Hotel Management

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    Perceived effects of service, process, and system digitization levels on guest retention rates in star rated hotels in Nairobi city county, Kenya
    (Chuka University, 2024) Nyakoe Dennis Makori
    The hospitality industry, particularly star-rated hotels in Nairobi City County, Kenya, is increasingly adopting digital technologies to enhance service quality and operational efficiency. As competition intensifies, guest retention has become crucial for sustaining business growth. Therefore, the study sought to address these knowledge gaps by evaluating the perceived effect of service, process, and system digitization on guest retention rates in star-rated hotels in Nairobi City County, Kenya. The study used a descriptive research design. It was conducted in 53 star-rated hotels in Nairobi that are listed in the Tourism Regulatory Authority. 6 departments were identified from each hotel purposively and a population of 450 respondents was allocated proportionately based on the number of hotels in each star rating category. A sample size of 211 respondents was calculated from the population using Yamanes (1967) formula. Proportionate Random Sampling was further used to allocate the 211 respondents accordingly. Data was collected by the researcher from June to August 2024 using structured questionnaires. The variables assessed included; service digitization, process digitization, and system digitization levels. The response rate was 100 percent while the reliability score of the entire dataset was 0.731 representing 73%. Descriptive data was analyzed through frequencies, percentages, and means. In objective i, inferential data on service digitization was conducted through factor analysis. In objective ii categorical regression was used for analyzing process digitization levels. In objective iii, Multinomial Logistic regression was used to analyze data on system digitization levels. Rotated factor analysis scores for service digitization levels indicated 3 major components. Component 1 had 12 factors related to electronic payment systems with scores ranging from 0.731- 0. 849. Component 2 indicated 2 factors relating to smartphone room keys, while Component 3 indicated 1 factor related to training of staff. The categorical regression fit for process digitization was quite robust scoring a value of (R² = 0.979, F= 395.237, p < 001). Significant coefficients in objective ii (process digitization) were; Porter robots (B= 0. 209, p < 001), Room assistant robots (β = 0.332, p < 001), Vacuum cleaning robots (β = 0.262, p < 001), and Artificial Intelligence (β = 0.153, p < 001). Multinomial Logistics regression model for system digitization had an acceptable model fit with scores of (R² = 0.711, F= 40.284, and p < 0.001). The significant coefficients were accounting systems (B = 0.163, p = 0.021) and security surveillance (B = 0.341, p < 0.001). Therefore, the study underscores the importance of digitizing services through electronic payment systems, and smart room technologies while integrating it with services rendered by trained employees. This points to the establishment of a Task- Technology- Fit by Star Rated Hotels. It demonstrated that process digitization can be achieved through robotics and artificial intelligence systems. These technologies may uplift the need of enhancing decision support through process digitization by hotels in Nairobi. Transparency of accounts, and customer’s security were also cited as important aspects systems digitization that may contribute to improve of guest loyalty in Nairobi’s star-rated hotels. The study recommends that the Star- Rated Hotels in Nairobi should integrate technological solutions with traditional personalized services to improve guest retention. Additionally, there is a need for constant staff training and the development of standard assessment criteria for these strategies to guarantee increased guest satisfaction and loyalty.
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    Effectiveness of visitor management strategies on the Sustainability of tourist attractions in Ol pejeta Conservancy, Laikipia county, Kenya
    (Chuka University, 2024) Mwangi Lucy Wanjiru
    Sustainability is one of the most essential strategic concepts being upheld in the Ol Pejeta conservancy. This is through initiatives like environmental conservation, income generation, and local community empowerment. However, the current rise in tourism numbers poses risks to establishing a balance between these efforts in the long term. Visitor management strategies including physical, interpretive, economic and regulatory management strategies have been adopted to oversee tourist flows in the conservancy. Despite adopting these management approaches, issues like humanwildlife conflict, unemployment, and water shortages still persist in the area. Besides, there is limited research on the effectiveness of these strategies on the sustainability of tourism in the area. Therefore, the main purpose of this study sought was to assess the effectiveness of visitor management strategies on the sustainability of tourist attractions in Ol Pejeta conservancy, Laikipia County, Kenya. Specifically, the study aimed to determine the relationship between physical management strategies and sustainability of tourist attractions, establish the relationship between interpretive management strategies and sustainability of tourist attractions in Ol Pejeta conservancy, Laikipia County, investigate the relationship between economic management strategies and sustainability of tourist attractions in Ol Pejeta conservancy, Laikipia County and assess the relationship between regulatory management strategies and sustainability of tourist attractions in Ol Pejeta conservancy, Laikipia County. The study employed convergent parallel mixed research design and was guided by World Commission on Protected Areas Framework. Data was collected from 167 visitors and 10 tourism officials using questionnaires and interview schedules respectively. Descriptive analysis and inferential statistics were used to analyse quantitative data while narrative analysis was employed in qualitative data analysis. Quantitative results revealed that interpretive strategies were the highest predictors of sustainability (β=0.436, p<0.001) compared to physical (β =0.177, p<0.008) and regulatory strategies (β=0.104, p<0.012). Narrative results also demonstrated that interpretive, physical and economic strategies were relevant in ensuring sustainability in the conservancy. The study concluded that interpretive, physical and economic management strategies were effective in ensuring sustainability, while regulatory strategies did not significantly influence the sustainability of tourist attractions in Ol Pejeta conservancy. The study recommended that tourism managers should reevaluate and refine regulatory strategies to address issues of enforcement and visitor compliance thus ensure their effectiveness in ensuring sustainability.
