Abstract
Purpose
This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.
Design/methodology/approach
The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.
Findings
The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.
Research limitations/implications
This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.
Originality/value
This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.
Keywords
Citation
Begashe, B.A., Mgonja, J.T. and Matotola, S. (2024), "Tanzania’s repeat tourists: unraveling choice of attractions patterns through demographic perspectives", Tourism Critiques, Vol. 5 No. 1, pp. 120-137. https://doi.org/10.1108/TRC-02-2024-0004
Publisher
:Emerald Publishing Limited
Copyright © 2024, Betty Amos Begashe, John Thomas Mgonja and Salum Matotola.
License
Published in Tourism Critiques: Practice and Theory. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The global tourism industry has undergone substantial growth in recent decades, marked by a significant increase in the number of individuals participating in international tourism for leisure and recreational purposes (UNWTO, 2021). In the realm of tourism, heightened tourist arrivals in a destination not only contribute to economic growth but also create employment opportunities, subsequently enhancing the living standards of the local population residing near these attractions (UNWTO, 2021). Thus, for destinations to realize benefits from tourism, they must strive to attain a competitive advantage over their counterparts in terms of attracting a substantial number of tourists (Mariani and Baggio, 2012; Balkaran and Maharaj, 2013).
In line to that, it is advised that, tourist destinations have to engage in returning tourists as a prime strategy to secure a competitive edge and enhance their market attendance (Oppermann, 2000). This approach is grounded in the observation that repeat tourists tend to prolong their stays, exhibit higher expenditures and enthusiastically promote the destination through positive word-of-mouth to their social circles, constituting a potent marketing avenue (Čaušević et al., 2020; Lim et al., 2016; Matzler et al., 2019). As a result, destinations are encouraged to prioritize their efforts on tourists retention to leverage economic and promotional benefits (Darnell and Johnson, 2001; Gitelson and Kerstetter, 1990).
Despite being crucial, the heterogeneity behavior of repeat tourists particularly in their decision-making within a destination, has proven to be a challenging fact (Almeida-Santana and Moreno-Gil, 2018; Lim et al., 2016). This phenomenon is attributed to the inherent nature of the industry, as it offers a variety of products where the market is heterogeneous (McKercher et al., 2021b). Numerous tourist destinations are grappling with challenges as they struggle to attract a sufficient number of repeat tourists, thereby impacting the sustainability of these destinations (Adam et al., 2015; Armenski et al., 2011; Assaker et al., 2011; Woyo and Woyo, 2019). This issue is similarly noticeable in Tanzania, where the country typically attracts just 20% of returning tourists on average. In contrast, South Africa and Kenya have higher rates, with 80% and 42% of tourists returning, respectively (Musembi et al., 2020; SIA, 2020; TRI, 2020).
Previous studies focused much on ensuring satisfaction level of tourists (Mat-Som and Bader-Badarneh, 2017; Perovic et al., 2018; Soni, 2018), increasing expenditure patterns (Wang, 2004), adhering to quality of tourism offerings (Emir and Kozak, 2011) and effective marketing strategies for attractions in the destination (Assaker et al., 2011; Dolnicar et al., 2015) as the way of ensuring destinations attract satisfactory number of repeat tourists. These studies overlooked the vital component of comprehending the characteristics holds repeat tourists and their role in shaping choices among various attractions alternatives within the destination (Matolo et al., 2021; Mlozi, 2014; Mlozi and Pesämaa, 2013; Perovic et al., 2018; Tosun et al., 2015; Wadood et al., 2020). McKercher et al. (2012) and McKercher et al. (2021a) emphasized that attracting repeat tourists lies on comprehending and promoting the choices available within the destination. These scholars further suggest that future research should concentrate on attractions that specifically motivate tourists for a return visit rather than viewing an entire country as the destination (McKercher et al., 2012a, 2021a, 2012b).
Hence, for a more comprehensive understanding of tourists’ choices of attractions within a destination, this study draws support from the theory of cumulative attractions, as proposed by Nelson (1958). The theory posits that a significant portion of tourist attractions shares visitors, and the visitation of an attraction is influenced not only by its intrinsic appeal but also by the generative power of other attractions (Lue et al., 1993). Hunt and Crompton (2008) underscored that a substantial proportion of tourist attractions engage in customer interchange to enhance their allure, and tourists are unlikely to visit one attraction without exploring others during the same trip.
