Index

Abstract

The study's goal was to investigate how revenue management (RM) techniques affect the financial performance of Kenya's star-rated hotels. The study aimed to examine if RM policies and implementation, the RM team, the application of RM methodologies, RM data and information, and the use of pricing and non-pricing instruments were all factors. The study used a cross-sectional survey research methodology and took a quantitative approach. The survey included 137 revenue managers from Kenyan all-star hotels. The structural equation modeling was used to test the linkages; revenue management strategies have an impact on hotel financial success, according to the research. The results indicated that RM practices explain variation in financial performance indicators by 42.7 percent (R2 =0.427), improved financial performance by 48.4 percent (R2 =0.48.4), and overall performance by 47.4 percent (R2 =0.474). The article recommends that hotels adopt RM tactics to fully achieve and maximize financial performance, including reducing operational expenses, forecasting hotel growth, improving yields, and generating income.

Keywords: Financial performance,Impacts,Hotels,Kenya,Revenue management practices,Star-rated.

Received: 16 June 2021 / Revised: 20 July 2021 / Accepted:23 August 2021/ Published: 6 September 2021

1. INTRODUCTION

The paper includes the following elements: research background, literature review, methodology used, findings and discussions, research implications, and conclusion.

1.1. The Background of the Research
Over the last two decades, the tourist industry has become increasingly important to African economies. In 2019, the sector accounted for more than 7% of Africa's GDP and contributed $169 billion to the continent's economy, approximately equaling the combined GDP of Côte d'Ivoire and Kenya (Monnier, 2021). The hotel industry is a vital part of Kenya's economy, as seen by its continued contribution to GDP, which increased from 14.4% to 16.6% in 2017 and 2018 (Kenya-National-Bureau-of-Statistics, 2020). Roughly 225 rated hotels range from one to five stars in Kenya, with approximately 16,156 sellable rooms and 26,786 sellable beds. There are currently 16,156 sellable rooms with a total capacity of 26,786 beds, up 3% from 2011 (Tourism Regulatory Authority (TRA), 2020). Between 2011 and 2015, Kenya's hotel business suffered a drop in room income, with an occupancy rate of 34.4 percent, compared to 59.4 percent in other African countries and more than 65.5 percent in European and American markets (Cytonn Real Estate, 2017). Moreover, from 40.3 percent in 2011, occupancy rates declined to 36.4 percent in 2012, 36.1 percent in 2013, 31.6 percent in 2014, and 29.1 percent in 2015 (Cytonn Real Estate, 2017; Kenya-National-Bureau-of-Statistics, 2020). Between 2017 and 2018, there was an increase of 30 percent and 32.500 percent before falling to 30.800 percent in 2019 (Kenya-National-Bureau-of-Statistics, 2020).

According to information from the CIEC (2020) and the Kenya National Bureau of Statistics (KNBS), the occupancy rate of Kenyan hotels is below average and varies greatly even across various regions within the country. Kenya's quantity of sellable hotel rooms is increasing, despite low hotel occupancy and a gradual growth over time. Occupancy rates may drop by 80 percent during low-season peak seasons (Irandu, 2006; Murimi, Wadongo, & Olielo, 2021). The Kenyan hotel sector is resistant to change and slow to absorb new ideas; they require a compelling reason to invest in new and innovative ways of doing similar jobs (Miricho, 2013; Murimi & Wadongo, 2021; Murimi, Wadongo, & Olielo, 2021). Hotels use revenue management tactics to control low occupancy rates and improve income (Ortega, 2016). Hotels can analyze their guests' preferences or booking habits, apply the optimal room prices, increase their business, and win against competition by implementing revenue management tactics (Patel, 2020). Revenue management is a tool that increases sales revenues by altering the rates at which frozen products such as hotel rooms are made available for sale based on present and expected demand (Hospitality-Professionals-Association, 2013). Integration of revenue management into hotel operations has been found to positively impact the hotel's performance and competitiveness and increase profitability for hotels and resorts (Ferguson & Smith, 2014). Revenue Management will use a standard method to maximize revenues by growing a hotel's total potential (González-Serrano & Talón-Ballestero, 2020). In the face of declining demand, hotels that employ a revenue management system outperform non-RMS users; RMSs have proven more effective in increasing occupancy (Ortega, 2016). Despite the numerous guarantees and improvements on the push for revenue management methods in the hotel sector, empirical research on the scope of RM practices and their impacts on hotel sector performance in Kenya is lacking (Murimi et al., 2021). As a result, this study aims to fill in the gaps and add to current knowledge by presenting empirical data and revenue management strategies and their effects on the financial performance of Kenya's star-rated hotels. The contingency theory, whose assumption is that there is no one-size-fits-all strategy to organizing, leading, or making decisions, established the groundwork for this research. Instead, internal and external factors determine the appropriate path of action (Fiedler, 1958). At a certain point in the organization's processes, contingent organizations can select and adjust straightforward methods to match the ever-changing environment. Few contingency evaluations of various scenarios prevalent in the hotel business in underdeveloped countries have been conducted (Chenhall, 2006; Wadongo & Abdel-Kader, 2014). Furthermore, applying proposed viewpoints from contingency studies determines and opposes the relationships between RM practices and hotel financial performance, as indicated by a theoretical framework (Murimi et al., 2021). Researchers have discovered a link between factors affecting contingency and performance in various businesses (Ferreira & Otley, 2010; Santoro, 2015; Speckbacher & Offenberger, 2010; Wadongo, 2014). Finally, this project responds to previous requests for more groundbreaking studies into the factors that determine hotel performance (Hernandez, 2015; Ortega, 2016).

