The role of social media influencers in shaping destination image and intention to visit Jordan: The moderating impact of social media usage intensity
Bibliographic record
Abstract
Social media influencers have become important motivators in shaping tourist attitudes and behaviors. This study analyzed how exposure to influencer content impacts key outcomes for the destination Jordan. A survey of tourists who visited Jordan in the past 3 years measured their perceptions of influencer credibility, content quality, awareness/interest, trust/engagement, destination image, general tourism behavior, and intentions to revisit. Results of SEM analysis found significant positive effects of influencer marketing on both destination image and visit intentions. Awareness/interest and trust/engagement were most impactful, highlighting influencers' role in sparking early motivation. Content quality additionally predicted visit intentions by informing decisions. Perceived credibility made recommendations more persuasive. Furthermore, usage intensity positively moderated the mediated relationships, amplifying effects among heavy social media users. Findings provide theoretical validation of how influencers act as digital opinion leaders. By enhancing destination image through compelling portrayals, influencers shape audience travel interests and behaviors. Managerial implications suggest destinations should invest in influencer campaigns for reach and inspiration while ensuring content quality. Performance tracking informs optimal platform and demographic targeting. Overall, influencer marketing demonstrated significant persuasive appeal for potential tourists. This quantitative study pioneer’s measurement of influencer marketing's tangible impacts on key tourist metrics. The results empirically substantiate the ability of strategically leverage influencers to motivate visitation and guide decision-making. As practitioners refine partnerships for audience growth and branding, academic research must also advance a nuanced understanding of this emerging phenomenon at the confluence of social media and tourism consumer behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".