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    Influence of peer-to-peer accommodation sharing platforms on competitiveness of star-rated hotels in Nairobi county, Kenya
    (Chuka University, 2024) Mwaniki Kennedy Wachira
    The rise of peer-to-peer accommodations has reshaped the traditional hotel industry sparking concerns about market fairness, safety and competitiveness. Despite the growing prevalence of P2P accommodation sharing platforms limited research has investigated their effects on the competitiveness of traditional hotels. Thus, the purpose of this study was to examine the influence of peer-to-peer accommodation sharing platforms on competitiveness of star-rated hotels in Nairobi County, Kenya. The specific objectives included: To investigate the impact of the cost of peer-to-peer accommodation sharing platforms on competitiveness of star-rated hotels; to determine the effects of social interaction of peer-to-peer accommodation sharing platforms on competitiveness of star-rated hotel; to examine the impact of environmental commitment of peer-to-peer accommodation sharing platforms on competitiveness of star-rated hotels; and to investigate the moderating influence of customer reviews on the relationship between peer-to-peer accommodation sharing platforms and competitiveness of star-rated hotels in Nairobi County. This study was guided by the Theory of Disruptive Innovation and Five forces framework and adopted a descriptive cross sectional survey design. The target population was made up of 288 top-two managers of both peer-to-peer accommodation and star-rated hotels in Nairobi County Kenya. The census method was used to collect data from peer-to-peer accommodation and star-rated hotels in Nairobi County Kenya. Structured questionnaires were used to collect data for this research. The data collected was analyzed using Statistical Package for Social Scientists Software (SPSS) version 25 and presented in tables. The reliability of the research instruments was tested using Cronbach’s Alpha at α ≥ 0.70. The study incorporated descriptive statistics and multiple linear regression analysis to predict the impact of P2P accommodation sharing platforms on competitiveness as well as the influence of customer reviews on the relationship between P2P accommodation sharing platforms and competitiveness. The study showed that social interaction were the highest predictors of competitiveness (β=0.24, p<0.05) compared to environmental commitment (β=0.18, p<0.05) and cost of peer-to-peer accommodation sharing platforms, (β=0.08, p<0.05). The moderating effect of customer review had positive coefficient therefore having an influence on P2P accommodation sharing platforms and competitiveness in star-rated hotels in Nairobi County, Kenya (β=0.09, P<0.05). In conclusion the study noted that there was a significant relationship between P2P accommodation sharing platforms and competitiveness of star-rated hotels. Thus starrated hotels in Nairobi County should monitor costs related to P2P accommodation sharing platforms, foster social interactions, adapt sustainable practices, and actively manage customer reviews so as to enhance competition. The study recommended that star-rated hotels managers should focus on optimizing cost strategies and enhancing social interactions to remain competitive, while also committing to environmental sustainability and effectively managing customer feedback, thus, boosting competitiveness.
