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Executive Summary
The important finding of the study is that there is a significant likelihood for people to travel into space. The descriptive statistics of income allocated to space tourism shows that about 90% of the respondents are willing to allocate the income that they earn within a period of less than a year. Additionally, descriptive statistics of respondents’ occupations indicate that the majority of respondents are white-collar professionals, blue-collar employees, and students.
Chi-square test and multiple regression analysis also shows that the level of education, age, and cost of space tourism are significant predictors of income allocated to space tourism. However, income allocated to space tourism does not vary according to the gender and occupation of potential tourists. In spite of the robust findings, the limitations are biased representation of potential customers and poor responses, which reduce both internal and external validities of the study.
Research Objectives
- To perform descriptive analysis of the income allocated to the space tourism and the current occupation of a potential space tourists.
- Test the hypothesis using one-sample t-test to establish the willingness of respondents to undertake space tourism.
- To determine the influence of gender on income allocated to space tourism using independent samples t-test.
- Perform one-way ANOVA to determine the differences of income allocated among the occupations of respondents.
- Chi-square test to determine the association between the amounts of income allocated to the space tourism and preferred length of stay in space.
- Determine predictors of income allocated to space tourism using multiple regression test.
Descriptive Statistics
Income Prepared to Allocated to the Space Trip
Analysis of the proportion of income that potential tourists are willing to spend on space tourism is important because it indicates their purchasing power. Gibson (2012) argues that a space tourism company needs to understand the amount of potential money that customers are willing to spend on space tourism so that it can design products and tourism packages that meet the demands of customers, as well as enable the company to optimize profits.
Table 1
From the descriptive table, measures of central tendency are 2.87, 3, and 3 for mean, median, and mode respectively. This means that the majority of the respondents are willing to spend on space tourism income that they earn in 3 months.
Regarding measures of dispersion, standard deviation indicates that the majority of respondents are willing to spend on space tourism between one week’s income and 3 months’ income (M = 2.87±1.337). Although respondents indicate that they are willing to spend the amount of money that ranges from a month’s income to 5-years’ income, the allocated income skews towards income earned in less than a year.
The frequency table indicates the distribution of respondents according to the amount of income they are willing to spend on space tourism. The significant information in the frequency table is that most of the respondents (86) are willing to spend 3 months’ income on space tourism, followed by a month’s income (82), and the third is a week’s income (38).
Figure 1
The histogram illustrates the distribution of income among different periods, which shows that most respondents are willing to spend between 3 months’ income to a month’s income on space tourism. Moreover, it illustrates the skewed distribution towards short periods of income.
Current Occupation
The current occupation of the potential customers is important to the space tourism because it enables marketing managers to understand the nature of customers they are targeting. For the space tourism to be successful, it must target certain kind of customers and define its market niche by targeting customers with specific socioeconomic attributes such as occupation.
Table 2
The measures of central tendency in the descriptive table are 4.22, 4, and 4 for mean, median, and mode respectively. These descriptive statistics imply that the white-collar professionals formed the majority of the respondents. Comparatively, measures of dispersion show that the respondents’ occupations comprise of students, blue-collar, white-collar, contractors and businesspersons (M = 4±2.54).
The frequency table above shows the distribution of respondents according to their occupations. The leading respondents are white-collar professionals (149) followed by students (123), and then blue-collar employees (73) come third. The histogram illustrates that white-collar professionals and students are major respondents.
Figure 2
Testing of Hypotheses
The Likelihood of Traveling in Space
H0: There is no significant likelihood of the person wanting to travel into space, having the interest, and having undertaking.
H1: There is a very significant likelihood of the person wanting to travel into space, having the interests and having undertaking.
Table 3
From the one-sample test table, it is evident that all variables that measure the likelihood of a person wanting to travel into space, having the interest, and having the undertakings are statistically significant (p<0.05). This implies that the test rejects the null hypothesis and accepts that alternative hypothesis, which states that there is a very significant likelihood of the person wanting to travel into space, having the interests and having undertaking.
Gender and Income Allocated to Space
Hypotheses
H0: The amounts of income that male and female respondents are willing to allocate to space tourism are not significantly different
H1: The amounts of income that male and female respondents are willing to allocate to space tourism are significantly different.
Table 4
The independent samples t-test shows that the difference in the amounts of income that male and female respondents are willing to allocate to space tourism are not significantly different (p>0.05). Hence, it implies that the independent t-test fails to reject the null hypothesis, and hence, male and female respondents have equal capacities of being space tourists.
Current Occupation and Income Allocated to Space Tourism
Hypotheses
H0: The amounts of income allocated to space tourism have no significant differences across the occupations.
H1: The amounts of income allocated to space tourism have significant differences across the occupations..
Table 5
The ANOVA table shows that there is no significant difference in the amounts of income allocated to space tourism among respondents in diverse occupations (p>0.05). Post hoc analysis also affirms that there is no significant difference in the amounts of income allocated among diverse occupations. Hence, the ANOVA test fails to reject the null hypothesis and affirms that occupations does not influence income allocated to space tourism.
The Association between Income Allocated and Preferred Length of Stay
Hypotheses
H0: There is no significant association between income allocated to space tourism and preferred length of stay.
H1: There is a significant association between income allocated to space tourism and preferred length of stay.
The chi-square test indicates that there is a significant association between income allocated to space tourism and preferred length of stay (p<0.05). This means that the amount of income allocated determines length of stay in space.
Multiple Regression Analysis
Hypotheses
H0: Level of education, preferred length of stay, cost, and age are not significant predictors of income allocated to tourism.
