This study sought to determine the extent to which demographic characteristics of residents influenced their domestic tourism participation behavior. This was achieved by establishing the relationship between seven demographic characteristics and participation behavior, followed by a comparison of the demographic characteristics of respondents participating and those not participating in domestic tourism. The purpose was to identify the segments with greatest potential for conversion from non-participation into participation in domestic tourists. The study targeted Nairobi residents aged above 18 years. Questionnaires were administered to 337 domestic tourists and 339 non-tourists. Chi square cross tabulation indicated that domestic tourism participation behavior was dependent on all the demographic characteristics of the respondents. Chi square goodness of fit test exhibited significant differences between tourists and non-tourists across all attributes of gender and level of education. For the other characteristics (namely age, occupation, income, marital status and family life cycle), the test revealed significant differences across some of the attributes while registering no significant difference across others. The segments with no significant difference were; Age (31-40), Occupation (students and retirees), Income (those earning Ksh.200,000-300,000 and above 300,000), marital status (the widowed and divorced), and family life cycle (those with young children and empty-nesters). The study, therefore, concluded that these were the segments with the greatest potential for conversion to domestic tourism participation. It further recommended the targeting of the segments identified above for domestic tourism in addition to the existing marketing efforts.
Keywords: Demographic characteristics, Domestic tourists, Domestic non-tourists, Participation behavior, Profiling, Segment, Targeting.
Received: 26 March 2020 / Revised: 11 May 2020 / Accepted: 16 June 2020/ Published: 29 June 2020
The paper’s primary contribution was the identification of the most viable demographic segments of non- tourist with the highest potential for conversion into domestic tourists. It is one of the few studies that compare tourists and non-tourists. Those with no significant difference are targeted as potential segments for domestic tourism.
Domestic tourism is a force to reckon with globally. Reports from UNWTO (2018) indicate that there were approximately 6 billion domestic tourists against 1,326 million international tourists globally in 2018. In the same year, domestic tourism accounted for 73% of total travel and tourism spending (World Travel Tourism Council, 2019). The magnitude and economic contribution of domestic tourism can therefore not be overstated and is often considered to be the key driver of the tourism sector globally.
Despite its significance globally, the potential of domestic tourism in Africa and specifically in Kenya has not been fully attained. According to the Economic Survey 2019, domestic tourism in Kenya accounted for 52.1% of total bed nights (4,489,800 bed nights) in the 2018 (Kenya National Bureau of Statistics, 2019; Tourism Research Institute, 2018). This is not only below the international threshold where domestic tourism uptake is more than 3 times that of international tourism (UNWTO, 2018) but is also below the country’s targets which according to the third medium-term plan of the Vision 2030 was set at 6.5 million bed-nights annually (Government of Kenya, 2018). This scenario has been partly attributed to the lack of targeted marketing specifically directed to the domestic market. Magableh and Kharabsheh (2013) alluded to this in their study that identified the inability to create, increase, and sustain local demand as key challenges facing domestic tourism. Tourism marketing and promotional efforts in Africa have for a long time been generally skewed towards international tourism with domestic tourism being less marketed (Okello, Kenana, & Kieti, 2012; Scheyvens, 2007). In most African destinations, domestic tourism is often treated as a stop-gap measure when international tourism is low rather than a significant market segment (Kihima, 2015) .
Various studies have shown that demographic, geographical, psychographic, and behavioral variables influence final participation in domestic tourism (Baker & Crompton, 2000; O’Leary & Deegan, 2005). These four characteristics are commonly used to profile tourists, a process that is significant during market segmentation (Holloway, Humphreys, & Davidson, 2009). This study focuses on demographic characteristics and their influence on tourism participation behavior. The choice of demographic characteristics among other profiling attributes was informed by the gap identified by Wang and Chen (2013) that sought to examine socio-demographic factors affecting tourism demand at the individual level.
Domestic tourism participation behavior in this study is conceptualized through participation and non-participation in domestic tourism. The significance of considering the views of both participants and non-participants in tourism has been emphasized by various studies including; Nyaupane and Andereck (2007); Hung and Petrick (2012); Li, Zhang, and Goh (2015); Li., Meng, and Zhang (2016) and Stone and Stone (2017). Tourism participants are defined as those who have participated in domestic tourism while non-participants are those who do not take part in tourism due to the existence of a barrier (Hung & Petrick, 2012). The inclusion of tourism non-participants is significant as understanding the characteristics of non- participation provides a fundamental basis for determining potential market segments that require targeting. The focus of successful businesses has shifted from pursuing larger market shares to generating new markets via developing current non-customers (Li et al., 2016). The significance of non-tourists in the tourism business and the dearth of current research on domestic tourism non-participation (Li et al., 2015; Park & Petrick, 2009) contributes to the gap for this study.
In this study, demographic characteristics were represented by age, gender, marital status, income, occupation, educational level, and stage in family lifecycle since these are considered to be the most commonly used demographics in the analysis of travel behavior (Blazey, 1987; Gilbert & Hudson, 2000; Nicolau & Más, 2006) . In his study on non-participation, Li et al. (2015) found out that occupation, income, education, family finance, awareness, gender, and health had a significant effect on the probability of one not participating in tourism.
Age has been cited as a significant determinant of tourism demand and eventual participation. It determines the amount of leisure time available, the freedom to travel, disposable income, the health, fitness, and overall mobility of a person. Hall (2005) argued that age was probably the key demographic factor that would affect the future of tourism. As one gets older, their needs and preferences change through age cohorts i.e. often in unison with others who are close to his/her age (Solomon, 2010) . Age also serves as an indicator of the choice and level of physical activity as well as a measure of the level of involvement in the vacation experience (Odunga, 2010) .
