Role of Deindividuation Between Perceived Crowding and Tourist Behaviors: Moderating Effect of Environmental Knowledge
Bibliographic record
Abstract
ABSTRACT Destination crowding has emerged as a serious issue for tourist sites and visitors alike. This research delves into the correlation between two‐dimensional perceived (spatial and human) crowding and two tourist behaviors (pro‐environmental and deviant behavior). Additionally, it explores the influence of deindividuation and environmental knowledge on these relationships. The study, based on 313 Chinese domestic tourists who recently visited the Great Wall, reveals that perceptions of spatial and human crowding significantly trigger deviant behavior. Conversely, pro‐environmental behavior is indirectly impeded by both forms of perceived crowding, with deindividuation acting as a mediating factor. The presence of environmental knowledge proves crucial in empowering tourists to make well‐informed behavioral decisions and mitigating the negative effects of deindividuation on their actions. This research contributes to the tourism literature by incorporating deindividuation to enhance the understanding of how perceived crowding affects tourist behaviors. It further advances the field by differentiating between pro‐environmental and deviant.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".