2. LITERATURE REVIEW

2.1. The Practice of Revenue Management in Hotels

In general, revenue management is defined as the art and science of determining demand while simultaneously modifying the price and accessibility of things to match that need (Queenan, Ferguson, & Stratman, 2011). Initially, hotel revenue management systems worked the same way as airline revenue management systems, examining past data and anticipating future booking patterns (Cross, Higbie, & Cross, 2011). By the year 2000, the great majority of hotel chains had begun to use RM systems extensively. Marriott, Hilton, Sheraton, Starwood, and InterContinental were among the first companies to use RM in the hotel business (Kimes, 2003). RM techniques have already begun to be implemented and executed in other industries such as restaurants, spas, clubs, and entertainment parks (Anderson & Xie, 2010; Torc'h, 2013).

The hotel industry saw the benefits of adopting the RM strategy, which the aviation industry had refined. However, the procedure's progress was first slowed by a lack of appropriate technology to manage information and a lack of vital visitor data (Hospitality-Professionals-Association, 2013). The rapid development and integration of technology-based tools, such as social media and mobile-device-based channels, has also impacted how RM is implemented in the hotel business (Noone, McGuire, & Rohlfs, 2011). Technology advancements are assisting in creating a more suitable atmosphere for RM (Morag, 2013).

Academics and industry have agreed that competitive revenue management needs success (Noh, Lee, & Lee, 2016). Effective revenue management policies and implementation are credited with generating more revenues. When adequately executed, revenue management causes a 33 percent increase in revenue over typical restaurant approaches (Karmarkar & Dutta, 2011). The use of current products or services and existing sets of consumers, clever pricing, and revenue management approaches have contributed billions of dollars to many organizations' bottom lines (Cross et al., 2011). When used appropriately, revenue management systems (RM) have been shown to directly increase sales by 5-10% and boost occupancy rates during low points in the economic cycle (Morag, 2013). Using restaurant revenue management data from an Atlanta, Georgia restaurant (Bertsimas & Popescu, 2003), the study discovered that implementing advanced revenue management models improved revenues from 3.5 percent to 7.3 percent compared to typical first-come-first-served methods.

2.2. Hotel RM Practices and Their Effects on Financial Performance

To achieve RM goals, the implementation of RM policies necessitates the employment of a variety of measures. RM goals, gathering relevant data, analyzing data, forecasting demand, decision making, decision execution, and decision monitoring are all essential RM phases (Kimes & Anderson, 2011). If RM policies are appropriately implemented and operationalized, they can help to reduce losses caused by the imperfect implementation (Lieberman, 1993). Performance and effective RM rules, without a doubt, impose established operational structures and tactics for staff to implement as needed (Anderson & Xie, 2010; Hernandez, 2015; Lieberman, 1993; Noone, Canina, & Enz, 2013; Wirtz & Kimes, 2007).