Moreover, scholars have demonstrated that tourists’ choices within a destination are influenced by demographic characteristics, including age, gender, nationality, occupation and income (Lee et al., 2017; Lew and McKercher, 2006; Lin, 2014; Woodside and Lysonski, 1989; Woodside and Macdonald, 1993; Yasami et al., 2021). Through a comprehensive comprehension of these crucial factors, with a specific emphasis on the demand side within the scope of this study, we can investigate inquiries such as discerning repeat tourists based on their demographic attributes and gauging their inclination toward similar or compatible attraction patterns during their revisits. Understanding this part is of high importance since destination contain variety of offering where market is heterogeneous (McKercher et al., 2021b).
Tailoring products and services to align with the preferences of specific demographic segments is anticipated to enhance tourists’ experiences in the destination and foster positive relationships between providers and their customers (Kara and Mkwizu, 2020). Consequently, this study not only contributes to the understanding of consumer behavior but also provides valuable insights for marketers and promoters aiming to align their offerings with tourists possessing specific demographic characteristics.
2. Literature review
2.1 Attraction compatibility theory
This theory was first developed by Nelson (1958) in retail and recreational business. The theory stands by the assumptions that, if two or more adjacent business affect each other by increased volume of sales, those business are compatible (Hunt and Crompton, 2008; Lue et al., 1993). Grounded in this theoretical framework, cumulative attractions can be categorized into two types: similar and complementary cumulative attractions. Similar cumulative attractions pertain to attractions within the same category, exerting an influence on choice behavior. Conversely, complementary cumulative attractions encompass those from different categories. The cumulative attraction theory posits that both similar and complementary accumulations of attractions play a role in influencing consumer decisions to visit a particular destination.
The theoretical framework of cumulative attractions finds application in the field of tourism, positing that a substantial portion of tourism businesses is shared (Hunt and Crompton, 2008; Lue et al., 1993). An attraction secures visitation not solely due to its inherent generative power but also owing to the generative power of other attractions. Therefore, based on the theory of cumulative attractions which also gain support from previous studies (Hunt and Crompton, 2008; Lue et al., 1993; Matthews et al., 2018), this offers a standpoint for the current study as it aims to unlock the attraction patterns preferred by international repeat tourists visiting Tanzania for leisure and recreation purposes.
While the theory of cumulative attractions provides a foundation for understanding tourist attraction choices, it does not offer a comprehensive perspective. The decision-making process of tourists in selecting a specific attraction pattern encompasses various factors, with sociodemographic characteristics being one of the significant contributors. To capture this multifaceted interrelation, the present study will augment the theoretical framework by incorporating the general framework of the tourism consumption system proposed by Woodside and Macdonald (1993). This addition aims to provide supplementary insights into the factors influencing tourists’ choices of attraction patterns.
2.2 General framework for tourism consumption system
This study is guided by the framework which was developed by Woodside and Macdonald (1993), frequently employed framework for tourists’ destination choice. This framework is recognized for its elucidation of how various tourists’ choice of different travel components are influenced by factors from both the tourists’ country of origin and the visited destination. Within this framework, tourists’ attractions are among the travel components explicated. Moreover, the framework underscores the pivotal role of attractions as the primary impetus for tourists in deciding to visit a particular destination. In addition, the authors emphasize that a diverse array of attractions within a destination not only stimulates initial visitation but also ensures repeat visits to the destination (McKercher and Wong, 2004; Niininen et al., 2004; Reitsamer and Brunner-Sperdin, 2017).
Hence, the alignment of this framework with the current study is pertinent, given its comprehensive overview of the available attractions within the destination as a key travel component, which directly corresponds to the primary focus of this investigation. In addition, the framework offers insights into factors influencing the selection of travel components, with demographic characteristics of tourists identified as one such influential factor. Consequently, the study is centered on elucidating the manner in which demographic features, including age, gender, marital status, income level, family size, region of origin, education level and occupation, influence the selection of attraction patterns among international repeat tourists visiting Tanzania.
2.3 Tourists’ choice of attraction patterns
A word “pattern” is applied in different fields including agriculture, which stands for species arrangement, irrigation distribution and species diversity (Guillera-Arroita, 2017; Minoli et al., 2019). Likewise, in geography, the term pattern is used to mean population distribution, geographical boundaries, geographical positioning and temporal arrangements (Hasnat and Hasan, 2018; Pironon et al., 2017; Schirpke et al., 2018). The term “pattern” is also applied within the realm of tourism studies, where it is associated with various facets of tourists, including their spending, movements, preferences, behaviors and distributions (Amir et al., 2015; Gyte and Phelps, 1989; Zhao et al., 2018; Zhong et al., 2019). Furthermore, existing literature differentiates several types of destination patterns based on the purpose of travel, encompassing categories such as sun and beach, cultural exploration, shopping, sports and outdoor activities, spa and wellness, cultural sightseeing, as well as festivals and events (Falk and Hagsten, 2018). In alignment with this, the geographical setting, such as urban areas, mountains, rural locales and cruise destinations, can be used to discern distinctive destination patterns (Huybers and Bennett, 2003).
