Influence of Cognitive Destination Brand Image on Wildlife Park Attractiveness: A Case Study of the Amboseli-Tsavo Ecosystem, Kenya

Influence of Cognitive Destination Brand Image on Wildlife Park Attractiveness: A Case Study of the Amboseli-Tsavo Ecosystem, Kenya

¹Paul O. Okumu, Msc, ²Dorothy A. Amwata, PhD, ³Mathews Godrick  Bulitia, PhD, ⁴John K.M. Wandaka, PhD
¹Post-Graduate Student (Corresponding Author) School of Hospitality and Tourism Management, Murang’a University of Technology, Kenya
²Senior Lecturer, School of Hospitality and Tourism Management, Murang’a University of Technology, Kenya
³Senior Lecturer, Department of Human Resource Management, Murang’a University of Technology, Kenya
Deputy Vice-Chancellor (Academics and Students Affairs) Maasai Mara University. Kenya
⁴Lecturer, Department of Tourism& Hospitality Management, Kenyatta University, Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2023.70743

Received: 11 May 2023; Accepted: 06 July 2023; Published: 01 August 2023

ABSTRACT

The attractiveness of a holiday destination motivates the development and growth of tourism in terms of tourists’ perceived value; however, the attractiveness of wildlife protected places, such as game parks, is little understood in previous tourism literature. The paper therefore explores the role of cognitive destination brand image on the attractiveness of wildlife parks in the Amboseli-Tsavo Ecosystem. The results showed a significant positive relationship between cognitive destination image and park attractiveness (β = 0.446, t = 6.661, p = .001). The study concludes that tourists exhibiting higher levels of cognitive destination image are more likely to perceive a tourist destination as being attractive. An embedded mixed-method research design was adopted to collect quantitative and qualitative data from 440 park visitors and 28 tourism experts. Simple and linear regressions were used to test the hypotheses, whereas qualitative data were analyzed using content analyses. This study aims to add to knowledge to tourism marketing literature on wildlife park attractiveness as perceived by tourists and gives recommendation on policy of controlling the provision of accommodation, attractions or activities within the protected parks to safeguard the ecosystem.

Keywords: Brand Image, Park Attractiveness, Tourism Marketing, Tourism Products, Wildlife parks,

INTRODUCTION

Globally, tourism destination attractiveness is the driving force for tourism in terms of tourists’ perceived value. Nonetheless, the attractiveness of wildlife protected places, such as national parks, is rarely understood in tourism literature. Most developing countries, particularly in Africa, have identified Wildlife-based tourism as a key economic growth pillar. The perception that potential tourists have of a destination is crucial in wildlife-based tourism. Since tourist products  are intangible in nature, brand images are considered even more significant than reality. Therefore, brand images are crucial in destination decisions (Sannleitner, 2011). Visitors enter through the gates of the wildlife-protected areas from time to time due to various motivational factors. The appeal displayed by tourist destinations have a large influence on a person’s holiday place of choice, expected satisfaction, intent to return, perceived benefits and incentives, favorable impressions of opinion leaders, and the amount of resources spent, as well as duration factors and the perceived expense of a vacation determine the destination that tourists will visit. In addition to maintaining the ecosystem, the parks provide visitors with recreational opportunities (Stemberk et al. 2018).

Kenya has tough competition from other African countries offering similar tourism products, such as the Republic of South Africa, Egypt, Morocco, Namibia, and Botswana (Christie et al. 2013). Therefore, there is a need to understand the factors influencing the choice of branded national parks for visitation by tourists and the possible interventions for improved attractiveness of the under-utilized parks. Several studies have been conducted in this field however, there are limited studies in wildlife tourism park attractiveness (Setiawan et al. 2021; Stemberk et al. 2018; Mohammed et al. 2021; Ariya et al. 2019).