Pricing is an important policy to consider when increasing consumer demand and improving hotel performance (Noone.. et al., 2013). Having an advance waitlist, securing reservations, and ensuring visitors sit as soon as they enter the hotel are three essential measures for coping with hotel demand (Wirtz & Kimes, 2007). Hotels with their RM structures might collaborate with other hotels and business partners to develop concrete frameworks for acquiring, administering, distributing, and combining such approaches (Hernandez, 2015).

Hernandez (2015) gathered and evaluated data from six fine-dining restaurants on reservation-related regulations that influenced the number of bookings and non-attendees. According to the study, reservation rules assist managers in operating hotels by providing the required methods and mechanisms to monitor and improve hotel performance. It also enhances hotel and employee retention, which leads to customer happiness by increasing patronage customers, controlling demand, inventory, and flexible prices.

The fragmented character of the hotel industry may be contributing to the slow implementation of RM policies. As a result, it's critical to see if RM policies are being implemented and if they're linked to hotel financial performance. Hoteliers may improve revenue by using new RM and reservation rules (Enz, Verma, Walsh, Kimes, & Siguaw, 2010). In the hospitality field, more research on RM policies is needed (Hernandez, 2015).

Pricing-optimization is an RM technique that manages guest room rates based on occupancy, price variety, and modest prices and is already implemented by more than 2,000 InterContinental Group hotels (Koushik, Higbie, & Eister, 2012). The goal of price optimization is to enhance revenue, and it employs a cutting-edge technology that considers the demand from a group of fragments as a separate entity from existing revenue management structures in hotels (Koushik et al., 2012).

Dynamic pricing is one of the essential concepts in today's valuation (Ivanov & Zhechev, 2012). By adding a price that reflects changes in demand and occupancy levels, hotels that use dynamic pricing can boost their returns and RevPAR (Tranter, Stuart-Hill, & Parker, 2008). Customers can track various fees related to the room's number and status and the length of time they are likely to remain while considering bookings, dependent on the current reservation (Ivanov & Zhechev, 2012; Tranter et al., 2008). Dynamic pricing provides additional benefits when used carefully with approved booking terms and conditions (Tranter et al., 2008).

Customers are supposed to be given price guarantees now and then (Demirciftci, Cobanoglu, Beldona, & Cummings, 2010). Through a choice pricing framework, Carvell and Quan (2008) determined that, for consumers to benefit from these types of lowest price assurances, the guarantee should protect them from the time of booking until arrival, which should not exceed 24 hours after making the booking. Liu (2012) created an optimizer tool for hotel booking to replace Cornell's standard price methodology for hotel booking. Because setting room rates are based on the desire to receive the room, the optimizer tool (Liu, 2012) focused on the tool's requirement while selecting a room rate. Noone and Mattila (2009) compared and contrasted two price strategies, assorted and non-assorted, and their impact on customers' capacity to pay via online platforms. The non-assorted strategy generated more booking excitement than the mixed method.

The use of demand forecasting strategies is critical to a hotel's success. Furthermore, revenue forecasting approaches need a decision-making process in tracking the business's performance. A study Haensel and Koole (2011) intended to estimate the collective reservation curve and the number of bookings expected within the reservation horizon found a combined contemplation of the connection and dynamic changes in reservations inside the reservation booking frameworks. It investigates the impact of revenue forecasting by tracking business and decision-making performance with an indication in a highly complex industry and providing probability to other service sectors to comprehend and manufacture their gadgets (Whitfield & Duffy, 2013).
Varini and Sirsi (2012) offered fresh approaches that will generate more money by incorporating social media when defining how the process adapts to RM. Hotels can choose to use all or any internet-based procedures as they become common and investigators figure out how to implement them. The more hotels use social media, the more likely they conduct responsible tourism (Noone et al., 2011). Many web-based demonstrations, such as virtual networking, survey and review, and social networking, are basic locus attentions that hotels can adapt to when determining how to develop products, services, and pricing (Varini & Sirsi, 2012).

The RM team's experience is full of complex hurdles, and they must be knowledgeable, possess the necessary skills, and be capable of overcoming these obstacles (Cetin, Demirciftci, & Bilgihan, 2016). Cetin et al. (2016) identified difficulties and capabilities to increase revenue management effectiveness utilizing data from 14 revenue managers obtained through interviews and focus group discussions with eight participants. Although RM tactics significantly impact hotel outcomes, much criticism of RM grievances and the lack of practical advantages required for pricing separation and overbooking procedures (Ivanov & Zhechev, 2012). the Carlson Rezidor Group of hotels increased income using demand management and price optimization approaches (Pekgün et al., 2013).