Pertaining to the focus of this study, attractions available within the country will contribute to the formation of diverse patterns as a consequence of tourist choices (Becken et al., 2003; Becken and Gnoth, 2004). Hence, when examining choice patterns among international repeat tourists seeking holiday and leisure experiences, the focus will encompass an array of both natural and cultural attractions found within the destination. This approach seeks to unravel the distinct preferences of repeat tourists by focusing on attractions choices (Arshad et al., 2018; Sertkan et al., 2019). It is worth noting that a country or region holds a multitude of potential attractions that could shape tourists’ patterns, essentially deciphering where exactly their preferences of attractions lie when selecting a specific destination (McKercher et al., 2021a; Zoltan and McKercher, 2015).
For example, in the case of Tanzania, which is often perceived as a unified destination, it encompasses a diverse array of offerings, including wildlife, lush vegetation, majestic mountains, picturesque beaches, vibrant birdlife, natural forests, indigenous cuisine, captivating arts and crafts, as well as historical monuments, among others. These myriad attractions collectively contribute to shaping unique choice of attraction patterns among international repeat tourists (Ettema and Nicolau, 2012; Liu et al., 2020).
2.4 Demographic characteristics and tourist’s choices of attractions patterns
Traditionally, the focus of studies on destination choices aimed to establish a connection between a traveler’s attributes and the offerings of the destination (Woodside and Lysonski, 1989; Woodside and Macdonald, 1993). As highlighted by Almeida-Santana and Moreno-Gil (2018), grasping the market’s dynamics is crucial for destination managers and tour providers. This consideration is of high importance, as it will enable marketers and promoters of tourism products to align with tourists needs and wants and gain competitive advantage over destinations such as Kenya and South Africa (Kara and Mkwizu, 2020).
In the body of literature, various studies have been conducted on the relationship between tourists’ demographic characteristics and the choice of destination. Starting with the study which was conducted by Almeida-Santana and Moreno-Gil (2018), concentrating on tourist loyalty to either single or multiple destinations, using a tourist-centered approach. Utilizing a binomial logit model in their investigation, they aimed to unveil the factors influencing the choice between horizontal and destination loyalty. The findings of their study unveiled a noteworthy association between tourists’ age and their proclivity to revisit. Specifically, older tourists demonstrated a higher inclination toward loyalty to destinations compared to their younger counterparts. Furthermore, tourists with higher disposable income exhibited a tendency to visit multiple destinations. Conversely, factors such as gender and education were deemed insignificant in their impact on the behavior of repeat tourists (Almeida-Santana and Moreno-Gil, 2018).
Furthermore, the study conducted by Moakler and Kim (2014) used a convenient sampling method to investigate the impact of sociodemographic characteristics on destination choice among Muslim travelers in Asia. Using descriptive analysis, factor analysis and paired t-test in their study, the findings indicated that Muslim tourists in their 20s predominantly favor independent tours compared to individuals in the age groups of 30s or older. In addition, the study which was conducted by Correia et al. (2015) suggested that older tourists are more inclined to be loyal to the destination as they tend to revisit the same destination more often compared to young tourists.
Similarly, Chiu et al. (2015) used cross-tabulation to assess the correlation between intentions of repeat tourist behavior and sociodemographic characteristics. In their study, they unveiled that sociodemographic characteristics of tourists are among the determinants for repeat visitation in relation to initial and subsequent visit (Chiu et al., 2015). Furthermore, Davison and Ryley (2010) conducted a research to explore tourists preferences in the destination by using cluster analysis. The results from chi-square demonstrated a statistically noteworthy association between income and segments of destination preferences such as tourists with income level exceeding £10,000 exhibited a preference over long vacations, especially for weekend getaways (Davison and Ryley, 2010).
The research carried out by Le-Klähn et al. (2015) investigated the factors influencing tourists’ decisions regarding both destination and mode of transport. The authors used a bivariate logit model to examine the interdependence of these two choices, namely, the selection of attractions to visit and the mode of transport to use. The results shed light on a positive correlation between tourists’ country of residence and their preferences for various recreational activities within the destination (Le-Klähn et al., 2015). In addition, the research undertaken by Lyngdoh et al. (2017) used descriptive analysis to investigate the influence of sociodemographic factors on tourists’ preferences for three Indian tiger reserves. The findings indicated that, among various factors, tourists’ nationality exhibited a positive correlation with the selection of various activities within the destination.
