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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".