Strategic Social Media Campaigns in the Tourism Industry; Their Influence on Consumer Behavior and Purchasing Decisions
Bibliographic record
Abstract
The tourism sector has undergone a significant transformation driven by the widespread adoption of social media, which is now essential for destination marketing and promotion. Social media platforms offer innovative ways to engage potential consumer through compelling visuals, authentic narratives, and real-time content. This engagement influences consumer perceptions and builds brand appeal well before purchase decisions are finalized, allowing to cultivate strong, emotionally resonant brands that attract diverse audiences. The impact of social media marketing extends beyond simple promotion; it fosters positive consumer attitudes and decisively shapes purchasing decisions. This research adopts a qualitative approach using purposive sampling, drawing on both primary observational data and secondary authoritative sources. Content analysis is systematically used to derive and support the study's findings. Furthermore, social media enhances the economic impact of tourism by attracting a broader visitor base, which benefits local enterprises and promotes sustainability by advocating for responsible travel practices. By examining successful digital marketing initiatives, this study identifies the practical mechanisms through which social media influences consumer behavior and purchase decisions, offering valuable insights for tourism stakeholders seeking to develop more effective marketing strategies and understanding how social media influence on consumer behavior and purchasing decisions.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".