When it comes to gender, men and women have been known to differ both physically and physiologically resulting in differences in travel preferences and decision making as attested to by various studies. Ryan (1998) outlines a detailed account of gender differences in leisure pursuits and activity preferences. Page and Connell (2006) acknowledged gender as an important determinant of participation in tourism. For instance, from a motivational point of view, women being the traditional home managers will not be motivated to visit a self-catering facility/holiday as that does not provide an escape from the usual home environment. A study by Mutinda and Mayaka (2012) concluded that more females than males participated in domestic tourism, hence reinforcing the notion that women travellers are increasingly forming the highest growth segment in domestic tourism. Contrary to this, Li et al. (2015) posited that there were more female non-participants than males. Okello et al. (2012) concluded that participation was independent of gender and age.
Education has for a long time been viewed as a means of widening one’s perspectives in life, providing experiences of other cultures and establishing curiosity. It is viewed not only as a primary motive for travel but also as a primary indicator of tastes and socio-economic status in society (Odunga, 2005) . It has also been proposed that individuals within the same educational cohorts tend to have the same perception, values, tastes, and preferences Kotler and Armstrong (2003) . This means that they will share attraction for the same tourism destinations and products. Others have gone ahead to argue that education is a better predictor of participation in tourism than income (Richards, 1996) . Education broadens horizons, raises the level of awareness, and stimulates the desire to travel hence increasing the likelihood of participation in tourism (Manono & Rotich, 2013; Torkildsen, 2005).
Income has also been cited as an important determinant of demand for tourism to a destination (Odunga & Folmer, 2004). Dieke (2003) perceived tourism as an activity that was costly and required discretionary income left after catering for routine living expenses. Kihima (2015) further argued that most people in Africa did not view tourism as a basic survival necessity but rather as a luxury. This makes income a significant determinant of tourism participation. People at different income levels tend to have quite different values, behaviors, and lifestyles which affect their participation in tourism (Peter & Olson, 2010). For instance, Richards (1996) noted that tourism participation rates were higher in high-income groups and amongst professionals. This trend is particularly evident with disposable income where it has been observed that as it increases, participation in tourism increases (Vanhove, 2018). Studies on park visitation by domestic tourists have indicated that participation was highly dependent on income (Manono & Rotich, 2013; Okello et al., 2012) .
Despite the economic variable seeming like an obvious determinant, studies have shown that a decrease in prices of cost of local tourism does not automatically translate into increased domestic tourism (Magableh & Kharabsheh, 2013) . This means that there are other significant determinants of domestic tourism participation apart from income or cost thus income is also not always positively correlated to the uptake of domestic tourism. For instance, studies have shown that in Australia, as disposable income increases, domestic tourism reduces as people choose to travel overseas (Gardiner, Grace, & King, 2014).
Similarly, an increase in debt does not reduce domestic tourism as people may incur debts to finance travel(Yap & Allen, 2011) . Besides, Mazimhaka (2007) stated that participation in leisure in most countries extended beyond the growing middle class to include lower middle class who are generally perceived to have lower income. This portends that income alone is not a determinant to participation in tourism. This study seeks to contribute to this debate.
Marital status has also been known to influence travel choices. People’s priorities, preferences, decision making process and disposable income vary according to their marital status. A family with young children will have different priorities from a retired childless couple. Solomon (2010) specifically gives an account of the longitudinal changes in spending priorities with changes in marital status. Mutinda and Mayaka (2012) asserted that more singles considered taking a holiday than married people indicating influence of marital status on participation in domestic tourism. Geerts (2017) concurred with this when he stated that career minded singles are more inclined to spend disposable income on themselves by participating in tourism. Indeed, family obligations by married people have been quoted to serve as a limitation to tourism demand (Kotler, Bowen, & Makens, 2002) . This study intends to contribute to this discussion.
The nature of one’s occupation has also been touted as a determinant of participation in tourism not only from an income point of view but also from other related factors. According to Li et al. (2015) , employees with steady jobs that offer paid leave were also more likely to participate in tourism than those without. Self-employed individuals were less likely to participate in domestic tourism due to lack of time. Retirees were also less likely to participate in tourism.
The family life cycle describes the various possible stages that households go through as influenced by the effects of age, marital status, income, and presence/absence of children (Lawson, 1991; Oppermann, 1995) . Many variations exist ranging from the traditional models by scholars such as Wells and Gubar (1966) to contemporary ones that have incorporated modern changes such as childless households (Backer & Lynch, 2017; Weaver & Lawton, 2010) . Family life cycle is considered a very significant predictor of behavior hence very crucial in market segmentation (Frash, Antun, & Hodges, 2008) . The main factors influencing tourism participation behavior from the perspective of the family life cycle are; availability of disposable income given family obligations, freedom to travel dependent on family responsibilities, safety based upon perceived risk of travel or the ensuing activities, presence of products tailored to meet both the individual and composite needs of family members, and the desire for socialization for single-person households. Differences have therefore been known to manifest across the different stages in expenditure (Hong, Fan, Palmer, & Bhargava, 2005) , participation (Lawson, 1991) and preferences (Kim, Baek, & Choe, 2020) . Backer and Lynch (2017) established that there was a significant difference in family life cycle composition between those participating in domestic VFR and the non- participants in VFR. This comparison helps to identify potential segments that require targeting.
There are many studies that outline demographic characteristics of domestic tourists as part of background information to their studies. However, these studies do not progress to analyze these characteristics as variables that affect participation in domestic tourism. Furthermore, there is a scarcity of studies in the African context that compare demographic characteristics of the domestic tourists versus those of the domestic non-tourists. This comparison is significant as it facilitates the identification of potential segments to target people not participating in domestic tourism hence transforming this latent demand into effective demand (Funk, Alexandris, & Ping, 2009) . This is critical for those destinations targeting to expand the domestic market like Kenya since it provides a strong foundation for effective marketing and product development.
This study, therefore, seeks to determine the extent to which demographic characteristics of residents influence their domestic tourism participation behavior in Nairobi County, Kenya.
The study seeks to test the following hypotheses:
H01: There is no significant difference in demographic characteristics between participants and non-participants of domestic tourism amongst residents of Nairobi County.
H02: There is no relationship between demographic characteristics and domestic tourism participation behavior amongst residents of Nairobi County.
