Yield Management Strategy in Kenya’s Town Hotels: Opportunities and Scope in Room-Stock Management
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Date
2014-02-19
Authors
Miricho, Moses Ngatia
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Abstract
Tourism in Kenya is not only cyclic, but suffers heavily from national and international politics. The result is an uncertain future for the country‟s hotel-bed occupancy. Part of the bed-occupancy solution may be found in creating a Kenyan Yield Management (YM) culture. YM has established an impressive record of benefiting space constrained operations including airlines, tourism facilities, and sporting avenues during low demand periods and excess demand periods. The main objective of this study was, therefore, to investigate the scope and application of YM in Kenya‟s town hotels, creating a room-stocks YM model for Kenya‟s hospitality manager. Consequently, the approach was through an attempt at establishing the capacity-utilization efficiencies of using the various YM ingredients, through the performance indicators of occupancy. The study, therefore, sought to determine both the YM status of the town hotels population in Kenya and their occupancy performances. Literature was reviewed through the five YM ingredients with occupancy being confirmed as the appropriate measure for evaluating the sample hotels‟ performances. The study‟s sample size was 46 hotels of Kenya‟s 53 registered town hotels; in effect a census was carried out on the total population of Kenya‟s registered town hotels. Data were collected using structured questionnaires, and focus group discussions, while the validity and reliability of the instruments were enhanced by pre-testing the tools. Cross tabulation and Chi-square analyzed the YM applications and occupancy performances to establish the relationship between the two variables. The analysis revealed that a significant statistical relationship existed between all the five YM ingredient applications and occupancy performances (p<0.001 to p-0.047). The results suggested that the application of YM, improved the hotel‟s occupancy performances, giving competitive advantage to hotels that had implemented YM and its ingredients. Moreover, multivariate regression analyses confirmed fourteen ingredient elements as the best occupancy predictors, making significant (p<0.001 to p<0.05) contributions to the town hotels occupancies. These determinant variables were assembled into an YM outcome model and presented as the most effective YM ingredient predictors for a hospitality facility. In addition, a leaner version of the model was also identified for the smaller hotel facilities and the budget constrained. A total of seven determinant variables with the biggest B coefficient values were identified and recommended as making the better contributions to occupancy. These predictor variables could then be implemented gradually starting with the variable with the highest impact (coefficient), increasing implementations as circumstances improve.
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Department of Hospitality Management, HD 60.7 .M5