As a result, it is critical to determine whether such strategies as RM policies implementation (Anderson & Xie, 2010; Hernandez, 2015; Lieberman, 1993; Noone et al., 2013; Wirtz & Kimes, 2007), Revenue forecasting (Whitfield & Duffy, 2013), integration of social-media procedures in Rm activities (Varini & Sirsi, 2012), demand forecasting (Noone & Mattila, 2009; Whitfield & Duffy, 2013), have an impact on hotel financial performance.

Daily activities monitoring, procedures followed principle indicators, and customer segmentation are all characteristics of revenue management systems in hotels (Wang, Yoonjoung Heo, Schwartz, Legohérel, & Specklin, 2015). Hotels that use RM systems perform better, according to a study that used a databank of three and above star-rated chain hotels (Ortega, 2016). In a pricing and capacity competition, the data suggested that RM systems are more effective at increasing occupancy than at achieving advanced rates and have no positive impact on employee productivity. Furthermore, RM practices can improve revenue, even if they are affected by changing market and economic conditions. Despite this, hotels have not embraced them because they do not significantly impact RevPAR (Ortega, 2016). Each night spent in a hotel room is treated as a separate asset in revenue management programming (Gallego & Van Ryzin, 1997). Great control strategies are created using the dynamic programming method (Zhang & Weatherford, 2017). By managing visitor stays, deterministic linear generates up to 2.9 percent more predictable profits than traditional RM methods (Weatherford, 1995).

Historical data from archives is used when projecting bookings, managing occupancy, and maximizing revenue in hotels (Wang et al., 2015). In an over-the-top hotel RM system, revenue forecasting necessitates data inputs, mainly information about clients (Morag, 2013). Torc'h (2013) defined RM as "automatic software that collects information on price rate, occupancy rate, and revenue from every room in a hotel for the previous years or seasons."

There are four essential methods for obtaining valuable RM data: hotels call competitors to inquire about their rates; they use GDSs to calculate prices for various products and services and make price modifications. They may also rely on external data providers to obtain hotel information by regularly searching reasonable competitors' websites. Online structures that provide their clients with valuable approximate details are also reliable sources (Oliveri Martínez-Pardo, 2017).

RMS is a globally recognized revenue management software programmed with strategic information useful to hotel managers (Torc'h, 2013). The software, however, comes at a considerable cost to hotels and requires expertise to implement it in their facilities. Carlson Rezidor Group employed JDA Software to enhance revenue, estimating a 2–4% increase in revenue, and compete against hotel competitors in various economic circumstances (Pekgün et al., 2013). Another well-established algorithm was able to anticipate revenue increases and decreases using hotel revenue records. The model could distinguish between short-term and long-term RM goals and assign shares accordingly (Padhi & Aggarwal, 2011). The system's framework involved developing and predicting demand algorithms that manage clustered reservations with parameters linked to reservations, no-shows, seasonality, trends, and length of visitor stay (El Gayar et al., 2011).

Price discrimination, price guarantee, dynamic pricing, behavioral pricing, rate fencing, and other techniques that have an impact on hotel costs are widely implemented in RM, albeit this depends on price rules, the structure of the hotel, level of the hotel, and its presentation (Ivanov & Zhechev, 2012). Price guarantee, price discrimination, and dynamic pricing are the most regularly used and researched tools (Choi & Kimes, 2002).

Where dynamic pricing is used, hotel service providers may offer different rates. Prices are regarded as expensive if they irrationally exceed the standard or capacity of the services or products they are tied to. As a result, each pricing should represent the advertised service or product (Anuwichanont, 2011). As a result, for pricing precision, hotels research by monitoring rivals' index ratings and ADR regularly to ensure that the prices are accurate and sustainable (Adedipe, 2018). Some Kenyan hotels base their pricing policy on market data provided by the Kenya Tourism Board.

