Understanding tourist-host interactions and their influence on quality tourism experiences
Bibliographic record
Abstract
In response to the rise in visitor harassment in tourist destinations, there is a need to further our understanding of its impacts on the tourist experience. The purpose of this study was to understand tourist-host interactions in the context of harassment and its influence on overall quality of the tourism experience. Tourist attitudes towards hosts is an under researched topic in the academic literature. Thus, this thesis makes use of the social exchange theory, tourism development cycles, and the concept of segmentation (traditionally used to explain residents’ attitudes towards tourism/tourists), to help understand tourists’ attitudes towards hosts. Similarly, there are few studies that investigate tourist harassment by local people. Tourists’ attitudes towards the local people of Jamaica and the island itself were examined generally, and then with reference to the host behavior towards tourists and its potential for causing dissatisfaction with the overall quality of the tourism experience.\nThis thesis utilized mixed methods in the form of surveys (quantitative data) and event-logs (qualitative data) to explore harassments impact on tourists’ perceptions, attitudes, and experiences. Data was collected on the island of Jamiaca, in Montego Bay and Negril. A total of 209 surveys were collected and 15 events were logged via BlackBerries over a weekend period. Both methodological approaches were employed during participants’ tourism experience, with the intent to capture their “in the moment” attitudinal responses towards the island, the local people, and the behavior of harassment.\nResults suggest that nearly 59% of participants experienced harassment, most often in the form of pestering vendors, and taking place on the street. Generally, participants’ attitudes towards the island of Jamaica and local people were positive. Although, when harassed and non-harassed participants were compared, those who were harassed expressed slightly more negative views. These negative views however, did not deter the majority of harassed participants from recommending or returning to Jamaica in the future. The findings of the present study raised important implications for tourism managers, operators, and planners, as harassment, although deemed an annoying local behavior, did not appear to negatively impact participants' tourism experience of Jamaica. Furthermore, this tehsis advocates the need for continued research on the topics of visitor harassment and host-guest interactions, specifically hosts ability to influence the quality of tourists’ experiences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".