to adopt such a technology.The findings also showed that if the last two dimensions (Insecurity and Discomfort) are present then such individuals exhibiting these behaviors are less likely to want to adopt this new technology.This implies that applications like the inDubai application and other similar applications need to address issues that lead to insecurity and discomfort among users if they are to attract a lot of adopters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".