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    Influence of biometric technology utilization on rated hotel operational performance in Nairobi Kenya
    (Chuka University, 2024) Njue John Munyi
    Biometric technology is one of such innovation that has been integrated into hotel property systems for use by employees and guests. Previous studies have primarily focused on the rate and readiness of hotel industry to adopt biometric which represents the adoption phase of this technology. However, very few studies have been conducted on the post installation impacts of biometric technology. This study therefore aimed at determining the impacts of biometric technology utilization on achievement of organizational goals and its overall effect on hotel operational performance. It particularly evaluated the performance of security processes, reservation processes, and room access. The study employed UTAUT2 and Balanced Score Card theories. A descriptive cross-sectional research design was used, targeting 7,367 respondents in star-rated hotels in Nairobi. Stratified and random sampling techniques were employed to select a sample of 243 guests and 122 employees. Primary data was collected using a structured questionnaire designed with a Likert scale. The reliability coefficients for the research instruments were α = 0.927 for employees and α = 0.815 for guests. Data analysis was conducted using SPSS (version 23) software. Exploratory factor analysis and Multiple Linear Regression were used to analyse the relationship between biometric technology utilization and hotel operational performance, with significance levels sought at α≤ 0.05. The response rate of the instruments was 85.25% and 81.49% for employee and guest respectively. Overall reliability of the data using Cronbach alpha was 0.927 and 0.815 for the employees and guest respectively. The factor analysis scores indicated 3 components: component 1 belong to security process, component 2, room access and component 3 reservation process. In objective 1, the regression scores of the model fit were (R2= 0.831, F= 96.651, p<0.001) for the employee and (Adjusted R2= 0.371, F= 30.084, p<.001) for the guest. The scores for the predictor variables for employees were: hotel access (β= 0.341, t= 4.643, p<0.001); control access (β= -0.226, t= -2.405, p= 0.018) and surveillance system CCTV (β= 0.629, t= 8.103, p<0.001). while for the guest was (β= 0.298, t= 4.994, p<0.001). In objective 2, the regression score for the predictor variable were (adjusted R2=0.915, F= 174.120, p<0.001) for employee and (Adjusted R2= 0.371, F= 30.084, p<0.001) for the guest. The variables that were significant from employee perspective were: identification check (β=0.239, t= 6.648, p<0.001), digital keys usage (β= -0.958, t= 0.469, p<0.001), entering information (β= 0.575, t= 4.295, p<0.001) security purposes (β= 0.823, t= 17.195, p<0.001). Hotel access (β= 0.298, t= 4.994, p< 0.001) was the only variable factor from guest side. In objective 3, the regression score for the predictor variable from employee was (R2 adjusted= 0.815, F= 109.328, p= 0.000) and (Adjusted R2= 0.321, F= 16.552, p<0.001) from guest. The variables that emerged significant from employee side were: rooms access (β= -0.219, t= -2.834, p=0.006), safe access (β= 1.562, t= 10.138, p<.001), and room lighting control (β= 0.799, t= 7.268, p<.001). The variables that were significant from guest were: use of biometric technology to check-in (β=-0.184, t= -2.565, p=0.011); use of biometric technology to enter details (β=0.465, t= 4.831 p<0.001) and securing data at reservation if biometric technology is used (β= 0.291, t= 4.045, p<0.001). The results indicated that both guest and employee usage of biometric technology in security processes had the highest effect, followed by room access usage and lastly reservation process. It was concluded that guest still prefer personal interaction at the reservation desk. Given Nairobi’s status as a smart city and its holds smart hotels, it was recommended that hotel should install and utilise biometric technology but have a continuance monitoring of the utilization in order to realise the intended installation reason.
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    Influence of customers’ brand perception, preferences and satisfaction on performance of airbnb facilities in Nyeri County, Kenya
    (Chuka University, 2015) Kamweru Esther Waithira
    The emergence of AirBnB has transformed the accommodation industry around the world by providing an accessible, affordable, and authentic local experience, making it a preferred alternative to traditional hotels. Nevertheless, its expansion in other areas like the Nyeri County in Kenya is minimal, and there has been a lack of empirical information on the impact of customer brand perception, preferences and satisfaction on the performance of AirBnB facilities. The objective of this research was to determine how these customer-related factors influence the performance of the AirBnB facilities within Nyeri County. The purpose of the study was to establish the role of brandperception on the performance of AirBnB facilities, to evaluate the influence of the customers’ preferences on the performance of AirBnB facilities and the influence of customer satisfaction on the performance of AirBnB facilities in Nyeri County. The study used mixed research design. The target population was 1600 AirBnB customers and 102 AirBnB hosts in Nyeri-county. The sample size was 200 for the guests and 41 for AirBnB owners was calculated using Cochran (1977) formula. The study employed purposive sampling to select sub-counties with AirBnB facilities, stratified random sampling to select facilities, a quota sampling for guests and purposive sampling for AirBnB owners. Structured questionnaires and interview guides were used to collect data from customers and managers, respectively. Multiple linear regression analysis was conducted at a 5% significance level to test three null hypotheses. The regression analysis revealed a strong positive correlation (R = 0.907) between customer satisfaction, preferences, and brand perception, explaining 82.2% of the variance in facility performance (R² = 0.822). Brand perception showed the highest influence (β = 0.533), followed by customer preferences (β = 0.378) and satisfaction (β = 0.313). All variables were statistically significant with p-values less than 0.05, leading to the rejection of the null hypotheses. The ANOVA test confirmed the model’s overall significance with an F-statistic of 249.565 (p < 0.000), supporting the claim that these customer-related factors are integral to performance outcomes. Interviews with AirBnB managers in Nyeri County reinforced the quantitative findings, with managers emphasizing the importance of maintaining a strong brand reputation, offering competitive pricing, and providing personalized services to meet customer preferences. These qualitative insights aligned with the regression results, highlighting that customer satisfaction directly influences performance. The study underscores the critical role of aligning services with customer expectations to drive success in the AirBnB market. The findings provide actionable recommendations for AirBnB hosts, property managers, and stakeholders, suggesting that improving brand perception, catering to customer preferences, and ensuring high levels of satisfaction can significantly enhance performance and foster long-term competitiveness in Nyeri County’s hospitality industry.