The target population for the study was Nairobi residents aged over 18 years. It comprised of both those who had participated (participants) and those who had not participated in domestic tourism (non-participants). For the participants, an on-site survey as recommended for this type of study by Chen and Funk (2010) was conducted. A sample of 337 domestic tourists was selected through systematic sampling from five tourist sites namely Nairobi National park, Giraffe Centre, Nairobi Safari Walk, Animal Orphanage, and the National Museum of Kenya. The distinction between the domestic and international tourists was made through the differentiated entrance rates charged at the destinations. For the non-participants, a three-step multistage sampling procedure was used to select 339 respondents from eight shopping malls drawn from the same constituencies that house the tourist sites listed above. The three stages comprised of purposively selecting 2 constituencies from the 17 that make Nairobi county, followed by randomly choosing 4 wards from the 2 constituencies and finally selecting 8 shopping centers from the 4 wards. The use of shopping centers as survey sites was also used by Mutinda and Mayaka (2012) and Li et al. (2015) in their studies on domestic tourism in Kenya and China respectively.
A street intercept was used as suggested by Veal (2017) who proposed the method as an appropriate technique for conducting tourism surveys at malls, shopping centers or on busy streets. After seeking permission from the management of the establishments, the respondents were intercepted either as they got in or left the shopping centers. Eligibility for inclusion in the study was first established and all those who had not participated in domestic tourism were included in the study until the quota for the site was achieved. In order to obtain the views of both genders, deliberate efforts were made by approaching both males and females within each site.
The study used a structured questionnaire to collect data from both sets of respondents. The study considered seven demographic attributes namely age, gender, level of education, marital status, occupation, income, and stage in family lifecycle. Descriptive statistics in the form of frequencies and percentages were used to display trends and patterns. The chi square test of goodness of fit was conducted to determine whether there was a significant difference in demographic characteristics between participants and non-participants of domestic tourism, hence testing the H01. Additionally, the chi-square test of independence was performed to examine the relationship between demographic characteristics of residents and participation in domestic tourism in Nairobi County, thereby testing H02.
The study sought to determine the extent to which seven demographic characteristics of the respondents influenced their participation in domestic tourism.
4.1. Age of Respondents and Participation in Domestic Tourism
Descriptive analysis revealed that amongst the respondents aged 18-30 years slightly more than half were participating in domestic tourism (60%, 205) while less than half were not participating (40%,138). For the respondents older than 30 years, the majority were not participating in domestic tourism. For the cohort aged between 31-40 years, 53% (94) were not participating while 47% (84) were the ones participating. 61% (68) of respondents aged between 41-50 years were not participating with only 39% (44) participating. For those aged between 51-60 years, only 9% (4) were participating with 91% (39) not participating see Table 1. Therefore, the study concluded that the youth were participating more in domestic tourism than the older respondents.
Further analysis using the χ2 test of goodness of fit to test significant difference between participants and non-participants resulted in differing findings across the various age cohorts. There was a significant difference between the participants and the non- participants for respondents aged 18-30 years, Χ2 (1, N=339) = 10.98, p< 0.001, with the participants being more (N=205) than the non- participants (N=138); those aged 41-50 years χ2(1, N=112) = 5.14, p= 0.023), with non-participants being more (N=68) than the participants (N= 44); and those aged 51-60 years old χ2(1, N=43) = 28.49, p< 0.001 with non-participants being more (N=39), than the participants (N= 4). However, there was no significant difference between the participants (N=84) and the non- participants (N=94) aged 31-40 years χ2 (1, 178) = 0.36, p =0.549, nssee Table 2. The null hypothesis (H01) was therefore rejected for the younger and older participants (cohorts 18-30 years, 41-50 years and 51-60 years). However, for the middle-aged (31-40 years cohort), the study failed to reject the null hypothesis (H01). Thus, the young people were participating more in tourism than the old while the middle-aged displayed no difference between the respondents participating in domestic tourism and those not participating. The cohort aged 31-40 therefore, presents a segment that can be targeted for the conversion of the non-tourists into tourists.
Lastly, with regards to the relationship between age and domestic tourism participation behavior, the results of the χ2 test of independence indicated that participation in domestic tourism was dependent on age, χ2 (4, N=676) = 48.9, p< 0.001) with the young people participating more than the older ones (see Table 3). The null hypothesis (H02) was therefore rejected. These findings concur with those by Solomon (2010) and Hall (2005) signifying the importance of age in determining participation behavior, contrary to Okello et al. (2012) who concluded that participation was independent of age. However, beyond the significance, the results are a pointer to the need to incentivize the older population to participate in domestic tourism by finding out the reasons for their non-participation and offering innovative solutions to overcome these. For instance, Prideaux, Wei, and Ruys (2001) infer that the older population considers the following to be barriers to travel, lack of companionship, safety, and security concerns, health concerns and lack of interest. As opined by Odunga (2010) age not only determines participation but also the nature of products and the level of involvement in tourism activities. Furthermore, this older segment may pose to be more lucrative than the younger market as it comprises a populace that may be more financially stable hence high spenders.
4.2. Gender and Participation in Domestic Tourism
The results further revealed that in terms of gender, slightly more than a third of men (39%, 146) were participating in domestic tourism while almost two thirds (61%, 233) were not. The scenario was reversed for women with almost two-thirds participating (64%, 191) while a third (36%, 106) were not participating in domestic tourism (see Table 1). Consistent with the descriptive findings, the χ2 test of goodness of fit was significantly different for gender with males, χ2 (1, N=379) = 19.97, p< 0.001, having more non-participants (N= 223) than participants (N= 146) while femalesχ2 (1, N=297) = 24.33, p< 0.001) had more participants (N= 191) than non-participants (N= 106). The study, therefore, rejected the null hypothesis (H01) and concluded that there were significant differences between the participants and the non-participants with regards to gender, with more females participating (N= 191) than men (N= 146) see Table 2.
