The Influence of Price Structures on Experience Quality and Behavior Intention in Hospitality Industry
Bibliographic record
Abstract
Tourism is one of the largest industries in the world as well as the significant contributors to the world's economy. According to the UNWTO, the export income enervated by international tourism ranks fourth after fuels, chemicals, and automotive product. In 2011, there were over 983 million international tourist arrivals worldwide, representing a growth of 4.6% when compared to 940 million in 2010. The attribution of pricing structures and experience quality played an important in determinant of buying behaviour in the sense where these principles are likely to remain important factors or elements whether shoppers are purchasing online or through another medium such as online travel sites or traditional agents. To narrow down the author research, author is selecting Malaysia as a based to examine how the influences of price structures are has affected the experience quality and customers behaviour intention in selecting a hotel to stay. A theoretical framework is formulated in order to achieve the results by revealing the influence of price structures towards the experience quality and behaviour intention with incorporated the Hedonic pricing model. This study has contribution from both a theoretical and a practical perspective. First, the relationships between price structures and experience quality, and behavioural intentions were examined. Second, little price structure toward experience quality research has been conducted in the area of hospitality context. This paper aims to provide a further study for future researchers, especially hotel managers to have an in depth understanding of price structures towards experience quality in the hospitality industry. While it also helps hotel managers to implement all their pricing strategies where pricing is an issue of paramount importance for practitioners in the hospitality industry and It is the only element in the accommodation marketing mix that impacts directly on revenues.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".