The sustainability of the wildlife parks is equally crucial for their posterity. Wildlife parks need visitors to be seen as adding value to the socio-economic life of a society, and the non-consumption of wildlife attractions is not viable (Burgin & Hardiman, 2015). According to Lozanov (2018), the basis of visitors’ decision to choose a site is highly essential in deciding the best places to go since it connects them to the critical aspects that may influence their decision-making. A tourist may first decide the destination to visit before the holiday experience, but in some circumstances, the reverse order would apply  as stated by Gunness and Oppewal (2016). These examples demonstrate that various situational conditions influence specific destination preferences and, as a result, travel intentions. While researching the destination brand image of Jakarta, Setiawan et al. (2021) stated that cognitive factors refer to beliefs and awareness of physical qualities of a place. The cognitive component of a brand image is typically established in the minds of tourists and is heavily influenced by the nature of marketing information  provided. This means that the tourist can make a general assessment of the area using this component destination depending on prior ideas about the tourism location.

Wildlife-based tourism is a critical component of Kenya’s tourist industry development. According to a 2019 World Travel and Tourism Council estimate, the tourism and travel sector’s direct contribution to Kenya’s GDP was around 8.8%, valued at Ksh 790 billion (or USD 7.9 billion) in 2019, as a result of direct, indirect, and induced effects. Most visitors are attracted to the wildlife variety, especially the Big Five: elephant, lion, rhino, buffalo and leopard. Yi et al. (2020) explains that a tourist’s perception of a destination’s traits or characteristics, including its attractions, infrastructure, surrounding environment, and other aspects, is a cognitive image. This study considered the aspects of cognitive destination brand image, which consisted of five facets: natural features (e.g., pleasant weather and scenic beauty), tourists’ facilities (e.g., outstanding hotels, and many shopping opportunities), attractions (e.g., selection of tourist activities, well-known attractions), accessibility (e.g., ease of access, developed infrastructure), and social environment (e.g., feelings of personal safety and security, hospitable local people, clean environment). The five facets were considered in the current study to represent the fundamental image of the Amboseli-Tsavo ecosystem as a tourist destination.

STATEMENT OF THE PROBLEM

According to Kenya Wildlife Service (2019), Amboseli National Park (ANP) under investigation was renamed ‘Kilimanjaro Royal Court’ on September 23, 2005, Tsavo West National Park (TWNP) as ‘Land of Lava, Springs, and Man-Eaters’ on November 8, 2005, and Tsavo East National Park (TENP) as ‘Theatre of the Wild ‘on December 9, 2005. Therefore, this study chose the Amboseli-Tsavo Ecosystem for having the country’s highest number of branded parks in Kenya. Yet, despite numerous attractions, some of its branded parks, such as Tsavo West and East National Parks, remain under- utilized regarding visitor numbers and economic returns (GoK, 2022). Therefore, strategies that would make the parks attractive are fundamental to enhance the performance of the under-utilized Tsavo West and Tsavo East National Parks, which Ritan (2013) argues have adequate tourist infrastructure, at least to the level of Amboseli National Park. Maingi et al. 2014 demonstrates that Kenya has created a distinct destination brand image, with leading wildlife parks and premium parks offering high-end travelers unique experiences in famous places such as; -Lake Nakuru National Park, Nairobi National Park, Maasai Mara National Reserve, and Maasai Mara National Reserve, Amboseli National Park and Mara National Reserve.

This study explores the influence of destination brand image on wildlife parks’ attractiveness in the Amboseli-Tsavo Ecosystem in Kenya. Despite being within the same ecosystem, Amboseli is classified as a premium park and Tsavo West and Tsavo East as under-utilized parks based on visitor numbers and revenue. According to GoK (2022), visitor numbers recorded at Amboseli National Park were 175,800; 191,700; 55,100 and 90,900 for 2018, 2019, 2020 and 2021, respectively. Further, within the same period, Tsavo West recorded 74100; 61300; 25,000 and 28,600 tourists, respectively, while Tsavo East reported 167000, 177900, 75100 and 76,200 for a similar period, respectively (see Table 1.1). Therefore, these branded parks have the potential to attract more visitors for better performance results. This study sought to examine the role of cognitive brand image on the attractiveness of wildlife parks in the Amboseli-Tsavo Ecosystem for optimum results.

