Active Sport Tourism in Europe: Applying Market Segmentation Model Based on Human Values

Abstract

The purpose of this study was to examine if human values sets indicating basic motivational aspects, shared by active sport participants, could be used for the creation of a specific market segmentation model. While the exploration of motivation scales and sport tourist typologies was vast, although limited to small samples, this research constructed a model that tested its applicability and validity on the large general samples. By using data collections from the European Social Survey, the model allowed comparability between surveyed countries by cross-checking multiple psychosocial and demographic factors. The findings identified four main factors that determine active sport tourist values, while at the same time, multiple-discriminant analysis indicated the existence of three active sport clusters, indicating preferences of different sport tourist segments. Furthermore, the study analyzed potential demand markets according to the geographical distribution of active sport segments. The study confirmed that basic human values play an important role in explaining motivation aspects in sport- and tourism-related choices. Overall, the findings may assist marketers in monitoring changes in preferences of sport participants and focus on specific marketing strategies for different segments.

Keywords

sport motivation values sport tourist profiles market segments

Being active in sport and tourism is an important aspect of contemporary society. Sport and tourism are represented by highly developed products and are strong motivational factors in choosing a holiday destination. Europe is traditionally a tourism-oriented market, attracting about 51% of shares in world tourism activity (671 million tourist arrivals in 2017) and engaging about 62% of European Union (EU) residents who took part in tourism (World Tourism Organization, 2018). EU countries represent the space for predominantly “holidays, leisure and recreation” purposes of travel (42.2%) and “visits to friends and relatives” (43.7%), with professional business travel having a relatively small share (9.4%). Yet participation in tourism trips differs significantly across European countries, ranging from over 80% of the population in Finland, Netherlands, Luxembourg, and the Czech Republic, to less than 30% in Bulgaria, Portugal, and Romania (Eurostat, 2018).

In line with this, the survey carried out in 2014 reported that about 44% of the EU adult population participated in leisure-related sport and recreation activities at least once a week. However, the practice of sport varies greatly across countries: Nordic countries (Finland, Denmark, Sweden, Iceland, and Norway) and Austria had over 70% of active sport participants, while Bulgaria, Romania, and Turkey had less than 10% of active sport participants (Eurostat, 2014).

The proper theoretical framework on the issue of sport tourism has yet to be determined. The determination of “sport tourist profiles” and defining specific markets has been an open debate within academic circles for over three decades. Underlining the basic connections, similarities, and differences in practicing sport and tourism; defining active and passive participation; explaining differences between “soft” and “hard” sports definitions; active and passive sport tourists: these have all resulted in extracting various motivational aspects with different levels of importance. In addition, the potential of defining specific markets and marketing tools for sport tourism and destination development (sport events, cycle routes, etc), are outlined.

Contrary to this research, the generalized conclusions were made based on different case studies and focused mainly on “convenience samples” (Finn, 2012; Funk & Bruun, 2007; Hodeck & Hovemann, 2016; Hungenberg et al., 2016; Kim & Ritchie, 2012). Most often they were conducted within the country, at major destinations and some mega-events, examining the preferences and travel habits of tourists or participants, or creating typology of sport tourists by using different motivation scales and models (Aicher & Brenner, 2015; Yoon & Uysal, 2005) or focusing on demographic variables affecting tourists’ choices (Slak Valek et al., 2014; Weed & Bull, 2009; Yu, 2010).

This study used a random sample with standardized methodology applied on a large general sample, so the results can be considered as a more general and universally applicable contribution to the field. The aim of the study was to examine if human values sets indicating basic motivational aspects, shared by active sports participants, could be used for the creation of specific market segmentation model.

To fulfill the main aim, the study had two objectives:

To create a psychological profile of active sport participants.

To indicate potential demand markets for different active sport tourism products. It was an attempt to understand the set of beliefs that manifest into sport tourism consumer value systems based on Hungenberg et al. (2016).

During the analysis, the focus was on the following questions:

Research Question 1: What are the main human values for active sport tourists?

Research Question 2: How can human values, shared and reflected by active sport participants, be grouped?

Research Question 3: Which sport- and tourism-related activities should be recommended to active sport tourists?

Literature Review

Human Values

Values were considered core conceptions of the “desirable” within every individual and the society, as they serve as standards or criteria to guide actions, judgment, choice, attitude, evaluation, argument, exhortation, and rationalization. Values are thought to be capable of being structurally organized within an individual and the society, not only in terms of priority but also in terms of extensiveness, the universality of application, and consistency (Rokeach, 2008).

The issues of human values were mostly based on psychological and sociological research, but there were also studies related to sports which incorporated the human value sets (C. K. Lee et al., 2006; Simmons & Dickinson, 1986). According to M. J. Lee at al. (2000, p. 310), “because values are considered universal in the sense, they are principles that govern all aspects of our lives, they should also govern sport participation.” To reveal and understand the behavior in sport and physical activity, it is necessary to know more about the nature of sport groups. Every group has its goals or objectives, and values which are shared and intimately interwoven in the development process of the group, which stands equally for social clubs, work groups, and sport teams (Carron, 1982).