The above findings were further reinforced by those from the χ2 test of independence which revealed that domestic tourism participation behavior was dependent on gender, χ2(1, N=676) = 44.29, p< 0.001) with females (N=191, 56.7%) participating more than males (N=146, 43.3%) (see Table 3). The null hypothesis (H02) was therefore rejected and the alternative adopted concluding that domestic tourism participation behavior was dependent on gender. This is consistent with findings by Page and Connell (2020).
The findings of this study also support the sentiments advanced by Mutinda and Mayaka (2012) who posited that females participated more in domestic tourism than males. The rise of female-headed households and women empowerment could also be the reason for the growth of this segment. Contrary to the above, Li et al. (2016) opined that in China, males participated more in tourism than females. The difference could be attributed to contrasting socio-cultural backgrounds where in the latter, social norms may be prohibitive towards exclusive female travel. In such cases, gender in itself becomes a barrier to travel through gender discrimination and gender stereotyping.
4.3. Level of Education and Participation in Domestic Tourism
The respondents were asked to state their level of education and from the responses, the study established that, generally, participation levels increased with an increase in the level of education. Only a quarter of those with high school education (17%, 28) had participated in domestic tourism compared to (61%, 65) diploma holders, (56%, 191) bachelor’s degree holders, (71%, 41) masters holders and (100%, 12) PHD holders. Conversely, non-participation decreased with an increase in the level of education. From the study, 83% (132) of respondents whose highest level of education was high school had not participated in domestic tourism, followed by 41% bachelor’s degree holders, 39% (42) diploma holders and 29% (17) masters holders see Table 1.
In addition, the χ2 test of goodness of fit revealed a significant difference between participants and non-participants across all the levels of education. There were more participants than non-participants for diploma holders (χ2(1, N=107) = 4.94, p= 0.026, participants (N=65) non-participants (N=42); bachelor’s degree holders χ2(1, N =339) = 5.45, p= 0.02), participants (N=191), non-participants (N=148); and masters holders χ2(1, N=58) = 9.93, p=0.002), participants (N=41) and non-participants (N=17). The trend was however reversed for those whose highest level of education was high school (χ2 (1, N=160) = 67.6, p< 0.001). In this case, the non-participants (N=132) were more than the participants (N=28). Thus, the study rejected the null hypothesis (H01), and concluded that there was a significant difference in participation across the various levels of education with participation increasing as the level of education increased.
Concerning the relationship between education and participation in domestic tourism behavior, the χ2 test of independence revealed that domestic tourism participation behavior was dependent on the level of education (χ2(4, N=676) = 99.92, p< 0.001), with the respondents who were more educated participating more than those who were less educated (see Table 3). The null hypothesis (H02) was therefore rejected. These findings are consistent with those by Richards (1996); Kotler and Armstrong (2003); Torkildsen (2005); Odunga (2005). Apart from directly influencing one’s perspectives, level of awareness, interests, and choice, education in most cases has an impact on one’s income and occupation are enablers of participation in leisure activities. Thus, the low participation of those with high school education could be attributed to their limited economic ability to afford tourism activities.
4.4. Occupation and Participation in Domestic Tourism
In relation to occupation, more than half of the students (54%, 119) and those employed (57%, 136) were participating in domestic tourism. In contrast, the majority of the self-employed (62%, 132) and the retired (60%, 3) were not participating in domestic tourism (see Table 1). The χ2 test of goodness of fit revealed varied significance across the various types of occupations for participating and non-participating respondents. There was no significant difference for students Χ2 (1, N=222) = 1.15, p= 0.283, ns), and retired respondents Χ2 (1, N= 5) = 0.20, p=0.655, ns) with the respondents having statistically similar numbers of participants and non-participants. The study, therefore, failed to reject the null hypothesis (H01), for students and retired respondents. However, there was a significant difference for the employed (Χ2 (1, N=237) = 5.17, p= 0.023) with the participants (N=136) being more than the non-participants (N=101). There was also significant difference for the self-employed (Χ2 (1, N=212) = 12.67, p< 0.001). However, for this cohort, the non-participants (N=132) were more than the participants (N=80). The study rejected the null hypothesis (H01) and concluded that there was a significant difference between participants and non- participants amongst the employed and the self-employed, with the employed participating more.
Concerning the association between occupation and participation, the χ2 test of independence revealed that domestic tourism participation behavior was dependent on occupation (χ2(3, N=676) = 19.27, p< 0.001), with those who were employed participating more followed by the students, then the self-employed, and lastly the retired (see Table 3). The null hypothesis (H02) was therefore rejected. These findings substantiates sentiments by Li et al. (2015) who described the self-employed and retirees as less likely to participate in tourism. Since these two groups have the potential to have both time and income, they also present segments that should be targeted with messages and products that appeal to them.
4.5. Income against Participation in Domestic Tourism
The results of the study showed that majority of the respondents earning below Ksh. 50,000 did not participate in domestic tourism (57%, 219). In contrast, the rest of the cohorts indicated majority of the respondents participating in domestic tourism with 58% comprising of those earning between 50,000 and 100,000, 60% earning between 100,000 to 300,000 and 67% earning above 300,000 see Table 1. This is consistent with many studies that cite income as a significant determinant of participation in domestic tourism Vanhove (2018); (Manono & Rotich, 2013); Manono and Rotich (2013) and Okello et al. (2012).
