3. MOTIVATIONS
3.2 Silence tourism related motivations
As the literature lacks the studies related to silence tourism motivations (Özdemir & Celebi, 2018), the motivation factors were searched from selected articles closely related to silence tourism. The selected articles were related to rural tourism (6 articles), nature tourism (3 arti-cles), wellbeing tourism (2 artiarti-cles), as well as slow tourism, eco-tourism, adventure tourism and spiritual tourism (1 article from each).
Table 1. Silence tourism related motivations in previous studies
Self-reflection & Discovery x x x x x x
Escape (desire to get away) x x x x x x x x x x x
Novelty-seeking / adventure x x x x x
Environmental concern x
Meet new people and/or
people with similar interests x x x x x x
Spending time with
Having a sense of comfort x x
Heritage & nature x x x
As can be noted from Table 1, relaxation and escape are the most common motivations in these previous studies, relaxation occurring in 12 and escape in 11 articles out of 15. Spending time with friends or family is third most common motivation in eight articles, while self-reflection
& discovery, meeting new people and/or people with similar interest, and learning were all found in six articles. Novelty-seeking and enjoying nature were both as travel motivations in five studies. Rest of the motivations occurred maximum in three articles.
Among the articles were some especially interesting findings. For example, it was found that visitors’ psychological needs, for example escape or the need to seek relaxing, serene and tran-quil environments may be more important motivation for visiting Buddhist temples than the temple itself. Therefore, it was suggested that when marketing sites such religional temples, tranquility and learning about new things should be emphasized. (Choe, Blazey & Mitas, 2015.) In the article related to adventure tourism, it was found that search for tranquility (silence and serenity in Table 1) was one of the major motives for scuba divers instead of searching for thrill of risk, as often expected (Fucs et. al., 2016). A motivation study revelead people with disabi-lities having some unique motivations for nature-based tourism, for example healing effect of nature, in addition to the same motives that able-bodied have (Chikuta, 2017).
4. METHODOLOGY
4.1 Research approach
This study was chosen to be a quantitative study as it aims to study relations between tourists’
motivations of travelling or buying silence tourism products, and general interest toward silence tourism. Even though a quantitative study is usually used for generalizing the results, it is not the case in this study. More precisely, the aim of this study is to figure out, with the help of formal concept analysis, what is the meaning of silence tourism. Secondly, this study aims to understand, through segmenting the respondents by hierarchical cluster analysis, how silence tourists differ from the other tourists especially in terms of travel motivations but also socio-demographic characteristics, interest toward silence tourism and travel behavior.
4.2 Data collection and method
The responses for the survey were collected online in social media using convenience sampling methodology. Convenience sampling is a nonprobability sampling method. It is useful when the researcher has limited resources, time and workforce as was the case with this study. Non-probability sampling can be utilized especially when the aim of the research is not to generate generalizable results for an entire population. Convenience sampling can provide insights for exploratory research (Landers & Behrend, 2015). Convenience sampling has often been used in tourism studies for example to predict tourist behavior (Kumar, 2016), study satisfaction (Araslı & Baradarani, 2014), and to understand tourists’ motivations and leisure activity prefe-rences (Hsieh & Chang, 2006).
The data were collected from 28.3.2018 to 19.4.2018. During this period altogether 517 answers were collected using an online survey link. Asked questions were based on theories of tourism motivations and travel behavior. The survey was distributed to various social media communi-ties and accounts related to travel and tourism. The data was validated using two approaches.
First, respondents who had answered to all Likert-scale motivations statements with the same number were deleted. It can be assumed that respondents who state similar importance for all travel motivations are not useful for the results of the study and have not probably paid enough attention to the questions. Second, among the travel activity statements, ‘Mingle with local
people’, was asked twice but with opposite meaning. Respondents who answered both versions of the question positively or negatively were removed from the analysis. This resulted in alto-gether 471 usable responses for further analysis.
4.3 Data analysis
4.3.1 Descriptive statistics and K-Means Cluster Analysis
Collected data was analyzed by using IBM SPSS (Statistical Package for Social Sciences) Sta-tistics 25 -program. At first, all the responses were analyzed by descriptive staSta-tistics for getting an overall image of the respondents. However, as the second research question reveals, seg-mentation of the respondents was one of the points of interest in this study to get a clue who those tourists are interested in silence tourism, in other words, what distinguishes them from the other tourists. Hence, the data was analyzed, and segmentation made by K-means clustering.
“Market segmentation in tourism research has been defined as a process of dividing a market into market segments, which are defined as groups of consumers who are expected to exhibit similar purchasing responses” (Smith in Rid, Ezeuduji & Pröbstl-Haider, 2014, 104). Obvi-ously, the interest in this study is on the tourists who would like to buy similar purchase, in this case, a silence tourism product. Segmentation used in this study is called activity-based seg-mentation.