Motivation in Sports and Recreation

Motivations play a significant role in sport participant outcomes, such as repeat behaviour and level of involvement, especially when individuals identify themselves with the value in the activity and integrate that value into their sense (Aicher & Brenner, 2015; Aicher et al., 2017). All sport activities, with formal and seemingly neutral rules, are given social meanings per contextual structures and cultures, partly resulting in informal rules of conduct, joint values, and norms. Practicing sports can confirm and/or construct part of people’s social identity or image, and these can be sports-related like “jock” or “climber,” but also refer to social group (Elling et al., 2001).

Group identity was deeply explored aiming to differ each group from other consumer segments, and consequently, many different motivational scales were argued in the literature (Hodeck & Hovemann, 2016; Hungenberg et al., 2016; Kim & Ritchie, 2012; Slak Valek et al., 2014). Recent studies suggested using specially designed Sport and Tourism Motivational Scale to explain the main reasons for active sport tourism consumption and to indicate unique market segments. However, such scales were applicable on relatively small convenience samples, gathered within a certain destination or event, while providing more “in-depth” analysis on motivational aspects.

The literature considered basically three types of motivation: intrinsic, extrinsic, and amotivation. Intrinsic motivation was based on doing an activity for itself, out of interest, for the pleasure and satisfaction derived simply from performing it (Deci & Ryan, 1985). It was argued that much of sports participation was in a function of intrinsic motivation and that such intrinsic motivation was facilitated by conditions conducive to autonomy or self-determination (Frederick & Ryan, 1995). In addition, intrinsic motivation (knowledge, accomplishments, and experience) included the innate, foundational needs for competence and self-determination, which motivated an ongoing process of seeking and attempting to conquer optimal challenges (Deci & Ryan, 1985; Ryan & Deci, 2000). On the other hand, extrinsic motivation pertained to a wide variety of behaviours where the goals of action extend beyond those inherent in the activity itself, reflected in athletes who practice their sport for the prestige or for competitive reasons (Fortier et al., 1995). The same was also evident when considering traveling as a “prestige” or having the opportunity to travel to “prestigious destinations.”

Sport Groups and Typology of Sport Tourists

To create a model that has the potential to indicate motivational aspects of large general samples, authors of this research went back to the basic psychological determinants: human values. The psychological theory adopted a definition of values as desirable, trans-situational goals, varying in importance, which serve as guiding principles in people’s lives, by specifying a comprehensive set of 10 motivationally distinct value constructs with which all individuals and societies must cope (Schwartz et al., 2001). The universe of values expresses a continuum of motivational goals (Schwartz, 2012). As sport active groups are considered coherent to a certain extent, motivation has a dominant role in determining the engagement level. It is considered that such a group could potentially share a specific set of basic human values.

Regarding human values and motivation, recent studies have confirmed that the highest-rated motives for organized sporting event athletes were competition, affiliation, enjoyment, and challenge. All of these motivational factors were intrinsic motivators (Hungenberg et al., 2016). On the other side, passive sport tourists (sport event spectators and sport fans) showed different motivational framework. More precisely, passive sport tourists were identified with the preferences of more “tourism-oriented” factors, “fan motives,” “entertainment,” “destination attributes,” and “relaxation” motives (Ottevanger, 2007).

By analyzing group of athletes, which took active participation in sporting events, three main types were identified: tourism-oriented sport tourists (preoccupied with travel exploration, destination attributes, and relaxation), sport-oriented tourists (oriented toward skill mastery, social needs, competitive desire, and physical fitness) and sport tourist enthusiasts (marked by self-enrichment, social needs, and physical fitness; Hungenberg et al., 2016). On the other side, Gibson (1998) also proposed three types of sport tourists: those who visit places to attend sport events (as sport championships), those who visit famous sport-related attractions (as sport facilities, museums, and stadiums), and those who travel with the objective to have active holidays and participate in sport- and recreation-related activities, such as outdoor activities, water-related activities, or sport-focused activities like golf or tennis. Hungenberg et al. (2016) also opened the question on how to identify the specific typology of sport tourists when there is so much difference within participants of specific events, among active and passive sport tourists, among recreation and professional sportsmen.

Methodology

Research Hypotheses

The basic human value sets determine the general motivation. Therefore, human values are an important factor influencing active sport tourist choices and behavior. Furthermore, it is possible to define specific segments (tourist niches) within active sport tourist groups which have specific value sets (by using factor analysis). Such groups have different sociodemographic characteristics, economic potential, and geographical distribution, based on which different marketing strategies can be developed and directed. The following null and alternative hypotheses link with the Research Question 2:

Hypothesis 1: There are no differences across the active sport cluster groups regarding sociodemographic characteristics of active sport tourists.

Hypothesis 1a: There are differences in the sociodemographic variables across active sport cluster groups.

Hypothesis 2: There are no differences across the active sport cluster groups regarding geographical distribution of active sport tourists.

Hypothesis 2a: There are differences in the geographical distribution across active sport clusters.

Study Methods and the Segmentation Model

A quantitative approach covered statistical analysis of the data set on the rotating module “Health issues” and the data on “Human values” within the core module in the Round 7 of the European Social Survey (ESS; 2014). The data were evaluated in SPSS 24.0 by performing principal component factor analysis, cluster analysis, and multiple discriminant analysis.

