Identifying the effect of emotions in government-citizen online (G2C) tourism based on the HEART metrics
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
Emotional factors in the use of technology have the potential to be studied, since the important role of user engagement in the information technology development cycle, emotional plays a role in influencing the relationship between consumers and service providers. Previous research has examined various emotional factors of a person in operating digital services through online sites, but it is necessary to find an empirical correlation between emotional variables and one's intention to reuse (IR) online services. This study aims to determine whether users' emotions affect their decision to reuse Government to Citizen (G2C) online tourism services in Indonesia through the HEART Metrics approach. Furthermore, this quantitative study distributed questionnaires using simple random sampling to respondents who had used online tourism. Then analyse 260 research data using the SEM-PLS method by running Warp-PLS 5.0. The findings of this study are among the 5 HEART Metrics factors, 3 of which affect IR, namely Engagement, Retention, and Task Success, while Happiness and Adoption empirically have no significant effect on IR. Our results show that to gain consumer engagement with online services, service providers must consider the emotional elements of the users so that service reuse goals can be achieved. Furthermore, this research can be considered as an alternative recommendation for online tourism service providers, as well as the findings of a new model proposed to contribute to similar research in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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