Hotels utilize price discrimination by charging varying prices for similar rooms to their customers. Differentials in prices targeting various market segments in the hospitality business could be linked to price discrimination. Consumers on business trips, for example, are less sensitive to hotel prices because they can afford to pay more outstanding prices than leisure customers (Ivanov & Zhechev, 2012). In hotels, price fences are instances in which apparent goods and services are made available on the market. They include guest characteristics (for example, government representatives and club members), length of stay, payment terms, adjustments, cancellations, and the main duration (Kimes, 2010). Price gates are designed to keep customers from taking advantage of low-cost services and products (Zhang & Bell, 2010). As a result, when clients make a reservation, the price fence terms should be made clear.

Non-pricing tools are linked to channel management and internal hotel mechanisms such as overbookings, capacity management, length of stay control, and guaranteed room availability. Traditional revenue management non-pricing approaches such as capacity management and overbookings (Karaesmen & Van Ryzin, 2004; Koide & Ishii, 2005; Talluri & Van-Ryzin, 2006), In comparison to the controlled length of stay, which has gotten little attention in research, overbooking is a well-studied tactic (Ivanov & Zhechev, 2012).

To summarize, implementing RM practices has a significant impact on hotel performance (Ortega, 2016). In Kenya, several star-rated hotels have been found to apply some RM techniques why others have not due to several internal and external factors associated with adoption and implementation (Murimi & Wadongo, 2021). However, it's critical to figure out the extent and the relationships between RM practices and the financial performance of hotels. The following Figure 1 displays the proposed relationships between various aspects of RM and the financial performance of hotels. 

Figure-1. SEM proposed relationships between RM practices on financial performance.

3. METHODOLOGY

This study used a cross-sectional survey design. The focus of this research was on Kenya's classified hotels. The businesses are rated one to five stars and can be located all around Kenya. These hotels are well established and continue to dominate a variety of market segments. The classified star-rated hotels were picked for study because of their wide range of application of policies and standard processes compared to non-classified hotels; classified star-rated hotels might adopt techniques that are perceived as more affluent due to the extent of operational activities (Murimi & Wadongo, 2021; Odawa, 2017). The 137 star-rated hotels designated star-rated businesses' clusters in Kenya responded to the research (Tourism Regulatory Authority (TRA), 2020). Those who answered included representatives from revenue management divisions. Because they play such an essential part in the hotel's revenue management procedures, the responders are specialists in their profession. There was one (1) respondent for each star-rated hotel, for a total of 225 respondents in the poll. A questionnaire was used to collect information from respondents. The researchers did a literature analysis for the questionnaire and adopted and modified items from earlier related literature to better analyze the factors. The correlations were tested using structural equation modeling.

4. FINDINGS AND DISCUSSIONS

A total of 148 of the targeted respondents responded. After cleaning the data, 11 surveys were deficient by more than 50%. The total number of valid respondents was 137, resulting in a useable response rate of 60.89 percent.

4.1. Hotel Revenue Management Practices

The results displayed in Table 1 below demonstrated a degree of revenue management strategies in star-rated institutions (M=2.96, SD=.989). In total, 40.9 percent of hotels have fully implemented RM procedures. According to the findings, RM is used to plan hotel growth (M=3.47, SD=.916), reduce operation expenses (M=3.50, SD=.994), improve yield (M=3.50, SD=.841), and generate income (M=3.67, SD=1.072). According to the findings, revenue management creates more significant revenues than traditional restaurant approaches (Karmarkar & Dutta, 2011). Furthermore, the responder had some RM skills proficiency (M=2.61, SD=.894). While the studies show that RM staffs are knowledgeable and skilled, they need to thoroughly equip them with the most up-to-date skills to achieve revenue management in hotels (Cetin et al., 2016); staff experience is full of complex challenges. They should be well-informed, possess relevant skills, and be capable of overcoming these obstacles. Hotels are more likely to apply RM if they can gradually incorporate internet-based procedures like virtual networking, survey and evaluation, and social networking (Noone, Enz, & Glassmire, 2017; Varini & Sirsi, 2012).

Table-1. On application of Revenue management practices.

