Exploring the impacts of advertisement and influencer on tourist destination selection: An empirical investigation of Cox’s Bazar tourism in Bangladesh
Bibliographic record
Abstract
,Purpose: This study aims to investigate the impact of social media advertising (SMA) and influencer endorsements on tourists' destination selection (TDS) for Cox’s Bazar, Bangladesh, and to determine the moderating role of generational cohorts in these relationships. Design/methodology/approach: A quantitative research design was employed, utilizing a self-administered survey. Data from 202 respondents were analyzed using confirmatory factor analysis (CFI=0.95, TLI=0.94, RMSEA=0.06) to validate the measurement model, followed by multiple regression and moderation analysis to test the hypothesized relationships. Findings: The regression results indicate that both SMA (β = 0.32, p < 0.001) and human influencer (HI) endorsements (β = 0.41, p < 0.001) significantly positively influence TDS. Furthermore, generational cohort significantly moderates these effects: SMA's impact is stronger for Gen Z (SMA×GenCohort: β = 0.12, p = 0.003), while conventional advertising is more effective for older cohorts (CA×GenCohort: β = -0.10, p = 0.001). Practical implications: Destination Marketing Organizations (DMOs) should adopt a generationally segmented strategy, prioritizing authentic human influencer partnerships and targeted social media campaigns for younger demographics, while maintaining a presence in conventional media for older tourists. Originality/value: By uniquely integrating the Elaboration Likelihood Model within an emerging market context, this research fills a critical gap in the literature, providing novel evidence on generational differences in processing destination marketing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".