Out of 40,185 respondents from 21 countries that participated in the Round 7 in 2014, the focus was on 29,968 (74.57%) respondents who declared to be sport/recreation active at least once a week. Therefore, the sample chosen is regarded as a possible active sport tourist segment. Those were respondents aged 15 years and older, who have participated in sport or recreation-related activities (nonwork-related, lasting for at least 30 minutes) at least once a week. However, some respondents did not complete the survey section on “Human Values,” so the sample focus was narrowed to the 28,106 valid responses. The demographic characteristic of a general sample defined as “potential sport active tourists” was reflected in equal distribution of male (49.8%) and female (50.2%) respondents. The equal distribution was also present between five age groups: (a) 19.76% up to 24 years, (b) 19.03% between 25 and 34 years, (c) the group from 35 to 44 as slightly larger (23.72%), (d) 16.98% between 45 and 54 years, and (e) 20.51% of the respondents were 55 years and older. The mean, calculated respondent’s age was 45.08 years (standard deviation = 14.31). Most respondents had secondary education (68.9%). Generally, respondents declared to be of good subjective health (74.4%) and having positive feelings about household incomes (81.4%), which are the basic preconditions for engaging in sport tourism. Most of the respondents were employed (57.9%), married (48.8%), or have never been married (35.9%), with two (27.5%) or four (24.4%) people living regularly as a member of their households (size of a household from 1 to 11 members).

The research constructed a grounded model based on the human value set defined within the ESS as a foundation for further systematic investigation. Such a model consisted of 21 indicators that fit 10 previously defined basic values suggested by Schwartz et al. (2001), as shown on Supplement Figure 1 (available online). The model was constructed on the following 10 basic values and should assist in the better understanding of the inner world of the respondents:

Power—two indicators: “Important to make own decisions and be free” and “Important to get respect from others.”

Achievement—two indicators: “Important to show abilities and be admired” and “Important to be successful and that people recognize achievements.”

Hedonism—three indicators: “Important to be rich, have money and expensive things,” “Important to have a good time,” and “Important to seek fun and things that give pleasure.”

Stimulation—two indicators: “Important to try new and different things in life” and “Important to seek adventures and have an exciting life.”

Self-direction—two indicators: “Important to think new ideas and being creative” and “Important to understand different people.”

Universalism—two indicators: “Important to care for nature and environment” and “Important that people are treated equally and have equal opportunities.”

Benevolence—two indicators: “Important to be loyal to friends and devote to people close” and “Important to help people and care for others well-being.”

Tradition—two indicators: “Important to follow traditions and customs” and “Important to be humble and modest, not draw attention.”

Conformity—two indicators: “Important to behave properly” and “Important to do what is told and follow rules.”

Security—two indicators: “Important to live in secure and safe surroundings” and “Important that government is strong and ensures safety.”

Regarding the formation of the attitudes, the segmentation model based on human values was considered to be cognitive and behavioral. Respondents created their motives based on information from the environment and from the residuals of past experiences. These motives could arise from respondents’ observation or might be inferred if they were the consequence of inference from several motives.

Results and Findings

Results on the Basic Human Value Sets

A principal component factor analysis was first applied to delineate the underlying dimensions of basic human values of sport active participants as potential sport tourists. Hence, 21 human values variables were analyzed by testing intercorrelations. The Bartlett’s test of sphericity was statistically significant (p < .001) and the measure of sampling adequacy (KMO) was 0.860, indicating that all variables were acceptable for conducting factor analysis, while Cronbach’s alpha was .808. Table 1 presents the results of varimax rotation with four factors identified with a total of 48.5% variance explained. In social sciences “it is not uncommon to consider a solution that accounts for 60% of the total variance and even less as satisfactory” (Hair et al., 2014, p. 107). Being a new and untested model so far may be one of the reasons why the percentage of variance explained was below 60%. Since this was the first extraction on the sample, the factor loadings only greater than 0.40 have been used. Higher loadings can sometimes counteract variance to make the factors as clear or directional as possible. The obtained percentage of variance explained can send a message that the model should be further refined, but it has a good basis.

Table 1

Results of Factor (F) Analysis

Variable, N = 28,106	Factor Loading	Eigenvalue	Variance Explained	Reliability Alpha	M	SD
F1: Conservatism		2.733	13.02	.701
Important to behave properly	0.703				2.67	1.25
Important to do what is told and follow rules	0.633				3.21	1.39
Important to live in secure and safe surroundings	0.591				2.37	1.24
Important that government is strong and ensures safety	0.562				2.32	1.18
Important to follow traditions and customs	0.620				2.82	1.36
Important to be humble and modest, not draw attention	0.470				2.71	1.25
F2: Self-transcendence		4.509	13.05	.699
Important to understand different people	0.668				2.32	1.06
Important that people are treated equally and have equal opportunities	0.673				2.00	1.02
Important to care for nature and environment	0.570				2.07	1.02
Important to help people and care for others well-being	0.625				2.12	0.97
Important to be loyal to friends and devote to people close	0.552				1.84	0.88
Important to think new ideas and being creative	0.501				2.45	1.20
F3: Self-enhancement		1.994	11.78	.707
Important to be successful and that people recognize achievements	0.711				3.09	1.34
Important to show abilities and be admired	0.724				3.17	1.39
Important to get respect from others	0.533				3.18	1.35
Important to be rich, have money and expensive things	0.671				4.08	1.33
Important to make own decisions and be free	0.440				2.11	1.06
F4: Openness to change		1.018	10.65	.738
Important to seek fun and things that give pleasure	0.814				2.88	1.32
Important to seek adventures and have an exciting life	0.640				3.71	1.45
Important to try new and different things in life	0.538				2.87	1.32
Important to have a good time	0.737				2.74	1.28

As shown in Table 1, four factors were labeled based on the consideration of basic value sets in the context of active sport participants:

Factor 1 was labeled “Conservatism” gathering six variables with reliability alpha calculated to .701.