In the broader context of investigating how tourists’ sociodemographic attributes affect destination choices, researchers have made efforts to depict this connection. However, the existing body of literature lacks sufficient substantiation regarding the manner in which sociodemographic attributes shape selection among real, returning tourists. Moreover, the existing literature has not fully illuminated how these sociodemographic factors function as decisive factors in shaping choice of attractions patterns among returning tourists, particularly within the Tanzanian context. Gaining insight into this correlation would empower tourism marketers and managers to categorize repeat tourists based on their sociodemographic traits with their corresponding choices based on attraction patterns.
3. Methods
3.1 Research design and study area
This study adopts a cross-sectional design to collect data from international tourists who engage in repeated visits to Tanzania as a tourist destination. In this design, information are collected from a specific subset of the population at a singular point in time (Allen, 2017). This method provides significant advantages in terms of both time and cost. It is also suitable for research that encompasses numerous variables and involves a considerable number of participants.
3.2 Sampling design and data collection
The article presents findings from the survey data which was collected from 1,550 international repeat tourists between November 2022 and August 2023. A convenience sampling technique was used to select respondents whereby tourists were selected based on their willingness to participate in this study. Drop-and-pick technique was used to distribute questionnaire in three main international airports, namely, Julius Nyerere International Airport (JNIA), Kilimanjaro International Airport (KIA) and Abeid Aman Karume International Airport (AAKIA). These airports serve as the main gateway for international tourists visiting Tanzania.
3.3 Variable measurements
This study engages an inclusive approach to measure various demographic variables that contribute to understanding choice of attraction patterns among international repeat tourists in Tanzania. The dependent variable in this study, denoted as “Choice of attractions patterns,” serves as a representation of the preferences for attractions among international repeat tourists visiting the country. The identified attractions are classified into categories such as wildlife, mountains, culture and beach. As per the Tanzania Tourism Sectoral Survey Report of (2019), the most popular attractions for international tourists align with these four categories. Tourists were requested to indicate the attractions they visited during their trips, with each attraction belonging to a specific category, as outlined in the Tanzania Tourism Sectoral Survey Report (TTSS) (2019).
Furthermore, as presented by previous studies, tourists normally tend to choose single or multiple attractions as their place to visit (Hunt and Crompton, 2008; Hwang et al., 2006; Lue et al., 1993; Masiero and Zoltan, 2013; Zoltan and McKercher, 2015). As outlined by Molinillo and Japutra (2017), attractions can be grouped based on choices, and they can be classified according to its types. Also research by Bhati and Pearce (2017), Molinillo and Japutra (2017) and Zhong et al. (2019) confirm the presence of attraction patterns as a result of tourists visitation single or multiple attractions within tourist destinations. Each pattern incorporates a variety of attraction types, and for a tourist to fall into a specific pattern, they must have visited all the attractions associated with that pattern. In addition, study gathered information regarding the demographic attributes of tourists, including factors such as age, gender, income, level of education, marital status, family size and nationality, with the aim of examining their influence on the choice of attraction patterns among international repeat tourists (Kara, 2016; Kozak et al., 2004).
3.4 Data analysis
This study employed multinomial logistic regression analysis to investigate the role of sociodemographic characteristics on the choice of attraction patterns among repeat tourists in Tanzania. The model aims to assess the relationships between sociodemographic variables among international repeat tourists and their selection of various attraction patterns within the destination, as expressed by the following equation:
P (Y = j) stands for the probability of selecting attraction pattern j;
P (Y = Ref) is the probability of selecting the reference category; and
β0j, β1j, β2j, β3j, β4j, β5j, β6j, β7j are the coefficients for each independent variable corresponding to choose pattern j.
3.5 Ethical considerations
All ethical procedures, including obtaining an introductory letter from the University of Dodoma ethical committee prior to initiating the data collection process, were strictly adhered to. The protocol for securing permission to collect data at three major international airports – JNIA, KIA and AAKIA – was meticulously followed. A comprehensive consent form outlining the study’s main objective and emphasizing the participants’ voluntary contribution to the study’s success was presented. The consent form explicitly communicated participants’ autonomy to withdraw from the study at any stage, ensuring that their involvement remained voluntary.