The χ2 test of goodness of fit conducted on income revealed significant differences between respondents earning below Ksh. 50,000, χ2 (1, N=384) = 7.59, p= 0.006, with the non-participants (N=219) being more than the participants (N=165). There was also a significant difference for respondents earning between Ksh. 50,000 to 100,000, χ2 (1, N= 167) = 4.36, p= 0.037) and those earning between 100,000 to 200,000, χ2 (1, N= 112) = 4.32, p= 0.038. However, in these two cases, the participants were more than non-participants. In contrast, there was no significant difference for respondents earning between Ksh. 200,000 to 300,000, χ2 (1, N= 10) = 0.4, p= 0.527, ns) and those earning above Ksh. 300,000, χ2 (1, N=3) = 0.333, p= 0.564, ns) see Table 2. Thus, the study rejected the null hypothesis (H01) for the respondents earning below Ksh. 200,000/= and concluded that there was a significant difference in income between participants and non-participants with respondents participating more with an increase in income. For respondents earning above Ksh 200,000/=, the study failed to reject the null hypothesis (H01) and concluded that there was no significant difference in income between participants and non-participants. These findings validate the view that income on its own is not the ultimate determinant of participation in domestic tourism and that those earning more may opt for international tourism instead (Gardiner et al., 2014; Magableh & Kharabsheh, 2013; Yap, 2010). It may also be an indicator of the fact that the current tourism products don’t appeal to the high-end market of domestic tourists. There is, therefore, the need to find out their preferences and offer competitive products as a local destination. Specifically, there is need to look into special interest tourism products that are customized to deliver unique experiences to this segment as per identified interests and preferences.
The χ2 test of independence revealed that domestic tourism participation behavior was dependent on monthly income (χ2(4, N=676) = 17.008, p= 0.002 with respondents earning above Ksh 50,000/= participating more in domestic tourism compared to those earning below Ksh. 50,000/= (see Table 3). The null hypothesis (H02) was therefore rejected.
4.6. Marital Status against Participation in Domestic Tourism
It was evident from the study that majority of the single respondents (60%, 186) were participating more in domestic tourism compared to those who were married (42%, 143), widowed (33%, 3) or divorced (31%, 5) as these had a higher non-participation rate (see Table 1). Further analysis using χ2 test of goodness of fit revealed both significant difference and non-significant differences across the different categories of marital status. The single and married respondents displayed significant differences between the participants and the non-participants. The singles χ2 (1, N=308) = 13.3, p< 0.001) had more participants (N=186) than non-participants (N=122) while for the married, χ2(1, 343) = 9.47, p=0.002, there were more non-participants (N=200) than participants (N=143) (see Table 2). The singles were participating more than the married, thus the study rejected the null hypothesis (H01) and concluded that there was significant difference in marital status between participants and non-participants for the singles and the married. These results authenticate the opinions of Geerts (2017); Mutinda and Mayaka (2012); Kotler et al. (2002). On the other hand, the widowed χ2(1, N=9) = 1.00, p=0.317, ns and divorced, χ2 (1, N=16) = 2.25, p= 0.134, ns respondents displayed no significant differences between participating and the non-participating respondents. Thus, the study failed to reject the null hypothesis (H01) and concluded that there was there was no significant difference in marital status between participants and non-participants for these two categories. The divorced and widowed may be hindered from participating by other constraints such as lack of a companion and financial constraints. It is therefore crucial that tourism product developers and market incorporate the social aspects and opportunities for social interactions.
The χ2 test of independence for marital status versus participation revealed that domestic tourism participation behavior was dependent on marital status (χ2(3, N=676) = 26.02, p< 0.001), with the single ones (N= 194, 57.6%) participating more compared to the married, widowed and divorced categories (N=143, 42.4%) (see Table 3). The null hypothesis (H02) was therefore rejected. The results corroborate with views by Solomon (2010).
4.7. Stage in Family Life Cycle against Participation in Domestic Tourism
The study revealed that slightly more than half of the respondents with no children (57%, 180) and those with young children (51%, 131) were participating more in domestic tourism. For the remaining cohorts, the majority were not participating in domestic tourism. For those with teenage children, only a third were participating (31%, 16), for those with adult children who were still at home, only slightly less than a quarter (17%, 7) were participating while for those with adult children who had left home, only a third were participating (33%,3) (see Table 1).
Table-1. Demographic characteristics of participating and non-participating respondents.
Participants |
Non-Participants |
Total |
|||
| Variable | Frequency |
Percent |
Frequency |
Percent |
Frequency |
| Age | 676 |
||||
| 18-30 | 205 |
60 |
138 |
40 |
343 |
| 31-40 | 84 |
48 |
94 |
52 |
178 |
| 41-50 | 44 |
39 |
68 |
61 |
112 |
| 51-60 | 4 |
9 |
39 |
91 |
43 |
| Gender | 676 |
||||
| Male | 146 |
39 |
233 |
61 |
379 |
| Female | 191 |
64 |
106 |
36 |
297 |
| Education | 676 |
||||
| High School | 28 |
18 |
132 |
82 |
160 |
| Diploma | 65 |
61 |
42 |
39 |
107 |
| Undergraduate | 191 |
56 |
148 |
44 |
339 |
| Masters | 41 |
71 |
17 |
29 |
58 |
| PHD | 12 |
100 |
0 |
0 |
12 |
| Income | 676 |
||||
| Below 50,000 | 165 |
43 |
219 |
57 |
384 |
| 50,000- 100,000 | 97 |
58 |
70 |
42 |
167 |
| 100,000-200,000 | 67 |
60 |
45 |
40 |
112 |
| 200,000-300,000 | 6 |
60 |
4 |
40 |
10 |
| Above 300,000 | 2 |
67 |
1 |
33 |
3 |
| Occupation | 676 |
||||
| Student | 119 |
54 |
103 |
46 |
222 |
| Self-Employed | 80 |
38 |
132 |
62 |
212 |
| Employed | 136 |
57 |
101 |
43 |
237 |
| Retired | 2 |
40 |
3 |
60 |
5 |
| Marital Status | 676 |
||||
| Single | 186 |
60 |
122 |
40 |
308 |
| Married | 143 |
42 |
200 |
58 |
343 |
| Widowed | 3 |
33 |
6 |
67 |
9 |
| Divorced/separated | 5 |
31 |
11 |
69 |
16 |
| Stage In Family Life Cycle | 676 |
||||
| No children | 180 |
57 |
138 |
43 |
318 |
| Young children | 131 |
51 |
126 |
49 |
257 |
| Teen children | 16 |
31 |
36 |
69 |
52 |
| Adult children at home | 7 |
18 |
33 |
83 |
40 |
| Adult children left home (Empty nesters) | 3 |
33 |
6 |
67 |
9 |
Source: Survey data, 2019.