Cluster analysis aims to group similar observations or variables to the same groups and is used especially in the situations where these can be grouped without knowing a classification crite-rion. Cluster analysis is hence an explorative way to make an analysis. (Metsämuuronen 2005, 812.) This was the case with the data used in this study, as the number of segments were possible and reasonable to determine to be three without knowing the reason why certain respondents will belong to the certain group, for example to the “Silence tourists”. The idea behind three segments was to divide tourists roughly enough to the groups which were seen as reasonable;
to the ones who are very interested in silence tourism, to the others who are somewhat interested and to a third segment which members are not interested in silence tourism, which relates to Beane’s & Ennis’ idea about using market segmentation for evaluating new opportunities to tourism products (in Rid et. al. 2014, 104). With the help of these three segments it can be evaluated if there are opportunities for silence tourism products. Due to the relatively big data
set (N=471), K-means Cluster Analysis was chosen instead of Hierarchical Cluster Analysis.
So, following two questions from the survey of this study were used for K-means clustering:
1. How interested would you be at this moment to travel to a tourism destination providing the described silent tourism services?
2. If you had a chance, how likely would you purchase a holiday in a destination where you could participate in silence tourism experiences?
Table 2. Forming of clusters using two selected questions.
Using K-means clustering, mentioned three segments were formed. However, SPSS presents the clusters as numbers, so it was needed to rename them. Therefore, clusters 1, 2 and 3 were renamed accordingly: 1 = Silence tourists, representing the ones who are very interested in silence tourism, 2 = Potential silence tourists, representing those who are somewhat interested in silence tourism, and 3 = Not interested in silence tourism (Table 3). Later on, profiles of mentioned segments are compared by crosstabulation and mean value.
4.3.2 Crosstabulation
After forming three clusters by K-means cluster analysis, the responses of each cluster were analyzed by using different analysis methods. At first, responses to the questions regarding clusters’ sociodemographic variables, background information and travel behaviour were com-pared by crosstabulation. In addition, responses regarding question three about silence tourism expectations, question four regarding interest in buying nature tourism services based on silence
experiences, and question seven regarding type of destination a respondent is interested in tra-velling at the moment were analyzed by crosstabulation as well. With the help of crosstabulation, a dependence between two or more variables can be illustrated (Metsämuuronen, 2005, p. 531).
4.3.3 Comparison of means
Responses to question six about holiday travel behaviour were analyzed by comparing means among the clusters. Comparison of means analyses in SPSS program can produce even 12 sta-tistics of which simple means compare is only one (Griffith, 2010, p. 229), and the one chosen to be used in this study. Chosen comparison of means analysis includes mean and standard deviation.
4.3.4 Multinomial logistic regression analysis
Multinomial logistic regression analysis was used to analyze responses to question five about travel motivations, in more detailed, how motivations affect to an interest toward silence tour-ism. Regression analysis, in general, is about predicting the unknown future based on collected data from the past (Griffith 2010, 240), which describes this study, as the data has been collected from respondents’ previous travel behavior. In logistic regression analysis, Dependent may be Binary Logistic which gets two values, or Multinomial Logistic, which gets more than two val-ues (Metsämuuronen, 2005, p. 701). Multinomial logistic regression analysis is used, according to its name, for predicting a membership of several categories (Field 2014). As there are several motivation factors in the survey of this study, a multinomial logistic regression analysis was chosen to analyze how the motivations affect to an interest toward silence tourism. In other words, how the motivations explain to which cluster (“Silence tourists”, “Potential silence tour-ists, or “Not interested in silence tourism”) is the one a respondent represents. Logistic regres-sion analysis aims to find best variables among several variables, in this case, motivations, that explain the phenomenon best. With the help of logistic regression analysis, it can be tested if some of the variables explain the results better than the others. Logistic regression analysis can be used when variables chosen to the analysis are relevant. Choosing irrelevant variables makes the results unreliable. (Metsämuuronen, 2005, p. 687-688.) In this study, motivation factors were chosen based on previous literature for ensuring they are relevant. However, as logistic
regression analysis tells just the association between variables, it must be kept in a mind that any of the motivation factors is not necessarily a reason for a respondent belonging to a certain cluster (Metsämuuronen, 2005, p. 688-689). In other word, there might be an association be-tween a single motivation factor and some of the clusters, but it does not say that the motivation factor is a reason why a respondent with the certain motivation belongs to the certain cluster.
When using logistic regression analysis, amount of responses must be sufficient to make sure there are differences between the clusters, and that there are enough responses compared to the variables, in this case, to motivation factors. Otherwise there is a danger of getting Independent variable for each of the responses, which is not a purpose. (Metsämuuronen, 2005, p. 688-689.) Mentioned problem did not occur in this study.