Factor 2 was labeled “Self-transcendence” with reliability alpha calculated to .699 and incorporating six variables.

Factor 3 was labeled “Self-enhancement” with reliability alpha calculated to .707, incorporating five variables.

Factor 4 was labeled “Openness to change” with reliability alpha calculated to .738, including four variables.

Results on Potential Active Sport Tourist Segments

Based on previously identified four groups of basic human values, further cluster analysis was conducted. The main aim was to indicate potential active sport tourist segments. As recommended by Weaver and Lawton (2005), the use of factors as variables is reasonable when the variance explained for all factors is weighted approximately equally, which is present in this case (from 10.65% for Factor 4 to 13.04% for Factor 1). Respondents were clustered by a K-means nonhierarchical cluster analysis and the centroids of the three clusters were used as the starting values to obtain the final cluster solution. The results in Table 2 show that 28,106 respondents were classified into three clusters. Cluster 1 was composed of 10,115 respondents (36.08%), Cluster 2 of 8,686 respondents (30.86%), and Cluster 3 of 9,305 respondents (33.06%).

Table 2

Final Clusters From the Nonhierarchical Analysis

Factor (F)	Cluster Centroids for the Three Clusters
Factor (F)	Cluster 1, n = 10,115 (36.08%)	Cluster 2, n = 8,686 (30.86%)		Cluster 3, n = 9,305 (33.06%)
F1: Conservatism	−0.43260	0.09833		0.43061
F2: Self-transcendence	−0.36140	1.07748		−0.66412
F3: Self-enhancement	−0.80446	0.25389		0.70565
F4: Openness to change	0.10378	0.24616		−0.33592
Factor	Active Sport Clusters
Factor	Cluster 1: “Adventurous”	Cluster 2: “Eco-Friendly and Socially Responsible”	Cluster 3: “Competitive”	F	Sig.
F1: Conservatism	2.43 ^{(2) (3)}	3.19 ⁽¹⁾	3.26 ⁽¹⁾	568.692	0.000
F2: Self-transcendence	1.90 ⁽²⁾	2.81 ^{(1) (3)}	1.80 ⁽²⁾	2958.435	0.000
F3: Self-enhancement	2.58 ^{(2) (3)}	3.76 ^{(1) (3)}	3.90 ^{(1) (2)}	1473.456	0.000
F4: Openness to change	2.89 ^{(2) (3)}	3.33 ^{(1) (3)}	2.68 ^{(1) (2)}	244.681	0.000

Note: (1) (2) (3) represent the mean of the cluster is significantly different from the mean of other clusters on each factor based on post hoc Tukey’s honestly significant difference test. Bold mean values indicate higher values than means values of the other groups and the total and the mean difference is significant (p < .05).

To label and better describe clusters, the mean values of the four factors (measured on a Likert-type scale) for each cluster were calculated. Cluster 1 was labeled “Adventurous” since this group of respondents had relatively higher mean scores on Factor 4 (“Openness to change”), but the lower mean score for all other factors. Cluster 2 was labeled “Eco-friendly and socially responsible” since this cluster had higher mean scores for almost every factor—F1 (“Conservatism”), F2 (“Self-transcendence”), and F4 (“Openness to change”) than mean scores of the other groups and the total. Cluster 3 was labeled “Competitive” since respondents in this group had higher mean scores of F1 (“Conservatism”) and F3 (“Self-enhancement”), than mean scores of the other groups and the total.

To discriminate between the three active sports clusters using previously defined factors, a multiple discriminant analysis was performed. Two functions were statistically significant if they are measured by the chi-square test (p < .001; Supplement Table 1[available online]). The significance can relate to a measure of canonical correlation which showed a relatively high degree of association between the discriminant scores and the defined groups (both values are close to 1.0—for Function 1 was 0.788 and for Function 2 was 0.750). Also, the results presented in Supplement Table 1 (available online) indicated that Cluster 2 tend to have high values on Function 1, highly motivated by Factor 2 (Self-transcendence), while Cluster 3 tend to have high values on Function 2 and the respondents in this group were strongly motivated by Factor 3 (Self-enhancement). The validation of the three active sport clusters was enhanced by these results. In addition, 92.12% of the respondents were correctly classified if all respondents are used to construct the discriminant functions.

Hypotheses Testing

The chi-square test was conducted to test whether the sociodemographic variables were statistically significant across the three active sports clusters. The results of this analysis showed that the alternative Hypothesis 1a was supported in terms of gender, age, employment status, legal marital status, highest level of education, and monthly income, that is, p < .05 for all variables (Table 3). Regarding this, the null Hypothesis 1 is rejected and it can be concluded that a significant difference does exist.