LITERATURE REVIEW

3.1 Theoretical Framework

3.1.1 Motivation Theory

Maslow was the first to write on motivation and personality in the workplace, which was published in 1954. People are motivated to achieve their basic needs first, according to Maslow’s hierarchy of needs. His strategy is based on the hierarchy of needs prerequisites, which claims that in order to understand the higher demands, a person must first take care of their most fundamental wants, such as air, food, and so on. It is presented in a pyramidal format. The pyramid’s base denotes survival, while its apex represents self-actualization. According to Maslow, a person’s requirements start with safety, and if those are met, they move on to social needs, esteem needs, and finally, self-actualization, which is the greatest level (Kotler and Keller, 2014). Motivation is crucial when it comes to drawing people to a particular location.

A desire to travel might be characterized as the tourist motivation. Echtner and Richie (1991) discovered that a destination’s image is formed from various sources, including media, references, friends, and associations. In addition, even if the tourist hasn’t been to the location, they might still build an impression. In essence, the tourist’s perception is shaped by information awareness, old and current news, and other relevant sources of information that they are exposed to. According to Chen and Phou (2013), most tourism research concurs that pre-visit decisions made by tourists regarding when, where, and what kind of tourism to engage in are heavily influenced by their motivation. In this context this theory helped in highlighting the role that motivation plays in forming tourists’ cognitive perception regarding a tourist destination.

3.1.2 Aaker’s Brand Value Model

In 1991, a well-known professor at the University of California, David Aaker, gave his name to the brand value concept. The Aaker Brand Value Model revealed that  brand equity results from a brand’s recognition, customer loyalty, and perceived value, (Aaker, 1996). Aaker’s model emphasizes on brand loyalty, awareness, perceived quality, associations, and other proprietary brand assets. According to the model, brand equity can aid consumers in learning more about a brand, comprehending it better, and retrieving more information about it. It can also boost consumers’ confidence in their purchase decisions due to their familiarity with the brand and give them a sense of security regarding the brand’s calibre, which can lead to higher satisfaction levels.

According to a critical evaluation of the literature, the Aaker’s model has been applied to comprehend tourist behavioural intentions in terms of aspects such as awareness, image, perception of quality, and value based on an analysis of the literature (Naseer, et al. 2021, Rahman et al. 2019; Yang et al. 2015; Zhang et al. 2021). Critics have argued that this model does not provide a theoretical framework to explain each dimension. In addition, the model has not been founded in behavioural theories to elaborate on how the characteristics might be combined to build brand equity. Despite these critiques, many studies have validated and tested the model (Battour et al. 2019, Rahman et al. 2019, Tsai et al. 2013, Tweneboah-Koduah et al. 2019,  Zhang et al. 2021), which claim that the criticism is sparse and that the model is more context-specific. Dartey-Baah and Amponsah-Tawiah, (2011) also argued that an organization can be able to enhance competitiveness and brand position in the eyes of the customers by giving them an information in advance , letting your customers know about your products and services. Customer-based brand equity creates brand awareness, thus impacting the cognitive brand image of consumers. Knowledge of different destinations allows visitors to compare the different dimensions of brand equity, such as price details, hence converting a potential tourist into a consumer.

3.2 Conceptual Framework

Figure 1.1 illustrates the study’s conceptual framework, showing the independent variables (cognitive destination brand image) and the dependent variables (park attractiveness). The conceptual framework suggests direct relationships between cognitive destination brand image and park attractiveness.

Figure1.1. Conceptual Framework

Source: Adopted and Modified from Cognitive Destination Image Scale (Stylidis et al. 2017),

3. 3 Review of Empirical Literature

The destination vision is fluid, relative, and personal. The perception of a destination that a traveler develops in their head is significantly influenced by the information they access and their life experiences. Literature places a higher value on the cognitive image. According to Zhang (2015), brand image influences customer purchase behavior, which could explain why the GoK decided to brand various wildlife parks. Customers acquire strong attachments and passions for select brands, according to Wu and Chen (2019), and are willing to wait till they secure the brand. With this in mind, you may discover people reserving in advance or willing to wait for availability for vacations to their favorite spots.