H1: Level of education, preferred length of stay, cost, and age are significant predictors of income allocated to tourism.
Table 6
The multiple regression coefficient (R) is 0.622, which means that this regression model predicts the income allocated to space tourism.
Table 7
Moreover, the multiple regression coefficient is significant (p<0.05). This means that the regression analysis rejects the null hypothesis and accepts the alternative one, which states that level of education, preferred length of stay, cost, and age are significant predictors of income allocated to tourism
Table 8
The coefficient table shows that all the independent variables are significant predictors except preferred length of stay. Hence, regression model has the following equation: –
Income allocated = 0.757 + (0.106 × education level) + (0.089 × preferred length of stay) + (0.585 × Cost) + (0.095 × age).
Discussion, Interpretation, and Implications of the Findings
The descriptive statistics of the income allocated to the space tourism indicates that most potential tourists are willing to spend less than a year’s income on space tourism. Specifically, most of the respondents are willing to spend between 6 months’ income and a week’s income on space tourism.
This implies that marketing managers need to tailor products of space tourism to suit the purchasing power of the potential customers, which should not exceed a year’s income. If the marketing managers design products that suit a year’s income, they will target about 90% of the potential customers, which is quite significant.
Regarding the descriptive statistics of the current occupation, the occupations of the majority of the respondents are white-collar professionals, blue-collar employees, and students. This means that marketing managers need to target people with these occupations because they are potential customers who are willing to travel in space.
According to Crouch (2001), estimation of the market size is a considerable challenge that space tourism is facing. In this view, the descriptive statistics indicate that white-collar professionals, blue-collar employees, and students are the potential customers, and thus enabling marketing managers to focus their attention on them.
Hypothesis testing to establish if there is a very significant likelihood of the people wanting to travel into space, having the interests, and having undertaking gives robust findings to the space tourism.
The hypothesis indicates that the existence of the significant likelihood that potential customers want to do astronomical observation, take risks in recreation and leisure, travel to the poles, play sports, and travel to space. These findings have considerable implication to space tourism as it informs marketing managers of the existence of a huge market, which they need to tap and utilize in expanding space tourism.
Gender is demographic factor that may influence the distribution of resources and consequently income allocated to space tourism. In establishing if there is any significant difference in the allocation of income between male and female respondents, the findings show that the difference is insignificant.
Jordan (2008) asserts that gendered space tourism creates an exclusive environment, which has detrimental effects on the growth of space tourism. In this view, marketing managers should target potential customers equally, irrespective of their gender because they have equal capacity in allocating their income to space tourism.
Analysis of variance indicates that the allocation of income to space tourism does not vary according to the occupation. Given that occupation does not influence allocation of income to space tourism, marketing managers should target potential customers equally without considering their occupations.
Moreover, marketing managers should understand that what matters to the space tourism is the purchasing power of customers, and not their occupation. Hjalager (2007) states that economic globalization is a driving force of tourism, which marketing managers need to harness in targeting potential customers.
The chi-square test established that there is a significant association between preferred length of stay and income allocated. This information is important to marketing managers because it helps them in designing tourism packages according to the purchasing power of customers and preferred length of stay.
In this view, marketing managers should understand that those who plan to use income earned in a short period want to take a short period in space, while those who plan to use income earned in a long period want to stay for a long period in space.
Multiple regression analysis shows that the level of education, cost of space tourism, and age are significant predictors of income allocated to space tourism. These predictors correlate positively with the income allocated to space tourism. Hence, marketing managers need to understand that educational level, pay for a two-day trip and one night stay, and age determine the amount of income that potential tourists allocate to space tourism.
This means that potential tourists with different education levels and different ages require unique packages that suit their needs. Botterill and Platenkamp (2012) argue that the tourism industry should customize their products according to the unique needs of their customers. Hence, marketing managers should utilize these findings in customizing their products according to the needs of space tourists.
Conclusion
Descriptive analysis of income allocated to space tourism shows that about 90% of the respondents are willing to spend income that they earn within a period of less than a year. Moreover, descriptive statistics of occupation indicate that the majority of respondents are white-collar professionals, blue-collar employees, and students. Importantly, the findings indicate that there is a significant likelihood for people to travel into space.
The chi-square test indicates that the length of stay associate with the income allocated to space tourism. Multiple regression analysis also shows that the level of education, age, and cost of space tourism are significant predictors of income allocated to space tourism. However, income allocated to space tourism does not vary according to the gender and occupation of potential tourists.
The limitation of the analysis is that most of the respondents are white-collar professionals, blue-collar employees, and students, and thus do not significantly represent the potential customers of space tourism. Additionally, about half of the respondents did not answer the questionnaires well regarding income allocated to space tourism; hence, making statistical analysis to have low internal and external validities.
References
Botterill, D & Platenkamp, V 2012, Key Concepts in Tourism Research, SAGE Publisher, New York.
Crouch, G. 2001, ‘The Market for Space Tourism: Early Indications’, Journal of Travel Research, vol. 40 no. 2, pp. 213-219.
Gibson, D 2012, Commercial Space Tourism: Impediments to Industrial Development and Strategic Communication Solutions, Bentham Science Publishers, Sharjah.
Hjalager, A 2007, ‘Stages in the Economic Globalization of Tourism’, Annals of Tourism Research, vol. 34 no. 2, pp. 437-457.
Jordan, F 2008, ‘Performing tourism: Exploring the Productive Consumption of Tourism in Enclavic Spaces’, International Journal of Tourism Research, vol. 10 no. 4, pp. 293-304.
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