Table 3

Sociodemographic Characteristics Across Clusters

Category	Active Sport Cluster			Pearson Chi-Square
Category	C1: Adventurous (%)	C2: Eco-Friendly and Socially Responsible (%)	C3: Competitive (%)	Pearson Chi-Square
Gender				.000
Male	40.3	31.8	27.9
Female	32	28.4	39.6
Age, years				.013
Up to 24	37.3	27.2	35.5
25 to 35	36.9	32.2	30.9
36 to 45	32.6	36.4	31
46 to 55	34.1	29.6	36.3
Older than 55	41.8	32.5	25.7
Employment status				.006
Employed	30.6	34.7	34.7
Self employed	44.4	29.9	25.7
Not in paid work	39.2	25.5	35.3
Legal marital status				.000
Married	41.8	34.5	23.7
Separated	38.2	24.4	37.4
Divorced	29.4	25.9	44.7
Widowed	36.9	25.5	37.6
Never married	35.8	29.9	34.3
Highest level of education				.000
Lower secondary education	45.2	32.7	22.1
Upper secondary education	50.3	20.1	29.6
Postsecondary nontertiary education	33.8	37.5	28.7
Tertiary education	19	40.7	40.3
Monthly income, €				.000
Less than 1,000	41.6	28.6	29.8
1,001-3,000	47.6	30.2	22.2
3,001-5,000	22.9	37.2	39.9
More than 5,000	7.7	55.8	36.5

Sport participation and consumption motives should be examined as a multidimensional construct, comprising multiple psychosociological factors including motivation and satisfaction issues as well as demographic factors (Hungenberg et al., 2016; Rohm et al., 2006). Hence, sociodemographic characteristics were important for defining clusters in sport tourism (Finn, 2012; Slak Valek et al., 2014). The study found certain differences across active sport clusters in terms of sociodemographic characteristics of three identified clusters. Males were more adventurous, eco-friendly and socially responsible than females, while females were the more competitive ones. Motivations for engaging in adventure/sports tourism seems to be different in relation to age: the younger age group (18-24 years) tended to look for a thrill; middle ages (45-54 years) tended to want to get away from it all; while older age group (55-64 years) tended to have an interest in the environment. Early research (Sung, 2004) found that adventure tourists are mainly men who often have preferences for hard adventure, whereas women have a higher propensity to engage in soft adventure, while study done by Pomfret and Bramwell (2016) indicated a significant growth and changing trend toward a more equal gender split. Despite the changes over time, gender affects the leisure behavior of men and women, reflected in the availability of leisure time and opportunities, as well as in differences in experiences, attitudes, and expectations of sport and recreation participation. It is found that women were risk sensitive and respond less to competition, while men were more eager to compete (Niederle & Vesterlund, 2011). On the other, competitive attitude differences in gender can be significantly influenced by the cultural environment, while in general, the gender lines are diminishing. Women’s leisure has changed from being centered on the family to taking time for their outdoor recreational activities (cycling, running, and intense fitness programs; McLean et al., 2019). The sport activities did not diminish with age, indicating that the oldest segment tend to be the most adventurous one. The most educated and the wealthiest segments were identified in “eco-friendly and socially responsible” group, which are also characteristics for people who originated from Nordic countries.

In addition, second alternative Hypothesis 2a was supported by the explanation of the differences in the geographical distribution across the active sport clusters, since the p value was less than .05 (Table 4). This leads to a conclusion that the second null Hypothesis 2 can be rejected.

Table 4

Geographical Distribution of Potential Active Sport Segments in 10 European Countries

Country		Cluster Number of Case			Total	Significance*
Country		C1: Adventurous	C2: Eco-Friendly and Socially Responsible	C3: Competitive	Total	Significance*
Netherlands	Count	1194	1177	1002	3373	.003
Netherlands	Percentage within country	35.4	34.9	29.7	100
Germany	Count	1351	869	872	3092
Germany	Percentage within country	43.7	28.1	28.2	100
Belgium	Count	851	573	965	2389
Belgium	Percentage within country	35.6	24	40.4	100
Sweden	Count	371	1103	746	2220
Sweden	Percentage within country	16.7	49.7	33.6	100
The United Kingdom	Count	868	741	583	2192
The United Kingdom	Percentage within country	39.6	33.8	26.6	100
Norway	Count	603	1070	434	2107
Norway	Percentage within country	28.6	50.8	20.6	100
Denmark	Count	396	694	933	2023
Denmark	Percentage within country	19.6	34.3	46.1	100
Ireland	Count	837	437	553	1827
Ireland	Percentage within country	45.8	23.9	30.3	100
Austria	Count	774	340	544	1658
Austria	Percentage within country	46.7	20.5	32.8	100
France	Count	261	390	838	1489
France	Percentage within country	17.5	26.2	56.3	100

Note: C = cluster.

Chi-square p < .05.

