Tourism Businesses’ Perceptions on Sustainable Practices and Barriers in Coastal North Carolina, USA
Bibliographic record
Abstract
The objective of this study was to investigate tourism business owners’ and operators’ perceptions of sustainable practices which would facilitate the success of their tourism businesses in the long term, as well as their perception of barriers to implementation of these sustainable practices. The study area was the North Carolina coastal counties where tourism is a major economic driver. Data were collected from 90 tourism businesses in 20 coastal counties in North Carolina. The study profiled the tourism business segments using a factor-cluster grouping approach that identified tourism business clusters. Three clusters were identified representing different levels of tourism business owners’ and operators’ perceptions of sustainable practices: Advocators, Accepters, and Anticipators. Comparative analyses were then conducted among these three groups to profile them based upon perceived barriers that prevent them from implementing sustainable practices. The results showed that prevalent barriers to implementing sustainable techniques were lack of funds, lack of financial incentives, complexity of implementation and cost. These findings could provide informed guidance to tourism entities when considering their sustainable actions, as well as provide data which may influence the role that state tourism officials play in advocating best practices that maintain and present tourism products in a sustainable manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".