Formal Application of RM in hotels	N					Mean	Std. Deviation
	Statistic	Never heard of RM	Have heard about RM but do not understand the meaning	Slightly	Fully	Mean Statistic	Std. Statistic
Revenue management is practiced in this hotel?	137	5.8%	32.8%	20.4%	40.9%	2.96	0.988
Degree of respondent skills on RM		Basic Level	Intermediate Level	Expert	Advanced Level	Mean Statistic	Std. Statistic
Rate your degree of expertise on Revenue management	137	9.5%	38.0%	35.8%	16.8%	2.61	0.894

Hotels are using RM policies (M=3.45, SD=.985) and have recruited RM implementers (M=3.47, SD=.993), according to the findings displayed in Table 2 below of the summary of RM practices. The RM teams at the hotels are capable of dealing with RM difficulties (M=3.69, SD=.999). There is a synergy between information technology and RM procedures (M=4.26, SD=.993). Guests have easy access to RM information (M=4.42 SD=1.241). Pricing tools were used (M=4.20 SD=.976). Non-pricing tools were used (M=4.58 SD=1.160). According to the summary, certain RM operations occur in the Kenyan hotel business, albeit just 40% of hotels have fully implemented RM standards. Inadequate implementation of RM practices could be due to the internal and external challenges associated with adopting and implementing such procedures. Non-pricing measures such as room availability guarantee, capacity management, length of stay control, and overbooking management are used by most hotels to manage revenues. The findings show that revenue management is still done through channel management and internal hotel processes (Ivanov & Zhechev, 2012; Koide & Ishii, 2005). 

In addition, hotels are using and adopting revenue management rules, proving that employing creative revenue management and reservation policies can help hoteliers improve revenue (Hernandez, 2015; Kimes, Enz, Siguaw, Verma, & Walsh, 2010). The usage of RM tools such as price optimization, dynamic pricing, revenue forecasting, and demand forecasting in hotels confirms their importance as a beneficial approach being employed today (Palmer & McMahon‐Beattie, 2008), and they are used by over 2,000 InterContinental Group hotels (Koushik et al., 2012). 

Table-2. Summary of RM practices.

	N						Mean	Std. Deviation
RM Policies & Implementation	Statistic	Strongly Disagree	Disagree	Average	Agree	Strongly Agree	Statistic	Statistic
Application of  RM policies	137	07%	18.2%	32.1%	33.6%	15.3%	3.45	0.985
Availability of RM implementer	137	2.9%	12.4%	34.3%	35.6%	15.3%	3.47	0.993
RM team able to handle RM challenges	137	0%	8.8%	34.5%	35.3%	21.2%	3.69	0.904
I.T. Integration in RM activities	137	2.9%	18.2%	41.6%	24.8%	12.4%	4.26	0.993
Provision of RM information	137	8.8%	13.1%	24.1%	33.6%	20.4%	4.42	1.241
Use of Pricing tools	137	6.6%	12.4%	43.1%	30.7%	7.3%	4.20	0.976
Use of Non-pricing tools	137	3.6%	12.4%	27.7%	23.4%	32.8%	4.58	1.160
Valid N (listwise)	137

Table-3. On Financial performance indicators of hotels.

Table-3. On Financial performance indicators of hotels.
	N						Mean	Std. Deviation
	Statistic	Very Rarely	Rarely	Occasionally	Frequently	Very Frequently	Statistic	Statistic
Average daily room rate	137	4.4%	19.0%	44.5%	17.5%	13.1%	4.28	1.168
Occupancy rate	137	5.8%	22.6%	40.1%	19.7%	11.7%	4.12	1.092
Revenue per available room (REVPAR)	137	5.8%	25.5%	40.9%	19.7%	11.7%	4.09	1.060
Revenue per occupied room (REVPOR)	137	5.8%	25.5%	40.9%	18.2%	9.5%	4.00	1.029
Gross operating profit per available room (GOPPAR)	137	5.1%	29.9%	29.9%	21.2%	14.6%	4.10	1.139
Return on Investments	137	3.7%	27%	29.2%	18.2%	21.9%	4.26	1.220
what extent your hotel use all the above following performance indicators?	137	5.1%	28.5%	45.3%	20.4%	0.7%	3.83	0.836
Valid N (listwise)	137

4.2. Findings on Financial Performance of Hotels

Table 3 below reveals that the findings of the most commonly used performance indicators in hotels' average daily rate (M=4.28. SD=1.17), the occupancy rate at (M=4.12. SD=1.09); revenue per available room (M=4.09. SD=1.07); revenue per occupied room (M=4.0. SD=1.03; gross operating profit per available room (M=4.10. SD=1.14; return on investment index (M=4.26. SD=1.12). The findings further revealed that the performance indicators are generally used by hotels (M=3.83. SD=0.84).