It is quite interesting that, according to Eurostat (2018), in terms of participation in tourism and sports, geographical distribution pattern within Europe was consistent, with the Top 10 tourism demand markets also being the countries with the highest levels of sport active populations. Therefore, the study continued with a further analysis of the geographical distribution of active sport tourist segments within the Top 10 European countries in terms of both sport and tourism (Table 4). The general aim of market segmentation is to identify segments that have the common characteristics shared by members of one group but as much as possible different compared with another group, allowing the marketers to develop a specific marketing mix that is particularly attractive to selected segments (Ernst & Dolnicar, 2018).

Based on the findings presented in Table 4, it can be concluded that adventurous tourist types were generally citizens from Austria, Ireland, Germany, and United Kingdom, often considered as a sort of adrenalin-seeking tourist. A fairly equal distribution was found for the Netherlands, pointing to almost equity of those who were adventurous (35.4%) and those who were eco-friendly and socially responsible (34.9%). Among those who were labeled as “eco-friendly and socially responsible,” the highest percentage went to Norway (50.8%) and Sweden (49.7%). The competitive tourist types were found to be citizens from France, Denmark, and Belgium.

Discussion

The primary focus of this study was to evaluate the basic human value set of sport active participants as potential sport tourists. The necessity to refine the factors, clusters, and specific typology emanates from previous research concerning authors’ propensity to omit large samples. The study identified four main factors that determine potential sport tourist groups along with defining active sport clusters, which allows an opportunity for tourism marketers to target different types of tourists more effectively:

The first identified factor (F1) was labeled as “Conservatism.” This is characteristically a nonrisking group of tourists whose behavior is based on human values interconnected to the personal importance of the safety and stability of self and society. Generally, they are passive individuals who follow norms and laws by maintaining cultural, family, and religious traditions, which act as push factors, as shown in research done by Hodeck and Hovemann (2016).

The second factor (F2) was labeled as “Self-transcendence.” This is an issue of sensitivity among a group of tourists whose general values are strongly interconnected to tolerance, equality, and justice. As many academics note, in most cases, they are self-directed and led by universalism and benevolence (Finn, 2012; Krippendorf, 1987), with a predominant responsible behavior in terms of nature and humanity aspects to protect themselves and meeting the needs of others.

The third identified factor (F3) was named “Self-enhancement.” It describes tourists belonging to a competitive group, whose inner value sets are closely connected with personal and professional success, along with fully enjoying life. This factor implies power and achievement by individuals as the main stimulation. Generally, this was found to be a strong push factor particularly in tourism, leisure, and recreation (Kozak, 2002; Robinson & Gammon, 2004; Weed & Bull, 2009). Nevertheless, being successful and recognizing achievements is also a noted factor in sport tourism (Bouchet et al., 2004; Hungenberg et al., 2016; Petrick et al., 2001).

The fourth factor (F4) addressed the motivational characteristics of the adventurous group of tourists labeled as “Openness to change.” This factor is based on enjoying in life while seeking excitement, novelty, and change. Trying new and different things while touring or sporting is often identified as a strong push factor that connects cross-cultural differences (Funk & Bruun, 2007). This is complemented with participation in sport or tourism activities that provide a good time, fun, pleasure, and excitement.

The study confirmed that intrinsic motivation is an extremely high motivating factor for being engaged in sport tourism challenges, as argued by Deci and Ryan (1985) and Frederick and Ryan (1995). This is in contrast with the extrinsic motivation factors where practicing sport is for prestige or for competitive reasons (Fortier et al., 1995). Regarding the potential demand markets for different active sport tourism products, the study confirmed findings of Galloway (2002) that both psychological, as well as sociodemographic variables, need to be considered for market segmentation. Consequently, the research revealed three types of active sport tourists, with the following profile:

Adventurous tourist types (as found for Austrians, Germans, Irish, and Britons) tend to involve in their vacation land-, air-, and water-based activities, ranging from short, adrenalin-fueled encounters (such as bungee jumping, rafting, surfing, snowboarding, paragliding, parachuting, and wind-surfing), to longer experiences(such as cruise expeditions, rock climbing, and mountaineering; Pomfret & Bramwell, 2016). They were generally identified as older male tourists (older than 55 years) engaging in fishing, hunting, tramping, and hang gliding, and those in the full strength (24-35 years) practicing mountain biking, surfing, and snowboarding. Generally, they are self-employed, mostly married or separated, with upper secondary education and a monthly income between 1,001€ and 3,000€. Among adventure tourists, there are specific segments labeled as “hard adventure” (extreme sports) and “soft-adventure” (eco-friendly) tourists.

Eco-friendly and socially responsible (as found for tourists from Sweden and Norway) most often choose sport-related vacations that include soft sport/adventure activities such as cycling (especially the Eurovelo network), backpacking, bird-watching, camping, hiking within protected nature parks and mountains, horse riding, kayaking, canoeing, fishing, safaris, research expeditions, and so on. They were mostly male in the group aged 36 to 45 years, employed, and mostly married, with the highest education level and the highest monthly income (over 5,000€). Furthermore, it is noted that a large proportion of eco-tourists appear to be childless or “empty-nesters.” This study confirmed previous findings that these tourists are led by responsibility for people and nature (Finn, 2012; Hodeck & Hovemann, 2016; Kim & Ritchie, 2012; Uysal & Jurowski, 1994; Weed & Bull, 2009).

