Assessing The Effect of Nutritional Knowledge on Menu Choice Decisions among Customers in Star-Rated Hotels in Nakuru County, Kenya
Bibliographic record
Abstract
Menus occupy a significant position in hotel business as they purpose to navigate customers to achieve satisfaction and exceed expectations in their food choices. Eating out has become an integral part of our modern society, and diners want to make informed choices for developing healthy eating habits that allow the body to meet its dietary needs and maintain the required weight. The main objective of this study was to assess the influence of nutritional knowledge on menu choice decisions amongst customers in star-rated hotels in Nakuru County, Kenya. The Food Choice Process Model and Theory of Planned Behaviour informed the study. The study employed an exploratory research design with a closed-ended questionnaire to collect data, which was analyzed using the Statistical Package for Social Sciences (SPSS) version 26.0, and hypothesis tested at p?0.05. The study findings show that the knowledge explained 27.9% of the variation in customer menu choice decisions. Nutritional knowledge (?1=0.608, p=0.000) positively and significantly influenced consumer menu choice. The study concluded that customers understanding of menu information depend on their knowledge to choose and consume foods that meet their nutritional needs. The study recommends that hotels provide in-menu nutrition information to guide customers toward healthy meal choices.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".