5. RESULTS
5.1. Respondents’ background information – Descriptive statistics
Table 3 presents division of respondents’ country of residence, which are later detailed in Table 4. Tables 4 and 5 presents respondents sociodemographic factors, background information and travel behaviour. These results were got from descriptive statistics in SPSS and are presented as numbers (n) and percentages (%). As some of the responds were removed after the data analysis due to respondents getting caught for not answering the questions logically, total num-ber of respondents (N) varies from 460 to 471.
Table 3. Division of respondents’ country of residence.
In total, 360 female (76,4%) and 102 male (21,7%) respondents answered to the survey in ad-dition to eight (1,7%) who preferred not to tell their gender, summing to 471 respondents. As can be noted, women and men are not equally represented. However, as the segmentation in this study is made based on interest toward silence tourism, and not for example on sociodemo-graphic characteristics, unequal stresses among the responses do not matter but instead, every respond give an important information of interest toward this specific field of tourism.
Age of respondents vary from young to elderly people. Over 40% (n=189, N=464) of respon-dents were born in years between 1983 and 1992 (mode: 1990, n=29). Responrespon-dents come from 46 countries. Great majority (n=298; 63,3%) of the respondents live in Finland. Next biggest group of respondents (n =16; 3,4%) live in United States. Both Italy and United Kingdom have 11 respondents (2,3%). Thirteen respondents represent their country alone. In addition to the respondents country of residence, size of their town of residence was also as point of interest.
Majority of respondents live in a city. Most of them (n=126; 26,8%) live in a medium city
Table 4. Overview of respondents' sociodemographic factors.
Table 5. Overview of respondents' background information and travel behaviour.
where is less than 100 000 inhabitants. In a very large city, comprising of more than 500 000 inhabitants, live 109 respondents (23,1%) and 80 respondents (17%) live in a large city, defined here as a city with less than 500 000 inhabitants. Small minority (n=11; 2,3%) live in a farm
referring to a location remote from any town or village. Respondents represent variety of diffe-rent levels of education. Most of respondents (n=194) present a bachelor’s degree as their highest level of education making it biggest group, representing 41,2% of all responds. Two respondents (0,4%) have no degree, 21 respondents (4,5%) have achieved a high school as their highest education and 65 respondents (13,8%) a vocational or technical school. Second biggest group is formed of those 160 respondents (34%) who have achieved a master’s degree. There are also 29 respondents (6,2%) who have got a doctoral degree. There is also variation regarding how the respondents live. A fourth of respondents (n=122; 25,9%) live alone but more respon-dents (n=176; 37,4%) have two persons in their household. Great majority of responrespon-dents (73,2%) do not have children under 16 years in their household. Some of the respondents (13,6%) have one child in their household. A number of over 64 years adults in the household was also asked to find out if there are pensioners among respondents or in their families. Most of respondents (88,7%) do not have over 64 years adults in their household while the rest have one (4,7%) or two (4,2%) which can be seen also from the responds regarding the birth year.
Table 5 give an overview of respondents’ background information and travel behaviour.
Households annual net income varies a lot. However, most of the respondents (18,3%) preferred not to say it, which after 20 000 - 29 999€ was the biggest group with 74 responds (15,7%, N=469). An approximate amount of money spent on leisure tourism in a year was also asked.
Majority (21,4%) of respondents reported their household spending 1001-2000 euros on leisure tourism, including transportation, accommodation, food, activities, tickets, and everything else.
Nearly as many (19,5%) spend 2001-3000 euros and 17,2% (n=81) spend 3001-5000 € on lei-sure tourism in a year (N=468). Most of the respondents (n=122; 25,9%, N=460) do two do-mestic leisure trip in a year and one (n=137; 29,1%) to two (n=134; 28,5%) international leisure trips in a year (N=467). Roughly half (54,1%) of the respondents have been on a nature tourism holiday while only 18% have been on nature tourism holiday that included silence tourism ex-periences (N = 470). Over half of the respondents travel typically with their friends (55,6%) or family (52,8%), while one third (34,2%) travel typically alone and almost as many (31,2%) with their spouse.
5.2. Formed clusters – K-Means Cluster Analysis
After getting an overall impression about respondents’, the more detailed information of each of the three cluster was gained. At first, number of cases in each cluster was solved by K-means Cluster Analysis as can be seen from Table 6. ”Silence tourists” proved being the biggest seg-ment with 252 respondents (53,5%). In addition, there are 143 ”Potential silence tourists”
(30,4%) and 76 respondents (16,1%) who are ”Not interested in silence tourism”.
Table 6. Number of cases in each cluster.