Weru (2021) advises destination marketers to pay close attention to a place’s cognitive image. He investigated how foreign MICE (Meetings, Incentives, Conferences, and Exhibitions) travelers’ impressions of the destination affected their behavior following their stay in his research study. The poll took place  in Nairobi, the country’s capital city. That study employed convenience sampling with 335 participants. A destination image and post-visit behavior model were developed and tested. Mohammed et al. (2021) conduct research to advance the literature while being bound by safety and security in the tourism business. The study findings discovered that heritage brand perceptions of quality and value had a positive and significant impact on travelers’ intentions to return. However, heritage brand awareness had little effect on visitors’ willingness to return. The association between legacy brand value and the likelihood of returning international tourists was considerably attenuated by safety and security.

Asfar (2019) investigated the perceived destination image of the Hashemite kingdom in Jordan. According to their findings, the desire and want or probably to revisit and recommend were discovered to have a moderate correlation with unique, affective and cognitive images. The researchers developed structured questionnaires to collect data from 250 visitors visiting Jordan during their study time. Employing the Spearman correlation test, the academicians demonstrate a positive correlation among the four components of destination image examined, that is, unique and holistic image and cognitive and affective image. Tourism scholars are interested in the concept of destination attractiveness. According to Lee et al. (2014), the uniqueness, abundance, accessibility, and visibility of wildlife attractions are important drivers of destination desirability. Ariya (2021) classified attractiveness into four dimensions: wildlife resources, park accessibility, attraction cost, and park image and revealed that all four dimensions had a direct favourable influence. The park image had a relatively low influence, likely contributing to the low pleasure experience, future behavioural intentions, and park ecological value. Furthermore, most repeat visitors agreed that there was increased wildlife disappearance, difficulty viewing key wildlife attractions at Lake Nakuru National Park, invasive new species within the park, changes in wildlife grazing grounds, infrastructure damage, and increased visibility of litter within the park.

3.4 Summary of Literature Review

It is evident that research has been conducted on the relationship between cognitive destination brand image and tourism destinations. However, there are limited studies in wildlife tourism park attractiveness (Ariya et al. 2019) the rest have targeted other tourism destinations (Weru 2021; Mohammed et al. 2021; Asfar, 2019). Weru’s (2021) research was based entirely on the three most prominent Meetings, Incentives, Conferences, and Exhibitions event sites in Nairobi, Kenya’s capital. The study’s findings cannot be extended without extreme caution because it specifically excluded any hotel locations offering meetings, incentives, conferences, and exposition events and focused on international MICE guests. Although Ayari et al. 2017 studied park attractiveness, there still exists a methodological gap since the study took a cross sectional approach and as a result it only provides a snapshot view of the situation. Additionally, the majority of prior research has been quantitative, there is a lack of deep insight and texture that a qualitative study could bring (Stemberk et al. 2018; Weru 2021; Sonnleitner, 2011; Setiawan et al. 2021). For these reasons, this study adopted a mixed method approach in the attempt to not only bring a better understanding but also yield complete evidence of the influence of cognitive destination brand image on park attractiveness.

METHODOLOGY

4.1 Research Design

The study adopted an  embedded research design to collect, analyze, and interpret the results. The embedded research design is a mixed methods approach in which one data collection (for example, qualitative) serves as a supplement to a study that is predominantly focused on quantitative data (Cresswell et al. 2011).  This study embedded a qualitative component within a quantitative design, with the qualitative data supporting the quantitative results.

4.2 Study Area

The Amboseli-Tsavo Ecosystem was studied as part of the Southern Kenya tourist circuit. Three branded national parks are located within the ecosystem: Amboseli, Tsavo East, and Tsavo West. Amboseli National Park, which covers around 392 square kilometers, was established in 1974 within Kajiado County. The Amboseli National Park is roughly 240 kilometers from Nairobi, near the Kenya-Tanzania border.