The χ2 test of goodness of fit for the respondents with no children, those with teenage children and those with adult children still staying at home displayed significant differences between participants and non-participants. Those with no children, χ2(1, N= 318) = 5.55, p= 0.019) had more participating (N= 180) than non-participating (N=138). Those with teenage children, χ2 (1, N= 52) = 7.69, p= 0.006) had more non-participating (N= 36) than participating (N=16). Those with adult children still staying at home, χ2 (1, N=40) = 16.9, p< 0.001) also had more non-participating (N= 33) than participating (N=7) (see Table 2).
The study, therefore, rejected the null hypothesis (H01) and concluded that there were significant differences in the stages in family lifecycle between participants and non- participants for those with no children, those with teenage children and those with adult children still staying at home. The respondents with young children, χ2 (1, N=257) = 0.097, p= 0.755, ns) and those with adult children who had left home (empty nesters) χ2 (1, N=9) = 1.00, p= 0.317, ns) displayed no significant difference between its participants and non-participants (see Table 2). Thus, the study failed to reject the null hypothesis (H01) for these two categories.
These findings supported those from a study by Backer and Lynch (2017) on significant differences between participants and non-participants in domestic tourism for those with no children, those with teenage children and those with adult children still staying at home, while it contradicted the same findings for those with young children and the “empty nesters”. It can, therefore, be extrapolated that families with teenagers and young adults still staying at home participate the least probably due to factors such as reduced discretionary income caused by huge financial commitments towards the children’s educational needs and upkeep as young adults. Since the respondents with young children and the “empty nesters” showed no significant difference between participants and non-participants, these present segments that can be easily targeted.
Lastly, the χ2 test of independence revealed that domestic tourism participation behavior was dependent on the stage at which the respondent was in the family life cycle (χ2(4, N=676) = 31.23, p< 0.001), with the respondents with no children, and those with young children participating more than those with older children and those whose children had left home (see Table 3). The null hypothesis (H05) was therefore rejected.
The findings of the research confirmed that all the demographic characteristics under the study influenced domestic tourism participation behavior hence were significant attributes to consider for domestic market segmentation.
However, out of the seven demographic characteristics, two attributes namely gender and level of education exhibited significant differences across all the cohorts between participants and non-participants. The other five characteristics (namely age, occupation, income, marital status and family life cycle) displayed significant differences across some of their cohorts while others registered no significant difference.
The implication of those that registered significant difference is that the non-participants under the attributes may be more affected by factors other than demographics such as preferences, motivation and constraints hence the substantial variance. On the contrary, the segments that registered no significant difference denote a thin line between the participants and non-participants hence presenting an easier target for conversion from potential to active domestic tourists. The segments with no significant differences were as follows: Age (31-40), Occupation (students and retirees), Income (those earning Ksh.200,000-300,000 and above 300,000), marital status (the widowed and divorced) and family life cycle (those with young children and empty-nesters).
Table-2. Comparison of demographic characteristics of participants versus non-participants of domestic tourism (Chi-square goodness of fit test).
| Variable | Χ2 |
Df |
P |
| Age | |||
| 18 – 30 years | 10.976 |
1 |
0.001 |
| 31 - 40 years | 0.0360 |
1 |
0.549 |
| 41 -50 years | 5.143 |
1 |
0.023 |
| 51- 60 years | 28.488 |
1 |
0.000 |
| Gender | |||
| Male | 19.971 |
1 |
0.000 |
| Female | 24.327 |
1 |
0.000 |
| Level of Education | |||
| High School | 67.600 |
1 |
0.000 |
| Diploma | 4.944 |
1 |
0.026 |
| Undergraduate | 5.454 |
1 |
0.020 |
| Masters | 9.931 |
1 |
0.002 |
| Occupation | |||
| Student | 1.153 |
1 |
0.283 |
| Self-employed | 12.755 |
1 |
0.000 |
| Employed | 5.169 |
1 |
0.023 |
| Retired | 0.200 |
1 |
0.655 |
| Monthly Income | |||
| Below 50,000 | 7.59 |
1 |
0.006 |
| 50,000 to 100,000 | 4.36 |
1 |
0.037 |
| 100,000 to 200,000 | 4.32 |
1 |
0.038 |
| 200,000 to 300,000 | 0.41 |
1 |
0.527 |
| Above 300,000 | 0.333 |
1 |
0.564 |
| Marital Status | |||
| Single | 13.3 |
1 |
0.001 |
| Married | 9.47 |
1 |
0.002 |
| Widowed | 1.00 |
1 |
0.317 |
| Divorced/Separated | 2.25 |
1 |
0.134 |
| Stage in Life Cycle | |||
| No Children | 5.547 |
1 |
0.019 |
| Young form age 0 to 12 years | 0.097 |
1 |
0.755 |
| Teenage Children from ages 13 to 19 | 7.692 |
1 |
0.006 |
| Adult children aged above 20 years still at home | 16.900 |
1 |
0.000 |
| Children left home (Empty nesters) | 1.000 |
1 |
0.317 |
Source: Survey data, 2019.
The study, therefore, recommended the targeting of the segments identified above for domestic tourism in addition to the marketing efforts already in place. The study further recommends the following:
The domestic market could also benefit from product bundling which involves combining attractions and activities to increase their competitiveness across the various segments. Future studies should move further and interrogate the preferences of each demographic segment to facilitate evidence-based product development and positioning.
