Corona Virus Disease (COVID-19) and Other External Factors as Determinants of Accommodation and Restaurant Services in Kenya
Bibliographic record
Abstract
Aim: The aim of this study is to investigate the influence of corona virus disease and other external factors on growth of accommodation and restaurant services (ARS) in Kenya.
 Study Design: The study employed quantitative research design involving quarterly time series data from quarter 1 of 2014 to quarter 1 of 2020. The data set was obtained from Kenya National Bureau of Statistics (KNBS).
 Methodology: The study employed unrestricted vector autoregression to investigate the changes in the growth of accommodation and restaurant services.
 Results: Results indicated that COVID-19, professional, administrative and support services, construction and past ARS growth at 1 to 3 lags influences growth of ARS in Kenya negatively. On the other hand, real estate growth, time trend, tax on products, other services, education, manufacturing, information and communication and past growth in ARS at lag 4 influences growth in ARS sector positively. It was also noted that growth in agriculture and transport and storage do not influence growth of ARS in Kenya.
 Conclusion: In conclusion, COVID-19, professional, administrative and support services, construction and past ARS growth, real estate growth, time trend, tax on products, other services, education, manufacturing, information and communication are the main determinants for the growth of ARS in Kenya.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".