Role of deindividuation between perceived crowding and tourist behaviors: Moderating effect of environmental knowledge
Bibliographic record
Abstract
Destination crowding has emerged as a serious issue for tourist sites and visitors alike. The deviant behaviors accompanied by crowding not only affect tourists’ travel experience but also damage the environment of the destinations. This study was then designed to examine the relationship between two dimensional perceived crowding (spatial and human crowding) and two types of tourist behaviors (pro-environmental and deviant behavior) as well as to explore the role of deindividuation and environmental knowledge on the stated relationships. The data comprised responses from 313 Chinese domestic tourists who have visited the Great Wall most recently in 2019. Using SPSS and AMOS, the empirical findings indicate that deviant behavior was significantly stimulated by both dimensions of perceived crowding, while pro-environmental behavior was indirectly restricted by perceived crowding, mediated by deindividuation. Perceived crowding, both spatial and human, significantly and positively influenced deindividuation, leading to a reduction of self-awareness. The level of environmental knowledge helped people make better behavioral decisions and diminished the negative effects of deindividuation on tourist behaviors. Overall, these findings offered an in-depth understanding of tourist behavior in crowding situations through deindividuation and provided theoretical and practical implications for sustainable tourism and destination management.
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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 | 0.002 |
| Research integrity | 0.001 | 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".