THE ROLE OF HOTELS IN THE CONSUMPTION OF CULTURAL TOURISM IN KENYA
Bibliographic record
Abstract
Tourism is Kenya's leading foreign exchange earner, yielding the country over US$500m annually. The industry contributes over 10% of the GDP to the national economy. However, considering that the industry experienced a slump over the period 1994-2003, there are challenges on how to sustain the current tempo of growth in the midst of growing competition, especially in wildlife-based tourism. There is a general feeling that the tourism industry in Kenya needs to avoid over reliance on wildlife and diversify its tourism product base. The industry stakeholders (led by the government) are seeking means and strategies of differentiating the tourism product offering in order to become a destination of choice in international markets. It is with the foregoing in mind that this study focused on investigating the possibility of incorporating cultural tourism as a means of augmenting and diversifying Kenya's tourism product. This is in view of the fact that the country has a vast ethnic diversity with a total of 42 cultural groupings. These groups spice up Kenya's heritage with various cultural attractions including music, food, dress, architecture, artifacts, dances, language, religious monuments, prayer and worship, family, government and leadership. The question that the research wanted to answer is how the hotels could contribute to the development of cultural tourism in Kenya. Being a key component and beneficiaries of improved performance in tourism, hotels, have a crucial role to play in shaping the nature of the cultural tourism product offering. The study established that many hotels have taken various specific measures in support of cultural tourism including: architectural designs and layouts that depict the surrounding culture; incorporation of local culture in branding and naming of facilities; inclusion of traditional tastes and choices in food; selection of staff uniform based on traditional designs and colours; emphasis on cultural uniqueness in overseas marketing campaigns; and formation of lobby groups seeking government support for cultural tourism The research concluded with a recommendation that it would be a great gain if hotels and the Kenya tourism fraternity could develop consistent frameworks for promoting culture as part of tourism consumption. This would then provide a unique strategic marketing formula for Kenya to have an edge over her competitors.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".