Influencers in Tourism Digital Marketing: A Comprehensive Literature Review
Bibliographic record
Abstract
Almost all business sectors in various developed and developing countries have realized the importance of transforming conventional marketing to digital marketing, the goal is to increase sales. Many marketing strategies can be applied to increase sales, including utilizing influencers in digital marketing. This study aims to identify digital marketing strategies that have been widely used by researchers in various countries and look for new models or new strategies that are relevant to be applied in developing countries after COVID-19 through a systematic literature review. The author searched for scientific articles on the Scopus database that were in English and fully accessible. This research reviewed 19 articles using a systematic literature review. The results showed that the majority of related research was published in 2018-2022, ten related articles were published in 2022 with three articles published in Spain. All authors proposed various variables, but generally conventional in digital marketing, while not many authors concentrated on the utilization of influencers in carrying out digital marketing. Therefore, this research offers a digital marketing strategy combined with the role of influencers in tourist destinations that have a competitive advantage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".