Table-3. Relationship between demographic characteristics of respondents and domestic tourism participation behavior (Chi-square test of independence).
| Variable | Χ2 |
Df |
P |
| Age | |||
| 18 – 30 years | 48.962 |
4 |
0.000 |
| 31 - 40 years | |||
| 41 -50 years | |||
| 51- 60 years | |||
| Gender | |||
| Male | 44.292 |
1 |
0.000 |
| Female | |||
| Level of Education | |||
| High School | 99.924 |
0.000 |
|
| Diploma | |||
| Undergraduate | |||
| Masters | |||
| Occupation | |||
| Stu ent |
19.271 |
3 |
0.000 |
| Self-employed | |||
| Employed | |||
| Retired | |||
| Monthly Income | |||
| Below 50,000 | 17.008 |
4 |
0.002 |
| 50,000 to 100,000 | |||
| 100,000 to 200,000 | |||
| 200,000 to 300,000 | |||
| Above 300,000 | |||
| Marital Status | |||
| Single | 26.015 |
3 |
0.000 |
| Married | |||
| Widowed | |||
| Divorced/Separated | |||
| Stage in Life Cycle | |||
| No Children | 31.231 |
4 |
0.000 |
| Young form age 0 to 12 years | |||
| Teenage Children from ages 13 to 19 | |||
| Adult children aged above 20 years still at home | |||
| Children left home (Empty nesters) |
Source: Survey data, 2019.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: All authors contributed equally to the conception and design of the study. |
Backer, E., & Lynch, D. (2017). Understanding the proclivity of visiting friends and relatives (VFR) travel across family life cycle stages in Australia. International Journal of Tourism Research, 19(4), 447–454.Available at: https://doi.org/10.1002/jtr.2087.
Baker, D. A., & Crompton, J. L. (2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research, 27(3), 785–804.Available at: https://doi.org/https://doi.org/10.1016/S0160-7383(99)00108-5.
Blazey, M. A. (1987). The differences between participants and non-participants in a senior travel program. Journal of Travel Research, 26(1), 7–12.Available at: https://doi.org/10.1177/004728758702600102.
Chen, N., & Funk, D. C. (2010). Exploring destination image, experience and revisit intention: A comparison of sport and non-sport tourist perceptions. Journal of Sport & Tourism, 15(3), 239-259.
Dieke, P. U. (2003). Tourism in Africa's economic development: policy implications. Management Decision, 41(3), 287-295.
Frash, J. R. E., Antun, J. M., & Hodges, H. E. (2008). Family life cycle segmentation for foodservice marketing: An exploratory case study. Journal of Foodservice Business Research, 11(4), 382–397.
Funk, D. C., Alexandris, K., & Ping, Y. (2009). To go or stay home and watch: exploring the balance between motives and perceived constraints for major events: A case study of the 2008 Beijing Olympic Games International Journal of Tourism Research, 11(1), 41–53.Available at: https://doi.org/10.1002/jtr.682.
Gardiner, S., Grace, D., & King, C. (2014). The generation effect: The future of domestic tourism in Australia. Journal of Travel Research, 53(6), 705-720.Available at: https://doi.org/10.1177/0047287514530810.
Geerts, W. (2017). Chinese travellers’ Lodging preferences. Rtrieved from: https://blog.euromonitor.com/chinese-travellers-lodging-preferences/.
Gilbert, D., & Hudson, S. (2000). Tourism demand constraints: A skiing participation. Annals of Tourism Research, 27(4), 906–925.Available at: https://doi.org/https://doi.org/10.1016/S0160-7383(99)00110-3.
Government of Kenya. (2018). Third medium term paper 2018-2022. Retrieved from: https://planning.go.ke/wp-content/uploads/2018/12/THIRD-MEDIUM-TERM-PLAN-2018-2022.pdf.
Hall, C. M. (2005). Reconsidering the geography of tourism and contemporary mobility. Geographical Research, 43(2), 125–139.Available at: https://doi.org/10.1111/j.1745-5871.2005.00308.x.
Holloway, J. C., Humphreys, C., & Davidson, R. (2009). The business of tourism (8th ed., pp. 66-70). England: Pearson Education Limited.
Hong, G.-S., Fan, J., Palmer, L., & Bhargava, V. (2005). Leisure travel expenditure patterns by family life cycle stages. Journal of Travel & Tourism Marketing, 15–30, 15–30.Available at: https://doi.org/10.1300/J073v18n02_02.
Hung, K., & Petrick, J. F. (2012). Comparing constraints to cruising Between cruisers and non-cruisers: A test of the constraint-Effects-mitigation model. Journal of Travel & Tourism Marketing, 29(3), 242–262.Available at: https://doi.org/10.1080/10548408.2012.666171.
Kenya National Bureau of Statistics. (2019). Economic Survey 2019. Retrieved from: https://www.theelephant.info/documents/kenya-national-bureau-of-statistics-economic-survey-2019/ .
Kihima, B. O. (2015). Domestic tourism in Kenya: Trends, Initiatives and practices. The East African Review, 50(May), 22–39.
Kim, H., Baek, J., & Choe, Y. (2020). Family life cycle and preferences for a mega-sporting event package: The case of the 2018 Pyeong Chang Winter Olympic Games. Tourism Economics, 1–21.Available at: https://doi.org/10.1177/1354816620907780.
Kotler, P., & Armstrong, G. (2003). Marketing management: Analysis, planning, implementation and control (10th ed.). Upper Saddle River, N.J: Pearson Education.
Kotler, P., Bowen, J. T., & Makens, J. C. (2002). Marketing for hospitality and tourism (3rd ed.). Upper Saddle River, N.J: Pearson Education.
Lawson, R. (1991). Patterns of tourist expenditure and types of vacation across the family life cycle. Journal of Travel Research, 29(4), 12–18.Available at: https://doi.org/10.1177/004728759102900403.
Li, H., Zhang, J., & Goh, C. (2015). Analyzing non-participation in domestic tourism: A combined framework. Journal of Travel & Tourism Marketing, 32, 454–473.Available at: https://doi.org/10.1080/10548408.2014.908160.
Li, H., Meng, F., & Zhang, Z. (2016). Non-participation of domestic tourism: Analyzing the influence of discouraging factors. International Journal of Tourism Research, 18(6), 567–578.Available at: https://doi.org/10.1002/jtr.2074.