4.3 Target Population

Ndivo (2013) describes the target group of the study as well-defined participants with comparable features who are anticipated to give the researcher information from which one can draw definitive conclusions about the greater community. The target population consisted of 444 visitors to the branded parks, 54 managers of tourism businesses, and 28 tourism professionals who work in or around Amboseli, Tsavo East, and Tsavo West national parks.

4.4 Sample Size and Sampling Techniques

This study used stratified, convenient, and purposeful sampling techniques. To select the park visitors, a stratified sampling technique was used to generate strata based on the branded parks, which yielded three strata—Amboseli, Tsavo West, and Tsavo East. In each stratum, a convenience sampling technique was used to select the required proportion of the sample size of park visitors. The study employed Yamane’s (1967) formula to compute the sample of park visitors as follows;

Which is valid where n is the sample size, N is the population size, and e is the desired level of precision at a 95% confidence level (5%). Thus,

Consequently, the required sample size for this study was 400 park visitors. In addition, 10% (n = 40) of the sample size was added to cater for the non-response bias associated with a questionnaire survey (Mugenda & Mugenda, 2003). Therefore, this study’s final required sample size was 440 park visitors distributed, as presented in the table below

Table 4.1. Sample Size Determination

In addition, managers of tourism enterprises were selected using the convenience sampling technique, while a purposeful sampling technique was used to select tourism experts as key informants for the interviews.

4.5 Data Collection Instruments

A semi-structured questionnaire was used to measure respondents’ cognitive destination brand image of Amboseli-Tsavo as a tourist destination. A multidimensional scale developed by Stylidis et al. (2017) grounded on previous studies (i.e., Beerli & Martin, 2004; Chen & Tsai, 2007; Chi & Qu, 2008) was used to measure. As applied by Bertram (2007), all items were measured on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In addition, a semi-structured interview guide was used to collect text data from the key study informants. The guide was designed to collect general information from the interviewees and information about national parks’ attractiveness.

4.6 Pretesting the Research Instruments

The semi-structured questionnaire targeting the park visitors was pretested to examine its usability. The questionnaire was pretested with 42 park visitors representing approximately 10% of the sample size, as suggested by Mugenda and Mugenda (2003). An equal number (n=14) of park visitors were selected from each branded park. Furthermore, the interview guide for tourism experts was pre-tested with four experts drawn from the national government, county government, park warden, and tourism associations, representing 14.3% of the sample size of all key informants. To eliminate bias, the 42 park visitors and the four tourism experts who participated in the pre-test exercises were excluded during the main data collection. Respondents used during pre-test were not used in the main data collection.

4.7 Validity and Reliability of Research Instruments

Validity refers to how well a research instrument measures what it is intended for (Kothari, 2012). This study used a content validity technique to gauge the validity of the questionnaires and interview guides. This technique was ascertained through several instruments’ reviews by the research supervisors and tourism experts. The study applied Cronbach’s α to examine the reliability of the measures used in the questionnaires. According to Vogt (1999), Cronbach’s α ranges from 0.000 (indicating no reliability) to 1.000 (demonstrating perfect reliability), with higher Cronbach’s α depicting the internal consistency of the measurement. A Cronbach’s α of at least  higher than 0.700 is considered acceptable (Hair et al., 1998). Six separate Cronbach’s α test was performed for the park visitors’ questionnaire as shown in Table 4.2

Table 4. 2. Results of Reliability Test for the Park Visitor’s Questionnaire

For this study, as shown in Table 4.2 Cronbach’s α coefficients ranged from 0.71 – 0.89. All the items in each construct were retained, as eliminating any item from each measurement would not have enhanced Cronbach’s α beyond the values displayed in Tables 4.2.The Cronbach’s α values for each construct exceeded the threshold of 0.70, demonstrating that all measurements were reliable for data analysis and reporting (Hair et al. 2014).

