Mark C Ushie | Robert Gordon University (original) (raw)
PhD Research student at Robert Gondon University Aberdeen Scotland UK.
Area of Interest: Lean Six Sigma, Statistics in hospitality and Strategic Business Management
Supervisors: Andrew Martin and Mentor/Supervisor
Address: Aberdeen, Scotland UK
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International Journal of Scientific Research and Engineering Development, 2024
The increase in revenue within a destination reflects the quality of service delivery, especially... more The increase in revenue within a destination reflects the quality of service delivery, especially in the hotel brand sector. This research explores the application of bilinear time series models to estimate the monthly revenue data of TH hotel brand Abuja. The study utilizes quantitative secondary data, analysing revenue figures from 2017 to 2022. The aim was to identify and estimate a suitable bilinear time series model for the revenue data of the TH hotel brand up to 2030. Both descriptive and inferential statistics were employed to test the hypothesis. The Akaike Information Criterion (AIC) was used to determine the best-fitting model, revealing that the bilinear time series model (6, 0, 6, 0) is most suitable for modelling the revenue series. Additionally, the Shapiro-Wilk test for normality was conducted, showing that the null hypothesis (H0) at an alpha level of 0.05 is rejected when the p-value is less than 0.05 (p < 0.05). This indicates that the data tested do not come from a normally distributed population, highlighting the necessity of this research. The results demonstrate that Bilinear Fit Six
International Journal of Scientific Research and Engineering Development, 2024
The increase in revenue within a destination reflects the quality of service delivery, especially... more The increase in revenue within a destination reflects the quality of service delivery, especially in the hotel brand sector. This research explores the application of bilinear time series models to estimate the monthly revenue data of TH hotel brand Abuja. The study utilizes quantitative secondary data, analysing revenue figures from 2017 to 2022. The aim was to identify and estimate a suitable bilinear time series model for the revenue data of the TH hotel brand up to 2030. Both descriptive and inferential statistics were employed to test the hypothesis. The Akaike Information Criterion (AIC) was used to determine the best-fitting model, revealing that the bilinear time series model (6, 0, 6, 0) is most suitable for modelling the revenue series. Additionally, the Shapiro-Wilk test for normality was conducted, showing that the null hypothesis (H0) at an alpha level of 0.05 is rejected when the p-value is less than 0.05 (p < 0.05). This indicates that the data tested do not come from a normally distributed population, highlighting the necessity of this research. The results demonstrate that Bilinear Fit Six