Competitive tourist types (as found for Frenchmen, Danish, and Belgians) would most probably seek low-risk holidays, including highly competitive sport activities (such as golf, tennis, volleyball, and cycling). In general, these tourists were identified as predominately females, in the group aged 46 to 55 years and the youngest group (up to 24 years), who were either not in a paid work position or were employed, mostly divorced, with the highest education level and with the highest monthly income from 3,001€ to 5,000€ euros. As already discussed by Hungenberg et al. (2016), Kim and Ritchie (2012), and Petrick et al. (2001), this segment looks for competition and challenges while practicing tourism and sport.

With a preference shift of contemporary tourists toward special interest tourism, exotic destinations, and active vacations, the general tourist risk is increased (related to unfamiliar environments, remote locations, unfamiliar activities, risk-taking, and challenge). Activities that were predominantly undertaken independently (tramping, surfing, mountaineering, mountain biking, and waterskiing), but also some commercial ones (diving and parachuting) were associated with a large proportion of incidents. The number of claims for adventure tourism/sports injuries showed strong domination of male claimants, mostly in the 21 to 40 years age range (50%), while claims were most expensive and injuries potentially most severe in the 60-plus years age range (Bentley et al., 2007). Even when the objectivity of quantitative risk exists, the effect of subjective risk perception is more obvious, depending on demographic variables and individual cognitive abilities (Cui et al., 2016). Hence, the “hard” adventure tourists are willingly engaged in more dangerous activities being fully aware of potential risks, compared with soft adventure groups (eco-tourists). Also, general risk-sensibility and responsibility for children delimitate female engagement in extreme and dangerous sports activities on vacation.

Holiday choices deeply depend also on personal interests, social trends, current supply, actuality, and popularity of certain destinations, availability of information, and marketing activities. Yet for some countries with long sporting and tourism tradition (such as Netherlands, Denmark, and Germany), it is most likely that tourists would seek almost any type of sport activities during the vacation. As the results confirmed that there are traditionally sport-oriented markets, with a constant high share in sport and tourism participation, it opens the question of how important a role social norms, traditions, and culture play in these aspects?

Practical Implications and Limitations of the Study

Since the study confirmed that psychological and sociodemographic variables need to be considered for market segmentation, for each specific profile of active sport tourists (adventurous, eco-friendly and socially responsible, and competitive), marketers should apply specific marketing mix and create diversified sport tourism products.

In the case of an adventurous tourist type (as found for Austrians, Germans, Irish, and Britons), they would be most likely attracted to such travel arrangements allowing escape from personal and social pressure, keen to engage in “hard adventure.” High-risk factors aligned with engagement in extreme sports activities should be taken into consideration. Having in mind that most of the extreme outdoor activities are self-organized, tourism policy makers must provide sufficient information about potential risks, limitations, strict control, and possible prevention, along with providing adequate needed equipment and professional training to the supporting institutions.

For those who are older (“third age” tourists; “baby boomers”; older than 55 years), being wealthier, more educated and more active, the marketers should focus on offering commercially organized educational adventure experiences. The focus should be on outdoor relatively “safe” sports activities and programs offering improved health, feeling youthful again, and enjoying bodily rejuvenation. Furthermore, when offering such activities within this tourist profile, one must consider the potential differences in gender and age combinations. Namely, the study emphasizes that older female prefers more age-related activities (e.g., horse riding, bird watching, and walking), while older men prefer activities more frequently associated with younger people (such as rock climbing, caving, and white-water rafting).

In the case of an eco-friendly and socially responsible tourist type (as found in the case of respondents from Sweden and Norway), responsibility for people and nature comes first. The extreme growth in eco-tourism in Europe outlines the high potential for tourist offer side. Due to the high-income levels, marketers may offer tailor-made and usually higher priced travel arrangements that will focus on uncrowded locations and wilderness areas, offering to learn about wildlife and nature, natives and cultures, along with enjoying soft adventure activities. This segment also has the strongest potential to directly contribute to the sustainability aspects of tourist destinations.

With regard to the case of competitive tourist type (as found for French, Danish, and Belgians), one may offer low-risk sport-oriented travel arrangements where competition and challenges are of high importance. Increasing competitiveness and sport/recreation participation levels among females signalize that tourism providers should focus on creating specific supply particularly designed for them. More precisely, the sport tourism products may consist of a combination of “soft” competitive sports activities (tennis, volleyball, golf, surfing, racing, and horse-riding) and traditional packages for beauty, wellness, and spa, as well allowing children engagement in sports.

By identifying tourist types one may provide valuable insights to explain their motivations, to create segments as homogeneous groups and to profile them. This enables pattering groups with personal and travel behavior in sport and tourism activities, which assists in developing marketing and managerial strategies. Hence, the comprehensive analysis of human values sets fully assists marketers in monitoring changes in preferences of sport active tourists.

The research was limited by several factors that can serve as productive starting points for future work. First, the applied model was driven by internal “push factors” (intrinsic motivations), while there is a little knowledge on the potential role of pull factors that are influenced by the existing supply side. This implies that the suggested model may be further upgraded with some new aspects, to be revised or even to test some new different models. Second, the ESS survey does not include data on tourism participation, so it is not possible to address the findings as the defined “sport tourist segments,” but rather “potential sport tourist” segments.