4. Results
4.1 Profiles of respondents
Participants in this study were international repeat tourists predominantly from the young and middle-age demographic, with a significant proportion having a high level of education, indicating attendance at the university level (67.61%). The majority hailed from Europe, and among them, slightly more were female (58.13%) than male (41.87%). A notable portion of respondents fell within the age bracket of 25–34 years (31.4%), followed by those aged 34–44 years (22.77%) and those above 55 years (19.23%). Over 72% of the participants were from Europe. Majority of respondents were employed (76.67%), while a smaller percentage were retired (17.29%). Marital status varied, with 46.15% of respondents being single and 47.68% being married. In addition, a significant proportion of international repeat tourists visiting Tanzania reported an income level below $30,000 (32.39%), followed by those with incomes between $30,000 and $49,000 (31.29%), and those within the range of $50,000–$49,000 (21.68%). In terms of family size, nearly half of the respondents came from families with fewer than three members (48.39%), while slightly more than half were from families with three members or more (51.68%). The detailed demographic profiles of the respondents are presented in Table 1.
4.2 Pattern among international repeat tourists
Seven distinct patterns were observed among international repeat tourists in Tanzania, with the following distribution: wildlife pattern accounted for 34.8%, culture and beach pattern for 17.0%, the combination of wildlife, culture and beach pattern for 16.8%, the combination of wildlife and beach pattern for 10.4%, the combination of culture, beach, wildlife and mountain pattern for 8.2%, the combination of mountain and wildlife pattern for 7.2% and the beach pattern alone for 5.6%. See Table 2.
4.3 The results from multinomial logistic regression model
A multinomial regression analysis was used to analyze the role of sociodemographics on repeat international tourists’ choice of attractions in Tanzania. All seven identified attraction patterns were employed as dependent variables in a multinomial logistic regression model. The model, expressed through coefficients, signifies the likelihood of an event occurring as a result of changes in independent variables. The statistical significance of the model is evident, as indicated by the chi-square test yielding a P-value close to zero (0.000). Furthermore, the likelihood ratio test, yielding a statistic of 467.32 with 108 degrees of freedom, provides additional evidence of the model’s improved fit.
The results reveal a noteworthy correlation between age (n = 1,550) and preferences for attraction patterns, specifically those involving the combination wildlife, culture, beach, as well as combinations such as culture and beach, wildlife and beach and the complex integration of wildlife, mountain, culture and beach (refer to Table 3). In all these instances, the P-values fall below 0.05, indicating statistical significance, with coefficients of −0.04, −0.03, −0.06 and −0.03, respectively. These findings suggest that with every one-unit increase in the age of repeat tourists, there is a reduced probability of selecting patterns that encompass the mentioned combinations over the pattern involving the combination of mountain and wildlife, when compared to the pattern involving the combination of wildlife and mountain.
Furthermore, the results highlight a significant correlation between married repeat tourists (n = 739) and their preferences for various attraction patterns. This is evident through P-values below 0.05 not only in the wildlife pattern in patterns involving combinations such as wildlife, culture and beach; culture and beach; wildlife and beach; and the intricate wildlife, mountain, culture and beach combination. This implies that married repeat tourists are more inclined to choose the wildlife pattern, as well as the patterns encompassing the mentioned attraction patterns over the pattern involving the combination of wildlife and mountain when compared to their single counterparts. In addition, the findings indicate a noteworthy correlation between repeat tourists who are divorced or widowed (N = 91) and their preferences for patterns that include combinations of culture and beach, as well as wildlife and beach. The P-values for both patterns are below 0.05, suggesting statistical significance. This implies that tourists who are widowed or divorced exhibit a higher likelihood of selecting the mentioned patterns as opposed to the pattern combining wildlife and mountain when compared to those who are single.
Findings indicate statistically significant correlations between families with more than three children (n = 801) and their preferences for both beach patterns and combinations incorporating culture and beach. This is evident through a P-value < 0.05 for both patterns and coefficients of −0.769 and −1.172, respectively. This suggests that, for each increment in family size by one unit, tourists fall within families exceeding three members are less likely to choose the said patterns when compared to the combination involving wildlife and mountain, particularly in comparison to the family with fewer than three members.
Furthermore, the findings highlight a substantial correlation between repeat tourists with an income range between US$30,000 and US$49,999 (N = 485) and their preferences. This connection extends beyond the wildlife pattern to various combinations, including wildlife, culture and beach; culture and beach; wildlife and beach; and the complex wildlife, mountain, culture and beach combination, with coefficients of 1.129, 0.645, 1.076, 1.37, 0.902 and −1.103, respectively. In addition, repeat tourists with an income range of US$50,000–US$69,999 (N = 336) exhibit a preference for beach patterns with a coefficient of −2.956, while those with an income range of US$70,000–US$89,999 (N = 84) lean toward patterns involving culture and beach with a coefficient of 2.204. The associated P-values for these income groups are all below 0.05. These findings suggest that, with each incremental increase in income by one unit, tourists within the US$30,000–US$49,999 income range are more inclined to opt for the mentioned patterns under the same income category and less likely to choose a beach pattern, as opposed to the wildlife and mountain pattern, in comparison to those with an income below US$30,000. Similarly, for tourists with incomes ranging from US$50,000 to US$69,999, each one-unit increase in income heightens the likelihood of choosing the previously mentioned patterns under the same income category over a wildlife and mountain pattern, compared to those with incomes below US$30,000.