Magableh, I. K., & Kharabsheh, R. (2013). Antecedents of local demand for domestic tourism in Jordan. International Journal of Culture, Tourism and Hospitality Research, 7(1), 78–92.Available at: https://doi.org/10.1108/17506181311301372.
Manono, G., & Rotich, D. (2013). Seasonality effects on trends of domestic and international tourism: A case of Nairobi National Park, Kenya. Journal of Natural Sciences Research, 3(1), 131-139.
Mazimhaka, J. (2007). Diversifying Rwanda’s tourism industry: A role for domestic tourism. Development Southern Africa, 24(3), 491–504.Available at: https://doi.org/10.1080/03768350701445590.
Mutinda, R., & Mayaka, M. (2012). Application of destination choice model: Factors influencing domestic tourists destination choice among residents of Nairobi, Kenya. Tourism Management, 33(6), 1593–1597.Available at: https://doi.org/https://doi.org/10.1016/j.tourman.2011.12.008.
Nicolau, J., & Más, F. (2006). The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations. Tourism Management, 27, 982–996.Available at: https://doi.org/10.1016/j.tourman.2005.09.009.
Nyaupane, G. P., & Andereck, K. L. (2007). Understanding travel constraints: Application and extension of a leisure constraints model. Journal of Travel Research 46(4), 433–439.Available at: https://doi.org/10.1177/0047287507308325.
O’Leary, S., & Deegan, J. (2005). Ireland’s image as a tourism destination in France: Attribute Importance and Performance. Journal of Travel Research, 43(3), 247–256.Available at: https://doi.org/10.1177/0047287504272025.
Odunga, P. (2010). Choice, expenditure and satisfaction of international tourists to Kenya: A structural equation modelling approach. Paper presented at the Operations Research Society of East Africa Conference.
Odunga, P., & Folmer, H. (2004). Profiling tourists for balanced utilization of tourism-based resources in Kenya. FEEM Working Paper No. 23.2004.
Odunga, P. (2005). Choice of attractions, expenditure and satisfaction of international tourists to Kenya. Nederland: Wageningen University.
Okello, M., Kenana, L., & Kieti, D. (2012). Factors influencing domestic tourism for urban and semiurban populations around Nairobi National Park, Kenya. Tourism Analysis, 17(1), 79–89.Available at: https://doi.org/10.3727/108354212X13330406124214.
Oppermann, D. M. (1995). Family life cycle and cohort effects. Journal of Travel & Tourism Marketing, 4(1), 23–44.Available at: https://doi.org/10.1300/J073v04n01_02.
Page, S. J., & Connell, J. (2020). Tourism: A modern synthesis. United Kingdom: Routledge.
Page, S., & Connell, J. (2006). Tourism: A modern synthesis (2nd ed.). Australia: Cengage Learning EMEA.
Park, S. Y., & Petrick, J. F. (2009). Examining current non-customers: A cruise vacation case. Journal of Vacation Marketing, 15(3), 275–293.Available at: https://doi.org/10.1177/1356766709104272.
Peter, J. P., & Olson, J. C. (2010). Consumer behavior & marketing strategy (9th ed.). New York: McGraw-Hill Irwin.
Prideaux, B., Wei, S., & Ruys, H. (2001). The senior drive tour market in Australia. Journal of Vacation Marketing, 7(3), 209-219.Available at: https://doi.org/10.1177/135676670100700302.
Richards, G. (1996). Production and consumption of European cultural tourism. Annals of Tourism Research, 23(2), 261–283.Available at: https://doi.org/10.1016/0160-7383(95)00063-1.
Ryan, C. (1998). The travel career ladder: An appraisal. Annals of Tourism Research, 25(4), 936–957.Available at: https://doi.org/10.1016/S0160-7383(98)00044-9.
Scheyvens, R. (2007). Poor cousins no more: Valuing the development potential of domestic and diaspora tourism. Progress in Development Studies, 7(4), 307–325.Available at: https://doi.org/10.1177/14649934070070040.
Solomon, M. R. (2010). Consumer behaviour: A European perspective (4th ed., pp. 422-441). England: Prentice Hall/Financial Times.
Stone, L. S., & Stone, T. M. (2017). Domestic tourism in Botswana: An examination of nature–based tourism constraints. Journal of Sustainable Tourism, 10(1), 97-116.
Torkildsen, G. (2005). Torkildsen’s sport and leisure management. London: Routledge.
Tourism Research Institute. (2018). Kenya tourism sector performance report 2018. Retrived from http://www.tri.go.ke/ .
UNWTO. (2018). UNWTO tourism highlights. International tourism trends 2017. Retrived from https://www.e-unwto.org/doi/pdf/10.18111/9789284419876 .
Vanhove, N. (2018). The economics of tourism destinations: Theory and practice (3rd ed.). London: Routledge.
Veal, A. J. (2017). Research methods for leisure and tourism. UK: Pearson.
Wang, D., & Chen, T. (2013). Temporal-spatial change and driving mechanism for regional difference of domestic tourism in china. Chinese Journal of Population Resources and Environment, 11(1), 69–78.Available at: https://doi.org/10.1080/10042857.2013.777525.
Weaver, D. B., & Lawton, L. (2010). Tourism management (4th ed.). Milton: John Wiley & Sons.
Wells, W. D., & Gubar, G. (1966). Life cycle concept in marketing research. Journal of Marketing Research, 3(4), 355–363.Available at: https://doi.org/10.1177/002224376600300403.
World Travel Tourism Council. (2019). Domestic tourism importance and economic impact. In World Travel & Tourism Council.
Yap, G. (2010). Australian domestic tourism demand analysis using panel data static regression. Economic Journal of Emerging Markets, 2(2), 113-127.
Yap, G., & Allen, D. (2011). Investigating other leading indicators influencing Australian domestic tourism demand. Mathematics and Computers in Simulation, 81(7), 1365-1374.Available at: https://doi.org/10.1016/j.matcom.2010.05.005.
Views and opinions expressed in this article are the views and opinions of the author(s), Journal of Tourism Management Research shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. |