4.8 Data Analysis and Presentation

4.8.1 Qualitative data analysis

The quantitative data analysis procedure included several steps. First, the completed Google Forms surveys were downloaded into Microsoft Excel, and the data was checked for accuracy.

The statistical package for social sciences (SPSS) v.25.0 software was used to analyze the data in this study. The Microsoft Excel files were imported into SPSS v.25.0 for data analysis. Second, to investigate the missing data, data cleaning with frequencies was performed. Third, new variables (aggregate of measurement items for each construct) were computed to modify the data. Fourth, to summarize the sample characteristics of park visitors, a descriptive analysis using frequencies and percentages was performed.  Means and standard deviations were also utilized to summarize the respondents’ replies to various metrics. Further, linear regression analysis to determine the relationship between independent and dependent variables.

Table 4. 3. Summary of Data Analysis Methods

Objective	Hypothesis	Analysis Method	Statistical Model	Decision Rule
To determine the influence of cognitive destination image on park attractiveness in the Amboseli-Tsavo Ecosystem	H01: Cognitive destination image does not significantly influence park attractiveness in Amboseli-Tsavo Ecosystem	●  Descriptive statistics ●  Multiple linear regression	Where: ● =Predicted Park attractiveness ● X1= Cognitive image ● X2 = Affective image ● X3 = Conative image ● β0 = Model constant ● β1 = Regression slope for X1 ● β2 = Regression slope for X2 ● β3 = Regression slope for X3 ● e = Model error	Reject H01 when β1 value is statistically significant (p< .05)

4.8.2 Quantitative data analysis

For the qualitative data, inductive analysis was used to analyze the data using thematic  analysis. During the qualitative analysis process, all the completed interviews were coded to easily identify the excerpts to be used while reporting the findings in the succeeding chapter. In this study, the coding consisted of allocating numbers to completed interviews, followed by the category of the key informant, followed by a code indicating that the participant was a tourism expert. For instance, for Participant #10, NGTE was used to mean a participant whose interview was the tenth to be completed and who was a national government tourism expert. The results of quantitative data analyses are reported and presented in graphs, charts, and tables.

RESULTS

5.1 Response Rate

A total of 440 questionnaires were issued to park visitors, 325 of which were returned. 50 returned questionnaires were deleted due to missing data, leaving 275 viable questionnaires for data analysis and a response rate of 62.5%. 42 questionnaires were provided to tourist stakeholders, 37 of which were returned. Seven of these questions were deleted due to inadequate data, leaving 30 useful questionnaires for data analysis and reporting with a 71.4% response rate. Finally, in terms of key informants, this study targeted 24 interviewees, 21 of whom completed interviews, representing an 87.5% success rate.

5.2 Demographic Information

The demographic information of this study comprised of: gender, age and education qualifications of the respondents. The demographic details were important to this study with the aim of providing information to tourism marketers.

5.2.1 Gender

Most (53.9%) of visitors to the Amboseli-Tsavo Ecosystem were female, with male visitors accounting for 46.1% (Figure 5.1).

Figure 5. 1. Distribution of visitors by gender

5.2.2 Age

As shown in Figure 5.2, the majority (32.4%) of the respondents ranged from 48 – 57 years, followed by those above 57 years (21.6%). The respondents aged 28 – 37 years accounted for the lowest number of visitors in the Amboseli-Tsavo Ecosystem and accounted for 10.8% of all the respondents.

 Figure 5. 2. Distribution of visitors by age

5.2.3 Education Qualifications

Results in Figure 5.3 show that the majority (47.1%) of respondents in this study were holders of undergraduate degree qualifications, followed by those with a master’s degree (19.6%), certificate (10.8%), doctorate (6.9%), diploma (5.9%), high-school (4.9%), and post-doctorate degree (2.9%), respectively. Only 2.0% of all the respondents in this study were holders of primary school certificates.