Moreover, the findings unveil a substantial correlation between repeat tourists originating from North America (n = 136) and Asia (n = 73) and their inclination toward the pattern encompassing wildlife, mountain, culture and beach with P-values below 0.05 for both regions, with coefficients of 1.343 and 2.9, respectively. In addition, tourists from Asia exhibit a significant association with the choice of the wildlife pattern over the combination of wildlife and beach, as indicated by P-values below 0.05 and a coefficient of 2.88. Consequently, the findings suggest that repeat tourists from North America and Asia are more likely to opt for the pattern involving the combination of wildlife, mountain, culture and beach over the wildlife and mountain pattern, compared to tourists from Europe. Furthermore, tourists from Asia express a preference for the wildlife pattern over the combination of wildlife and mountain compared to those originating from Europe.
In addition, the results illuminate a significant relationship between female repeat tourists (n = 901) and their preference for patterns involving the combination of culture and beach, as well as combinations of wildlife, mountain, culture and beach, along with the standalone beach pattern. This correlation is substantiated by P-values below 0.05 for all patterns, with coefficients of 0.567, 0.6 and −0.76, respectively. This implies that female repeat tourists are more inclined to choose the aforementioned patterns over the combination of wildlife and mountain patterns, in comparison to their male counterparts. Furthermore, female repeat tourists show less interest in visiting beach patterns over the combination of mountain and wildlife patterns compared to males.
Furthermore, the results highlight a significant correlation between the preferences of repeat tourists with college (n = 299) and university education (n = 1,048). This correlation extends beyond the wildlife and beach patterns to various combinations, including wildlife, culture and beach; culture and beach; wildlife and beach; and the complex wildlife, mountain, culture and beach combination. The associated P-values for both education groups are below 0.05, with coefficients of 1.976, 0.925, 1.044, 1.270, 1.37 and 2.757, respectively, for tourists with a college education level; and 2.037, 1.459, 1.046, 1.54, 1.02 and 2.919, respectively, for those with a university education level. These findings suggest that repeat tourists who attended college and university education are more inclined to choose the aforementioned patterns over the pattern involving the combination of mountain and wildlife compared to repeat tourists with a secondary education level.
5. Discussion
This study illustrates that the choice of attraction patterns among international repeat tourists in Tanzania can be accurately predicted by sociodemographic factors, including age, marital status, income and educational level. The negative correlation observed between the preference for attraction combinations and the variable of age suggests that, as individuals’ age increases, they tend to prefer simpler tourist packages with fewer attractions (Mihalic, 2002). This inclination could be attributed by their health status, with evidence suggesting that, older tourists tend to gravitate toward fewer, more specific attractions, while younger tourists opt for a combination of diverse and complex attractions (Lin et al., 2015). Furthermore, it has been argued that as age increases, energy levels decrease, leading tourists to select specific areas to visit, ensuring to preserve their limited energy while maximizing their overall enjoyment (Lin et al., 2015). Therefore, destination managers and tour providers should make sure that they design simple packages for this specific market segment to retain them.
Conversely, the results revealed a positive correlation between married repeat tourists and their preference for all seven attraction patterns. This inclination may be attributed to the propensity of married couples to allocate their leisure time for travel outside their usual environment during holiday periods. This aligns with research conducted by Shuai et al. (2022) indicating that, recently married couples often engage in global leisure travel by visiting varieties of tourists attractions before assuming the responsibilities of parenthood. In addition, the study conducted by Kuo et al. (2011) revealed that unmarried tourists have more leisure constraints since they do not have a partner to share their leisure activities with. Therefore, it is advisable for destination providers to focus on creating packages that incorporate a variety of attractions catering to the interests of married couples to attract a substantial number of repeat tourists to the country.
Moreover, the inclination of tourists toward varied choices is linked to their income levels. The findings indicate that tourists with an income level of 2, ranging between US$30,000 and US$49,999, exhibit a positive association with the selection of almost all patterns, excluding the beach pattern, which demonstrates negative coefficients. This can be attributed to the fact that the preferred attractions for this income group often involve combination of attractions. This is due to, necessitating additional budget allocation for tourists to fulfill their interests by visiting a combination of attractions, unlike visiting a single beach attraction, which tends to be less expensive. The literature also underscores income as a one of important factors which affect tourists behavior, especially on the choice of attractions to visit within the destination and trip duration (Oeconomica, 2014; Seyidov and Adomaitienė, 2017). Consequently, destination providers should prioritize catering to the needs of tourists with an income level between US$30,000 and US$49,999 by designing packages that align with their preferences, aiming to retain them as potential repeat tourists in the country.
