Visitor Segmentation Through Sustainable Travel Behaviors: Insights from Indonesia
Bibliographic record
Abstract
Tourism's potential to contribute to sustainable development is strongly influenced by tourist behaviors.To advance sustainability in destination planning, segmentation studies tailored to behavioral patterns are essential.This study aims to identify distinct segments of urban visitors based on environmental and social sustainability practices, while also exploring generational differences.Using a two-step cluster analysis on survey data from 1,053 respondents in the Jakarta Metropolitan Area, visitors were grouped based on their reported sustainable behaviors.The analysis first determined the optimal number of clusters using the Bayesian Information Criterion (BIC) and then validated the solution through silhouette analysis.The study reveals two primary visitor clusters: one characterized by consistent pro-environmental and pro-social behaviors, predominantly comprising individuals with higher education and income; and another with more sporadic sustainable behaviors, largely associated with lower socioeconomic status.Generational distinctions were evident, with Gen Y and Gen X dominating the former cluster, while Gen Z and Baby Boomers were more prevalent in the latter.The study offers actionable insights for tailoring sustainability-focused tourism strategies across demographic profiles and contributes an important perspective from the Global South, addressing the literature's current bias toward Western contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".