The Table 4 below shows the effects of RM practices on financial indicators. Respondents feel that RM practices affects Average daily rate (M=2.39, SD=0.807); improves occupancy rate (M=2.42, SD=0.745); REVPAR (M=2.42, SD=0.764); REVPOR (M=2.40, SD=0.771); GOPPAR (M=2.47, SD=0.767); and return on investments (M=2.51, SD=0.698).

Table-4. Effect of RM practices on financial performance indicators.

	N				Mean	Std. Deviation
	Statistic	Lowly	Moderately	Highly	Statistic	Statistic
RM practices improves Average daily room rate	137	20.4%	20.4%	59.1%	2.39	0.807
RM practices improves Occupancy rate	137	15.3%	27.0%	57.7%	2.42	0.745
RM practices improves Revenue per available room (REVPAR)	137	16.8%	24.1%	59.1%	2.42	0.764
RM practices  improves Revenue per occupied room (REVPOR)	137	17.5%	24.8%	57.7%	2.40	0.771
RM practices improves Gross operating profit per available room (GOPPAR)	137	16.8%	19.7%	63.5%	2.47	0.767
RM practices improves Return on Investments	137	11.7%	25.5%	62.8%	2.51	0.698
Valid N (listwise)	137

Table-5. On application RM tools and procedures on general hotel performance.

	N						Mean	Std. Deviation
	Statistic	Strongly Disagree	Disagree	Average	Agree	Strongly Agree	Statistic	Statistic
Application of RM practices contributes positively to the revenues of our hotel.	137	8.0%	19.7%	23.4%	14.6%	34.3%	3.47	1.351
Revenues attributable to RM applications exceed the costs attributable to revenue management applications	137	8.0%	22.6%	24.8%	22.6%	21.9%	3.28	1.259
Hotel is monitoring and adopting recent applications and Technologies for revenue management	137	4.4%	25.5%	29.9%	21.2%	19.0%	3.25	1.162
RM practices contributes towards gaining and improvement of competitive advantage	137	6.6%	24.8%	21.2%	30.7%	16.8%	3.26	1.196
RM contributes towards developing efficient competition strategies	137	4.4%	25.5%	27.7%	23.4%	19.0%	3.27	1.166
RM practices assists in decreasing idle capacity	137	5.8%	24.8%	21.9%	21.2%	26.3%	3.37	1.272
RM practices increases our total revenue	137	5.1%	17.5%	25.5%	19.7%	32.1%	3.56	1.248
Valid N (listwise)	137

The findings presented in Table 5 show that application of RM practices improves performance positively to the revenues of hotels (M=3.47, SD=1.35); Revenues attributable to revenue management applications exceed the costs attributable to revenue management applications (M=3.28, SD=1.26); that hotels are monitoring and adopting recent applications and Technologies for revenue management (M=3.25, SD=1.16); Revenue management contributes towards gaining and improvement of competitive advantage (M=3.26, SD=1.20); Revenue management contributes towards developing efficient competition strategies (M=3.27, SD=1.17); Revenue management assists in decreasing idle capacity (M=3.37, SD=1.27); Revenue management applications increase our total revenue (M=3.56, SD=1.25).

On average, on the use of performance of indicators (M=4.0669, SD=0.881), on the application of RM (M=2.4355, SD=0.671), application of RM on financial performance (M=3.3525, SD=1.05435). About 35% of respondents reported that Rm had increased revenues by less than 5%, while 24.8% said revenues had increased with average (6-10%). About 19% said their revenues had increased by (11-20%); while 11.7% registered increment by (21-30%); 9.5% revealed that their revenues had increased by over 30%. The findings supported by Karmarkar and Dutta (2011) argued that revenue management results in 33% higher payments than traditional restaurants' traditional methods when appropriately implemented.