Furthermore, a positive correlation has been uncovered between repeat tourists who have undergone college and university education and their choices of all attraction patterns. This connection may stem the evidence provided in the literature that, tourists with high level of education are more connected with exposure (Park et al., 2019). Therefore, as the level of education increase, tourists need of exploration for the aim of education fulfillment (Kim et al., 2007). Studies shows that, tourists who are more educated normally see the opportunities for exploration compared to those with low level of education (Celik, 2019). This desire stimulate them to revisit various tourists destinations for the main aim of seeking deeper understanding and fulfill their travel desires (Celik, 2019). In alignment with this study, the results shows that repeat tourists with the age group 25–35, mostly have diploma and university level of education attainment. Therefore, destination marketers and tour providers should ensure that they design packages to include attraction patterns that facilitate exploration and education activities to retain this group.
6. Conclusion and recommendations
The findings of this study provide valuable and comprehensive insights into the complex dynamics that exist in the relationship between demographic characteristics and the preferences for attraction patterns within the context of Tanzania as a tourist destination. Through the application of multinomial logistic regression, the research has successfully identified and established a predictive connection between key demographic factors, namely, age, marital status, income level and education level, and the specific choices made by international repeat tourists when selecting attraction patterns in Tanzania.
In summary, this research contributes not only to the academic understanding of the interplay between demographic characteristics and tourist behavior but also provides practical implications for the tourism industry in Tanzania. By recognizing and catering to the diverse preferences influenced by demographic factors, stakeholders can tailor their offerings to create a more personalized and engaging experience for international repeat tourists, thereby enhancing the overall attractiveness and competitiveness of Tanzania as a tourist destination.
Recommendations can be made to tour providers and marketers of tourism products that, if they want to attract repeat tourists in the country, they have to ensure that they design, package and market their products and services by considering products specifically for adults’ tourists, those who are marriage, with income level between US$30,000 and US$49,000 and those who attained college and university level of education.
6.1 Study implications
6.1.1 Theoretical implication.
This study makes a valuable contribution, especially in the field of tourism, particularly in the context of the choice of attraction patterns among international repeat tourists in Tanzania. In addition, it enhances our comprehension of the demographic factors that shape the preferences for attraction patterns among repeat tourists.
6.1.2 Managerial implications.
Tour operators and marketers of tourist attractions will gain a comprehensive understanding of their repeat tourists who select diverse attraction patterns, taking into account factors such as age, marital status, income level and education. This insight will enable them to tailor their products to meet the specific needs of their target market, ensuring satisfaction and fostering repeat visits to the country.
7. Limitation of the study and future area
This research primarily concentrates on international repeat tourists, limiting the opportunity to comprehend the preferences of domestic tourists. To enhance efforts in attracting more domestic tourists to the country, future studies should prioritize understanding the choice patterns of domestic tourists in connection with their demographic characteristics.
Demographic profiles of respondents
| Variable | Category | % |
|---|---|---|
| Age | 18–24 | 12.13 |
| 25–34 | 31.94 | |
| 35–44 | 22.77 | |
