Love at first site: exploring the influence of place attachment on real-Time tourist sharing behaviour
Bibliographic record
Abstract
This study explores the relationship between place attachment and tourists’ On-the-Spot Behaviour (OSB), focusing on how distinct elements of a destination drive real-time social sharing. Building on existing research in electronic word-of-mouth (eWOM) and place attachment, we differentiate between two dimensions of attachment: place love, an emotional bond, and place dependence, a functional reliance on a destination. Using survey data collected from tourists across various locations, we test how these forms of attachment influence OSB. The findings show that place love significantly predicts OSB since emotional attachment encourages tourists to share experiences in real-time. On the contrary, place dependence is surprisingly negatively associated with OSB, which suggests that functional attachments alone may not be able to trigger the emotional arousal that can cause immediate social sharing. This may be an unexpected result, indicating the complexity of place attachment and its consequences for behaviours. We provide theoretical explanations for this divergence by including intrinsic motivation driving OSB and how tourists’ travel influences their sharing propensity. The study has practical implications for destination marketers in that cultivating emotional place attachment is the key to encouraging spontaneous online sharing. Meanwhile, functional elements should be optimised to facilitate emotional place attachment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".