Figure 5. 3. Distribution of visitors by education qualifications

5.3 Cognitive Destination Image

The respondents were requested to specify their level of agreement or disagreement with several characteristics used to describe the Amboseli-Tsavo ecosystem as a tourist destination (Table 5.4).

Notes: n=275. M-Mean. SD-Standard Deviation. Scale: Likert-type (Mean Classification): 1= Strongly Disagree (1.00 – 1.80), 2=Disagree (1.80 – 2.60), 3=neither Agree nor Disagree (2.60 – 3.40), 4=Agree (3.40 – 4.20), 5=Strongly Agree (4.20 – 5.00)

Concerning the natural environment, the respondents reported positive opinions (mean scores of over 3.00) of the parks in the Amboseli-Tsavo ecosystem, including scenic beauty (M = 4.44, SD = 0.61), enjoyable weather and climate (M = 4.16, SD = 0.74), and breath-taking water attractions (M = 4.04, SD = 0.87).  Additionally, the majority of respondents had relatively positive perceptions of the Amboseli-Tsavo ecosystem’s parks in terms of a number of tourist infrastructure components, including enticing dining options in lodges and tented camps (M = 4.23, SD = 0.63), high-quality lodging options (M = 4.31, SD = 0.68), good shopping options (M = 3.89, SD = 0.94), and exceptional service quality (M = 3.89, SD = 0.80). The majority of respondents, however, had rather pessimistic perceptions of the Amboseli-Tsavo ecosystem’s parks, which included various observable accessibility factors like convenient transportation (M = 3.04, SD = 0.94), developed infrastructure (M = 3.26, SD = 0.85), and ease of access (M = 3.24, SD = 1.08).

Despite this, the respondents expressed favorable views of the Amboseli-Tsavo ecosystem parks in regards to a number of social environment factors, including feelings of personal safety and security (M = 3.80, SD = 0.75), friendliness of the locals (M = 3.84, SD = 0.81), good value for money (M = 3.52, SD = 0.91), and a clean and tidy environment (M = 3.81, SD = 0.84). Additionally, the Amboseli-Tsavo ecosystem’s national parks had a favorable overall cognitive destination image (M = 3.74, SD = 0.44).

CONCLUSION

The aim of the current study sought to determine the influence of cognitive destination image on park attractiveness, which was addressed through H ₀₁ . The results show a significant positive relationship was established between cognitive destination image and park attractiveness (β = 0.446, t = 6.661, p = .001).Consequently, H ₀₁ was rejected.

Consistent with previous research (Kim et al., 2019; Setiawan et al., 2021; Weru, 2021; Xu et al., 2018), based on the results of the current study, tourists exhibiting higher levels of cognitive destination image are more likely to perceive a tourist destination as being attractive.

According to the study’s findings, the presence of sufficient lodging, accessibility, attractions, amenities, and activities demonstrates a positive destination brand image. Friendly service fees and persuasive promotional efforts are important factors in travelers’ choice of destinations. According to the study, most visitors believe that the parks have strong infrastructure and road signage that prevent them from getting lost in the wilderness and that they have a high perception of the destination brand image of the parks under consideration. Additionally, the findings showed that park visitors thought they were getting their money’s worth. Providing high-quality goods and services encourages visitors to have favorable opinions of the parks as a whole.

5.5 RECOMMENDATION

Based on the results of the study and the discussions on the specific objective, it is observable that tourism products are created to attract potential visitors to the destination. Amboseli Tsavo Ecosystem as a tourist destination becomes attractive for holidays if it can increase the level of its tourists’ cognitive destination image in the following key components of tourism; – suitable natural environment, tourist infrastructure, accessibility, attraction, and social environment. Since potential tourists gain more information about a site, which in turn develop familiarity and competence, this study recommends communicating the benefits of Amboseli Tsavo Ecosystem to potential tourists as the most important concepts of strategic destination marketing.  The destination becomes favourable for holidays if it can offer the five key components of tourism; – suitable accommodation, accessibility, attraction, activities and amenities at an affordable price to the target market. The study therefore recommends policies that can enhance attractiveness of the destination.

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