5.3 Comparison of clusters’ background information – Crosstabulation
Crosstabulation after formed clusters enables a comparison between clusters’. Most of the va-riables related to respondents’ sociodemographic characteristics, background information, or travel behaviour does not differ statistically significantly between the groups as can be noted from Tables 7, 8, and 9. Value column in the tables shows the value of Goodman and Kruskal tau, in which zero (0) refers to no association and one (1) to complete or perfect association.
Sig. column in the tables shows the p-value, which is statistically significant when <0,05.
In Table 7, the only statistically significant variable is household size (p=0,012). Most of the
”Silence tourists” (38,3%) live in a two persons’ household, which is more compared to ”Po-tential silence tourists” (32,2%) but less than ”Not interested in silence tourism” (47,4%). There are least one person households among ”Silence tourists” (22%) compared to ”Potential silence
Table 7. Comparison of clusters’ sociodemographic variables. *p<0,05
Table 8. Comparison of clusters’ background information and travel behaviour 1.
tourists” of which 32,9% live alone or to ”Not interested in silence tourism” (26,3%). Otherwise the results are alike among the clusters. Most of the respondents’ in all clusters are female, they were born in between 1983 and 1992, and have bachelor’s degree as their highest education. In great majority of respondents households do not live children under 16 years nor adults over 64 years.
Regarding the variables presented in Table 8, the clusters do not differ statistically significantly from each others. Nearly a fifth of ”Silence tourists” and similarly, 21,1% of ”Not interested in silence tourism” prefer not to tell their household’s annual net income forming the biggest group of respondents in their clusters. Most of ”Potential silence tourists” (20,3%) reported 20 000 - 29 999€ as their annual net income. Households’ money spend on leisure tourism is prevalent among the different amount of money in each cluster. Roughly one third of ”Silence tourist”
and ”Potential silence tourists” make one international leisure trip in a year while 30,7% of
”Not interested in silence tourism” make two international leisure trips in a year. Most of the respondents’ in every cluster make two domestic leisure trips in a year.
As can be seen in Table 9, there is a statistical significance between the clusters’ regarding experiences of a nature tourism holiday (p=0,002) and experiences of a nature tourism holiday with silence tourism experiences (p=0,002). ”Silence tourists” and ”Potential silence tourists”
have notably more experience of nature tourism holidays (59,8% and 53,8%) compared to ”Not interested in silence tourism” (36,8%). Similarly, ” Silence tourists” and ”Potential silence tou-rists” have notably more experience of nature tourism holidays with silence tourism experiences (20,3% and 21,7%) compared to ”Not interested in silence tourism” (3,9%).
Other variables presented in Table 9 do not differ statistically significantly between the clusters.
It is noteworthy that regarding Typical travel party, respondents were able to choose several answer options which sums to total frequencies over maximum cases in each cluster and per-centages over 100%. Over half of the respondents in each cluster travel typically with their friends and family. About one third in all clusters travel typically alone. Size of the town of residence was also asked. In medium cities live most of ”Silence tourists” (25,8%) and
”Potential silence tourists” (31,5%). Majority (26,3%) of those who are ”Not interested in si-lence tourism” live in very large cities.
Table 9. Comparison of clusters’ background information and travel behaviour 2. *p<0,05
As presented in Table 10, the clusters represent a great variety of respondent’s countries of residence. ”Silence tourists” come from 43 different countries, ”Potential silence tourists” rep-resent 22 countries of residence and ”Not interested in silence tourism” 15 countries of resi-dence. Comparison between the clusters’ country of residence is statistically significnt (p<0,05). However, over half of the respondents in each cluster come from Finland, which has caused this result. It is noteworthy, that the interest of this study is not in clusters’
Sociodemographic variable Value Sig.
Typical travel party n (N=252) % n (N=143) % n (N=76) %
Alone 84 33,3 50 35 27 35,5 0 0,914
Husband/wife/spouse 84 33,3 43 30,1 20 26,3 0,003 0,482
Boyfriend/girlfriend 55 21,8 39 27,3 27 35,5 0,013 0,05
Friends 130 51,6 89 62,2 43 56,6 0,009 0,121
Family 131 52 76 53,1 42 55,3 0,001 0,879
People who are not close 8 3,2 1 0,7 3 3,9 0,006 0,228
Large City 43 17,1 22 15,4 15 19,7
Very Large City 61 24,2 28 19,6 20 26,3
0,004 0,619 0,026 0,002*
0,026 0,002*
Comparison of clusters' background information and travel behaviour 2 Silence tourists Potential silence
tourists
Not interested in silence tourism
sociodemographic factors, but in their motivations. The comparison is interesting in this study even though the results are not generalisable.
Table 10. Comparison of clusters’ country of residence. p<0,05
Table 10. Comparison of clusters’ country of residence. p<0,05