4.3. The Linkages between RM Practices and Hotel Financial Performance

This study finds a connection between revenue management practices variables and the financial performance of hotels. The RM practices under consideration were RM policies and implementation, having an RM team, integrating social media in Rm activities, solid RM data, and information, using RM tools, and applying pricing and non-pricing techniques. The standardized regression weights (Beta) and two-tailed significance levels (p) in Figure 2 below display the connections between the variables. At the same time, the following table shows the results which indicate that RM practices explain variation in financial performance as follows, financial performance indicators by 42.7% (R2 =0.427), improved financial performance by 48.4% (R2 =0.48.4), and general performance 47.4% (R2 =0.474).

Figure-2. SEM results on relationship RM practices on financial performance.

The Table 6 below displays findings that demonstrate there is significant RM team with all the three aspects of financial performance of hotels (B = .430, P = ***), (B = .222, P =.002), (B = .382, P =.001). There was significant relationship between RM pricing techniques and all the three aspects of financial performance (B = .169, P = .010), (B = .276, P = ***), (B = .214, P =.010). There was significant relationship between RM techniques (B = .217, P =.025),and insignificant relationship with improved financial performance (B = .106, P =.155) and with general performance (B = -.206, P =.093).There was significant relationship between RM policy and implementation with financial performance indicators (B = .224, P =.005) and insignificant relationship with improved performance (B = .002, P =.971) and general performance (B = .062, P =.539). There was insignificant relationship between RM social media integration with financial performance indicators (B = .072, P =.422) but significant relationship with improved performance (B = .216, P =.422) and with general performance (B = .47, P = ***). There was insignificant relationship between RM data and information with financial performance indicators (B =.070, P =.360). There is significant relationship between RM data and information with improved financial performance (B = .174, P =.003) and with hotel general performance (B = .221, P =.022). There was insignificant relationship between RM non-pricing techniques with financial performance indicators (B = .169, P =.010) and improved financial performance (B = -.018, P =.422) a significant relationship with general financial performance (B = .211, P =.015).

Table-6. On the relationships between Rm practices and financial performance.

Rm practices	Financial performance indicators R2 =42.7% beta	P value	Improved financial performance R2 =48.4% beta	P value	General financial performance R2 =47.4% beta	P value
RM policy and implementation	0.224	0.005	0.002	0.971	0.062	0.539
RM Team	0.430	***	0.222	0.002	0.382	0.001
RM Techniques	0.217	0.025	0.106	0.155	-0.206	0.093
RM social media integration	0.072	0.422	0.216	0.002	0.470	***
RM data and information	0.070	0.360	0.174	0.003	0.221	0.022
RM Pricing techniques	0.169	0.010	0.276	***	0.214	0.010
RM non-pricing techniques	0.021	0.761	-0.018	0.731	0.211	0.015

5. CONCLUSION AND IMPLICATIONS OF THE STUDY

This study aimed to look at the essentials that impact the revenue management methods of star-rated hotels in Kenya. Previous and current researches on RM gave primary foundations for this paper. The use of a structured questionnaire collected the secondary information for this study. The metrics used for RM practices were adopted from past research, modified, and accustomed for this study as measured. Based on the results presented, the paper provides empirical evidence that key aspects of revenue management practices affect hotel financial performance in star-rated hotels in Kenya. The empirical evidence presented here reveals that star-rated hotels in Kenya are taking the bold step of fully adopting RM strategies to realize benefits associated with financial performance. They may be able to predict the growth of the hotel, increase yields, reduce operations costs, and generate revenues. There is a need for full actualization of RM practices in all-star-rated hotels to realize the full potential of the hospitality sector.

This work presents empirical evidence that has been developed through an extensive cross-sectional survey. Based on literature from other academic disciplines, the study strengthens the collective evidence for conceptualizing and describing revenue management practices. The empirical evidence for this study may advance future studies on revenue management practices in hotels. This study adds to the growing body of literature on revenue management practices in the Hospitality sector, particularly enhancing our understanding of the value of RM application in hotels. 

List of Abbreviations

KNBS                     Kenya National Bureau of Statistics.

REVPAR              Revenue per Available Room.

RMS                       Revenue Management System.

TRA                       Tourism Regulatory Authority.

RM                         Revenue Management.

SNAP                     Stay Night Automated Pricing.

Funding: Acknowledge the support with Research Funds by Maseno University School of Graduate Studies- Kenya. Acknowledge the support with Scholarship Fund by Higher Education Loans Board –Kenya.

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: Thank all the respondents from classified star-rated hotels in Kenya for sharing their information during the survey.

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