| 45–54 | 13.94 | |
| 55 and above | 19.23 | |
| Gender | Male | 41.87 |
| Female | 58.13 | |
| Marital status | Single | 46.15 |
| Married | 47.68 | |
| Others | 5.07 | |
| Education level | Primary education | 1.48 |
| Secondary education | 11.61 | |
| College education | 19.29 | |
| University and above | 67.61 | |
| Family size | Below 3 | 48.32 |
| Above 3 | 51.68 | |
| Region of origin | Europe | 72.23 |
| North America | 9.04 | |
| South America | 4.85 | |
| Asia | 4.85 | |
| Sub-Saharan Africa | 8.57 | |
| Australia | 0.47 | |
| Income (USD) | Less than 30,000 | 32.39 |
| 30,000–49,999 | 31.29 | |
| 50,000–69,999 | 21.68 | |
| 70,000–89,000 | 5.42 | |
| 90,000 and above | 9.23 | |
| Occupation | Employed | 76.65 |
| Retired | 17.29 | |
| Students | 6.06 |
Source: Table by authors
Attraction patterns among international repeat tourists
| Attraction patterns | Frequency | % |
|---|---|---|
| Wildlife | 539 | 34.8 |
| Mountain and wildlife | 111 | 7.2 |
| Culture and beach | 264 | 17.0 |
| Beach | 87 | 5.6 |
| Wildlife culture and beach | 261 | 16.8 |
| Wild and beach | 161 | 10.4 |
| Culture, beach, wildlife and mountain | 127 | 8.2 |
| Total | 1550 | 100.0 |
Source: Table by authors
Coefficient values of the multinominal logit model (N == 1,550)
| Variables | Wild, cult, beach P | Wildlife P | Cult, beach P | Wild, beach P | Wild, mount, cult, beach P | Beach P |
|---|---|---|---|---|---|---|
| Age | −0.03944 (0.000 | −0.01351 (0.171) | −0.02659 (0.014) | −0.06432 (0.000) | −0.02969 (0.016) | −0.01859 (0.191) |
| Married | 1.41782 (0.000) | 1.65303 (0.000) | 1.39060 (0.000) | 1.76195 (0.000) | 1.62534 (0.000) | 1.52321 (0.000) |
| Divorced and widowed | 0.87871 (0.192) | 1.11195 (0.071) | 1.32886 (0.041) | 2.62914 (0.000) | 1.32067 (0.077) | 1.26741 (0.123) |
| Above3 | −0.37042 (0.187) | −0.22264 (0.400) | −1.17219 (0.000) | 0.04856 (0.874) | −0.42915 (0.175) | −0.76944 (0.027) |
| Inc2s | 1.12940 (0.001) | 0.64467 (0.045) | 1.07569 (0.002) | 1.36982 (0.000) | 0.90237 (0.021) | −1.10307 (0.018) |
| Inc3 | 0.59285 (0.097) | −0.44774 (0.177) | −0.10750 (0.771) | 0.08229 (0.837) | −0.09872 (0.813) | −2.95613 (0.000) |
| Inc4 | 1.86636 (0.081) | 1.54831 (0.138) | 2.20364 (0.037) | 1.31631 (0.246) | 1.62810 (0.145) | −0.87071 (0.551) |
| Inc5 | 0.13424 (0.784) | −0.32091 (0.469) | 0.33648 (0.478) | 0.12264 (0.823) | 0.20073 (0.712) | −0.2968 (0.568) |
| Employed | −0.23713 (0.461) | −0.24605 (0.391) | 0.41012 (0.237) | −0.21978 (0.523) | 0.13494 (0.730) | 0.08063 (0.856) |
| Students | 1.53233 (0.053) | 0.73825 (0.325) | 0.93368 (0.258) | 0.89819 (0.310) | 0.51682 (0.575) | −0.61403 (0.644) |
| Region2 | 0.59338 (0.271) | 0.98683 (0.053) | 0.21254 (0.705) | 1.04222 (0.069) | 1.34278 (0.018) | 0.15199 (0.848) |
| Region3 | 12.70925 (0.974) | 13.72991 (0.972) | 14.43377 (0.970) | 13.92459 (0.971) | 14.66063 (0.970) | 13.09976 (0.973) |
| Region4 | 1.95387 (0.077) | 2.88032 (0.006) | 1.99747 (0.070) | 2.09511 (0.057) | 2.90055 (0.009) | −0.05049 (0.973) |
| Region5 | 0.25170 (0.580) | 0.28052 (0.507) | −0.38924 (0.416) | 0.17974 (0.718) | 0.14518 (0.782) | 0.26564 (0.620) |
| Female | 0.51261 (0.055) | 0.37960 (0.122) | 0.56691 (0.035) | 0.18810 (0.510) | 0.60039 (0.047) | −0.78973 (0.021) |
| Primary | 0.17755 (0.891) | −0.39360 (0.691) | 0.28791 (0.767) | 2.15826 (0.020) | 0.94945 (0.358) | 2.90511 (0.024) |
| College education | 1.97621 (0.000) | 0.92493 (0.016) | 1.04383 (0.017) | 1.27012 (0.006) | 1.37090 (0.005) | 2.75738 (0.001) |
| University education | 2.03659 (0.000) | 1.45862 (0.000) | 1.04599 (0.004) | 1.53929 (0.000) | 1.01889 (0.014) | 2.91918 (0.000) |
| _cons | −0.37534 (0.514) | 0.23641 (0.639) | −0.03608 (0.950) | 0.23850 (0.689) | −1.01732 (0.115) | −1.32521 (0.157) |
Number of observations = 1,550; Log likelihood = −2404.5757; LR chi2(108) = 467.32; Prob > chi2 = 0.0000; Pseudo R2 = 0.0886
Source: Table by authors
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Acknowledgements
Funding: The author confirms that financial support for the creation and submission of this manuscript was provided by The University of Dodoma as internal funding to support the pursuit of PhD studies.