Identifying the effect of emotions in government-citizen online (G2C) tourism based on the HEART metrics
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".