Consequently, it is rather difficult to indicate which sport respondents are practicing or are likely to be attracted to. Third, the research was focused only on participants’ choice and personal motivation when being sport active. This opens considerations for including other aspects of perception and motivation in some prospecting work. And fourth, methodologically, the analysis was based on general standard statistical tests, indicating a possibility for further application of new tests. The applied model may go toward checking the number of items for latent factors (whether the existing number of items is too small/too large or whether their number needs to be revised) and to determine the nature of the constructs (e.g., whether they are too extensive).

The limitations, however, do not diminish the significance of the findings, but rather they suggest some broad directions for further research, mainly being focused on tourist market segments, new theoretical lens, and cross-disciplinary approaches.

Conclusion

The focus of this study was to determinate the role of basic human value sets in predicting the sport tourism-related preferences and identifying different segments of active sport participants. The EES initiative was used as a possibility to address such issues, to validate and support the findings already discussed in numerous case studies.

There is no general study examining motivational aspects on large unspecific samples that can properly address such issues since previous studies examined mostly convenient samples. Besides this, there were scarce statistical data within European space on tourism preferences of tourists, as data collected was focused mostly on accommodation occupancy, primary motives of visits, inbound, outbound and domestic trips, length of stay, and economic issues and effects. Such data may point to the demand markets and their share, but is rather limited when it comes to issues of motivation and preferences. On top of that, as leisure and tourism preferences are constantly changing, demand markets are growing and tourist supply is expanding. People’s choices cannot be simply perceived as facts and figures, as motivation and destination choices lie deeply in the personal interests and habits of individuals or groups (tourist segments, niches). Hence, there is a close interconnection with the personal value set (human values), which tourists seek on a destination.

The research indicated the existence of four different factors to determine sport participants: conservative (low risking, security at first), self-transcendence (sensitive), self-enhancement (competitive and challenge seeking); and open to change (looking for excitement and adventure). Considering that groups are coherent social units bounded by representative strength, the study identified three clusters, which intimately share same or similar value sets and characteristics, based on which different preferences were constructed. Each group has its goals or objectives which are shared within the group and interwoven in the development process of the group, which stands equally for social clubs, tourism groups, and sport teams. Taking into consideration that sport groups are coherent to a certain extent and share the defined set of basic values, the study examined the active participating members of sport/outdoor activity clubs and aimed to define the sociodemographic characteristics of potential active sport tourists and outlined the main human values that they identify with.

It contributed to the general segmentation pattern of different “sport participant segments,” which are highly likely to represent sport tourism markets, as their active sport participation in everyday life indicated their high potential to choose sport-related holidays and activities, and to respond to specific tourist supply directed to their personal interest and expectations, and be involved in more or less risky, adventurous type of holidays.

The study found that a typical active sport participant should be reflected in the high identification with determinants reflected in human values under “achievement,” “stimulation,” “power,” and “self-direction.” On the other hand, the study revealed that typical sportsmen may unlikely be identified by “conformity,” “tradition,” and “security” determinants, which may retain within sport fans and passive sport tourist value sets.

Finally, human values are not as much subject to constant changes as the supply-side itself, or other stimulus factors, like prices, new destinations, new fashion trends, attractions, and activities. So, the motivation and the demand side (push factors) are factors on which tourism managers can rely on in the longer term, while the destination choices and activities depend mostly on the attractiveness of the destination, available supply, and price ranges (pull factors). By defining the universal human values set shared by “active sport participants” and by further examination, the study indicated potential markets and groups of people (nations) who are likely to choose sport-related vacations. Hence, market segmentation is enabled urging the construction of specific marketing strategies.

Supplemental Material

supplemental_information – Supplemental material for Active Sport Tourism in Europe: Applying Market Segmentation Model Based on Human Values

Supplemental material, supplemental_information for Active Sport Tourism in Europe: Applying Market Segmentation Model Based on Human Values by Aleksandra Terzić, Dunja Demirović, Biljana Petrevska and Wolfgang Limbert in Journal of Hospitality & Tourism Research

Footnotes

Authors’ Note:

We have no conflicts of interests to disclose. The article is a result of project funded by the Ministry of Sciences and Technological Development, the Republic of Serbia (III 47007).

ORCID iD

Aleksandra Terzić

Supplemental Material

Supplemental material for this article is available online.

Aleksandra Terzić, PhD (e-mail: a.terzic@gi.sanu.ac.rs), is currently working as a senior research associate at the Social Geography Department of the Geographical Institute “Jovan Cvijić,” Serbian Academy of Sciences and Arts, Belgrade, Serbia. Dunja Demirović, PhD (e-mail: d.demirovic@gi.sanu.ac.rs), is a research associate at the Social Geography Department of the Geographical Institute “Jovan Cvijić,” Serbian Academy of Sciences and Arts, Belgrade, Serbia. Biljana Petrevska, PhD (e-mail: biljana.petrevska@ugd.edu.mk), is an associate professor at Faculty of Tourism and Business Logistics at the Goce Delchev University, Stip, Macedonia. Wolfgang Limbert (e-mail: wl.orgamarket@gmail.com) is a partner of ORGAMARKET GmbH, Munich, Germany.

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