Inbound marketing and customer loyalty in the tourism sector
Bibliographic record
Abstract
The inbound marketing strategies employed by tourism companies have proven to be crucial in post-COVID-19 scenarios, facilitating their adaptability, visibility, and customer retention in adverse environments. The study aimed to determine the relationship between inbound marketing and customer loyalty in the tourism sector. It is a quantitative, correlational, and non-experimental study. The sample consisted of 380 tourists aged 18 and above who visited Trujillo, Peru, in the first quarter of 2022. Data collection utilized the survey technique with a questionnaire as the research instrument. Survey data were tabulated in MS Excel and analyzed using Jamovi 2.3.38, employing descriptive and inferential statistics. The correlation was assessed using the Spearman coefficient. The results highlighted that 40.8% of participants agreed that inbound marketing strategies implemented by Trujillo's tourism sector were highly effective, while 60.0% expressed strong loyalty to these companies. Ultimately, a significance level below 0.05 was identified between the variables, indicating a significant relationship between them. Consequently, the study concluded the existence of a direct and moderate relationship between inbound marketing and customer loyalty in the investigated group. AcknowledgmentWe express our gratitude to Antenor Orrego Private University for their invaluable support in the development of our research thesis. The guidance and resources provided were instrumental in achieving our academic objectives.We extend special thanks to the Faculty of Economic Sciences for their constant support, guidance, and valuable suggestions that have significantly enriched the quality of this research work. Additionally, we appreciate the teaching staff for their inspiration and dedication, which has positively influenced our academic and professional growth.The infrastructure and learning environment of Antenor Orrego Private University have played a crucial role in the achievement of this milestone. This research article is the outcome of the solid foundation we have acquired during our time at this institution.Finally, our sincere thanks to Antenor Orrego Private University for being the cornerstone of our academic education and for providing us with the necessary tools to carry out this research project.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".