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Record W4386574273 · doi:10.6007/ijarbss/v13-i9/17796

Assessing The Effect of Nutritional Knowledge on Menu Choice Decisions among Customers in Star-Rated Hotels in Nakuru County, Kenya

2023· article· en· W4386574273 on OpenAlexaff
Mildred J. Limo, Catherine Sempele, Stella Barsulai

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMarketingExploratory researchFood choiceBusinessNutritional informationPsychologyMedicineFood scienceSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.150
GPT teacher head0.458
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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